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env/ env/
__pycache__/ __pycache__/
.DS_Store .DS_Store
*.csv *.csv
src/ src/
eval_results/ eval_results/
eval_data/ eval_data/
*.egg-info/ *.egg-info/
results/ results/
.env .env
tradingagents/dataflows/data_cache/
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{
// Use IntelliSense to learn about possible attributes.
// Hover to view descriptions of existing attributes.
// For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
"version": "0.2.0",
"configurations": [
{
"name": "Python Debugger: main.py",
"type": "debugpy",
"request": "launch",
"program": "${workspaceFolder}/main.py",
"console": "integratedTerminal"
}
]
}

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--- ---
# TradingAgents: Multi-Agents LLM Financial Trading Framework # TradingAgents: Multi-Agents LLM Financial Trading Framework
> 🎉 **TradingAgents** officially released! We have received numerous inquiries about the work, and we would like to express our thanks for the enthusiasm in our community. > 🎉 **TradingAgents** officially released! We have received numerous inquiries about the work, and we would like to express our thanks for the enthusiasm in our community.
> >
> So we decided to fully open-source the framework. Looking forward to building impactful projects with you! > So we decided to fully open-source the framework. Looking forward to building impactful projects with you!
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</picture> </picture>
</a> </a>
</div> </div>
<div align="center"> <div align="center">
🚀 [TradingAgents](#tradingagents-framework) | ⚡ [Installation & CLI](#installation-and-cli) | 🎬 [Demo](https://www.youtube.com/watch?v=90gr5lwjIho) | 📦 [Package Usage](#tradingagents-package) | 🤝 [Contributing](#contributing) | 📄 [Citation](#citation) 🚀 [TradingAgents](#tradingagents-framework) | ⚡ [Installation & CLI](#installation-and-cli) | 🎬 [Demo](https://www.youtube.com/watch?v=90gr5lwjIho) | 📦 [Package Usage](#tradingagents-package) | 🤝 [Contributing](#contributing) | 📄 [Citation](#citation)
</div> </div>
## TradingAgents Framework ## TradingAgents Framework
TradingAgents is a multi-agent trading framework that mirrors the dynamics of real-world trading firms. By deploying specialized LLM-powered agents: from fundamental analysts, sentiment experts, and technical analysts, to trader, risk management team, the platform collaboratively evaluates market conditions and informs trading decisions. Moreover, these agents engage in dynamic discussions to pinpoint the optimal strategy. TradingAgents is a multi-agent trading framework that mirrors the dynamics of real-world trading firms. By deploying specialized LLM-powered agents: from fundamental analysts, sentiment experts, and technical analysts, to trader, risk management team, the platform collaboratively evaluates market conditions and informs trading decisions. Moreover, these agents engage in dynamic discussions to pinpoint the optimal strategy.
<p align="center"> <p align="center">
<img src="assets/schema.png" style="width: 100%; height: auto;"> <img src="assets/schema.png" style="width: 100%; height: auto;">
</p> </p>
> TradingAgents framework is designed for research purposes. Trading performance may vary based on many factors, including the chosen backbone language models, model temperature, trading periods, the quality of data, and other non-deterministic factors. [It is not intended as financial, investment, or trading advice.](https://tauric.ai/disclaimer/) > TradingAgents framework is designed for research purposes. Trading performance may vary based on many factors, including the chosen backbone language models, model temperature, trading periods, the quality of data, and other non-deterministic factors. [It is not intended as financial, investment, or trading advice.](https://tauric.ai/disclaimer/)
Our framework decomposes complex trading tasks into specialized roles. This ensures the system achieves a robust, scalable approach to market analysis and decision-making. Our framework decomposes complex trading tasks into specialized roles. This ensures the system achieves a robust, scalable approach to market analysis and decision-making.
### Analyst Team ### Analyst Team
- Fundamentals Analyst: Evaluates company financials and performance metrics, identifying intrinsic values and potential red flags. - Fundamentals Analyst: Evaluates company financials and performance metrics, identifying intrinsic values and potential red flags.
- Sentiment Analyst: Analyzes social media and public sentiment using sentiment scoring algorithms to gauge short-term market mood. - Sentiment Analyst: Analyzes social media and public sentiment using sentiment scoring algorithms to gauge short-term market mood.
- News Analyst: Monitors global news and macroeconomic indicators, interpreting the impact of events on market conditions. - News Analyst: Monitors global news and macroeconomic indicators, interpreting the impact of events on market conditions.
- Technical Analyst: Utilizes technical indicators (like MACD and RSI) to detect trading patterns and forecast price movements. - Technical Analyst: Utilizes technical indicators (like MACD and RSI) to detect trading patterns and forecast price movements.
<p align="center"> <p align="center">
<img src="assets/analyst.png" width="100%" style="display: inline-block; margin: 0 2%;"> <img src="assets/analyst.png" width="100%" style="display: inline-block; margin: 0 2%;">
</p> </p>
### Researcher Team ### Researcher Team
- Comprises both bullish and bearish researchers who critically assess the insights provided by the Analyst Team. Through structured debates, they balance potential gains against inherent risks. - Comprises both bullish and bearish researchers who critically assess the insights provided by the Analyst Team. Through structured debates, they balance potential gains against inherent risks.
<p align="center"> <p align="center">
<img src="assets/researcher.png" width="70%" style="display: inline-block; margin: 0 2%;"> <img src="assets/researcher.png" width="70%" style="display: inline-block; margin: 0 2%;">
</p> </p>
### Trader Agent ### Trader Agent
- Composes reports from the analysts and researchers to make informed trading decisions. It determines the timing and magnitude of trades based on comprehensive market insights. - Composes reports from the analysts and researchers to make informed trading decisions. It determines the timing and magnitude of trades based on comprehensive market insights.
<p align="center"> <p align="center">
<img src="assets/trader.png" width="70%" style="display: inline-block; margin: 0 2%;"> <img src="assets/trader.png" width="70%" style="display: inline-block; margin: 0 2%;">
</p> </p>
### Risk Management and Portfolio Manager ### Risk Management and Portfolio Manager
- Continuously evaluates portfolio risk by assessing market volatility, liquidity, and other risk factors. The risk management team evaluates and adjusts trading strategies, providing assessment reports to the Portfolio Manager for final decision. - Continuously evaluates portfolio risk by assessing market volatility, liquidity, and other risk factors. The risk management team evaluates and adjusts trading strategies, providing assessment reports to the Portfolio Manager for final decision.
- The Portfolio Manager approves/rejects the transaction proposal. If approved, the order will be sent to the simulated exchange and executed. - The Portfolio Manager approves/rejects the transaction proposal. If approved, the order will be sent to the simulated exchange and executed.
<p align="center"> <p align="center">
<img src="assets/risk.png" width="70%" style="display: inline-block; margin: 0 2%;"> <img src="assets/risk.png" width="70%" style="display: inline-block; margin: 0 2%;">
</p> </p>
## Installation and CLI ## Installation and CLI
### Installation ### Installation
Clone TradingAgents: Clone TradingAgents:
```bash ```bash
git clone https://github.com/TauricResearch/TradingAgents.git git clone https://github.com/TauricResearch/TradingAgents.git
cd TradingAgents cd TradingAgents
``` ```
Create a virtual environment in any of your favorite environment managers: Create a virtual environment in any of your favorite environment managers:
```bash ```bash
conda create -n tradingagents python=3.13 conda create -n tradingagents python=3.13
conda activate tradingagents conda activate tradingagents
``` ```
Install dependencies: Install dependencies:
```bash ```bash
pip install -r requirements.txt pip install -r requirements.txt
``` ```
### Required APIs ### Required APIs
You will also need the FinnHub API for financial data. All of our code is implemented with the free tier. You will also need the FinnHub API for financial data. All of our code is implemented with the free tier.
```bash ```bash
export FINNHUB_API_KEY=$YOUR_FINNHUB_API_KEY export FINNHUB_API_KEY=$YOUR_FINNHUB_API_KEY
``` ```
You will need the OpenAI API or GEMINI API for all the agents. You will need the OpenAI API or GEMINI API for all the agents.
```bash ```bash
export OPENAI_API_KEY=$YOUR_OPENAI_API_KEY export OPENAI_API_KEY=$YOUR_OPENAI_API_KEY
export GEMINI_API_KEY=$YOUR_GEMINI_API_KEY export GEMINI_API_KEY=$YOUR_GEMINI_API_KEY
export GOOGLE_API_KEY=$YOUR_GEMINI_API_KEY export GOOGLE_API_KEY=$YOUR_GEMINI_API_KEY
``` ```
### CLI Usage ### CLI Usage
You can also try out the CLI directly by running: You can also try out the CLI directly by running:
```bash ```bash
python -m cli.main python -m cli.main
``` ```
You will see a screen where you can select your desired tickers, date, LLMs, research depth, etc. You will see a screen where you can select your desired tickers, date, LLMs, research depth, etc.
<p align="center"> <p align="center">
<img src="assets/cli/cli_init.png" width="100%" style="display: inline-block; margin: 0 2%;"> <img src="assets/cli/cli_init.png" width="100%" style="display: inline-block; margin: 0 2%;">
</p> </p>
An interface will appear showing results as they load, letting you track the agent's progress as it runs. An interface will appear showing results as they load, letting you track the agent's progress as it runs.
<p align="center"> <p align="center">
<img src="assets/cli/cli_news.png" width="100%" style="display: inline-block; margin: 0 2%;"> <img src="assets/cli/cli_news.png" width="100%" style="display: inline-block; margin: 0 2%;">
</p> </p>
<p align="center"> <p align="center">
<img src="assets/cli/cli_transaction.png" width="100%" style="display: inline-block; margin: 0 2%;"> <img src="assets/cli/cli_transaction.png" width="100%" style="display: inline-block; margin: 0 2%;">
</p> </p>
## TradingAgents Package ## TradingAgents Package
### Implementation Details ### Implementation Details
We built TradingAgents with LangGraph to ensure flexibility and modularity. We utilize `o1-preview` and `gpt-4o` as our deep thinking and fast thinking LLMs for our experiments. However, for testing purposes, we recommend you use `o4-mini` and `gpt-4.1-mini` to save on costs as our framework makes **lots of** API calls. We built TradingAgents with LangGraph to ensure flexibility and modularity. We utilize `o1-preview` and `gpt-4o` as our deep thinking and fast thinking LLMs for our experiments. However, for testing purposes, we recommend you use `o4-mini` and `gpt-4.1-mini` to save on costs as our framework makes **lots of** API calls.
### Python Usage ### Python Usage
To use TradingAgents inside your code, you can import the `tradingagents` module and initialize a `TradingAgentsGraph()` object. The `.propagate()` function will return a decision. You can run `main.py`, here's also a quick example: To use TradingAgents inside your code, you can import the `tradingagents` module and initialize a `TradingAgentsGraph()` object. The `.propagate()` function will return a decision. You can run `main.py`, here's also a quick example:
```python ```python
from tradingagents.graph.trading_graph import TradingAgentsGraph from tradingagents.graph.trading_graph import TradingAgentsGraph
from tradingagents.default_config import DEFAULT_CONFIG from tradingagents.default_config import DEFAULT_CONFIG
ta = TradingAgentsGraph(debug=True, config=DEFAULT_CONFIG.copy()) ta = TradingAgentsGraph(debug=True, config=DEFAULT_CONFIG.copy())
# forward propagate # forward propagate
_, decision = ta.propagate("NVDA", "2024-05-10") _, decision = ta.propagate("NVDA", "2024-05-10")
print(decision) print(decision)
``` ```
You can also adjust the default configuration to set your own choice of LLMs, debate rounds, etc. You can also adjust the default configuration to set your own choice of LLMs, debate rounds, etc.
```python ```python
from tradingagents.graph.trading_graph import TradingAgentsGraph from tradingagents.graph.trading_graph import TradingAgentsGraph
from tradingagents.default_config import DEFAULT_CONFIG from tradingagents.default_config import DEFAULT_CONFIG
# Create a custom config # Create a custom config
config = DEFAULT_CONFIG.copy() config = DEFAULT_CONFIG.copy()
config["deep_think_llm"] = "gpt-4.1-nano" # Use a different model config["deep_think_llm"] = "gpt-4.1-nano" # Use a different model
config["quick_think_llm"] = "gpt-4.1-nano" # Use a different model config["quick_think_llm"] = "gpt-4.1-nano" # Use a different model
config["max_debate_rounds"] = 1 # Increase debate rounds config["max_debate_rounds"] = 1 # Increase debate rounds
config["online_tools"] = True # Use online tools or cached data config["online_tools"] = True # Use online tools or cached data
# Initialize with custom config # Initialize with custom config
ta = TradingAgentsGraph(debug=True, config=config) ta = TradingAgentsGraph(debug=True, config=config)
# forward propagate # forward propagate
_, decision = ta.propagate("NVDA", "2024-05-10") _, decision = ta.propagate("NVDA", "2024-05-10")
print(decision) print(decision)
``` ```
> For `online_tools`, we recommend enabling them for experimentation, as they provide access to real-time data. The agents' offline tools rely on cached data from our **Tauric TradingDB**, a curated dataset we use for backtesting. We're currently in the process of refining this dataset, and we plan to release it soon alongside our upcoming projects. Stay tuned! > For `online_tools`, we recommend enabling them for experimentation, as they provide access to real-time data. The agents' offline tools rely on cached data from our **Tauric TradingDB**, a curated dataset we use for backtesting. We're currently in the process of refining this dataset, and we plan to release it soon alongside our upcoming projects. Stay tuned!
You can view the full list of configurations in `tradingagents/default_config.py`. You can view the full list of configurations in `tradingagents/default_config.py`.
## Contributing ## Contributing
We welcome contributions from the community! Whether it's fixing a bug, improving documentation, or suggesting a new feature, your input helps make this project better. If you are interested in this line of research, please consider joining our open-source financial AI research community [Tauric Research](https://tauric.ai/). We welcome contributions from the community! Whether it's fixing a bug, improving documentation, or suggesting a new feature, your input helps make this project better. If you are interested in this line of research, please consider joining our open-source financial AI research community [Tauric Research](https://tauric.ai/).
## Citation ## Citation
Please reference our work if you find *TradingAgents* provides you with some help :) Please reference our work if you find *TradingAgents* provides you with some help :)
``` ```
@misc{xiao2025tradingagentsmultiagentsllmfinancial, @misc{xiao2025tradingagentsmultiagentsllmfinancial,
title={TradingAgents: Multi-Agents LLM Financial Trading Framework}, title={TradingAgents: Multi-Agents LLM Financial Trading Framework},
author={Yijia Xiao and Edward Sun and Di Luo and Wei Wang}, author={Yijia Xiao and Edward Sun and Di Luo and Wei Wang},
year={2025}, year={2025},
eprint={2412.20138}, eprint={2412.20138},
archivePrefix={arXiv}, archivePrefix={arXiv},
primaryClass={q-fin.TR}, primaryClass={q-fin.TR},
url={https://arxiv.org/abs/2412.20138}, url={https://arxiv.org/abs/2412.20138},
} }
``` ```

2
cli/utils.py
View File

@ -132,6 +132,8 @@ def select_shallow_thinking_agent(provider) -> str:
("GPT-4.1-nano - Ultra-lightweight model for basic operations", "gpt-4.1-nano"), ("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-4.1-mini - Compact model with good performance", "gpt-4.1-mini"),
("GPT-4o - Standard model with solid capabilities", "gpt-4o"), ("GPT-4o - Standard model with solid capabilities", "gpt-4o"),
("o4-mini - Specialized reasoning model (compact)", "o4-mini"),
("o3 - Full advanced reasoning model", "o3"),
], ],
"anthropic": [ "anthropic": [
("Claude Haiku 3.5 - Fast inference and standard capabilities", "claude-3-5-haiku-latest"), ("Claude Haiku 3.5 - Fast inference and standard capabilities", "claude-3-5-haiku-latest"),

59
docker-compose.yml Normal file
View File

@ -0,0 +1,59 @@
version: '3.8'
services:
mysql:
image: mysql:8.0
container_name: tradingagents_mysql
restart: unless-stopped
environment:
MYSQL_ROOT_PASSWORD: ${DB_PASSWORD:-password}
MYSQL_DATABASE: ${DB_NAME:-tradingagents_db}
MYSQL_USER: ${DB_USER:-tradinguser}
MYSQL_PASSWORD: ${DB_PASSWORD:-password}
ports:
- "3306:3306"
volumes:
- /home/hskim/mysql_data:/var/lib/mysql
- /home/hskim/docker/mysql/init:/docker-entrypoint-initdb.d
command: --default-authentication-plugin=mysql_native_password --character-set-server=utf8mb4 --collation-server=utf8mb4_unicode_ci
networks:
- tradingagents_network
redis:
image: redis:7-alpine
container_name: tradingagents_redis
restart: unless-stopped
ports:
- "6379:6379"
volumes:
- redis_data:/data
command: redis-server --appendonly yes
networks:
- tradingagents_network
# 개발용 phpMyAdmin (선택사항)
# phpmyadmin:
# image: phpmyadmin/phpmyadmin
# container_name: tradingagents_phpmyadmin
# restart: unless-stopped
# environment:
# PMA_HOST: mysql
# PMA_PORT: 3306
# PMA_USER: root
# PMA_PASSWORD: ${DB_PASSWORD:-password}
# ports:
# - "8080:80"
# depends_on:
# - mysql
# networks:
# - tradingagents_network
volumes:
mysql_data:
driver: local
redis_data:
driver: local
networks:
tradingagents_network:
driver: bridge

21
main.py
View File

@ -1,21 +0,0 @@
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-2.5-pro" # Use a different model
config["quick_think_llm"] = "gemini-2.5-flash-lite-preview-06-17" # 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

68
pyproject.toml
View File

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

54
requirements.txt
View File

@ -1,27 +1,27 @@
typing-extensions typing-extensions
langchain-openai langchain-openai
langchain-experimental langchain-experimental
pandas pandas
yfinance yfinance
praw praw
feedparser feedparser
stockstats stockstats
eodhd eodhd
langgraph langgraph
chromadb chromadb
setuptools setuptools
backtrader backtrader
akshare akshare
tushare tushare
finnhub-python finnhub-python
parsel parsel
requests requests
tqdm tqdm
pytz pytz
redis redis
chainlit chainlit
rich rich
questionary questionary
langchain_anthropic langchain_anthropic
langchain-google-genai langchain-google-genai
google-genai google-genai

86
setup.py
View File

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

4
tradingagents/agents/utils/embedding_provider_factory.py
View File

@ -4,11 +4,11 @@ from .embedding_providers import (
GeminiEmbeddingProvider, GeminiEmbeddingProvider,
OllamaEmbeddingProvider OllamaEmbeddingProvider
) )
from typing import Any
class EmbeddingProviderFactory: class EmbeddingProviderFactory:
@staticmethod @staticmethod
def create_provider(config : dict[str, any])->EmbeddingProvider: def create_provider(config : dict[str, Any])->EmbeddingProvider:
backend_url = config["backend_url"] backend_url = config["backend_url"]
if "generativelanguage.googleapis.com" in backend_url: if "generativelanguage.googleapis.com" in backend_url:

2
tradingagents/dataflows/config.py
View File

@ -3,7 +3,7 @@ from typing import Dict, Optional
# Use default config but allow it to be overridden # Use default config but allow it to be overridden
_config: Optional[Dict] = None _config: Optional[Dict] = None
DATA_DIR: Optional[str] = None DATA_DIR: str = ""
def initialize_config(): def initialize_config():

12
tradingagents/dataflows/googlenews_utils.py
View File

@ -74,10 +74,14 @@ def getNewsData(query, start_date, end_date):
for el in results_on_page: for el in results_on_page:
try: try:
link = el.find("a")["href"] link = el.find("a")["href"]
title = el.select_one("div.MBeuO").get_text() title_el = el.select_one("div.MBeuO")
snippet = el.select_one(".GI74Re").get_text() title = title_el.get_text() if title_el else ""
date = el.select_one(".LfVVr").get_text() snippet_el = el.select_one(".GI74Re")
source = el.select_one(".NUnG9d span").get_text() snippet = snippet_el.get_text() if snippet_el else ""
date_el = el.select_one(".LfVVr")
date = date_el.get_text() if date_el else ""
source_el = el.select_one(".NUnG9d span")
source = source_el.get_text() if source_el else ""
news_results.append( news_results.append(
{ {
"link": link, "link": link,

106
tradingagents/dataflows/interface.py
View File

@ -1,4 +1,4 @@
from typing import Annotated, Dict from typing import Annotated, Dict, Tuple
from .reddit_utils import fetch_top_from_category from .reddit_utils import fetch_top_from_category
from .yfin_utils import * from .yfin_utils import *
from .stockstats_utils import * from .stockstats_utils import *
@ -14,7 +14,24 @@ from tqdm import tqdm
import yfinance as yf import yfinance as yf
from openai import OpenAI from openai import OpenAI
from .config import get_config, set_config, DATA_DIR from .config import get_config, set_config, DATA_DIR
from .search_provider_factory import SearchProviderFactory from .search_provider_factory import SearchProviderFactory, create_search_provider_factory
def parse_date_range(curr_date: str, look_back_days: int) -> Tuple[str, str]:
"""
Parse date range and return start and end dates.
Args:
curr_date: Current date in yyyy-mm-dd format
look_back_days: Number of days to look back
Returns:
Tuple of (start_date, end_date) as strings
"""
end_date = curr_date
start_date_obj = datetime.strptime(curr_date, "%Y-%m-%d")
before = start_date_obj - relativedelta(days=look_back_days)
return before.strftime("%Y-%m-%d"), end_date
def get_finnhub_news( def get_finnhub_news(
@ -37,9 +54,7 @@ def get_finnhub_news(
""" """
start_date = datetime.strptime(curr_date, "%Y-%m-%d") before, _ = parse_date_range(curr_date, look_back_days)
before = start_date - relativedelta(days=look_back_days)
before = before.strftime("%Y-%m-%d")
result = get_data_in_range(ticker, before, curr_date, "news_data", DATA_DIR) result = get_data_in_range(ticker, before, curr_date, "news_data", DATA_DIR)
@ -76,9 +91,7 @@ def get_finnhub_company_insider_sentiment(
str: a report of the sentiment in the past 15 days starting at curr_date 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, _ = parse_date_range(curr_date, look_back_days)
before = date_obj - relativedelta(days=look_back_days)
before = before.strftime("%Y-%m-%d")
data = get_data_in_range(ticker, before, curr_date, "insider_senti", DATA_DIR) data = get_data_in_range(ticker, before, curr_date, "insider_senti", DATA_DIR)
@ -117,9 +130,7 @@ def get_finnhub_company_insider_transactions(
str: a report of the company's insider transaction/trading informtaion in the past 15 days 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, _ = parse_date_range(curr_date, look_back_days)
before = date_obj - relativedelta(days=look_back_days)
before = before.strftime("%Y-%m-%d")
data = get_data_in_range(ticker, before, curr_date, "insider_trans", DATA_DIR) data = get_data_in_range(ticker, before, curr_date, "insider_trans", DATA_DIR)
@ -290,9 +301,7 @@ def get_google_news(
) -> str: ) -> str:
query = query.replace(" ", "+") query = query.replace(" ", "+")
start_date = datetime.strptime(curr_date, "%Y-%m-%d") before, _ = parse_date_range(curr_date, look_back_days)
before = start_date - relativedelta(days=look_back_days)
before = before.strftime("%Y-%m-%d")
news_results = getNewsData(query, before, curr_date) news_results = getNewsData(query, before, curr_date)
@ -323,18 +332,17 @@ def get_reddit_global_news(
str: A formatted dataframe containing the latest news articles posts on reddit and meta information in these columns: "created_utc", "id", "title", "selftext", "score", "num_comments", "url" str: A formatted dataframe containing the latest news articles posts on reddit and meta information in these columns: "created_utc", "id", "title", "selftext", "score", "num_comments", "url"
""" """
start_date = datetime.strptime(start_date, "%Y-%m-%d") before, start_date_str = parse_date_range(start_date, look_back_days)
before = start_date - relativedelta(days=look_back_days) start_date_dt = datetime.strptime(start_date_str, "%Y-%m-%d")
before = before.strftime("%Y-%m-%d")
posts = [] posts = []
# iterate from start_date to end_date # iterate from start_date to end_date
curr_date = datetime.strptime(before, "%Y-%m-%d") curr_date = datetime.strptime(before, "%Y-%m-%d")
total_iterations = (start_date - curr_date).days + 1 total_iterations = (start_date_dt - curr_date).days + 1
pbar = tqdm(desc=f"Getting Global News on {start_date}", total=total_iterations) pbar = tqdm(desc=f"Getting Global News on {start_date_dt}", total=total_iterations)
while curr_date <= start_date: while curr_date <= start_date_dt:
curr_date_str = curr_date.strftime("%Y-%m-%d") curr_date_str = curr_date.strftime("%Y-%m-%d")
fetch_result = fetch_top_from_category( fetch_result = fetch_top_from_category(
"global_news", "global_news",
@ -377,21 +385,20 @@ def get_reddit_company_news(
str: A formatted dataframe containing the latest news articles posts on reddit and meta information in these columns: "created_utc", "id", "title", "selftext", "score", "num_comments", "url" str: A formatted dataframe containing the latest news articles posts on reddit and meta information in these columns: "created_utc", "id", "title", "selftext", "score", "num_comments", "url"
""" """
start_date = datetime.strptime(start_date, "%Y-%m-%d") before, start_date_str = parse_date_range(start_date, look_back_days)
before = start_date - relativedelta(days=look_back_days) start_date_dt = datetime.strptime(start_date_str, "%Y-%m-%d")
before = before.strftime("%Y-%m-%d")
posts = [] posts = []
# iterate from start_date to end_date # iterate from start_date to end_date
curr_date = datetime.strptime(before, "%Y-%m-%d") curr_date = datetime.strptime(before, "%Y-%m-%d")
total_iterations = (start_date - curr_date).days + 1 total_iterations = (start_date_dt - curr_date).days + 1
pbar = tqdm( pbar = tqdm(
desc=f"Getting Company News for {ticker} on {start_date}", desc=f"Getting Company News for {ticker} on {start_date_dt}",
total=total_iterations, total=total_iterations,
) )
while curr_date <= start_date: while curr_date <= start_date_dt:
curr_date_str = curr_date.strftime("%Y-%m-%d") curr_date_str = curr_date.strftime("%Y-%m-%d")
fetch_result = fetch_top_from_category( fetch_result = fetch_top_from_category(
"company_news", "company_news",
@ -509,8 +516,9 @@ def get_stock_stats_indicators_window(
) )
end_date = curr_date end_date = curr_date
curr_date = datetime.strptime(curr_date, "%Y-%m-%d") before_str, _ = parse_date_range(curr_date, look_back_days)
before = curr_date - relativedelta(days=look_back_days) before_dt = datetime.strptime(before_str, "%Y-%m-%d")
curr_date_dt = datetime.strptime(curr_date, "%Y-%m-%d")
if not online: if not online:
# read from YFin data # read from YFin data
@ -524,30 +532,30 @@ def get_stock_stats_indicators_window(
dates_in_df = data["Date"].astype(str).str[:10] dates_in_df = data["Date"].astype(str).str[:10]
ind_string = "" ind_string = ""
while curr_date >= before: while curr_date_dt >= before_dt:
# only do the trading dates # only do the trading dates
if curr_date.strftime("%Y-%m-%d") in dates_in_df.values: if curr_date_dt.strftime("%Y-%m-%d") in dates_in_df.values:
indicator_value = get_stockstats_indicator( indicator_value = get_stockstats_indicator(
symbol, indicator, curr_date.strftime("%Y-%m-%d"), online symbol, indicator, curr_date_dt.strftime("%Y-%m-%d"), online
) )
ind_string += f"{curr_date.strftime('%Y-%m-%d')}: {indicator_value}\n" ind_string += f"{curr_date_dt.strftime('%Y-%m-%d')}: {indicator_value}\n"
curr_date = curr_date - relativedelta(days=1) curr_date_dt = curr_date_dt - relativedelta(days=1)
else: else:
# online gathering # online gathering
ind_string = "" ind_string = ""
while curr_date >= before: while curr_date_dt >= before_dt:
indicator_value = get_stockstats_indicator( indicator_value = get_stockstats_indicator(
symbol, indicator, curr_date.strftime("%Y-%m-%d"), online symbol, indicator, curr_date_dt.strftime("%Y-%m-%d"), online
) )
ind_string += f"{curr_date.strftime('%Y-%m-%d')}: {indicator_value}\n" ind_string += f"{curr_date_dt.strftime('%Y-%m-%d')}: {indicator_value}\n"
curr_date = curr_date - relativedelta(days=1) curr_date_dt = curr_date_dt - relativedelta(days=1)
result_str = ( result_str = (
f"## {indicator} values from {before.strftime('%Y-%m-%d')} to {end_date}:\n\n" f"## {indicator} values from {before_dt.strftime('%Y-%m-%d')} to {end_date}:\n\n"
+ ind_string + ind_string
+ "\n\n" + "\n\n"
+ best_ind_params.get(indicator, "No description available.") + best_ind_params.get(indicator, "No description available.")
@ -565,20 +573,20 @@ def get_stockstats_indicator(
online: Annotated[bool, "to fetch data online or offline"], online: Annotated[bool, "to fetch data online or offline"],
) -> str: ) -> str:
curr_date = datetime.strptime(curr_date, "%Y-%m-%d") curr_date_dt = datetime.strptime(curr_date, "%Y-%m-%d")
curr_date = curr_date.strftime("%Y-%m-%d") curr_date_str = curr_date_dt.strftime("%Y-%m-%d")
try: try:
indicator_value = StockstatsUtils.get_stock_stats( indicator_value = StockstatsUtils.get_stock_stats(
symbol, symbol,
indicator, indicator,
curr_date, curr_date_str,
os.path.join(DATA_DIR, "market_data", "price_data"), os.path.join(DATA_DIR, "market_data", "price_data"),
online=online, online=online,
) )
except Exception as e: except Exception as e:
print( print(
f"Error getting stockstats indicator data for indicator {indicator} on {curr_date}: {e}" f"Error getting stockstats indicator data for indicator {indicator} on {curr_date_str}: {e}"
) )
return "" return ""
@ -591,9 +599,7 @@ def get_YFin_data_window(
look_back_days: Annotated[int, "how many days to look back"], look_back_days: Annotated[int, "how many days to look back"],
) -> str: ) -> str:
# calculate past days # calculate past days
date_obj = datetime.strptime(curr_date, "%Y-%m-%d") start_date, _ = parse_date_range(curr_date, look_back_days)
before = date_obj - relativedelta(days=look_back_days)
start_date = before.strftime("%Y-%m-%d")
# read in data # read in data
data = pd.read_csv( data = pd.read_csv(
@ -703,9 +709,13 @@ def get_YFin_data(
return filtered_data return filtered_data
# Enhanced search provider factory instance (singleton)
_search_factory = create_search_provider_factory()
def get_stock_news(ticker, curr_date): def get_stock_news(ticker, curr_date):
config = get_config() config = get_config()
search_provider = SearchProviderFactory.create_provider(config) search_provider = _search_factory.create_provider(config)
query = f"Can you search Social Media for {ticker} from 7 days before {curr_date} to {curr_date}? Make sure you only get the data posted during that period." query = f"Can you search Social Media for {ticker} from 7 days before {curr_date} to {curr_date}? Make sure you only get the data posted during that period."
return search_provider.search(query) return search_provider.search(query)
@ -713,7 +723,7 @@ def get_stock_news(ticker, curr_date):
def get_global_news(curr_date): def get_global_news(curr_date):
config = get_config() config = get_config()
search_provider = SearchProviderFactory.create_provider(config) search_provider = _search_factory.create_provider(config)
query = f"Search for global macroeconomic news and financial market updates from 7 days before {curr_date} to {curr_date}. Focus on central bank decisions, economic indicators, geopolitical events, and market-moving news that would be important for trading decisions." query = f"Search for global macroeconomic news and financial market updates from 7 days before {curr_date} to {curr_date}. Focus on central bank decisions, economic indicators, geopolitical events, and market-moving news that would be important for trading decisions."
return search_provider.search(query) return search_provider.search(query)
@ -721,7 +731,7 @@ def get_global_news(curr_date):
def get_fundamentals(ticker, curr_date): def get_fundamentals(ticker, curr_date):
config = get_config() config = get_config()
search_provider = SearchProviderFactory.create_provider(config) search_provider = _search_factory.create_provider(config)
query = f"Search for fundamental analysis data and financial metrics for {ticker} stock from the month before {curr_date} to the month of {curr_date}. Look for earnings reports, financial ratios like PE, PS, cash flow, revenue growth, analyst ratings, and any fundamental analysis discussions. Please present key metrics in a structured format." query = f"Search for fundamental analysis data and financial metrics for {ticker} stock from the month before {curr_date} to the month of {curr_date}. Look for earnings reports, financial ratios like PE, PS, cash flow, revenue growth, analyst ratings, and any fundamental analysis discussions. Please present key metrics in a structured format."
return search_provider.search(query) return search_provider.search(query)

2
tradingagents/dataflows/search_provider.py
View File

@ -7,7 +7,7 @@ from abc import ABC, abstractmethod
class SearchProvider(ABC): class SearchProvider(ABC):
@abstractmethod @abstractmethod
def search(self, query: str, ticker: str, curr_date: str) -> str: def search(self, query: str) -> str:
pass pass

136
tradingagents/dataflows/search_provider_factory.py
View File

@ -1,47 +1,133 @@
from .search_provider import ( from .search_provider import SearchProvider
SearchProvider,
GoogleSearchProvider,
OpenAISearchProvider
)
import hashlib import hashlib
import json import json
from typing import Dict, Callable, Any
from abc import ABC, abstractmethod
class SearchProviderFactory: class ProviderSelector(ABC):
_cache = {} # 클래스 레벨 캐시 """Abstract base class for provider selection strategies."""
@staticmethod @abstractmethod
def create_provider(config: dict[str, any]) -> SearchProvider: def select_provider_type(self, config: Dict[str, Any]) -> str:
"""Select provider type based on configuration."""
pass
class MappingBasedProviderSelector(ProviderSelector):
"""Selects provider based on URL pattern mapping table."""
def __init__(self, mappings: Dict[str, str], default_provider: str = "openai"):
self._mappings = mappings
self._default_provider = default_provider
def select_provider_type(self, config: Dict[str, Any]) -> str:
backend_url = config.get("backend_url", "")
for pattern, provider_type in self._mappings.items():
if pattern in backend_url:
return provider_type
return self._default_provider
class SearchProviderRegistry:
"""Registry for search provider creation functions."""
def __init__(self):
self._providers: Dict[str, Callable[[Dict[str, Any]], SearchProvider]] = {}
def register(self, provider_type: str, creator: Callable[[Dict[str, Any]], SearchProvider]):
"""Register a provider creator function."""
self._providers[provider_type] = creator
def create(self, provider_type: str, config: Dict[str, Any]) -> SearchProvider:
"""Create a provider instance using registered creator."""
if provider_type not in self._providers:
raise ValueError(f"Unknown provider type: {provider_type}")
return self._providers[provider_type](config)
def get_available_types(self) -> list[str]:
"""Get list of available provider types."""
return list(self._providers.keys())
class SearchProviderFactoryImpl:
"""Enhanced factory for creating SearchProvider instances with caching and extensibility."""
def __init__(self, registry: SearchProviderRegistry, selector: ProviderSelector):
self._registry = registry
self._selector = selector
self._cache: Dict[str, SearchProvider] = {}
def create_provider(self, config: Dict[str, Any]) -> SearchProvider:
""" """
Create a SearchProvider with caching to avoid creating new instances. Create a SearchProvider with caching to avoid creating new instances.
Uses config hash as cache key for efficient reuse. Uses config hash as cache key for efficient reuse.
""" """
# Create cache key from relevant config values # Create cache key from relevant config values
cache_key_data = { cache_key_data = {
"backend_url": config["backend_url"], "backend_url": config.get("backend_url", ""),
"model": config["quick_think_llm"] "model": config.get("quick_think_llm", "")
} }
cache_key = hashlib.md5(json.dumps(cache_key_data, sort_keys=True).encode()).hexdigest() cache_key = hashlib.md5(json.dumps(cache_key_data, sort_keys=True).encode()).hexdigest()
# Return cached instance if exists # Return cached instance if exists
if cache_key in SearchProviderFactory._cache: if cache_key in self._cache:
return SearchProviderFactory._cache[cache_key] return self._cache[cache_key]
# Create new instance # Select and create provider
backend_url = config["backend_url"] provider_type = self._selector.select_provider_type(config)
model = config["quick_think_llm"] provider = self._registry.create(provider_type, config)
if "generativelanguage.googleapis.com" in backend_url:
provider = GoogleSearchProvider(model)
else:
provider = OpenAISearchProvider(model, backend_url)
# Cache and return # Cache and return
SearchProviderFactory._cache[cache_key] = provider self._cache[cache_key] = provider
return provider return provider
def clear_cache(self):
"""Clear the provider cache (useful for testing or config changes)."""
self._cache.clear()
def get_available_provider_types(self) -> list[str]:
"""Get list of available provider types."""
return self._registry.get_available_types()
def create_search_provider_factory() -> SearchProviderFactoryImpl:
"""Create a configured SearchProviderFactory with default providers."""
registry = SearchProviderRegistry()
# Register default providers
def create_google_provider(config: Dict[str, Any]) -> SearchProvider:
from .search_provider import GoogleSearchProvider
return GoogleSearchProvider(config["quick_think_llm"])
def create_openai_provider(config: Dict[str, Any]) -> SearchProvider:
from .search_provider import OpenAISearchProvider
return OpenAISearchProvider(config["quick_think_llm"], config["backend_url"])
registry.register("google", create_google_provider)
registry.register("openai", create_openai_provider)
# Create URL pattern mappings (easily extensible)
url_mappings = {
"generativelanguage.googleapis.com": "google",
"api.openai.com": "openai",
}
selector = MappingBasedProviderSelector(url_mappings, default_provider="openai")
return SearchProviderFactoryImpl(registry, selector)
# Backward compatibility - singleton instance
_default_factory = create_search_provider_factory()
class SearchProviderFactory:
"""Backward compatibility wrapper for the old static factory."""
@staticmethod
def create_provider(config: Dict[str, Any]) -> SearchProvider:
return _default_factory.create_provider(config)
@staticmethod @staticmethod
def clear_cache(): def clear_cache():
"""Clear the provider cache (useful for testing or config changes).""" _default_factory.clear_cache()
SearchProviderFactory._cache.clear()

2
tradingagents/default_config.py
View File

@ -3,7 +3,7 @@ import os
DEFAULT_CONFIG = { DEFAULT_CONFIG = {
"project_dir": os.path.abspath(os.path.join(os.path.dirname(__file__), ".")), "project_dir": os.path.abspath(os.path.join(os.path.dirname(__file__), ".")),
"results_dir": os.getenv("TRADINGAGENTS_RESULTS_DIR", "./results"), "results_dir": os.getenv("TRADINGAGENTS_RESULTS_DIR", "./results"),
"data_dir": "/Users/yluo/Documents/Code/ScAI/FR1-data", "data_dir": os.getenv("TRADINGAGENTS_DATA_DIR", "./data"),
"data_cache_dir": os.path.join( "data_cache_dir": os.path.join(
os.path.abspath(os.path.join(os.path.dirname(__file__), ".")), os.path.abspath(os.path.join(os.path.dirname(__file__), ".")),
"dataflows/data_cache", "dataflows/data_cache",

16
tradingagents/graph/trading_graph.py
View File

@ -127,7 +127,7 @@ class TradingAgentsGraph:
# online tools # online tools
self.toolkit.get_stock_news, self.toolkit.get_stock_news,
# offline tools # offline tools
self.toolkit.get_reddit_stock_info, # self.toolkit.get_reddit_stock_info,
] ]
), ),
"news": ToolNode( "news": ToolNode(
@ -136,8 +136,8 @@ class TradingAgentsGraph:
self.toolkit.get_global_news, self.toolkit.get_global_news,
self.toolkit.get_google_news, self.toolkit.get_google_news,
# offline tools # offline tools
self.toolkit.get_finnhub_news, # self.toolkit.get_finnhub_news,
self.toolkit.get_reddit_news, # self.toolkit.get_reddit_news,
] ]
), ),
"fundamentals": ToolNode( "fundamentals": ToolNode(
@ -145,11 +145,11 @@ class TradingAgentsGraph:
# online tools # online tools
self.toolkit.get_fundamentals, self.toolkit.get_fundamentals,
# offline tools # offline tools
self.toolkit.get_finnhub_company_insider_sentiment, # self.toolkit.get_finnhub_company_insider_sentiment,
self.toolkit.get_finnhub_company_insider_transactions, # self.toolkit.get_finnhub_company_insider_transactions,
self.toolkit.get_simfin_balance_sheet, # self.toolkit.get_simfin_balance_sheet,
self.toolkit.get_simfin_cashflow, # self.toolkit.get_simfin_cashflow,
self.toolkit.get_simfin_income_stmt, # self.toolkit.get_simfin_income_stmt,
] ]
), ),
} }

10810
uv.lock
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