From 78de587a0b7d78d641fb298cd52880e41a3fe2fa Mon Sep 17 00:00:00 2001 From: Manav Chaudhary <120240843+manav4499@users.noreply.github.com> Date: Thu, 19 Feb 2026 21:16:43 -0500 Subject: [PATCH] updated the readme --- README.md | 77 ------------------------------------------------------- 1 file changed, 77 deletions(-) diff --git a/README.md b/README.md index 47bd41cd..562d5f13 100644 --- a/README.md +++ b/README.md @@ -1,83 +1,6 @@ ### Agentic AI system for stock trading ### TradeDog = LangGraph + Specialized Agents + Real-Time Loop + Paper Execution -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). -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). -What TradingAgents Actually Is -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. -Core agents/roles (as of v0.2.0): - -Fundamentals Analyst — evaluates company financials, earnings, balance sheets, valuations (P/E, DCF, etc.) -Sentiment Analyst — processes news, social media (X/Twitter), Reddit, etc. for market mood -Technical Analyst — looks at charts, indicators (RSI, MACD, moving averages, volume patterns) -News / Researcher Agent — digs deeper into events, filings, macroeconomic data -Trader Agents — multiple versions with different risk appetites (conservative, aggressive, balanced) -Risk Manager — enforces position sizing, stop-loss rules, portfolio limits, drawdown controls - -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. -Key strengths in 2026: - -Supports many LLM providers out of the box: Grok, Claude 4, GPT-5 series, Gemini 3, OpenRouter, local Ollama — easy to switch or mix -Built on modern agent frameworks (likely LangGraph / similar graph-based orchestration) -Includes backtesting mode + simulation (paper trading style) -Focuses on explainability: every decision has detailed reasoning chain you can log/review - -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). -How to Use It for Your TradeDog Project -Your goal = autonomous agent that invests → monitors → takes profits/exits automatically in paper trading mode. -TradingAgents is an excellent base because: - -It already has the "brain" (multi-agent reasoning + decision) -You just need to add the "hands" (execution via Alpaca paper API) and "memory/guardian" (position tracking + profit/exit logic) - - - -Step-by-step plan to turn it into TradeDog: - -Clone and Run Baseline (Do This Today)Bashgit clone https://github.com/TauricResearch/TradingAgents.git -cd TradingAgents -# Follow their README — usually: -pip install -r requirements.txt -# Set up .env with your LLM API keys (e.g. GROQ_API_KEY or others) -# Run example / demo script (they have CLI + Jupyter examples) -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. -Add Your Profit-Taking / Exit Logic (Core TradeDog Feature) -Extend the framework by adding a new agent or post-processing node called ProfitGuard or ExitManager. -It runs after every decision loop: -Checks open positions (you'll need to add simple portfolio state — dict or SQLite) -Calculates current profit % , trailing high, days held -Forces SELL if: profit > 10–15%, trailing stop hit (e.g. -7% from peak), or reversal signal from technical/sentiment agents - -Example pseudocode to integrate:Python# In the main loop or as new graph node -def exit_check(portfolio, current_data): - for pos in portfolio: - profit = (current_data[pos.ticker].price - pos.entry_price) / pos.entry_price - if profit >= TAKE_PROFIT_PCT or trailing_stop_triggered(pos): - return {"action": "SELL", "quantity": pos.quantity, "reason": f"Profit hit {profit*100:.1f}%"} - return {"action": "HOLD"} - -Connect to Paper Trading Execution -Use Alpaca (easiest for paper mode): -Sign up → get paper keys → install alpaca-py -Add an Executor module that turns agent decisions into real API calls:Pythonfrom alpaca.trading.client import TradingClient -client = TradingClient(API_KEY, SECRET_KEY, paper=True) -# When agents decide BUY 10 shares SHOP -client.submit_order(symbol="SHOP", qty=10, side="buy", type="market") - -Start with US-listed or cross-listed TSX names (SHOP, TD, etc.). Later add IBKR for full TSX if needed. - -Make It Run Autonomously (24/7-ish) -Wrap in a loop with schedule lib or asyncio: run full agent cycle every 15–60 min during market hours. -Add logging: save every decision + reasoning + P&L to file/DB. -Build a simple Streamlit dashboard: current portfolio, equity curve, recent agent debates. - -Iteration Path for Serious Profits in Paper Mode -Week 1–2: Get baseline running + add basic exit rules → paper test on 5–10 stocks -Week 3–4: Tune prompts (make agents more conservative/profitable), add X sentiment via tools -Month 2: Track metrics (Sharpe, win rate, max drawdown) vs SPY/TSX benchmark -Month 3+: A/B test configs (different take-profit %, risk levels), add more data sources - -