bfx-hf-backtest

Bitfinex Honey Framework Backtesting Tools for Node.JS

This repo provides an interface for executing backtests using either offline data, or a bfx-hf-data-server instance for historical Bitfinex market data.

Features

• Offline backtest execution with user-supplied trade & candle data
• Online backtest execution with data from bfx-hf-data-server
• Simulates trades within a candle if none are provided

Installation

npm i --save bfx-hf-backtest

Quickstart

const HFS = require('bfx-hf-strategy')
const HFBT = require('bfx-hf-backtest')
const Strategy = ... // strategy instance
const candles = [/* ... */]

const candleKey = HFS.candleMarketDataKey({
  symbol: SYMBOLS.BTC_USD,
  tf: TIME_FRAMES.ONE_HOUR
})

HFBT.execOffline(strat, {
  trades: {},
  candles: {
    [candleKey]: candles,
  }
}).then((btState) => {
  const { trades = [] } = btState

  // analyze backtest trades...
})

Docs

Refer to docs/exec.md for JSDoc-generated API documentation, and the examples/ folder for executable examples.

Examples

Offline Backtests

To execute a backtest of a trading strategy using historical data, the execOffline method is provided which will run the strategy against each trade & candle in-order by timestamp:

const HFS = require('bfx-hf-strategy')
const HFBT = require('bfx-hf-backtest')

const EMAStrategy = require('bfx-hf-strategy/examples/ema_cross')
const { Candle } = require('bfx-api-node-models')
const { SYMBOLS, TIME_FRAMES } = require('bfx-hf-util')
const rawCandleData = require('./btc_candle_data.json')

// During real execution, candles can arrive from any market/at any time (if
// sub'ed to multiple time frames); hence, each candle must include its origin
// symbol/time frame pair.
const market = {
  symbol: SYMBOLS.BTC_USD,
  tf: TIME_FRAMES.ONE_HOUR
}

const candleKey = HFS.candleMarketDataKey(market)
const strat = EMAStrategy(market)
const candles = rawCandleData
  .sort((a, b) => a[0] - b[0])
  .map(c => ({
    ...(new Candle(c).toJS()),
    ...market // attach market data
  }))

const run = async () => {
  await HFBT.execOffline(strat, {
    trades: {},
    candles: {
      [candleKey]: candles,
    }
  })
}

try {
  run()
} catch (e) {
  console.error(e)
}

Online Backtests

Online backtests are executed a running bfx-hf-data-server instance, which will automatically synchronize historical data as needed and pass it to the backtesting logic:

const HFBT = require('bfx-hf-backtest')
const EMAStrategy = require('bfx-hf-strategy/examples/ema_cross')
const { SYMBOLS, TIME_FRAMES } = require('bfx-hf-util')

const now = Date.now()
const market = {
  symbol: SYMBOLS.XMR_USD,
  tf: TIME_FRAMES.ONE_MINUTE
}

const strat = EMAStrategy(market)
const run = async () => {
  await HFBT.execOnline([strat], {
    exchange: 'bitfinex',
    from: now - (2 * 24 * 60 * 60 * 1000),
    to: now,
    trades: true,
    candles: true,
    ...market
  })
}

try {
  run()
} catch (e) {
  console.error(e)
}

Dazaar Market Data

Dazaar Market data can be used to run a strategy:

const hypercore = require('hypercore')
const Hyperbee = require('hyperbee')
const replicate = require('@hyperswarm/replicator')

const HFBT = require('bfx-hf-backtest')
const { SYMBOLS, TIME_FRAMES } = require('bfx-hf-util')
const EMAStrategy = require('bfx-hf-strategy/examples/ema_cross')

const hopts = {}

const hbOpts = {
  valueEncoding: 'json'
}

const strat = EMAStrategy(market)

// pu in your dazaar market data key here
const key = Buffer.from('7c6057e7ac2f19fcacbee2853554b9a24da85797b1cef31b330465d0f24d7fb1', 'hex')
const feed = hypercore(path.join(__dirname, 'dbs', 'testCOPY'), key, { sparse: true })

const db = new Hyperbee(feed, hbOpts)

db.feed.ready(async () => {
  replicate(feed, { lookup: true, live: true })

  const { exec, onEnd } = await HFBT.execStream(strat, market, {
    from,
    to
  })

  // dazaar market data 5min candles
  const kp = 'c!5m!'

  const from = 1358182044000
  const to = 1358342119000

  const s = db.createReadStream({ gte: kp + from, lte: kp + to })

  let btState
  s.on('data', async (data) => {
    const { key, value } = data
    btState = await exec(key, value)
  })

  s.on('end', async () => {
    btState = await onEnd(btState)
  })
})

Contributing

1. Fork it
2. Create your feature branch (git checkout -b my-new-feature)
3. Commit your changes (git commit -am 'Add some feature')
4. Push to the branch (git push origin my-new-feature)
5. Create a new Pull Request