🧿 Backtest Kit

> A TypeScript framework for backtesting and live trading strategies on multi-asset, crypto, forex or DEX (peer-to-peer marketplace), spot, futures with crash-safe persistence, signal validation, and AI optimization.

!screenshot

![TypeScript]()

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

🚀 Quick Start

$3

> Create a production-ready trading bot in seconds:

``bash

Create project with npx (recommended)

npx -y @backtest-kit/sidekick my-trading-bot
cd my-trading-bot
npm start
`

$3

> Want to see the code? 👉 Demo app 👈

`bash
npm install backtest-kit ccxt ollama uuid
`

✨ 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: 350+ 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
- Partial profit/loss levels
- Trailing stop-loss
- Breakeven protection

📚 Code Samples

$3

`typescript
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
});
`

$3

`typescript
import ccxt from 'ccxt';
import { addExchangeSchema, addStrategySchema, addFrameSchema, addRiskSchema } from 'backtest-kit';

// Exchange (data source)
addExchangeSchema({
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
addRiskSchema({
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
addFrameSchema({
frameName: '1d-test',
interval: '1m',
startDate: new Date('2025-12-01'),
endDate: new Date('2025-12-02'),
});
`

$3

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

addStrategySchema({
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 dumpSignalData(resultId, messages, signal); // Log

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

$3

`typescript
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
});
`

$3

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

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

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

$3

- 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.

$3

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



The Math

For a candle with:
- timestamp = candle open time (openTime)
- stepMs = interval duration (e.g., 60000ms for "1m")
- Candle close time = timestamp + stepMs

Alignment: All timestamps are aligned down to interval boundary.
For example, for 15m interval: 00:17 → 00:15, 00:44 → 00:30

Adapter contract:
- First candle.timestamp must equal aligned since
- Adapter must return exactly limit candles
- Sequential timestamps: since + i * stepMs for i = 0..limit-1

How since is calculated from when:
- when = current execution context time (from AsyncLocalStorage)
- alignedWhen = Math.floor(when / stepMs) * stepMs (aligned down to interval boundary)
- since = alignedWhen - limit * stepMs (go back limit candles from aligned when)

Boundary semantics (inclusive/exclusive):
- since is always inclusive — first candle has timestamp === since
- Exactly limit candles are returned
- Last candle has timestamp === since + (limit - 1) * stepMs — inclusive
- For getCandles: alignedWhen is exclusive — candle at that timestamp is NOT included (it's a pending/incomplete candle)
- For getRawCandles: eDate is exclusive — candle at that timestamp is NOT included (it's a pending/incomplete candle)
- For getNextCandles: alignedWhen is inclusive — first candle starts at alignedWhen (it's the current candle for backtest, already closed in historical data)

- getCandles(symbol, interval, limit) - Returns exactly limit candles
- Aligns when down to interval boundary
- Calculates since = alignedWhen - limit * stepMs
- since — inclusive, first candle.timestamp === since
- alignedWhen — exclusive, candle at alignedWhen is NOT returned
- Range: [since, alignedWhen) — half-open interval
- Example: getCandles("BTCUSDT", "1m", 100) returns 100 candles ending before aligned when

- getNextCandles(symbol, interval, limit) - Returns exactly limit candles (backtest only)
- Aligns when down to interval boundary
- since = alignedWhen (starts from aligned when, going forward)
- since — inclusive, first candle.timestamp === since
- Range: [alignedWhen, alignedWhen + limit * stepMs) — half-open interval
- Throws error in live mode to prevent look-ahead bias
- Example: getNextCandles("BTCUSDT", "1m", 10) returns next 10 candles starting from aligned when

- getRawCandles(symbol, interval, limit?, sDate?, eDate?) - Flexible parameter combinations:
- (limit) - since = alignedWhen - limit * stepMs, range [since, alignedWhen)
- (limit, sDate) - since = align(sDate), returns limit candles forward, range [since, since + limit * stepMs)
- (limit, undefined, eDate) - since = align(eDate) - limit * stepMs, eDate — exclusive, range [since, eDate)
- (undefined, sDate, eDate) - since = align(sDate), limit calculated from range, sDate — inclusive, eDate — exclusive, range [sDate, eDate)
- (limit, sDate, eDate) - since = align(sDate), returns limit candles, sDate — inclusive
- All combinations respect look-ahead bias protection (eDate/endTime <= when)

Persistent Cache:
- Cache lookup calculates expected timestamps: since + i * stepMs for i = 0..limit-1
- Returns all candles if found, null if any missing (cache miss)
- Cache and runtime use identical timestamp calculation logic





#### Candle Timestamp Convention:

According to this timestamp of a candle in backtest-kit is exactly the openTime, not ~~closeTime~~

Key principles:
- All timestamps are aligned down to interval boundary
- First candle.timestamp must equal aligned since
- Adapter must return exactly limit candles
- Sequential timestamps: since + i * stepMs

$3

Why align timestamps to interval boundaries?

Because candle APIs return data starting from exact interval boundaries:

`typescript
// 15-minute interval example:
when = 1704067920000 // 00:12:00
step = 15 // 15 minutes
stepMs = 15 * 60000 // 900000ms

// Alignment: round down to nearest interval boundary
alignedWhen = Math.floor(when / stepMs) * stepMs
// = Math.floor(1704067920000 / 900000) * 900000
// = 1704067200000 (00:00:00)

// Calculate since for 4 candles backwards:
since = alignedWhen - 4 * stepMs
// = 1704067200000 - 4 * 900000
// = 1704063600000 (23:00:00 previous day)

// Expected candles:
// [0] timestamp = 1704063600000 (23:00)
// [1] timestamp = 1704064500000 (23:15)
// [2] timestamp = 1704065400000 (23:30)
// [3] timestamp = 1704066300000 (23:45)
`

Pending candle exclusion: The candle at 00:00:00 (alignedWhen) is NOT included in the result. At when=00:12:00, this candle covers the period 00:00, 00:15) and is still open (pending). Pending candles have incomplete OHLCV data that would distort technical indicators. Only fully closed candles are returned.

Validation is applied consistently across:
- ✅ getCandles() - validates first timestamp and count
- ✅ getNextCandles() - validates first timestamp and count
- ✅ getRawCandles() - validates first timestamp and count
- ✅ Cache read - calculates exact expected timestamps
- ✅ Cache write - stores validated candles

Result: Deterministic candle retrieval with exact timestamp matching.

$3

All candle timestamp alignment uses UTC (Unix epoch). For intervals like 4h, boundaries are 00:00, 04:00, 08:00, 12:00, 16:00, 20:00 UTC. If your local timezone offset is not a multiple of the interval, the since timestamps will look "uneven" in local time.

For example, in UTC+5 the same 4h candle request logs as:

`
since: Sat Sep 20 2025 13:00:00 GMT+0500 ← looks uneven (13:00)
since: Sat Sep 20 2025 17:00:00 GMT+0500 ← looks uneven (17:00)
since: Sat Sep 20 2025 21:00:00 GMT+0500 ← looks uneven (21:00)
since: Sun Sep 21 2025 05:00:00 GMT+0500 ← looks uneven (05:00)
`

But in UTC these are perfectly aligned 4h boundaries:

`
since: Sat, 20 Sep 2025 08:00:00 GMT ← 08:00 UTC ✓
since: Sat, 20 Sep 2025 12:00:00 GMT ← 12:00 UTC ✓
since: Sat, 20 Sep 2025 16:00:00 GMT ← 16:00 UTC ✓
since: Sun, 21 Sep 2025 00:00:00 GMT ← 00:00 UTC ✓
`

Use toUTCString() or toISOString() in callbacks to see the actual aligned UTC times.

$3

- 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 - all time boundaries are enforced
- Same code works in both backtest and live modes
- Boundary semantics prevent edge cases in signal generation

🧠 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.

$3

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

`typescript
Backtest.background('BTCUSDT', config);

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

$3

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

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

⚔️ Think of it as...

Open-source QuantConnect/MetaTrader 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#/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

🌍 Ecosystem

The backtest-kit ecosystem extends beyond the core library, offering complementary packages and tools to enhance your trading system development experience:

$3

> Explore on NPM 📜

The @backtest-kit/pinets package lets you run TradingView Pine Script strategies directly in Node.js. Port your existing Pine Script indicators to backtest-kit with zero rewrite using the PineTS runtime.

#### Key Features
- 📜 Pine Script v5/v6: Native TradingView syntax with 1:1 compatibility
- 🎯 60+ Indicators: SMA, EMA, RSI, MACD, Bollinger Bands, ATR, Stochastic built-in
- 📁 File or Code: Load .pine files or pass code strings directly
- 🗺️ Plot Extraction: Flexible mapping from Pine plot() outputs to structured signals
- ⚡ Cached Execution: Memoized file reads for repeated strategy runs

#### Use Case
Perfect for traders who already have working TradingView strategies. Instead of rewriting your Pine Script logic in JavaScript, simply copy your .pine file and use getSignal() to extract trading signals. Works seamlessly with backtest-kit's temporal context - no look-ahead bias possible.

#### Get Started
`bash
npm install @backtest-kit/pinets pinets backtest-kit
`

$3

> Explore on NPM 📊

The @backtest-kit/ui package is a full-stack UI framework for visualizing cryptocurrency trading signals, backtests, and real-time market data. Combines a Node.js backend server with a React dashboard - all in one package.

#### Key Features
- 📈 Interactive Charts: Candlestick visualization with Lightweight Charts (1m, 15m, 1h timeframes)
- 🎯 Signal Tracking: View opened, closed, scheduled, and cancelled signals with full details
- 📊 Risk Analysis: Monitor risk rejections and position management
- 🔔 Notifications: Real-time notification system for all trading events
- 💹 Trailing & Breakeven: Visualize trailing stop/take and breakeven events
- 🎨 Material Design: Beautiful UI with MUI 5 and Mantine components

#### Use Case
Perfect for monitoring your trading bots in production. Instead of building custom dashboards, @backtest-kit/ui provides a complete visualization layer out of the box. Each signal view includes detailed information forms, multi-timeframe candlestick charts, and JSON export for all data.

#### Get Started
`bash
npm install @backtest-kit/ui backtest-kit ccxt
`

$3

> Explore on NPM 🤖

The @backtest-kit/ollama package is a multi-provider LLM inference library that supports 10+ providers including OpenAI, Claude, DeepSeek, Grok, Mistral, Perplexity, Cohere, Alibaba, Hugging Face, and Ollama with unified API and automatic token rotation.

#### Key Features
- 🔌 10+ LLM Providers: OpenAI, Claude, DeepSeek, Grok, Mistral, Perplexity, Cohere, Alibaba, Hugging Face, Ollama
- 🔄 Token Rotation: Automatic API key rotation for Ollama (others throw clear errors)
- 🎯 Structured Output: Enforced JSON schema for trading signals (position, price levels, risk notes)
- 🔑 Flexible Auth: Context-based API keys or environment variables
- ⚡ Unified API: Single interface across all providers
- 📊 Trading-First: Built for backtest-kit with position sizing and risk management

#### Use Case
Ideal for building multi-provider LLM strategies with fallback chains and ensemble predictions. The package returns structured trading signals with validated TP/SL levels, making it perfect for use in getSignal functions. Supports both backtest and live trading modes.

#### Get Started
`bash
npm install @backtest-kit/ollama agent-swarm-kit backtest-kit
`

$3

> Explore on NPM 📊

The @backtest-kit/signals package is a technical analysis and trading signal generation library designed for AI-powered trading systems. It computes 50+ indicators across 4 timeframes and generates markdown reports optimized for LLM consumption.

#### Key Features
- 📈 Multi-Timeframe Analysis: 1m, 15m, 30m, 1h with synchronized indicator computation
- 🎯 50+ Technical Indicators: RSI, MACD, Bollinger Bands, Stochastic, ADX, ATR, CCI, Fibonacci, Support/Resistance
- 📊 Order Book Analysis: Bid/ask depth, spread, liquidity imbalance, top 20 levels
- 🤖 AI-Ready Output: Markdown reports formatted for LLM context injection
- ⚡ Performance Optimized: Intelligent caching with configurable TTL per timeframe

#### Use Case
Perfect for injecting comprehensive market context into your LLM-powered strategies. Instead of manually calculating indicators, @backtest-kit/signals provides a single function call that adds all technical analysis to your message context. Works seamlessly with getSignal function in backtest-kit strategies.

#### Get Started
`bash
npm install @backtest-kit/signals backtest-kit
`

$3

> Explore on NPM 🧿

The @backtest-kit/sidekick package is the easiest way to create a new Backtest Kit trading bot project. Like create-react-app, but for algorithmic trading.

#### Key Features
- 🚀 Zero Config: Get started with one command - no setup required
- 📦 Complete Template: Includes backtest strategy, risk management, and LLM integration
- 🤖 AI-Powered: Pre-configured with DeepSeek, Claude, and GPT-5 fallback chain
- 📊 Technical Analysis: Built-in 50+ indicators via @backtest-kit/signals
- 🔑 Environment Setup: Auto-generated .env with all API key placeholders
- 📝 Best Practices: Production-ready code structure with examples

#### Use Case
The fastest way to bootstrap a new trading bot project. Instead of manually setting up dependencies, configurations, and boilerplate code, simply run one command and get a working project with LLM-powered strategy, multi-timeframe technical analysis, and risk management validation.

#### Get Started
`bash
npx -y @backtest-kit/sidekick my-trading-bot
cd my-trading-bot
npm start
``

🤖 Are you a robot?

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

✅ Tested & Reliable

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

🤝 Contribute

Fork/PR on GitHub.

📜 License

MIT © tripolskypetr