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TradingAgents
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Maytekin 2025-10-14 20:34:14 +01:00
parent 05d3730e5b
commit 94558f2227
58 changed files with 11753 additions and 10445 deletions
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tradingagents/utils/memory
def __init__(self, name, config):
if config["backend_url"] == "http://192.168.0.20:11434/v1":
self.embedding = "nomic-embed-text"
elif config["backend_url"] == "http://192.168.0.20:1234/v1":
self.embedding = "text-embedding-nomic-embed-text-v1.5"
else:
self.embedding = "ttext-embedding-nomic-embed-text-v1.5"
self.client = OpenAI(base_url=config["backend_url"], api_key=config["api_key"])
tradingagents/graph/trading_graph.py
# Initialize LLMs
if self.config["llm_provider"].lower() == "openai" or self.config["llm_provider"] == "ollama" or self.config["llm_provider"] == "openrouter":
self.deep_thinking_llm = ChatOpenAI(model=self.config["deep_think_llm"], base_url=self.config["backend_url"],api_key=self.config["api_key"])
self.quick_thinking_llm = ChatOpenAI(model=self.config["quick_think_llm"], base_url=self.config["backend_url"],api_key=self.config["api_key"])
elif self.config["llm_provider"].lower() == "anthropic":
self.deep_thinking_llm = ChatAnthropic(model=self.config["deep_think_llm"], base_url=self.config["backend_url"],api_key=self.config["api_key"])
self.quick_thinking_llm = ChatAnthropic(model=self.config["quick_think_llm"], base_url=self.config["backend_url"],api_key=self.config["api_key"])
elif self.config["llm_provider"].lower() == "google":
self.deep_thinking_llm = ChatGoogleGenerativeAI(model=self.config["deep_think_llm"])
self.quick_thinking_llm = ChatGoogleGenerativeAI(model=self.config["quick_think_llm"])
elif self.config["llm_provider"].lower() == "lmstudio":
self.deep_thinking_llm = ChatOpenAI(model=self.config["deep_think_llm"], base_url=self.config["backend_url"], api_key=self.config["api_key"])
self.quick_thinking_llm = ChatOpenAI(model=self.config["quick_think_llm"], base_url=self.config["backend_url"],api_key=self.config["api_key"])
default_config.py
# Default LLM is set to local LMStudio instance
"llm_provider": "lmstudio",
"deep_think_llm": "qwen/qwen3-4b-thinking-2507",
"quick_think_llm": "openai/gpt-oss-20b",
"backend_url": "http://192.168.0.20/v1",
"api_key": "blablabla",
utils.py
BASE_URLS = [
("LMStudio", "http://192.168.0.20:1234/v1"),
("OpenAI Local", "http://192.168.0.20:1234/v1"),
("OpenAI", "https://api.openai.com/v1"),
("Anthropic", "https://api.anthropic.com/"),
("Google", "https://generativelanguage.googleapis.com/v1"),
("Openrouter", "https://openrouter.ai/api/v1"),
("Ollama", "http://localhost:11434/v1"),
Anthropic section
("CCR", "openai/gpt-oss-20b"),
both to shallow thinking and deep thinking
,
"openrouter": [
("Meta: Llama 4 Scout", "meta-llama/llama-4-scout:free"),
("Meta: Llama 3.3 8B Instruct - A lightweight and ultra-fast variant of Llama 3.3 70B", "meta-llama/llama-3.3-8b-instruct:free"),
("google/gemini-2.0-flash-exp:free - Gemini Flash 2.0 offers a significantly faster time to first token", "google/gemini-2.0-flash-exp:free"),
],
"ollama": [
("llama3.1 local", "llama3.1"),
("qwen3", "qwen3"),
],
"lmstudio": [
("LMStudio Qwen 4b Thinking","qwen/qwen3-4b-thinking-2507"),
("LMStudio GLM", "glm-4.5-air-mlx"),
("LMStudio OSS 120b","openai/gpt-oss-120b"),
("LMStudio Kimi","kimi-dev-72b-dwq"),
]

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cli/main.py
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cli/models.py
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from enum import Enum
from typing import List, Optional, Dict
from pydantic import BaseModel
class AnalystType(str, Enum):
MARKET = "market"
SOCIAL = "social"
NEWS = "news"
FUNDAMENTALS = "fundamentals"
from enum import Enum
from typing import List, Optional, Dict
from pydantic import BaseModel
class AnalystType(str, Enum):
MARKET = "market"
SOCIAL = "social"
NEWS = "news"
FUNDAMENTALS = "fundamentals"

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cli/utils.py
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import questionary
from typing import List, Optional, Tuple, Dict
from cli.models import AnalystType
ANALYST_ORDER = [
("Market Analyst", AnalystType.MARKET),
("Social Media Analyst", AnalystType.SOCIAL),
("News Analyst", AnalystType.NEWS),
("Fundamentals Analyst", AnalystType.FUNDAMENTALS),
]
def get_ticker() -> str:
"""Prompt the user to enter a ticker symbol."""
ticker = questionary.text(
"Enter the ticker symbol to analyze:",
validate=lambda x: len(x.strip()) > 0 or "Please enter a valid ticker symbol.",
style=questionary.Style(
[
("text", "fg:green"),
("highlighted", "noinherit"),
]
),
).ask()
if not ticker:
console.print("\n[red]No ticker symbol provided. Exiting...[/red]")
exit(1)
return ticker.strip().upper()
def get_analysis_date() -> str:
"""Prompt the user to enter a date in YYYY-MM-DD format."""
import re
from datetime import datetime
def validate_date(date_str: str) -> bool:
if not re.match(r"^\d{4}-\d{2}-\d{2}$", date_str):
return False
try:
datetime.strptime(date_str, "%Y-%m-%d")
return True
except ValueError:
return False
date = questionary.text(
"Enter the analysis date (YYYY-MM-DD):",
validate=lambda x: validate_date(x.strip())
or "Please enter a valid date in YYYY-MM-DD format.",
style=questionary.Style(
[
("text", "fg:green"),
("highlighted", "noinherit"),
]
),
).ask()
if not date:
console.print("\n[red]No date provided. Exiting...[/red]")
exit(1)
return date.strip()
def select_analysts() -> List[AnalystType]:
"""Select analysts using an interactive checkbox."""
choices = questionary.checkbox(
"Select Your [Analysts Team]:",
choices=[
questionary.Choice(display, value=value) for display, value in ANALYST_ORDER
],
instruction="\n- Press Space to select/unselect analysts\n- Press 'a' to select/unselect all\n- Press Enter when done",
validate=lambda x: len(x) > 0 or "You must select at least one analyst.",
style=questionary.Style(
[
("checkbox-selected", "fg:green"),
("selected", "fg:green noinherit"),
("highlighted", "noinherit"),
("pointer", "noinherit"),
]
),
).ask()
if not choices:
console.print("\n[red]No analysts selected. Exiting...[/red]")
exit(1)
return choices
def select_research_depth() -> int:
"""Select research depth using an interactive selection."""
# Define research depth options with their corresponding values
DEPTH_OPTIONS = [
("Shallow - Quick research, few debate and strategy discussion rounds", 1),
("Medium - Middle ground, moderate debate rounds and strategy discussion", 3),
("Deep - Comprehensive research, in depth debate and strategy discussion", 5),
]
choice = questionary.select(
"Select Your [Research Depth]:",
choices=[
questionary.Choice(display, value=value) for display, value in DEPTH_OPTIONS
],
instruction="\n- Use arrow keys to navigate\n- Press Enter to select",
style=questionary.Style(
[
("selected", "fg:yellow noinherit"),
("highlighted", "fg:yellow noinherit"),
("pointer", "fg:yellow noinherit"),
]
),
).ask()
if choice is None:
console.print("\n[red]No research depth selected. Exiting...[/red]")
exit(1)
return choice
def select_shallow_thinking_agent(provider) -> str:
"""Select shallow thinking llm engine using an interactive selection."""
# Define shallow thinking llm engine options with their corresponding model names
SHALLOW_AGENT_OPTIONS = {
"openai": [
("GPT-4o-mini - Fast and efficient for quick tasks", "gpt-4o-mini"),
("GPT-4.1-nano - Ultra-lightweight model for basic operations", "gpt-4.1-nano"),
("GPT-4.1-mini - Compact model with good performance", "gpt-4.1-mini"),
("GPT-4o - Standard model with solid capabilities", "gpt-4o"),
("LMStudio OSS 20b","openai/gpt-oss-20b"),
],
"anthropic": [
("Claude Haiku 3.5 - Fast inference and standard capabilities", "claude-3-5-haiku-latest"),
("Claude Sonnet 3.5 - Highly capable standard model", "claude-3-5-sonnet-latest"),
("Claude Sonnet 3.7 - Exceptional hybrid reasoning and agentic capabilities", "claude-3-7-sonnet-latest"),
("Claude Sonnet 4 - High performance and excellent reasoning", "claude-sonnet-4-0"),
("CCR", "openai/gpt-oss-20b"),
],
"google": [
("Gemini 2.0 Flash-Lite - Cost efficiency and low latency", "gemini-2.0-flash-lite"),
("Gemini 2.0 Flash - Next generation features, speed, and thinking", "gemini-2.0-flash"),
("Gemini 2.5 Flash - Adaptive thinking, cost efficiency", "gemini-2.5-flash-preview-05-20"),
],
"openrouter": [
("Meta: Llama 4 Scout", "meta-llama/llama-4-scout:free"),
("Meta: Llama 3.3 8B Instruct - A lightweight and ultra-fast variant of Llama 3.3 70B", "meta-llama/llama-3.3-8b-instruct:free"),
("google/gemini-2.0-flash-exp:free - Gemini Flash 2.0 offers a significantly faster time to first token", "google/gemini-2.0-flash-exp:free"),
],
"ollama": [
("llama3.1 local", "llama3.1"),
("llama3.2 local", "llama3.2"),
],
"lmstudio": [
("LMStudio OSS 20b","openai/gpt-oss-20b"),
("LMStudio GLM", "glm-4.5-air-mlx"),
("LMStudio Qwen 4b Thinking","qwen/qwen3-4b-thinking-2507"),
("LMStudio Kimi","kimi-dev-72b-dwq"),
]
}
choice = questionary.select(
"Select Your [Quick-Thinking LLM Engine]:",
choices=[
questionary.Choice(display, value=value)
for display, value in SHALLOW_AGENT_OPTIONS[provider.lower()]
],
instruction="\n- Use arrow keys to navigate\n- Press Enter to select",
style=questionary.Style(
[
("selected", "fg:magenta noinherit"),
("highlighted", "fg:magenta noinherit"),
("pointer", "fg:magenta noinherit"),
]
),
).ask()
if choice is None:
console.print(
"\n[red]No shallow thinking llm engine selected. Exiting...[/red]"
)
exit(1)
return choice
def select_deep_thinking_agent(provider) -> str:
"""Select deep thinking llm engine using an interactive selection."""
# Define deep thinking llm engine options with their corresponding model names
DEEP_AGENT_OPTIONS = {
"openai": [
("GPT-4.1-nano - Ultra-lightweight model for basic operations", "gpt-4.1-nano"),
("GPT-4.1-mini - Compact model with good performance", "gpt-4.1-mini"),
("GPT-4o - Standard model with solid capabilities", "gpt-4o"),
("o4-mini - Specialized reasoning model (compact)", "o4-mini"),
("o3-mini - Advanced reasoning model (lightweight)", "o3-mini"),
("o3 - Full advanced reasoning model", "o3"),
("o1 - Premier reasoning and problem-solving model", "o1"),
("LMStudio Qwen 4b Thinking","qwen/qwen3-4b-thinking-2507"),
],
"anthropic": [
("Claude Haiku 3.5 - Fast inference and standard capabilities", "claude-3-5-haiku-latest"),
("Claude Sonnet 3.5 - Highly capable standard model", "claude-3-5-sonnet-latest"),
("Claude Sonnet 3.7 - Exceptional hybrid reasoning and agentic capabilities", "claude-3-7-sonnet-latest"),
("Claude Sonnet 4 - High performance and excellent reasoning", "claude-sonnet-4-0"),
("Claude Opus 4 - Most powerful Anthropic model", " claude-opus-4-0"),
("CCR", "LMStudio Qwen 4b Thinking","qwen/qwen3-4b-thinking-2507"),
],
"google": [
("Gemini 2.0 Flash-Lite - Cost efficiency and low latency", "gemini-2.0-flash-lite"),
("Gemini 2.0 Flash - Next generation features, speed, and thinking", "gemini-2.0-flash"),
("Gemini 2.5 Flash - Adaptive thinking, cost efficiency", "gemini-2.5-flash-preview-05-20"),
("Gemini 2.5 Pro", "gemini-2.5-pro-preview-06-05"),
],
"openrouter": [
("DeepSeek V3 - a 685B-parameter, mixture-of-experts model", "deepseek/deepseek-chat-v3-0324:free"),
("Deepseek - latest iteration of the flagship chat model family from the DeepSeek team.", "deepseek/deepseek-chat-v3-0324:free"),
],
"ollama": [
("llama3.1 local", "llama3.1"),
("qwen3", "qwen3"),
],
"lmstudio": [
("LMStudio Qwen 4b Thinking","qwen/qwen3-4b-thinking-2507"),
("LMStudio GLM", "glm-4.5-air-mlx"),
("LMStudio OSS 120b","openai/gpt-oss-120b"),
("LMStudio Kimi","kimi-dev-72b-dwq"),
]
}
choice = questionary.select(
"Select Your [Deep-Thinking LLM Engine]:",
choices=[
questionary.Choice(display, value=value)
for display, value in DEEP_AGENT_OPTIONS[provider.lower()]
],
instruction="\n- Use arrow keys to navigate\n- Press Enter to select",
style=questionary.Style(
[
("selected", "fg:magenta noinherit"),
("highlighted", "fg:magenta noinherit"),
("pointer", "fg:magenta noinherit"),
]
),
).ask()
if choice is None:
console.print("\n[red]No deep thinking llm engine selected. Exiting...[/red]")
exit(1)
return choice
def select_llm_provider() -> tuple[str, str]:
"""Select the OpenAI api url using interactive selection."""
# Define OpenAI api options with their corresponding endpoints
BASE_URLS = [
("LMStudio", "http://192.168.0.20:1234/v1"),
("OpenAI Local", "http://192.168.0.20:1234/v1"),
("OpenAI", "https://api.openai.com/v1"),
("Anthropic", "https://api.anthropic.com/"),
("Google", "https://generativelanguage.googleapis.com/v1"),
("Openrouter", "https://openrouter.ai/api/v1"),
("Ollama", "http://localhost:11434/v1"),
]
choice = questionary.select(
"Select your LLM Provider:",
choices=[
questionary.Choice(display, value=(display, value))
for display, value in BASE_URLS
],
instruction="\n- Use arrow keys to navigate\n- Press Enter to select",
style=questionary.Style(
[
("selected", "fg:magenta noinherit"),
("highlighted", "fg:magenta noinherit"),
("pointer", "fg:magenta noinherit"),
]
),
).ask()
if choice is None:
console.print("\n[red]no OpenAI backend selected. Exiting...[/red]")
exit(1)
display_name, url = choice
print(f"You selected: {display_name}\tURL: {url}")
return display_name, url
import questionary
from typing import List, Optional, Tuple, Dict
from cli.models import AnalystType
ANALYST_ORDER = [
("Market Analyst", AnalystType.MARKET),
("Social Media Analyst", AnalystType.SOCIAL),
("News Analyst", AnalystType.NEWS),
("Fundamentals Analyst", AnalystType.FUNDAMENTALS),
]
def get_ticker() -> str:
"""Prompt the user to enter a ticker symbol."""
ticker = questionary.text(
"Enter the ticker symbol to analyze:",
validate=lambda x: len(x.strip()) > 0 or "Please enter a valid ticker symbol.",
style=questionary.Style(
[
("text", "fg:green"),
("highlighted", "noinherit"),
]
),
).ask()
if not ticker:
console.print("\n[red]No ticker symbol provided. Exiting...[/red]")
exit(1)
return ticker.strip().upper()
def get_analysis_date() -> str:
"""Prompt the user to enter a date in YYYY-MM-DD format."""
import re
from datetime import datetime
def validate_date(date_str: str) -> bool:
if not re.match(r"^\d{4}-\d{2}-\d{2}$", date_str):
return False
try:
datetime.strptime(date_str, "%Y-%m-%d")
return True
except ValueError:
return False
date = questionary.text(
"Enter the analysis date (YYYY-MM-DD):",
validate=lambda x: validate_date(x.strip())
or "Please enter a valid date in YYYY-MM-DD format.",
style=questionary.Style(
[
("text", "fg:green"),
("highlighted", "noinherit"),
]
),
).ask()
if not date:
console.print("\n[red]No date provided. Exiting...[/red]")
exit(1)
return date.strip()
def select_analysts() -> List[AnalystType]:
"""Select analysts using an interactive checkbox."""
choices = questionary.checkbox(
"Select Your [Analysts Team]:",
choices=[
questionary.Choice(display, value=value) for display, value in ANALYST_ORDER
],
instruction="\n- Press Space to select/unselect analysts\n- Press 'a' to select/unselect all\n- Press Enter when done",
validate=lambda x: len(x) > 0 or "You must select at least one analyst.",
style=questionary.Style(
[
("checkbox-selected", "fg:green"),
("selected", "fg:green noinherit"),
("highlighted", "noinherit"),
("pointer", "noinherit"),
]
),
).ask()
if not choices:
console.print("\n[red]No analysts selected. Exiting...[/red]")
exit(1)
return choices
def select_research_depth() -> int:
"""Select research depth using an interactive selection."""
# Define research depth options with their corresponding values
DEPTH_OPTIONS = [
("Shallow - Quick research, few debate and strategy discussion rounds", 1),
("Medium - Middle ground, moderate debate rounds and strategy discussion", 3),
("Deep - Comprehensive research, in depth debate and strategy discussion", 5),
]
choice = questionary.select(
"Select Your [Research Depth]:",
choices=[
questionary.Choice(display, value=value) for display, value in DEPTH_OPTIONS
],
instruction="\n- Use arrow keys to navigate\n- Press Enter to select",
style=questionary.Style(
[
("selected", "fg:yellow noinherit"),
("highlighted", "fg:yellow noinherit"),
("pointer", "fg:yellow noinherit"),
]
),
).ask()
if choice is None:
console.print("\n[red]No research depth selected. Exiting...[/red]")
exit(1)
return choice
def select_shallow_thinking_agent(provider) -> str:
"""Select shallow thinking llm engine using an interactive selection."""
# Define shallow thinking llm engine options with their corresponding model names
SHALLOW_AGENT_OPTIONS = {
"openai": [
("GPT-4o-mini - Fast and efficient for quick tasks", "gpt-4o-mini"),
("GPT-4.1-nano - Ultra-lightweight model for basic operations", "gpt-4.1-nano"),
("GPT-4.1-mini - Compact model with good performance", "gpt-4.1-mini"),
("GPT-4o - Standard model with solid capabilities", "gpt-4o"),
],
"anthropic": [
("Claude Haiku 3.5 - Fast inference and standard capabilities", "claude-3-5-haiku-latest"),
("Claude Sonnet 3.5 - Highly capable standard model", "claude-3-5-sonnet-latest"),
("Claude Sonnet 3.7 - Exceptional hybrid reasoning and agentic capabilities", "claude-3-7-sonnet-latest"),
("Claude Sonnet 4 - High performance and excellent reasoning", "claude-sonnet-4-0"),
("CCR", "openai/gpt-oss-20b"),
],
"google": [
("Gemini 2.0 Flash-Lite - Cost efficiency and low latency", "gemini-2.0-flash-lite"),
("Gemini 2.0 Flash - Next generation features, speed, and thinking", "gemini-2.0-flash"),
("Gemini 2.5 Flash - Adaptive thinking, cost efficiency", "gemini-2.5-flash-preview-05-20"),
],
"openrouter": [
("Meta: Llama 4 Scout", "meta-llama/llama-4-scout:free"),
("Meta: Llama 3.3 8B Instruct - A lightweight and ultra-fast variant of Llama 3.3 70B", "meta-llama/llama-3.3-8b-instruct:free"),
("google/gemini-2.0-flash-exp:free - Gemini Flash 2.0 offers a significantly faster time to first token", "google/gemini-2.0-flash-exp:free"),
],
"ollama": [
("llama3.1 local", "llama3.1"),
("llama3.2 local", "llama3.2"),
],
"lmstudio": [
("LMStudio Qwen 4b Thinking","qwen/qwen3-4b-thinking-2507"),
("LMStudio GLM", "glm-4.5-air-mlx"),
("LMStudio OSS 120b","openai/gpt-oss-120b"),
("LMStudio Kimi","kimi-dev-72b-dwq"),
]
}
choice = questionary.select(
"Select Your [Quick-Thinking LLM Engine]:",
choices=[
questionary.Choice(display, value=value)
for display, value in SHALLOW_AGENT_OPTIONS[provider.lower()]
],
instruction="\n- Use arrow keys to navigate\n- Press Enter to select",
style=questionary.Style(
[
("selected", "fg:magenta noinherit"),
("highlighted", "fg:magenta noinherit"),
("pointer", "fg:magenta noinherit"),
]
),
).ask()
if choice is None:
console.print(
"\n[red]No shallow thinking llm engine selected. Exiting...[/red]"
)
exit(1)
return choice
def select_deep_thinking_agent(provider) -> str:
"""Select deep thinking llm engine using an interactive selection."""
# Define deep thinking llm engine options with their corresponding model names
DEEP_AGENT_OPTIONS = {
"openai": [
("GPT-4.1-nano - Ultra-lightweight model for basic operations", "gpt-4.1-nano"),
("GPT-4.1-mini - Compact model with good performance", "gpt-4.1-mini"),
("GPT-4o - Standard model with solid capabilities", "gpt-4o"),
("o4-mini - Specialized reasoning model (compact)", "o4-mini"),
("o3-mini - Advanced reasoning model (lightweight)", "o3-mini"),
("o3 - Full advanced reasoning model", "o3"),
("o1 - Premier reasoning and problem-solving model", "o1"),
],
"anthropic": [
("Claude Haiku 3.5 - Fast inference and standard capabilities", "claude-3-5-haiku-latest"),
("Claude Sonnet 3.5 - Highly capable standard model", "claude-3-5-sonnet-latest"),
("Claude Sonnet 3.7 - Exceptional hybrid reasoning and agentic capabilities", "claude-3-7-sonnet-latest"),
("Claude Sonnet 4 - High performance and excellent reasoning", "claude-sonnet-4-0"),
("Claude Opus 4 - Most powerful Anthropic model", " claude-opus-4-0"),
],
"google": [
("Gemini 2.0 Flash-Lite - Cost efficiency and low latency", "gemini-2.0-flash-lite"),
("Gemini 2.0 Flash - Next generation features, speed, and thinking", "gemini-2.0-flash"),
("Gemini 2.5 Flash - Adaptive thinking, cost efficiency", "gemini-2.5-flash-preview-05-20"),
("Gemini 2.5 Pro", "gemini-2.5-pro-preview-06-05"),
],
"openrouter": [
("DeepSeek V3 - a 685B-parameter, mixture-of-experts model", "deepseek/deepseek-chat-v3-0324:free"),
("Deepseek - latest iteration of the flagship chat model family from the DeepSeek team.", "deepseek/deepseek-chat-v3-0324:free"),
],
"ollama": [
("llama3.1 local", "llama3.1"),
("qwen3", "qwen3"),
],
"lmstudio": [
("LMStudio Qwen 4b Thinking","qwen/qwen3-4b-thinking-2507"),
("LMStudio GLM", "glm-4.5-air-mlx"),
("LMStudio OSS 20b","openai/gpt-oss-20b"),
("LMStudio Kimi","kimi-dev-72b-dwq"),
]
}
choice = questionary.select(
"Select Your [Deep-Thinking LLM Engine]:",
choices=[
questionary.Choice(display, value=value)
for display, value in DEEP_AGENT_OPTIONS[provider.lower()]
],
instruction="\n- Use arrow keys to navigate\n- Press Enter to select",
style=questionary.Style(
[
("selected", "fg:magenta noinherit"),
("highlighted", "fg:magenta noinherit"),
("pointer", "fg:magenta noinherit"),
]
),
).ask()
if choice is None:
console.print("\n[red]No deep thinking llm engine selected. Exiting...[/red]")
exit(1)
return choice
def select_llm_provider() -> tuple[str, str]:
"""Select the OpenAI api url using interactive selection."""
# Define OpenAI api options with their corresponding endpoints
BASE_URLS = [
("LMStudio", "http://192.168.0.20:1234/v1"),
("OpenAI Local", "http://192.168.0.20:1234/v1"),
("OpenAI", "https://api.openai.com/v1"),
("Anthropic", "https://api.anthropic.com/"),
("Google", "https://generativelanguage.googleapis.com/v1"),
("Openrouter", "https://openrouter.ai/api/v1"),
("Ollama", "http://localhost:11434/v1"),
]
choice = questionary.select(
"Select your LLM Provider:",
choices=[
questionary.Choice(display, value=(display, value))
for display, value in BASE_URLS
],
instruction="\n- Use arrow keys to navigate\n- Press Enter to select",
style=questionary.Style(
[
("selected", "fg:magenta noinherit"),
("highlighted", "fg:magenta noinherit"),
("pointer", "fg:magenta noinherit"),
]
),
).ask()
if choice is None:
console.print("\n[red]no OpenAI backend selected. Exiting...[/red]")
exit(1)
display_name, url = choice
print(f"You selected: {display_name}\tURL: {url}")
return display_name, url

52
main.py
View File

@ -1,21 +1,31 @@
from tradingagents.graph.trading_graph import TradingAgentsGraph
from tradingagents.default_config import DEFAULT_CONFIG
# Create a custom config
config = DEFAULT_CONFIG.copy()
config["llm_provider"] = "google" # Use a different model
config["backend_url"] = "https://generativelanguage.googleapis.com/v1" # Use a different backend
config["deep_think_llm"] = "gemini-2.0-flash" # Use a different model
config["quick_think_llm"] = "gemini-2.0-flash" # Use a different model
config["max_debate_rounds"] = 1 # Increase debate rounds
config["online_tools"] = True # Increase debate rounds
# Initialize with custom config
ta = TradingAgentsGraph(debug=True, config=config)
# forward propagate
_, decision = ta.propagate("NVDA", "2024-05-10")
print(decision)
# Memorize mistakes and reflect
# ta.reflect_and_remember(1000) # parameter is the position returns
from tradingagents.graph.trading_graph import TradingAgentsGraph
from tradingagents.default_config import DEFAULT_CONFIG
from dotenv import load_dotenv
# Load environment variables from .env file
load_dotenv()
# Create a custom config
config = DEFAULT_CONFIG.copy()
config["deep_think_llm"] = "gpt-4o-mini" # Use a different model
config["quick_think_llm"] = "gpt-4o-mini" # Use a different model
config["max_debate_rounds"] = 1 # Increase debate rounds
# Configure data vendors (default uses yfinance and alpha_vantage)
config["data_vendors"] = {
"core_stock_apis": "yfinance", # Options: yfinance, alpha_vantage, local
"technical_indicators": "yfinance", # Options: yfinance, alpha_vantage, local
"fundamental_data": "alpha_vantage", # Options: openai, alpha_vantage, local
"news_data": "alpha_vantage", # Options: openai, alpha_vantage, google, local
}
# Initialize with custom config
ta = TradingAgentsGraph(debug=True, config=config)
# forward propagate
_, decision = ta.propagate("NVDA", "2024-05-10")
print(decision)
# Memorize mistakes and reflect
# ta.reflect_and_remember(1000) # parameter is the position returns

69
pyproject.toml
View File

@ -1,34 +1,35 @@
[project]
name = "tradingagents"
version = "0.1.0"
description = "Add your description here"
readme = "README.md"
requires-python = ">=3.10"
dependencies = [
"akshare>=1.16.98",
"backtrader>=1.9.78.123",
"chainlit>=2.5.5",
"chromadb>=1.0.12",
"eodhd>=1.0.32",
"feedparser>=6.0.11",
"finnhub-python>=2.4.23",
"langchain-anthropic>=0.3.15",
"langchain-experimental>=0.3.4",
"langchain-google-genai>=2.1.5",
"langchain-openai>=0.3.23",
"langgraph>=0.4.8",
"pandas>=2.3.0",
"parsel>=1.10.0",
"praw>=7.8.1",
"pytz>=2025.2",
"questionary>=2.1.0",
"redis>=6.2.0",
"requests>=2.32.4",
"rich>=14.0.0",
"setuptools>=80.9.0",
"stockstats>=0.6.5",
"tqdm>=4.67.1",
"tushare>=1.4.21",
"typing-extensions>=4.14.0",
"yfinance>=0.2.63",
]
[project]
name = "tradingagents"
version = "0.1.0"
description = "Add your description here"
readme = "README.md"
requires-python = ">=3.10"
dependencies = [
"akshare>=1.16.98",
"backtrader>=1.9.78.123",
"chainlit>=2.5.5",
"chromadb>=1.0.12",
"eodhd>=1.0.32",
"feedparser>=6.0.11",
"finnhub-python>=2.4.23",
"grip>=4.6.2",
"langchain-anthropic>=0.3.15",
"langchain-experimental>=0.3.4",
"langchain-google-genai>=2.1.5",
"langchain-openai>=0.3.23",
"langgraph>=0.4.8",
"pandas>=2.3.0",
"parsel>=1.10.0",
"praw>=7.8.1",
"pytz>=2025.2",
"questionary>=2.1.0",
"redis>=6.2.0",
"requests>=2.32.4",
"rich>=14.0.0",
"setuptools>=80.9.0",
"stockstats>=0.6.5",
"tqdm>=4.67.1",
"tushare>=1.4.21",
"typing-extensions>=4.14.0",
"yfinance>=0.2.63",
]

86
setup.py
View File

@ -1,43 +1,43 @@
"""
Setup script for the TradingAgents package.
"""
from setuptools import setup, find_packages
setup(
name="tradingagents",
version="0.1.0",
description="Multi-Agents LLM Financial Trading Framework",
author="TradingAgents Team",
author_email="yijia.xiao@cs.ucla.edu",
url="https://github.com/TauricResearch",
packages=find_packages(),
install_requires=[
"langchain>=0.1.0",
"langchain-openai>=0.0.2",
"langchain-experimental>=0.0.40",
"langgraph>=0.0.20",
"numpy>=1.24.0",
"pandas>=2.0.0",
"praw>=7.7.0",
"stockstats>=0.5.4",
"yfinance>=0.2.31",
"typer>=0.9.0",
"rich>=13.0.0",
"questionary>=2.0.1",
],
python_requires=">=3.10",
entry_points={
"console_scripts": [
"tradingagents=cli.main:app",
],
},
classifiers=[
"Development Status :: 3 - Alpha",
"Intended Audience :: Financial and Trading Industry",
"License :: OSI Approved :: Apache Software License",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.10",
"Topic :: Office/Business :: Financial :: Investment",
],
)
"""
Setup script for the TradingAgents package.
"""
from setuptools import setup, find_packages
setup(
name="tradingagents",
version="0.1.0",
description="Multi-Agents LLM Financial Trading Framework",
author="TradingAgents Team",
author_email="yijia.xiao@cs.ucla.edu",
url="https://github.com/TauricResearch",
packages=find_packages(),
install_requires=[
"langchain>=0.1.0",
"langchain-openai>=0.0.2",
"langchain-experimental>=0.0.40",
"langgraph>=0.0.20",
"numpy>=1.24.0",
"pandas>=2.0.0",
"praw>=7.7.0",
"stockstats>=0.5.4",
"yfinance>=0.2.31",
"typer>=0.9.0",
"rich>=13.0.0",
"questionary>=2.0.1",
],
python_requires=">=3.10",
entry_points={
"console_scripts": [
"tradingagents=cli.main:app",
],
},
classifiers=[
"Development Status :: 3 - Alpha",
"Intended Audience :: Financial and Trading Industry",
"License :: OSI Approved :: Apache Software License",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.10",
"Topic :: Office/Business :: Financial :: Investment",
],
)

11
test.py Normal file
View File

@ -0,0 +1,11 @@
import time
from tradingagents.dataflows.y_finance import get_YFin_data_online, get_stock_stats_indicators_window, get_balance_sheet as get_yfinance_balance_sheet, get_cashflow as get_yfinance_cashflow, get_income_statement as get_yfinance_income_statement, get_insider_transactions as get_yfinance_insider_transactions
print("Testing optimized implementation with 30-day lookback:")
start_time = time.time()
result = get_stock_stats_indicators_window("AAPL", "macd", "2024-11-01", 30)
end_time = time.time()
print(f"Execution time: {end_time - start_time:.2f} seconds")
print(f"Result length: {len(result)} characters")
print(result)

81
tradingagents/agents/__init__.py
View File

@ -1,41 +1,40 @@
from .utils.agent_utils import Toolkit, create_msg_delete
from .utils.agent_states import AgentState, InvestDebateState, RiskDebateState
from .utils.memory import FinancialSituationMemory
from .analysts.fundamentals_analyst import create_fundamentals_analyst
from .analysts.market_analyst import create_market_analyst
from .analysts.news_analyst import create_news_analyst
from .analysts.social_media_analyst import create_social_media_analyst
from .researchers.bear_researcher import create_bear_researcher
from .researchers.bull_researcher import create_bull_researcher
from .risk_mgmt.aggresive_debator import create_risky_debator
from .risk_mgmt.conservative_debator import create_safe_debator
from .risk_mgmt.neutral_debator import create_neutral_debator
from .managers.research_manager import create_research_manager
from .managers.risk_manager import create_risk_manager
from .trader.trader import create_trader
__all__ = [
"FinancialSituationMemory",
"Toolkit",
"AgentState",
"create_msg_delete",
"InvestDebateState",
"RiskDebateState",
"create_bear_researcher",
"create_bull_researcher",
"create_research_manager",
"create_fundamentals_analyst",
"create_market_analyst",
"create_neutral_debator",
"create_news_analyst",
"create_risky_debator",
"create_risk_manager",
"create_safe_debator",
"create_social_media_analyst",
"create_trader",
]
from .utils.agent_utils import create_msg_delete
from .utils.agent_states import AgentState, InvestDebateState, RiskDebateState
from .utils.memory import FinancialSituationMemory
from .analysts.fundamentals_analyst import create_fundamentals_analyst
from .analysts.market_analyst import create_market_analyst
from .analysts.news_analyst import create_news_analyst
from .analysts.social_media_analyst import create_social_media_analyst
from .researchers.bear_researcher import create_bear_researcher
from .researchers.bull_researcher import create_bull_researcher
from .risk_mgmt.aggresive_debator import create_risky_debator
from .risk_mgmt.conservative_debator import create_safe_debator
from .risk_mgmt.neutral_debator import create_neutral_debator
from .managers.research_manager import create_research_manager
from .managers.risk_manager import create_risk_manager
from .trader.trader import create_trader
__all__ = [
"FinancialSituationMemory",
"AgentState",
"create_msg_delete",
"InvestDebateState",
"RiskDebateState",
"create_bear_researcher",
"create_bull_researcher",
"create_research_manager",
"create_fundamentals_analyst",
"create_market_analyst",
"create_neutral_debator",
"create_news_analyst",
"create_risky_debator",
"create_risk_manager",
"create_safe_debator",
"create_social_media_analyst",
"create_trader",
]

127
tradingagents/agents/analysts/fundamentals_analyst.py
View File

@ -1,64 +1,63 @@
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
import time
import json
def create_fundamentals_analyst(llm, toolkit):
def fundamentals_analyst_node(state):
current_date = state["trade_date"]
ticker = state["company_of_interest"]
company_name = state["company_of_interest"]
if toolkit.config["online_tools"]:
tools = [toolkit.get_fundamentals_openai]
else:
tools = [
toolkit.get_finnhub_company_insider_sentiment,
toolkit.get_finnhub_company_insider_transactions,
toolkit.get_simfin_balance_sheet,
toolkit.get_simfin_cashflow,
toolkit.get_simfin_income_stmt,
]
system_message = (
"You are a researcher tasked with analyzing fundamental information over the past week about a company. Please write a comprehensive report of the company's fundamental information such as financial documents, company profile, basic company financials, company financial history, insider sentiment and insider transactions to gain a full view of the company's fundamental information to inform traders. Make sure to include as much detail as possible. Do not simply state the trends are mixed, provide detailed and finegrained analysis and insights that may help traders make decisions."
+ " Make sure to append a Markdown table at the end of the report to organize key points in the report, organized and easy to read.",
)
prompt = ChatPromptTemplate.from_messages(
[
(
"system",
"You are a helpful AI assistant, collaborating with other assistants."
" Use the provided tools to progress towards answering the question."
" If you are unable to fully answer, that's OK; another assistant with different tools"
" will help where you left off. Execute what you can to make progress."
" If you or any other assistant has the FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** or deliverable,"
" prefix your response with FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** so the team knows to stop."
" You have access to the following tools: {tool_names}.\n{system_message}"
"For your reference, the current date is {current_date}. The company we want to look at is {ticker}",
),
MessagesPlaceholder(variable_name="messages"),
]
)
prompt = prompt.partial(system_message=system_message)
prompt = prompt.partial(tool_names=", ".join([tool.name for tool in tools]))
prompt = prompt.partial(current_date=current_date)
prompt = prompt.partial(ticker=ticker)
chain = prompt | llm.bind_tools(tools)
result = chain.invoke(state["messages"])
report = ""
if len(result.tool_calls) == 0:
report = result.content
return {
"messages": [result],
"fundamentals_report": report,
}
return fundamentals_analyst_node
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
import time
import json
from tradingagents.agents.utils.agent_utils import get_fundamentals, get_balance_sheet, get_cashflow, get_income_statement, get_insider_sentiment, get_insider_transactions
from tradingagents.dataflows.config import get_config
def create_fundamentals_analyst(llm):
def fundamentals_analyst_node(state):
current_date = state["trade_date"]
ticker = state["company_of_interest"]
company_name = state["company_of_interest"]
tools = [
get_fundamentals,
get_balance_sheet,
get_cashflow,
get_income_statement,
]
system_message = (
"You are a researcher tasked with analyzing fundamental information over the past week about a company. Please write a comprehensive report of the company's fundamental information such as financial documents, company profile, basic company financials, and company financial history to gain a full view of the company's fundamental information to inform traders. Make sure to include as much detail as possible. Do not simply state the trends are mixed, provide detailed and finegrained analysis and insights that may help traders make decisions."
+ " Make sure to append a Markdown table at the end of the report to organize key points in the report, organized and easy to read."
+ " Use the available tools: `get_fundamentals` for comprehensive company analysis, `get_balance_sheet`, `get_cashflow`, and `get_income_statement` for specific financial statements.",
)
prompt = ChatPromptTemplate.from_messages(
[
(
"system",
"You are a helpful AI assistant, collaborating with other assistants."
" Use the provided tools to progress towards answering the question."
" If you are unable to fully answer, that's OK; another assistant with different tools"
" will help where you left off. Execute what you can to make progress."
" If you or any other assistant has the FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** or deliverable,"
" prefix your response with FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** so the team knows to stop."
" You have access to the following tools: {tool_names}.\n{system_message}"
"For your reference, the current date is {current_date}. The company we want to look at is {ticker}",
),
MessagesPlaceholder(variable_name="messages"),
]
)
prompt = prompt.partial(system_message=system_message)
prompt = prompt.partial(tool_names=", ".join([tool.name for tool in tools]))
prompt = prompt.partial(current_date=current_date)
prompt = prompt.partial(ticker=ticker)
chain = prompt | llm.bind_tools(tools)
result = chain.invoke(state["messages"])
report = ""
if len(result.tool_calls) == 0:
report = result.content
return {
"messages": [result],
"fundamentals_report": report,
}
return fundamentals_analyst_node

174
tradingagents/agents/analysts/market_analyst.py
View File

@ -1,89 +1,85 @@
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
import time
import json
def create_market_analyst(llm, toolkit):
def market_analyst_node(state):
current_date = state["trade_date"]
ticker = state["company_of_interest"]
company_name = state["company_of_interest"]
if toolkit.config["online_tools"]:
tools = [
toolkit.get_YFin_data_online,
toolkit.get_stockstats_indicators_report_online,
]
else:
tools = [
toolkit.get_YFin_data,
toolkit.get_stockstats_indicators_report,
]
system_message = (
"""You are a trading assistant tasked with analyzing financial markets. Your role is to select the **most relevant indicators** for a given market condition or trading strategy from the following list. The goal is to choose up to **8 indicators** that provide complementary insights without redundancy. Categories and each category's indicators are:
Moving Averages:
- close_50_sma: 50 SMA: A medium-term trend indicator. Usage: Identify trend direction and serve as dynamic support/resistance. Tips: It lags price; combine with faster indicators for timely signals.
- close_200_sma: 200 SMA: A long-term trend benchmark. Usage: Confirm overall market trend and identify golden/death cross setups. Tips: It reacts slowly; best for strategic trend confirmation rather than frequent trading entries.
- close_10_ema: 10 EMA: A responsive short-term average. Usage: Capture quick shifts in momentum and potential entry points. Tips: Prone to noise in choppy markets; use alongside longer averages for filtering false signals.
MACD Related:
- macd: MACD: Computes momentum via differences of EMAs. Usage: Look for crossovers and divergence as signals of trend changes. Tips: Confirm with other indicators in low-volatility or sideways markets.
- macds: MACD Signal: An EMA smoothing of the MACD line. Usage: Use crossovers with the MACD line to trigger trades. Tips: Should be part of a broader strategy to avoid false positives.
- macdh: MACD Histogram: Shows the gap between the MACD line and its signal. Usage: Visualize momentum strength and spot divergence early. Tips: Can be volatile; complement with additional filters in fast-moving markets.
Momentum Indicators:
- rsi: RSI: Measures momentum to flag overbought/oversold conditions. Usage: Apply 70/30 thresholds and watch for divergence to signal reversals. Tips: In strong trends, RSI may remain extreme; always cross-check with trend analysis.
Volatility Indicators:
- boll: Bollinger Middle: A 20 SMA serving as the basis for Bollinger Bands. Usage: Acts as a dynamic benchmark for price movement. Tips: Combine with the upper and lower bands to effectively spot breakouts or reversals.
- boll_ub: Bollinger Upper Band: Typically 2 standard deviations above the middle line. Usage: Signals potential overbought conditions and breakout zones. Tips: Confirm signals with other tools; prices may ride the band in strong trends.
- boll_lb: Bollinger Lower Band: Typically 2 standard deviations below the middle line. Usage: Indicates potential oversold conditions. Tips: Use additional analysis to avoid false reversal signals.
- atr: ATR: Averages true range to measure volatility. Usage: Set stop-loss levels and adjust position sizes based on current market volatility. Tips: It's a reactive measure, so use it as part of a broader risk management strategy.
Volume-Based Indicators:
- vwma: VWMA: A moving average weighted by volume. Usage: Confirm trends by integrating price action with volume data. Tips: Watch for skewed results from volume spikes; use in combination with other volume analyses.
- Select indicators that provide diverse and complementary information. Avoid redundancy (e.g., do not select both rsi and stochrsi). Also briefly explain why they are suitable for the given market context. When you tool call, please use the exact name of the indicators provided above as they are defined parameters, otherwise your call will fail. Please make sure to call get_YFin_data first to retrieve the CSV that is needed to generate indicators. Write a very detailed and nuanced report of the trends you observe. Do not simply state the trends are mixed, provide detailed and finegrained analysis and insights that may help traders make decisions."""
+ """ Make sure to append a Markdown table at the end of the report to organize key points in the report, organized and easy to read."""
)
prompt = ChatPromptTemplate.from_messages(
[
(
"system",
"You are a helpful AI assistant, collaborating with other assistants."
" Use the provided tools to progress towards answering the question."
" If you are unable to fully answer, that's OK; another assistant with different tools"
" will help where you left off. Execute what you can to make progress."
" If you or any other assistant has the FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** or deliverable,"
" prefix your response with FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** so the team knows to stop."
" You have access to the following tools: {tool_names}.\n{system_message}"
"For your reference, the current date is {current_date}. The company we want to look at is {ticker}",
),
MessagesPlaceholder(variable_name="messages"),
]
)
prompt = prompt.partial(system_message=system_message)
prompt = prompt.partial(tool_names=", ".join([tool.name for tool in tools]))
prompt = prompt.partial(current_date=current_date)
prompt = prompt.partial(ticker=ticker)
chain = prompt | llm.bind_tools(tools)
result = chain.invoke(state["messages"])
report = ""
if len(result.tool_calls) == 0:
report = result.content
return {
"messages": [result],
"market_report": report,
}
return market_analyst_node
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
import time
import json
from tradingagents.agents.utils.agent_utils import get_stock_data, get_indicators
from tradingagents.dataflows.config import get_config
def create_market_analyst(llm):
def market_analyst_node(state):
current_date = state["trade_date"]
ticker = state["company_of_interest"]
company_name = state["company_of_interest"]
tools = [
get_stock_data,
get_indicators,
]
system_message = (
"""You are a trading assistant tasked with analyzing financial markets. Your role is to select the **most relevant indicators** for a given market condition or trading strategy from the following list. The goal is to choose up to **8 indicators** that provide complementary insights without redundancy. Categories and each category's indicators are:
Moving Averages:
- close_50_sma: 50 SMA: A medium-term trend indicator. Usage: Identify trend direction and serve as dynamic support/resistance. Tips: It lags price; combine with faster indicators for timely signals.
- close_200_sma: 200 SMA: A long-term trend benchmark. Usage: Confirm overall market trend and identify golden/death cross setups. Tips: It reacts slowly; best for strategic trend confirmation rather than frequent trading entries.
- close_10_ema: 10 EMA: A responsive short-term average. Usage: Capture quick shifts in momentum and potential entry points. Tips: Prone to noise in choppy markets; use alongside longer averages for filtering false signals.
MACD Related:
- macd: MACD: Computes momentum via differences of EMAs. Usage: Look for crossovers and divergence as signals of trend changes. Tips: Confirm with other indicators in low-volatility or sideways markets.
- macds: MACD Signal: An EMA smoothing of the MACD line. Usage: Use crossovers with the MACD line to trigger trades. Tips: Should be part of a broader strategy to avoid false positives.
- macdh: MACD Histogram: Shows the gap between the MACD line and its signal. Usage: Visualize momentum strength and spot divergence early. Tips: Can be volatile; complement with additional filters in fast-moving markets.
Momentum Indicators:
- rsi: RSI: Measures momentum to flag overbought/oversold conditions. Usage: Apply 70/30 thresholds and watch for divergence to signal reversals. Tips: In strong trends, RSI may remain extreme; always cross-check with trend analysis.
Volatility Indicators:
- boll: Bollinger Middle: A 20 SMA serving as the basis for Bollinger Bands. Usage: Acts as a dynamic benchmark for price movement. Tips: Combine with the upper and lower bands to effectively spot breakouts or reversals.
- boll_ub: Bollinger Upper Band: Typically 2 standard deviations above the middle line. Usage: Signals potential overbought conditions and breakout zones. Tips: Confirm signals with other tools; prices may ride the band in strong trends.
- boll_lb: Bollinger Lower Band: Typically 2 standard deviations below the middle line. Usage: Indicates potential oversold conditions. Tips: Use additional analysis to avoid false reversal signals.
- atr: ATR: Averages true range to measure volatility. Usage: Set stop-loss levels and adjust position sizes based on current market volatility. Tips: It's a reactive measure, so use it as part of a broader risk management strategy.
Volume-Based Indicators:
- vwma: VWMA: A moving average weighted by volume. Usage: Confirm trends by integrating price action with volume data. Tips: Watch for skewed results from volume spikes; use in combination with other volume analyses.
- Select indicators that provide diverse and complementary information. Avoid redundancy (e.g., do not select both rsi and stochrsi). Also briefly explain why they are suitable for the given market context. When you tool call, please use the exact name of the indicators provided above as they are defined parameters, otherwise your call will fail. Please make sure to call get_stock_data first to retrieve the CSV that is needed to generate indicators. Then use get_indicators with the specific indicator names. Write a very detailed and nuanced report of the trends you observe. Do not simply state the trends are mixed, provide detailed and finegrained analysis and insights that may help traders make decisions."""
+ """ Make sure to append a Markdown table at the end of the report to organize key points in the report, organized and easy to read."""
)
prompt = ChatPromptTemplate.from_messages(
[
(
"system",
"You are a helpful AI assistant, collaborating with other assistants."
" Use the provided tools to progress towards answering the question."
" If you are unable to fully answer, that's OK; another assistant with different tools"
" will help where you left off. Execute what you can to make progress."
" If you or any other assistant has the FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** or deliverable,"
" prefix your response with FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** so the team knows to stop."
" You have access to the following tools: {tool_names}.\n{system_message}"
"For your reference, the current date is {current_date}. The company we want to look at is {ticker}",
),
MessagesPlaceholder(variable_name="messages"),
]
)
prompt = prompt.partial(system_message=system_message)
prompt = prompt.partial(tool_names=", ".join([tool.name for tool in tools]))
prompt = prompt.partial(current_date=current_date)
prompt = prompt.partial(ticker=ticker)
chain = prompt | llm.bind_tools(tools)
result = chain.invoke(state["messages"])
report = ""
if len(result.tool_calls) == 0:
report = result.content
return {
"messages": [result],
"market_report": report,
}
return market_analyst_node

118
tradingagents/agents/analysts/news_analyst.py
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@ -1,60 +1,58 @@
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
import time
import json
def create_news_analyst(llm, toolkit):
def news_analyst_node(state):
current_date = state["trade_date"]
ticker = state["company_of_interest"]
if toolkit.config["online_tools"]:
tools = [toolkit.get_global_news_openai, toolkit.get_google_news]
else:
tools = [
toolkit.get_finnhub_news,
toolkit.get_reddit_news,
toolkit.get_google_news,
]
system_message = (
"You are a news researcher tasked with analyzing recent news and trends over the past week. Please write a comprehensive report of the current state of the world that is relevant for trading and macroeconomics. Look at news from EODHD, and finnhub to be comprehensive. Do not simply state the trends are mixed, provide detailed and finegrained analysis and insights that may help traders make decisions."
+ """ Make sure to append a Makrdown table at the end of the report to organize key points in the report, organized and easy to read."""
)
prompt = ChatPromptTemplate.from_messages(
[
(
"system",
"You are a helpful AI assistant, collaborating with other assistants."
" Use the provided tools to progress towards answering the question."
" If you are unable to fully answer, that's OK; another assistant with different tools"
" will help where you left off. Execute what you can to make progress."
" If you or any other assistant has the FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** or deliverable,"
" prefix your response with FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** so the team knows to stop."
" You have access to the following tools: {tool_names}.\n{system_message}"
"For your reference, the current date is {current_date}. We are looking at the company {ticker}",
),
MessagesPlaceholder(variable_name="messages"),
]
)
prompt = prompt.partial(system_message=system_message)
prompt = prompt.partial(tool_names=", ".join([tool.name for tool in tools]))
prompt = prompt.partial(current_date=current_date)
prompt = prompt.partial(ticker=ticker)
chain = prompt | llm.bind_tools(tools)
result = chain.invoke(state["messages"])
report = ""
if len(result.tool_calls) == 0:
report = result.content
return {
"messages": [result],
"news_report": report,
}
return news_analyst_node
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
import time
import json
from tradingagents.agents.utils.agent_utils import get_news, get_global_news
from tradingagents.dataflows.config import get_config
def create_news_analyst(llm):
def news_analyst_node(state):
current_date = state["trade_date"]
ticker = state["company_of_interest"]
tools = [
get_news,
get_global_news,
]
system_message = (
"You are a news researcher tasked with analyzing recent news and trends over the past week. Please write a comprehensive report of the current state of the world that is relevant for trading and macroeconomics. Use the available tools: get_news(query, start_date, end_date) for company-specific or targeted news searches, and get_global_news(curr_date, look_back_days, limit) for broader macroeconomic news. Do not simply state the trends are mixed, provide detailed and finegrained analysis and insights that may help traders make decisions."
+ """ Make sure to append a Markdown table at the end of the report to organize key points in the report, organized and easy to read."""
)
prompt = ChatPromptTemplate.from_messages(
[
(
"system",
"You are a helpful AI assistant, collaborating with other assistants."
" Use the provided tools to progress towards answering the question."
" If you are unable to fully answer, that's OK; another assistant with different tools"
" will help where you left off. Execute what you can to make progress."
" If you or any other assistant has the FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** or deliverable,"
" prefix your response with FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** so the team knows to stop."
" You have access to the following tools: {tool_names}.\n{system_message}"
"For your reference, the current date is {current_date}. We are looking at the company {ticker}",
),
MessagesPlaceholder(variable_name="messages"),
]
)
prompt = prompt.partial(system_message=system_message)
prompt = prompt.partial(tool_names=", ".join([tool.name for tool in tools]))
prompt = prompt.partial(current_date=current_date)
prompt = prompt.partial(ticker=ticker)
chain = prompt | llm.bind_tools(tools)
result = chain.invoke(state["messages"])
report = ""
if len(result.tool_calls) == 0:
report = result.content
return {
"messages": [result],
"news_report": report,
}
return news_analyst_node

119
tradingagents/agents/analysts/social_media_analyst.py
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@ -1,60 +1,59 @@
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
import time
import json
def create_social_media_analyst(llm, toolkit):
def social_media_analyst_node(state):
current_date = state["trade_date"]
ticker = state["company_of_interest"]
company_name = state["company_of_interest"]
if toolkit.config["online_tools"]:
tools = [toolkit.get_stock_news_openai]
else:
tools = [
toolkit.get_reddit_stock_info,
]
system_message = (
"You are a social media and company specific news researcher/analyst tasked with analyzing social media posts, recent company news, and public sentiment for a specific company over the past week. You will be given a company's name your objective is to write a comprehensive long report detailing your analysis, insights, and implications for traders and investors on this company's current state after looking at social media and what people are saying about that company, analyzing sentiment data of what people feel each day about the company, and looking at recent company news. Try to look at all sources possible from social media to sentiment to news. Do not simply state the trends are mixed, provide detailed and finegrained analysis and insights that may help traders make decisions."
+ """ Make sure to append a Makrdown table at the end of the report to organize key points in the report, organized and easy to read.""",
)
prompt = ChatPromptTemplate.from_messages(
[
(
"system",
"You are a helpful AI assistant, collaborating with other assistants."
" Use the provided tools to progress towards answering the question."
" If you are unable to fully answer, that's OK; another assistant with different tools"
" will help where you left off. Execute what you can to make progress."
" If you or any other assistant has the FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** or deliverable,"
" prefix your response with FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** so the team knows to stop."
" You have access to the following tools: {tool_names}.\n{system_message}"
"For your reference, the current date is {current_date}. The current company we want to analyze is {ticker}",
),
MessagesPlaceholder(variable_name="messages"),
]
)
prompt = prompt.partial(system_message=system_message)
prompt = prompt.partial(tool_names=", ".join([tool.name for tool in tools]))
prompt = prompt.partial(current_date=current_date)
prompt = prompt.partial(ticker=ticker)
chain = prompt | llm.bind_tools(tools)
result = chain.invoke(state["messages"])
report = ""
if len(result.tool_calls) == 0:
report = result.content
return {
"messages": [result],
"sentiment_report": report,
}
return social_media_analyst_node
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
import time
import json
from tradingagents.agents.utils.agent_utils import get_news
from tradingagents.dataflows.config import get_config
def create_social_media_analyst(llm):
def social_media_analyst_node(state):
current_date = state["trade_date"]
ticker = state["company_of_interest"]
company_name = state["company_of_interest"]
tools = [
get_news,
]
system_message = (
"You are a social media and company specific news researcher/analyst tasked with analyzing social media posts, recent company news, and public sentiment for a specific company over the past week. You will be given a company's name your objective is to write a comprehensive long report detailing your analysis, insights, and implications for traders and investors on this company's current state after looking at social media and what people are saying about that company, analyzing sentiment data of what people feel each day about the company, and looking at recent company news. Use the get_news(query, start_date, end_date) tool to search for company-specific news and social media discussions. Try to look at all sources possible from social media to sentiment to news. Do not simply state the trends are mixed, provide detailed and finegrained analysis and insights that may help traders make decisions."
+ """ Make sure to append a Markdown table at the end of the report to organize key points in the report, organized and easy to read.""",
)
prompt = ChatPromptTemplate.from_messages(
[
(
"system",
"You are a helpful AI assistant, collaborating with other assistants."
" Use the provided tools to progress towards answering the question."
" If you are unable to fully answer, that's OK; another assistant with different tools"
" will help where you left off. Execute what you can to make progress."
" If you or any other assistant has the FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** or deliverable,"
" prefix your response with FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** so the team knows to stop."
" You have access to the following tools: {tool_names}.\n{system_message}"
"For your reference, the current date is {current_date}. The current company we want to analyze is {ticker}",
),
MessagesPlaceholder(variable_name="messages"),
]
)
prompt = prompt.partial(system_message=system_message)
prompt = prompt.partial(tool_names=", ".join([tool.name for tool in tools]))
prompt = prompt.partial(current_date=current_date)
prompt = prompt.partial(ticker=ticker)
chain = prompt | llm.bind_tools(tools)
result = chain.invoke(state["messages"])
report = ""
if len(result.tool_calls) == 0:
report = result.content
return {
"messages": [result],
"sentiment_report": report,
}
return social_media_analyst_node

110
tradingagents/agents/managers/research_manager.py
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@ -1,55 +1,55 @@
import time
import json
def create_research_manager(llm, memory):
def research_manager_node(state) -> dict:
history = state["investment_debate_state"].get("history", "")
market_research_report = state["market_report"]
sentiment_report = state["sentiment_report"]
news_report = state["news_report"]
fundamentals_report = state["fundamentals_report"]
investment_debate_state = state["investment_debate_state"]
curr_situation = f"{market_research_report}\n\n{sentiment_report}\n\n{news_report}\n\n{fundamentals_report}"
past_memories = memory.get_memories(curr_situation, n_matches=2)
past_memory_str = ""
for i, rec in enumerate(past_memories, 1):
past_memory_str += rec["recommendation"] + "\n\n"
prompt = f"""As the portfolio manager and debate facilitator, your role is to critically evaluate this round of debate and make a definitive decision: align with the bear analyst, the bull analyst, or choose Hold only if it is strongly justified based on the arguments presented.
Summarize the key points from both sides concisely, focusing on the most compelling evidence or reasoning. Your recommendation—Buy, Sell, or Hold—must be clear and actionable. Avoid defaulting to Hold simply because both sides have valid points; commit to a stance grounded in the debate's strongest arguments.
Additionally, develop a detailed investment plan for the trader. This should include:
Your Recommendation: A decisive stance supported by the most convincing arguments.
Rationale: An explanation of why these arguments lead to your conclusion.
Strategic Actions: Concrete steps for implementing the recommendation.
Take into account your past mistakes on similar situations. Use these insights to refine your decision-making and ensure you are learning and improving. Present your analysis conversationally, as if speaking naturally, without special formatting.
Here are your past reflections on mistakes:
\"{past_memory_str}\"
Here is the debate:
Debate History:
{history}"""
response = llm.invoke(prompt)
new_investment_debate_state = {
"judge_decision": response.content,
"history": investment_debate_state.get("history", ""),
"bear_history": investment_debate_state.get("bear_history", ""),
"bull_history": investment_debate_state.get("bull_history", ""),
"current_response": response.content,
"count": investment_debate_state["count"],
}
return {
"investment_debate_state": new_investment_debate_state,
"investment_plan": response.content,
}
return research_manager_node
import time
import json
def create_research_manager(llm, memory):
def research_manager_node(state) -> dict:
history = state["investment_debate_state"].get("history", "")
market_research_report = state["market_report"]
sentiment_report = state["sentiment_report"]
news_report = state["news_report"]
fundamentals_report = state["fundamentals_report"]
investment_debate_state = state["investment_debate_state"]
curr_situation = f"{market_research_report}\n\n{sentiment_report}\n\n{news_report}\n\n{fundamentals_report}"
past_memories = memory.get_memories(curr_situation, n_matches=2)
past_memory_str = ""
for i, rec in enumerate(past_memories, 1):
past_memory_str += rec["recommendation"] + "\n\n"
prompt = f"""As the portfolio manager and debate facilitator, your role is to critically evaluate this round of debate and make a definitive decision: align with the bear analyst, the bull analyst, or choose Hold only if it is strongly justified based on the arguments presented.
Summarize the key points from both sides concisely, focusing on the most compelling evidence or reasoning. Your recommendation—Buy, Sell, or Hold—must be clear and actionable. Avoid defaulting to Hold simply because both sides have valid points; commit to a stance grounded in the debate's strongest arguments.
Additionally, develop a detailed investment plan for the trader. This should include:
Your Recommendation: A decisive stance supported by the most convincing arguments.
Rationale: An explanation of why these arguments lead to your conclusion.
Strategic Actions: Concrete steps for implementing the recommendation.
Take into account your past mistakes on similar situations. Use these insights to refine your decision-making and ensure you are learning and improving. Present your analysis conversationally, as if speaking naturally, without special formatting.
Here are your past reflections on mistakes:
\"{past_memory_str}\"
Here is the debate:
Debate History:
{history}"""
response = llm.invoke(prompt)
new_investment_debate_state = {
"judge_decision": response.content,
"history": investment_debate_state.get("history", ""),
"bear_history": investment_debate_state.get("bear_history", ""),
"bull_history": investment_debate_state.get("bull_history", ""),
"current_response": response.content,
"count": investment_debate_state["count"],
}
return {
"investment_debate_state": new_investment_debate_state,
"investment_plan": response.content,
}
return research_manager_node

132
tradingagents/agents/managers/risk_manager.py
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@ -1,66 +1,66 @@
import time
import json
def create_risk_manager(llm, memory):
def risk_manager_node(state) -> dict:
company_name = state["company_of_interest"]
history = state["risk_debate_state"]["history"]
risk_debate_state = state["risk_debate_state"]
market_research_report = state["market_report"]
news_report = state["news_report"]
fundamentals_report = state["news_report"]
sentiment_report = state["sentiment_report"]
trader_plan = state["investment_plan"]
curr_situation = f"{market_research_report}\n\n{sentiment_report}\n\n{news_report}\n\n{fundamentals_report}"
past_memories = memory.get_memories(curr_situation, n_matches=2)
past_memory_str = ""
for i, rec in enumerate(past_memories, 1):
past_memory_str += rec["recommendation"] + "\n\n"
prompt = f"""As the Risk Management Judge and Debate Facilitator, your goal is to evaluate the debate between three risk analysts—Risky, Neutral, and Safe/Conservative—and determine the best course of action for the trader. Your decision must result in a clear recommendation: Buy, Sell, or Hold. Choose Hold only if strongly justified by specific arguments, not as a fallback when all sides seem valid. Strive for clarity and decisiveness.
Guidelines for Decision-Making:
1. **Summarize Key Arguments**: Extract the strongest points from each analyst, focusing on relevance to the context.
2. **Provide Rationale**: Support your recommendation with direct quotes and counterarguments from the debate.
3. **Refine the Trader's Plan**: Start with the trader's original plan, **{trader_plan}**, and adjust it based on the analysts' insights.
4. **Learn from Past Mistakes**: Use lessons from **{past_memory_str}** to address prior misjudgments and improve the decision you are making now to make sure you don't make a wrong BUY/SELL/HOLD call that loses money.
Deliverables:
- A clear and actionable recommendation: Buy, Sell, or Hold.
- Detailed reasoning anchored in the debate and past reflections.
---
**Analysts Debate History:**
{history}
---
Focus on actionable insights and continuous improvement. Build on past lessons, critically evaluate all perspectives, and ensure each decision advances better outcomes."""
response = llm.invoke(prompt)
new_risk_debate_state = {
"judge_decision": response.content,
"history": risk_debate_state["history"],
"risky_history": risk_debate_state["risky_history"],
"safe_history": risk_debate_state["safe_history"],
"neutral_history": risk_debate_state["neutral_history"],
"latest_speaker": "Judge",
"current_risky_response": risk_debate_state["current_risky_response"],
"current_safe_response": risk_debate_state["current_safe_response"],
"current_neutral_response": risk_debate_state["current_neutral_response"],
"count": risk_debate_state["count"],
}
return {
"risk_debate_state": new_risk_debate_state,
"final_trade_decision": response.content,
}
return risk_manager_node
import time
import json
def create_risk_manager(llm, memory):
def risk_manager_node(state) -> dict:
company_name = state["company_of_interest"]
history = state["risk_debate_state"]["history"]
risk_debate_state = state["risk_debate_state"]
market_research_report = state["market_report"]
news_report = state["news_report"]
fundamentals_report = state["news_report"]
sentiment_report = state["sentiment_report"]
trader_plan = state["investment_plan"]
curr_situation = f"{market_research_report}\n\n{sentiment_report}\n\n{news_report}\n\n{fundamentals_report}"
past_memories = memory.get_memories(curr_situation, n_matches=2)
past_memory_str = ""
for i, rec in enumerate(past_memories, 1):
past_memory_str += rec["recommendation"] + "\n\n"
prompt = f"""As the Risk Management Judge and Debate Facilitator, your goal is to evaluate the debate between three risk analysts—Risky, Neutral, and Safe/Conservative—and determine the best course of action for the trader. Your decision must result in a clear recommendation: Buy, Sell, or Hold. Choose Hold only if strongly justified by specific arguments, not as a fallback when all sides seem valid. Strive for clarity and decisiveness.
Guidelines for Decision-Making:
1. **Summarize Key Arguments**: Extract the strongest points from each analyst, focusing on relevance to the context.
2. **Provide Rationale**: Support your recommendation with direct quotes and counterarguments from the debate.
3. **Refine the Trader's Plan**: Start with the trader's original plan, **{trader_plan}**, and adjust it based on the analysts' insights.
4. **Learn from Past Mistakes**: Use lessons from **{past_memory_str}** to address prior misjudgments and improve the decision you are making now to make sure you don't make a wrong BUY/SELL/HOLD call that loses money.
Deliverables:
- A clear and actionable recommendation: Buy, Sell, or Hold.
- Detailed reasoning anchored in the debate and past reflections.
---
**Analysts Debate History:**
{history}
---
Focus on actionable insights and continuous improvement. Build on past lessons, critically evaluate all perspectives, and ensure each decision advances better outcomes."""
response = llm.invoke(prompt)
new_risk_debate_state = {
"judge_decision": response.content,
"history": risk_debate_state["history"],
"risky_history": risk_debate_state["risky_history"],
"safe_history": risk_debate_state["safe_history"],
"neutral_history": risk_debate_state["neutral_history"],
"latest_speaker": "Judge",
"current_risky_response": risk_debate_state["current_risky_response"],
"current_safe_response": risk_debate_state["current_safe_response"],
"current_neutral_response": risk_debate_state["current_neutral_response"],
"count": risk_debate_state["count"],
}
return {
"risk_debate_state": new_risk_debate_state,
"final_trade_decision": response.content,
}
return risk_manager_node

122
tradingagents/agents/researchers/bear_researcher.py
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@ -1,61 +1,61 @@
from langchain_core.messages import AIMessage
import time
import json
def create_bear_researcher(llm, memory):
def bear_node(state) -> dict:
investment_debate_state = state["investment_debate_state"]
history = investment_debate_state.get("history", "")
bear_history = investment_debate_state.get("bear_history", "")
current_response = investment_debate_state.get("current_response", "")
market_research_report = state["market_report"]
sentiment_report = state["sentiment_report"]
news_report = state["news_report"]
fundamentals_report = state["fundamentals_report"]
curr_situation = f"{market_research_report}\n\n{sentiment_report}\n\n{news_report}\n\n{fundamentals_report}"
past_memories = memory.get_memories(curr_situation, n_matches=2)
past_memory_str = ""
for i, rec in enumerate(past_memories, 1):
past_memory_str += rec["recommendation"] + "\n\n"
prompt = f"""You are a Bear Analyst making the case against investing in the stock. Your goal is to present a well-reasoned argument emphasizing risks, challenges, and negative indicators. Leverage the provided research and data to highlight potential downsides and counter bullish arguments effectively.
Key points to focus on:
- Risks and Challenges: Highlight factors like market saturation, financial instability, or macroeconomic threats that could hinder the stock's performance.
- Competitive Weaknesses: Emphasize vulnerabilities such as weaker market positioning, declining innovation, or threats from competitors.
- Negative Indicators: Use evidence from financial data, market trends, or recent adverse news to support your position.
- Bull Counterpoints: Critically analyze the bull argument with specific data and sound reasoning, exposing weaknesses or over-optimistic assumptions.
- Engagement: Present your argument in a conversational style, directly engaging with the bull analyst's points and debating effectively rather than simply listing facts.
Resources available:
Market research report: {market_research_report}
Social media sentiment report: {sentiment_report}
Latest world affairs news: {news_report}
Company fundamentals report: {fundamentals_report}
Conversation history of the debate: {history}
Last bull argument: {current_response}
Reflections from similar situations and lessons learned: {past_memory_str}
Use this information to deliver a compelling bear argument, refute the bull's claims, and engage in a dynamic debate that demonstrates the risks and weaknesses of investing in the stock. You must also address reflections and learn from lessons and mistakes you made in the past.
"""
response = llm.invoke(prompt)
argument = f"Bear Analyst: {response.content}"
new_investment_debate_state = {
"history": history + "\n" + argument,
"bear_history": bear_history + "\n" + argument,
"bull_history": investment_debate_state.get("bull_history", ""),
"current_response": argument,
"count": investment_debate_state["count"] + 1,
}
return {"investment_debate_state": new_investment_debate_state}
return bear_node
from langchain_core.messages import AIMessage
import time
import json
def create_bear_researcher(llm, memory):
def bear_node(state) -> dict:
investment_debate_state = state["investment_debate_state"]
history = investment_debate_state.get("history", "")
bear_history = investment_debate_state.get("bear_history", "")
current_response = investment_debate_state.get("current_response", "")
market_research_report = state["market_report"]
sentiment_report = state["sentiment_report"]
news_report = state["news_report"]
fundamentals_report = state["fundamentals_report"]
curr_situation = f"{market_research_report}\n\n{sentiment_report}\n\n{news_report}\n\n{fundamentals_report}"
past_memories = memory.get_memories(curr_situation, n_matches=2)
past_memory_str = ""
for i, rec in enumerate(past_memories, 1):
past_memory_str += rec["recommendation"] + "\n\n"
prompt = f"""You are a Bear Analyst making the case against investing in the stock. Your goal is to present a well-reasoned argument emphasizing risks, challenges, and negative indicators. Leverage the provided research and data to highlight potential downsides and counter bullish arguments effectively.
Key points to focus on:
- Risks and Challenges: Highlight factors like market saturation, financial instability, or macroeconomic threats that could hinder the stock's performance.
- Competitive Weaknesses: Emphasize vulnerabilities such as weaker market positioning, declining innovation, or threats from competitors.
- Negative Indicators: Use evidence from financial data, market trends, or recent adverse news to support your position.
- Bull Counterpoints: Critically analyze the bull argument with specific data and sound reasoning, exposing weaknesses or over-optimistic assumptions.
- Engagement: Present your argument in a conversational style, directly engaging with the bull analyst's points and debating effectively rather than simply listing facts.
Resources available:
Market research report: {market_research_report}
Social media sentiment report: {sentiment_report}
Latest world affairs news: {news_report}
Company fundamentals report: {fundamentals_report}
Conversation history of the debate: {history}
Last bull argument: {current_response}
Reflections from similar situations and lessons learned: {past_memory_str}
Use this information to deliver a compelling bear argument, refute the bull's claims, and engage in a dynamic debate that demonstrates the risks and weaknesses of investing in the stock. You must also address reflections and learn from lessons and mistakes you made in the past.
"""
response = llm.invoke(prompt)
argument = f"Bear Analyst: {response.content}"
new_investment_debate_state = {
"history": history + "\n" + argument,
"bear_history": bear_history + "\n" + argument,
"bull_history": investment_debate_state.get("bull_history", ""),
"current_response": argument,
"count": investment_debate_state["count"] + 1,
}
return {"investment_debate_state": new_investment_debate_state}
return bear_node

118
tradingagents/agents/researchers/bull_researcher.py
View File

@ -1,59 +1,59 @@
from langchain_core.messages import AIMessage
import time
import json
def create_bull_researcher(llm, memory):
def bull_node(state) -> dict:
investment_debate_state = state["investment_debate_state"]
history = investment_debate_state.get("history", "")
bull_history = investment_debate_state.get("bull_history", "")
current_response = investment_debate_state.get("current_response", "")
market_research_report = state["market_report"]
sentiment_report = state["sentiment_report"]
news_report = state["news_report"]
fundamentals_report = state["fundamentals_report"]
curr_situation = f"{market_research_report}\n\n{sentiment_report}\n\n{news_report}\n\n{fundamentals_report}"
past_memories = memory.get_memories(curr_situation, n_matches=2)
past_memory_str = ""
for i, rec in enumerate(past_memories, 1):
past_memory_str += rec["recommendation"] + "\n\n"
prompt = f"""You are a Bull Analyst advocating for investing in the stock. Your task is to build a strong, evidence-based case emphasizing growth potential, competitive advantages, and positive market indicators. Leverage the provided research and data to address concerns and counter bearish arguments effectively.
Key points to focus on:
- Growth Potential: Highlight the company's market opportunities, revenue projections, and scalability.
- Competitive Advantages: Emphasize factors like unique products, strong branding, or dominant market positioning.
- Positive Indicators: Use financial health, industry trends, and recent positive news as evidence.
- Bear Counterpoints: Critically analyze the bear argument with specific data and sound reasoning, addressing concerns thoroughly and showing why the bull perspective holds stronger merit.
- Engagement: Present your argument in a conversational style, engaging directly with the bear analyst's points and debating effectively rather than just listing data.
Resources available:
Market research report: {market_research_report}
Social media sentiment report: {sentiment_report}
Latest world affairs news: {news_report}
Company fundamentals report: {fundamentals_report}
Conversation history of the debate: {history}
Last bear argument: {current_response}
Reflections from similar situations and lessons learned: {past_memory_str}
Use this information to deliver a compelling bull argument, refute the bear's concerns, and engage in a dynamic debate that demonstrates the strengths of the bull position. You must also address reflections and learn from lessons and mistakes you made in the past.
"""
response = llm.invoke(prompt)
argument = f"Bull Analyst: {response.content}"
new_investment_debate_state = {
"history": history + "\n" + argument,
"bull_history": bull_history + "\n" + argument,
"bear_history": investment_debate_state.get("bear_history", ""),
"current_response": argument,
"count": investment_debate_state["count"] + 1,
}
return {"investment_debate_state": new_investment_debate_state}
return bull_node
from langchain_core.messages import AIMessage
import time
import json
def create_bull_researcher(llm, memory):
def bull_node(state) -> dict:
investment_debate_state = state["investment_debate_state"]
history = investment_debate_state.get("history", "")
bull_history = investment_debate_state.get("bull_history", "")
current_response = investment_debate_state.get("current_response", "")
market_research_report = state["market_report"]
sentiment_report = state["sentiment_report"]
news_report = state["news_report"]
fundamentals_report = state["fundamentals_report"]
curr_situation = f"{market_research_report}\n\n{sentiment_report}\n\n{news_report}\n\n{fundamentals_report}"
past_memories = memory.get_memories(curr_situation, n_matches=2)
past_memory_str = ""
for i, rec in enumerate(past_memories, 1):
past_memory_str += rec["recommendation"] + "\n\n"
prompt = f"""You are a Bull Analyst advocating for investing in the stock. Your task is to build a strong, evidence-based case emphasizing growth potential, competitive advantages, and positive market indicators. Leverage the provided research and data to address concerns and counter bearish arguments effectively.
Key points to focus on:
- Growth Potential: Highlight the company's market opportunities, revenue projections, and scalability.
- Competitive Advantages: Emphasize factors like unique products, strong branding, or dominant market positioning.
- Positive Indicators: Use financial health, industry trends, and recent positive news as evidence.
- Bear Counterpoints: Critically analyze the bear argument with specific data and sound reasoning, addressing concerns thoroughly and showing why the bull perspective holds stronger merit.
- Engagement: Present your argument in a conversational style, engaging directly with the bear analyst's points and debating effectively rather than just listing data.
Resources available:
Market research report: {market_research_report}
Social media sentiment report: {sentiment_report}
Latest world affairs news: {news_report}
Company fundamentals report: {fundamentals_report}
Conversation history of the debate: {history}
Last bear argument: {current_response}
Reflections from similar situations and lessons learned: {past_memory_str}
Use this information to deliver a compelling bull argument, refute the bear's concerns, and engage in a dynamic debate that demonstrates the strengths of the bull position. You must also address reflections and learn from lessons and mistakes you made in the past.
"""
response = llm.invoke(prompt)
argument = f"Bull Analyst: {response.content}"
new_investment_debate_state = {
"history": history + "\n" + argument,
"bull_history": bull_history + "\n" + argument,
"bear_history": investment_debate_state.get("bear_history", ""),
"current_response": argument,
"count": investment_debate_state["count"] + 1,
}
return {"investment_debate_state": new_investment_debate_state}
return bull_node

110
tradingagents/agents/risk_mgmt/aggresive_debator.py
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@ -1,55 +1,55 @@
import time
import json
def create_risky_debator(llm):
def risky_node(state) -> dict:
risk_debate_state = state["risk_debate_state"]
history = risk_debate_state.get("history", "")
risky_history = risk_debate_state.get("risky_history", "")
current_safe_response = risk_debate_state.get("current_safe_response", "")
current_neutral_response = risk_debate_state.get("current_neutral_response", "")
market_research_report = state["market_report"]
sentiment_report = state["sentiment_report"]
news_report = state["news_report"]
fundamentals_report = state["fundamentals_report"]
trader_decision = state["trader_investment_plan"]
prompt = f"""As the Risky Risk Analyst, your role is to actively champion high-reward, high-risk opportunities, emphasizing bold strategies and competitive advantages. When evaluating the trader's decision or plan, focus intently on the potential upside, growth potential, and innovative benefits—even when these come with elevated risk. Use the provided market data and sentiment analysis to strengthen your arguments and challenge the opposing views. Specifically, respond directly to each point made by the conservative and neutral analysts, countering with data-driven rebuttals and persuasive reasoning. Highlight where their caution might miss critical opportunities or where their assumptions may be overly conservative. Here is the trader's decision:
{trader_decision}
Your task is to create a compelling case for the trader's decision by questioning and critiquing the conservative and neutral stances to demonstrate why your high-reward perspective offers the best path forward. Incorporate insights from the following sources into your arguments:
Market Research Report: {market_research_report}
Social Media Sentiment Report: {sentiment_report}
Latest World Affairs Report: {news_report}
Company Fundamentals Report: {fundamentals_report}
Here is the current conversation history: {history} Here are the last arguments from the conservative analyst: {current_safe_response} Here are the last arguments from the neutral analyst: {current_neutral_response}. If there are no responses from the other viewpoints, do not halluncinate and just present your point.
Engage actively by addressing any specific concerns raised, refuting the weaknesses in their logic, and asserting the benefits of risk-taking to outpace market norms. Maintain a focus on debating and persuading, not just presenting data. Challenge each counterpoint to underscore why a high-risk approach is optimal. Output conversationally as if you are speaking without any special formatting."""
response = llm.invoke(prompt)
argument = f"Risky Analyst: {response.content}"
new_risk_debate_state = {
"history": history + "\n" + argument,
"risky_history": risky_history + "\n" + argument,
"safe_history": risk_debate_state.get("safe_history", ""),
"neutral_history": risk_debate_state.get("neutral_history", ""),
"latest_speaker": "Risky",
"current_risky_response": argument,
"current_safe_response": risk_debate_state.get("current_safe_response", ""),
"current_neutral_response": risk_debate_state.get(
"current_neutral_response", ""
),
"count": risk_debate_state["count"] + 1,
}
return {"risk_debate_state": new_risk_debate_state}
return risky_node
import time
import json
def create_risky_debator(llm):
def risky_node(state) -> dict:
risk_debate_state = state["risk_debate_state"]
history = risk_debate_state.get("history", "")
risky_history = risk_debate_state.get("risky_history", "")
current_safe_response = risk_debate_state.get("current_safe_response", "")
current_neutral_response = risk_debate_state.get("current_neutral_response", "")
market_research_report = state["market_report"]
sentiment_report = state["sentiment_report"]
news_report = state["news_report"]
fundamentals_report = state["fundamentals_report"]
trader_decision = state["trader_investment_plan"]
prompt = f"""As the Risky Risk Analyst, your role is to actively champion high-reward, high-risk opportunities, emphasizing bold strategies and competitive advantages. When evaluating the trader's decision or plan, focus intently on the potential upside, growth potential, and innovative benefits—even when these come with elevated risk. Use the provided market data and sentiment analysis to strengthen your arguments and challenge the opposing views. Specifically, respond directly to each point made by the conservative and neutral analysts, countering with data-driven rebuttals and persuasive reasoning. Highlight where their caution might miss critical opportunities or where their assumptions may be overly conservative. Here is the trader's decision:
{trader_decision}
Your task is to create a compelling case for the trader's decision by questioning and critiquing the conservative and neutral stances to demonstrate why your high-reward perspective offers the best path forward. Incorporate insights from the following sources into your arguments:
Market Research Report: {market_research_report}
Social Media Sentiment Report: {sentiment_report}
Latest World Affairs Report: {news_report}
Company Fundamentals Report: {fundamentals_report}
Here is the current conversation history: {history} Here are the last arguments from the conservative analyst: {current_safe_response} Here are the last arguments from the neutral analyst: {current_neutral_response}. If there are no responses from the other viewpoints, do not halluncinate and just present your point.
Engage actively by addressing any specific concerns raised, refuting the weaknesses in their logic, and asserting the benefits of risk-taking to outpace market norms. Maintain a focus on debating and persuading, not just presenting data. Challenge each counterpoint to underscore why a high-risk approach is optimal. Output conversationally as if you are speaking without any special formatting."""
response = llm.invoke(prompt)
argument = f"Risky Analyst: {response.content}"
new_risk_debate_state = {
"history": history + "\n" + argument,
"risky_history": risky_history + "\n" + argument,
"safe_history": risk_debate_state.get("safe_history", ""),
"neutral_history": risk_debate_state.get("neutral_history", ""),
"latest_speaker": "Risky",
"current_risky_response": argument,
"current_safe_response": risk_debate_state.get("current_safe_response", ""),
"current_neutral_response": risk_debate_state.get(
"current_neutral_response", ""
),
"count": risk_debate_state["count"] + 1,
}
return {"risk_debate_state": new_risk_debate_state}
return risky_node

116
tradingagents/agents/risk_mgmt/conservative_debator.py
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@ -1,58 +1,58 @@
from langchain_core.messages import AIMessage
import time
import json
def create_safe_debator(llm):
def safe_node(state) -> dict:
risk_debate_state = state["risk_debate_state"]
history = risk_debate_state.get("history", "")
safe_history = risk_debate_state.get("safe_history", "")
current_risky_response = risk_debate_state.get("current_risky_response", "")
current_neutral_response = risk_debate_state.get("current_neutral_response", "")
market_research_report = state["market_report"]
sentiment_report = state["sentiment_report"]
news_report = state["news_report"]
fundamentals_report = state["fundamentals_report"]
trader_decision = state["trader_investment_plan"]
prompt = f"""As the Safe/Conservative Risk Analyst, your primary objective is to protect assets, minimize volatility, and ensure steady, reliable growth. You prioritize stability, security, and risk mitigation, carefully assessing potential losses, economic downturns, and market volatility. When evaluating the trader's decision or plan, critically examine high-risk elements, pointing out where the decision may expose the firm to undue risk and where more cautious alternatives could secure long-term gains. Here is the trader's decision:
{trader_decision}
Your task is to actively counter the arguments of the Risky and Neutral Analysts, highlighting where their views may overlook potential threats or fail to prioritize sustainability. Respond directly to their points, drawing from the following data sources to build a convincing case for a low-risk approach adjustment to the trader's decision:
Market Research Report: {market_research_report}
Social Media Sentiment Report: {sentiment_report}
Latest World Affairs Report: {news_report}
Company Fundamentals Report: {fundamentals_report}
Here is the current conversation history: {history} Here is the last response from the risky analyst: {current_risky_response} Here is the last response from the neutral analyst: {current_neutral_response}. If there are no responses from the other viewpoints, do not halluncinate and just present your point.
Engage by questioning their optimism and emphasizing the potential downsides they may have overlooked. Address each of their counterpoints to showcase why a conservative stance is ultimately the safest path for the firm's assets. Focus on debating and critiquing their arguments to demonstrate the strength of a low-risk strategy over their approaches. Output conversationally as if you are speaking without any special formatting."""
response = llm.invoke(prompt)
argument = f"Safe Analyst: {response.content}"
new_risk_debate_state = {
"history": history + "\n" + argument,
"risky_history": risk_debate_state.get("risky_history", ""),
"safe_history": safe_history + "\n" + argument,
"neutral_history": risk_debate_state.get("neutral_history", ""),
"latest_speaker": "Safe",
"current_risky_response": risk_debate_state.get(
"current_risky_response", ""
),
"current_safe_response": argument,
"current_neutral_response": risk_debate_state.get(
"current_neutral_response", ""
),
"count": risk_debate_state["count"] + 1,
}
return {"risk_debate_state": new_risk_debate_state}
return safe_node
from langchain_core.messages import AIMessage
import time
import json
def create_safe_debator(llm):
def safe_node(state) -> dict:
risk_debate_state = state["risk_debate_state"]
history = risk_debate_state.get("history", "")
safe_history = risk_debate_state.get("safe_history", "")
current_risky_response = risk_debate_state.get("current_risky_response", "")
current_neutral_response = risk_debate_state.get("current_neutral_response", "")
market_research_report = state["market_report"]
sentiment_report = state["sentiment_report"]
news_report = state["news_report"]
fundamentals_report = state["fundamentals_report"]
trader_decision = state["trader_investment_plan"]
prompt = f"""As the Safe/Conservative Risk Analyst, your primary objective is to protect assets, minimize volatility, and ensure steady, reliable growth. You prioritize stability, security, and risk mitigation, carefully assessing potential losses, economic downturns, and market volatility. When evaluating the trader's decision or plan, critically examine high-risk elements, pointing out where the decision may expose the firm to undue risk and where more cautious alternatives could secure long-term gains. Here is the trader's decision:
{trader_decision}
Your task is to actively counter the arguments of the Risky and Neutral Analysts, highlighting where their views may overlook potential threats or fail to prioritize sustainability. Respond directly to their points, drawing from the following data sources to build a convincing case for a low-risk approach adjustment to the trader's decision:
Market Research Report: {market_research_report}
Social Media Sentiment Report: {sentiment_report}
Latest World Affairs Report: {news_report}
Company Fundamentals Report: {fundamentals_report}
Here is the current conversation history: {history} Here is the last response from the risky analyst: {current_risky_response} Here is the last response from the neutral analyst: {current_neutral_response}. If there are no responses from the other viewpoints, do not halluncinate and just present your point.
Engage by questioning their optimism and emphasizing the potential downsides they may have overlooked. Address each of their counterpoints to showcase why a conservative stance is ultimately the safest path for the firm's assets. Focus on debating and critiquing their arguments to demonstrate the strength of a low-risk strategy over their approaches. Output conversationally as if you are speaking without any special formatting."""
response = llm.invoke(prompt)
argument = f"Safe Analyst: {response.content}"
new_risk_debate_state = {
"history": history + "\n" + argument,
"risky_history": risk_debate_state.get("risky_history", ""),
"safe_history": safe_history + "\n" + argument,
"neutral_history": risk_debate_state.get("neutral_history", ""),
"latest_speaker": "Safe",
"current_risky_response": risk_debate_state.get(
"current_risky_response", ""
),
"current_safe_response": argument,
"current_neutral_response": risk_debate_state.get(
"current_neutral_response", ""
),
"count": risk_debate_state["count"] + 1,
}
return {"risk_debate_state": new_risk_debate_state}
return safe_node

110
tradingagents/agents/risk_mgmt/neutral_debator.py
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@ -1,55 +1,55 @@
import time
import json
def create_neutral_debator(llm):
def neutral_node(state) -> dict:
risk_debate_state = state["risk_debate_state"]
history = risk_debate_state.get("history", "")
neutral_history = risk_debate_state.get("neutral_history", "")
current_risky_response = risk_debate_state.get("current_risky_response", "")
current_safe_response = risk_debate_state.get("current_safe_response", "")
market_research_report = state["market_report"]
sentiment_report = state["sentiment_report"]
news_report = state["news_report"]
fundamentals_report = state["fundamentals_report"]
trader_decision = state["trader_investment_plan"]
prompt = f"""As the Neutral Risk Analyst, your role is to provide a balanced perspective, weighing both the potential benefits and risks of the trader's decision or plan. You prioritize a well-rounded approach, evaluating the upsides and downsides while factoring in broader market trends, potential economic shifts, and diversification strategies.Here is the trader's decision:
{trader_decision}
Your task is to challenge both the Risky and Safe Analysts, pointing out where each perspective may be overly optimistic or overly cautious. Use insights from the following data sources to support a moderate, sustainable strategy to adjust the trader's decision:
Market Research Report: {market_research_report}
Social Media Sentiment Report: {sentiment_report}
Latest World Affairs Report: {news_report}
Company Fundamentals Report: {fundamentals_report}
Here is the current conversation history: {history} Here is the last response from the risky analyst: {current_risky_response} Here is the last response from the safe analyst: {current_safe_response}. If there are no responses from the other viewpoints, do not halluncinate and just present your point.
Engage actively by analyzing both sides critically, addressing weaknesses in the risky and conservative arguments to advocate for a more balanced approach. Challenge each of their points to illustrate why a moderate risk strategy might offer the best of both worlds, providing growth potential while safeguarding against extreme volatility. Focus on debating rather than simply presenting data, aiming to show that a balanced view can lead to the most reliable outcomes. Output conversationally as if you are speaking without any special formatting."""
response = llm.invoke(prompt)
argument = f"Neutral Analyst: {response.content}"
new_risk_debate_state = {
"history": history + "\n" + argument,
"risky_history": risk_debate_state.get("risky_history", ""),
"safe_history": risk_debate_state.get("safe_history", ""),
"neutral_history": neutral_history + "\n" + argument,
"latest_speaker": "Neutral",
"current_risky_response": risk_debate_state.get(
"current_risky_response", ""
),
"current_safe_response": risk_debate_state.get("current_safe_response", ""),
"current_neutral_response": argument,
"count": risk_debate_state["count"] + 1,
}
return {"risk_debate_state": new_risk_debate_state}
return neutral_node
import time
import json
def create_neutral_debator(llm):
def neutral_node(state) -> dict:
risk_debate_state = state["risk_debate_state"]
history = risk_debate_state.get("history", "")
neutral_history = risk_debate_state.get("neutral_history", "")
current_risky_response = risk_debate_state.get("current_risky_response", "")
current_safe_response = risk_debate_state.get("current_safe_response", "")
market_research_report = state["market_report"]
sentiment_report = state["sentiment_report"]
news_report = state["news_report"]
fundamentals_report = state["fundamentals_report"]
trader_decision = state["trader_investment_plan"]
prompt = f"""As the Neutral Risk Analyst, your role is to provide a balanced perspective, weighing both the potential benefits and risks of the trader's decision or plan. You prioritize a well-rounded approach, evaluating the upsides and downsides while factoring in broader market trends, potential economic shifts, and diversification strategies.Here is the trader's decision:
{trader_decision}
Your task is to challenge both the Risky and Safe Analysts, pointing out where each perspective may be overly optimistic or overly cautious. Use insights from the following data sources to support a moderate, sustainable strategy to adjust the trader's decision:
Market Research Report: {market_research_report}
Social Media Sentiment Report: {sentiment_report}
Latest World Affairs Report: {news_report}
Company Fundamentals Report: {fundamentals_report}
Here is the current conversation history: {history} Here is the last response from the risky analyst: {current_risky_response} Here is the last response from the safe analyst: {current_safe_response}. If there are no responses from the other viewpoints, do not halluncinate and just present your point.
Engage actively by analyzing both sides critically, addressing weaknesses in the risky and conservative arguments to advocate for a more balanced approach. Challenge each of their points to illustrate why a moderate risk strategy might offer the best of both worlds, providing growth potential while safeguarding against extreme volatility. Focus on debating rather than simply presenting data, aiming to show that a balanced view can lead to the most reliable outcomes. Output conversationally as if you are speaking without any special formatting."""
response = llm.invoke(prompt)
argument = f"Neutral Analyst: {response.content}"
new_risk_debate_state = {
"history": history + "\n" + argument,
"risky_history": risk_debate_state.get("risky_history", ""),
"safe_history": risk_debate_state.get("safe_history", ""),
"neutral_history": neutral_history + "\n" + argument,
"latest_speaker": "Neutral",
"current_risky_response": risk_debate_state.get(
"current_risky_response", ""
),
"current_safe_response": risk_debate_state.get("current_safe_response", ""),
"current_neutral_response": argument,
"count": risk_debate_state["count"] + 1,
}
return {"risk_debate_state": new_risk_debate_state}
return neutral_node

92
tradingagents/agents/trader/trader.py
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@ -1,46 +1,46 @@
import functools
import time
import json
def create_trader(llm, memory):
def trader_node(state, name):
company_name = state["company_of_interest"]
investment_plan = state["investment_plan"]
market_research_report = state["market_report"]
sentiment_report = state["sentiment_report"]
news_report = state["news_report"]
fundamentals_report = state["fundamentals_report"]
curr_situation = f"{market_research_report}\n\n{sentiment_report}\n\n{news_report}\n\n{fundamentals_report}"
past_memories = memory.get_memories(curr_situation, n_matches=2)
past_memory_str = ""
if past_memories:
for i, rec in enumerate(past_memories, 1):
past_memory_str += rec["recommendation"] + "\n\n"
else:
past_memory_str = "No past memories found."
context = {
"role": "user",
"content": f"Based on a comprehensive analysis by a team of analysts, here is an investment plan tailored for {company_name}. This plan incorporates insights from current technical market trends, macroeconomic indicators, and social media sentiment. Use this plan as a foundation for evaluating your next trading decision.\n\nProposed Investment Plan: {investment_plan}\n\nLeverage these insights to make an informed and strategic decision.",
}
messages = [
{
"role": "system",
"content": f"""You are a trading agent analyzing market data to make investment decisions. Based on your analysis, provide a specific recommendation to buy, sell, or hold. End with a firm decision and always conclude your response with 'FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL**' to confirm your recommendation. Do not forget to utilize lessons from past decisions to learn from your mistakes. Here is some reflections from similar situatiosn you traded in and the lessons learned: {past_memory_str}""",
},
context,
]
result = llm.invoke(messages)
return {
"messages": [result],
"trader_investment_plan": result.content,
"sender": name,
}
return functools.partial(trader_node, name="Trader")
import functools
import time
import json
def create_trader(llm, memory):
def trader_node(state, name):
company_name = state["company_of_interest"]
investment_plan = state["investment_plan"]
market_research_report = state["market_report"]
sentiment_report = state["sentiment_report"]
news_report = state["news_report"]
fundamentals_report = state["fundamentals_report"]
curr_situation = f"{market_research_report}\n\n{sentiment_report}\n\n{news_report}\n\n{fundamentals_report}"
past_memories = memory.get_memories(curr_situation, n_matches=2)
past_memory_str = ""
if past_memories:
for i, rec in enumerate(past_memories, 1):
past_memory_str += rec["recommendation"] + "\n\n"
else:
past_memory_str = "No past memories found."
context = {
"role": "user",
"content": f"Based on a comprehensive analysis by a team of analysts, here is an investment plan tailored for {company_name}. This plan incorporates insights from current technical market trends, macroeconomic indicators, and social media sentiment. Use this plan as a foundation for evaluating your next trading decision.\n\nProposed Investment Plan: {investment_plan}\n\nLeverage these insights to make an informed and strategic decision.",
}
messages = [
{
"role": "system",
"content": f"""You are a trading agent analyzing market data to make investment decisions. Based on your analysis, provide a specific recommendation to buy, sell, or hold. End with a firm decision and always conclude your response with 'FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL**' to confirm your recommendation. Do not forget to utilize lessons from past decisions to learn from your mistakes. Here is some reflections from similar situatiosn you traded in and the lessons learned: {past_memory_str}""",
},
context,
]
result = llm.invoke(messages)
return {
"messages": [result],
"trader_investment_plan": result.content,
"sender": name,
}
return functools.partial(trader_node, name="Trader")

152
tradingagents/agents/utils/agent_states.py
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@ -1,76 +1,76 @@
from typing import Annotated, Sequence
from datetime import date, timedelta, datetime
from typing_extensions import TypedDict, Optional
from langchain_openai import ChatOpenAI
from tradingagents.agents import *
from langgraph.prebuilt import ToolNode
from langgraph.graph import END, StateGraph, START, MessagesState
# Researcher team state
class InvestDebateState(TypedDict):
bull_history: Annotated[
str, "Bullish Conversation history"
] # Bullish Conversation history
bear_history: Annotated[
str, "Bearish Conversation history"
] # Bullish Conversation history
history: Annotated[str, "Conversation history"] # Conversation history
current_response: Annotated[str, "Latest response"] # Last response
judge_decision: Annotated[str, "Final judge decision"] # Last response
count: Annotated[int, "Length of the current conversation"] # Conversation length
# Risk management team state
class RiskDebateState(TypedDict):
risky_history: Annotated[
str, "Risky Agent's Conversation history"
] # Conversation history
safe_history: Annotated[
str, "Safe Agent's Conversation history"
] # Conversation history
neutral_history: Annotated[
str, "Neutral Agent's Conversation history"
] # Conversation history
history: Annotated[str, "Conversation history"] # Conversation history
latest_speaker: Annotated[str, "Analyst that spoke last"]
current_risky_response: Annotated[
str, "Latest response by the risky analyst"
] # Last response
current_safe_response: Annotated[
str, "Latest response by the safe analyst"
] # Last response
current_neutral_response: Annotated[
str, "Latest response by the neutral analyst"
] # Last response
judge_decision: Annotated[str, "Judge's decision"]
count: Annotated[int, "Length of the current conversation"] # Conversation length
class AgentState(MessagesState):
company_of_interest: Annotated[str, "Company that we are interested in trading"]
trade_date: Annotated[str, "What date we are trading at"]
sender: Annotated[str, "Agent that sent this message"]
# research step
market_report: Annotated[str, "Report from the Market Analyst"]
sentiment_report: Annotated[str, "Report from the Social Media Analyst"]
news_report: Annotated[
str, "Report from the News Researcher of current world affairs"
]
fundamentals_report: Annotated[str, "Report from the Fundamentals Researcher"]
# researcher team discussion step
investment_debate_state: Annotated[
InvestDebateState, "Current state of the debate on if to invest or not"
]
investment_plan: Annotated[str, "Plan generated by the Analyst"]
trader_investment_plan: Annotated[str, "Plan generated by the Trader"]
# risk management team discussion step
risk_debate_state: Annotated[
RiskDebateState, "Current state of the debate on evaluating risk"
]
final_trade_decision: Annotated[str, "Final decision made by the Risk Analysts"]
from typing import Annotated, Sequence
from datetime import date, timedelta, datetime
from typing_extensions import TypedDict, Optional
from langchain_openai import ChatOpenAI
from tradingagents.agents import *
from langgraph.prebuilt import ToolNode
from langgraph.graph import END, StateGraph, START, MessagesState
# Researcher team state
class InvestDebateState(TypedDict):
bull_history: Annotated[
str, "Bullish Conversation history"
] # Bullish Conversation history
bear_history: Annotated[
str, "Bearish Conversation history"
] # Bullish Conversation history
history: Annotated[str, "Conversation history"] # Conversation history
current_response: Annotated[str, "Latest response"] # Last response
judge_decision: Annotated[str, "Final judge decision"] # Last response
count: Annotated[int, "Length of the current conversation"] # Conversation length
# Risk management team state
class RiskDebateState(TypedDict):
risky_history: Annotated[
str, "Risky Agent's Conversation history"
] # Conversation history
safe_history: Annotated[
str, "Safe Agent's Conversation history"
] # Conversation history
neutral_history: Annotated[
str, "Neutral Agent's Conversation history"
] # Conversation history
history: Annotated[str, "Conversation history"] # Conversation history
latest_speaker: Annotated[str, "Analyst that spoke last"]
current_risky_response: Annotated[
str, "Latest response by the risky analyst"
] # Last response
current_safe_response: Annotated[
str, "Latest response by the safe analyst"
] # Last response
current_neutral_response: Annotated[
str, "Latest response by the neutral analyst"
] # Last response
judge_decision: Annotated[str, "Judge's decision"]
count: Annotated[int, "Length of the current conversation"] # Conversation length
class AgentState(MessagesState):
company_of_interest: Annotated[str, "Company that we are interested in trading"]
trade_date: Annotated[str, "What date we are trading at"]
sender: Annotated[str, "Agent that sent this message"]
# research step
market_report: Annotated[str, "Report from the Market Analyst"]
sentiment_report: Annotated[str, "Report from the Social Media Analyst"]
news_report: Annotated[
str, "Report from the News Researcher of current world affairs"
]
fundamentals_report: Annotated[str, "Report from the Fundamentals Researcher"]
# researcher team discussion step
investment_debate_state: Annotated[
InvestDebateState, "Current state of the debate on if to invest or not"
]
investment_plan: Annotated[str, "Plan generated by the Analyst"]
trader_investment_plan: Annotated[str, "Plan generated by the Trader"]
# risk management team discussion step
risk_debate_state: Annotated[
RiskDebateState, "Current state of the debate on evaluating risk"
]
final_trade_decision: Annotated[str, "Final decision made by the Risk Analysts"]

458
tradingagents/agents/utils/agent_utils.py
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@ -1,419 +1,39 @@
from langchain_core.messages import BaseMessage, HumanMessage, ToolMessage, AIMessage
from typing import List
from typing import Annotated
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain_core.messages import RemoveMessage
from langchain_core.tools import tool
from datetime import date, timedelta, datetime
import functools
import pandas as pd
import os
from dateutil.relativedelta import relativedelta
from langchain_openai import ChatOpenAI
import tradingagents.dataflows.interface as interface
from tradingagents.default_config import DEFAULT_CONFIG
from langchain_core.messages import HumanMessage
def create_msg_delete():
def delete_messages(state):
"""Clear messages and add placeholder for Anthropic compatibility"""
messages = state["messages"]
# Remove all messages
removal_operations = [RemoveMessage(id=m.id) for m in messages]
# Add a minimal placeholder message
placeholder = HumanMessage(content="Continue")
return {"messages": removal_operations + [placeholder]}
return delete_messages
class Toolkit:
_config = DEFAULT_CONFIG.copy()
@classmethod
def update_config(cls, config):
"""Update the class-level configuration."""
cls._config.update(config)
@property
def config(self):
"""Access the configuration."""
return self._config
def __init__(self, config=None):
if config:
self.update_config(config)
@staticmethod
@tool
def get_reddit_news(
curr_date: Annotated[str, "Date you want to get news for in yyyy-mm-dd format"],
) -> str:
"""
Retrieve global news from Reddit within a specified time frame.
Args:
curr_date (str): Date you want to get news for in yyyy-mm-dd format
Returns:
str: A formatted dataframe containing the latest global news from Reddit in the specified time frame.
"""
global_news_result = interface.get_reddit_global_news(curr_date, 7, 5)
return global_news_result
@staticmethod
@tool
def get_finnhub_news(
ticker: Annotated[
str,
"Search query of a company, e.g. 'AAPL, TSM, etc.",
],
start_date: Annotated[str, "Start date in yyyy-mm-dd format"],
end_date: Annotated[str, "End date in yyyy-mm-dd format"],
):
"""
Retrieve the latest news about a given stock from Finnhub within a date range
Args:
ticker (str): Ticker of a company. e.g. AAPL, TSM
start_date (str): Start date in yyyy-mm-dd format
end_date (str): End date in yyyy-mm-dd format
Returns:
str: A formatted dataframe containing news about the company within the date range from start_date to end_date
"""
end_date_str = end_date
end_date = datetime.strptime(end_date, "%Y-%m-%d")
start_date = datetime.strptime(start_date, "%Y-%m-%d")
look_back_days = (end_date - start_date).days
finnhub_news_result = interface.get_finnhub_news(
ticker, end_date_str, look_back_days
)
return finnhub_news_result
@staticmethod
@tool
def get_reddit_stock_info(
ticker: Annotated[
str,
"Ticker of a company. e.g. AAPL, TSM",
],
curr_date: Annotated[str, "Current date you want to get news for"],
) -> str:
"""
Retrieve the latest news about a given stock from Reddit, given the current date.
Args:
ticker (str): Ticker of a company. e.g. AAPL, TSM
curr_date (str): current date in yyyy-mm-dd format to get news for
Returns:
str: A formatted dataframe containing the latest news about the company on the given date
"""
stock_news_results = interface.get_reddit_company_news(ticker, curr_date, 7, 5)
return stock_news_results
@staticmethod
@tool
def get_YFin_data(
symbol: Annotated[str, "ticker symbol of the company"],
start_date: Annotated[str, "Start date in yyyy-mm-dd format"],
end_date: Annotated[str, "End date in yyyy-mm-dd format"],
) -> str:
"""
Retrieve the stock price data for a given ticker symbol from Yahoo Finance.
Args:
symbol (str): Ticker symbol of the company, e.g. AAPL, TSM
start_date (str): Start date in yyyy-mm-dd format
end_date (str): End date in yyyy-mm-dd format
Returns:
str: A formatted dataframe containing the stock price data for the specified ticker symbol in the specified date range.
"""
result_data = interface.get_YFin_data(symbol, start_date, end_date)
return result_data
@staticmethod
@tool
def get_YFin_data_online(
symbol: Annotated[str, "ticker symbol of the company"],
start_date: Annotated[str, "Start date in yyyy-mm-dd format"],
end_date: Annotated[str, "End date in yyyy-mm-dd format"],
) -> str:
"""
Retrieve the stock price data for a given ticker symbol from Yahoo Finance.
Args:
symbol (str): Ticker symbol of the company, e.g. AAPL, TSM
start_date (str): Start date in yyyy-mm-dd format
end_date (str): End date in yyyy-mm-dd format
Returns:
str: A formatted dataframe containing the stock price data for the specified ticker symbol in the specified date range.
"""
result_data = interface.get_YFin_data_online(symbol, start_date, end_date)
return result_data
@staticmethod
@tool
def get_stockstats_indicators_report(
symbol: Annotated[str, "ticker symbol of the company"],
indicator: Annotated[
str, "technical indicator to get the analysis and report of"
],
curr_date: Annotated[
str, "The current trading date you are trading on, YYYY-mm-dd"
],
look_back_days: Annotated[int, "how many days to look back"] = 30,
) -> str:
"""
Retrieve stock stats indicators for a given ticker symbol and indicator.
Args:
symbol (str): Ticker symbol of the company, e.g. AAPL, TSM
indicator (str): Technical indicator to get the analysis and report of
curr_date (str): The current trading date you are trading on, YYYY-mm-dd
look_back_days (int): How many days to look back, default is 30
Returns:
str: A formatted dataframe containing the stock stats indicators for the specified ticker symbol and indicator.
"""
result_stockstats = interface.get_stock_stats_indicators_window(
symbol, indicator, curr_date, look_back_days, False
)
return result_stockstats
@staticmethod
@tool
def get_stockstats_indicators_report_online(
symbol: Annotated[str, "ticker symbol of the company"],
indicator: Annotated[
str, "technical indicator to get the analysis and report of"
],
curr_date: Annotated[
str, "The current trading date you are trading on, YYYY-mm-dd"
],
look_back_days: Annotated[int, "how many days to look back"] = 30,
) -> str:
"""
Retrieve stock stats indicators for a given ticker symbol and indicator.
Args:
symbol (str): Ticker symbol of the company, e.g. AAPL, TSM
indicator (str): Technical indicator to get the analysis and report of
curr_date (str): The current trading date you are trading on, YYYY-mm-dd
look_back_days (int): How many days to look back, default is 30
Returns:
str: A formatted dataframe containing the stock stats indicators for the specified ticker symbol and indicator.
"""
result_stockstats = interface.get_stock_stats_indicators_window(
symbol, indicator, curr_date, look_back_days, True
)
return result_stockstats
@staticmethod
@tool
def get_finnhub_company_insider_sentiment(
ticker: Annotated[str, "ticker symbol for the company"],
curr_date: Annotated[
str,
"current date of you are trading at, yyyy-mm-dd",
],
):
"""
Retrieve insider sentiment information about a company (retrieved from public SEC information) for the past 30 days
Args:
ticker (str): ticker symbol of the company
curr_date (str): current date you are trading at, yyyy-mm-dd
Returns:
str: a report of the sentiment in the past 30 days starting at curr_date
"""
data_sentiment = interface.get_finnhub_company_insider_sentiment(
ticker, curr_date, 30
)
return data_sentiment
@staticmethod
@tool
def get_finnhub_company_insider_transactions(
ticker: Annotated[str, "ticker symbol"],
curr_date: Annotated[
str,
"current date you are trading at, yyyy-mm-dd",
],
):
"""
Retrieve insider transaction information about a company (retrieved from public SEC information) for the past 30 days
Args:
ticker (str): ticker symbol of the company
curr_date (str): current date you are trading at, yyyy-mm-dd
Returns:
str: a report of the company's insider transactions/trading information in the past 30 days
"""
data_trans = interface.get_finnhub_company_insider_transactions(
ticker, curr_date, 30
)
return data_trans
@staticmethod
@tool
def get_simfin_balance_sheet(
ticker: Annotated[str, "ticker symbol"],
freq: Annotated[
str,
"reporting frequency of the company's financial history: annual/quarterly",
],
curr_date: Annotated[str, "current date you are trading at, yyyy-mm-dd"],
):
"""
Retrieve the most recent balance sheet of a company
Args:
ticker (str): ticker symbol of the company
freq (str): reporting frequency of the company's financial history: annual / quarterly
curr_date (str): current date you are trading at, yyyy-mm-dd
Returns:
str: a report of the company's most recent balance sheet
"""
data_balance_sheet = interface.get_simfin_balance_sheet(ticker, freq, curr_date)
return data_balance_sheet
@staticmethod
@tool
def get_simfin_cashflow(
ticker: Annotated[str, "ticker symbol"],
freq: Annotated[
str,
"reporting frequency of the company's financial history: annual/quarterly",
],
curr_date: Annotated[str, "current date you are trading at, yyyy-mm-dd"],
):
"""
Retrieve the most recent cash flow statement of a company
Args:
ticker (str): ticker symbol of the company
freq (str): reporting frequency of the company's financial history: annual / quarterly
curr_date (str): current date you are trading at, yyyy-mm-dd
Returns:
str: a report of the company's most recent cash flow statement
"""
data_cashflow = interface.get_simfin_cashflow(ticker, freq, curr_date)
return data_cashflow
@staticmethod
@tool
def get_simfin_income_stmt(
ticker: Annotated[str, "ticker symbol"],
freq: Annotated[
str,
"reporting frequency of the company's financial history: annual/quarterly",
],
curr_date: Annotated[str, "current date you are trading at, yyyy-mm-dd"],
):
"""
Retrieve the most recent income statement of a company
Args:
ticker (str): ticker symbol of the company
freq (str): reporting frequency of the company's financial history: annual / quarterly
curr_date (str): current date you are trading at, yyyy-mm-dd
Returns:
str: a report of the company's most recent income statement
"""
data_income_stmt = interface.get_simfin_income_statements(
ticker, freq, curr_date
)
return data_income_stmt
@staticmethod
@tool
def get_google_news(
query: Annotated[str, "Query to search with"],
curr_date: Annotated[str, "Curr date in yyyy-mm-dd format"],
):
"""
Retrieve the latest news from Google News based on a query and date range.
Args:
query (str): Query to search with
curr_date (str): Current date in yyyy-mm-dd format
look_back_days (int): How many days to look back
Returns:
str: A formatted string containing the latest news from Google News based on the query and date range.
"""
google_news_results = interface.get_google_news(query, curr_date, 7)
return google_news_results
@staticmethod
@tool
def get_stock_news_openai(
ticker: Annotated[str, "the company's ticker"],
curr_date: Annotated[str, "Current date in yyyy-mm-dd format"],
):
"""
Retrieve the latest news about a given stock by using OpenAI's news API.
Args:
ticker (str): Ticker of a company. e.g. AAPL, TSM
curr_date (str): Current date in yyyy-mm-dd format
Returns:
str: A formatted string containing the latest news about the company on the given date.
"""
openai_news_results = interface.get_stock_news_openai(ticker, curr_date)
return openai_news_results
@staticmethod
@tool
def get_global_news_openai(
curr_date: Annotated[str, "Current date in yyyy-mm-dd format"],
):
"""
Retrieve the latest macroeconomics news on a given date using OpenAI's macroeconomics news API.
Args:
curr_date (str): Current date in yyyy-mm-dd format
Returns:
str: A formatted string containing the latest macroeconomic news on the given date.
"""
openai_news_results = interface.get_global_news_openai(curr_date)
return openai_news_results
@staticmethod
@tool
def get_fundamentals_openai(
ticker: Annotated[str, "the company's ticker"],
curr_date: Annotated[str, "Current date in yyyy-mm-dd format"],
):
"""
Retrieve the latest fundamental information about a given stock on a given date by using OpenAI's news API.
Args:
ticker (str): Ticker of a company. e.g. AAPL, TSM
curr_date (str): Current date in yyyy-mm-dd format
Returns:
str: A formatted string containing the latest fundamental information about the company on the given date.
"""
openai_fundamentals_results = interface.get_fundamentals_openai(
ticker, curr_date
)
return openai_fundamentals_results
from langchain_core.messages import HumanMessage, RemoveMessage
# Import tools from separate utility files
from tradingagents.agents.utils.core_stock_tools import (
get_stock_data
)
from tradingagents.agents.utils.technical_indicators_tools import (
get_indicators
)
from tradingagents.agents.utils.fundamental_data_tools import (
get_fundamentals,
get_balance_sheet,
get_cashflow,
get_income_statement
)
from tradingagents.agents.utils.news_data_tools import (
get_news,
get_insider_sentiment,
get_insider_transactions,
get_global_news
)
def create_msg_delete():
def delete_messages(state):
"""Clear messages and add placeholder for Anthropic compatibility"""
messages = state["messages"]
# Remove all messages
removal_operations = [RemoveMessage(id=m.id) for m in messages]
# Add a minimal placeholder message
placeholder = HumanMessage(content="Continue")
return {"messages": removal_operations + [placeholder]}
return delete_messages

22
tradingagents/agents/utils/core_stock_tools.py Normal file
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from langchain_core.tools import tool
from typing import Annotated
from tradingagents.dataflows.interface import route_to_vendor
@tool
def get_stock_data(
symbol: Annotated[str, "ticker symbol of the company"],
start_date: Annotated[str, "Start date in yyyy-mm-dd format"],
end_date: Annotated[str, "End date in yyyy-mm-dd format"],
) -> str:
"""
Retrieve stock price data (OHLCV) for a given ticker symbol.
Uses the configured core_stock_apis vendor.
Args:
symbol (str): Ticker symbol of the company, e.g. AAPL, TSM
start_date (str): Start date in yyyy-mm-dd format
end_date (str): End date in yyyy-mm-dd format
Returns:
str: A formatted dataframe containing the stock price data for the specified ticker symbol in the specified date range.
"""
return route_to_vendor("get_stock_data", symbol, start_date, end_date)

77
tradingagents/agents/utils/fundamental_data_tools.py Normal file
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from langchain_core.tools import tool
from typing import Annotated
from tradingagents.dataflows.interface import route_to_vendor
@tool
def get_fundamentals(
ticker: Annotated[str, "ticker symbol"],
curr_date: Annotated[str, "current date you are trading at, yyyy-mm-dd"],
) -> str:
"""
Retrieve comprehensive fundamental data for a given ticker symbol.
Uses the configured fundamental_data vendor.
Args:
ticker (str): Ticker symbol of the company
curr_date (str): Current date you are trading at, yyyy-mm-dd
Returns:
str: A formatted report containing comprehensive fundamental data
"""
return route_to_vendor("get_fundamentals", ticker, curr_date)
@tool
def get_balance_sheet(
ticker: Annotated[str, "ticker symbol"],
freq: Annotated[str, "reporting frequency: annual/quarterly"] = "quarterly",
curr_date: Annotated[str, "current date you are trading at, yyyy-mm-dd"] = None,
) -> str:
"""
Retrieve balance sheet data for a given ticker symbol.
Uses the configured fundamental_data vendor.
Args:
ticker (str): Ticker symbol of the company
freq (str): Reporting frequency: annual/quarterly (default quarterly)
curr_date (str): Current date you are trading at, yyyy-mm-dd
Returns:
str: A formatted report containing balance sheet data
"""
return route_to_vendor("get_balance_sheet", ticker, freq, curr_date)
@tool
def get_cashflow(
ticker: Annotated[str, "ticker symbol"],
freq: Annotated[str, "reporting frequency: annual/quarterly"] = "quarterly",
curr_date: Annotated[str, "current date you are trading at, yyyy-mm-dd"] = None,
) -> str:
"""
Retrieve cash flow statement data for a given ticker symbol.
Uses the configured fundamental_data vendor.
Args:
ticker (str): Ticker symbol of the company
freq (str): Reporting frequency: annual/quarterly (default quarterly)
curr_date (str): Current date you are trading at, yyyy-mm-dd
Returns:
str: A formatted report containing cash flow statement data
"""
return route_to_vendor("get_cashflow", ticker, freq, curr_date)
@tool
def get_income_statement(
ticker: Annotated[str, "ticker symbol"],
freq: Annotated[str, "reporting frequency: annual/quarterly"] = "quarterly",
curr_date: Annotated[str, "current date you are trading at, yyyy-mm-dd"] = None,
) -> str:
"""
Retrieve income statement data for a given ticker symbol.
Uses the configured fundamental_data vendor.
Args:
ticker (str): Ticker symbol of the company
freq (str): Reporting frequency: annual/quarterly (default quarterly)
curr_date (str): Current date you are trading at, yyyy-mm-dd
Returns:
str: A formatted report containing income statement data
"""
return route_to_vendor("get_income_statement", ticker, freq, curr_date)

237
tradingagents/agents/utils/memory.py
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@ -1,116 +1,121 @@
import chromadb
from chromadb.config import Settings
from openai import OpenAI
class FinancialSituationMemory:
def __init__(self, name, config):
if config["backend_url"] == "http://localhost:11434/v1":
self.embedding = "nomic-embed-text"
elif config["backend_url"] == "http://localhost:1234/v1":
self.embedding = "text-embedding-nomic-embed-text-v1.5"
else:
self.embedding = "text-embedding-3-small"
self.client = OpenAI(base_url=config["backend_url"])
self.chroma_client = chromadb.Client(Settings(allow_reset=True))
self.situation_collection = self.chroma_client.create_collection(name=name)
def get_embedding(self, text):
"""Get OpenAI embedding for a text"""
response = self.client.embeddings.create(
model=self.embedding, input=text
)
return response.data[0].embedding
def add_situations(self, situations_and_advice):
"""Add financial situations and their corresponding advice. Parameter is a list of tuples (situation, rec)"""
situations = []
advice = []
ids = []
embeddings = []
offset = self.situation_collection.count()
for i, (situation, recommendation) in enumerate(situations_and_advice):
situations.append(situation)
advice.append(recommendation)
ids.append(str(offset + i))
embeddings.append(self.get_embedding(situation))
self.situation_collection.add(
documents=situations,
metadatas=[{"recommendation": rec} for rec in advice],
embeddings=embeddings,
ids=ids,
)
def get_memories(self, current_situation, n_matches=1):
"""Find matching recommendations using OpenAI embeddings"""
query_embedding = self.get_embedding(current_situation)
results = self.situation_collection.query(
query_embeddings=[query_embedding],
n_results=n_matches,
include=["metadatas", "documents", "distances"],
)
matched_results = []
for i in range(len(results["documents"][0])):
matched_results.append(
{
"matched_situation": results["documents"][0][i],
"recommendation": results["metadatas"][0][i]["recommendation"],
"similarity_score": 1 - results["distances"][0][i],
}
)
return matched_results
if __name__ == "__main__":
# Example usage
matcher = FinancialSituationMemory()
# Example data
example_data = [
(
"High inflation rate with rising interest rates and declining consumer spending",
"Consider defensive sectors like consumer staples and utilities. Review fixed-income portfolio duration.",
),
(
"Tech sector showing high volatility with increasing institutional selling pressure",
"Reduce exposure to high-growth tech stocks. Look for value opportunities in established tech companies with strong cash flows.",
),
(
"Strong dollar affecting emerging markets with increasing forex volatility",
"Hedge currency exposure in international positions. Consider reducing allocation to emerging market debt.",
),
(
"Market showing signs of sector rotation with rising yields",
"Rebalance portfolio to maintain target allocations. Consider increasing exposure to sectors benefiting from higher rates.",
),
]
# Add the example situations and recommendations
matcher.add_situations(example_data)
# Example query
current_situation = """
Market showing increased volatility in tech sector, with institutional investors
reducing positions and rising interest rates affecting growth stock valuations
"""
try:
recommendations = matcher.get_memories(current_situation, n_matches=2)
for i, rec in enumerate(recommendations, 1):
print(f"\nMatch {i}:")
print(f"Similarity Score: {rec['similarity_score']:.2f}")
print(f"Matched Situation: {rec['matched_situation']}")
print(f"Recommendation: {rec['recommendation']}")
except Exception as e:
print(f"Error during recommendation: {str(e)}")
import chromadb
from chromadb.config import Settings
from openai import OpenAI
class FinancialSituationMemory:
def __init__(self, name, config):
#if config["backend_url"] == "http://localhost:11434/v1":
# self.embedding = "nomic-embed-text"
#else:
# self.embedding = "text-embedding-3-small"
if config["backend_url"] == "http://localhost:11434/v1":
self.embedding = "nomic-embed-text"
elif config["backend_url"] == "http://192.168.0.20:1234/v1":
self.embedding = "text-embedding-nomic-embed-text-v1.5"
else:
self.embedding = "ttext-embedding-nomic-embed-text-v1.5"
self.client = OpenAI(base_url=config["backend_url"])
self.chroma_client = chromadb.Client(Settings(allow_reset=True))
self.situation_collection = self.chroma_client.create_collection(name=name)
def get_embedding(self, text):
"""Get OpenAI embedding for a text"""
response = self.client.embeddings.create(
model=self.embedding, input=text
)
return response.data[0].embedding
def add_situations(self, situations_and_advice):
"""Add financial situations and their corresponding advice. Parameter is a list of tuples (situation, rec)"""
situations = []
advice = []
ids = []
embeddings = []
offset = self.situation_collection.count()
for i, (situation, recommendation) in enumerate(situations_and_advice):
situations.append(situation)
advice.append(recommendation)
ids.append(str(offset + i))
embeddings.append(self.get_embedding(situation))
self.situation_collection.add(
documents=situations,
metadatas=[{"recommendation": rec} for rec in advice],
embeddings=embeddings,
ids=ids,
)
def get_memories(self, current_situation, n_matches=1):
"""Find matching recommendations using OpenAI embeddings"""
query_embedding = self.get_embedding(current_situation)
results = self.situation_collection.query(
query_embeddings=[query_embedding],
n_results=n_matches,
include=["metadatas", "documents", "distances"],
)
matched_results = []
for i in range(len(results["documents"][0])):
matched_results.append(
{
"matched_situation": results["documents"][0][i],
"recommendation": results["metadatas"][0][i]["recommendation"],
"similarity_score": 1 - results["distances"][0][i],
}
)
return matched_results
if __name__ == "__main__":
# Example usage
matcher = FinancialSituationMemory()
# Example data
example_data = [
(
"High inflation rate with rising interest rates and declining consumer spending",
"Consider defensive sectors like consumer staples and utilities. Review fixed-income portfolio duration.",
),
(
"Tech sector showing high volatility with increasing institutional selling pressure",
"Reduce exposure to high-growth tech stocks. Look for value opportunities in established tech companies with strong cash flows.",
),
(
"Strong dollar affecting emerging markets with increasing forex volatility",
"Hedge currency exposure in international positions. Consider reducing allocation to emerging market debt.",
),
(
"Market showing signs of sector rotation with rising yields",
"Rebalance portfolio to maintain target allocations. Consider increasing exposure to sectors benefiting from higher rates.",
),
]
# Add the example situations and recommendations
matcher.add_situations(example_data)
# Example query
current_situation = """
Market showing increased volatility in tech sector, with institutional investors
reducing positions and rising interest rates affecting growth stock valuations
"""
try:
recommendations = matcher.get_memories(current_situation, n_matches=2)
for i, rec in enumerate(recommendations, 1):
print(f"\nMatch {i}:")
print(f"Similarity Score: {rec['similarity_score']:.2f}")
print(f"Matched Situation: {rec['matched_situation']}")
print(f"Recommendation: {rec['recommendation']}")
except Exception as e:
print(f"Error during recommendation: {str(e)}")

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from langchain_core.tools import tool
from typing import Annotated
from tradingagents.dataflows.interface import route_to_vendor
@tool
def get_news(
ticker: Annotated[str, "Ticker symbol"],
start_date: Annotated[str, "Start date in yyyy-mm-dd format"],
end_date: Annotated[str, "End date in yyyy-mm-dd format"],
) -> str:
"""
Retrieve news data for a given ticker symbol.
Uses the configured news_data vendor.
Args:
ticker (str): Ticker symbol
start_date (str): Start date in yyyy-mm-dd format
end_date (str): End date in yyyy-mm-dd format
Returns:
str: A formatted string containing news data
"""
return route_to_vendor("get_news", ticker, start_date, end_date)
@tool
def get_global_news(
curr_date: Annotated[str, "Current date in yyyy-mm-dd format"],
look_back_days: Annotated[int, "Number of days to look back"] = 7,
limit: Annotated[int, "Maximum number of articles to return"] = 5,
) -> str:
"""
Retrieve global news data.
Uses the configured news_data vendor.
Args:
curr_date (str): Current date in yyyy-mm-dd format
look_back_days (int): Number of days to look back (default 7)
limit (int): Maximum number of articles to return (default 5)
Returns:
str: A formatted string containing global news data
"""
return route_to_vendor("get_global_news", curr_date, look_back_days, limit)
@tool
def get_insider_sentiment(
ticker: Annotated[str, "ticker symbol for the company"],
curr_date: Annotated[str, "current date you are trading at, yyyy-mm-dd"],
) -> str:
"""
Retrieve insider sentiment information about a company.
Uses the configured news_data vendor.
Args:
ticker (str): Ticker symbol of the company
curr_date (str): Current date you are trading at, yyyy-mm-dd
Returns:
str: A report of insider sentiment data
"""
return route_to_vendor("get_insider_sentiment", ticker, curr_date)
@tool
def get_insider_transactions(
ticker: Annotated[str, "ticker symbol"],
curr_date: Annotated[str, "current date you are trading at, yyyy-mm-dd"],
) -> str:
"""
Retrieve insider transaction information about a company.
Uses the configured news_data vendor.
Args:
ticker (str): Ticker symbol of the company
curr_date (str): Current date you are trading at, yyyy-mm-dd
Returns:
str: A report of insider transaction data
"""
return route_to_vendor("get_insider_transactions", ticker, curr_date)

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from langchain_core.tools import tool
from typing import Annotated
from tradingagents.dataflows.interface import route_to_vendor
@tool
def get_indicators(
symbol: Annotated[str, "ticker symbol of the company"],
indicator: Annotated[str, "technical indicator to get the analysis and report of"],
curr_date: Annotated[str, "The current trading date you are trading on, YYYY-mm-dd"],
look_back_days: Annotated[int, "how many days to look back"] = 30,
) -> str:
"""
Retrieve technical indicators for a given ticker symbol.
Uses the configured technical_indicators vendor.
Args:
symbol (str): Ticker symbol of the company, e.g. AAPL, TSM
indicator (str): Technical indicator to get the analysis and report of
curr_date (str): The current trading date you are trading on, YYYY-mm-dd
look_back_days (int): How many days to look back, default is 30
Returns:
str: A formatted dataframe containing the technical indicators for the specified ticker symbol and indicator.
"""
return route_to_vendor("get_indicators", symbol, indicator, curr_date, look_back_days)

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from .finnhub_utils import get_data_in_range
from .googlenews_utils import getNewsData
from .yfin_utils import YFinanceUtils
from .reddit_utils import fetch_top_from_category
from .stockstats_utils import StockstatsUtils
from .yfin_utils import YFinanceUtils
from .interface import (
# News and sentiment functions
get_finnhub_news,
get_finnhub_company_insider_sentiment,
get_finnhub_company_insider_transactions,
get_google_news,
get_reddit_global_news,
get_reddit_company_news,
# Financial statements functions
get_simfin_balance_sheet,
get_simfin_cashflow,
get_simfin_income_statements,
# Technical analysis functions
get_stock_stats_indicators_window,
get_stockstats_indicator,
# Market data functions
get_YFin_data_window,
get_YFin_data,
)
__all__ = [
# News and sentiment functions
"get_finnhub_news",
"get_finnhub_company_insider_sentiment",
"get_finnhub_company_insider_transactions",
"get_google_news",
"get_reddit_global_news",
"get_reddit_company_news",
# Financial statements functions
"get_simfin_balance_sheet",
"get_simfin_cashflow",
"get_simfin_income_statements",
# Technical analysis functions
"get_stock_stats_indicators_window",
"get_stockstats_indicator",
# Market data functions
"get_YFin_data_window",
"get_YFin_data",
]

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# Import functions from specialized modules
from .alpha_vantage_stock import get_stock
from .alpha_vantage_indicator import get_indicator
from .alpha_vantage_fundamentals import get_fundamentals, get_balance_sheet, get_cashflow, get_income_statement
from .alpha_vantage_news import get_news, get_insider_transactions

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import os
import requests
import pandas as pd
import json
from datetime import datetime
from io import StringIO
API_BASE_URL = "https://www.alphavantage.co/query"
def get_api_key() -> str:
"""Retrieve the API key for Alpha Vantage from environment variables."""
api_key = os.getenv("ALPHA_VANTAGE_API_KEY")
if not api_key:
raise ValueError("ALPHA_VANTAGE_API_KEY environment variable is not set.")
return api_key
def format_datetime_for_api(date_input) -> str:
"""Convert various date formats to YYYYMMDDTHHMM format required by Alpha Vantage API."""
if isinstance(date_input, str):
# If already in correct format, return as-is
if len(date_input) == 13 and 'T' in date_input:
return date_input
# Try to parse common date formats
try:
dt = datetime.strptime(date_input, "%Y-%m-%d")
return dt.strftime("%Y%m%dT0000")
except ValueError:
try:
dt = datetime.strptime(date_input, "%Y-%m-%d %H:%M")
return dt.strftime("%Y%m%dT%H%M")
except ValueError:
raise ValueError(f"Unsupported date format: {date_input}")
elif isinstance(date_input, datetime):
return date_input.strftime("%Y%m%dT%H%M")
else:
raise ValueError(f"Date must be string or datetime object, got {type(date_input)}")
class AlphaVantageRateLimitError(Exception):
"""Exception raised when Alpha Vantage API rate limit is exceeded."""
pass
def _make_api_request(function_name: str, params: dict) -> dict | str:
"""Helper function to make API requests and handle responses.
Raises:
AlphaVantageRateLimitError: When API rate limit is exceeded
"""
# Create a copy of params to avoid modifying the original
api_params = params.copy()
api_params.update({
"function": function_name,
"apikey": get_api_key(),
"source": "trading_agents",
})
# Handle entitlement parameter if present in params or global variable
current_entitlement = globals().get('_current_entitlement')
entitlement = api_params.get("entitlement") or current_entitlement
if entitlement:
api_params["entitlement"] = entitlement
elif "entitlement" in api_params:
# Remove entitlement if it's None or empty
api_params.pop("entitlement", None)
response = requests.get(API_BASE_URL, params=api_params)
response.raise_for_status()
response_text = response.text
# Check if response is JSON (error responses are typically JSON)
try:
response_json = json.loads(response_text)
# Check for rate limit error
if "Information" in response_json:
info_message = response_json["Information"]
if "rate limit" in info_message.lower() or "api key" in info_message.lower():
raise AlphaVantageRateLimitError(f"Alpha Vantage rate limit exceeded: {info_message}")
except json.JSONDecodeError:
# Response is not JSON (likely CSV data), which is normal
pass
return response_text
def _filter_csv_by_date_range(csv_data: str, start_date: str, end_date: str) -> str:
"""
Filter CSV data to include only rows within the specified date range.
Args:
csv_data: CSV string from Alpha Vantage API
start_date: Start date in yyyy-mm-dd format
end_date: End date in yyyy-mm-dd format
Returns:
Filtered CSV string
"""
if not csv_data or csv_data.strip() == "":
return csv_data
try:
# Parse CSV data
df = pd.read_csv(StringIO(csv_data))
# Assume the first column is the date column (timestamp)
date_col = df.columns[0]
df[date_col] = pd.to_datetime(df[date_col])
# Filter by date range
start_dt = pd.to_datetime(start_date)
end_dt = pd.to_datetime(end_date)
filtered_df = df[(df[date_col] >= start_dt) & (df[date_col] <= end_dt)]
# Convert back to CSV string
return filtered_df.to_csv(index=False)
except Exception as e:
# If filtering fails, return original data with a warning
print(f"Warning: Failed to filter CSV data by date range: {e}")
return csv_data

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from .alpha_vantage_common import _make_api_request
def get_fundamentals(ticker: str, curr_date: str = None) -> str:
"""
Retrieve comprehensive fundamental data for a given ticker symbol using Alpha Vantage.
Args:
ticker (str): Ticker symbol of the company
curr_date (str): Current date you are trading at, yyyy-mm-dd (not used for Alpha Vantage)
Returns:
str: Company overview data including financial ratios and key metrics
"""
params = {
"symbol": ticker,
}
return _make_api_request("OVERVIEW", params)
def get_balance_sheet(ticker: str, freq: str = "quarterly", curr_date: str = None) -> str:
"""
Retrieve balance sheet data for a given ticker symbol using Alpha Vantage.
Args:
ticker (str): Ticker symbol of the company
freq (str): Reporting frequency: annual/quarterly (default quarterly) - not used for Alpha Vantage
curr_date (str): Current date you are trading at, yyyy-mm-dd (not used for Alpha Vantage)
Returns:
str: Balance sheet data with normalized fields
"""
params = {
"symbol": ticker,
}
return _make_api_request("BALANCE_SHEET", params)
def get_cashflow(ticker: str, freq: str = "quarterly", curr_date: str = None) -> str:
"""
Retrieve cash flow statement data for a given ticker symbol using Alpha Vantage.
Args:
ticker (str): Ticker symbol of the company
freq (str): Reporting frequency: annual/quarterly (default quarterly) - not used for Alpha Vantage
curr_date (str): Current date you are trading at, yyyy-mm-dd (not used for Alpha Vantage)
Returns:
str: Cash flow statement data with normalized fields
"""
params = {
"symbol": ticker,
}
return _make_api_request("CASH_FLOW", params)
def get_income_statement(ticker: str, freq: str = "quarterly", curr_date: str = None) -> str:
"""
Retrieve income statement data for a given ticker symbol using Alpha Vantage.
Args:
ticker (str): Ticker symbol of the company
freq (str): Reporting frequency: annual/quarterly (default quarterly) - not used for Alpha Vantage
curr_date (str): Current date you are trading at, yyyy-mm-dd (not used for Alpha Vantage)
Returns:
str: Income statement data with normalized fields
"""
params = {
"symbol": ticker,
}
return _make_api_request("INCOME_STATEMENT", params)

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from .alpha_vantage_common import _make_api_request
def get_indicator(
symbol: str,
indicator: str,
curr_date: str,
look_back_days: int,
interval: str = "daily",
time_period: int = 14,
series_type: str = "close"
) -> str:
"""
Returns Alpha Vantage technical indicator values over a time window.
Args:
symbol: ticker symbol of the company
indicator: technical indicator to get the analysis and report of
curr_date: The current trading date you are trading on, YYYY-mm-dd
look_back_days: how many days to look back
interval: Time interval (daily, weekly, monthly)
time_period: Number of data points for calculation
series_type: The desired price type (close, open, high, low)
Returns:
String containing indicator values and description
"""
from datetime import datetime
from dateutil.relativedelta import relativedelta
supported_indicators = {
"close_50_sma": ("50 SMA", "close"),
"close_200_sma": ("200 SMA", "close"),
"close_10_ema": ("10 EMA", "close"),
"macd": ("MACD", "close"),
"macds": ("MACD Signal", "close"),
"macdh": ("MACD Histogram", "close"),
"rsi": ("RSI", "close"),
"boll": ("Bollinger Middle", "close"),
"boll_ub": ("Bollinger Upper Band", "close"),
"boll_lb": ("Bollinger Lower Band", "close"),
"atr": ("ATR", None),
"vwma": ("VWMA", "close")
}
indicator_descriptions = {
"close_50_sma": "50 SMA: A medium-term trend indicator. Usage: Identify trend direction and serve as dynamic support/resistance. Tips: It lags price; combine with faster indicators for timely signals.",
"close_200_sma": "200 SMA: A long-term trend benchmark. Usage: Confirm overall market trend and identify golden/death cross setups. Tips: It reacts slowly; best for strategic trend confirmation rather than frequent trading entries.",
"close_10_ema": "10 EMA: A responsive short-term average. Usage: Capture quick shifts in momentum and potential entry points. Tips: Prone to noise in choppy markets; use alongside longer averages for filtering false signals.",
"macd": "MACD: Computes momentum via differences of EMAs. Usage: Look for crossovers and divergence as signals of trend changes. Tips: Confirm with other indicators in low-volatility or sideways markets.",
"macds": "MACD Signal: An EMA smoothing of the MACD line. Usage: Use crossovers with the MACD line to trigger trades. Tips: Should be part of a broader strategy to avoid false positives.",
"macdh": "MACD Histogram: Shows the gap between the MACD line and its signal. Usage: Visualize momentum strength and spot divergence early. Tips: Can be volatile; complement with additional filters in fast-moving markets.",
"rsi": "RSI: Measures momentum to flag overbought/oversold conditions. Usage: Apply 70/30 thresholds and watch for divergence to signal reversals. Tips: In strong trends, RSI may remain extreme; always cross-check with trend analysis.",
"boll": "Bollinger Middle: A 20 SMA serving as the basis for Bollinger Bands. Usage: Acts as a dynamic benchmark for price movement. Tips: Combine with the upper and lower bands to effectively spot breakouts or reversals.",
"boll_ub": "Bollinger Upper Band: Typically 2 standard deviations above the middle line. Usage: Signals potential overbought conditions and breakout zones. Tips: Confirm signals with other tools; prices may ride the band in strong trends.",
"boll_lb": "Bollinger Lower Band: Typically 2 standard deviations below the middle line. Usage: Indicates potential oversold conditions. Tips: Use additional analysis to avoid false reversal signals.",
"atr": "ATR: Averages true range to measure volatility. Usage: Set stop-loss levels and adjust position sizes based on current market volatility. Tips: It's a reactive measure, so use it as part of a broader risk management strategy.",
"vwma": "VWMA: A moving average weighted by volume. Usage: Confirm trends by integrating price action with volume data. Tips: Watch for skewed results from volume spikes; use in combination with other volume analyses."
}
if indicator not in supported_indicators:
raise ValueError(
f"Indicator {indicator} is not supported. Please choose from: {list(supported_indicators.keys())}"
)
curr_date_dt = datetime.strptime(curr_date, "%Y-%m-%d")
before = curr_date_dt - relativedelta(days=look_back_days)
# Get the full data for the period instead of making individual calls
_, required_series_type = supported_indicators[indicator]
# Use the provided series_type or fall back to the required one
if required_series_type:
series_type = required_series_type
try:
# Get indicator data for the period
if indicator == "close_50_sma":
data = _make_api_request("SMA", {
"symbol": symbol,
"interval": interval,
"time_period": "50",
"series_type": series_type,
"datatype": "csv"
})
elif indicator == "close_200_sma":
data = _make_api_request("SMA", {
"symbol": symbol,
"interval": interval,
"time_period": "200",
"series_type": series_type,
"datatype": "csv"
})
elif indicator == "close_10_ema":
data = _make_api_request("EMA", {
"symbol": symbol,
"interval": interval,
"time_period": "10",
"series_type": series_type,
"datatype": "csv"
})
elif indicator == "macd":
data = _make_api_request("MACD", {
"symbol": symbol,
"interval": interval,
"series_type": series_type,
"datatype": "csv"
})
elif indicator == "macds":
data = _make_api_request("MACD", {
"symbol": symbol,
"interval": interval,
"series_type": series_type,
"datatype": "csv"
})
elif indicator == "macdh":
data = _make_api_request("MACD", {
"symbol": symbol,
"interval": interval,
"series_type": series_type,
"datatype": "csv"
})
elif indicator == "rsi":
data = _make_api_request("RSI", {
"symbol": symbol,
"interval": interval,
"time_period": str(time_period),
"series_type": series_type,
"datatype": "csv"
})
elif indicator in ["boll", "boll_ub", "boll_lb"]:
data = _make_api_request("BBANDS", {
"symbol": symbol,
"interval": interval,
"time_period": "20",
"series_type": series_type,
"datatype": "csv"
})
elif indicator == "atr":
data = _make_api_request("ATR", {
"symbol": symbol,
"interval": interval,
"time_period": str(time_period),
"datatype": "csv"
})
elif indicator == "vwma":
# Alpha Vantage doesn't have direct VWMA, so we'll return an informative message
# In a real implementation, this would need to be calculated from OHLCV data
return f"## VWMA (Volume Weighted Moving Average) for {symbol}:\n\nVWMA calculation requires OHLCV data and is not directly available from Alpha Vantage API.\nThis indicator would need to be calculated from the raw stock data using volume-weighted price averaging.\n\n{indicator_descriptions.get('vwma', 'No description available.')}"
else:
return f"Error: Indicator {indicator} not implemented yet."
# Parse CSV data and extract values for the date range
lines = data.strip().split('\n')
if len(lines) < 2:
return f"Error: No data returned for {indicator}"
# Parse header and data
header = [col.strip() for col in lines[0].split(',')]
try:
date_col_idx = header.index('time')
except ValueError:
return f"Error: 'time' column not found in data for {indicator}. Available columns: {header}"
# Map internal indicator names to expected CSV column names from Alpha Vantage
col_name_map = {
"macd": "MACD", "macds": "MACD_Signal", "macdh": "MACD_Hist",
"boll": "Real Middle Band", "boll_ub": "Real Upper Band", "boll_lb": "Real Lower Band",
"rsi": "RSI", "atr": "ATR", "close_10_ema": "EMA",
"close_50_sma": "SMA", "close_200_sma": "SMA"
}
target_col_name = col_name_map.get(indicator)
if not target_col_name:
# Default to the second column if no specific mapping exists
value_col_idx = 1
else:
try:
value_col_idx = header.index(target_col_name)
except ValueError:
return f"Error: Column '{target_col_name}' not found for indicator '{indicator}'. Available columns: {header}"
result_data = []
for line in lines[1:]:
if not line.strip():
continue
values = line.split(',')
if len(values) > value_col_idx:
try:
date_str = values[date_col_idx].strip()
# Parse the date
date_dt = datetime.strptime(date_str, "%Y-%m-%d")
# Check if date is in our range
if before <= date_dt <= curr_date_dt:
value = values[value_col_idx].strip()
result_data.append((date_dt, value))
except (ValueError, IndexError):
continue
# Sort by date and format output
result_data.sort(key=lambda x: x[0])
ind_string = ""
for date_dt, value in result_data:
ind_string += f"{date_dt.strftime('%Y-%m-%d')}: {value}\n"
if not ind_string:
ind_string = "No data available for the specified date range.\n"
result_str = (
f"## {indicator.upper()} values from {before.strftime('%Y-%m-%d')} to {curr_date}:\n\n"
+ ind_string
+ "\n\n"
+ indicator_descriptions.get(indicator, "No description available.")
)
return result_str
except Exception as e:
print(f"Error getting Alpha Vantage indicator data for {indicator}: {e}")
return f"Error retrieving {indicator} data: {str(e)}"

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from .alpha_vantage_common import _make_api_request, format_datetime_for_api
def get_news(ticker, start_date, end_date) -> dict[str, str] | str:
"""Returns live and historical market news & sentiment data from premier news outlets worldwide.
Covers stocks, cryptocurrencies, forex, and topics like fiscal policy, mergers & acquisitions, IPOs.
Args:
ticker: Stock symbol for news articles.
start_date: Start date for news search.
end_date: End date for news search.
Returns:
Dictionary containing news sentiment data or JSON string.
"""
params = {
"tickers": ticker,
"time_from": format_datetime_for_api(start_date),
"time_to": format_datetime_for_api(end_date),
"sort": "LATEST",
"limit": "50",
}
return _make_api_request("NEWS_SENTIMENT", params)
def get_insider_transactions(symbol: str) -> dict[str, str] | str:
"""Returns latest and historical insider transactions by key stakeholders.
Covers transactions by founders, executives, board members, etc.
Args:
symbol: Ticker symbol. Example: "IBM".
Returns:
Dictionary containing insider transaction data or JSON string.
"""
params = {
"symbol": symbol,
}
return _make_api_request("INSIDER_TRANSACTIONS", params)

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tradingagents/dataflows/alpha_vantage_stock.py Normal file
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from datetime import datetime
from .alpha_vantage_common import _make_api_request, _filter_csv_by_date_range
def get_stock(
symbol: str,
start_date: str,
end_date: str
) -> str:
"""
Returns raw daily OHLCV values, adjusted close values, and historical split/dividend events
filtered to the specified date range.
Args:
symbol: The name of the equity. For example: symbol=IBM
start_date: Start date in yyyy-mm-dd format
end_date: End date in yyyy-mm-dd format
Returns:
CSV string containing the daily adjusted time series data filtered to the date range.
"""
# Parse dates to determine the range
start_dt = datetime.strptime(start_date, "%Y-%m-%d")
today = datetime.now()
# Choose outputsize based on whether the requested range is within the latest 100 days
# Compact returns latest 100 data points, so check if start_date is recent enough
days_from_today_to_start = (today - start_dt).days
outputsize = "compact" if days_from_today_to_start < 100 else "full"
params = {
"symbol": symbol,
"outputsize": outputsize,
"datatype": "csv",
}
response = _make_api_request("TIME_SERIES_DAILY_ADJUSTED", params)
return _filter_csv_by_date_range(response, start_date, end_date)

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tradingagents/dataflows/config.py
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import tradingagents.default_config as default_config
from typing import Dict, Optional
# Use default config but allow it to be overridden
_config: Optional[Dict] = None
DATA_DIR: Optional[str] = None
def initialize_config():
"""Initialize the configuration with default values."""
global _config, DATA_DIR
if _config is None:
_config = default_config.DEFAULT_CONFIG.copy()
DATA_DIR = _config["data_dir"]
def set_config(config: Dict):
"""Update the configuration with custom values."""
global _config, DATA_DIR
if _config is None:
_config = default_config.DEFAULT_CONFIG.copy()
_config.update(config)
DATA_DIR = _config["data_dir"]
def get_config() -> Dict:
"""Get the current configuration."""
if _config is None:
initialize_config()
return _config.copy()
# Initialize with default config
initialize_config()
import tradingagents.default_config as default_config
from typing import Dict, Optional
# Use default config but allow it to be overridden
_config: Optional[Dict] = None
DATA_DIR: Optional[str] = None
def initialize_config():
"""Initialize the configuration with default values."""
global _config, DATA_DIR
if _config is None:
_config = default_config.DEFAULT_CONFIG.copy()
DATA_DIR = _config["data_dir"]
def set_config(config: Dict):
"""Update the configuration with custom values."""
global _config, DATA_DIR
if _config is None:
_config = default_config.DEFAULT_CONFIG.copy()
_config.update(config)
DATA_DIR = _config["data_dir"]
def get_config() -> Dict:
"""Get the current configuration."""
if _config is None:
initialize_config()
return _config.copy()
# Initialize with default config
initialize_config()

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tradingagents/dataflows/google.py Normal file
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from typing import Annotated
from datetime import datetime
from dateutil.relativedelta import relativedelta
from .googlenews_utils import getNewsData
def get_google_news(
query: Annotated[str, "Query to search with"],
curr_date: Annotated[str, "Curr date in yyyy-mm-dd format"],
look_back_days: Annotated[int, "how many days to look back"],
) -> str:
query = query.replace(" ", "+")
start_date = datetime.strptime(curr_date, "%Y-%m-%d")
before = start_date - relativedelta(days=look_back_days)
before = before.strftime("%Y-%m-%d")
news_results = getNewsData(query, before, curr_date)
news_str = ""
for news in news_results:
news_str += (
f"### {news['title']} (source: {news['source']}) \n\n{news['snippet']}\n\n"
)
if len(news_results) == 0:
return ""
return f"## {query} Google News, from {before} to {curr_date}:\n\n{news_str}"

216
tradingagents/dataflows/googlenews_utils.py
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@ -1,108 +1,108 @@
import json
import requests
from bs4 import BeautifulSoup
from datetime import datetime
import time
import random
from tenacity import (
retry,
stop_after_attempt,
wait_exponential,
retry_if_exception_type,
retry_if_result,
)
def is_rate_limited(response):
"""Check if the response indicates rate limiting (status code 429)"""
return response.status_code == 429
@retry(
retry=(retry_if_result(is_rate_limited)),
wait=wait_exponential(multiplier=1, min=4, max=60),
stop=stop_after_attempt(5),
)
def make_request(url, headers):
"""Make a request with retry logic for rate limiting"""
# Random delay before each request to avoid detection
time.sleep(random.uniform(2, 6))
response = requests.get(url, headers=headers)
return response
def getNewsData(query, start_date, end_date):
"""
Scrape Google News search results for a given query and date range.
query: str - search query
start_date: str - start date in the format yyyy-mm-dd or mm/dd/yyyy
end_date: str - end date in the format yyyy-mm-dd or mm/dd/yyyy
"""
if "-" in start_date:
start_date = datetime.strptime(start_date, "%Y-%m-%d")
start_date = start_date.strftime("%m/%d/%Y")
if "-" in end_date:
end_date = datetime.strptime(end_date, "%Y-%m-%d")
end_date = end_date.strftime("%m/%d/%Y")
headers = {
"User-Agent": (
"Mozilla/5.0 (Windows NT 10.0; Win64; x64) "
"AppleWebKit/537.36 (KHTML, like Gecko) "
"Chrome/101.0.4951.54 Safari/537.36"
)
}
news_results = []
page = 0
while True:
offset = page * 10
url = (
f"https://www.google.com/search?q={query}"
f"&tbs=cdr:1,cd_min:{start_date},cd_max:{end_date}"
f"&tbm=nws&start={offset}"
)
try:
response = make_request(url, headers)
soup = BeautifulSoup(response.content, "html.parser")
results_on_page = soup.select("div.SoaBEf")
if not results_on_page:
break # No more results found
for el in results_on_page:
try:
link = el.find("a")["href"]
title = el.select_one("div.MBeuO").get_text()
snippet = el.select_one(".GI74Re").get_text()
date = el.select_one(".LfVVr").get_text()
source = el.select_one(".NUnG9d span").get_text()
news_results.append(
{
"link": link,
"title": title,
"snippet": snippet,
"date": date,
"source": source,
}
)
except Exception as e:
print(f"Error processing result: {e}")
# If one of the fields is not found, skip this result
continue
# Update the progress bar with the current count of results scraped
# Check for the "Next" link (pagination)
next_link = soup.find("a", id="pnnext")
if not next_link:
break
page += 1
except Exception as e:
print(f"Failed after multiple retries: {e}")
break
return news_results
import json
import requests
from bs4 import BeautifulSoup
from datetime import datetime
import time
import random
from tenacity import (
retry,
stop_after_attempt,
wait_exponential,
retry_if_exception_type,
retry_if_result,
)
def is_rate_limited(response):
"""Check if the response indicates rate limiting (status code 429)"""
return response.status_code == 429
@retry(
retry=(retry_if_result(is_rate_limited)),
wait=wait_exponential(multiplier=1, min=4, max=60),
stop=stop_after_attempt(5),
)
def make_request(url, headers):
"""Make a request with retry logic for rate limiting"""
# Random delay before each request to avoid detection
time.sleep(random.uniform(2, 6))
response = requests.get(url, headers=headers)
return response
def getNewsData(query, start_date, end_date):
"""
Scrape Google News search results for a given query and date range.
query: str - search query
start_date: str - start date in the format yyyy-mm-dd or mm/dd/yyyy
end_date: str - end date in the format yyyy-mm-dd or mm/dd/yyyy
"""
if "-" in start_date:
start_date = datetime.strptime(start_date, "%Y-%m-%d")
start_date = start_date.strftime("%m/%d/%Y")
if "-" in end_date:
end_date = datetime.strptime(end_date, "%Y-%m-%d")
end_date = end_date.strftime("%m/%d/%Y")
headers = {
"User-Agent": (
"Mozilla/5.0 (Windows NT 10.0; Win64; x64) "
"AppleWebKit/537.36 (KHTML, like Gecko) "
"Chrome/101.0.4951.54 Safari/537.36"
)
}
news_results = []
page = 0
while True:
offset = page * 10
url = (
f"https://www.google.com/search?q={query}"
f"&tbs=cdr:1,cd_min:{start_date},cd_max:{end_date}"
f"&tbm=nws&start={offset}"
)
try:
response = make_request(url, headers)
soup = BeautifulSoup(response.content, "html.parser")
results_on_page = soup.select("div.SoaBEf")
if not results_on_page:
break # No more results found
for el in results_on_page:
try:
link = el.find("a")["href"]
title = el.select_one("div.MBeuO").get_text()
snippet = el.select_one(".GI74Re").get_text()
date = el.select_one(".LfVVr").get_text()
source = el.select_one(".NUnG9d span").get_text()
news_results.append(
{
"link": link,
"title": title,
"snippet": snippet,
"date": date,
"source": source,
}
)
except Exception as e:
print(f"Error processing result: {e}")
# If one of the fields is not found, skip this result
continue
# Update the progress bar with the current count of results scraped
# Check for the "Next" link (pagination)
next_link = soup.find("a", id="pnnext")
if not next_link:
break
page += 1
except Exception as e:
print(f"Failed after multiple retries: {e}")
break
return news_results

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tradingagents/dataflows/interface.py
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tradingagents/dataflows/local.py Normal file
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from typing import Annotated
import pandas as pd
import os
from .config import DATA_DIR
from datetime import datetime
from dateutil.relativedelta import relativedelta
import json
from .reddit_utils import fetch_top_from_category
from tqdm import tqdm
def get_YFin_data_window(
symbol: Annotated[str, "ticker symbol of the company"],
curr_date: Annotated[str, "Start date in yyyy-mm-dd format"],
look_back_days: Annotated[int, "how many days to look back"],
) -> str:
# calculate past days
date_obj = datetime.strptime(curr_date, "%Y-%m-%d")
before = date_obj - relativedelta(days=look_back_days)
start_date = before.strftime("%Y-%m-%d")
# read in data
data = pd.read_csv(
os.path.join(
DATA_DIR,
f"market_data/price_data/{symbol}-YFin-data-2015-01-01-2025-03-25.csv",
)
)
# Extract just the date part for comparison
data["DateOnly"] = data["Date"].str[:10]
# Filter data between the start and end dates (inclusive)
filtered_data = data[
(data["DateOnly"] >= start_date) & (data["DateOnly"] <= curr_date)
]
# Drop the temporary column we created
filtered_data = filtered_data.drop("DateOnly", axis=1)
# Set pandas display options to show the full DataFrame
with pd.option_context(
"display.max_rows", None, "display.max_columns", None, "display.width", None
):
df_string = filtered_data.to_string()
return (
f"## Raw Market Data for {symbol} from {start_date} to {curr_date}:\n\n"
+ df_string
)
def get_YFin_data(
symbol: Annotated[str, "ticker symbol of the company"],
start_date: Annotated[str, "Start date in yyyy-mm-dd format"],
end_date: Annotated[str, "End date in yyyy-mm-dd format"],
) -> str:
# read in data
data = pd.read_csv(
os.path.join(
DATA_DIR,
f"market_data/price_data/{symbol}-YFin-data-2015-01-01-2025-03-25.csv",
)
)
if end_date > "2025-03-25":
raise Exception(
f"Get_YFin_Data: {end_date} is outside of the data range of 2015-01-01 to 2025-03-25"
)
# Extract just the date part for comparison
data["DateOnly"] = data["Date"].str[:10]
# Filter data between the start and end dates (inclusive)
filtered_data = data[
(data["DateOnly"] >= start_date) & (data["DateOnly"] <= end_date)
]
# Drop the temporary column we created
filtered_data = filtered_data.drop("DateOnly", axis=1)
# remove the index from the dataframe
filtered_data = filtered_data.reset_index(drop=True)
return filtered_data
def get_finnhub_news(
query: Annotated[str, "Search query or ticker symbol"],
start_date: Annotated[str, "Start date in yyyy-mm-dd format"],
end_date: Annotated[str, "End date in yyyy-mm-dd format"],
):
"""
Retrieve news about a company within a time frame
Args
query (str): Search query or ticker symbol
start_date (str): Start date in yyyy-mm-dd format
end_date (str): End date in yyyy-mm-dd format
Returns
str: dataframe containing the news of the company in the time frame
"""
result = get_data_in_range(query, start_date, end_date, "news_data", DATA_DIR)
if len(result) == 0:
return ""
combined_result = ""
for day, data in result.items():
if len(data) == 0:
continue
for entry in data:
current_news = (
"### " + entry["headline"] + f" ({day})" + "\n" + entry["summary"]
)
combined_result += current_news + "\n\n"
return f"## {query} News, from {start_date} to {end_date}:\n" + str(combined_result)
def get_finnhub_company_insider_sentiment(
ticker: Annotated[str, "ticker symbol for the company"],
curr_date: Annotated[str, "current date you are trading at, yyyy-mm-dd"],
):
"""
Retrieve insider sentiment about a company (retrieved from public SEC information) for the past 15 days
Args:
ticker (str): ticker symbol of the company
curr_date (str): current date you are trading on, yyyy-mm-dd
Returns:
str: a report of the sentiment in the past 15 days starting at curr_date
"""
date_obj = datetime.strptime(curr_date, "%Y-%m-%d")
before = date_obj - relativedelta(days=15) # Default 15 days lookback
before = before.strftime("%Y-%m-%d")
data = get_data_in_range(ticker, before, curr_date, "insider_senti", DATA_DIR)
if len(data) == 0:
return ""
result_str = ""
seen_dicts = []
for date, senti_list in data.items():
for entry in senti_list:
if entry not in seen_dicts:
result_str += f"### {entry['year']}-{entry['month']}:\nChange: {entry['change']}\nMonthly Share Purchase Ratio: {entry['mspr']}\n\n"
seen_dicts.append(entry)
return (
f"## {ticker} Insider Sentiment Data for {before} to {curr_date}:\n"
+ result_str
+ "The change field refers to the net buying/selling from all insiders' transactions. The mspr field refers to monthly share purchase ratio."
)
def get_finnhub_company_insider_transactions(
ticker: Annotated[str, "ticker symbol"],
curr_date: Annotated[str, "current date you are trading at, yyyy-mm-dd"],
):
"""
Retrieve insider transcaction information about a company (retrieved from public SEC information) for the past 15 days
Args:
ticker (str): ticker symbol of the company
curr_date (str): current date you are trading at, yyyy-mm-dd
Returns:
str: a report of the company's insider transaction/trading informtaion in the past 15 days
"""
date_obj = datetime.strptime(curr_date, "%Y-%m-%d")
before = date_obj - relativedelta(days=15) # Default 15 days lookback
before = before.strftime("%Y-%m-%d")
data = get_data_in_range(ticker, before, curr_date, "insider_trans", DATA_DIR)
if len(data) == 0:
return ""
result_str = ""
seen_dicts = []
for date, senti_list in data.items():
for entry in senti_list:
if entry not in seen_dicts:
result_str += f"### Filing Date: {entry['filingDate']}, {entry['name']}:\nChange:{entry['change']}\nShares: {entry['share']}\nTransaction Price: {entry['transactionPrice']}\nTransaction Code: {entry['transactionCode']}\n\n"
seen_dicts.append(entry)
return (
f"## {ticker} insider transactions from {before} to {curr_date}:\n"
+ result_str
+ "The change field reflects the variation in share count—here a negative number indicates a reduction in holdings—while share specifies the total number of shares involved. The transactionPrice denotes the per-share price at which the trade was executed, and transactionDate marks when the transaction occurred. The name field identifies the insider making the trade, and transactionCode (e.g., S for sale) clarifies the nature of the transaction. FilingDate records when the transaction was officially reported, and the unique id links to the specific SEC filing, as indicated by the source. Additionally, the symbol ties the transaction to a particular company, isDerivative flags whether the trade involves derivative securities, and currency notes the currency context of the transaction."
)
def get_data_in_range(ticker, start_date, end_date, data_type, data_dir, period=None):
"""
Gets finnhub data saved and processed on disk.
Args:
start_date (str): Start date in YYYY-MM-DD format.
end_date (str): End date in YYYY-MM-DD format.
data_type (str): Type of data from finnhub to fetch. Can be insider_trans, SEC_filings, news_data, insider_senti, or fin_as_reported.
data_dir (str): Directory where the data is saved.
period (str): Default to none, if there is a period specified, should be annual or quarterly.
"""
if period:
data_path = os.path.join(
data_dir,
"finnhub_data",
data_type,
f"{ticker}_{period}_data_formatted.json",
)
else:
data_path = os.path.join(
data_dir, "finnhub_data", data_type, f"{ticker}_data_formatted.json"
)
data = open(data_path, "r")
data = json.load(data)
# filter keys (date, str in format YYYY-MM-DD) by the date range (str, str in format YYYY-MM-DD)
filtered_data = {}
for key, value in data.items():
if start_date <= key <= end_date and len(value) > 0:
filtered_data[key] = value
return filtered_data
def get_simfin_balance_sheet(
ticker: Annotated[str, "ticker symbol"],
freq: Annotated[
str,
"reporting frequency of the company's financial history: annual / quarterly",
],
curr_date: Annotated[str, "current date you are trading at, yyyy-mm-dd"],
):
data_path = os.path.join(
DATA_DIR,
"fundamental_data",
"simfin_data_all",
"balance_sheet",
"companies",
"us",
f"us-balance-{freq}.csv",
)
df = pd.read_csv(data_path, sep=";")
# Convert date strings to datetime objects and remove any time components
df["Report Date"] = pd.to_datetime(df["Report Date"], utc=True).dt.normalize()
df["Publish Date"] = pd.to_datetime(df["Publish Date"], utc=True).dt.normalize()
# Convert the current date to datetime and normalize
curr_date_dt = pd.to_datetime(curr_date, utc=True).normalize()
# Filter the DataFrame for the given ticker and for reports that were published on or before the current date
filtered_df = df[(df["Ticker"] == ticker) & (df["Publish Date"] <= curr_date_dt)]
# Check if there are any available reports; if not, return a notification
if filtered_df.empty:
print("No balance sheet available before the given current date.")
return ""
# Get the most recent balance sheet by selecting the row with the latest Publish Date
latest_balance_sheet = filtered_df.loc[filtered_df["Publish Date"].idxmax()]
# drop the SimFinID column
latest_balance_sheet = latest_balance_sheet.drop("SimFinId")
return (
f"## {freq} balance sheet for {ticker} released on {str(latest_balance_sheet['Publish Date'])[0:10]}: \n"
+ str(latest_balance_sheet)
+ "\n\nThis includes metadata like reporting dates and currency, share details, and a breakdown of assets, liabilities, and equity. Assets are grouped as current (liquid items like cash and receivables) and noncurrent (long-term investments and property). Liabilities are split between short-term obligations and long-term debts, while equity reflects shareholder funds such as paid-in capital and retained earnings. Together, these components ensure that total assets equal the sum of liabilities and equity."
)
def get_simfin_cashflow(
ticker: Annotated[str, "ticker symbol"],
freq: Annotated[
str,
"reporting frequency of the company's financial history: annual / quarterly",
],
curr_date: Annotated[str, "current date you are trading at, yyyy-mm-dd"],
):
data_path = os.path.join(
DATA_DIR,
"fundamental_data",
"simfin_data_all",
"cash_flow",
"companies",
"us",
f"us-cashflow-{freq}.csv",
)
df = pd.read_csv(data_path, sep=";")
# Convert date strings to datetime objects and remove any time components
df["Report Date"] = pd.to_datetime(df["Report Date"], utc=True).dt.normalize()
df["Publish Date"] = pd.to_datetime(df["Publish Date"], utc=True).dt.normalize()
# Convert the current date to datetime and normalize
curr_date_dt = pd.to_datetime(curr_date, utc=True).normalize()
# Filter the DataFrame for the given ticker and for reports that were published on or before the current date
filtered_df = df[(df["Ticker"] == ticker) & (df["Publish Date"] <= curr_date_dt)]
# Check if there are any available reports; if not, return a notification
if filtered_df.empty:
print("No cash flow statement available before the given current date.")
return ""
# Get the most recent cash flow statement by selecting the row with the latest Publish Date
latest_cash_flow = filtered_df.loc[filtered_df["Publish Date"].idxmax()]
# drop the SimFinID column
latest_cash_flow = latest_cash_flow.drop("SimFinId")
return (
f"## {freq} cash flow statement for {ticker} released on {str(latest_cash_flow['Publish Date'])[0:10]}: \n"
+ str(latest_cash_flow)
+ "\n\nThis includes metadata like reporting dates and currency, share details, and a breakdown of cash movements. Operating activities show cash generated from core business operations, including net income adjustments for non-cash items and working capital changes. Investing activities cover asset acquisitions/disposals and investments. Financing activities include debt transactions, equity issuances/repurchases, and dividend payments. The net change in cash represents the overall increase or decrease in the company's cash position during the reporting period."
)
def get_simfin_income_statements(
ticker: Annotated[str, "ticker symbol"],
freq: Annotated[
str,
"reporting frequency of the company's financial history: annual / quarterly",
],
curr_date: Annotated[str, "current date you are trading at, yyyy-mm-dd"],
):
data_path = os.path.join(
DATA_DIR,
"fundamental_data",
"simfin_data_all",
"income_statements",
"companies",
"us",
f"us-income-{freq}.csv",
)
df = pd.read_csv(data_path, sep=";")
# Convert date strings to datetime objects and remove any time components
df["Report Date"] = pd.to_datetime(df["Report Date"], utc=True).dt.normalize()
df["Publish Date"] = pd.to_datetime(df["Publish Date"], utc=True).dt.normalize()
# Convert the current date to datetime and normalize
curr_date_dt = pd.to_datetime(curr_date, utc=True).normalize()
# Filter the DataFrame for the given ticker and for reports that were published on or before the current date
filtered_df = df[(df["Ticker"] == ticker) & (df["Publish Date"] <= curr_date_dt)]
# Check if there are any available reports; if not, return a notification
if filtered_df.empty:
print("No income statement available before the given current date.")
return ""
# Get the most recent income statement by selecting the row with the latest Publish Date
latest_income = filtered_df.loc[filtered_df["Publish Date"].idxmax()]
# drop the SimFinID column
latest_income = latest_income.drop("SimFinId")
return (
f"## {freq} income statement for {ticker} released on {str(latest_income['Publish Date'])[0:10]}: \n"
+ str(latest_income)
+ "\n\nThis includes metadata like reporting dates and currency, share details, and a comprehensive breakdown of the company's financial performance. Starting with Revenue, it shows Cost of Revenue and resulting Gross Profit. Operating Expenses are detailed, including SG&A, R&D, and Depreciation. The statement then shows Operating Income, followed by non-operating items and Interest Expense, leading to Pretax Income. After accounting for Income Tax and any Extraordinary items, it concludes with Net Income, representing the company's bottom-line profit or loss for the period."
)
def get_reddit_global_news(
curr_date: Annotated[str, "Current date in yyyy-mm-dd format"],
look_back_days: Annotated[int, "Number of days to look back"] = 7,
limit: Annotated[int, "Maximum number of articles to return"] = 5,
) -> str:
"""
Retrieve the latest top reddit news
Args:
curr_date: Current date in yyyy-mm-dd format
look_back_days: Number of days to look back (default 7)
limit: Maximum number of articles to return (default 5)
Returns:
str: A formatted string containing the latest news articles posts on reddit
"""
curr_date_dt = datetime.strptime(curr_date, "%Y-%m-%d")
before = curr_date_dt - relativedelta(days=look_back_days)
before = before.strftime("%Y-%m-%d")
posts = []
# iterate from before to curr_date
curr_iter_date = datetime.strptime(before, "%Y-%m-%d")
total_iterations = (curr_date_dt - curr_iter_date).days + 1
pbar = tqdm(desc=f"Getting Global News on {curr_date}", total=total_iterations)
while curr_iter_date <= curr_date_dt:
curr_date_str = curr_iter_date.strftime("%Y-%m-%d")
fetch_result = fetch_top_from_category(
"global_news",
curr_date_str,
limit,
data_path=os.path.join(DATA_DIR, "reddit_data"),
)
posts.extend(fetch_result)
curr_iter_date += relativedelta(days=1)
pbar.update(1)
pbar.close()
if len(posts) == 0:
return ""
news_str = ""
for post in posts:
if post["content"] == "":
news_str += f"### {post['title']}\n\n"
else:
news_str += f"### {post['title']}\n\n{post['content']}\n\n"
return f"## Global News Reddit, from {before} to {curr_date}:\n{news_str}"
def get_reddit_company_news(
query: Annotated[str, "Search query or ticker symbol"],
start_date: Annotated[str, "Start date in yyyy-mm-dd format"],
end_date: Annotated[str, "End date in yyyy-mm-dd format"],
) -> str:
"""
Retrieve the latest top reddit news
Args:
query: Search query or ticker symbol
start_date: Start date in yyyy-mm-dd format
end_date: End date in yyyy-mm-dd format
Returns:
str: A formatted string containing news articles posts on reddit
"""
start_date_dt = datetime.strptime(start_date, "%Y-%m-%d")
end_date_dt = datetime.strptime(end_date, "%Y-%m-%d")
posts = []
# iterate from start_date to end_date
curr_date = start_date_dt
total_iterations = (end_date_dt - curr_date).days + 1
pbar = tqdm(
desc=f"Getting Company News for {query} from {start_date} to {end_date}",
total=total_iterations,
)
while curr_date <= end_date_dt:
curr_date_str = curr_date.strftime("%Y-%m-%d")
fetch_result = fetch_top_from_category(
"company_news",
curr_date_str,
10, # max limit per day
query,
data_path=os.path.join(DATA_DIR, "reddit_data"),
)
posts.extend(fetch_result)
curr_date += relativedelta(days=1)
pbar.update(1)
pbar.close()
if len(posts) == 0:
return ""
news_str = ""
for post in posts:
if post["content"] == "":
news_str += f"### {post['title']}\n\n"
else:
news_str += f"### {post['title']}\n\n{post['content']}\n\n"
return f"##{query} News Reddit, from {start_date} to {end_date}:\n\n{news_str}"

107
tradingagents/dataflows/openai.py Normal file
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from openai import OpenAI
from .config import get_config
def get_stock_news_openai(query, start_date, end_date):
config = get_config()
client = OpenAI(base_url=config["backend_url"])
response = client.responses.create(
model=config["quick_think_llm"],
input=[
{
"role": "system",
"content": [
{
"type": "input_text",
"text": f"Can you search Social Media for {query} from {start_date} to {end_date}? Make sure you only get the data posted during that period.",
}
],
}
],
text={"format": {"type": "text"}},
reasoning={},
tools=[
{
"type": "web_search_preview",
"user_location": {"type": "approximate"},
"search_context_size": "low",
}
],
temperature=1,
max_output_tokens=4096,
top_p=1,
store=True,
)
return response.output[1].content[0].text
def get_global_news_openai(curr_date, look_back_days=7, limit=5):
config = get_config()
client = OpenAI(base_url=config["backend_url"])
response = client.responses.create(
model=config["quick_think_llm"],
input=[
{
"role": "system",
"content": [
{
"type": "input_text",
"text": f"Can you search global or macroeconomics news from {look_back_days} days before {curr_date} to {curr_date} that would be informative for trading purposes? Make sure you only get the data posted during that period. Limit the results to {limit} articles.",
}
],
}
],
text={"format": {"type": "text"}},
reasoning={},
tools=[
{
"type": "web_search_preview",
"user_location": {"type": "approximate"},
"search_context_size": "low",
}
],
temperature=1,
max_output_tokens=4096,
top_p=1,
store=True,
)
return response.output[1].content[0].text
def get_fundamentals_openai(ticker, curr_date):
config = get_config()
client = OpenAI(base_url=config["backend_url"])
response = client.responses.create(
model=config["quick_think_llm"],
input=[
{
"role": "system",
"content": [
{
"type": "input_text",
"text": f"Can you search Fundamental for discussions on {ticker} during of the month before {curr_date} to the month of {curr_date}. Make sure you only get the data posted during that period. List as a table, with PE/PS/Cash flow/ etc",
}
],
}
],
text={"format": {"type": "text"}},
reasoning={},
tools=[
{
"type": "web_search_preview",
"user_location": {"type": "approximate"},
"search_context_size": "low",
}
],
temperature=1,
max_output_tokens=4096,
top_p=1,
store=True,
)
return response.output[1].content[0].text

270
tradingagents/dataflows/reddit_utils.py
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@ -1,135 +1,135 @@
import requests
import time
import json
from datetime import datetime, timedelta
from contextlib import contextmanager
from typing import Annotated
import os
import re
ticker_to_company = {
"AAPL": "Apple",
"MSFT": "Microsoft",
"GOOGL": "Google",
"AMZN": "Amazon",
"TSLA": "Tesla",
"NVDA": "Nvidia",
"TSM": "Taiwan Semiconductor Manufacturing Company OR TSMC",
"JPM": "JPMorgan Chase OR JP Morgan",
"JNJ": "Johnson & Johnson OR JNJ",
"V": "Visa",
"WMT": "Walmart",
"META": "Meta OR Facebook",
"AMD": "AMD",
"INTC": "Intel",
"QCOM": "Qualcomm",
"BABA": "Alibaba",
"ADBE": "Adobe",
"NFLX": "Netflix",
"CRM": "Salesforce",
"PYPL": "PayPal",
"PLTR": "Palantir",
"MU": "Micron",
"SQ": "Block OR Square",
"ZM": "Zoom",
"CSCO": "Cisco",
"SHOP": "Shopify",
"ORCL": "Oracle",
"X": "Twitter OR X",
"SPOT": "Spotify",
"AVGO": "Broadcom",
"ASML": "ASML ",
"TWLO": "Twilio",
"SNAP": "Snap Inc.",
"TEAM": "Atlassian",
"SQSP": "Squarespace",
"UBER": "Uber",
"ROKU": "Roku",
"PINS": "Pinterest",
}
def fetch_top_from_category(
category: Annotated[
str, "Category to fetch top post from. Collection of subreddits."
],
date: Annotated[str, "Date to fetch top posts from."],
max_limit: Annotated[int, "Maximum number of posts to fetch."],
query: Annotated[str, "Optional query to search for in the subreddit."] = None,
data_path: Annotated[
str,
"Path to the data folder. Default is 'reddit_data'.",
] = "reddit_data",
):
base_path = data_path
all_content = []
if max_limit < len(os.listdir(os.path.join(base_path, category))):
raise ValueError(
"REDDIT FETCHING ERROR: max limit is less than the number of files in the category. Will not be able to fetch any posts"
)
limit_per_subreddit = max_limit // len(
os.listdir(os.path.join(base_path, category))
)
for data_file in os.listdir(os.path.join(base_path, category)):
# check if data_file is a .jsonl file
if not data_file.endswith(".jsonl"):
continue
all_content_curr_subreddit = []
with open(os.path.join(base_path, category, data_file), "rb") as f:
for i, line in enumerate(f):
# skip empty lines
if not line.strip():
continue
parsed_line = json.loads(line)
# select only lines that are from the date
post_date = datetime.utcfromtimestamp(
parsed_line["created_utc"]
).strftime("%Y-%m-%d")
if post_date != date:
continue
# if is company_news, check that the title or the content has the company's name (query) mentioned
if "company" in category and query:
search_terms = []
if "OR" in ticker_to_company[query]:
search_terms = ticker_to_company[query].split(" OR ")
else:
search_terms = [ticker_to_company[query]]
search_terms.append(query)
found = False
for term in search_terms:
if re.search(
term, parsed_line["title"], re.IGNORECASE
) or re.search(term, parsed_line["selftext"], re.IGNORECASE):
found = True
break
if not found:
continue
post = {
"title": parsed_line["title"],
"content": parsed_line["selftext"],
"url": parsed_line["url"],
"upvotes": parsed_line["ups"],
"posted_date": post_date,
}
all_content_curr_subreddit.append(post)
# sort all_content_curr_subreddit by upvote_ratio in descending order
all_content_curr_subreddit.sort(key=lambda x: x["upvotes"], reverse=True)
all_content.extend(all_content_curr_subreddit[:limit_per_subreddit])
return all_content
import requests
import time
import json
from datetime import datetime, timedelta
from contextlib import contextmanager
from typing import Annotated
import os
import re
ticker_to_company = {
"AAPL": "Apple",
"MSFT": "Microsoft",
"GOOGL": "Google",
"AMZN": "Amazon",
"TSLA": "Tesla",
"NVDA": "Nvidia",
"TSM": "Taiwan Semiconductor Manufacturing Company OR TSMC",
"JPM": "JPMorgan Chase OR JP Morgan",
"JNJ": "Johnson & Johnson OR JNJ",
"V": "Visa",
"WMT": "Walmart",
"META": "Meta OR Facebook",
"AMD": "AMD",
"INTC": "Intel",
"QCOM": "Qualcomm",
"BABA": "Alibaba",
"ADBE": "Adobe",
"NFLX": "Netflix",
"CRM": "Salesforce",
"PYPL": "PayPal",
"PLTR": "Palantir",
"MU": "Micron",
"SQ": "Block OR Square",
"ZM": "Zoom",
"CSCO": "Cisco",
"SHOP": "Shopify",
"ORCL": "Oracle",
"X": "Twitter OR X",
"SPOT": "Spotify",
"AVGO": "Broadcom",
"ASML": "ASML ",
"TWLO": "Twilio",
"SNAP": "Snap Inc.",
"TEAM": "Atlassian",
"SQSP": "Squarespace",
"UBER": "Uber",
"ROKU": "Roku",
"PINS": "Pinterest",
}
def fetch_top_from_category(
category: Annotated[
str, "Category to fetch top post from. Collection of subreddits."
],
date: Annotated[str, "Date to fetch top posts from."],
max_limit: Annotated[int, "Maximum number of posts to fetch."],
query: Annotated[str, "Optional query to search for in the subreddit."] = None,
data_path: Annotated[
str,
"Path to the data folder. Default is 'reddit_data'.",
] = "reddit_data",
):
base_path = data_path
all_content = []
if max_limit < len(os.listdir(os.path.join(base_path, category))):
raise ValueError(
"REDDIT FETCHING ERROR: max limit is less than the number of files in the category. Will not be able to fetch any posts"
)
limit_per_subreddit = max_limit // len(
os.listdir(os.path.join(base_path, category))
)
for data_file in os.listdir(os.path.join(base_path, category)):
# check if data_file is a .jsonl file
if not data_file.endswith(".jsonl"):
continue
all_content_curr_subreddit = []
with open(os.path.join(base_path, category, data_file), "rb") as f:
for i, line in enumerate(f):
# skip empty lines
if not line.strip():
continue
parsed_line = json.loads(line)
# select only lines that are from the date
post_date = datetime.utcfromtimestamp(
parsed_line["created_utc"]
).strftime("%Y-%m-%d")
if post_date != date:
continue
# if is company_news, check that the title or the content has the company's name (query) mentioned
if "company" in category and query:
search_terms = []
if "OR" in ticker_to_company[query]:
search_terms = ticker_to_company[query].split(" OR ")
else:
search_terms = [ticker_to_company[query]]
search_terms.append(query)
found = False
for term in search_terms:
if re.search(
term, parsed_line["title"], re.IGNORECASE
) or re.search(term, parsed_line["selftext"], re.IGNORECASE):
found = True
break
if not found:
continue
post = {
"title": parsed_line["title"],
"content": parsed_line["selftext"],
"url": parsed_line["url"],
"upvotes": parsed_line["ups"],
"posted_date": post_date,
}
all_content_curr_subreddit.append(post)
# sort all_content_curr_subreddit by upvote_ratio in descending order
all_content_curr_subreddit.sort(key=lambda x: x["upvotes"], reverse=True)
all_content.extend(all_content_curr_subreddit[:limit_per_subreddit])
return all_content

169
tradingagents/dataflows/stockstats_utils.py
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@ -1,87 +1,82 @@
import pandas as pd
import yfinance as yf
from stockstats import wrap
from typing import Annotated
import os
from .config import get_config
class StockstatsUtils:
@staticmethod
def get_stock_stats(
symbol: Annotated[str, "ticker symbol for the company"],
indicator: Annotated[
str, "quantitative indicators based off of the stock data for the company"
],
curr_date: Annotated[
str, "curr date for retrieving stock price data, YYYY-mm-dd"
],
data_dir: Annotated[
str,
"directory where the stock data is stored.",
],
online: Annotated[
bool,
"whether to use online tools to fetch data or offline tools. If True, will use online tools.",
] = False,
):
df = None
data = None
if not online:
try:
data = pd.read_csv(
os.path.join(
data_dir,
f"{symbol}-YFin-data-2015-01-01-2025-03-25.csv",
)
)
df = wrap(data)
except FileNotFoundError:
raise Exception("Stockstats fail: Yahoo Finance data not fetched yet!")
else:
# Get today's date as YYYY-mm-dd to add to cache
today_date = pd.Timestamp.today()
curr_date = pd.to_datetime(curr_date)
end_date = today_date
start_date = today_date - pd.DateOffset(years=15)
start_date = start_date.strftime("%Y-%m-%d")
end_date = end_date.strftime("%Y-%m-%d")
# Get config and ensure cache directory exists
config = get_config()
os.makedirs(config["data_cache_dir"], exist_ok=True)
data_file = os.path.join(
config["data_cache_dir"],
f"{symbol}-YFin-data-{start_date}-{end_date}.csv",
)
if os.path.exists(data_file):
data = pd.read_csv(data_file)
data["Date"] = pd.to_datetime(data["Date"])
else:
data = yf.download(
symbol,
start=start_date,
end=end_date,
multi_level_index=False,
progress=False,
auto_adjust=True,
)
data = data.reset_index()
data.to_csv(data_file, index=False)
df = wrap(data)
df["Date"] = df["Date"].dt.strftime("%Y-%m-%d")
curr_date = curr_date.strftime("%Y-%m-%d")
df[indicator] # trigger stockstats to calculate the indicator
matching_rows = df[df["Date"].str.startswith(curr_date)]
if not matching_rows.empty:
indicator_value = matching_rows[indicator].values[0]
return indicator_value
else:
return "N/A: Not a trading day (weekend or holiday)"
import pandas as pd
import yfinance as yf
from stockstats import wrap
from typing import Annotated
import os
from .config import get_config, DATA_DIR
class StockstatsUtils:
@staticmethod
def get_stock_stats(
symbol: Annotated[str, "ticker symbol for the company"],
indicator: Annotated[
str, "quantitative indicators based off of the stock data for the company"
],
curr_date: Annotated[
str, "curr date for retrieving stock price data, YYYY-mm-dd"
],
):
# Get config and set up data directory path
config = get_config()
online = config["data_vendors"]["technical_indicators"] != "local"
df = None
data = None
if not online:
try:
data = pd.read_csv(
os.path.join(
DATA_DIR,
f"{symbol}-YFin-data-2015-01-01-2025-03-25.csv",
)
)
df = wrap(data)
except FileNotFoundError:
raise Exception("Stockstats fail: Yahoo Finance data not fetched yet!")
else:
# Get today's date as YYYY-mm-dd to add to cache
today_date = pd.Timestamp.today()
curr_date = pd.to_datetime(curr_date)
end_date = today_date
start_date = today_date - pd.DateOffset(years=15)
start_date = start_date.strftime("%Y-%m-%d")
end_date = end_date.strftime("%Y-%m-%d")
# Get config and ensure cache directory exists
os.makedirs(config["data_cache_dir"], exist_ok=True)
data_file = os.path.join(
config["data_cache_dir"],
f"{symbol}-YFin-data-{start_date}-{end_date}.csv",
)
if os.path.exists(data_file):
data = pd.read_csv(data_file)
data["Date"] = pd.to_datetime(data["Date"])
else:
data = yf.download(
symbol,
start=start_date,
end=end_date,
multi_level_index=False,
progress=False,
auto_adjust=True,
)
data = data.reset_index()
data.to_csv(data_file, index=False)
df = wrap(data)
df["Date"] = df["Date"].dt.strftime("%Y-%m-%d")
curr_date = curr_date.strftime("%Y-%m-%d")
df[indicator] # trigger stockstats to calculate the indicator
matching_rows = df[df["Date"].str.startswith(curr_date)]
if not matching_rows.empty:
indicator_value = matching_rows[indicator].values[0]
return indicator_value
else:
return "N/A: Not a trading day (weekend or holiday)"

78
tradingagents/dataflows/utils.py
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@ -1,39 +1,39 @@
import os
import json
import pandas as pd
from datetime import date, timedelta, datetime
from typing import Annotated
SavePathType = Annotated[str, "File path to save data. If None, data is not saved."]
def save_output(data: pd.DataFrame, tag: str, save_path: SavePathType = None) -> None:
if save_path:
data.to_csv(save_path)
print(f"{tag} saved to {save_path}")
def get_current_date():
return date.today().strftime("%Y-%m-%d")
def decorate_all_methods(decorator):
def class_decorator(cls):
for attr_name, attr_value in cls.__dict__.items():
if callable(attr_value):
setattr(cls, attr_name, decorator(attr_value))
return cls
return class_decorator
def get_next_weekday(date):
if not isinstance(date, datetime):
date = datetime.strptime(date, "%Y-%m-%d")
if date.weekday() >= 5:
days_to_add = 7 - date.weekday()
next_weekday = date + timedelta(days=days_to_add)
return next_weekday
else:
return date
import os
import json
import pandas as pd
from datetime import date, timedelta, datetime
from typing import Annotated
SavePathType = Annotated[str, "File path to save data. If None, data is not saved."]
def save_output(data: pd.DataFrame, tag: str, save_path: SavePathType = None) -> None:
if save_path:
data.to_csv(save_path)
print(f"{tag} saved to {save_path}")
def get_current_date():
return date.today().strftime("%Y-%m-%d")
def decorate_all_methods(decorator):
def class_decorator(cls):
for attr_name, attr_value in cls.__dict__.items():
if callable(attr_value):
setattr(cls, attr_name, decorator(attr_value))
return cls
return class_decorator
def get_next_weekday(date):
if not isinstance(date, datetime):
date = datetime.strptime(date, "%Y-%m-%d")
if date.weekday() >= 5:
days_to_add = 7 - date.weekday()
next_weekday = date + timedelta(days=days_to_add)
return next_weekday
else:
return date

407
tradingagents/dataflows/y_finance.py Normal file
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from typing import Annotated
from datetime import datetime
from dateutil.relativedelta import relativedelta
import yfinance as yf
import os
from .stockstats_utils import StockstatsUtils
def get_YFin_data_online(
symbol: Annotated[str, "ticker symbol of the company"],
start_date: Annotated[str, "Start date in yyyy-mm-dd format"],
end_date: Annotated[str, "End date in yyyy-mm-dd format"],
):
datetime.strptime(start_date, "%Y-%m-%d")
datetime.strptime(end_date, "%Y-%m-%d")
# Create ticker object
ticker = yf.Ticker(symbol.upper())
# Fetch historical data for the specified date range
data = ticker.history(start=start_date, end=end_date)
# Check if data is empty
if data.empty:
return (
f"No data found for symbol '{symbol}' between {start_date} and {end_date}"
)
# Remove timezone info from index for cleaner output
if data.index.tz is not None:
data.index = data.index.tz_localize(None)
# Round numerical values to 2 decimal places for cleaner display
numeric_columns = ["Open", "High", "Low", "Close", "Adj Close"]
for col in numeric_columns:
if col in data.columns:
data[col] = data[col].round(2)
# Convert DataFrame to CSV string
csv_string = data.to_csv()
# Add header information
header = f"# Stock data for {symbol.upper()} from {start_date} to {end_date}\n"
header += f"# Total records: {len(data)}\n"
header += f"# Data retrieved on: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n\n"
return header + csv_string
def get_stock_stats_indicators_window(
symbol: Annotated[str, "ticker symbol of the company"],
indicator: Annotated[str, "technical indicator to get the analysis and report of"],
curr_date: Annotated[
str, "The current trading date you are trading on, YYYY-mm-dd"
],
look_back_days: Annotated[int, "how many days to look back"],
) -> str:
best_ind_params = {
# Moving Averages
"close_50_sma": (
"50 SMA: A medium-term trend indicator. "
"Usage: Identify trend direction and serve as dynamic support/resistance. "
"Tips: It lags price; combine with faster indicators for timely signals."
),
"close_200_sma": (
"200 SMA: A long-term trend benchmark. "
"Usage: Confirm overall market trend and identify golden/death cross setups. "
"Tips: It reacts slowly; best for strategic trend confirmation rather than frequent trading entries."
),
"close_10_ema": (
"10 EMA: A responsive short-term average. "
"Usage: Capture quick shifts in momentum and potential entry points. "
"Tips: Prone to noise in choppy markets; use alongside longer averages for filtering false signals."
),
# MACD Related
"macd": (
"MACD: Computes momentum via differences of EMAs. "
"Usage: Look for crossovers and divergence as signals of trend changes. "
"Tips: Confirm with other indicators in low-volatility or sideways markets."
),
"macds": (
"MACD Signal: An EMA smoothing of the MACD line. "
"Usage: Use crossovers with the MACD line to trigger trades. "
"Tips: Should be part of a broader strategy to avoid false positives."
),
"macdh": (
"MACD Histogram: Shows the gap between the MACD line and its signal. "
"Usage: Visualize momentum strength and spot divergence early. "
"Tips: Can be volatile; complement with additional filters in fast-moving markets."
),
# Momentum Indicators
"rsi": (
"RSI: Measures momentum to flag overbought/oversold conditions. "
"Usage: Apply 70/30 thresholds and watch for divergence to signal reversals. "
"Tips: In strong trends, RSI may remain extreme; always cross-check with trend analysis."
),
# Volatility Indicators
"boll": (
"Bollinger Middle: A 20 SMA serving as the basis for Bollinger Bands. "
"Usage: Acts as a dynamic benchmark for price movement. "
"Tips: Combine with the upper and lower bands to effectively spot breakouts or reversals."
),
"boll_ub": (
"Bollinger Upper Band: Typically 2 standard deviations above the middle line. "
"Usage: Signals potential overbought conditions and breakout zones. "
"Tips: Confirm signals with other tools; prices may ride the band in strong trends."
),
"boll_lb": (
"Bollinger Lower Band: Typically 2 standard deviations below the middle line. "
"Usage: Indicates potential oversold conditions. "
"Tips: Use additional analysis to avoid false reversal signals."
),
"atr": (
"ATR: Averages true range to measure volatility. "
"Usage: Set stop-loss levels and adjust position sizes based on current market volatility. "
"Tips: It's a reactive measure, so use it as part of a broader risk management strategy."
),
# Volume-Based Indicators
"vwma": (
"VWMA: A moving average weighted by volume. "
"Usage: Confirm trends by integrating price action with volume data. "
"Tips: Watch for skewed results from volume spikes; use in combination with other volume analyses."
),
"mfi": (
"MFI: The Money Flow Index is a momentum indicator that uses both price and volume to measure buying and selling pressure. "
"Usage: Identify overbought (>80) or oversold (<20) conditions and confirm the strength of trends or reversals. "
"Tips: Use alongside RSI or MACD to confirm signals; divergence between price and MFI can indicate potential reversals."
),
}
if indicator not in best_ind_params:
raise ValueError(
f"Indicator {indicator} is not supported. Please choose from: {list(best_ind_params.keys())}"
)
end_date = curr_date
curr_date_dt = datetime.strptime(curr_date, "%Y-%m-%d")
before = curr_date_dt - relativedelta(days=look_back_days)
# Optimized: Get stock data once and calculate indicators for all dates
try:
indicator_data = _get_stock_stats_bulk(symbol, indicator, curr_date)
# Generate the date range we need
current_dt = curr_date_dt
date_values = []
while current_dt >= before:
date_str = current_dt.strftime('%Y-%m-%d')
# Look up the indicator value for this date
if date_str in indicator_data:
indicator_value = indicator_data[date_str]
else:
indicator_value = "N/A: Not a trading day (weekend or holiday)"
date_values.append((date_str, indicator_value))
current_dt = current_dt - relativedelta(days=1)
# Build the result string
ind_string = ""
for date_str, value in date_values:
ind_string += f"{date_str}: {value}\n"
except Exception as e:
print(f"Error getting bulk stockstats data: {e}")
# Fallback to original implementation if bulk method fails
ind_string = ""
curr_date_dt = datetime.strptime(curr_date, "%Y-%m-%d")
while curr_date_dt >= before:
indicator_value = get_stockstats_indicator(
symbol, indicator, curr_date_dt.strftime("%Y-%m-%d")
)
ind_string += f"{curr_date_dt.strftime('%Y-%m-%d')}: {indicator_value}\n"
curr_date_dt = curr_date_dt - relativedelta(days=1)
result_str = (
f"## {indicator} values from {before.strftime('%Y-%m-%d')} to {end_date}:\n\n"
+ ind_string
+ "\n\n"
+ best_ind_params.get(indicator, "No description available.")
)
return result_str
def _get_stock_stats_bulk(
symbol: Annotated[str, "ticker symbol of the company"],
indicator: Annotated[str, "technical indicator to calculate"],
curr_date: Annotated[str, "current date for reference"]
) -> dict:
"""
Optimized bulk calculation of stock stats indicators.
Fetches data once and calculates indicator for all available dates.
Returns dict mapping date strings to indicator values.
"""
from .config import get_config
import pandas as pd
from stockstats import wrap
import os
config = get_config()
online = config["data_vendors"]["technical_indicators"] != "local"
if not online:
# Local data path
try:
data = pd.read_csv(
os.path.join(
config.get("data_cache_dir", "data"),
f"{symbol}-YFin-data-2015-01-01-2025-03-25.csv",
)
)
df = wrap(data)
except FileNotFoundError:
raise Exception("Stockstats fail: Yahoo Finance data not fetched yet!")
else:
# Online data fetching with caching
today_date = pd.Timestamp.today()
curr_date_dt = pd.to_datetime(curr_date)
end_date = today_date
start_date = today_date - pd.DateOffset(years=15)
start_date_str = start_date.strftime("%Y-%m-%d")
end_date_str = end_date.strftime("%Y-%m-%d")
os.makedirs(config["data_cache_dir"], exist_ok=True)
data_file = os.path.join(
config["data_cache_dir"],
f"{symbol}-YFin-data-{start_date_str}-{end_date_str}.csv",
)
if os.path.exists(data_file):
data = pd.read_csv(data_file)
data["Date"] = pd.to_datetime(data["Date"])
else:
data = yf.download(
symbol,
start=start_date_str,
end=end_date_str,
multi_level_index=False,
progress=False,
auto_adjust=True,
)
data = data.reset_index()
data.to_csv(data_file, index=False)
df = wrap(data)
df["Date"] = df["Date"].dt.strftime("%Y-%m-%d")
# Calculate the indicator for all rows at once
df[indicator] # This triggers stockstats to calculate the indicator
# Create a dictionary mapping date strings to indicator values
result_dict = {}
for _, row in df.iterrows():
date_str = row["Date"]
indicator_value = row[indicator]
# Handle NaN/None values
if pd.isna(indicator_value):
result_dict[date_str] = "N/A"
else:
result_dict[date_str] = str(indicator_value)
return result_dict
def get_stockstats_indicator(
symbol: Annotated[str, "ticker symbol of the company"],
indicator: Annotated[str, "technical indicator to get the analysis and report of"],
curr_date: Annotated[
str, "The current trading date you are trading on, YYYY-mm-dd"
],
) -> str:
curr_date_dt = datetime.strptime(curr_date, "%Y-%m-%d")
curr_date = curr_date_dt.strftime("%Y-%m-%d")
try:
indicator_value = StockstatsUtils.get_stock_stats(
symbol,
indicator,
curr_date,
)
except Exception as e:
print(
f"Error getting stockstats indicator data for indicator {indicator} on {curr_date}: {e}"
)
return ""
return str(indicator_value)
def get_balance_sheet(
ticker: Annotated[str, "ticker symbol of the company"],
freq: Annotated[str, "frequency of data: 'annual' or 'quarterly'"] = "quarterly",
curr_date: Annotated[str, "current date (not used for yfinance)"] = None
):
"""Get balance sheet data from yfinance."""
try:
ticker_obj = yf.Ticker(ticker.upper())
if freq.lower() == "quarterly":
data = ticker_obj.quarterly_balance_sheet
else:
data = ticker_obj.balance_sheet
if data.empty:
return f"No balance sheet data found for symbol '{ticker}'"
# Convert to CSV string for consistency with other functions
csv_string = data.to_csv()
# Add header information
header = f"# Balance Sheet data for {ticker.upper()} ({freq})\n"
header += f"# Data retrieved on: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n\n"
return header + csv_string
except Exception as e:
return f"Error retrieving balance sheet for {ticker}: {str(e)}"
def get_cashflow(
ticker: Annotated[str, "ticker symbol of the company"],
freq: Annotated[str, "frequency of data: 'annual' or 'quarterly'"] = "quarterly",
curr_date: Annotated[str, "current date (not used for yfinance)"] = None
):
"""Get cash flow data from yfinance."""
try:
ticker_obj = yf.Ticker(ticker.upper())
if freq.lower() == "quarterly":
data = ticker_obj.quarterly_cashflow
else:
data = ticker_obj.cashflow
if data.empty:
return f"No cash flow data found for symbol '{ticker}'"
# Convert to CSV string for consistency with other functions
csv_string = data.to_csv()
# Add header information
header = f"# Cash Flow data for {ticker.upper()} ({freq})\n"
header += f"# Data retrieved on: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n\n"
return header + csv_string
except Exception as e:
return f"Error retrieving cash flow for {ticker}: {str(e)}"
def get_income_statement(
ticker: Annotated[str, "ticker symbol of the company"],
freq: Annotated[str, "frequency of data: 'annual' or 'quarterly'"] = "quarterly",
curr_date: Annotated[str, "current date (not used for yfinance)"] = None
):
"""Get income statement data from yfinance."""
try:
ticker_obj = yf.Ticker(ticker.upper())
if freq.lower() == "quarterly":
data = ticker_obj.quarterly_income_stmt
else:
data = ticker_obj.income_stmt
if data.empty:
return f"No income statement data found for symbol '{ticker}'"
# Convert to CSV string for consistency with other functions
csv_string = data.to_csv()
# Add header information
header = f"# Income Statement data for {ticker.upper()} ({freq})\n"
header += f"# Data retrieved on: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n\n"
return header + csv_string
except Exception as e:
return f"Error retrieving income statement for {ticker}: {str(e)}"
def get_insider_transactions(
ticker: Annotated[str, "ticker symbol of the company"]
):
"""Get insider transactions data from yfinance."""
try:
ticker_obj = yf.Ticker(ticker.upper())
data = ticker_obj.insider_transactions
if data is None or data.empty:
return f"No insider transactions data found for symbol '{ticker}'"
# Convert to CSV string for consistency with other functions
csv_string = data.to_csv()
# Add header information
header = f"# Insider Transactions data for {ticker.upper()}\n"
header += f"# Data retrieved on: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n\n"
return header + csv_string
except Exception as e:
return f"Error retrieving insider transactions for {ticker}: {str(e)}"

234
tradingagents/dataflows/yfin_utils.py
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@ -1,117 +1,117 @@
# gets data/stats
import yfinance as yf
from typing import Annotated, Callable, Any, Optional
from pandas import DataFrame
import pandas as pd
from functools import wraps
from .utils import save_output, SavePathType, decorate_all_methods
def init_ticker(func: Callable) -> Callable:
"""Decorator to initialize yf.Ticker and pass it to the function."""
@wraps(func)
def wrapper(symbol: Annotated[str, "ticker symbol"], *args, **kwargs) -> Any:
ticker = yf.Ticker(symbol)
return func(ticker, *args, **kwargs)
return wrapper
@decorate_all_methods(init_ticker)
class YFinanceUtils:
def get_stock_data(
symbol: Annotated[str, "ticker symbol"],
start_date: Annotated[
str, "start date for retrieving stock price data, YYYY-mm-dd"
],
end_date: Annotated[
str, "end date for retrieving stock price data, YYYY-mm-dd"
],
save_path: SavePathType = None,
) -> DataFrame:
"""retrieve stock price data for designated ticker symbol"""
ticker = symbol
# add one day to the end_date so that the data range is inclusive
end_date = pd.to_datetime(end_date) + pd.DateOffset(days=1)
end_date = end_date.strftime("%Y-%m-%d")
stock_data = ticker.history(start=start_date, end=end_date)
# save_output(stock_data, f"Stock data for {ticker.ticker}", save_path)
return stock_data
def get_stock_info(
symbol: Annotated[str, "ticker symbol"],
) -> dict:
"""Fetches and returns latest stock information."""
ticker = symbol
stock_info = ticker.info
return stock_info
def get_company_info(
symbol: Annotated[str, "ticker symbol"],
save_path: Optional[str] = None,
) -> DataFrame:
"""Fetches and returns company information as a DataFrame."""
ticker = symbol
info = ticker.info
company_info = {
"Company Name": info.get("shortName", "N/A"),
"Industry": info.get("industry", "N/A"),
"Sector": info.get("sector", "N/A"),
"Country": info.get("country", "N/A"),
"Website": info.get("website", "N/A"),
}
company_info_df = DataFrame([company_info])
if save_path:
company_info_df.to_csv(save_path)
print(f"Company info for {ticker.ticker} saved to {save_path}")
return company_info_df
def get_stock_dividends(
symbol: Annotated[str, "ticker symbol"],
save_path: Optional[str] = None,
) -> DataFrame:
"""Fetches and returns the latest dividends data as a DataFrame."""
ticker = symbol
dividends = ticker.dividends
if save_path:
dividends.to_csv(save_path)
print(f"Dividends for {ticker.ticker} saved to {save_path}")
return dividends
def get_income_stmt(symbol: Annotated[str, "ticker symbol"]) -> DataFrame:
"""Fetches and returns the latest income statement of the company as a DataFrame."""
ticker = symbol
income_stmt = ticker.financials
return income_stmt
def get_balance_sheet(symbol: Annotated[str, "ticker symbol"]) -> DataFrame:
"""Fetches and returns the latest balance sheet of the company as a DataFrame."""
ticker = symbol
balance_sheet = ticker.balance_sheet
return balance_sheet
def get_cash_flow(symbol: Annotated[str, "ticker symbol"]) -> DataFrame:
"""Fetches and returns the latest cash flow statement of the company as a DataFrame."""
ticker = symbol
cash_flow = ticker.cashflow
return cash_flow
def get_analyst_recommendations(symbol: Annotated[str, "ticker symbol"]) -> tuple:
"""Fetches the latest analyst recommendations and returns the most common recommendation and its count."""
ticker = symbol
recommendations = ticker.recommendations
if recommendations.empty:
return None, 0 # No recommendations available
# Assuming 'period' column exists and needs to be excluded
row_0 = recommendations.iloc[0, 1:] # Exclude 'period' column if necessary
# Find the maximum voting result
max_votes = row_0.max()
majority_voting_result = row_0[row_0 == max_votes].index.tolist()
return majority_voting_result[0], max_votes
# gets data/stats
import yfinance as yf
from typing import Annotated, Callable, Any, Optional
from pandas import DataFrame
import pandas as pd
from functools import wraps
from .utils import save_output, SavePathType, decorate_all_methods
def init_ticker(func: Callable) -> Callable:
"""Decorator to initialize yf.Ticker and pass it to the function."""
@wraps(func)
def wrapper(symbol: Annotated[str, "ticker symbol"], *args, **kwargs) -> Any:
ticker = yf.Ticker(symbol)
return func(ticker, *args, **kwargs)
return wrapper
@decorate_all_methods(init_ticker)
class YFinanceUtils:
def get_stock_data(
symbol: Annotated[str, "ticker symbol"],
start_date: Annotated[
str, "start date for retrieving stock price data, YYYY-mm-dd"
],
end_date: Annotated[
str, "end date for retrieving stock price data, YYYY-mm-dd"
],
save_path: SavePathType = None,
) -> DataFrame:
"""retrieve stock price data for designated ticker symbol"""
ticker = symbol
# add one day to the end_date so that the data range is inclusive
end_date = pd.to_datetime(end_date) + pd.DateOffset(days=1)
end_date = end_date.strftime("%Y-%m-%d")
stock_data = ticker.history(start=start_date, end=end_date)
# save_output(stock_data, f"Stock data for {ticker.ticker}", save_path)
return stock_data
def get_stock_info(
symbol: Annotated[str, "ticker symbol"],
) -> dict:
"""Fetches and returns latest stock information."""
ticker = symbol
stock_info = ticker.info
return stock_info
def get_company_info(
symbol: Annotated[str, "ticker symbol"],
save_path: Optional[str] = None,
) -> DataFrame:
"""Fetches and returns company information as a DataFrame."""
ticker = symbol
info = ticker.info
company_info = {
"Company Name": info.get("shortName", "N/A"),
"Industry": info.get("industry", "N/A"),
"Sector": info.get("sector", "N/A"),
"Country": info.get("country", "N/A"),
"Website": info.get("website", "N/A"),
}
company_info_df = DataFrame([company_info])
if save_path:
company_info_df.to_csv(save_path)
print(f"Company info for {ticker.ticker} saved to {save_path}")
return company_info_df
def get_stock_dividends(
symbol: Annotated[str, "ticker symbol"],
save_path: Optional[str] = None,
) -> DataFrame:
"""Fetches and returns the latest dividends data as a DataFrame."""
ticker = symbol
dividends = ticker.dividends
if save_path:
dividends.to_csv(save_path)
print(f"Dividends for {ticker.ticker} saved to {save_path}")
return dividends
def get_income_stmt(symbol: Annotated[str, "ticker symbol"]) -> DataFrame:
"""Fetches and returns the latest income statement of the company as a DataFrame."""
ticker = symbol
income_stmt = ticker.financials
return income_stmt
def get_balance_sheet(symbol: Annotated[str, "ticker symbol"]) -> DataFrame:
"""Fetches and returns the latest balance sheet of the company as a DataFrame."""
ticker = symbol
balance_sheet = ticker.balance_sheet
return balance_sheet
def get_cash_flow(symbol: Annotated[str, "ticker symbol"]) -> DataFrame:
"""Fetches and returns the latest cash flow statement of the company as a DataFrame."""
ticker = symbol
cash_flow = ticker.cashflow
return cash_flow
def get_analyst_recommendations(symbol: Annotated[str, "ticker symbol"]) -> tuple:
"""Fetches the latest analyst recommendations and returns the most common recommendation and its count."""
ticker = symbol
recommendations = ticker.recommendations
if recommendations.empty:
return None, 0 # No recommendations available
# Assuming 'period' column exists and needs to be excluded
row_0 = recommendations.iloc[0, 1:] # Exclude 'period' column if necessary
# Find the maximum voting result
max_votes = row_0.max()
majority_voting_result = row_0[row_0 == max_votes].index.tolist()
return majority_voting_result[0], max_votes

71
tradingagents/default_config.py
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@ -1,29 +1,42 @@
import os
DEFAULT_CONFIG = {
"project_dir": os.path.abspath(os.path.join(os.path.dirname(__file__), ".")),
"results_dir": os.getenv("TRADINGAGENTS_RESULTS_DIR", "./results"),
"data_dir": "/Users/yluo/Documents/Code/ScAI/FR1-data",
"data_cache_dir": os.path.join(
os.path.abspath(os.path.join(os.path.dirname(__file__), ".")),
"dataflows/data_cache",
),
# LLM settings
#"llm_provider": "openai",
#"deep_think_llm": "o4-mini",
#"quick_think_llm": "gpt-4o-mini",
#"backend_url": "https://api.openai.com/v1",
# Default LLM is set to local LMStudio instance
"llm_provider": "lmstudio",
"deep_think_llm": "qwen/qwen3-4b-thinking-2507",
"quick_think_llm": "openai/gpt-oss-20b",
"backend_url": "http://192.168.0.20/v1",
# Debate and discussion settings
"max_debate_rounds": 1,
"max_risk_discuss_rounds": 1,
"max_recur_limit": 100,
# Tool settings
"online_tools": True,
}
import os
DEFAULT_CONFIG = {
"project_dir": os.path.abspath(os.path.join(os.path.dirname(__file__), ".")),
"results_dir": os.getenv("TRADINGAGENTS_RESULTS_DIR", "./results"),
"data_dir": "/Users/yluo/Documents/Code/ScAI/FR1-data",
"data_cache_dir": os.path.join(
os.path.abspath(os.path.join(os.path.dirname(__file__), ".")),
"dataflows/data_cache",
),
# LLM settings
#"llm_provider": "openai",
#"deep_think_llm": "o4-mini",
#"quick_think_llm": "gpt-4o-mini",
#"backend_url": "https://api.openai.com/v1",
# Debate and discussion settings
"max_debate_rounds": 1,
"max_risk_discuss_rounds": 1,
"max_recur_limit": 100,
# Data vendor configuration
# Category-level configuration (default for all tools in category)
# Default LLM is set to local LMStudio instance
"llm_provider": "lmstudio",
"deep_think_llm": "qwen/qwen3-4b-thinking-2507",
"quick_think_llm": "openai/gpt-oss-20b",
"backend_url": "http://192.168.0.20/v1",
"api_key": "blablabla",
"data_vendors": {
"core_stock_apis": "yfinance", # Options: yfinance, alpha_vantage, local
"technical_indicators": "yfinance", # Options: yfinance, alpha_vantage, local
"fundamental_data": "alpha_vantage", # Options: openai, alpha_vantage, local
"news_data": "alpha_vantage", # Options: openai, alpha_vantage, google, local
},
# Tool-level configuration (takes precedence over category-level)
"tool_vendors": {
# Example: "get_stock_data": "alpha_vantage", # Override category default
# Example: "get_news": "openai", # Override category default
},
}

34
tradingagents/graph/__init__.py
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@ -1,17 +1,17 @@
# TradingAgents/graph/__init__.py
from .trading_graph import TradingAgentsGraph
from .conditional_logic import ConditionalLogic
from .setup import GraphSetup
from .propagation import Propagator
from .reflection import Reflector
from .signal_processing import SignalProcessor
__all__ = [
"TradingAgentsGraph",
"ConditionalLogic",
"GraphSetup",
"Propagator",
"Reflector",
"SignalProcessor",
]
# TradingAgents/graph/__init__.py
from .trading_graph import TradingAgentsGraph
from .conditional_logic import ConditionalLogic
from .setup import GraphSetup
from .propagation import Propagator
from .reflection import Reflector
from .signal_processing import SignalProcessor
__all__ = [
"TradingAgentsGraph",
"ConditionalLogic",
"GraphSetup",
"Propagator",
"Reflector",
"SignalProcessor",
]

134
tradingagents/graph/conditional_logic.py
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@ -1,67 +1,67 @@
# TradingAgents/graph/conditional_logic.py
from tradingagents.agents.utils.agent_states import AgentState
class ConditionalLogic:
"""Handles conditional logic for determining graph flow."""
def __init__(self, max_debate_rounds=1, max_risk_discuss_rounds=1):
"""Initialize with configuration parameters."""
self.max_debate_rounds = max_debate_rounds
self.max_risk_discuss_rounds = max_risk_discuss_rounds
def should_continue_market(self, state: AgentState):
"""Determine if market analysis should continue."""
messages = state["messages"]
last_message = messages[-1]
if last_message.tool_calls:
return "tools_market"
return "Msg Clear Market"
def should_continue_social(self, state: AgentState):
"""Determine if social media analysis should continue."""
messages = state["messages"]
last_message = messages[-1]
if last_message.tool_calls:
return "tools_social"
return "Msg Clear Social"
def should_continue_news(self, state: AgentState):
"""Determine if news analysis should continue."""
messages = state["messages"]
last_message = messages[-1]
if last_message.tool_calls:
return "tools_news"
return "Msg Clear News"
def should_continue_fundamentals(self, state: AgentState):
"""Determine if fundamentals analysis should continue."""
messages = state["messages"]
last_message = messages[-1]
if last_message.tool_calls:
return "tools_fundamentals"
return "Msg Clear Fundamentals"
def should_continue_debate(self, state: AgentState) -> str:
"""Determine if debate should continue."""
if (
state["investment_debate_state"]["count"] >= 2 * self.max_debate_rounds
): # 3 rounds of back-and-forth between 2 agents
return "Research Manager"
if state["investment_debate_state"]["current_response"].startswith("Bull"):
return "Bear Researcher"
return "Bull Researcher"
def should_continue_risk_analysis(self, state: AgentState) -> str:
"""Determine if risk analysis should continue."""
if (
state["risk_debate_state"]["count"] >= 3 * self.max_risk_discuss_rounds
): # 3 rounds of back-and-forth between 3 agents
return "Risk Judge"
if state["risk_debate_state"]["latest_speaker"].startswith("Risky"):
return "Safe Analyst"
if state["risk_debate_state"]["latest_speaker"].startswith("Safe"):
return "Neutral Analyst"
return "Risky Analyst"
# TradingAgents/graph/conditional_logic.py
from tradingagents.agents.utils.agent_states import AgentState
class ConditionalLogic:
"""Handles conditional logic for determining graph flow."""
def __init__(self, max_debate_rounds=1, max_risk_discuss_rounds=1):
"""Initialize with configuration parameters."""
self.max_debate_rounds = max_debate_rounds
self.max_risk_discuss_rounds = max_risk_discuss_rounds
def should_continue_market(self, state: AgentState):
"""Determine if market analysis should continue."""
messages = state["messages"]
last_message = messages[-1]
if last_message.tool_calls:
return "tools_market"
return "Msg Clear Market"
def should_continue_social(self, state: AgentState):
"""Determine if social media analysis should continue."""
messages = state["messages"]
last_message = messages[-1]
if last_message.tool_calls:
return "tools_social"
return "Msg Clear Social"
def should_continue_news(self, state: AgentState):
"""Determine if news analysis should continue."""
messages = state["messages"]
last_message = messages[-1]
if last_message.tool_calls:
return "tools_news"
return "Msg Clear News"
def should_continue_fundamentals(self, state: AgentState):
"""Determine if fundamentals analysis should continue."""
messages = state["messages"]
last_message = messages[-1]
if last_message.tool_calls:
return "tools_fundamentals"
return "Msg Clear Fundamentals"
def should_continue_debate(self, state: AgentState) -> str:
"""Determine if debate should continue."""
if (
state["investment_debate_state"]["count"] >= 2 * self.max_debate_rounds
): # 3 rounds of back-and-forth between 2 agents
return "Research Manager"
if state["investment_debate_state"]["current_response"].startswith("Bull"):
return "Bear Researcher"
return "Bull Researcher"
def should_continue_risk_analysis(self, state: AgentState) -> str:
"""Determine if risk analysis should continue."""
if (
state["risk_debate_state"]["count"] >= 3 * self.max_risk_discuss_rounds
): # 3 rounds of back-and-forth between 3 agents
return "Risk Judge"
if state["risk_debate_state"]["latest_speaker"].startswith("Risky"):
return "Safe Analyst"
if state["risk_debate_state"]["latest_speaker"].startswith("Safe"):
return "Neutral Analyst"
return "Risky Analyst"

98
tradingagents/graph/propagation.py
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@ -1,49 +1,49 @@
# TradingAgents/graph/propagation.py
from typing import Dict, Any
from tradingagents.agents.utils.agent_states import (
AgentState,
InvestDebateState,
RiskDebateState,
)
class Propagator:
"""Handles state initialization and propagation through the graph."""
def __init__(self, max_recur_limit=100):
"""Initialize with configuration parameters."""
self.max_recur_limit = max_recur_limit
def create_initial_state(
self, company_name: str, trade_date: str
) -> Dict[str, Any]:
"""Create the initial state for the agent graph."""
return {
"messages": [("human", company_name)],
"company_of_interest": company_name,
"trade_date": str(trade_date),
"investment_debate_state": InvestDebateState(
{"history": "", "current_response": "", "count": 0}
),
"risk_debate_state": RiskDebateState(
{
"history": "",
"current_risky_response": "",
"current_safe_response": "",
"current_neutral_response": "",
"count": 0,
}
),
"market_report": "",
"fundamentals_report": "",
"sentiment_report": "",
"news_report": "",
}
def get_graph_args(self) -> Dict[str, Any]:
"""Get arguments for the graph invocation."""
return {
"stream_mode": "values",
"config": {"recursion_limit": self.max_recur_limit},
}
# TradingAgents/graph/propagation.py
from typing import Dict, Any
from tradingagents.agents.utils.agent_states import (
AgentState,
InvestDebateState,
RiskDebateState,
)
class Propagator:
"""Handles state initialization and propagation through the graph."""
def __init__(self, max_recur_limit=100):
"""Initialize with configuration parameters."""
self.max_recur_limit = max_recur_limit
def create_initial_state(
self, company_name: str, trade_date: str
) -> Dict[str, Any]:
"""Create the initial state for the agent graph."""
return {
"messages": [("human", company_name)],
"company_of_interest": company_name,
"trade_date": str(trade_date),
"investment_debate_state": InvestDebateState(
{"history": "", "current_response": "", "count": 0}
),
"risk_debate_state": RiskDebateState(
{
"history": "",
"current_risky_response": "",
"current_safe_response": "",
"current_neutral_response": "",
"count": 0,
}
),
"market_report": "",
"fundamentals_report": "",
"sentiment_report": "",
"news_report": "",
}
def get_graph_args(self) -> Dict[str, Any]:
"""Get arguments for the graph invocation."""
return {
"stream_mode": "values",
"config": {"recursion_limit": self.max_recur_limit},
}

242
tradingagents/graph/reflection.py
View File

@ -1,121 +1,121 @@
# TradingAgents/graph/reflection.py
from typing import Dict, Any
from langchain_openai import ChatOpenAI
class Reflector:
"""Handles reflection on decisions and updating memory."""
def __init__(self, quick_thinking_llm: ChatOpenAI):
"""Initialize the reflector with an LLM."""
self.quick_thinking_llm = quick_thinking_llm
self.reflection_system_prompt = self._get_reflection_prompt()
def _get_reflection_prompt(self) -> str:
"""Get the system prompt for reflection."""
return """
You are an expert financial analyst tasked with reviewing trading decisions/analysis and providing a comprehensive, step-by-step analysis.
Your goal is to deliver detailed insights into investment decisions and highlight opportunities for improvement, adhering strictly to the following guidelines:
1. Reasoning:
- For each trading decision, determine whether it was correct or incorrect. A correct decision results in an increase in returns, while an incorrect decision does the opposite.
- Analyze the contributing factors to each success or mistake. Consider:
- Market intelligence.
- Technical indicators.
- Technical signals.
- Price movement analysis.
- Overall market data analysis
- News analysis.
- Social media and sentiment analysis.
- Fundamental data analysis.
- Weight the importance of each factor in the decision-making process.
2. Improvement:
- For any incorrect decisions, propose revisions to maximize returns.
- Provide a detailed list of corrective actions or improvements, including specific recommendations (e.g., changing a decision from HOLD to BUY on a particular date).
3. Summary:
- Summarize the lessons learned from the successes and mistakes.
- Highlight how these lessons can be adapted for future trading scenarios and draw connections between similar situations to apply the knowledge gained.
4. Query:
- Extract key insights from the summary into a concise sentence of no more than 1000 tokens.
- Ensure the condensed sentence captures the essence of the lessons and reasoning for easy reference.
Adhere strictly to these instructions, and ensure your output is detailed, accurate, and actionable. You will also be given objective descriptions of the market from a price movements, technical indicator, news, and sentiment perspective to provide more context for your analysis.
"""
def _extract_current_situation(self, current_state: Dict[str, Any]) -> str:
"""Extract the current market situation from the state."""
curr_market_report = current_state["market_report"]
curr_sentiment_report = current_state["sentiment_report"]
curr_news_report = current_state["news_report"]
curr_fundamentals_report = current_state["fundamentals_report"]
return f"{curr_market_report}\n\n{curr_sentiment_report}\n\n{curr_news_report}\n\n{curr_fundamentals_report}"
def _reflect_on_component(
self, component_type: str, report: str, situation: str, returns_losses
) -> str:
"""Generate reflection for a component."""
messages = [
("system", self.reflection_system_prompt),
(
"human",
f"Returns: {returns_losses}\n\nAnalysis/Decision: {report}\n\nObjective Market Reports for Reference: {situation}",
),
]
result = self.quick_thinking_llm.invoke(messages).content
return result
def reflect_bull_researcher(self, current_state, returns_losses, bull_memory):
"""Reflect on bull researcher's analysis and update memory."""
situation = self._extract_current_situation(current_state)
bull_debate_history = current_state["investment_debate_state"]["bull_history"]
result = self._reflect_on_component(
"BULL", bull_debate_history, situation, returns_losses
)
bull_memory.add_situations([(situation, result)])
def reflect_bear_researcher(self, current_state, returns_losses, bear_memory):
"""Reflect on bear researcher's analysis and update memory."""
situation = self._extract_current_situation(current_state)
bear_debate_history = current_state["investment_debate_state"]["bear_history"]
result = self._reflect_on_component(
"BEAR", bear_debate_history, situation, returns_losses
)
bear_memory.add_situations([(situation, result)])
def reflect_trader(self, current_state, returns_losses, trader_memory):
"""Reflect on trader's decision and update memory."""
situation = self._extract_current_situation(current_state)
trader_decision = current_state["trader_investment_plan"]
result = self._reflect_on_component(
"TRADER", trader_decision, situation, returns_losses
)
trader_memory.add_situations([(situation, result)])
def reflect_invest_judge(self, current_state, returns_losses, invest_judge_memory):
"""Reflect on investment judge's decision and update memory."""
situation = self._extract_current_situation(current_state)
judge_decision = current_state["investment_debate_state"]["judge_decision"]
result = self._reflect_on_component(
"INVEST JUDGE", judge_decision, situation, returns_losses
)
invest_judge_memory.add_situations([(situation, result)])
def reflect_risk_manager(self, current_state, returns_losses, risk_manager_memory):
"""Reflect on risk manager's decision and update memory."""
situation = self._extract_current_situation(current_state)
judge_decision = current_state["risk_debate_state"]["judge_decision"]
result = self._reflect_on_component(
"RISK JUDGE", judge_decision, situation, returns_losses
)
risk_manager_memory.add_situations([(situation, result)])
# TradingAgents/graph/reflection.py
from typing import Dict, Any
from langchain_openai import ChatOpenAI
class Reflector:
"""Handles reflection on decisions and updating memory."""
def __init__(self, quick_thinking_llm: ChatOpenAI):
"""Initialize the reflector with an LLM."""
self.quick_thinking_llm = quick_thinking_llm
self.reflection_system_prompt = self._get_reflection_prompt()
def _get_reflection_prompt(self) -> str:
"""Get the system prompt for reflection."""
return """
You are an expert financial analyst tasked with reviewing trading decisions/analysis and providing a comprehensive, step-by-step analysis.
Your goal is to deliver detailed insights into investment decisions and highlight opportunities for improvement, adhering strictly to the following guidelines:
1. Reasoning:
- For each trading decision, determine whether it was correct or incorrect. A correct decision results in an increase in returns, while an incorrect decision does the opposite.
- Analyze the contributing factors to each success or mistake. Consider:
- Market intelligence.
- Technical indicators.
- Technical signals.
- Price movement analysis.
- Overall market data analysis
- News analysis.
- Social media and sentiment analysis.
- Fundamental data analysis.
- Weight the importance of each factor in the decision-making process.
2. Improvement:
- For any incorrect decisions, propose revisions to maximize returns.
- Provide a detailed list of corrective actions or improvements, including specific recommendations (e.g., changing a decision from HOLD to BUY on a particular date).
3. Summary:
- Summarize the lessons learned from the successes and mistakes.
- Highlight how these lessons can be adapted for future trading scenarios and draw connections between similar situations to apply the knowledge gained.
4. Query:
- Extract key insights from the summary into a concise sentence of no more than 1000 tokens.
- Ensure the condensed sentence captures the essence of the lessons and reasoning for easy reference.
Adhere strictly to these instructions, and ensure your output is detailed, accurate, and actionable. You will also be given objective descriptions of the market from a price movements, technical indicator, news, and sentiment perspective to provide more context for your analysis.
"""
def _extract_current_situation(self, current_state: Dict[str, Any]) -> str:
"""Extract the current market situation from the state."""
curr_market_report = current_state["market_report"]
curr_sentiment_report = current_state["sentiment_report"]
curr_news_report = current_state["news_report"]
curr_fundamentals_report = current_state["fundamentals_report"]
return f"{curr_market_report}\n\n{curr_sentiment_report}\n\n{curr_news_report}\n\n{curr_fundamentals_report}"
def _reflect_on_component(
self, component_type: str, report: str, situation: str, returns_losses
) -> str:
"""Generate reflection for a component."""
messages = [
("system", self.reflection_system_prompt),
(
"human",
f"Returns: {returns_losses}\n\nAnalysis/Decision: {report}\n\nObjective Market Reports for Reference: {situation}",
),
]
result = self.quick_thinking_llm.invoke(messages).content
return result
def reflect_bull_researcher(self, current_state, returns_losses, bull_memory):
"""Reflect on bull researcher's analysis and update memory."""
situation = self._extract_current_situation(current_state)
bull_debate_history = current_state["investment_debate_state"]["bull_history"]
result = self._reflect_on_component(
"BULL", bull_debate_history, situation, returns_losses
)
bull_memory.add_situations([(situation, result)])
def reflect_bear_researcher(self, current_state, returns_losses, bear_memory):
"""Reflect on bear researcher's analysis and update memory."""
situation = self._extract_current_situation(current_state)
bear_debate_history = current_state["investment_debate_state"]["bear_history"]
result = self._reflect_on_component(
"BEAR", bear_debate_history, situation, returns_losses
)
bear_memory.add_situations([(situation, result)])
def reflect_trader(self, current_state, returns_losses, trader_memory):
"""Reflect on trader's decision and update memory."""
situation = self._extract_current_situation(current_state)
trader_decision = current_state["trader_investment_plan"]
result = self._reflect_on_component(
"TRADER", trader_decision, situation, returns_losses
)
trader_memory.add_situations([(situation, result)])
def reflect_invest_judge(self, current_state, returns_losses, invest_judge_memory):
"""Reflect on investment judge's decision and update memory."""
situation = self._extract_current_situation(current_state)
judge_decision = current_state["investment_debate_state"]["judge_decision"]
result = self._reflect_on_component(
"INVEST JUDGE", judge_decision, situation, returns_losses
)
invest_judge_memory.add_situations([(situation, result)])
def reflect_risk_manager(self, current_state, returns_losses, risk_manager_memory):
"""Reflect on risk manager's decision and update memory."""
situation = self._extract_current_situation(current_state)
judge_decision = current_state["risk_debate_state"]["judge_decision"]
result = self._reflect_on_component(
"RISK JUDGE", judge_decision, situation, returns_losses
)
risk_manager_memory.add_situations([(situation, result)])

407
tradingagents/graph/setup.py
View File

@ -1,205 +1,202 @@
# TradingAgents/graph/setup.py
from typing import Dict, Any
from langchain_openai import ChatOpenAI
from langgraph.graph import END, StateGraph, START
from langgraph.prebuilt import ToolNode
from tradingagents.agents import *
from tradingagents.agents.utils.agent_states import AgentState
from tradingagents.agents.utils.agent_utils import Toolkit
from .conditional_logic import ConditionalLogic
class GraphSetup:
"""Handles the setup and configuration of the agent graph."""
def __init__(
self,
quick_thinking_llm: ChatOpenAI,
deep_thinking_llm: ChatOpenAI,
toolkit: Toolkit,
tool_nodes: Dict[str, ToolNode],
bull_memory,
bear_memory,
trader_memory,
invest_judge_memory,
risk_manager_memory,
conditional_logic: ConditionalLogic,
):
"""Initialize with required components."""
self.quick_thinking_llm = quick_thinking_llm
self.deep_thinking_llm = deep_thinking_llm
self.toolkit = toolkit
self.tool_nodes = tool_nodes
self.bull_memory = bull_memory
self.bear_memory = bear_memory
self.trader_memory = trader_memory
self.invest_judge_memory = invest_judge_memory
self.risk_manager_memory = risk_manager_memory
self.conditional_logic = conditional_logic
def setup_graph(
self, selected_analysts=["market", "social", "news", "fundamentals"]
):
"""Set up and compile the agent workflow graph.
Args:
selected_analysts (list): List of analyst types to include. Options are:
- "market": Market analyst
- "social": Social media analyst
- "news": News analyst
- "fundamentals": Fundamentals analyst
"""
if len(selected_analysts) == 0:
raise ValueError("Trading Agents Graph Setup Error: no analysts selected!")
# Create analyst nodes
analyst_nodes = {}
delete_nodes = {}
tool_nodes = {}
if "market" in selected_analysts:
analyst_nodes["market"] = create_market_analyst(
self.quick_thinking_llm, self.toolkit
)
delete_nodes["market"] = create_msg_delete()
tool_nodes["market"] = self.tool_nodes["market"]
if "social" in selected_analysts:
analyst_nodes["social"] = create_social_media_analyst(
self.quick_thinking_llm, self.toolkit
)
delete_nodes["social"] = create_msg_delete()
tool_nodes["social"] = self.tool_nodes["social"]
if "news" in selected_analysts:
analyst_nodes["news"] = create_news_analyst(
self.quick_thinking_llm, self.toolkit
)
delete_nodes["news"] = create_msg_delete()
tool_nodes["news"] = self.tool_nodes["news"]
if "fundamentals" in selected_analysts:
analyst_nodes["fundamentals"] = create_fundamentals_analyst(
self.quick_thinking_llm, self.toolkit
)
delete_nodes["fundamentals"] = create_msg_delete()
tool_nodes["fundamentals"] = self.tool_nodes["fundamentals"]
# Create researcher and manager nodes
bull_researcher_node = create_bull_researcher(
self.quick_thinking_llm, self.bull_memory
)
bear_researcher_node = create_bear_researcher(
self.quick_thinking_llm, self.bear_memory
)
research_manager_node = create_research_manager(
self.deep_thinking_llm, self.invest_judge_memory
)
trader_node = create_trader(self.quick_thinking_llm, self.trader_memory)
# Create risk analysis nodes
risky_analyst = create_risky_debator(self.quick_thinking_llm)
neutral_analyst = create_neutral_debator(self.quick_thinking_llm)
safe_analyst = create_safe_debator(self.quick_thinking_llm)
risk_manager_node = create_risk_manager(
self.deep_thinking_llm, self.risk_manager_memory
)
# Create workflow
workflow = StateGraph(AgentState)
# Add analyst nodes to the graph
for analyst_type, node in analyst_nodes.items():
workflow.add_node(f"{analyst_type.capitalize()} Analyst", node)
workflow.add_node(
f"Msg Clear {analyst_type.capitalize()}", delete_nodes[analyst_type]
)
workflow.add_node(f"tools_{analyst_type}", tool_nodes[analyst_type])
# Add other nodes
workflow.add_node("Bull Researcher", bull_researcher_node)
workflow.add_node("Bear Researcher", bear_researcher_node)
workflow.add_node("Research Manager", research_manager_node)
workflow.add_node("Trader", trader_node)
workflow.add_node("Risky Analyst", risky_analyst)
workflow.add_node("Neutral Analyst", neutral_analyst)
workflow.add_node("Safe Analyst", safe_analyst)
workflow.add_node("Risk Judge", risk_manager_node)
# Define edges
# Start with the first analyst
first_analyst = selected_analysts[0]
workflow.add_edge(START, f"{first_analyst.capitalize()} Analyst")
# Connect analysts in sequence
for i, analyst_type in enumerate(selected_analysts):
current_analyst = f"{analyst_type.capitalize()} Analyst"
current_tools = f"tools_{analyst_type}"
current_clear = f"Msg Clear {analyst_type.capitalize()}"
# Add conditional edges for current analyst
workflow.add_conditional_edges(
current_analyst,
getattr(self.conditional_logic, f"should_continue_{analyst_type}"),
[current_tools, current_clear],
)
workflow.add_edge(current_tools, current_analyst)
# Connect to next analyst or to Bull Researcher if this is the last analyst
if i < len(selected_analysts) - 1:
next_analyst = f"{selected_analysts[i+1].capitalize()} Analyst"
workflow.add_edge(current_clear, next_analyst)
else:
workflow.add_edge(current_clear, "Bull Researcher")
# Add remaining edges
workflow.add_conditional_edges(
"Bull Researcher",
self.conditional_logic.should_continue_debate,
{
"Bear Researcher": "Bear Researcher",
"Research Manager": "Research Manager",
},
)
workflow.add_conditional_edges(
"Bear Researcher",
self.conditional_logic.should_continue_debate,
{
"Bull Researcher": "Bull Researcher",
"Research Manager": "Research Manager",
},
)
workflow.add_edge("Research Manager", "Trader")
workflow.add_edge("Trader", "Risky Analyst")
workflow.add_conditional_edges(
"Risky Analyst",
self.conditional_logic.should_continue_risk_analysis,
{
"Safe Analyst": "Safe Analyst",
"Risk Judge": "Risk Judge",
},
)
workflow.add_conditional_edges(
"Safe Analyst",
self.conditional_logic.should_continue_risk_analysis,
{
"Neutral Analyst": "Neutral Analyst",
"Risk Judge": "Risk Judge",
},
)
workflow.add_conditional_edges(
"Neutral Analyst",
self.conditional_logic.should_continue_risk_analysis,
{
"Risky Analyst": "Risky Analyst",
"Risk Judge": "Risk Judge",
},
)
workflow.add_edge("Risk Judge", END)
# Compile and return
return workflow.compile()
# TradingAgents/graph/setup.py
from typing import Dict, Any
from langchain_openai import ChatOpenAI
from langgraph.graph import END, StateGraph, START
from langgraph.prebuilt import ToolNode
from tradingagents.agents import *
from tradingagents.agents.utils.agent_states import AgentState
from .conditional_logic import ConditionalLogic
class GraphSetup:
"""Handles the setup and configuration of the agent graph."""
def __init__(
self,
quick_thinking_llm: ChatOpenAI,
deep_thinking_llm: ChatOpenAI,
tool_nodes: Dict[str, ToolNode],
bull_memory,
bear_memory,
trader_memory,
invest_judge_memory,
risk_manager_memory,
conditional_logic: ConditionalLogic,
):
"""Initialize with required components."""
self.quick_thinking_llm = quick_thinking_llm
self.deep_thinking_llm = deep_thinking_llm
self.tool_nodes = tool_nodes
self.bull_memory = bull_memory
self.bear_memory = bear_memory
self.trader_memory = trader_memory
self.invest_judge_memory = invest_judge_memory
self.risk_manager_memory = risk_manager_memory
self.conditional_logic = conditional_logic
def setup_graph(
self, selected_analysts=["market", "social", "news", "fundamentals"]
):
"""Set up and compile the agent workflow graph.
Args:
selected_analysts (list): List of analyst types to include. Options are:
- "market": Market analyst
- "social": Social media analyst
- "news": News analyst
- "fundamentals": Fundamentals analyst
"""
if len(selected_analysts) == 0:
raise ValueError("Trading Agents Graph Setup Error: no analysts selected!")
# Create analyst nodes
analyst_nodes = {}
delete_nodes = {}
tool_nodes = {}
if "market" in selected_analysts:
analyst_nodes["market"] = create_market_analyst(
self.quick_thinking_llm
)
delete_nodes["market"] = create_msg_delete()
tool_nodes["market"] = self.tool_nodes["market"]
if "social" in selected_analysts:
analyst_nodes["social"] = create_social_media_analyst(
self.quick_thinking_llm
)
delete_nodes["social"] = create_msg_delete()
tool_nodes["social"] = self.tool_nodes["social"]
if "news" in selected_analysts:
analyst_nodes["news"] = create_news_analyst(
self.quick_thinking_llm
)
delete_nodes["news"] = create_msg_delete()
tool_nodes["news"] = self.tool_nodes["news"]
if "fundamentals" in selected_analysts:
analyst_nodes["fundamentals"] = create_fundamentals_analyst(
self.quick_thinking_llm
)
delete_nodes["fundamentals"] = create_msg_delete()
tool_nodes["fundamentals"] = self.tool_nodes["fundamentals"]
# Create researcher and manager nodes
bull_researcher_node = create_bull_researcher(
self.quick_thinking_llm, self.bull_memory
)
bear_researcher_node = create_bear_researcher(
self.quick_thinking_llm, self.bear_memory
)
research_manager_node = create_research_manager(
self.deep_thinking_llm, self.invest_judge_memory
)
trader_node = create_trader(self.quick_thinking_llm, self.trader_memory)
# Create risk analysis nodes
risky_analyst = create_risky_debator(self.quick_thinking_llm)
neutral_analyst = create_neutral_debator(self.quick_thinking_llm)
safe_analyst = create_safe_debator(self.quick_thinking_llm)
risk_manager_node = create_risk_manager(
self.deep_thinking_llm, self.risk_manager_memory
)
# Create workflow
workflow = StateGraph(AgentState)
# Add analyst nodes to the graph
for analyst_type, node in analyst_nodes.items():
workflow.add_node(f"{analyst_type.capitalize()} Analyst", node)
workflow.add_node(
f"Msg Clear {analyst_type.capitalize()}", delete_nodes[analyst_type]
)
workflow.add_node(f"tools_{analyst_type}", tool_nodes[analyst_type])
# Add other nodes
workflow.add_node("Bull Researcher", bull_researcher_node)
workflow.add_node("Bear Researcher", bear_researcher_node)
workflow.add_node("Research Manager", research_manager_node)
workflow.add_node("Trader", trader_node)
workflow.add_node("Risky Analyst", risky_analyst)
workflow.add_node("Neutral Analyst", neutral_analyst)
workflow.add_node("Safe Analyst", safe_analyst)
workflow.add_node("Risk Judge", risk_manager_node)
# Define edges
# Start with the first analyst
first_analyst = selected_analysts[0]
workflow.add_edge(START, f"{first_analyst.capitalize()} Analyst")
# Connect analysts in sequence
for i, analyst_type in enumerate(selected_analysts):
current_analyst = f"{analyst_type.capitalize()} Analyst"
current_tools = f"tools_{analyst_type}"
current_clear = f"Msg Clear {analyst_type.capitalize()}"
# Add conditional edges for current analyst
workflow.add_conditional_edges(
current_analyst,
getattr(self.conditional_logic, f"should_continue_{analyst_type}"),
[current_tools, current_clear],
)
workflow.add_edge(current_tools, current_analyst)
# Connect to next analyst or to Bull Researcher if this is the last analyst
if i < len(selected_analysts) - 1:
next_analyst = f"{selected_analysts[i+1].capitalize()} Analyst"
workflow.add_edge(current_clear, next_analyst)
else:
workflow.add_edge(current_clear, "Bull Researcher")
# Add remaining edges
workflow.add_conditional_edges(
"Bull Researcher",
self.conditional_logic.should_continue_debate,
{
"Bear Researcher": "Bear Researcher",
"Research Manager": "Research Manager",
},
)
workflow.add_conditional_edges(
"Bear Researcher",
self.conditional_logic.should_continue_debate,
{
"Bull Researcher": "Bull Researcher",
"Research Manager": "Research Manager",
},
)
workflow.add_edge("Research Manager", "Trader")
workflow.add_edge("Trader", "Risky Analyst")
workflow.add_conditional_edges(
"Risky Analyst",
self.conditional_logic.should_continue_risk_analysis,
{
"Safe Analyst": "Safe Analyst",
"Risk Judge": "Risk Judge",
},
)
workflow.add_conditional_edges(
"Safe Analyst",
self.conditional_logic.should_continue_risk_analysis,
{
"Neutral Analyst": "Neutral Analyst",
"Risk Judge": "Risk Judge",
},
)
workflow.add_conditional_edges(
"Neutral Analyst",
self.conditional_logic.should_continue_risk_analysis,
{
"Risky Analyst": "Risky Analyst",
"Risk Judge": "Risk Judge",
},
)
workflow.add_edge("Risk Judge", END)
# Compile and return
return workflow.compile()

62
tradingagents/graph/signal_processing.py
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@ -1,31 +1,31 @@
# TradingAgents/graph/signal_processing.py
from langchain_openai import ChatOpenAI
class SignalProcessor:
"""Processes trading signals to extract actionable decisions."""
def __init__(self, quick_thinking_llm: ChatOpenAI):
"""Initialize with an LLM for processing."""
self.quick_thinking_llm = quick_thinking_llm
def process_signal(self, full_signal: str) -> str:
"""
Process a full trading signal to extract the core decision.
Args:
full_signal: Complete trading signal text
Returns:
Extracted decision (BUY, SELL, or HOLD)
"""
messages = [
(
"system",
"You are an efficient assistant designed to analyze paragraphs or financial reports provided by a group of analysts. Your task is to extract the investment decision: SELL, BUY, or HOLD. Provide only the extracted decision (SELL, BUY, or HOLD) as your output, without adding any additional text or information.",
),
("human", full_signal),
]
return self.quick_thinking_llm.invoke(messages).content
# TradingAgents/graph/signal_processing.py
from langchain_openai import ChatOpenAI
class SignalProcessor:
"""Processes trading signals to extract actionable decisions."""
def __init__(self, quick_thinking_llm: ChatOpenAI):
"""Initialize with an LLM for processing."""
self.quick_thinking_llm = quick_thinking_llm
def process_signal(self, full_signal: str) -> str:
"""
Process a full trading signal to extract the core decision.
Args:
full_signal: Complete trading signal text
Returns:
Extracted decision (BUY, SELL, or HOLD)
"""
messages = [
(
"system",
"You are an efficient assistant designed to analyze paragraphs or financial reports provided by a group of analysts. Your task is to extract the investment decision: SELL, BUY, or HOLD. Provide only the extracted decision (SELL, BUY, or HOLD) as your output, without adding any additional text or information.",
),
("human", full_signal),
]
return self.quick_thinking_llm.invoke(messages).content

514
tradingagents/graph/trading_graph.py
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@ -1,257 +1,257 @@
# TradingAgents/graph/trading_graph.py
import os
from pathlib import Path
import json
from datetime import date
from typing import Dict, Any, Tuple, List, Optional
from langchain_openai import ChatOpenAI
from langchain_anthropic import ChatAnthropic
from langchain_google_genai import ChatGoogleGenerativeAI
from langgraph.prebuilt import ToolNode
from tradingagents.agents import *
from tradingagents.default_config import DEFAULT_CONFIG
from tradingagents.agents.utils.memory import FinancialSituationMemory
from tradingagents.agents.utils.agent_states import (
AgentState,
InvestDebateState,
RiskDebateState,
)
from tradingagents.dataflows.interface import set_config
from .conditional_logic import ConditionalLogic
from .setup import GraphSetup
from .propagation import Propagator
from .reflection import Reflector
from .signal_processing import SignalProcessor
class TradingAgentsGraph:
"""Main class that orchestrates the trading agents framework."""
def __init__(
self,
selected_analysts=["market", "social", "news", "fundamentals"],
debug=False,
config: Dict[str, Any] = None,
):
"""Initialize the trading agents graph and components.
Args:
selected_analysts: List of analyst types to include
debug: Whether to run in debug mode
config: Configuration dictionary. If None, uses default config
"""
self.debug = debug
self.config = config or DEFAULT_CONFIG
# Update the interface's config
set_config(self.config)
# Create necessary directories
os.makedirs(
os.path.join(self.config["project_dir"], "dataflows/data_cache"),
exist_ok=True,
)
# Initialize LLMs
if self.config["llm_provider"].lower() == "openai" or self.config["llm_provider"] == "ollama" or self.config["llm_provider"] == "openrouter":
self.deep_thinking_llm = ChatOpenAI(model=self.config["deep_think_llm"], base_url=self.config["backend_url"])
self.quick_thinking_llm = ChatOpenAI(model=self.config["quick_think_llm"], base_url=self.config["backend_url"])
elif self.config["llm_provider"].lower() == "anthropic":
self.deep_thinking_llm = ChatAnthropic(model=self.config["deep_think_llm"], base_url=self.config["backend_url"])
self.quick_thinking_llm = ChatAnthropic(model=self.config["quick_think_llm"], base_url=self.config["backend_url"])
elif self.config["llm_provider"].lower() == "google":
self.deep_thinking_llm = ChatGoogleGenerativeAI(model=self.config["deep_think_llm"])
self.quick_thinking_llm = ChatGoogleGenerativeAI(model=self.config["quick_think_llm"])
elif self.config["llm_provider"].lower() == "lmstudio":
self.deep_thinking_llm = ChatOpenAI(model=self.config["deep_think_llm"], base_url=self.config["backend_url"])
self.quick_thinking_llm = ChatOpenAI(model=self.config["quick_think_llm"], base_url=self.config["backend_url"])
else:
raise ValueError(f"Unsupported LLM provider: {self.config['llm_provider']}")
self.toolkit = Toolkit(config=self.config)
# Initialize memories
self.bull_memory = FinancialSituationMemory("bull_memory", self.config)
self.bear_memory = FinancialSituationMemory("bear_memory", self.config)
self.trader_memory = FinancialSituationMemory("trader_memory", self.config)
self.invest_judge_memory = FinancialSituationMemory("invest_judge_memory", self.config)
self.risk_manager_memory = FinancialSituationMemory("risk_manager_memory", self.config)
# Create tool nodes
self.tool_nodes = self._create_tool_nodes()
# Initialize components
self.conditional_logic = ConditionalLogic()
self.graph_setup = GraphSetup(
self.quick_thinking_llm,
self.deep_thinking_llm,
self.toolkit,
self.tool_nodes,
self.bull_memory,
self.bear_memory,
self.trader_memory,
self.invest_judge_memory,
self.risk_manager_memory,
self.conditional_logic,
)
self.propagator = Propagator()
self.reflector = Reflector(self.quick_thinking_llm)
self.signal_processor = SignalProcessor(self.quick_thinking_llm)
# State tracking
self.curr_state = None
self.ticker = None
self.log_states_dict = {} # date to full state dict
# Set up the graph
self.graph = self.graph_setup.setup_graph(selected_analysts)
def _create_tool_nodes(self) -> Dict[str, ToolNode]:
"""Create tool nodes for different data sources."""
return {
"market": ToolNode(
[
# online tools
self.toolkit.get_YFin_data_online,
self.toolkit.get_stockstats_indicators_report_online,
# offline tools
self.toolkit.get_YFin_data,
self.toolkit.get_stockstats_indicators_report,
]
),
"social": ToolNode(
[
# online tools
self.toolkit.get_stock_news_openai,
# offline tools
self.toolkit.get_reddit_stock_info,
]
),
"news": ToolNode(
[
# online tools
self.toolkit.get_global_news_openai,
self.toolkit.get_google_news,
# offline tools
self.toolkit.get_finnhub_news,
self.toolkit.get_reddit_news,
]
),
"fundamentals": ToolNode(
[
# online tools
self.toolkit.get_fundamentals_openai,
# offline tools
self.toolkit.get_finnhub_company_insider_sentiment,
self.toolkit.get_finnhub_company_insider_transactions,
self.toolkit.get_simfin_balance_sheet,
self.toolkit.get_simfin_cashflow,
self.toolkit.get_simfin_income_stmt,
]
),
}
def propagate(self, company_name, trade_date):
"""Run the trading agents graph for a company on a specific date."""
self.ticker = company_name
# Initialize state
init_agent_state = self.propagator.create_initial_state(
company_name, trade_date
)
args = self.propagator.get_graph_args()
if self.debug:
# Debug mode with tracing
trace = []
for chunk in self.graph.stream(init_agent_state, **args):
if len(chunk["messages"]) == 0:
pass
else:
chunk["messages"][-1].pretty_print()
trace.append(chunk)
final_state = trace[-1]
else:
# Standard mode without tracing
final_state = self.graph.invoke(init_agent_state, **args)
# Store current state for reflection
self.curr_state = final_state
# Log state
self._log_state(trade_date, final_state)
# Return decision and processed signal
return final_state, self.process_signal(final_state["final_trade_decision"])
def _log_state(self, trade_date, final_state):
"""Log the final state to a JSON file."""
self.log_states_dict[str(trade_date)] = {
"company_of_interest": final_state["company_of_interest"],
"trade_date": final_state["trade_date"],
"market_report": final_state["market_report"],
"sentiment_report": final_state["sentiment_report"],
"news_report": final_state["news_report"],
"fundamentals_report": final_state["fundamentals_report"],
"investment_debate_state": {
"bull_history": final_state["investment_debate_state"]["bull_history"],
"bear_history": final_state["investment_debate_state"]["bear_history"],
"history": final_state["investment_debate_state"]["history"],
"current_response": final_state["investment_debate_state"][
"current_response"
],
"judge_decision": final_state["investment_debate_state"][
"judge_decision"
],
},
"trader_investment_decision": final_state["trader_investment_plan"],
"risk_debate_state": {
"risky_history": final_state["risk_debate_state"]["risky_history"],
"safe_history": final_state["risk_debate_state"]["safe_history"],
"neutral_history": final_state["risk_debate_state"]["neutral_history"],
"history": final_state["risk_debate_state"]["history"],
"judge_decision": final_state["risk_debate_state"]["judge_decision"],
},
"investment_plan": final_state["investment_plan"],
"final_trade_decision": final_state["final_trade_decision"],
}
# Save to file
directory = Path(f"eval_results/{self.ticker}/TradingAgentsStrategy_logs/")
directory.mkdir(parents=True, exist_ok=True)
with open(
f"eval_results/{self.ticker}/TradingAgentsStrategy_logs/full_states_log_{trade_date}.json",
"w",
) as f:
json.dump(self.log_states_dict, f, indent=4)
def reflect_and_remember(self, returns_losses):
"""Reflect on decisions and update memory based on returns."""
self.reflector.reflect_bull_researcher(
self.curr_state, returns_losses, self.bull_memory
)
self.reflector.reflect_bear_researcher(
self.curr_state, returns_losses, self.bear_memory
)
self.reflector.reflect_trader(
self.curr_state, returns_losses, self.trader_memory
)
self.reflector.reflect_invest_judge(
self.curr_state, returns_losses, self.invest_judge_memory
)
self.reflector.reflect_risk_manager(
self.curr_state, returns_losses, self.risk_manager_memory
)
def process_signal(self, full_signal):
"""Process a signal to extract the core decision."""
return self.signal_processor.process_signal(full_signal)
# TradingAgents/graph/trading_graph.py
import os
from pathlib import Path
import json
from datetime import date
from typing import Dict, Any, Tuple, List, Optional
from langchain_openai import ChatOpenAI
from langchain_anthropic import ChatAnthropic
from langchain_google_genai import ChatGoogleGenerativeAI
from langgraph.prebuilt import ToolNode
from tradingagents.agents import *
from tradingagents.default_config import DEFAULT_CONFIG
from tradingagents.agents.utils.memory import FinancialSituationMemory
from tradingagents.agents.utils.agent_states import (
AgentState,
InvestDebateState,
RiskDebateState,
)
from tradingagents.dataflows.config import set_config
# Import the new abstract tool methods from agent_utils
from tradingagents.agents.utils.agent_utils import (
get_stock_data,
get_indicators,
get_fundamentals,
get_balance_sheet,
get_cashflow,
get_income_statement,
get_news,
get_insider_sentiment,
get_insider_transactions,
get_global_news
)
from .conditional_logic import ConditionalLogic
from .setup import GraphSetup
from .propagation import Propagator
from .reflection import Reflector
from .signal_processing import SignalProcessor
class TradingAgentsGraph:
"""Main class that orchestrates the trading agents framework."""
def __init__(
self,
selected_analysts=["market", "social", "news", "fundamentals"],
debug=False,
config: Dict[str, Any] = None,
):
"""Initialize the trading agents graph and components.
Args:
selected_analysts: List of analyst types to include
debug: Whether to run in debug mode
config: Configuration dictionary. If None, uses default config
"""
self.debug = debug
self.config = config or DEFAULT_CONFIG
# Update the interface's config
set_config(self.config)
# Create necessary directories
os.makedirs(
os.path.join(self.config["project_dir"], "dataflows/data_cache"),
exist_ok=True,
)
# Initialize LLMs
if self.config["llm_provider"].lower() == "openai" or self.config["llm_provider"] == "ollama" or self.config["llm_provider"] == "lmstudio" or self.config["llm_provider"] == "openrouter":
self.deep_thinking_llm = ChatOpenAI(model=self.config["deep_think_llm"], base_url=self.config["backend_url"],api_key=self.config["api_key"])
self.quick_thinking_llm = ChatOpenAI(model=self.config["quick_think_llm"], base_url=self.config["backend_url"],api_key=self.config["api_key"])
elif self.config["llm_provider"].lower() == "anthropic":
self.deep_thinking_llm = ChatAnthropic(model=self.config["deep_think_llm"], base_url=self.config["backend_url"])
self.quick_thinking_llm = ChatAnthropic(model=self.config["quick_think_llm"], base_url=self.config["backend_url"])
elif self.config["llm_provider"].lower() == "google":
self.deep_thinking_llm = ChatGoogleGenerativeAI(model=self.config["deep_think_llm"])
self.quick_thinking_llm = ChatGoogleGenerativeAI(model=self.config["quick_think_llm"])
else:
raise ValueError(f"Unsupported LLM provider: {self.config['llm_provider']}")
# Initialize memories
self.bull_memory = FinancialSituationMemory("bull_memory", self.config)
self.bear_memory = FinancialSituationMemory("bear_memory", self.config)
self.trader_memory = FinancialSituationMemory("trader_memory", self.config)
self.invest_judge_memory = FinancialSituationMemory("invest_judge_memory", self.config)
self.risk_manager_memory = FinancialSituationMemory("risk_manager_memory", self.config)
# Create tool nodes
self.tool_nodes = self._create_tool_nodes()
# Initialize components
self.conditional_logic = ConditionalLogic()
self.graph_setup = GraphSetup(
self.quick_thinking_llm,
self.deep_thinking_llm,
self.tool_nodes,
self.bull_memory,
self.bear_memory,
self.trader_memory,
self.invest_judge_memory,
self.risk_manager_memory,
self.conditional_logic,
)
self.propagator = Propagator()
self.reflector = Reflector(self.quick_thinking_llm)
self.signal_processor = SignalProcessor(self.quick_thinking_llm)
# State tracking
self.curr_state = None
self.ticker = None
self.log_states_dict = {} # date to full state dict
# Set up the graph
self.graph = self.graph_setup.setup_graph(selected_analysts)
def _create_tool_nodes(self) -> Dict[str, ToolNode]:
"""Create tool nodes for different data sources using abstract methods."""
return {
"market": ToolNode(
[
# Core stock data tools
get_stock_data,
# Technical indicators
get_indicators,
]
),
"social": ToolNode(
[
# News tools for social media analysis
get_news,
]
),
"news": ToolNode(
[
# News and insider information
get_news,
get_global_news,
get_insider_sentiment,
get_insider_transactions,
]
),
"fundamentals": ToolNode(
[
# Fundamental analysis tools
get_fundamentals,
get_balance_sheet,
get_cashflow,
get_income_statement,
]
),
}
def propagate(self, company_name, trade_date):
"""Run the trading agents graph for a company on a specific date."""
self.ticker = company_name
# Initialize state
init_agent_state = self.propagator.create_initial_state(
company_name, trade_date
)
args = self.propagator.get_graph_args()
if self.debug:
# Debug mode with tracing
trace = []
for chunk in self.graph.stream(init_agent_state, **args):
if len(chunk["messages"]) == 0:
pass
else:
chunk["messages"][-1].pretty_print()
trace.append(chunk)
final_state = trace[-1]
else:
# Standard mode without tracing
final_state = self.graph.invoke(init_agent_state, **args)
# Store current state for reflection
self.curr_state = final_state
# Log state
self._log_state(trade_date, final_state)
# Return decision and processed signal
return final_state, self.process_signal(final_state["final_trade_decision"])
def _log_state(self, trade_date, final_state):
"""Log the final state to a JSON file."""
self.log_states_dict[str(trade_date)] = {
"company_of_interest": final_state["company_of_interest"],
"trade_date": final_state["trade_date"],
"market_report": final_state["market_report"],
"sentiment_report": final_state["sentiment_report"],
"news_report": final_state["news_report"],
"fundamentals_report": final_state["fundamentals_report"],
"investment_debate_state": {
"bull_history": final_state["investment_debate_state"]["bull_history"],
"bear_history": final_state["investment_debate_state"]["bear_history"],
"history": final_state["investment_debate_state"]["history"],
"current_response": final_state["investment_debate_state"][
"current_response"
],
"judge_decision": final_state["investment_debate_state"][
"judge_decision"
],
},
"trader_investment_decision": final_state["trader_investment_plan"],
"risk_debate_state": {
"risky_history": final_state["risk_debate_state"]["risky_history"],
"safe_history": final_state["risk_debate_state"]["safe_history"],
"neutral_history": final_state["risk_debate_state"]["neutral_history"],
"history": final_state["risk_debate_state"]["history"],
"judge_decision": final_state["risk_debate_state"]["judge_decision"],
},
"investment_plan": final_state["investment_plan"],
"final_trade_decision": final_state["final_trade_decision"],
}
# Save to file
directory = Path(f"eval_results/{self.ticker}/TradingAgentsStrategy_logs/")
directory.mkdir(parents=True, exist_ok=True)
with open(
f"eval_results/{self.ticker}/TradingAgentsStrategy_logs/full_states_log_{trade_date}.json",
"w",
) as f:
json.dump(self.log_states_dict, f, indent=4)
def reflect_and_remember(self, returns_losses):
"""Reflect on decisions and update memory based on returns."""
self.reflector.reflect_bull_researcher(
self.curr_state, returns_losses, self.bull_memory
)
self.reflector.reflect_bear_researcher(
self.curr_state, returns_losses, self.bear_memory
)
self.reflector.reflect_trader(
self.curr_state, returns_losses, self.trader_memory
)
self.reflector.reflect_invest_judge(
self.curr_state, returns_losses, self.invest_judge_memory
)
self.reflector.reflect_risk_manager(
self.curr_state, returns_losses, self.risk_manager_memory
)
def process_signal(self, full_signal):
"""Process a signal to extract the core decision."""
return self.signal_processor.process_signal(full_signal)

30
updated_files/default_config.py Normal file
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import os
DEFAULT_CONFIG = {
"project_dir": os.path.abspath(os.path.join(os.path.dirname(__file__), ".")),
"results_dir": os.getenv("TRADINGAGENTS_RESULTS_DIR", "./results"),
"data_dir": "/Users/yluo/Documents/Code/ScAI/FR1-data",
"data_cache_dir": os.path.join(
os.path.abspath(os.path.join(os.path.dirname(__file__), ".")),
"dataflows/data_cache",
),
# LLM settings
#"llm_provider": "openai",
#"deep_think_llm": "o4-mini",
#"quick_think_llm": "gpt-4o-mini",
#"backend_url": "https://api.openai.com/v1",
# Default LLM is set to local LMStudio instance
"llm_provider": "lmstudio",
"deep_think_llm": "qwen/qwen3-4b-thinking-2507",
"quick_think_llm": "openai/gpt-oss-20b",
"backend_url": "http://192.168.0.20/v1",
"api_key": "blablabla",
# Debate and discussion settings
"max_debate_rounds": 1,
"max_risk_discuss_rounds": 1,
"max_recur_limit": 100,
# Tool settings
"online_tools": True,
}

116
updated_files/memory.py Normal file
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import chromadb
from chromadb.config import Settings
from openai import OpenAI
class FinancialSituationMemory:
def __init__(self, name, config):
if config["backend_url"] == "http://192.168.0.20:11434/v1":
self.embedding = "nomic-embed-text"
elif config["backend_url"] == "http://192.168.0.20:1234/v1":
self.embedding = "text-embedding-nomic-embed-text-v1.5"
else:
self.embedding = "ttext-embedding-nomic-embed-text-v1.5"
self.client = OpenAI(base_url=config["backend_url"], api_key=config["api_key"])
self.chroma_client = chromadb.Client(Settings(allow_reset=True))
self.situation_collection = self.chroma_client.create_collection(name=name)
def get_embedding(self, text):
"""Get OpenAI embedding for a text"""
response = self.client.embeddings.create(
model=self.embedding, input=text
)
return response.data[0].embedding
def add_situations(self, situations_and_advice):
"""Add financial situations and their corresponding advice. Parameter is a list of tuples (situation, rec)"""
situations = []
advice = []
ids = []
embeddings = []
offset = self.situation_collection.count()
for i, (situation, recommendation) in enumerate(situations_and_advice):
situations.append(situation)
advice.append(recommendation)
ids.append(str(offset + i))
embeddings.append(self.get_embedding(situation))
self.situation_collection.add(
documents=situations,
metadatas=[{"recommendation": rec} for rec in advice],
embeddings=embeddings,
ids=ids,
)
def get_memories(self, current_situation, n_matches=1):
"""Find matching recommendations using OpenAI embeddings"""
query_embedding = self.get_embedding(current_situation)
results = self.situation_collection.query(
query_embeddings=[query_embedding],
n_results=n_matches,
include=["metadatas", "documents", "distances"],
)
matched_results = []
for i in range(len(results["documents"][0])):
matched_results.append(
{
"matched_situation": results["documents"][0][i],
"recommendation": results["metadatas"][0][i]["recommendation"],
"similarity_score": 1 - results["distances"][0][i],
}
)
return matched_results
if __name__ == "__main__":
# Example usage
matcher = FinancialSituationMemory()
# Example data
example_data = [
(
"High inflation rate with rising interest rates and declining consumer spending",
"Consider defensive sectors like consumer staples and utilities. Review fixed-income portfolio duration.",
),
(
"Tech sector showing high volatility with increasing institutional selling pressure",
"Reduce exposure to high-growth tech stocks. Look for value opportunities in established tech companies with strong cash flows.",
),
(
"Strong dollar affecting emerging markets with increasing forex volatility",
"Hedge currency exposure in international positions. Consider reducing allocation to emerging market debt.",
),
(
"Market showing signs of sector rotation with rising yields",
"Rebalance portfolio to maintain target allocations. Consider increasing exposure to sectors benefiting from higher rates.",
),
]
# Add the example situations and recommendations
matcher.add_situations(example_data)
# Example query
current_situation = """
Market showing increased volatility in tech sector, with institutional investors
reducing positions and rising interest rates affecting growth stock valuations
"""
try:
recommendations = matcher.get_memories(current_situation, n_matches=2)
for i, rec in enumerate(recommendations, 1):
print(f"\nMatch {i}:")
print(f"Similarity Score: {rec['similarity_score']:.2f}")
print(f"Matched Situation: {rec['matched_situation']}")
print(f"Recommendation: {rec['recommendation']}")
except Exception as e:
print(f"Error during recommendation: {str(e)}")

257
updated_files/trading_graph.py Normal file
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# TradingAgents/graph/trading_graph.py
import os
from pathlib import Path
import json
from datetime import date
from typing import Dict, Any, Tuple, List, Optional
from langchain_openai import ChatOpenAI
from langchain_anthropic import ChatAnthropic
from langchain_google_genai import ChatGoogleGenerativeAI
from langgraph.prebuilt import ToolNode
from tradingagents.agents import *
from tradingagents.default_config import DEFAULT_CONFIG
from tradingagents.agents.utils.memory import FinancialSituationMemory
from tradingagents.agents.utils.agent_states import (
AgentState,
InvestDebateState,
RiskDebateState,
)
from tradingagents.dataflows.interface import set_config
from .conditional_logic import ConditionalLogic
from .setup import GraphSetup
from .propagation import Propagator
from .reflection import Reflector
from .signal_processing import SignalProcessor
class TradingAgentsGraph:
"""Main class that orchestrates the trading agents framework."""
def __init__(
self,
selected_analysts=["market", "social", "news", "fundamentals"],
debug=False,
config: Dict[str, Any] = None,
):
"""Initialize the trading agents graph and components.
Args:
selected_analysts: List of analyst types to include
debug: Whether to run in debug mode
config: Configuration dictionary. If None, uses default config
"""
self.debug = debug
self.config = config or DEFAULT_CONFIG
# Update the interface's config
set_config(self.config)
# Create necessary directories
os.makedirs(
os.path.join(self.config["project_dir"], "dataflows/data_cache"),
exist_ok=True,
)
# Initialize LLMs
if self.config["llm_provider"].lower() == "openai" or self.config["llm_provider"] == "ollama" or self.config["llm_provider"] == "openrouter":
self.deep_thinking_llm = ChatOpenAI(model=self.config["deep_think_llm"], base_url=self.config["backend_url"],api_key=self.config["api_key"])
self.quick_thinking_llm = ChatOpenAI(model=self.config["quick_think_llm"], base_url=self.config["backend_url"],api_key=self.config["api_key"])
elif self.config["llm_provider"].lower() == "anthropic":
self.deep_thinking_llm = ChatAnthropic(model=self.config["deep_think_llm"], base_url=self.config["backend_url"],api_key=self.config["api_key"])
self.quick_thinking_llm = ChatAnthropic(model=self.config["quick_think_llm"], base_url=self.config["backend_url"],api_key=self.config["api_key"])
elif self.config["llm_provider"].lower() == "google":
self.deep_thinking_llm = ChatGoogleGenerativeAI(model=self.config["deep_think_llm"])
self.quick_thinking_llm = ChatGoogleGenerativeAI(model=self.config["quick_think_llm"])
elif self.config["llm_provider"].lower() == "lmstudio":
self.deep_thinking_llm = ChatOpenAI(model=self.config["deep_think_llm"], base_url=self.config["backend_url"], api_key=self.config["api_key"])
self.quick_thinking_llm = ChatOpenAI(model=self.config["quick_think_llm"], base_url=self.config["backend_url"],api_key=self.config["api_key"])
else:
raise ValueError(f"Unsupported LLM provider: {self.config['llm_provider']}")
self.toolkit = Toolkit(config=self.config)
# Initialize memories
self.bull_memory = FinancialSituationMemory("bull_memory", self.config)
self.bear_memory = FinancialSituationMemory("bear_memory", self.config)
self.trader_memory = FinancialSituationMemory("trader_memory", self.config)
self.invest_judge_memory = FinancialSituationMemory("invest_judge_memory", self.config)
self.risk_manager_memory = FinancialSituationMemory("risk_manager_memory", self.config)
# Create tool nodes
self.tool_nodes = self._create_tool_nodes()
# Initialize components
self.conditional_logic = ConditionalLogic()
self.graph_setup = GraphSetup(
self.quick_thinking_llm,
self.deep_thinking_llm,
self.toolkit,
self.tool_nodes,
self.bull_memory,
self.bear_memory,
self.trader_memory,
self.invest_judge_memory,
self.risk_manager_memory,
self.conditional_logic,
)
self.propagator = Propagator()
self.reflector = Reflector(self.quick_thinking_llm)
self.signal_processor = SignalProcessor(self.quick_thinking_llm)
# State tracking
self.curr_state = None
self.ticker = None
self.log_states_dict = {} # date to full state dict
# Set up the graph
self.graph = self.graph_setup.setup_graph(selected_analysts)
def _create_tool_nodes(self) -> Dict[str, ToolNode]:
"""Create tool nodes for different data sources."""
return {
"market": ToolNode(
[
# online tools
self.toolkit.get_YFin_data_online,
self.toolkit.get_stockstats_indicators_report_online,
# offline tools
self.toolkit.get_YFin_data,
self.toolkit.get_stockstats_indicators_report,
]
),
"social": ToolNode(
[
# online tools
self.toolkit.get_stock_news_openai,
# offline tools
self.toolkit.get_reddit_stock_info,
]
),
"news": ToolNode(
[
# online tools
self.toolkit.get_global_news_openai,
self.toolkit.get_google_news,
# offline tools
self.toolkit.get_finnhub_news,
self.toolkit.get_reddit_news,
]
),
"fundamentals": ToolNode(
[
# online tools
self.toolkit.get_fundamentals_openai,
# offline tools
self.toolkit.get_finnhub_company_insider_sentiment,
self.toolkit.get_finnhub_company_insider_transactions,
self.toolkit.get_simfin_balance_sheet,
self.toolkit.get_simfin_cashflow,
self.toolkit.get_simfin_income_stmt,
]
),
}
def propagate(self, company_name, trade_date):
"""Run the trading agents graph for a company on a specific date."""
self.ticker = company_name
# Initialize state
init_agent_state = self.propagator.create_initial_state(
company_name, trade_date
)
args = self.propagator.get_graph_args()
if self.debug:
# Debug mode with tracing
trace = []
for chunk in self.graph.stream(init_agent_state, **args):
if len(chunk["messages"]) == 0:
pass
else:
chunk["messages"][-1].pretty_print()
trace.append(chunk)
final_state = trace[-1]
else:
# Standard mode without tracing
final_state = self.graph.invoke(init_agent_state, **args)
# Store current state for reflection
self.curr_state = final_state
# Log state
self._log_state(trade_date, final_state)
# Return decision and processed signal
return final_state, self.process_signal(final_state["final_trade_decision"])
def _log_state(self, trade_date, final_state):
"""Log the final state to a JSON file."""
self.log_states_dict[str(trade_date)] = {
"company_of_interest": final_state["company_of_interest"],
"trade_date": final_state["trade_date"],
"market_report": final_state["market_report"],
"sentiment_report": final_state["sentiment_report"],
"news_report": final_state["news_report"],
"fundamentals_report": final_state["fundamentals_report"],
"investment_debate_state": {
"bull_history": final_state["investment_debate_state"]["bull_history"],
"bear_history": final_state["investment_debate_state"]["bear_history"],
"history": final_state["investment_debate_state"]["history"],
"current_response": final_state["investment_debate_state"][
"current_response"
],
"judge_decision": final_state["investment_debate_state"][
"judge_decision"
],
},
"trader_investment_decision": final_state["trader_investment_plan"],
"risk_debate_state": {
"risky_history": final_state["risk_debate_state"]["risky_history"],
"safe_history": final_state["risk_debate_state"]["safe_history"],
"neutral_history": final_state["risk_debate_state"]["neutral_history"],
"history": final_state["risk_debate_state"]["history"],
"judge_decision": final_state["risk_debate_state"]["judge_decision"],
},
"investment_plan": final_state["investment_plan"],
"final_trade_decision": final_state["final_trade_decision"],
}
# Save to file
directory = Path(f"eval_results/{self.ticker}/TradingAgentsStrategy_logs/")
directory.mkdir(parents=True, exist_ok=True)
with open(
f"eval_results/{self.ticker}/TradingAgentsStrategy_logs/full_states_log_{trade_date}.json",
"w",
) as f:
json.dump(self.log_states_dict, f, indent=4)
def reflect_and_remember(self, returns_losses):
"""Reflect on decisions and update memory based on returns."""
self.reflector.reflect_bull_researcher(
self.curr_state, returns_losses, self.bull_memory
)
self.reflector.reflect_bear_researcher(
self.curr_state, returns_losses, self.bear_memory
)
self.reflector.reflect_trader(
self.curr_state, returns_losses, self.trader_memory
)
self.reflector.reflect_invest_judge(
self.curr_state, returns_losses, self.invest_judge_memory
)
self.reflector.reflect_risk_manager(
self.curr_state, returns_losses, self.risk_manager_memory
)
def process_signal(self, full_signal):
"""Process a signal to extract the core decision."""
return self.signal_processor.process_signal(full_signal)

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