updated the readme
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Manav Chaudhary
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README.md
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README.md
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### Agentic AI system for stock trading
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### TradeDog = LangGraph + Specialized Agents + Real-Time Loop + Paper Execution
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TradingAgents (full name: TradingAgents: Multi-Agents LLM Financial Trading Framework) is currently one of the strongest and most popular open-source projects for exactly what you're building: an agentic AI system for stock trading powered by large language models (LLMs).
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It was developed by Tauric Research (a group focused on AI for trading intelligence) and released openly on GitHub. As of February 2026, it has massive traction: ~30k stars, very active updates (v0.2.0 came out in early February 2026 with major improvements), an associated arXiv paper, and it's inspiring many forks/extensions (including ones that add real broker integrations like Alpaca).
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What TradingAgents Actually Is
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It's a multi-agent simulation of a real trading desk / hedge fund inside code. Instead of one single LLM deciding trades, it creates a team of specialized AI agents that work together, debate, and reach a consensus — just like analysts, traders, and risk managers in a professional firm.
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Core agents/roles (as of v0.2.0):
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Fundamentals Analyst — evaluates company financials, earnings, balance sheets, valuations (P/E, DCF, etc.)
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Sentiment Analyst — processes news, social media (X/Twitter), Reddit, etc. for market mood
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Technical Analyst — looks at charts, indicators (RSI, MACD, moving averages, volume patterns)
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News / Researcher Agent — digs deeper into events, filings, macroeconomic data
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Trader Agents — multiple versions with different risk appetites (conservative, aggressive, balanced)
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Risk Manager — enforces position sizing, stop-loss rules, portfolio limits, drawdown controls
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They communicate in a structured way (often via "debate" rounds or message passing), produce reasoning, and finally output a trading decision: BUY, SELL, HOLD + size + confidence.
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Key strengths in 2026:
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Supports many LLM providers out of the box: Grok, Claude 4, GPT-5 series, Gemini 3, OpenRouter, local Ollama — easy to switch or mix
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Built on modern agent frameworks (likely LangGraph / similar graph-based orchestration)
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Includes backtesting mode + simulation (paper trading style)
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Focuses on explainability: every decision has detailed reasoning chain you can log/review
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It's not a plug-and-play live trading bot yet — the base repo is more about analysis + decision-making in simulated or backtest environments. But it's designed to be extended with execution layers (which is perfect for your TradeDog plan).
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How to Use It for Your TradeDog Project
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Your goal = autonomous agent that invests → monitors → takes profits/exits automatically in paper trading mode.
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TradingAgents is an excellent base because:
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It already has the "brain" (multi-agent reasoning + decision)
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You just need to add the "hands" (execution via Alpaca paper API) and "memory/guardian" (position tracking + profit/exit logic)
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Step-by-step plan to turn it into TradeDog:
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Clone and Run Baseline (Do This Today)Bashgit clone https://github.com/TauricResearch/TradingAgents.git
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cd TradingAgents
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# Follow their README — usually:
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pip install -r requirements.txt
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# Set up .env with your LLM API keys (e.g. GROQ_API_KEY or others)
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# Run example / demo script (they have CLI + Jupyter examples)
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python -m tradingagents run --ticker SHOP --mode backtest→ You should see agents debating SHOP (Shopify — great Toronto/TSX example) and outputting a simulated trade recommendation.
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Add Your Profit-Taking / Exit Logic (Core TradeDog Feature)
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Extend the framework by adding a new agent or post-processing node called ProfitGuard or ExitManager.
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It runs after every decision loop:
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Checks open positions (you'll need to add simple portfolio state — dict or SQLite)
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Calculates current profit % , trailing high, days held
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Forces SELL if: profit > 10–15%, trailing stop hit (e.g. -7% from peak), or reversal signal from technical/sentiment agents
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Example pseudocode to integrate:Python# In the main loop or as new graph node
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def exit_check(portfolio, current_data):
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for pos in portfolio:
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profit = (current_data[pos.ticker].price - pos.entry_price) / pos.entry_price
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if profit >= TAKE_PROFIT_PCT or trailing_stop_triggered(pos):
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return {"action": "SELL", "quantity": pos.quantity, "reason": f"Profit hit {profit*100:.1f}%"}
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return {"action": "HOLD"}
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Connect to Paper Trading Execution
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Use Alpaca (easiest for paper mode):
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Sign up → get paper keys → install alpaca-py
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Add an Executor module that turns agent decisions into real API calls:Pythonfrom alpaca.trading.client import TradingClient
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client = TradingClient(API_KEY, SECRET_KEY, paper=True)
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# When agents decide BUY 10 shares SHOP
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client.submit_order(symbol="SHOP", qty=10, side="buy", type="market")
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Start with US-listed or cross-listed TSX names (SHOP, TD, etc.). Later add IBKR for full TSX if needed.
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Make It Run Autonomously (24/7-ish)
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Wrap in a loop with schedule lib or asyncio: run full agent cycle every 15–60 min during market hours.
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Add logging: save every decision + reasoning + P&L to file/DB.
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Build a simple Streamlit dashboard: current portfolio, equity curve, recent agent debates.
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Iteration Path for Serious Profits in Paper Mode
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Week 1–2: Get baseline running + add basic exit rules → paper test on 5–10 stocks
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Week 3–4: Tune prompts (make agents more conservative/profitable), add X sentiment via tools
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Month 2: Track metrics (Sharpe, win rate, max drawdown) vs SPY/TSX benchmark
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Month 3+: A/B test configs (different take-profit %, risk levels), add more data sources
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