# 🧠 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.