# Decentralized Prediction Market Agent Bot - Planning Document

**Project**: Autonomous Agent Bot for Event Trader Platform
**Version**: 1.0
**Date**: November 21, 2025
**Status**: Planning Phase

## Executive Summary

Build an autonomous agent bot that uses AI-driven analysis, technical indicators, and market data to make intelligent trading decisions on the Event Trader prediction market platform. The bot will analyze market conditions, identify opportunities, execute trades, and manage risk across multiple prediction markets simultaneously.

## Table of Contents

1. [Project Goals](#project-goals)
2. [Architecture Overview](#architecture-overview)
3. [Implementation Phases](#implementation-phases)
4. [Technical Requirements](#technical-requirements)
5. [Risk Management](#risk-management)
6. [Performance Metrics](#performance-metrics)
7. [Timeline & Milestones](#timeline--milestones)

## Project Goals

### Primary Objectives

1. **Automated Trading**: Execute trades autonomously based on predefined strategies
2. **Market Analysis**: Continuously analyze market conditions and asset probabilities
3. **Risk Management**: Implement position sizing, stop-losses, and portfolio diversification
4. **Performance Optimization**: Learn from past trades and optimize strategies
5. **Real-Time Monitoring**: Track positions, P&L, and market changes in real-time

### Success Criteria

- **Profitability**: Achieve positive ROI over 30-day rolling window
- **Win Rate**: Maintain >55% winning trades
- **Risk Control**: Maximum drawdown <15% of capital
- **Uptime**: 99.5% availability during market hours
- **Latency**: Execute trades within 500ms of signal generation

## Architecture Overview

### System Components

```
┌─────────────────────────────────────────────────────────────┐
│                     Agent Bot System                       │
├─────────────────────────────────────────────────────────────┤
│                                                               │
│  ┌──────────────┐  ┌──────────────┐  ┌──────────────┐      │
│  │   Market     │  │   Strategy   │  │    Risk      │      │
│  │   Monitor    │──│   Engine     │──│   Manager    │      │
│  └──────────────┘  └──────────────┘  └──────────────┘      │
│         │                  │                  │              │
│         └──────────────────┴──────────────────┘              │
│                            │                                 │
│                   ┌────────▼─────────┐                      │
│                   │   Trade          │                      │
│                   │   Executor       │                      │
│                   └────────┬─────────┘                      │
│                            │                                 │
│         ┌──────────────────┼──────────────────┐            │
│         │                  │                  │             │
│  ┌──────▼──────┐  ┌────────▼────────┐  ┌─────▼─────┐     │
│  │  Portfolio  │  │   Performance   │  │  Logging  │     │
│  │  Manager    │  │   Analytics     │  │  & Alerts │     │
│  └─────────────┘  └─────────────────┘  └───────────┘     │
│                                                             │
└─────────────────────────────────────────────────────────────┘
                            │
                   ┌────────▼─────────┐
                   │  Event Trader    │
                   │  API (v2)        │
                   └──────────────────┘
```

### Core Modules

1. **Market Monitor**
   - Continuously fetches market data via API v2
   - Tracks orderbook changes, price movements, volume
   - Detects market events and anomalies
   - Caches data for performance

2. **Strategy Engine**
   - Implements multiple trading strategies
   - Generates buy/sell signals based on analysis
   - Calculates entry/exit prices
   - Manages strategy parameters

3. **Risk Manager**
   - Enforces position limits and diversification
   - Calculates position sizing using Kelly Criterion
   - Implements stop-loss and take-profit levels
   - Monitors portfolio exposure

4. **Trade Executor**
   - Places orders via API v2
   - Manages order lifecycle (open → filled)
   - Implements retry logic and error handling
   - Tracks order execution quality

5. **Portfolio Manager**
   - Tracks all open positions
   - Calculates P&L and performance metrics
   - Manages capital allocation
   - Rebalances portfolio periodically

6. **Performance Analytics**
   - Records all trades and outcomes
   - Calculates performance metrics (Sharpe ratio, max drawdown, etc.)
   - Identifies winning/losing strategies
   - Generates performance reports

## Implementation Phases

### Phase 1: Foundation (Week 1-2)

**Goal**: Build core infrastructure and data pipeline

#### Tasks:

1. **Bot Framework**
   - Create main bot controller class
   - Implement event loop and scheduling
   - Add configuration management
   - Set up logging and monitoring

2. **API Integration**
   - Integrate with Event Trader API v2
   - Implement API client with rate limiting
   - Add authentication and error handling
   - Create data models for markets/orders/trades

3. **Market Monitor**
   - Build market data fetcher
   - Implement real-time market tracking
   - Add orderbook depth analysis
   - Create market state cache

4. **Database Schema**
   - Design tables for trades, positions, signals
   - Set up SQLite/PostgreSQL database
   - Create migration scripts
   - Add data access layer

**Deliverables**:
- Bot can connect to API and fetch market data
- Market data is stored and cached locally
- Basic logging and error handling works
- Configuration system in place

### Phase 2: Strategy Implementation (Week 3-4)

**Goal**: Implement core trading strategies

#### Strategies to Implement:

1. **Mean Reversion Strategy**
   - Identify overbought/oversold conditions
   - Trade against extreme movements
   - Use RSI, Bollinger Bands
   - Exit when price reverts to mean

2. **Momentum Strategy**
   - Identify assets with strong trends
   - Enter positions in direction of momentum
   - Use MACD, moving averages
   - Exit when momentum weakens

3. **Arbitrage Strategy**
   - Find mispriced assets across markets
   - Exploit probability inconsistencies
   - Quick entry/exit for small profits
   - Low risk, high frequency

4. **Market Making Strategy**
   - Provide liquidity by placing bid/ask orders
   - Capture spread as profit
   - Adjust quotes based on inventory
   - Manage adverse selection risk

5. **AI-Driven Strategy**
   - Use Claude AI service for market analysis
   - Analyze news, sentiment, correlations
   - Generate probability estimates
   - Combine with technical indicators

#### Tasks:

1. **Strategy Framework**
   - Create base Strategy class
   - Implement strategy registry
   - Add strategy backtesting framework
   - Build signal generation pipeline

2. **Technical Indicators**
   - Implement RSI, MACD, Bollinger Bands
   - Add moving averages (SMA, EMA)
   - Calculate volume indicators
   - Create indicator library

3. **Signal Generator**
   - Combine multiple signals
   - Weight signals by confidence
   - Filter false signals
   - Generate actionable trade signals

4. **Backtesting Engine**
   - Replay historical data
   - Simulate strategy execution
   - Calculate performance metrics
   - Optimize strategy parameters

**Deliverables**:
- 5 working trading strategies
- Backtesting framework operational
- Strategy performance reports
- Parameter optimization complete

### Phase 3: Risk Management (Week 5)

**Goal**: Implement comprehensive risk controls

#### Tasks:

1. **Position Sizing**
   - Implement Kelly Criterion
   - Add fixed fractional sizing
   - Create risk-based sizing
   - Set maximum position limits

2. **Stop-Loss Management**
   - Implement trailing stops
   - Add time-based stops
   - Create volatility-adjusted stops
   - Set maximum loss limits

3. **Portfolio Constraints**
   - Enforce diversification rules
   - Limit correlated positions
   - Set sector/asset limits
   - Manage overall portfolio risk

4. **Risk Metrics**
   - Calculate Value at Risk (VaR)
   - Track portfolio volatility
   - Monitor correlation matrix
   - Measure risk-adjusted returns

**Deliverables**:
- Risk management system operational
- Position sizing algorithms working
- Stop-loss system implemented
- Risk reports generated

### Phase 4: Trade Execution (Week 6)

**Goal**: Build robust trade execution system

#### Tasks:

1. **Order Management**
   - Place market orders
   - Place limit orders with price optimization
   - Implement order routing logic
   - Handle partial fills

2. **Execution Quality**
   - Measure slippage
   - Track fill rates
   - Optimize execution timing
   - Minimize market impact

3. **Order Book Analysis**
   - Analyze bid-ask spread
   - Detect liquidity gaps
   - Identify order book imbalances
   - Optimize limit order placement

4. **Error Handling**
   - Retry failed orders
   - Handle rate limits
   - Manage API errors
   - Implement circuit breakers

**Deliverables**:
- Order execution system complete
- Order management working
- Execution quality metrics tracked
- Error handling robust

### Phase 5: Portfolio Management (Week 7)

**Goal**: Implement portfolio tracking and management

#### Tasks:

1. **Position Tracking**
   - Track all open positions
   - Monitor position P&L
   - Calculate unrealized gains/losses
   - Update positions on market changes

2. **Capital Allocation**
   - Allocate capital across strategies
   - Rebalance periodically
   - Manage cash reserves
   - Handle withdrawals/deposits

3. **Performance Calculation**
   - Calculate daily/weekly/monthly returns
   - Compute Sharpe ratio
   - Measure maximum drawdown
   - Track win rate and profit factor

4. **Portfolio Rebalancing**
   - Implement rebalancing rules
   - Close underperforming positions
   - Scale up winning positions
   - Maintain target allocations

**Deliverables**:
- Portfolio tracking system
- P&L calculation accurate
- Performance metrics computed
- Rebalancing logic implemented

### Phase 6: Monitoring & Analytics (Week 8)

**Goal**: Build comprehensive monitoring and reporting

#### Tasks:

1. **Real-Time Dashboard**
   - Display active positions
   - Show current P&L
   - Monitor strategy performance
   - Track system health

2. **Alerting System**
   - Alert on large losses
   - Notify on system errors
   - Alert on risk limit breaches
   - Send performance summaries

3. **Performance Reports**
   - Daily performance summary
   - Weekly strategy review
   - Monthly P&L statement
   - Trade execution analysis

4. **Logging & Debugging**
   - Comprehensive trade logging
   - System event logging
   - Error tracking
   - Performance profiling

**Deliverables**:
- Dashboard operational
- Alerting system working
- Reports generated automatically
- Logging comprehensive

### Phase 7: Testing & Optimization (Week 9-10)

**Goal**: Comprehensive testing and performance optimization

#### Tasks:

1. **Unit Testing**
   - Test all core components
   - Test strategy logic
   - Test risk management
   - Achieve >80% code coverage

2. **Integration Testing**
   - Test end-to-end workflows
   - Test API integration
   - Test error scenarios
   - Test concurrent operations

3. **Performance Testing**
   - Load test API client
   - Benchmark strategy execution
   - Profile memory usage
   - Optimize bottlenecks

4. **Paper Trading**
   - Run bot in simulation mode
   - Use real market data
   - Track virtual portfolio
   - Validate strategies

**Deliverables**:
- Test suite complete
- Integration tests passing
- Performance optimized
- Paper trading results validated

### Phase 8: Production Deployment (Week 11-12)

**Goal**: Deploy to production and monitor

#### Tasks:

1. **Infrastructure Setup**
   - Set up production server
   - Configure database
   - Set up monitoring
   - Configure backups

2. **Deployment**
   - Deploy bot to production
   - Configure API keys
   - Set initial capital
   - Start with small positions

3. **Monitoring**
   - Monitor performance daily
   - Track system health
   - Review trade logs
   - Analyze P&L

4. **Optimization**
   - Tune strategy parameters
   - Adjust risk limits
   - Optimize execution
   - Scale capital allocation

**Deliverables**:
- Bot deployed to production
- Monitoring operational
- Initial trades executed
- Performance tracked

## Technical Requirements

### Technology Stack

**Backend**:
- Python 3.12+
- FastAPI (for bot API/dashboard)
- SQLAlchemy (database ORM)
- Redis (caching, rate limiting)
- Pandas (data analysis)
- NumPy (numerical computing)
- TA-Lib (technical indicators)

**Data & ML**:
- scikit-learn (machine learning)
- Claude AI API (market analysis)
- Anthropic SDK

**Infrastructure**:
- Docker (containerization)
- PostgreSQL (production database)
- Grafana (monitoring dashboard)
- Prometheus (metrics collection)

### API Requirements

**Event Trader API v2**:
- `/api/v2/markets` - Fetch market data
- `/api/v2/orders` - Place and manage orders
- `/api/v2/trades` - Fetch trade history
- API key with write permissions
- Rate limit: 100-1000 req/min depending on tier

### System Requirements

**Compute**:
- 2+ CPU cores
- 4GB+ RAM
- 20GB+ storage
- Low-latency network connection

**Uptime**:
- 99.5% availability during market hours
- Automatic restart on failure
- Health checks every 60 seconds

## Risk Management

### Trading Risks

1. **Market Risk**
   - **Mitigation**: Diversify across multiple markets
   - **Limit**: Max 20% capital per market

2. **Liquidity Risk**
   - **Mitigation**: Only trade liquid markets (>$1000 pool)
   - **Limit**: Max 10% of market volume per order

3. **Model Risk**
   - **Mitigation**: Use multiple strategies, validate with backtesting
   - **Limit**: Stop trading if drawdown >15%

4. **Execution Risk**
   - **Mitigation**: Implement retry logic, use limit orders
   - **Limit**: Cancel orders if not filled within 5 minutes

5. **Technology Risk**
   - **Mitigation**: Comprehensive testing, monitoring, alerts
   - **Limit**: Circuit breaker stops trading on system errors

### Risk Limits

| Risk Type | Limit | Action on Breach |
|-----------|-------|------------------|
| Max position size | 20% of capital | Reject new orders |
| Max drawdown | 15% | Stop all trading |
| Daily loss limit | 5% of capital | Stop trading for day |
| Max open positions | 10 | Close oldest position |
| Max correlated positions | 3 | Reject correlated orders |
| VaR (95%) | 10% of capital | Reduce position sizes |

### Position Sizing

**Kelly Criterion** (aggressive):
```
f* = (bp - q) / b
where:
  f* = fraction of capital to bet
  b = odds (profit/loss ratio)
  p = probability of win
  q = probability of loss (1-p)
```

**Half-Kelly** (conservative):
```
f = 0.5 * f*
```

**Maximum Position Size**:
```
position_size = min(
    kelly_size,
    max_position_pct * capital,
    max_market_pct * market_volume
)
```

## Performance Metrics

### Trading Metrics

1. **Return Metrics**
   - Total Return %
   - Daily/Weekly/Monthly Returns
   - Risk-Free Rate Adjusted Returns
   - Compound Annual Growth Rate (CAGR)

2. **Risk Metrics**
   - Standard Deviation (volatility)
   - Maximum Drawdown
   - Value at Risk (VaR)
   - Conditional Value at Risk (CVaR)

3. **Risk-Adjusted Returns**
   - Sharpe Ratio: `(Return - RiskFreeRate) / StdDev`
   - Sortino Ratio: `(Return - RiskFreeRate) / DownsideStdDev`
   - Calmar Ratio: `CAGR / MaxDrawdown`
   - Omega Ratio

4. **Trading Efficiency**
   - Win Rate %
   - Profit Factor: `GrossProfit / GrossLoss`
   - Average Win / Average Loss
   - Expectancy: `(WinRate * AvgWin) - (LossRate * AvgLoss)`

5. **Execution Quality**
   - Average Slippage
   - Fill Rate %
   - Time to Fill
   - Market Impact

### Target Performance

| Metric | Target | Minimum |
|--------|--------|---------|
| Monthly Return | 5-10% | 2% |
| Sharpe Ratio | >1.5 | >1.0 |
| Win Rate | >55% | >50% |
| Profit Factor | >1.5 | >1.2 |
| Max Drawdown | <10% | <15% |
| Daily VaR (95%) | <3% | <5% |

## Timeline & Milestones

### 12-Week Implementation Plan

| Week | Phase | Milestone | Deliverables |
|------|-------|-----------|--------------|
| 1-2 | Foundation | Core infrastructure ready | Bot framework, API client, market monitor |
| 3-4 | Strategies | Trading strategies implemented | 5 strategies, backtesting framework |
| 5 | Risk Management | Risk controls operational | Position sizing, stop-loss, limits |
| 6 | Execution | Trade execution system ready | Order management, execution quality |
| 7 | Portfolio | Portfolio management complete | Position tracking, P&L, rebalancing |
| 8 | Monitoring | Dashboard and alerts working | Real-time dashboard, alerting system |
| 9-10 | Testing | All tests passing | Unit tests, integration tests, paper trading |
| 11-12 | Production | Bot live in production | Production deployment, monitoring |

### Key Milestones

- **Week 2**: First successful market data fetch and storage
- **Week 4**: First backtest results showing profitable strategy
- **Week 6**: First simulated trade executed successfully
- **Week 8**: Dashboard displaying real-time portfolio
- **Week 10**: Paper trading shows positive returns over 2 weeks
- **Week 12**: Production bot making profitable trades

## Next Steps

1. **Immediate Actions**:
   - Review and approve planning document
   - Set up development environment
   - Create project repository
   - Initialize database schema

2. **Week 1 Tasks**:
   - Implement bot framework (Day 1-2)
   - Build API client integration (Day 3-4)
   - Create market monitor (Day 5-7)

3. **Dependencies**:
   - API v2 access (available ✓)
   - API keys with write permissions (available ✓)
   - Historical market data for backtesting (need to collect)
   - Production server (can use current server)

## Appendix

### A. Strategy Descriptions

**Mean Reversion**:
- Assumes prices revert to average over time
- Enters when price deviates significantly from mean
- Exits when price returns to mean
- Works best in ranging markets

**Momentum**:
- Assumes trends persist in short-term
- Enters in direction of strong trends
- Exits when trend weakens
- Works best in trending markets

**Arbitrage**:
- Exploits price discrepancies
- Enters when assets are mispriced
- Exits immediately for profit
- Works best with high liquidity

**Market Making**:
- Provides liquidity for profit
- Places bid/ask orders around mid-price
- Captures spread as profit
- Works best with stable markets

**AI-Driven**:
- Uses Claude AI for analysis
- Analyzes complex market dynamics
- Generates probability estimates
- Works best with sufficient context

### B. Risk Formulas

**Kelly Criterion**:
```python
def kelly_criterion(win_prob, win_loss_ratio):
    """
    Calculate optimal position size using Kelly Criterion

    Args:
        win_prob: Probability of winning trade (0-1)
        win_loss_ratio: Ratio of average win to average loss

    Returns:
        Fraction of capital to risk (0-1)
    """
    q = 1 - win_prob
    return (win_prob * win_loss_ratio - q) / win_loss_ratio
```

**Value at Risk (VaR)**:
```python
def calculate_var(returns, confidence=0.95):
    """
    Calculate Value at Risk

    Args:
        returns: Array of historical returns
        confidence: Confidence level (default 95%)

    Returns:
        VaR as fraction of portfolio
    """
    return np.percentile(returns, (1 - confidence) * 100)
```

### C. Configuration Template

```yaml
# config.yaml
bot:
  name: "EventTrader Bot v1"
  mode: "paper"  # paper or live
  initial_capital: 10000.0

api:
  base_url: "http://localhost:8000"
  api_key: "evt_..."
  rate_limit: 100

strategies:
  - name: "mean_reversion"
    enabled: true
    weight: 0.3
    params:
      lookback_period: 20
      entry_threshold: 2.0
      exit_threshold: 0.5

  - name: "momentum"
    enabled: true
    weight: 0.3
    params:
      fast_period: 10
      slow_period: 30
      entry_threshold: 0.02

  - name: "ai_driven"
    enabled: true
    weight: 0.4
    params:
      min_confidence: 0.65
      analysis_frequency: 3600

risk:
  max_position_size: 0.20
  max_drawdown: 0.15
  daily_loss_limit: 0.05
  max_open_positions: 10
  max_correlated_positions: 3

execution:
  order_type: "limit"  # limit or market
  time_in_force: 300  # seconds
  min_liquidity: 1000  # min pool size
  max_slippage: 0.01
```

---

**Document Status**: Draft for Review
**Last Updated**: November 21, 2025
**Next Review**: Before Phase 1 Implementation