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Ekliptor

WolfBot

Crypto currency trading bot written in TypeScript for NodeJS.

ApplicationsSpecialistsTypeScript
Open on GitHub
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788
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225
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8
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Created 2018-04-23 · Updated 2026-10-02 · #13814 today
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EkliptorkirschteXL-Reaper

Ekliptor/WolfBot is ranked 13,814 with 788 stars.

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Ekliptor/WolfBot is ranked 13,814 among these 8 peers; agnaistic/agnai is ranked 13,815.

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Key parameters and specifications
Key parameters and specifications
ParameterValue
LanguageTypeScript
CategoryApplications
SubcategorySpecialists
KeywordsFinance
Stars788
Forks225
Contributors8
Created date2018-04-23
Updated date2026-10-02
Stars in 1 day0
Stars in 7 days0
Rank13,814
Previous rank13,707
Rank change-107
7-day stars rank14,134
Previous 7-day stars rank14,155
7-day stars rank change21
Total stars rank13,519
Previous total stars rank13,504
Total stars rank change-15
CountryGermany
README summary

WolfBot This is the source of the Crypto currency trading bot running on: It is written in TypeScript for NodeJS + MongoDB. Features **Trading**: buying + selling, portfolio management (sync balances with exchanges) **Margin Trading**: leveraged trading including short selling and futures trading **Arbitrage**: profit from price differences between 2 exchanges (done "on the books" with balances on both exchanges, no withdrawals from exchanges required) **Lending**: lend your coins on the lending market of supported crypto exchanges for the highest possible interest

rates **Backtesting**: test your trade strategies in simulation on historical data **Web Plugins**: Access social media data from Twitter, Reddit, Telegram Channels, RSS Feeds,. to trade based on news and real world events (not part of open source version yet) Key trading advantages **Indicators**: Over 200 technical indicators (MACD, RSI, EMA,. ), candlestick pattern recognition (Doji, Tri-Star,. ) of 20+ most common patterns, Fibonacci Retracements (upwards & downwards), automatic trendline (support & resistance) detection **Strategy events**: All strategies emit buy/sell events that

can be forwarded to other strategies using different candle sizes before trade execution. This gives you the ability to easily configure your bot to “zoom in” on candlestick chart data (for example from 12h candles trendline to 1h MACD to 10min RSI). **Realtime**: Trades come in realtime via websocket connection from the exchange. Strategies can process (and react) on every single trade. Furthermore all indicators can be updated within the current (latest) candle as new data comes in. **Backtesting**: Advanced

automatic parameter optimization using: Screenshots of the Trading UI: Trading Chart Live Strategy data Strategy configuration A more detailed list of all features: The list of supported exchanges: Additionally WolfBot supports over 130 exchanges using CCXT Library (no WebSockets, no margin trading). The full strategy documentation: Getting Started Requirements Installation You can use the flag if you only want to run the bot and not make any code changes. Start trading Rename the file in the project root directory to

and add at least (plus some exchange API keys if you want to trade). After running TypeScript (automatically in your IDE or run the command in the project root dir) you will see a file: will show you some errors (due to shared code with missing types between server and client side, I will refactor this later). Just ignore these errors and make sure is not set (the default) and that you have a dir in the project root as

well as for all packages under which contain a file. Use the directory as the working directory and run: The parameter must be a JSON file from the directory. For a list of all parameters look at the top of the file. Docker support To you can install WolfBot with all its dependencies using Docker: Writing your own trading strategies There is documentation available here: Look into the folder for more examples. Incoming trades flow diagram Outgoing trades flow diagram

Modifying the UI In the project root directory, run: You should also run the bot with the flag so that

Frequently asked questions

How many stars and forks are recorded?

Ekliptor/WolfBot has 788 stars and 225 forks.

What is the repository rank?

Ekliptor/WolfBot is ranked 13,814.

What language and category are recorded?

Ekliptor/WolfBot has 788 stars, uses TypeScript and belongs to Applications.

Which repository is closest in rank?

Ekliptor/WolfBot is compared with agnaistic/agnai, ranked 13,815 with 788 stars.

README

WolfBot

This is the source of the Crypto currency trading bot running on: https://wolfbot.org

It is written in TypeScript for NodeJS + MongoDB.

Features

  • Trading: buying + selling, portfolio management (sync balances with exchanges)
  • Margin Trading: leveraged trading including short selling and futures trading
  • Arbitrage: profit from price differences between 2 exchanges (done "on the books" with balances on both exchanges, no withdrawals from exchanges required)
  • Lending: lend your coins on the lending market of supported crypto exchanges for the highest possible interest rates
  • Backtesting: test your trade strategies in simulation on historical data
  • Web Plugins: Access social media data from Twitter, Reddit, Telegram Channels, RSS Feeds,... to trade based on news and real world events (not part of open source version yet)

Key trading advantages

  • Indicators: Over 200 technical indicators (MACD, RSI, EMA,..), candlestick pattern recognition (Doji, Tri-Star,...) of 20+ most common patterns, Fibonacci Retracements (upwards & downwards), automatic trendline (support & resistance) detection
  • Strategy events: All strategies emit buy/sell events that can be forwarded to other strategies using different candle sizes before trade execution. This gives you the ability to easily configure your bot to “zoom in” on candlestick chart data (for example from 12h candles trendline to 1h MACD to 10min RSI).
  • Realtime: Trades come in realtime via websocket connection from the exchange. Strategies can process (and react) on every single trade. Furthermore all indicators can be updated within the current (latest) candle as new data comes in.
  • Backtesting: Advanced automatic parameter optimization using:
    • the Cartesian product to try all permutations of given config parameters or
    • a genetic algorithm to find the most profitable config parameters within given parameter ranges

Screenshots of the Trading UI:

Trading Chart

Live Strategy data

Strategy configuration

A more detailed list of all features: https://wolfbot.org/features/

The list of supported exchanges: https://wolfbot.org/features/exchanges/

Additionally WolfBot supports over 130 exchanges using CCXT Library (no WebSockets, no margin trading).

The full strategy documentation: https://wolfbot.org/strategy-docs/

Getting Started

Requirements

NodeJS >= 12 && <= 14
MongoDB >= 4.0
TypeScript >= 3.5
yarn >= 1.9.4 (npm should work too, but no support given if you run into errors)
Webpack >= 4 (only for UI modifications)

Installation

git clone https://github.com/Ekliptor/WolfBot
yarn install

You can use the --production flag if you only want to run the bot and not make any code changes.

Start trading

Rename the configLocal-sample.ts file in the project root directory to configLocal.ts and add at least mongoUrl (plus some exchange API keys if you want to trade).

After running TypeScript (automatically in your IDE or run the tsc command in the project root dir) you will see a file:

build/app.js

tsc will show you some errors (due to shared code with missing types between server and client side, I will refactor this later). Just ignore these errors and make sure noEmitOnError is not set (the default) and that you have a build/ dir in the project root as well as for all packages under node_modules/@ekliptor which contain a tsconfig.json file. Use the build directory as the working directory and run:

node app.js --debug --config=Noop --trader=RealTimeTrader --noUpdate --noBrowser

The config parameter must be a JSON file from the config directory. For a list of all parameters look at the top of the app.ts file.

Docker support

To you can install WolfBot with all its dependencies using Docker:

docker-compose up

Writing your own trading strategies

There is documentation available here: https://forum.wolfbot.org/forums/strategy-development.17/

Look into the /src/Strategies folder for more examples.

Incoming trades flow diagram Outgoing trades flow diagram

Modifying the UI

In the project root directory, run:

yarn watch

You should also run the bot with the --uiDev flag so that changes to HTML template files are reloaded from disk.

The UI uses a single persistent WebSocket connection. UI related code is in the following directories:

├ project root
├─── public <- all code shipped to the browser
├────── js <- all TypeScript code to be compiled with WebPack
├─── src
├────── WebSocket <- all server-side logic to push updates to the browser
├─── views <- all HTML templates

REST and WebSocket JSON APIs

If you want to connect WolfBot to your trading terminal, please take a look at the API docs.

This let's you:

  • create new cloud bot instances and earn commission for every referral (not applicable to open source version)
  • import the trades book with all trades WolfBot made via REST API call
  • subscribe to live trades via WebSocket to display them in your trading terminal
  • subscribe to live strategy data and indicator values via WebSocket

Contact

Follow me on Twitter and Memo.

No donations, I actually make money trading;)

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