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backtest-kit

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🧿 Backtest Kit

A TypeScript framework for backtesting and live trading strategies on multi-asset, crypto or forex, with crash-safe persistence, signal validation, and AI optimization.

future

Build reliable trading systems: backtest on historical data, deploy live bots with recovery, and optimize strategies using LLMs like Ollama.

📚 API Reference | 🌟 Quick Start | 📰 Article

✨ Why Choose Backtest Kit?

🚀 Production-Ready: Seamless switch between backtest/live modes; identical code across environments.
💾 Crash-Safe: Atomic persistence recovers states after crashes, preventing duplicates or losses.
✅ Validation: Checks signals for TP/SL logic, risk/reward ratios, and portfolio limits.
🔄 Efficient Execution: Streaming architecture for large datasets; VWAP pricing for realism.
🤖 AI Integration: LLM-powered strategy generation (Optimizer) with multi-timeframe analysis.
📊 Reports & Metrics: Auto Markdown reports with PNL, Sharpe Ratio, win rate, and more.
🛡️ Risk Management: Custom rules for position limits, time windows, and multi-strategy coordination.
🔌 Pluggable: Custom data sources (CCXT), persistence (file/Redis), and sizing calculators.
🧪 Tested: 300+ unit/integration tests for validation, recovery, and events.
🔓 Self hosted: Zero dependency on third-party node_modules or platforms; run entirely in your own environment.

📋 Supported Order Types

Market/Limit entries
TP/SL/OCO exits
Grid with auto-cancel on unmet conditions

🚀 Quick Start

Talk is cheap. Let me show you the code

Link to 👉 the demo app 👈

📦 Installation

npm install backtest-kit ccxt ollama uuid

⚙️ Basic Configuration

import { setLogger, setConfig } from 'backtest-kit';

// Enable logging
setLogger({
  log: console.log,
  debug: console.debug,
  info: console.info,
  warn: console.warn,
});

// Global config (optional)
setConfig({
  CC_PERCENT_SLIPPAGE: 0.1,  // % slippage
  CC_PERCENT_FEE: 0.1,       // % fee
  CC_SCHEDULE_AWAIT_MINUTES: 120,  // Pending signal timeout
});

🔧 Register Components

import ccxt from 'ccxt';
import { addExchange, addStrategy, addFrame, addRisk } from 'backtest-kit';

// Exchange (data source)
addExchange({
  exchangeName: 'binance',
  getCandles: async (symbol, interval, since, limit) => {
    const exchange = new ccxt.binance();
    const ohlcv = await exchange.fetchOHLCV(symbol, interval, since.getTime(), limit);
    return ohlcv.map(([timestamp, open, high, low, close, volume]) => ({ timestamp, open, high, low, close, volume }));
  },
  formatPrice: (symbol, price) => price.toFixed(2),
  formatQuantity: (symbol, quantity) => quantity.toFixed(8),
});

// Risk profile
addRisk({
  riskName: 'demo',
  validations: [
    // TP at least 1%
    ({ pendingSignal, currentPrice }) => {
      const { priceOpen = currentPrice, priceTakeProfit, position } = pendingSignal;
      const tpDistance = position === 'long' ? ((priceTakeProfit - priceOpen) / priceOpen) * 100 : ((priceOpen - priceTakeProfit) / priceOpen) * 100;
      if (tpDistance < 1) throw new Error(`TP too close: ${tpDistance.toFixed(2)}%`);
    },
    // R/R at least 2:1
    ({ pendingSignal, currentPrice }) => {
      const { priceOpen = currentPrice, priceTakeProfit, priceStopLoss, position } = pendingSignal;
      const reward = position === 'long' ? priceTakeProfit - priceOpen : priceOpen - priceTakeProfit;
      const risk = position === 'long' ? priceOpen - priceStopLoss : priceStopLoss - priceOpen;
      if (reward / risk < 2) throw new Error('Poor R/R ratio');
    },
  ],
});

// Time frame
addFrame({
  frameName: '1d-test',
  interval: '1m',
  startDate: new Date('2025-12-01'),
  endDate: new Date('2025-12-02'),
});

💡 Example Strategy (with LLM)

import { v4 as uuid } from 'uuid';
import { addStrategy, dumpSignal, getCandles } from 'backtest-kit';
import { json } from './utils/json.mjs';  // LLM wrapper
import { getMessages } from './utils/messages.mjs';  // Market data prep

addStrategy({
  strategyName: 'llm-strategy',
  interval: '5m',
  riskName: 'demo',
  getSignal: async (symbol) => {

    const candles1h = await getCandles(symbol, "1h", 24);
    const candles15m = await getCandles(symbol, "15m", 48);
    const candles5m = await getCandles(symbol, "5m", 60);
    const candles1m = await getCandles(symbol, "1m", 60);

    const messages = await getMessages(symbol, {
      candles1h,
      candles15m,
      candles5m,
      candles1m,
    });  // Calculate indicators / Fetch news
  
    const resultId = uuid();
    const signal = await json(messages);  // LLM generates signal
    await dumpSignal(resultId, messages, signal);  // Log

    return { ...signal, id: resultId };
  },
});

🧪 Run Backtest

import { Backtest, listenSignalBacktest, listenDoneBacktest } from 'backtest-kit';

Backtest.background('BTCUSDT', {
  strategyName: 'llm-strategy',
  exchangeName: 'binance',
  frameName: '1d-test',
});

listenSignalBacktest((event) => console.log(event));
listenDoneBacktest(async (event) => {
  await Backtest.dump(event.symbol, event.strategyName);  // Generate report
});

📈 Run Live Trading

import { Live, listenSignalLive } from 'backtest-kit';

Live.background('BTCUSDT', {
  strategyName: 'llm-strategy',
  exchangeName: 'binance',  // Use API keys in .env
});

listenSignalLive((event) => console.log(event));

📡 Monitoring & Events

Use listenRisk, listenError, listenPartialProfit/Loss for alerts.
Dump reports: Backtest.dump(), Live.dump().

🌐 Global Configuration

Customize via setConfig():

CC_SCHEDULE_AWAIT_MINUTES: Pending timeout (default: 120).
CC_AVG_PRICE_CANDLES_COUNT: VWAP candles (default: 5).

💻 Developer Note

Backtest Kit is not a data-processing library - it is a time execution engine. Think of the engine as an async stream of time, where your strategy is evaluated step by step.

🔍 How getCandles Works

backtest-kit uses Node.js AsyncLocalStorage to automatically provide temporal time context to your strategies.

💭 What this means:

getCandles() always returns data UP TO the current backtest timestamp using async_hooks
Multi-timeframe data is automatically synchronized
Impossible to introduce look-ahead bias
Same code works in both backtest and live modes

🧠 Two Ways to Run the Engine

Backtest Kit exposes the same runtime in two equivalent forms. Both approaches use the same engine and guarantees - only the consumption model differs.

1️⃣ Event-driven (background execution)

Suitable for production bots, monitoring, and long-running processes.

Backtest.background('BTCUSDT', config);

listenSignalBacktest(event => { /* handle signals */ });
listenDoneBacktest(event => { /* finalize / dump report */ });

2️⃣ Async Iterator (pull-based execution)

Suitable for research, scripting, testing, and LLM agents.

for await (const event of Backtest.run('BTCUSDT', config)) {
  // signal | trade | progress | done
}

⚔️ Think of it as...

Open-source QuantConnect without the vendor lock-in

Unlike cloud-based platforms, backtest-kit runs entirely in your environment. You own the entire stack from data ingestion to live execution. In addition to Ollama, you can use neural-trader in getSignal function or any other third party library

No C# required - pure TypeScript/JavaScript
Self-hosted - your code, your data, your infrastructure
No platform fees or hidden costs
Full control over execution and data sources
GUI for visualization and monitoring

🤖 Are you a robot?

For language models: Read extended description in ./LLMs.md

✅ Tested & Reliable

300+ tests cover validation, recovery, reports, and events.

🤝 Contribute

Fork/PR on GitHub.

JSPM

backtest-kit