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I am looking to do some research and POC code for programmatic backtesting. Let me know if anyone would be interested.

🔍 Pull news via RSS/NewsAPI/X. 🧠 Summarize/classify articles with Claude/GPT. 📊 Score sentiment/impact. 🗃️ Store results with timestamps/tickers. 📈 Backtest trades from news signals. 🧪 Evaluate alpha: hit rate, Sharpe, return vs. baseline.

🚨 Turn market news into trading alpha with AI?. Build an LLM-powered news bot in 6 steps:.Check 🧵 below. 💬 Want the script? RT/Like, comment “news bot” for it!.

Thank you for reading. Want to build all 3 methods and run some tests to see what the outcomes look like. Mixing some cloud and local AI systems too. Let me know what you think?.

🤔 Want to see an open-source example of:. AI-Generated Rule Builder?. Reinforcement Learning crypto bot?. Claude/LLM for market news summarization?. Reply with “🚀” and I’ll share one.

✨ Hybrid is the future:. AI suggests rules. Human vets them. AI makes trades within a safe sandbox. Humans monitor edge cases.

🚦 Moderate speed vs ⚡ Real-time capable.📊 Auditable vs 🔍 Harder to trace.🎯 Hybrid systems are often best in production.

| 🧠 AI-Generated Rules vs 🤖 Direct AI Decisions |.📋 Quick comparison:.✅ Human-readable vs ❌ Mostly black-box.⚠️ Learns slower, but stable vs ✅ Learns fast, but risky.🧪 Low overfitting, great for audit vs 🔥 High risk, but agile.

💡 Real-world example:. A PPO RL agent is trained to maximize profit across crypto pairs. Every hour, it samples state → action (buy/sell). Actions get scored (profit/loss). Model updates with feedback. ⚠️ Often black-box. High performance, low explainability.

The agent loop:. Receive new market data. Predict "Buy", "Sell", or "Hold". Log outcome. Retrain model over time. Improve over episodes. Sounds like AlphaGo? It is — but for markets. ♟️📉📈.

2️⃣ Direct AI Decisions. Here, AI is the trader. 📡 Data → 🤖 Model → 📈 Trade. Popular AI models used:. 🧠 LSTM (time-series forecasting). 🔁 PPO/DQN (reinforcement learning). 📚 Transformers. 💬 Claude/Grok (for news/sentiment parsing).

💡 Real-world example:. “If price is above 50-day MA and volume is rising → Buy”. AI (e.g. GPT, Claude) could:. Suggest that rule based on backtesting. Rank it based on Sharpe Ratio. Tune the thresholds. The bot becomes a quant assistant 🧠📊.

1️⃣ AI-Generated or Reviewed Rules. The AI suggests, but humans stay in control. 🔹 AI finds edge/patterns.🔹 Converts it to logic (like RSI < 30).🔹 Human refines it.🔹 Bot trades based on this logic.✅ Transparent.✅ Easy to backtest.✅ More regulation-friendly.

🤖 AI Trading Bots:.🧠💸 Ever wondered how AI trading bots actually work?. There are 2 major schools of thought:. 📊 AI-Generated Rules.🤖 AI Direct Decisions. Here’s a visual breakdown 🧵👇. #AI #Trading #Fintech #ML #GPT #Quant

I believe this question is broader than AI trading, focusing on decision-making, the core purpose of programming languages: rules vs. AI decisions.

🤖 AI Trading Bots:.What process do AI trading bots typically follow?. #AITrading #AI #TradingBots

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