Initial commit: Add TradingAgents with seeking_alpha_analyst

.env.example

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+ALPHA_VANTAGE_API_KEY=alpha_vantage_api_key_placeholder
+OPENAI_API_KEY=openai_api_key_placeholder

.gitignore

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+.venv
+results
+env/
+__pycache__/
+.DS_Store
+*.csv
+src/
+eval_results/
+eval_data/
+*.egg-info/
+.env

.python-version

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+3.10

GIT_REMOTE_SETUP.md

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+# 连接远程 Git 仓库指南
+
+## 📋 快速步骤
+
+### 情况 1: 本地已有代码，需要连接到远程仓库
+
+```bash
+# 1. 初始化 git 仓库（如果还没有）
+git init
+
+# 2. 添加远程仓库
+git remote add origin <你的远程仓库URL>
+
+# 3. 添加所有文件
+git add .
+
+# 4. 创建初始提交
+git commit -m "Initial commit: Add TradingAgents with seeking_alpha_analyst"
+
+# 5. 推送到远程（如果是新仓库）
+git branch -M main  # 将分支重命名为 main（如果远程使用 main）
+git push -u origin main
+
+# 或者如果远程使用 master
+git push -u origin master
+```
+
+### 情况 2: 远程仓库已有代码，需要克隆并连接
+
+```bash
+# 1. 克隆远程仓库
+git clone <你的远程仓库URL>
+
+# 2. 进入目录
+cd <仓库名>
+
+# 3. 查看远程仓库
+git remote -v
+```
+
+---
+
+## 🔗 远程仓库 URL 格式
+
+### HTTPS 方式（推荐新手）
+```bash
+git remote add origin https://github.com/username/repo-name.git
+```
+
+### SSH 方式（需要配置 SSH key）
+```bash
+git remote add origin git@github.com:username/repo-name.git
+```
+
+---
+
+## 📝 完整示例
+
+假设你的远程仓库是 `https://github.com/yourusername/TradingAgents.git`：
+
+```bash
+# 1. 初始化
+git init
+
+# 2. 添加远程
+git remote add origin https://github.com/yourusername/TradingAgents.git
+
+# 3. 检查远程配置
+git remote -v
+# 应该显示:
+# origin  https://github.com/yourusername/TradingAgents.git (fetch)
+# origin  https://github.com/yourusername/TradingAgents.git (push)
+
+# 4. 添加文件
+git add .
+
+# 5. 提交
+git commit -m "Initial commit: TradingAgents with seeking_alpha_analyst"
+
+# 6. 推送到远程
+git branch -M main
+git push -u origin main
+```
+
+---
+
+## 🔧 常用命令
+
+### 查看远程仓库
+```bash
+git remote -v
+```
+
+### 修改远程仓库 URL
+```bash
+git remote set-url origin <新的URL>
+```
+
+### 删除远程仓库
+```bash
+git remote remove origin
+```
+
+### 重命名远程仓库
+```bash
+git remote rename origin upstream
+```
+
+### 拉取远程更新
+```bash
+git pull origin main
+```
+
+### 推送本地更新
+```bash
+git push origin main
+```
+
+---
+
+## ⚠️ 常见问题
+
+### 问题 1: 远程仓库已存在内容
+如果远程仓库已经有代码，需要先拉取：
+
+```bash
+# 拉取远程代码
+git pull origin main --allow-unrelated-histories
+
+# 解决可能的冲突后
+git add .
+git commit -m "Merge remote and local"
+git push origin main
+```
+
+### 问题 2: 认证失败
+如果使用 HTTPS，可能需要配置 token：
+
+1. GitHub: Settings → Developer settings → Personal access tokens
+2. 生成 token 后，使用 token 作为密码
+
+或者配置 SSH key（推荐）：
+```bash
+# 生成 SSH key
+ssh-keygen -t ed25519 -C "your_email@example.com"
+
+# 添加到 ssh-agent
+eval "$(ssh-agent -s)"
+ssh-add ~/.ssh/id_ed25519
+
+# 复制公钥到 GitHub/GitLab
+cat ~/.ssh/id_ed25519.pub
+```
+
+### 问题 3: 分支名称不匹配
+```bash
+# 查看当前分支
+git branch
+
+# 重命名分支
+git branch -M main  # 重命名为 main
+# 或
+git branch -M master  # 重命名为 master
+```
+
+---
+
+## 🚀 使用提供的脚本
+
+我已经创建了 `connect_remote_git.sh` 脚本，你可以这样使用：
+
+```bash
+# 使用脚本（需要提供远程 URL）
+./connect_remote_git.sh https://github.com/username/repo.git
+
+# 或者指定远程名称
+./connect_remote_git.sh https://github.com/username/repo.git origin
+```
+
+---
+
+## 📌 下一步
+
+连接成功后，你可以：
+
+1. **继续开发**: 正常使用 `git add`, `git commit`, `git push`
+2. **创建分支**: `git checkout -b feature/new-feature`
+3. **协作**: 其他人可以 `git clone` 你的仓库
+
+---
+
+## 💡 提示
+
+- 首次推送使用 `-u` 参数设置上游分支: `git push -u origin main`
+- 之后可以直接使用 `git push` 和 `git pull`
+- 建议定期提交和推送，避免丢失代码
+

INPUT_OUTPUT_GUIDE.md

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+# TradingAgents 输入输出检查指南
+
+## 📍 快速定位
+
+### 1. **状态定义位置** (输入/输出的数据结构)
+**文件**: `tradingagents/agents/utils/agent_states.py`
+
+这是所有输入输出的数据结构定义，包含三个阶段的所有字段：
+
+```python
+class AgentState:
+    # 输入
+    company_of_interest: str      # 公司名称
+    trade_date: str               # 交易日期
+    messages: List[Message]       # 消息历史
+    
+    # 阶段1输出: 分析师报告
+    market_report: str           # 市场分析师报告
+    sentiment_report: str         # 社交媒体分析师报告
+    news_report: str             # 新闻分析师报告
+    fundamentals_report: str     # 基本面分析师报告
+    
+    # 阶段2输出: 投资辩论
+    investment_debate_state: InvestDebateState  # 包含 bull_history, bear_history, judge_decision
+    investment_plan: str          # 研究经理的投资计划
+    trader_investment_plan: str  # 交易员的投资计划
+    
+    # 阶段3输出: 风险分析
+    risk_debate_state: RiskDebateState  # 包含 risky/safe/neutral 历史
+    final_trade_decision: str    # 最终交易决策
+```
+
+---
+
+## 🔍 各阶段输入输出检查位置
+
+### **阶段 1: 分析师阶段 (Analyst Phase)**
+
+#### 输入检查:
+- **初始状态**: `tradingagents/graph/propagation.py` 第 18-42 行
+  ```python
+  init_state = {
+      "company_of_interest": company_name,
+      "trade_date": trade_date,
+      "market_report": "",      # 初始为空
+      "sentiment_report": "",    # 初始为空
+      "news_report": "",         # 初始为空
+      "fundamentals_report": "", # 初始为空
+      ...
+  }
+  ```
+
+#### 输出检查:
+- **运行时 (Debug模式)**: `tradingagents/graph/trading_graph.py` 第 171-179 行
+  ```python
+  for chunk in self.graph.stream(init_agent_state, **args):
+      # chunk 包含每个节点的输出
+      if "market_report" in chunk:
+          print(chunk["market_report"])
+  ```
+
+- **最终状态**: `tradingagents/graph/trading_graph.py` 第 184 行
+  ```python
+  final_state = self.graph.invoke(init_agent_state, **args)
+  # 访问各报告:
+  final_state["market_report"]
+  final_state["sentiment_report"]
+  final_state["news_report"]
+  final_state["fundamentals_report"]
+  ```
+
+- **日志文件**: `tradingagents/graph/trading_graph.py` 第 195-225 行
+  - 保存位置: `eval_results/{ticker}/TradingAgentsStrategy_logs/full_states_log_{date}.json`
+  - 包含所有报告字段
+
+- **Agent实现**: 查看各分析师如何写入报告
+  - Market: `tradingagents/agents/analysts/market_analyst.py` 第 80-83 行
+  - Social: `tradingagents/agents/analysts/social_media_analyst.py`
+  - News: `tradingagents/agents/analysts/news_analyst.py`
+  - Fundamentals: `tradingagents/agents/analysts/fundamentals_analyst.py`
+
+---
+
+### **阶段 2: 研究辩论阶段 (Research Debate Phase)**
+
+#### 输入检查:
+- **从阶段1接收**: `final_state["market_report"]`, `final_state["sentiment_report"]`, etc.
+- **初始辩论状态**: `tradingagents/graph/propagation.py` 第 26-28 行
+  ```python
+  "investment_debate_state": {
+      "history": "",
+      "current_response": "",
+      "count": 0
+  }
+  ```
+
+#### 输出检查:
+- **运行时**: 在 `graph.stream()` 的 chunk 中检查
+  ```python
+  if "investment_debate_state" in chunk:
+      debate_state = chunk["investment_debate_state"]
+      print(f"Bull history: {debate_state['bull_history']}")
+      print(f"Bear history: {debate_state['bear_history']}")
+      print(f"Judge decision: {debate_state['judge_decision']}")
+  ```
+
+- **最终状态**:
+  ```python
+  final_state["investment_debate_state"]["bull_history"]
+  final_state["investment_debate_state"]["bear_history"]
+  final_state["investment_debate_state"]["judge_decision"]
+  final_state["investment_plan"]              # Research Manager 的输出
+  final_state["trader_investment_plan"]      # Trader 的输出
+  ```
+
+- **日志文件**: `tradingagents/graph/trading_graph.py` 第 204-214 行
+  ```json
+  {
+    "investment_debate_state": {
+      "bull_history": "...",
+      "bear_history": "...",
+      "history": "...",
+      "current_response": "...",
+      "judge_decision": "..."
+    },
+    "trader_investment_decision": "..."
+  }
+  ```
+
+- **Agent实现**:
+  - Bull Researcher: `tradingagents/agents/researchers/bull_researcher.py`
+  - Bear Researcher: `tradingagents/agents/researchers/bear_researcher.py`
+  - Research Manager: `tradingagents/agents/managers/research_manager.py`
+  - Trader: `tradingagents/agents/trader/trader.py`
+
+---
+
+### **阶段 3: 风险分析阶段 (Risk Analysis Phase)**
+
+#### 输入检查:
+- **从阶段2接收**: `final_state["trader_investment_plan"]`
+- **初始风险状态**: `tradingagents/graph/propagation.py` 第 29-37 行
+  ```python
+  "risk_debate_state": {
+      "history": "",
+      "current_risky_response": "",
+      "current_safe_response": "",
+      "current_neutral_response": "",
+      "count": 0
+  }
+  ```
+
+#### 输出检查:
+- **运行时**: 在 `graph.stream()` 的 chunk 中检查
+  ```python
+  if "risk_debate_state" in chunk:
+      risk_state = chunk["risk_debate_state"]
+      print(f"Risky history: {risk_state['risky_history']}")
+      print(f"Safe history: {risk_state['safe_history']}")
+      print(f"Neutral history: {risk_state['neutral_history']}")
+      print(f"Judge decision: {risk_state['judge_decision']}")
+  ```
+
+- **最终状态**:
+  ```python
+  final_state["risk_debate_state"]["risky_history"]
+  final_state["risk_debate_state"]["safe_history"]
+  final_state["risk_debate_state"]["neutral_history"]
+  final_state["risk_debate_state"]["judge_decision"]
+  final_state["final_trade_decision"]  # 最终交易决策
+  ```
+
+- **日志文件**: `tradingagents/graph/trading_graph.py` 第 216-222 行
+  ```json
+  {
+    "risk_debate_state": {
+      "risky_history": "...",
+      "safe_history": "...",
+      "neutral_history": "...",
+      "history": "...",
+      "judge_decision": "..."
+    },
+    "final_trade_decision": "..."
+  }
+  ```
+
+- **Agent实现**:
+  - Risky Analyst: `tradingagents/agents/risk_mgmt/aggresive_debator.py`
+  - Safe Analyst: `tradingagents/agents/risk_mgmt/conservative_debator.py`
+  - Neutral Analyst: `tradingagents/agents/risk_mgmt/neutral_debator.py`
+  - Risk Manager: `tradingagents/agents/managers/risk_manager.py`
+
+---
+
+## 🛠️ 实际使用示例
+
+### 方法 1: 在代码中检查 (推荐用于调试)
+
+```python
+from tradingagents.graph.trading_graph import TradingAgentsGraph
+from tradingagents.default_config import DEFAULT_CONFIG
+
+ta = TradingAgentsGraph(debug=True, config=DEFAULT_CONFIG.copy())
+
+# 运行分析
+final_state, decision = ta.propagate("NVDA", "2024-05-10")
+
+# 检查阶段1输出
+print("=== 阶段1: 分析师报告 ===")
+print(f"Market Report: {final_state['market_report']}")
+print(f"Sentiment Report: {final_state['sentiment_report']}")
+print(f"News Report: {final_state['news_report']}")
+print(f"Fundamentals Report: {final_state['fundamentals_report']}")
+
+# 检查阶段2输出
+print("\n=== 阶段2: 投资辩论 ===")
+debate = final_state['investment_debate_state']
+print(f"Bull History: {debate['bull_history']}")
+print(f"Bear History: {debate['bear_history']}")
+print(f"Judge Decision: {debate['judge_decision']}")
+print(f"Investment Plan: {final_state['investment_plan']}")
+print(f"Trader Plan: {final_state['trader_investment_plan']}")
+
+# 检查阶段3输出
+print("\n=== 阶段3: 风险分析 ===")
+risk = final_state['risk_debate_state']
+print(f"Risky History: {risk['risky_history']}")
+print(f"Safe History: {risk['safe_history']}")
+print(f"Neutral History: {risk['neutral_history']}")
+print(f"Risk Judge Decision: {risk['judge_decision']}")
+print(f"Final Trade Decision: {final_state['final_trade_decision']}")
+```
+
+### 方法 2: 查看日志文件
+
+运行后，检查 JSON 日志文件:
+```bash
+cat eval_results/NVDA/TradingAgentsStrategy_logs/full_states_log_2024-05-10.json
+```
+
+### 方法 3: 使用 Debug 模式实时查看
+
+在 `trading_graph.py` 第 171-179 行，debug 模式会打印每个节点的输出:
+```python
+for chunk in self.graph.stream(init_agent_state, **args):
+    if len(chunk["messages"]) == 0:
+        pass
+    else:
+        chunk["messages"][-1].pretty_print()  # 打印消息
+        # 可以在这里检查 chunk 中的其他字段
+```
+
+### 方法 4: 使用 CLI 界面
+
+运行 CLI 可以看到实时输出:
+```bash
+python -m cli.main
+```
+
+CLI 会显示每个阶段的进度和输出 (`cli/main.py` 第 888-923 行处理各阶段的输出显示)
+
+---
+
+## 📊 数据流总结
+
+```
+输入 → 阶段1 → 阶段2 → 阶段3 → 输出
+     ↓         ↓         ↓
+  reports   debate   risk    final_decision
+```
+
+- **输入**: `propagation.py` 的 `create_initial_state()`
+- **阶段1输出**: `AgentState` 中的 `*_report` 字段
+- **阶段2输出**: `AgentState` 中的 `investment_debate_state` 和 `trader_investment_plan`
+- **阶段3输出**: `AgentState` 中的 `risk_debate_state` 和 `final_trade_decision`
+- **最终日志**: `eval_results/{ticker}/TradingAgentsStrategy_logs/full_states_log_{date}.json`
+

LICENSE

@@ -0,0 +1,201 @@
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README.md

@@ -0,0 +1,225 @@
+<p align="center">
+  <img src="assets/TauricResearch.png" style="width: 60%; height: auto;">
+</p>
+
+<div align="center" style="line-height: 1;">
+  <a href="https://arxiv.org/abs/2412.20138" target="_blank"><img alt="arXiv" src="https://img.shields.io/badge/arXiv-2412.20138-B31B1B?logo=arxiv"/></a>
+  <a href="https://discord.com/invite/hk9PGKShPK" target="_blank"><img alt="Discord" src="https://img.shields.io/badge/Discord-TradingResearch-7289da?logo=discord&logoColor=white&color=7289da"/></a>
+  <a href="./assets/wechat.png" target="_blank"><img alt="WeChat" src="https://img.shields.io/badge/WeChat-TauricResearch-brightgreen?logo=wechat&logoColor=white"/></a>
+  <a href="https://x.com/TauricResearch" target="_blank"><img alt="X Follow" src="https://img.shields.io/badge/X-TauricResearch-white?logo=x&logoColor=white"/></a>
+  <br>
+  <a href="https://github.com/TauricResearch/" target="_blank"><img alt="Community" src="https://img.shields.io/badge/Join_GitHub_Community-TauricResearch-14C290?logo=discourse"/></a>
+</div>
+
+<div align="center">
+  <!-- Keep these links. Translations will automatically update with the README. -->
+  <a href="https://www.readme-i18n.com/TauricResearch/TradingAgents?lang=de">Deutsch</a> | 
+  <a href="https://www.readme-i18n.com/TauricResearch/TradingAgents?lang=es">Español</a> | 
+  <a href="https://www.readme-i18n.com/TauricResearch/TradingAgents?lang=fr">français</a> | 
+  <a href="https://www.readme-i18n.com/TauricResearch/TradingAgents?lang=ja">日本語</a> | 
+  <a href="https://www.readme-i18n.com/TauricResearch/TradingAgents?lang=ko">한국어</a> | 
+  <a href="https://www.readme-i18n.com/TauricResearch/TradingAgents?lang=pt">Português</a> | 
+  <a href="https://www.readme-i18n.com/TauricResearch/TradingAgents?lang=ru">Русский</a> | 
+  <a href="https://www.readme-i18n.com/TauricResearch/TradingAgents?lang=zh">中文</a>
+</div>
+
+---
+
+# 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.
+>
+> So we decided to fully open-source the framework. Looking forward to building impactful projects with you!
+
+<div align="center">
+<a href="https://www.star-history.com/#TauricResearch/TradingAgents&Date">
+ <picture>
+   <source media="(prefers-color-scheme: dark)" srcset="https://api.star-history.com/svg?repos=TauricResearch/TradingAgents&type=Date&theme=dark" />
+   <source media="(prefers-color-scheme: light)" srcset="https://api.star-history.com/svg?repos=TauricResearch/TradingAgents&type=Date" />
+   <img alt="TradingAgents Star History" src="https://api.star-history.com/svg?repos=TauricResearch/TradingAgents&type=Date" style="width: 80%; height: auto;" />
+ </picture>
+</a>
+</div>
+
+<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)
+
+</div>
+
+## 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.
+
+<p align="center">
+  <img src="assets/schema.png" style="width: 100%; height: auto;">
+</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/)
+
+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
+- 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.
+- 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.
+
+<p align="center">
+  <img src="assets/analyst.png" width="100%" style="display: inline-block; margin: 0 2%;">
+</p>
+
+### 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.
+
+<p align="center">
+  <img src="assets/researcher.png" width="70%" style="display: inline-block; margin: 0 2%;">
+</p>
+
+### 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.
+
+<p align="center">
+  <img src="assets/trader.png" width="70%" style="display: inline-block; margin: 0 2%;">
+</p>
+
+### 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.
+- The Portfolio Manager approves/rejects the transaction proposal. If approved, the order will be sent to the simulated exchange and executed.
+
+<p align="center">
+  <img src="assets/risk.png" width="70%" style="display: inline-block; margin: 0 2%;">
+</p>
+
+## Installation and CLI
+
+### Installation
+
+Clone TradingAgents:
+```bash
+git clone https://github.com/TauricResearch/TradingAgents.git
+cd TradingAgents
+```
+
+Create a virtual environment in any of your favorite environment managers:
+```bash
+conda create -n tradingagents python=3.13
+conda activate tradingagents
+```
+
+Install dependencies:
+```bash
+pip install -r requirements.txt
+```
+
+### Required APIs
+
+You will need the OpenAI API for all the agents, and [Alpha Vantage API](https://www.alphavantage.co/support/#api-key) for fundamental and news data (default configuration).
+
+```bash
+export OPENAI_API_KEY=$YOUR_OPENAI_API_KEY
+export ALPHA_VANTAGE_API_KEY=$YOUR_ALPHA_VANTAGE_API_KEY
+```
+
+Alternatively, you can create a `.env` file in the project root with your API keys (see `.env.example` for reference):
+```bash
+cp .env.example .env
+# Edit .env with your actual API keys
+```
+
+**Note:** We are happy to partner with Alpha Vantage to provide robust API support for TradingAgents. You can get a free AlphaVantage API [here](https://www.alphavantage.co/support/#api-key), TradingAgents-sourced requests also have increased rate limits to 60 requests per minute with no daily limits. Typically the quota is sufficient for performing complex tasks with TradingAgents thanks to Alpha Vantage’s open-source support program. If you prefer to use OpenAI for these data sources instead, you can modify the data vendor settings in `tradingagents/default_config.py`.
+
+### CLI Usage
+
+You can also try out the CLI directly by running:
+```bash
+python -m cli.main
+```
+You will see a screen where you can select your desired tickers, date, LLMs, research depth, etc.
+
+<p align="center">
+  <img src="assets/cli/cli_init.png" width="100%" style="display: inline-block; margin: 0 2%;">
+</p>
+
+An interface will appear showing results as they load, letting you track the agent's progress as it runs.
+
+<p align="center">
+  <img src="assets/cli/cli_news.png" width="100%" style="display: inline-block; margin: 0 2%;">
+</p>
+
+<p align="center">
+  <img src="assets/cli/cli_transaction.png" width="100%" style="display: inline-block; margin: 0 2%;">
+</p>
+
+## TradingAgents Package
+
+### 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.
+
+### 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:
+
+```python
+from tradingagents.graph.trading_graph import TradingAgentsGraph
+from tradingagents.default_config import DEFAULT_CONFIG
+
+ta = TradingAgentsGraph(debug=True, config=DEFAULT_CONFIG.copy())
+
+# forward propagate
+_, decision = ta.propagate("NVDA", "2024-05-10")
+print(decision)
+```
+
+You can also adjust the default configuration to set your own choice of LLMs, debate rounds, etc.
+
+```python
+from tradingagents.graph.trading_graph import TradingAgentsGraph
+from tradingagents.default_config import DEFAULT_CONFIG
+
+# Create a custom config
+config = DEFAULT_CONFIG.copy()
+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["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)
+```
+
+> The default configuration uses yfinance for stock price and technical data, and Alpha Vantage for fundamental and news data. For production use or if you encounter rate limits, consider upgrading to [Alpha Vantage Premium](https://www.alphavantage.co/premium/) for more stable and reliable data access. For offline experimentation, there's a local data vendor option that uses our **Tauric TradingDB**, a curated dataset for backtesting, though this is still in development. We're currently refining this dataset and plan to release it soon alongside our upcoming projects. Stay tuned!
+
+You can view the full list of configurations in `tradingagents/default_config.py`.
+
+## 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/).
+
+## Citation
+
+Please reference our work if you find *TradingAgents* provides you with some help :)
+
+```
+@misc{xiao2025tradingagentsmultiagentsllmfinancial,
+      title={TradingAgents: Multi-Agents LLM Financial Trading Framework}, 
+      author={Yijia Xiao and Edward Sun and Di Luo and Wei Wang},
+      year={2025},
+      eprint={2412.20138},
+      archivePrefix={arXiv},
+      primaryClass={q-fin.TR},
+      url={https://arxiv.org/abs/2412.20138}, 
+}
+```

check_phase_outputs.py

@@ -0,0 +1 @@
+

cli/models.py

@@ -0,0 +1,10 @@
+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"

cli/static/welcome.txt

@@ -0,0 +1,7 @@
+
+  ______               ___             ___                    __      
+ /_  __/________ _____/ (_)___  ____ _/   | ____ ____  ____  / /______
+  / / / ___/ __ `/ __  / / __ \/ __ `/ /| |/ __ `/ _ \/ __ \/ __/ ___/
+ / / / /  / /_/ / /_/ / / / / / /_/ / ___ / /_/ /  __/ / / / /_(__  ) 
+/_/ /_/   \__,_/\__,_/_/_/ /_/\__, /_/  |_\__, /\___/_/ /_/\__/____/  
+                             /____/      /____/                       

cli/utils.py

@@ -0,0 +1,276 @@
+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"),
+        ],
+        "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"),
+        ]
+    }
+
+    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"),
+        ]
+    }
+    
+    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 = [
+        ("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

connect_remote_git.sh

@@ -0,0 +1,52 @@
+#!/bin/bash
+# 连接远程 Git 仓库的脚本
+
+# 步骤 1: 初始化 git 仓库（如果还没有）
+if [ ! -d .git ]; then
+    echo "初始化 git 仓库..."
+    git init
+    echo "✅ Git 仓库已初始化"
+fi
+
+# 步骤 2: 添加远程仓库
+# 请将 YOUR_REMOTE_URL 替换为你的实际远程仓库 URL
+# 例如: https://github.com/username/repo.git 或 git@github.com:username/repo.git
+
+REMOTE_URL="${1:-YOUR_REMOTE_URL}"
+REMOTE_NAME="${2:-origin}"
+
+if [ "$REMOTE_URL" = "YOUR_REMOTE_URL" ]; then
+    echo "⚠️  请提供远程仓库 URL"
+    echo ""
+    echo "使用方法:"
+    echo "  ./connect_remote_git.sh <remote_url> [remote_name]"
+    echo ""
+    echo "示例:"
+    echo "  ./connect_remote_git.sh https://github.com/username/repo.git"
+    echo "  ./connect_remote_git.sh git@github.com:username/repo.git origin"
+    echo ""
+    echo "或者手动执行:"
+    echo "  git remote add origin <your_remote_url>"
+    exit 1
+fi
+
+# 检查是否已有远程仓库
+if git remote | grep -q "^${REMOTE_NAME}$"; then
+    echo "远程仓库 '${REMOTE_NAME}' 已存在，更新 URL..."
+    git remote set-url ${REMOTE_NAME} ${REMOTE_URL}
+else
+    echo "添加远程仓库 '${REMOTE_NAME}'..."
+    git remote add ${REMOTE_NAME} ${REMOTE_URL}
+fi
+
+echo "✅ 远程仓库已添加/更新"
+echo ""
+echo "当前远程仓库:"
+git remote -v
+
+echo ""
+echo "下一步操作:"
+echo "1. 添加文件: git add ."
+echo "2. 提交: git commit -m 'Initial commit'"
+echo "3. 推送到远程: git push -u origin main  (或 git push -u origin master)"
+

main.py

@@ -0,0 +1,31 @@
+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

pyproject.toml

@@ -0,0 +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",
+    "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",
+]

requirements.txt

@@ -0,0 +1,26 @@
+typing-extensions
+langchain-openai
+langchain-experimental
+pandas
+yfinance
+praw
+feedparser
+stockstats
+eodhd
+langgraph
+chromadb
+setuptools
+backtrader
+akshare
+tushare
+finnhub-python
+parsel
+requests
+tqdm
+pytz
+redis
+chainlit
+rich
+questionary
+langchain_anthropic
+langchain-google-genai

setup.py

@@ -0,0 +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",
+    ],
+)

test.py

@@ -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)

tradingagents/agents/__init__.py

@@ -0,0 +1,42 @@
+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 .analysts.seeking_alpha_analyst import create_seeking_alpha_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_seeking_alpha_analyst",
+    "create_social_media_analyst",
+    "create_trader",
+]

tradingagents/agents/analysts/fundamentals_analyst.py

@@ -0,0 +1,63 @@
+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

tradingagents/agents/analysts/market_analyst.py

@@ -0,0 +1,85 @@
+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

tradingagents/agents/analysts/news_analyst.py

@@ -0,0 +1,58 @@
+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

tradingagents/agents/analysts/seeking_alpha_analyst.py

@@ -0,0 +1,71 @@
+from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
+import time
+import json
+from tradingagents.agents.utils.seeking_alpha_tools import get_seeking_alpha_pdfs
+from tradingagents.dataflows.config import get_config
+
+
+def create_seeking_alpha_analyst(llm):
+    def seeking_alpha_analyst_node(state):
+        current_date = state["trade_date"]
+        ticker = state["company_of_interest"]
+        company_name = state["company_of_interest"]
+
+        tools = [
+            get_seeking_alpha_pdfs,
+        ]
+
+        system_message = (
+            "You are a research analyst specializing in analyzing Seeking Alpha reports and research documents. "
+            "Your role is to extract and analyze key insights from PDF research documents about companies. "
+            "These documents typically contain detailed analysis, financial projections, investment theses, and expert opinions. "
+            "Please read through the PDF documents carefully and write a comprehensive report that includes:\n"
+            "- Key investment theses and arguments presented\n"
+            "- Financial analysis and projections mentioned\n"
+            "- Risk factors and concerns highlighted\n"
+            "- Expert opinions and recommendations\n"
+            "- Any quantitative metrics or data points\n"
+            "- Overall sentiment and outlook\n\n"
+            "Make sure to provide detailed and nuanced analysis. Do not simply state that the information is mixed - "
+            "provide specific insights, numbers, and detailed analysis 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],
+            "seeking_alpha_report": report,
+        }
+
+    return seeking_alpha_analyst_node
+

tradingagents/agents/analysts/social_media_analyst.py

@@ -0,0 +1,59 @@
+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

tradingagents/agents/managers/research_manager.py

@@ -0,0 +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

tradingagents/agents/managers/risk_manager.py

@@ -0,0 +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

tradingagents/agents/researchers/bear_researcher.py

@@ -0,0 +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

tradingagents/agents/researchers/bull_researcher.py

@@ -0,0 +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

tradingagents/agents/risk_mgmt/aggresive_debator.py

@@ -0,0 +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

tradingagents/agents/risk_mgmt/conservative_debator.py

@@ -0,0 +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

tradingagents/agents/risk_mgmt/neutral_debator.py

@@ -0,0 +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

tradingagents/agents/trader/trader.py

@@ -0,0 +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")

tradingagents/agents/utils/agent_states.py

@@ -0,0 +1,77 @@
+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"]
+    seeking_alpha_report: Annotated[str, "Report from the Seeking Alpha Analyst"]
+
+    # 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"]

tradingagents/agents/utils/agent_utils.py

@@ -0,0 +1,42 @@
+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
+)
+from tradingagents.agents.utils.seeking_alpha_tools import (
+    get_seeking_alpha_pdfs
+)
+
+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
+
+
+        

tradingagents/agents/utils/core_stock_tools.py

@@ -0,0 +1,22 @@
+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)

tradingagents/agents/utils/fundamental_data_tools.py

@@ -0,0 +1,77 @@
+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)

tradingagents/agents/utils/memory.py

@@ -0,0 +1,113 @@
+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"
+        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)}")

tradingagents/agents/utils/news_data_tools.py

@@ -0,0 +1,71 @@
+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)

tradingagents/agents/utils/seeking_alpha_tools.py

@@ -0,0 +1,76 @@
+from langchain_core.tools import tool
+from typing import Annotated
+from pathlib import Path
+import glob
+import os
+
+try:
+    import PyPDF2
+    PDF_AVAILABLE = True
+    PDF_LIB = "PyPDF2"
+except ImportError:
+    try:
+        import pypdf
+        PDF_AVAILABLE = True
+        PDF_LIB = "pypdf"
+    except ImportError:
+        PDF_AVAILABLE = False
+        PDF_LIB = None
+
+
+@tool
+def get_seeking_alpha_pdfs(
+    ticker: Annotated[str, "ticker symbol or stock name"],
+    base_dir: Annotated[str, "base directory containing stock folders"] = "/",
+) -> str:
+    """
+    Retrieve and extract text content from PDF files in the local directory.
+    Looks for PDF files in {base_dir}/{ticker}/*.pdf
+    
+    Args:
+        ticker (str): Ticker symbol or stock name (used as folder name)
+        base_dir (str): Base directory path containing stock folders (default: "/")
+    
+    Returns:
+        str: Extracted text content from all PDF files found
+    """
+    if not PDF_AVAILABLE:
+        return "Error: PyPDF2 or pypdf library is not installed. Please install it with: pip install PyPDF2 or pip install pypdf"
+    
+    # Construct the path pattern
+    pdf_pattern = os.path.join(base_dir, ticker, "*.pdf")
+    pdf_files = glob.glob(pdf_pattern)
+    
+    if not pdf_files:
+        return f"No PDF files found in {os.path.join(base_dir, ticker)}/"
+    
+    all_text = []
+    
+    for pdf_path in sorted(pdf_files):
+        try:
+            with open(pdf_path, 'rb') as file:
+                if PDF_LIB == "PyPDF2":
+                    pdf_reader = PyPDF2.PdfReader(file)
+                elif PDF_LIB == "pypdf":
+                    import pypdf
+                    pdf_reader = pypdf.PdfReader(file)
+                else:
+                    all_text.append(f"Error: No PDF library available for {pdf_path}\n")
+                    continue
+                
+                pdf_text = []
+                for page_num in range(len(pdf_reader.pages)):
+                    page = pdf_reader.pages[page_num]
+                    pdf_text.append(page.extract_text())
+                text_content = "\n".join(pdf_text)
+            
+            all_text.append(f"=== File: {os.path.basename(pdf_path)} ===\n{text_content}\n")
+            
+        except Exception as e:
+            all_text.append(f"Error reading {pdf_path}: {str(e)}\n")
+    
+    if not all_text:
+        return f"Found PDF files but could not extract text from any of them in {os.path.join(base_dir, ticker)}/"
+    
+    return "\n".join(all_text)
+

tradingagents/agents/utils/technical_indicators_tools.py

@@ -0,0 +1,23 @@
+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)

tradingagents/dataflows/alpha_vantage.py

@@ -0,0 +1,5 @@
+# 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

tradingagents/dataflows/alpha_vantage_common.py

@@ -0,0 +1,122 @@
+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

tradingagents/dataflows/alpha_vantage_fundamentals.py

@@ -0,0 +1,77 @@
+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)
+

tradingagents/dataflows/alpha_vantage_indicator.py

@@ -0,0 +1,222 @@
+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)}"

tradingagents/dataflows/alpha_vantage_news.py

@@ -0,0 +1,43 @@
+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)

tradingagents/dataflows/alpha_vantage_stock.py

@@ -0,0 +1,38 @@
+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)

tradingagents/dataflows/config.py

@@ -0,0 +1,34 @@
+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()

tradingagents/dataflows/google.py

@@ -0,0 +1,30 @@
+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}"

tradingagents/dataflows/googlenews_utils.py

@@ -0,0 +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

tradingagents/dataflows/interface.py

@@ -0,0 +1,244 @@
+from typing import Annotated
+
+# Import from vendor-specific modules
+from .local import get_YFin_data, get_finnhub_news, get_finnhub_company_insider_sentiment, get_finnhub_company_insider_transactions, get_simfin_balance_sheet, get_simfin_cashflow, get_simfin_income_statements, get_reddit_global_news, get_reddit_company_news
+from .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
+from .google import get_google_news
+from .openai import get_stock_news_openai, get_global_news_openai, get_fundamentals_openai
+from .alpha_vantage import (
+    get_stock as get_alpha_vantage_stock,
+    get_indicator as get_alpha_vantage_indicator,
+    get_fundamentals as get_alpha_vantage_fundamentals,
+    get_balance_sheet as get_alpha_vantage_balance_sheet,
+    get_cashflow as get_alpha_vantage_cashflow,
+    get_income_statement as get_alpha_vantage_income_statement,
+    get_insider_transactions as get_alpha_vantage_insider_transactions,
+    get_news as get_alpha_vantage_news
+)
+from .alpha_vantage_common import AlphaVantageRateLimitError
+
+# Configuration and routing logic
+from .config import get_config
+
+# Tools organized by category
+TOOLS_CATEGORIES = {
+    "core_stock_apis": {
+        "description": "OHLCV stock price data",
+        "tools": [
+            "get_stock_data"
+        ]
+    },
+    "technical_indicators": {
+        "description": "Technical analysis indicators",
+        "tools": [
+            "get_indicators"
+        ]
+    },
+    "fundamental_data": {
+        "description": "Company fundamentals",
+        "tools": [
+            "get_fundamentals",
+            "get_balance_sheet",
+            "get_cashflow",
+            "get_income_statement"
+        ]
+    },
+    "news_data": {
+        "description": "News (public/insiders, original/processed)",
+        "tools": [
+            "get_news",
+            "get_global_news",
+            "get_insider_sentiment",
+            "get_insider_transactions",
+        ]
+    }
+}
+
+VENDOR_LIST = [
+    "local",
+    "yfinance",
+    "openai",
+    "google"
+]
+
+# Mapping of methods to their vendor-specific implementations
+VENDOR_METHODS = {
+    # core_stock_apis
+    "get_stock_data": {
+        "alpha_vantage": get_alpha_vantage_stock,
+        "yfinance": get_YFin_data_online,
+        "local": get_YFin_data,
+    },
+    # technical_indicators
+    "get_indicators": {
+        "alpha_vantage": get_alpha_vantage_indicator,
+        "yfinance": get_stock_stats_indicators_window,
+        "local": get_stock_stats_indicators_window
+    },
+    # fundamental_data
+    "get_fundamentals": {
+        "alpha_vantage": get_alpha_vantage_fundamentals,
+        "openai": get_fundamentals_openai,
+    },
+    "get_balance_sheet": {
+        "alpha_vantage": get_alpha_vantage_balance_sheet,
+        "yfinance": get_yfinance_balance_sheet,
+        "local": get_simfin_balance_sheet,
+    },
+    "get_cashflow": {
+        "alpha_vantage": get_alpha_vantage_cashflow,
+        "yfinance": get_yfinance_cashflow,
+        "local": get_simfin_cashflow,
+    },
+    "get_income_statement": {
+        "alpha_vantage": get_alpha_vantage_income_statement,
+        "yfinance": get_yfinance_income_statement,
+        "local": get_simfin_income_statements,
+    },
+    # news_data
+    "get_news": {
+        "alpha_vantage": get_alpha_vantage_news,
+        "openai": get_stock_news_openai,
+        "google": get_google_news,
+        "local": [get_finnhub_news, get_reddit_company_news, get_google_news],
+    },
+    "get_global_news": {
+        "openai": get_global_news_openai,
+        "local": get_reddit_global_news
+    },
+    "get_insider_sentiment": {
+        "local": get_finnhub_company_insider_sentiment
+    },
+    "get_insider_transactions": {
+        "alpha_vantage": get_alpha_vantage_insider_transactions,
+        "yfinance": get_yfinance_insider_transactions,
+        "local": get_finnhub_company_insider_transactions,
+    },
+}
+
+def get_category_for_method(method: str) -> str:
+    """Get the category that contains the specified method."""
+    for category, info in TOOLS_CATEGORIES.items():
+        if method in info["tools"]:
+            return category
+    raise ValueError(f"Method '{method}' not found in any category")
+
+def get_vendor(category: str, method: str = None) -> str:
+    """Get the configured vendor for a data category or specific tool method.
+    Tool-level configuration takes precedence over category-level.
+    """
+    config = get_config()
+
+    # Check tool-level configuration first (if method provided)
+    if method:
+        tool_vendors = config.get("tool_vendors", {})
+        if method in tool_vendors:
+            return tool_vendors[method]
+
+    # Fall back to category-level configuration
+    return config.get("data_vendors", {}).get(category, "default")
+
+def route_to_vendor(method: str, *args, **kwargs):
+    """Route method calls to appropriate vendor implementation with fallback support."""
+    category = get_category_for_method(method)
+    vendor_config = get_vendor(category, method)
+
+    # Handle comma-separated vendors
+    primary_vendors = [v.strip() for v in vendor_config.split(',')]
+
+    if method not in VENDOR_METHODS:
+        raise ValueError(f"Method '{method}' not supported")
+
+    # Get all available vendors for this method for fallback
+    all_available_vendors = list(VENDOR_METHODS[method].keys())
+    
+    # Create fallback vendor list: primary vendors first, then remaining vendors as fallbacks
+    fallback_vendors = primary_vendors.copy()
+    for vendor in all_available_vendors:
+        if vendor not in fallback_vendors:
+            fallback_vendors.append(vendor)
+
+    # Debug: Print fallback ordering
+    primary_str = " → ".join(primary_vendors)
+    fallback_str = " → ".join(fallback_vendors)
+    print(f"DEBUG: {method} - Primary: [{primary_str}] | Full fallback order: [{fallback_str}]")
+
+    # Track results and execution state
+    results = []
+    vendor_attempt_count = 0
+    any_primary_vendor_attempted = False
+    successful_vendor = None
+
+    for vendor in fallback_vendors:
+        if vendor not in VENDOR_METHODS[method]:
+            if vendor in primary_vendors:
+                print(f"INFO: Vendor '{vendor}' not supported for method '{method}', falling back to next vendor")
+            continue
+
+        vendor_impl = VENDOR_METHODS[method][vendor]
+        is_primary_vendor = vendor in primary_vendors
+        vendor_attempt_count += 1
+
+        # Track if we attempted any primary vendor
+        if is_primary_vendor:
+            any_primary_vendor_attempted = True
+
+        # Debug: Print current attempt
+        vendor_type = "PRIMARY" if is_primary_vendor else "FALLBACK"
+        print(f"DEBUG: Attempting {vendor_type} vendor '{vendor}' for {method} (attempt #{vendor_attempt_count})")
+
+        # Handle list of methods for a vendor
+        if isinstance(vendor_impl, list):
+            vendor_methods = [(impl, vendor) for impl in vendor_impl]
+            print(f"DEBUG: Vendor '{vendor}' has multiple implementations: {len(vendor_methods)} functions")
+        else:
+            vendor_methods = [(vendor_impl, vendor)]
+
+        # Run methods for this vendor
+        vendor_results = []
+        for impl_func, vendor_name in vendor_methods:
+            try:
+                print(f"DEBUG: Calling {impl_func.__name__} from vendor '{vendor_name}'...")
+                result = impl_func(*args, **kwargs)
+                vendor_results.append(result)
+                print(f"SUCCESS: {impl_func.__name__} from vendor '{vendor_name}' completed successfully")
+                    
+            except AlphaVantageRateLimitError as e:
+                if vendor == "alpha_vantage":
+                    print(f"RATE_LIMIT: Alpha Vantage rate limit exceeded, falling back to next available vendor")
+                    print(f"DEBUG: Rate limit details: {e}")
+                # Continue to next vendor for fallback
+                continue
+            except Exception as e:
+                # Log error but continue with other implementations
+                print(f"FAILED: {impl_func.__name__} from vendor '{vendor_name}' failed: {e}")
+                continue
+
+        # Add this vendor's results
+        if vendor_results:
+            results.extend(vendor_results)
+            successful_vendor = vendor
+            result_summary = f"Got {len(vendor_results)} result(s)"
+            print(f"SUCCESS: Vendor '{vendor}' succeeded - {result_summary}")
+            
+            # Stopping logic: Stop after first successful vendor for single-vendor configs
+            # Multiple vendor configs (comma-separated) may want to collect from multiple sources
+            if len(primary_vendors) == 1:
+                print(f"DEBUG: Stopping after successful vendor '{vendor}' (single-vendor config)")
+                break
+        else:
+            print(f"FAILED: Vendor '{vendor}' produced no results")
+
+    # Final result summary
+    if not results:
+        print(f"FAILURE: All {vendor_attempt_count} vendor attempts failed for method '{method}'")
+        raise RuntimeError(f"All vendor implementations failed for method '{method}'")
+    else:
+        print(f"FINAL: Method '{method}' completed with {len(results)} result(s) from {vendor_attempt_count} vendor attempt(s)")
+
+    # Return single result if only one, otherwise concatenate as string
+    if len(results) == 1:
+        return results[0]
+    else:
+        # Convert all results to strings and concatenate
+        return '\n'.join(str(result) for result in results)

tradingagents/dataflows/local.py

@@ -0,0 +1,475 @@
+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}"

tradingagents/dataflows/openai.py

@@ -0,0 +1,107 @@
+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

tradingagents/dataflows/reddit_utils.py

@@ -0,0 +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

tradingagents/dataflows/stockstats_utils.py

@@ -0,0 +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, 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)"

tradingagents/dataflows/utils.py

@@ -0,0 +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

tradingagents/dataflows/y_finance.py

@@ -0,0 +1,407 @@
+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)}"

tradingagents/dataflows/yfin_utils.py

@@ -0,0 +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

tradingagents/default_config.py

@@ -0,0 +1,33 @@
+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)
+    "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
+    },
+}

tradingagents/graph/__init__.py

@@ -0,0 +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/conditional_logic.py

@@ -0,0 +1,75 @@
+# 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_seeking_alpha(self, state: AgentState):
+        """Determine if seeking alpha analysis should continue."""
+        messages = state["messages"]
+        last_message = messages[-1]
+        if last_message.tool_calls:
+            return "tools_seeking_alpha"
+        return "Msg Clear Seeking Alpha"
+
+    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/propagation.py

@@ -0,0 +1,50 @@
+# 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": "",
+            "seeking_alpha_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/reflection.py

@@ -0,0 +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/setup.py

@@ -0,0 +1,210 @@
+# 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
+                - "seeking_alpha": Seeking Alpha 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"]
+
+        if "seeking_alpha" in selected_analysts:
+            analyst_nodes["seeking_alpha"] = create_seeking_alpha_analyst(
+                self.quick_thinking_llm
+            )
+            delete_nodes["seeking_alpha"] = create_msg_delete()
+            tool_nodes["seeking_alpha"] = self.tool_nodes["seeking_alpha"]
+
+        # 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/signal_processing.py

@@ -0,0 +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/trading_graph.py

@@ -0,0 +1,265 @@
+# 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,
+    get_seeking_alpha_pdfs
+)
+
+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"])
+        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,
+                ]
+            ),
+            "seeking_alpha": ToolNode(
+                [
+                    # Seeking Alpha PDF analysis tools
+                    get_seeking_alpha_pdfs,
+                ]
+            ),
+        }
+
+    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"],
+            "seeking_alpha_report": final_state.get("seeking_alpha_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)

workflow_diagram.md

@@ -0,0 +1,74 @@
+# TradingAgents Workflow Diagram
+
+## 完整工作流程图
+
+```mermaid
+graph TD
+    START([START]) --> FirstAnalyst[第一个分析师<br/>Market/Social/News/Fundamentals]
+    
+    subgraph AnalystLoop["分析师循环 (可配置)"]
+        Analyst[分析师节点] -->|需要工具调用| Tools[工具节点<br/>tools_market/social/news/fundamentals]
+        Tools --> Analyst
+        Analyst -->|分析完成| ClearMsg[清理消息节点<br/>Msg Clear]
+        ClearMsg -->|下一个分析师| NextAnalyst[下一个分析师]
+        ClearMsg -->|最后一个分析师完成| BullResearcher
+    end
+    
+    FirstAnalyst --> AnalystLoop
+    
+    subgraph ResearchPhase["研究与投资决策阶段"]
+        BullResearcher[Bull Researcher<br/>看涨研究员] -->|继续辩论| BearResearcher[Bear Researcher<br/>看跌研究员]
+        BearResearcher -->|继续辩论| BullResearcher
+        BullResearcher -->|达到最大轮次| ResearchManager[Research Manager<br/>研究经理/投资裁判]
+        BearResearcher -->|达到最大轮次| ResearchManager
+        ResearchManager --> Trader[Trader<br/>交易员]
+    end
+    
+    subgraph RiskPhase["风险分析阶段"]
+        Trader --> RiskyAnalyst[Risky Analyst<br/>激进分析师]
+        RiskyAnalyst -->|继续辩论| SafeAnalyst[Safe Analyst<br/>保守分析师]
+        SafeAnalyst -->|继续辩论| NeutralAnalyst[Neutral Analyst<br/>中性分析师]
+        NeutralAnalyst -->|继续辩论| RiskyAnalyst
+        RiskyAnalyst -->|达到最大轮次| RiskJudge[Risk Judge<br/>风险经理]
+        SafeAnalyst -->|达到最大轮次| RiskJudge
+        NeutralAnalyst -->|达到最大轮次| RiskJudge
+    end
+    
+    RiskJudge --> END([END])
+    
+    style START fill:#90EE90
+    style END fill:#FFB6C1
+    style ResearchManager fill:#87CEEB
+    style Trader fill:#DDA0DD
+    style RiskJudge fill:#F0E68C
+```
+
+## 详细流程说明
+
+### 阶段 1: 分析师阶段 (Analyst Phase)
+- **顺序执行**: 根据 `selected_analysts` 配置，按顺序执行各个分析师
+- **每个分析师**:
+  1. 分析师节点分析市场数据
+  2. 如果需要数据 → 调用工具节点 (`tools_xxx`)
+  3. 工具返回数据 → 回到分析师节点继续分析
+  4. 分析完成 → 清理消息 → 进入下一个分析师
+
+### 阶段 2: 研究辩论阶段 (Research Debate Phase)
+- **Bull Researcher** ↔ **Bear Researcher** 循环辩论
+- 辩论轮次由 `max_debate_rounds` 控制
+- 达到最大轮次后 → **Research Manager** 做出投资判断
+- **Research Manager** → **Trader** 制定交易计划
+
+### 阶段 3: 风险分析阶段 (Risk Analysis Phase)
+- **Trader** → **Risky Analyst** (激进观点)
+- **Risky** → **Safe** → **Neutral** → **Risky** (循环辩论)
+- 辩论轮次由 `max_risk_discuss_rounds` 控制
+- 达到最大轮次后 → **Risk Judge** 做出最终风险决策
+- **Risk Judge** → **END** (输出最终交易决策)
+
+## 条件判断逻辑
+
+- **分析师条件**: 检查是否有 `tool_calls`，决定是否需要调用工具
+- **辩论条件**: 检查辩论轮次 (`count`) 和当前响应者，决定继续辩论或进入下一阶段
+- **风险分析条件**: 检查风险讨论轮次和最新发言者，决定继续讨论或进入风险判断
+

workflow_text_diagram.txt

@@ -0,0 +1,121 @@
+═══════════════════════════════════════════════════════════════════
+          TradingAgents 工作流程图 (Workflow Diagram)
+═══════════════════════════════════════════════════════════════════
+
+[START]
+    │
+    ▼
+┌─────────────────────────────────────────────────────────────┐
+│  阶段 1: 分析师阶段 (Analyst Phase)                          │
+│  ────────────────────────────────────────────────────────   │
+│                                                               │
+│  [Market Analyst] ──┐                                         │
+│                     │ (需要数据?)                             │
+│                     ▼                                         │
+│              [tools_market] ──┐                               │
+│                               │ (返回数据)                    │
+│                               ▼                               │
+│                        [Market Analyst]                       │
+│                               │                               │
+│                               ▼ (分析完成)                    │
+│                        [Msg Clear Market]                     │
+│                               │                               │
+│                               ▼                               │
+│  [Social Analyst] ──┐                                         │
+│                     │ (需要数据?)                             │
+│                     ▼                                         │
+│              [tools_social] ──┐                               │
+│                               │                               │
+│                               ▼                               │
+│                        [Social Analyst]                       │
+│                               │                               │
+│                               ▼                               │
+│                        [Msg Clear Social]                     │
+│                               │                               │
+│                               ▼                               │
+│  [News Analyst] ──┐                                           │
+│                   │                                           │
+│                   ▼                                           │
+│            [tools_news] ──┐                                   │
+│                           │                                   │
+│                           ▼                                   │
+│                    [News Analyst]                             │
+│                           │                                   │
+│                           ▼                                   │
+│                    [Msg Clear News]                           │
+│                           │                                   │
+│                           ▼                                   │
+│  [Fundamentals Analyst] ──┐                                   │
+│                           │                                   │
+│                           ▼                                   │
+│                    [tools_fundamentals] ──┐                    │
+│                                          │                    │
+│                                          ▼                    │
+│                                   [Fundamentals Analyst]      │
+│                                          │                    │
+│                                          ▼                    │
+│                                   [Msg Clear Fundamentals]    │
+└─────────────────────────────────────────────────────────────┘
+                                │
+                                ▼
+┌─────────────────────────────────────────────────────────────┐
+│  阶段 2: 研究辩论阶段 (Research Debate Phase)               │
+│  ────────────────────────────────────────────────────────   │
+│                                                               │
+│                    [Bull Researcher]                         │
+│                         ↕ (循环辩论)                         │
+│                    [Bear Researcher]                          │
+│                         │                                     │
+│                         ▼ (达到最大轮次)                      │
+│                  [Research Manager]                           │
+│                         │                                     │
+│                         ▼                                     │
+│                      [Trader]                                 │
+└─────────────────────────────────────────────────────────────┘
+                                │
+                                ▼
+┌─────────────────────────────────────────────────────────────┐
+│  阶段 3: 风险分析阶段 (Risk Analysis Phase)                 │
+│  ────────────────────────────────────────────────────────   │
+│                                                               │
+│                    [Risky Analyst]                            │
+│                         │                                     │
+│                         ▼                                     │
+│                    [Safe Analyst]                             │
+│                         │                                     │
+│                         ▼                                     │
+│                  [Neutral Analyst]                            │
+│                         │                                     │
+│                         └───────┐                             │
+│                                 │ (循环辩论)                  │
+│                                 ▼                             │
+│                          [Risky Analyst]                      │
+│                                 │                             │
+│                                 ▼ (达到最大轮次)              │
+│                          [Risk Judge]                         │
+└─────────────────────────────────────────────────────────────┘
+                                │
+                                ▼
+                            [END]
+
+═══════════════════════════════════════════════════════════════════
+关键说明:
+═══════════════════════════════════════════════════════════════════
+
+1. 分析师阶段:
+   - 每个分析师可以调用工具获取数据 (tools_xxx)
+   - 工具调用后会返回分析师节点继续分析
+   - 分析完成后清理消息，进入下一个分析师
+
+2. 研究辩论阶段:
+   - Bull 和 Bear 研究员进行多轮辩论
+   - 辩论轮次由 max_debate_rounds 控制
+   - Research Manager 根据辩论结果做出投资判断
+
+3. 风险分析阶段:
+   - Risky → Safe → Neutral → Risky (循环)
+   - 辩论轮次由 max_risk_discuss_rounds 控制
+   - Risk Judge 做出最终风险决策
+
+═══════════════════════════════════════════════════════════════════
+