Displaying 1 to 6 from 6 results

StockSharp - Algorithmic trading and quantitative trading open source platform to develop trading robots (stock markets, forex, bitcoins and options)

  •    CSharp

StockSharp (shortly S#) – are free set of programs for trading at any markets of the world (American, European, Asian, Russian, stocks, futures, options, Bitcoins, forex, etc.). You will be able to trade manually or automated trading (algorithmic trading robots, conventional or HFT).Available connections: FIX/FAST, LMAX, Rithmic, Fusion/Blackwood, Interactive Brokers, OpenECry, Sterling, IQFeed, ITCH, FXCM, QuantHouse, E*Trade, BTCE, BitStamp and many other. Any broker or partner broker (benefits).

tribeca - A high frequency, market making cryptocurrency trading platform in node.js

  •    TypeScript

tribeca is a very low latency cryptocurrency market making trading bot with a full featured web client, backtester, and supports direct connectivity to several cryptocoin exchanges. On modern hardware, it can react to market data by placing and canceling orders in under a millisecond. Runs on the latest node.js (v7.8 or greater). Persistence is acheived using mongodb. Installation is recommended via Docker, but manual installation is also supported.

crypto-algotrading - Algorithmic trading framework for cryptocurrencies.

  •    Python

Crypto AlgoTrading Framework is a repository with tools to build and run working trading bots, backtest strategies, assist on trading, define simple stop losses and trailing stop losses, etc. This framework work with data directly from Crypto exchanges API, from a DB or csv files. Can be used for data-driven and event-driven systems. Made exclusively for crypto markets for now and written in Python. In realtime, Trading Bot operates in real time, with live data from exchanges APIs. It doesn't need pre stored data or DB to work. In this mode, bot can trade real money, simulate or alert user when is time to buy or sell, based on entry and exit strategies defined by user. Can also simulate user's strategies and present the results in real time.




example-hftish - Example Order Book Imbalance Algorithm

  •    Python

The aim of this algorithm is to capture slight moves in the bid/ask spread as they happen. It is only intended to work for high-volume stocks where there are frequent moves of 1 cent exactly. It is one of the trading strategies based on order book imbalance. For more details about it, please refer to Darryl Shen, 2015 or other online articles. This algorithm will make many trades on the same security each day, so any account running it will quickly encounter PDT rules. Please make sure your account balance is well above $25,000 before running this script in a live environment.

neural-finance - Neural Network for HFT-trading [experimental]

  •    Python

Neural Network for High Frequency Trading. Right now there is no doc. If you found something useful for you - star this repo. I really appreciate it.





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