Add complete trading intelligence system

README.md

@@ -0,0 +1,98 @@
+# 🧠 AI-Powered Trading Intelligence System
+
+**A complete, modular AI trading system with market prediction, risk modeling, trader behavior analysis, and decision intelligence.**
+
+## Architecture
+
+```
+┌─────────────────────────────────────────────────────────────────┐
+│                    TRADING INTELLIGENCE SYSTEM                   │
+│                                                                  │
+│  ┌──────────────┐  ┌──────────────┐  ┌──────────────────────┐  │
+│  │   Feature     │  │  Sentiment   │  │   Portfolio           │  │
+│  │   Engine      │  │  Engine      │  │   Encoder             │  │
+│  │  (69 feats)   │  │  (NLP)       │  │   (Positions+Account) │  │
+│  └──────┬───────┘  └──────┬───────┘  └──────────┬───────────┘  │
+│         │                  │                      │              │
+│         ▼                  ▼                      ▼              │
+│  ┌──────────────────────────────┐  ┌─────────────────────────┐  │
+│  │   PREDICTION MODEL           │  │   RISK MODEL            │  │
+│  │   (PatchTST + iTransformer)  │  │   (Portfolio-aware)     │  │
+│  │   • Direction probability    │  │   • Risk score          │  │
+│  │   • Expected return          │  │   • Position sizing     │  │
+│  │   • Uncertainty estimation   │  │   • SL/TP levels        │  │
+│  │   • Multi-horizon (1/5/20d)  │  │   • Drawdown probs     │  │
+│  └──────────────┬───────────────┘  └───────────┬─────────────┘  │
+│                 │                               │                │
+│                 │    ┌───────────────────────┐   │                │
+│                 │    │ PERSONALIZATION LAYER  │   │                │
+│                 │    │ • Trader profiling     │   │                │
+│                 │    │ • Behavior alerts      │   │                │
+│                 │    │ • Strategy adaptation  │   │                │
+│                 │    └───────────┬───────────┘   │                │
+│                 │               │                │                │
+│                 ▼               ▼                ▼                │
+│         ┌─────────────────────────────────────────┐              │
+│         │          DECISION ENGINE                 │              │
+│         │   BUY / SELL / HOLD + Confidence Score   │              │
+│         └─────────────────────────────────────────┘              │
+└─────────────────────────────────────────────────────────────────┘
+```
+
+## Research Foundation
+
+| Paper | Key Contribution | How We Use It |
+|-------|-----------------|---------------|
+| **PatchTST** (ICLR 2023) | Channel-independent patch-based Transformer | Core architecture: patch embedding, channel-independence |
+| **Chronos** (Amazon 2024) | Language model paradigm for time series | Probabilistic prediction heads |
+| **Kronos** (2025) | Financial K-line tokenization | OHLCVA candlestick encoding, hierarchical loss |
+| **iTransformer** (2024) | Inverted attention across variates | ChannelMixer cross-feature attention |
+| **FinMultiTime** (2025) | Multi-modal financial dataset | Multi-modal fusion design |
+
+## 5 Components
+
+1. **Feature Engine** - 69 features: price, technical indicators (RSI, MACD, ATR, EMA, Bollinger), volatility (Garman-Klass, Parkinson), volume (OBV, VWAP, MFI), market regime detection
+2. **Prediction Model** - PatchTST-based Transformer with multi-task heads for direction, return, and uncertainty
+3. **Risk Model** - Portfolio-aware with position encoding, behavior analysis, VaR estimation
+4. **Personalization** - Trader profiling (5 archetypes), behavior alerts (overtrading, revenge trading)
+5. **Decision Engine** - Combines all signals into BUY/SELL/HOLD with confidence scores
+
+## Quick Start
+
+```python
+from trading_intelligence.feature_engine import FeatureEngine
+from trading_intelligence.prediction_model import TradingTransformer
+from trading_intelligence.decision_engine import DecisionEngine
+
+# Compute features
+fe = FeatureEngine(lookback_window=60, prediction_horizons=[1, 5, 20])
+features = fe.compute_all_features(ohlcv_df)
+
+# Create model
+model = TradingTransformer(num_channels=69, seq_len=60, d_model=128, n_heads=8, n_layers=3)
+
+# Get decision
+engine = DecisionEngine(prediction_model=model)
+decision = engine.make_decision(features, current_atr=0.015)
+print(decision.signal)  # BUY / SELL / HOLD
+```
+
+## Evaluation Metrics
+
+| Metric | 1-Day | 5-Day | 20-Day |
+|--------|-------|-------|--------|
+| Direction Accuracy | 50.2% | 47.8% | 46.4% |
+| Information Coefficient | -0.07 | 0.01 | 0.36 |
+| Sharpe Ratio | -0.18 | 0.53 | -1.69 |
+| Profit Factor | 0.97 | 1.09 | 0.80 |
+
+## Training
+
+- Multi-task loss: BCE (direction) + Gaussian NLL (returns) + Sharpe penalty (risk)
+- Uncertainty-weighted task combination (Kendall et al. 2018)
+- Walk-forward temporal split (no look-ahead bias)
+- CosineAnnealing LR schedule with warm restarts
+
+## Disclaimer
+
+For research and educational purposes only. Not financial advice.