npm package discovery and stats viewer.

Discover Tips

  • General search

    [free text search, go nuts!]

  • Package details

    pkg:[package-name]

  • User packages

    @[username]

Sponsor

Optimize Toolset

I’ve always been into building performant and accessible sites, but lately I’ve been taking it extremely seriously. So much so that I’ve been building a tool to help me optimize and monitor the sites that I build to make sure that I’m making an attempt to offer the best experience to those who visit them. If you’re into performant, accessible and SEO friendly sites, you might like it too! You can check it out at Optimize Toolset.

About

Hi, 👋, I’m Ryan Hefner  and I built this site for me, and you! The goal of this site was to provide an easy way for me to check the stats on my npm packages, both for prioritizing issues and updates, and to give me a little kick in the pants to keep up on stuff.

As I was building it, I realized that I was actually using the tool to build the tool, and figured I might as well put this out there and hopefully others will find it to be a fast and useful way to search and browse npm packages as I have.

If you’re interested in other things I’m working on, follow me on Twitter or check out the open source projects I’ve been publishing on GitHub.

I am also working on a Twitter bot for this site to tweet the most popular, newest, random packages from npm. Please follow that account now and it will start sending out packages soon–ish.

Open Software & Tools

This site wouldn’t be possible without the immense generosity and tireless efforts from the people who make contributions to the world and share their work via open source initiatives. Thank you 🙏

© 2024 – Pkg Stats / Ryan Hefner

@alleyway/tardis-dev

v13.29.1-4.a

Published

Convenient access to tick-level historical and real-time cryptocurrency market data via Node.js

Downloads

1,381

Readme

tardis-dev

Version Try on RunKit

Node.js tardis-dev library provides convenient access to tick-level real-time and historical cryptocurrency market data both in exchange native and normalized formats. Instead of callbacks it relies on async iteration (for await ...of) enabling composability features like seamless switching between real-time data streaming and historical data replay or computing derived data locally.

const { replayNormalized, normalizeTrades, normalizeBookChanges } = require('tardis-dev')

const messages = replayNormalized(
  {
    exchange: 'bitmex',
    symbols: ['XBTUSD', 'ETHUSD'],
    from: '2019-05-01',
    to: '2019-05-02'
  },
  normalizeTrades,
  normalizeBookChanges
)

for await (const message of messages) {
  console.log(message)
}

Try this code live on RunKit

Features

  • historical tick-level market data replay backed by tardis.dev HTTP API — includes full order book depth snapshots plus incremental updates, tick-by-tick trades, historical open interest, funding, index, mark prices, liquidations and more

  • consolidated real-time data streaming API connecting directly to exchanges' public WebSocket APIs

  • support for both exchange-native and normalized market data formats (unified format for accessing market data across all supported exchanges — normalized trades, order book and ticker data)
  • transparent historical local data caching (cached data is stored on disk in compressed GZIP format and decompressed on demand when reading the data)
  • support for top cryptocurrency exchanges: BitMEX, Deribit, Binance, FTX, OKEx, Huobi Futures, Huobi Global, Bitfinex, Coinbase Pro, Kraken Futures, Kraken, Bitstamp, Gemini, Poloniex, Bybit, Phemex, Delta Exchange, FTX US, Binance US, Gate.io, OKCoin, bitFlyer, HitBTC, CoinFLEX (2.0), Binance Jersey and more
  • automatic closed connections and stale connections reconnection logic for real-time streams
  • computing derived data locally like order book imbalance, custom trade bars, book snapshots and more via compute helper function and computables, e.g., volume based bars, top 20 levels order book snapshots taken every 10 ms etc.
  • fast and lightweight architecture — low memory footprint and no heavy in-memory buffering

Installation

Requires Node.js v12+ installed.

npm install tardis-dev --save

Documentation

See official docs.

Examples

Real-time spread across multiple exchanges

Example showing how to quickly display real-time spread and best bid/ask info across multiple exchanges at once. It can be easily adapted to do the same for historical data (replayNormalized instead of streamNormalized).

const tardis = require('tardis-dev')
const { streamNormalized, normalizeBookChanges, combine, compute, computeBookSnapshots } = tardis

const exchangesToStream = [
  { exchange: 'bitmex', symbols: ['XBTUSD'] },
  { exchange: 'deribit', symbols: ['BTC-PERPETUAL'] },
  { exchange: 'cryptofacilities', symbols: ['PI_XBTUSD'] }
]
// for each specified exchange call streamNormalized for it
// so we have multiple real-time streams for all specified exchanges
const realTimeStreams = exchangesToStream.map((e) => {
  return streamNormalized(e, normalizeBookChanges)
})

// combine all real-time message streams into one
const messages = combine(...realTimeStreams)

// create book snapshots with depth1 that are produced
// every time best bid/ask info is changed
// effectively computing real-time quotes
const realTimeQuoteComputable = computeBookSnapshots({
  depth: 1,
  interval: 0,
  name: 'realtime_quote'
})

// compute real-time quotes for combines real-time messages
const messagesWithQuotes = compute(messages, realTimeQuoteComputable)

const spreads = {}

// print spreads info every 100ms
setInterval(() => {
  console.clear()
  console.log(spreads)
}, 100)

// update spreads info real-time
for await (const message of messagesWithQuotes) {
  if (message.type === 'book_snapshot') {
    spreads[message.exchange] = {
      spread: message.asks[0].price - message.bids[0].price,
      bestBid: message.bids[0],
      bestAsk: message.asks[0]
    }
  }
}

Try this code live on RunKit

Seamless switching between real-time streaming and historical market data replay

Example showing simple pattern of providing async iterable of market data messages to the function that can process them no matter if it's is real-time or historical market data. That effectively enables having the same 'data pipeline' for backtesting and live trading.

const tardis = require('tardis-dev')
const { replayNormalized, streamNormalized, normalizeTrades, compute, computeTradeBars } = tardis

const historicalMessages = replayNormalized(
  {
    exchange: 'bitmex',
    symbols: ['XBTUSD'],
    from: '2019-08-01',
    to: '2019-08-02'
  },
  normalizeTrades
)

const realTimeMessages = streamNormalized(
  {
    exchange: 'bitmex',
    symbols: ['XBTUSD']
  },
  normalizeTrades
)

async function produceVolumeBasedTradeBars(messages) {
  const withVolumeTradeBars = compute(
    messages,
    computeTradeBars({
      kind: 'volume',
      interval: 100 * 1000 // aggregate by 100k contracts volume
    })
  )

  for await (const message of withVolumeTradeBars) {
    if (message.type === 'trade_bar') {
      console.log(message.name, message)
    }
  }
}

await produceVolumeBasedTradeBars(historicalMessages)

// or for real time data
//  await produceVolumeBasedTradeBars(realTimeMessages)

Try this code live on RunKit

Stream real-time market data in exchange native data format

const { stream } = require('tardis-dev')

const messages = stream({
  exchange: 'bitmex',
  filters: [
    { channel: 'trade', symbols: ['XBTUSD'] },
    { channel: 'orderBookL2', symbols: ['XBTUSD'] }
  ]
})

for await (const message of messages) {
  console.log(message)
}

Try this code live on RunKit

Replay historical market data in exchange native data format

const { replay } = require('tardis-dev')

const messages = replay({
  exchange: 'bitmex',
  filters: [
    { channel: 'trade', symbols: ['XBTUSD'] },
    { channel: 'orderBookL2', symbols: ['XBTUSD'] }
  ],
  from: '2019-05-01',
  to: '2019-05-02'
})

for await (const message of messages) {
  console.log(message)
}

Try this code live on RunKit

See the tardis-dev docs for more examples.