charieswei
/
TradingAgents
mirror of https://github.com/TauricResearch/TradingAgents.git
1
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity

Merge branch 'main' into roadmap/realistic-timeline-march-2026

This commit is contained in:
Manav Chaudhary 2026-03-16 11:07:32 -04:00 committed by GitHub
parent 805ab3d913 f362a160c3
commit a2936b6dfe
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
16 changed files with 124 additions and 93 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

4
README.md
View File

@ -5,6 +5,10 @@
## News
- [2026-03] **TradingAgents v0.2.1** released with GPT-5.4, Gemini 3.1, Claude 4.6 model coverage and improved system stability.
- [2026-02] **TradingAgents v0.2.0** released with multi-provider LLM support (GPT-5.x, Gemini 3.x, Claude 4.x, Grok 4.x) and improved system architecture.
- [2026-01] **Trading-R1** [Technical Report](https://arxiv.org/abs/2509.11420) released, with [Terminal](https://github.com/TauricResearch/Trading-R1) expected to land soon.

8
cli/main.py
View File

@ -462,7 +462,7 @@ def update_display(layout, spinner_text=None, stats_handler=None, start_time=Non
def get_user_selections():
"""Get all user selections before starting the analysis display."""
# Display ASCII art welcome message
with open("./cli/static/welcome.txt", "r") as f:
with open("./cli/static/welcome.txt", "r", encoding="utf-8") as f:
welcome_ascii = f.read()
# Create welcome box content
@ -948,7 +948,7 @@ def run_analysis():
func(*args, **kwargs)
timestamp, message_type, content = obj.messages[-1]
content = content.replace("\n", " ") # Replace newlines with spaces
with open(log_file, "a") as f:
with open(log_file, "a", encoding="utf-8") as f:
f.write(f"{timestamp} [{message_type}] {content}\n")
return wrapper
@ -959,7 +959,7 @@ def run_analysis():
func(*args, **kwargs)
timestamp, tool_name, args = obj.tool_calls[-1]
args_str = ", ".join(f"{k}={v}" for k, v in args.items())
with open(log_file, "a") as f:
with open(log_file, "a", encoding="utf-8") as f:
f.write(f"{timestamp} [Tool Call] {tool_name}({args_str})\n")
return wrapper
@ -972,7 +972,7 @@ def run_analysis():
content = obj.report_sections[section_name]
if content:
file_name = f"{section_name}.md"
with open(report_dir / file_name, "w") as f:
with open(report_dir / file_name, "w", encoding="utf-8") as f:
f.write(content)
return wrapper

53
cli/utils.py
View File

@ -1,8 +1,12 @@
import questionary
from typing import List, Optional, Tuple, Dict
from rich.console import Console
from cli.models import AnalystType
console = Console()
ANALYST_ORDER = [
("Market Analyst", AnalystType.MARKET),
("Social Media Analyst", AnalystType.SOCIAL),
@ -126,30 +130,30 @@ 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
# Ordering: medium → light → heavy (balanced first for quick tasks)
# Within same tier, newer models first
SHALLOW_AGENT_OPTIONS = {
"openai": [
("GPT-5 Mini - Cost-optimized reasoning", "gpt-5-mini"),
("GPT-5 Nano - Ultra-fast, high-throughput", "gpt-5-nano"),
("GPT-5.2 - Latest flagship", "gpt-5.2"),
("GPT-5.1 - Flexible reasoning", "gpt-5.1"),
("GPT-4.1 - Smartest non-reasoning, 1M context", "gpt-4.1"),
("GPT-5 Mini - Balanced speed, cost, and capability", "gpt-5-mini"),
("GPT-5 Nano - High-throughput, simple tasks", "gpt-5-nano"),
("GPT-5.4 - Latest frontier, 1M context", "gpt-5.4"),
("GPT-4.1 - Smartest non-reasoning model", "gpt-4.1"),
],
"anthropic": [
("Claude Haiku 4.5 - Fast + extended thinking", "claude-haiku-4-5"),
("Claude Sonnet 4.5 - Best for agents/coding", "claude-sonnet-4-5"),
("Claude Sonnet 4 - High-performance", "claude-sonnet-4-20250514"),
("Claude Sonnet 4.6 - Best speed and intelligence balance", "claude-sonnet-4-6"),
("Claude Haiku 4.5 - Fast, near-instant responses", "claude-haiku-4-5"),
("Claude Sonnet 4.5 - Agents and coding", "claude-sonnet-4-5"),
],
"google": [
("Gemini 3 Flash - Next-gen fast", "gemini-3-flash-preview"),
("Gemini 2.5 Flash - Balanced, recommended", "gemini-2.5-flash"),
("Gemini 3 Pro - Reasoning-first", "gemini-3-pro-preview"),
("Gemini 2.5 Flash - Balanced, stable", "gemini-2.5-flash"),
("Gemini 3.1 Flash Lite - Most cost-efficient", "gemini-3.1-flash-lite-preview"),
("Gemini 2.5 Flash Lite - Fast, low-cost", "gemini-2.5-flash-lite"),
],
"xai": [
("Grok 4.1 Fast (Non-Reasoning) - Speed optimized, 2M ctx", "grok-4-1-fast-non-reasoning"),
("Grok 4 Fast (Non-Reasoning) - Speed optimized", "grok-4-fast-non-reasoning"),
("Grok 4.1 Fast (Reasoning) - High-performance, 2M ctx", "grok-4-1-fast-reasoning"),
("Grok 4 Fast (Reasoning) - High-performance", "grok-4-fast-reasoning"),
],
"openrouter": [
("NVIDIA Nemotron 3 Nano 30B (free)", "nvidia/nemotron-3-nano-30b-a3b:free"),
@ -191,33 +195,32 @@ 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
# Ordering: heavy → medium → light (most capable first for deep tasks)
# Within same tier, newer models first
DEEP_AGENT_OPTIONS = {
"openai": [
("GPT-5.2 - Latest flagship", "gpt-5.2"),
("GPT-5.1 - Flexible reasoning", "gpt-5.1"),
("GPT-5 - Advanced reasoning", "gpt-5"),
("GPT-4.1 - Smartest non-reasoning, 1M context", "gpt-4.1"),
("GPT-5 Mini - Cost-optimized reasoning", "gpt-5-mini"),
("GPT-5 Nano - Ultra-fast, high-throughput", "gpt-5-nano"),
("GPT-5.4 - Latest frontier, 1M context", "gpt-5.4"),
("GPT-5.2 - Strong reasoning, cost-effective", "gpt-5.2"),
("GPT-5 Mini - Balanced speed, cost, and capability", "gpt-5-mini"),
("GPT-5.4 Pro - Most capable, expensive ($30/$180 per 1M tokens)", "gpt-5.4-pro"),
],
"anthropic": [
("Claude Sonnet 4.5 - Best for agents/coding", "claude-sonnet-4-5"),
("Claude Opus 4.6 - Most intelligent, agents and coding", "claude-opus-4-6"),
("Claude Opus 4.5 - Premium, max intelligence", "claude-opus-4-5"),
("Claude Opus 4.1 - Most capable model", "claude-opus-4-1-20250805"),
("Claude Haiku 4.5 - Fast + extended thinking", "claude-haiku-4-5"),
("Claude Sonnet 4 - High-performance", "claude-sonnet-4-20250514"),
("Claude Sonnet 4.6 - Best speed and intelligence balance", "claude-sonnet-4-6"),
("Claude Sonnet 4.5 - Agents and coding", "claude-sonnet-4-5"),
],
"google": [
("Gemini 3 Pro - Reasoning-first", "gemini-3-pro-preview"),
("Gemini 3.1 Pro - Reasoning-first, complex workflows", "gemini-3.1-pro-preview"),
("Gemini 3 Flash - Next-gen fast", "gemini-3-flash-preview"),
("Gemini 2.5 Flash - Balanced, recommended", "gemini-2.5-flash"),
("Gemini 2.5 Pro - Stable pro model", "gemini-2.5-pro"),
("Gemini 2.5 Flash - Balanced, stable", "gemini-2.5-flash"),
],
"xai": [
("Grok 4 - Flagship model", "grok-4-0709"),
("Grok 4.1 Fast (Reasoning) - High-performance, 2M ctx", "grok-4-1-fast-reasoning"),
("Grok 4 Fast (Reasoning) - High-performance", "grok-4-fast-reasoning"),
("Grok 4 - Flagship model", "grok-4-0709"),
("Grok 4.1 Fast (Non-Reasoning) - Speed optimized, 2M ctx", "grok-4-1-fast-non-reasoning"),
("Grok 4 Fast (Non-Reasoning) - Speed optimized", "grok-4-fast-non-reasoning"),
],
"openrouter": [
("Z.AI GLM 4.5 Air (free)", "z-ai/glm-4.5-air:free"),

3
pyproject.toml
View File

@ -4,14 +4,13 @@ build-backend = "setuptools.build_meta"
[project]
name = "tradingagents"
version = "0.2.0"
version = "0.2.1"
description = "TradingAgents: Multi-Agents LLM Financial Trading Framework"
readme = "README.md"
requires-python = ">=3.10"
dependencies = [
"langchain-core>=0.3.81",
"backtrader>=1.9.78.123",
"chainlit>=2.5.5",
"langchain-anthropic>=0.3.15",
"langchain-experimental>=0.3.4",
"langchain-google-genai>=2.1.5",

1
requirements.txt
View File

@ -14,7 +14,6 @@ requests
tqdm
pytz
redis
chainlit
rich
typer
questionary

0
tradingagents/__init__.py Normal file
View File

14
tradingagents/agents/utils/technical_indicators_tools.py
View File

@ -10,14 +10,22 @@ def get_indicators(
look_back_days: Annotated[int, "how many days to look back"] = 30,
) -> str:
"""
Retrieve technical indicators for a given ticker symbol.
Retrieve a single technical indicator 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
indicator (str): A single technical indicator name, e.g. 'rsi', 'macd'. Call this tool once per indicator.
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)
# LLMs sometimes pass multiple indicators as a comma-separated string;
# split and process each individually.
indicators = [i.strip() for i in indicator.split(",") if i.strip()]
if len(indicators) > 1:
results = []
for ind in indicators:
results.append(route_to_vendor("get_indicators", symbol, ind, curr_date, look_back_days))
return "\n\n".join(results)
return route_to_vendor("get_indicators", symbol, indicator.strip(), curr_date, look_back_days)

17
tradingagents/dataflows/stockstats_utils.py
View File

@ -6,6 +6,19 @@ import os
from .config import get_config
def _clean_dataframe(data: pd.DataFrame) -> pd.DataFrame:
"""Normalize a stock DataFrame for stockstats: parse dates, drop invalid rows, fill price gaps."""
data["Date"] = pd.to_datetime(data["Date"], errors="coerce")
data = data.dropna(subset=["Date"])
price_cols = [c for c in ["Open", "High", "Low", "Close", "Volume"] if c in data.columns]
data[price_cols] = data[price_cols].apply(pd.to_numeric, errors="coerce")
data = data.dropna(subset=["Close"])
data[price_cols] = data[price_cols].ffill().bfill()
return data
class StockstatsUtils:
@staticmethod
def get_stock_stats(
@ -36,8 +49,7 @@ class StockstatsUtils:
)
if os.path.exists(data_file):
data = pd.read_csv(data_file)
data["Date"] = pd.to_datetime(data["Date"])
data = pd.read_csv(data_file, on_bad_lines="skip")
else:
data = yf.download(
symbol,
@ -50,6 +62,7 @@ class StockstatsUtils:
data = data.reset_index()
data.to_csv(data_file, index=False)
data = _clean_dataframe(data)
df = wrap(data)
df["Date"] = df["Date"].dt.strftime("%Y-%m-%d")
curr_date_str = curr_date_dt.strftime("%Y-%m-%d")

24
tradingagents/dataflows/y_finance.py
View File

@ -3,7 +3,7 @@ from datetime import datetime
from dateutil.relativedelta import relativedelta
import yfinance as yf
import os
from .stockstats_utils import StockstatsUtils
from .stockstats_utils import StockstatsUtils, _clean_dataframe
def get_YFin_data_online(
symbol: Annotated[str, "ticker symbol of the company"],
@ -209,31 +209,30 @@ def _get_stock_stats_bulk(
os.path.join(
config.get("data_cache_dir", "data"),
f"{symbol}-YFin-data-2015-01-01-2025-03-25.csv",
)
),
on_bad_lines="skip",
)
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"])
data = pd.read_csv(data_file, on_bad_lines="skip")
else:
data = yf.download(
symbol,
@ -245,9 +244,10 @@ def _get_stock_stats_bulk(
)
data = data.reset_index()
data.to_csv(data_file, index=False)
df = wrap(data)
df["Date"] = df["Date"].dt.strftime("%Y-%m-%d")
data = _clean_dataframe(data)
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

14
tradingagents/graph/propagation.py
View File

@ -24,14 +24,26 @@ class Propagator:
"company_of_interest": company_name,
"trade_date": str(trade_date),
"investment_debate_state": InvestDebateState(
{"history": "", "current_response": "", "count": 0}
{
"bull_history": "",
"bear_history": "",
"history": "",
"current_response": "",
"judge_decision": "",
"count": 0,
}
),
"risk_debate_state": RiskDebateState(
{
"aggressive_history": "",
"conservative_history": "",
"neutral_history": "",
"history": "",
"latest_speaker": "",
"current_aggressive_response": "",
"current_conservative_response": "",
"current_neutral_response": "",
"judge_decision": "",
"count": 0,
}
),

6
tradingagents/graph/trading_graph.py
View File

@ -105,7 +105,10 @@ class TradingAgentsGraph:
self.tool_nodes = self._create_tool_nodes()
# Initialize components
self.conditional_logic = ConditionalLogic()
self.conditional_logic = ConditionalLogic(
max_debate_rounds=self.config["max_debate_rounds"],
max_risk_discuss_rounds=self.config["max_risk_discuss_rounds"],
)
self.graph_setup = GraphSetup(
self.quick_thinking_llm,
self.deep_thinking_llm,
@ -257,6 +260,7 @@ class TradingAgentsGraph:
with open(
f"eval_results/{self.ticker}/TradingAgentsStrategy_logs/full_states_log_{trade_date}.json",
"w",
encoding="utf-8",
) as f:
json.dump(self.log_states_dict, f, indent=4)

2
tradingagents/llm_clients/anthropic_client.py
View File

@ -16,7 +16,7 @@ class AnthropicClient(BaseLLMClient):
"""Return configured ChatAnthropic instance."""
llm_kwargs = {"model": self.model}
for key in ("timeout", "max_retries", "api_key", "max_tokens", "callbacks"):
for key in ("timeout", "max_retries", "api_key", "max_tokens", "callbacks", "http_client", "http_async_client"):
if key in self.kwargs:
llm_kwargs[key] = self.kwargs[key]

6
tradingagents/llm_clients/factory.py
View File

@ -19,6 +19,12 @@ def create_llm_client(
model: Model name/identifier
base_url: Optional base URL for API endpoint
**kwargs: Additional provider-specific arguments
- http_client: Custom httpx.Client for SSL proxy or certificate customization
- http_async_client: Custom httpx.AsyncClient for async operations
- timeout: Request timeout in seconds
- max_retries: Maximum retry attempts
- api_key: API key for the provider
- callbacks: LangChain callbacks
Returns:
Configured BaseLLMClient instance

2
tradingagents/llm_clients/google_client.py
View File

@ -38,7 +38,7 @@ class GoogleClient(BaseLLMClient):
"""Return configured ChatGoogleGenerativeAI instance."""
llm_kwargs = {"model": self.model}
for key in ("timeout", "max_retries", "google_api_key", "callbacks"):
for key in ("timeout", "max_retries", "google_api_key", "callbacks", "http_client", "http_async_client"):
if key in self.kwargs:
llm_kwargs[key] = self.kwargs[key]

26
tradingagents/llm_clients/openai_client.py
View File

@ -8,25 +8,23 @@ from .validators import validate_model
class UnifiedChatOpenAI(ChatOpenAI):
"""ChatOpenAI subclass that strips incompatible params for certain models."""
"""ChatOpenAI subclass that strips temperature/top_p for GPT-5 family models.
GPT-5 family models use reasoning natively. temperature/top_p are only
accepted when reasoning.effort is 'none'; with any other effort level
(or for older GPT-5/GPT-5-mini/GPT-5-nano which always reason) the API
rejects these params. Langchain defaults temperature=0.7, so we must
strip it to avoid errors.
Non-GPT-5 models (GPT-4.1, xAI, Ollama, etc.) are unaffected.
"""
def __init__(self, **kwargs):
model = kwargs.get("model", "")
if self._is_reasoning_model(model):
if "gpt-5" in kwargs.get("model", "").lower():
kwargs.pop("temperature", None)
kwargs.pop("top_p", None)
super().__init__(**kwargs)
@staticmethod
def _is_reasoning_model(model: str) -> bool:
"""Check if model is a reasoning model that doesn't support temperature."""
model_lower = model.lower()
return (
model_lower.startswith("o1")
or model_lower.startswith("o3")
or "gpt-5" in model_lower
)
class OpenAIClient(BaseLLMClient):
"""Client for OpenAI, Ollama, OpenRouter, and xAI providers."""
@ -61,7 +59,7 @@ class OpenAIClient(BaseLLMClient):
elif self.base_url:
llm_kwargs["base_url"] = self.base_url
for key in ("timeout", "max_retries", "reasoning_effort", "api_key", "callbacks"):
for key in ("timeout", "max_retries", "reasoning_effort", "api_key", "callbacks", "http_client", "http_async_client"):
if key in self.kwargs:
llm_kwargs[key] = self.kwargs[key]

37
tradingagents/llm_clients/validators.py
View File

@ -6,59 +6,44 @@ Let LLM providers use their own defaults for unspecified params.
VALID_MODELS = {
"openai": [
# GPT-5 series (2025)
# GPT-5 series
"gpt-5.4-pro",
"gpt-5.4",
"gpt-5.2",
"gpt-5.1",
"gpt-5",
"gpt-5-mini",
"gpt-5-nano",
# GPT-4.1 series (2025)
# GPT-4.1 series
"gpt-4.1",
"gpt-4.1-mini",
"gpt-4.1-nano",
# o-series reasoning models
"o4-mini",
"o3",
"o3-mini",
"o1",
"o1-preview",
# GPT-4o series (legacy but still supported)
"gpt-4o",
"gpt-4o-mini",
],
"anthropic": [
# Claude 4.5 series (2025)
# Claude 4.6 series (latest)
"claude-opus-4-6",
"claude-sonnet-4-6",
# Claude 4.5 series
"claude-opus-4-5",
"claude-sonnet-4-5",
"claude-haiku-4-5",
# Claude 4.x series
"claude-opus-4-1-20250805",
"claude-sonnet-4-20250514",
# Claude 3.7 series
"claude-3-7-sonnet-20250219",
# Claude 3.5 series (legacy)
"claude-3-5-haiku-20241022",
"claude-3-5-sonnet-20241022",
],
"google": [
# Gemini 3.1 series (preview)
"gemini-3.1-pro-preview",
"gemini-3.1-flash-lite-preview",
# Gemini 3 series (preview)
"gemini-3-pro-preview",
"gemini-3-flash-preview",
# Gemini 2.5 series
"gemini-2.5-pro",
"gemini-2.5-flash",
"gemini-2.5-flash-lite",
# Gemini 2.0 series
"gemini-2.0-flash",
"gemini-2.0-flash-lite",
],
"xai": [
# Grok 4.1 series
"grok-4-1-fast",
"grok-4-1-fast-reasoning",
"grok-4-1-fast-non-reasoning",
# Grok 4 series
"grok-4",
"grok-4-0709",
"grok-4-fast-reasoning",
"grok-4-fast-non-reasoning",