backtest

name	backtest
description	Test trading strategies on historical data with Monte Carlo simulation
emoji	📈

Backtest - Complete API Reference

Validate trading strategies using historical data, walk-forward analysis, and Monte Carlo simulation.

---

Chat Commands

Run Backtest

/backtest momentum --from 2024-01-01 --to 2024-12-31
/backtest mean-reversion --market "Trump 2028" --days 90
/backtest my-strategy --capital 10000

Quick Stats

/backtest stats momentum           Show strategy metrics
/backtest compare momentum arb     Compare two strategies
/backtest monte-carlo momentum     Run Monte Carlo simulation

Results

/backtest results                  Show recent results
/backtest results <id> --detailed  Detailed breakdown
/backtest export <id>              Export to CSV

---

TypeScript API Reference

Create Backtest Engine

import { createBacktestEngine } from 'clodds/backtest';

const backtest = createBacktestEngine({
  // Data source
  dataSource: 'polymarket',  // or custom data provider

  // Capital
  initialCapital: 10000,

  // Fees (Polymarket: 0% on most markets; Kalshi: ~1.2% avg)
  fees: {
    maker: 0,       // 0% maker fee (Polymarket most markets)
    taker: 0,       // 0% taker fee (Polymarket most markets)
    // For 15-min crypto markets or Kalshi, use: taker: 0.012
  },

  // Slippage model
  slippageModel: 'realistic',  // 'none' | 'fixed' | 'realistic'
  slippageBps: 10,
});

Run Basic Backtest

const result = await backtest.run({
  strategy: 'momentum',
  startDate: '2024-01-01',
  endDate: '2024-12-31',
  parameters: {
    lookbackPeriod: 14,
    entryThreshold: 0.02,
    exitThreshold: 0.01,
  },
});

console.log(`Total Return: ${result.totalReturn}%`);
console.log(`Sharpe Ratio: ${result.sharpeRatio}`);
console.log(`Max Drawdown: ${result.maxDrawdown}%`);
console.log(`Win Rate: ${result.winRate}%`);
console.log(`Profit Factor: ${result.profitFactor}`);

Walk-Forward Analysis

// Out-of-sample validation
const wf = await backtest.walkForward({
  strategy: 'momentum',
  startDate: '2023-01-01',
  endDate: '2024-12-31',

  // Train/test split
  trainPeriod: '6M',
  testPeriod: '1M',
  step: '1M',

  // Optimization
  optimize: ['lookbackPeriod', 'entryThreshold'],
  optimizationMetric: 'sharpe',
});

console.log(`In-Sample Sharpe: ${wf.inSampleSharpe}`);
console.log(`Out-of-Sample Sharpe: ${wf.outOfSampleSharpe}`);
console.log(`Overfitting Ratio: ${wf.overfitRatio}`);

Monte Carlo Simulation

// Stress test with randomization
const mc = await backtest.monteCarlo({
  strategy: 'momentum',
  trades: historicalTrades,

  // Simulation settings
  simulations: 10000,
  confidenceLevel: 0.95,

  // Randomization
  shuffleTrades: true,
  randomizeReturns: true,
});

console.log(`Expected Return: ${mc.expectedReturn}%`);
console.log(`95% VaR: ${mc.valueAtRisk}%`);
console.log(`Worst Case: ${mc.worstCase}%`);
console.log(`Best Case: ${mc.bestCase}%`);
console.log(`Probability of Profit: ${mc.probProfit}%`);

Performance Metrics

const metrics = await backtest.getMetrics(result);

console.log('=== Performance ===');
console.log(`Total Return: ${metrics.totalReturn}%`);
console.log(`CAGR: ${metrics.cagr}%`);
console.log(`Volatility: ${metrics.volatility}%`);

console.log('=== Risk ===');
console.log(`Sharpe Ratio: ${metrics.sharpeRatio}`);
console.log(`Sortino Ratio: ${metrics.sortinoRatio}`);
console.log(`Max Drawdown: ${metrics.maxDrawdown}%`);
console.log(`Max Drawdown Duration: ${metrics.maxDrawdownDuration} days`);

console.log('=== Trading ===');
console.log(`Total Trades: ${metrics.totalTrades}`);
console.log(`Win Rate: ${metrics.winRate}%`);
console.();
.();
.();
.();

Custom Strategy

// Define custom strategy
const myStrategy = {
  name: 'my-strategy',

  onData: async (data, context) => {
    const price = data.price;
    const sma = data.indicators.sma(20);

    if (price < sma * 0.95 && !context.hasPosition) {
      return { action: 'buy', size: context.availableCapital * 0.1 };
    }

    if (price > sma * 1.05 && context.hasPosition) {
      return { action: 'sell', size: 'all' };
    }

    return { action: 'hold' };
  },
};

const result = await backtest.run({
  strategy: myStrategy,
  startDate: '2024-01-01',
  endDate: '2024-12-31',
});

---

Built-in Strategies

Strategy	Description
momentum	Follow price trends
mean-reversion	Buy dips, sell rallies
arbitrage	Cross-platform price differences
breakout	Enter on range breakouts
pairs	Correlated market pairs

---

Metrics Explained

Metric	Good Value	Description
Sharpe Ratio	> 1.0	Risk-adjusted return
Sortino Ratio	> 1.5	Downside-adjusted return
Max Drawdown	< 20%	Worst peak-to-trough
Win Rate	> 50%	Winning trades %
Profit Factor	> 1.5	Gross profit / gross loss
Expectancy	> 0	Expected $ per trade

---

Best Practices

1. Use walk-forward — Avoid overfitting
2. Include fees — Realistic cost modeling
3. Test multiple periods — Don't cherry-pick dates
4. Monte Carlo — Understand variance
5. Out-of-sample — Always validate on unseen data