| # 🧠 AI-Powered Trading Intelligence System |
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| **A complete, modular AI trading system with market prediction, risk modeling, trader behavior analysis, and decision intelligence.** |
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| ## Architecture |
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| ``` |
| ┌─────────────────────────────────────────────────────────────────┐ |
| │ 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 │ │ |
| │ └─────────────────────────────────────────┘ │ |
| └─────────────────────────────────────────────────────────────────┘ |
| ``` |
|
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| ## Research Foundation |
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| | 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 | |
|
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| ## 5 Components |
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| 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 |
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| ## Quick Start |
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| ```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 |
| ``` |
|
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| ## Evaluation Metrics |
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| | 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 | |
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| ## Training |
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| - 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 |
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| ## Disclaimer |
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| For research and educational purposes only. Not financial advice. |
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