# WIA-FIN-017: AI Trading Standard v1.0

**Status:** Release Candidate  
**Date:** 2025-01-15  
**Authors:** WIA Technical Committee  
**Category:** Finance/Economy (FIN)

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## Abstract

WIA-FIN-017 defines a comprehensive standard for AI-powered trading systems, covering data ingestion, model training, strategy execution, risk management, and regulatory compliance. This standard enables interoperability between AI trading platforms and provides best practices for deploying machine learning models in production trading environments.

## 1. Introduction

### 1.1 Purpose

This standard provides:
- Unified data formats for market data and trading signals
- API specifications for AI trading system components
- Best practices for model training, validation, and deployment
- Risk management frameworks and compliance requirements
- Performance metrics and reporting standards

### 1.2 Scope

This standard applies to:
- Algorithmic trading systems using AI/ML
- High-frequency trading (HFT) platforms
- Quantitative hedge funds and prop trading firms
- Cryptocurrency and DeFi trading bots
- Retail algorithmic trading platforms

### 1.3 Definitions

- **AI Trading**: Automated trading using artificial intelligence and machine learning
- **Alpha**: Risk-adjusted excess returns above benchmark
- **Backtest**: Testing strategy on historical data
- **Slippage**: Difference between expected and actual execution price
- **Sharpe Ratio**: Risk-adjusted return metric

## 2. Architecture

### 2.1 System Components

```
┌─────────────────────────────────────────────────────────┐
│                    AI Trading System                     │
├─────────────────────────────────────────────────────────┤
│  ┌───────────┐  ┌───────────┐  ┌────────────┐         │
│  │   Data    │  │    ML     │  │  Strategy  │         │
│  │ Ingestion │→ │  Models   │→ │  Execution │         │
│  └───────────┘  └───────────┘  └────────────┘         │
│       ↓               ↓              ↓                  │
│  ┌───────────────────────────────────────────┐         │
│  │         Risk Management & Monitoring       │         │
│  └───────────────────────────────────────────┘         │
└─────────────────────────────────────────────────────────┘
```

### 2.2 Data Layer

**Market Data Sources:**
- Real-time quotes and trades
- Order book snapshots (L2/L3 data)
- Historical OHLCV (Open, High, Low, Close, Volume)
- News and sentiment data
- Alternative data sources

**Data Format:**
```json
{
  "symbol": "BTC/USDT",
  "timestamp": 1704067200000,
  "type": "quote",
  "bid": 42150.50,
  "ask": 42151.00,
  "bid_size": 1.5,
  "ask_size": 2.3,
  "exchange": "binance"
}
```

### 2.3 ML Model Layer

**Supported Model Types:**
- Supervised Learning (LSTM, GRU, Transformers)
- Reinforcement Learning (DQN, PPO, SAC)
- Ensemble Methods (Random Forest, XGBoost)

**Model Metadata:**
```json
{
  "model_id": "lstm_btc_predictor_v1",
  "version": "1.0.0",
  "type": "lstm",
  "input_features": 50,
  "output_type": "regression",
  "training_data": {
    "start_date": "2023-01-01",
    "end_date": "2024-12-31",
    "samples": 250000
  },
  "performance": {
    "sharpe_ratio": 2.34,
    "max_drawdown": -0.124,
    "win_rate": 0.68
  }
}
```

### 2.4 Strategy Execution

**Signal Format:**
```json
{
  "timestamp": 1704067200000,
  "symbol": "BTC/USDT",
  "signal": "buy",
  "strength": 0.85,
  "confidence": 0.92,
  "price": 42150.50,
  "size": 0.5,
  "strategy_id": "ml_momentum_v1"
}
```

**Order Specification:**
```json
{
  "order_id": "uuid-12345",
  "symbol": "BTC/USDT",
  "side": "buy",
  "type": "limit",
  "quantity": 0.5,
  "price": 42150.00,
  "time_in_force": "GTC",
  "reduce_only": false
}
```

## 3. Risk Management

### 3.1 Position Limits

- Maximum position size: 10% of portfolio per asset
- Maximum leverage: 3x for tested strategies
- Maximum correlation: 0.7 between positions

### 3.2 Stop-Loss Requirements

All positions MUST have:
- Hard stop-loss: Maximum 2% loss per position
- Portfolio stop-loss: Maximum 5% daily loss
- Trailing stop option: Minimum 1% trail distance

### 3.3 Circuit Breakers

Trading MUST halt when:
- Daily loss exceeds 5% of portfolio value
- Drawdown from peak exceeds 20%
- Model prediction confidence drops below 50%
- Exchange connectivity lost for >60 seconds

## 4. Performance Metrics

### 4.1 Required Metrics

All systems MUST report:
- Total Return
- Sharpe Ratio
- Sortino Ratio
- Maximum Drawdown
- Win Rate
- Profit Factor
- Average Win / Average Loss

### 4.2 Reporting Format

```json
{
  "period": "2024-01-01T00:00:00Z/2024-12-31T23:59:59Z",
  "metrics": {
    "total_return": 0.425,
    "sharpe_ratio": 2.34,
    "sortino_ratio": 3.12,
    "max_drawdown": -0.124,
    "win_rate": 0.68,
    "profit_factor": 2.8,
    "total_trades": 1247,
    "avg_win": 285.50,
    "avg_loss": -142.30
  }
}
```

## 5. Compliance & Auditing

### 5.1 Audit Trail

All systems MUST maintain:
- Complete order history with timestamps
- Model predictions and actual outcomes
- Risk limit breaches and responses
- System errors and downtime logs

### 5.2 Data Retention

Minimum retention periods:
- Trade data: 7 years
- Model predictions: 3 years
- System logs: 1 year
- Risk reports: 5 years

## 6. Security Requirements

### 6.1 Authentication

- API keys MUST use minimum 256-bit encryption
- Multi-factor authentication REQUIRED for live trading
- Key rotation RECOMMENDED every 90 days

### 6.2 Data Protection

- All data in transit MUST use TLS 1.3+
- Sensitive data at rest MUST be encrypted (AES-256)
- PII MUST comply with GDPR/CCPA

## 7. API Specification

### 7.1 REST API Endpoints

```
POST   /api/v1/strategies/{strategy_id}/signals
GET    /api/v1/strategies/{strategy_id}/performance
POST   /api/v1/orders
GET    /api/v1/orders/{order_id}
DELETE /api/v1/orders/{order_id}
GET    /api/v1/positions
GET    /api/v1/portfolio/metrics
```

### 7.2 WebSocket Streams

```
ws://api.example.com/v1/stream/market-data
ws://api.example.com/v1/stream/signals
ws://api.example.com/v1/stream/orders
ws://api.example.com/v1/stream/positions
```

## 8. Testing Requirements

### 8.1 Backtesting

Strategies MUST be backtested with:
- Minimum 2 years of historical data
- Realistic transaction costs (0.1% min)
- Walk-forward analysis (min 4 periods)
- Monte Carlo simulation (min 1000 runs)

### 8.2 Paper Trading

Before live deployment, strategies MUST:
- Paper trade for minimum 30 days
- Achieve Sharpe ratio > 1.5 in paper trading
- Demonstrate <10% deviation from backtest results

## 9. Implementation Guidelines

### 9.1 Recommended Technology Stack

**Languages:**
- Python 3.10+ (research, backtesting)
- C++17/Rust (production, HFT)
- TypeScript (web interfaces)

**Frameworks:**
- PyTorch/TensorFlow (ML models)
- Backtrader/Zipline (backtesting)
- FastAPI (REST APIs)
- Redis (real-time data)

### 9.2 Infrastructure

**Minimum Requirements:**
- 99.9% uptime SLA
- <10ms median API latency
- <100ms P99 latency
- Auto-scaling capability

## 10. Versioning & Updates

This specification follows semantic versioning:
- MAJOR version: incompatible API changes
- MINOR version: backward-compatible functionality
- PATCH version: backward-compatible bug fixes

## Appendix A: Example Implementation

See: https://github.com/WIA-Official/ai-trading-reference

## Appendix B: Compliance Checklist

- [ ] Risk limits configured
- [ ] Circuit breakers tested
- [ ] Audit logging enabled
- [ ] Performance metrics implemented
- [ ] Security requirements met
- [ ] Backtesting completed
- [ ] Paper trading validated

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