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BACKTEST_QUICKSTART.md

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Backtesting Quick Start Guide

Get started with PR3DICT backtesting in 5 minutes.

Step 1: Generate Sample Data (if needed)

cd ~/.openclaw/workspace/pr3dict

python3 -m src.backtest.run \
  --generate-sample \
  --output data/sample.csv \
  --sample-markets 5 \
  --sample-days 30

Step 2: Run Your First Backtest

python3 -m src.backtest.run \
  --strategy arbitrage \
  --data data/sample.csv \
  --start 2024-01-01 \
  --end 2024-01-31

Step 3: View Results

Reports are saved to ./backtest_reports/

Console output shows:

  • Total return %
  • Sharpe ratio
  • Win rate
  • Max drawdown
  • Trade count

Common Commands

Test All Strategies

python3 -m src.backtest.run \
  --strategy all \
  --data data/sample.csv \
  --start 2024-01-01 \
  --end 2024-12-31

Custom Settings

python3 -m src.backtest.run \
  --strategy market_making \
  --data data/sample.csv \
  --start 2024-01-01 \
  --end 2024-06-30 \
  --balance 50000 \
  --commission 0.02 \
  --slippage 10

Use Real Historical Data

python3 -m src.backtest.run \
  --strategy behavioral \
  --data-dir data/historical/ \
  --platforms kalshi polymarket \
  --start 2024-01-01 \
  --end 2024-12-31

Programmatic Usage

from pathlib import Path
from datetime import datetime
from decimal import Decimal

from src.backtest import BacktestEngine, BacktestConfig
from src.backtest.data import HistoricalDataLoader
from src.strategies.arbitrage import ArbitrageStrategy

# Load data
loader = HistoricalDataLoader()
loader.load_csv(Path("data/sample.csv"))

# Configure
config = BacktestConfig(
    start_date=datetime(2024, 1, 1),
    end_date=datetime(2024, 12, 31),
    initial_balance=Decimal("10000"),
    commission_rate=Decimal("0.01"),
    slippage_bps=Decimal("5")
)

# Run
engine = BacktestEngine(
    data_loader=loader,
    strategies=[ArbitrageStrategy()],
    config=config
)

results = engine.run()

# Analyze
print(f"Final Balance: ${results['final_balance']}")
print(f"Total Return: {results['total_return']:.2%}")
print(f"Sharpe Ratio: {results['sharpe_ratio']:.2f}")

CLI Options Reference

Option Description Default
--strategy Strategy name or "all" arbitrage
--start Start date (YYYY-MM-DD) required
--end End date (YYYY-MM-DD) required
--data Path to CSV file auto-load
--balance Initial balance USD 10000
--commission Commission rate (decimal) 0.01
--slippage Slippage in bps 5
--max-positions Max concurrent positions 10
--output Report output directory ./backtest_reports

Interpreting Results

Good Performance

  • Sharpe Ratio: > 1.5
  • Win Rate: > 55%
  • Profit Factor: > 1.5
  • Max Drawdown: < 20%

Warning Signs

  • Sharpe < 0.5 (poor risk-adjusted returns)
  • Win rate < 45% (more losers than winners)
  • Profit factor < 1.2 (barely profitable)
  • Max drawdown > 30% (too risky)

Historical Data Format

CSV with these columns:

timestamp,market_id,ticker,title,yes_price,no_price,volume,liquidity,close_time,resolved,platform

Example row:

2024-01-01T10:00:00,MKT-1,TICKER1,Will event occur?,0.65,0.34,10000,50000,2024-02-01T00:00:00,false,kalshi

Troubleshooting

"No historical data loaded"

  • Check data file exists
  • Verify date range matches data
  • Try --generate-sample first

"Insufficient balance"

  • Increase --balance
  • Reduce position sizes

"ImportError"

  • Run from pr3dict/ directory
  • Use python3 -m src.backtest.run format

Next Steps

  1. Read full docs: BACKTESTING.md
  2. Run test: python3 test_backtest.py
  3. See examples: examples/backtest_example.py
  4. Collect real data and start optimizing!

Need Help? Check BACKTESTING.md for detailed documentation.