Displaying 1 to 20 from 24 results

Zipline - A Pythonic Algorithmic Trading Library

  •    Python

Zipline is a Pythonic algorithmic trading library. It is an event-driven system that supports both backtesting and live-trading. Zipline is currently used in production as the backtesting and live-trading engine powering Quantopian -- a free, community-centered, hosted platform for building and executing trading strategies.Note: Installing Zipline via pip is slightly more involved than the average Python package. Simply running pip install zipline will likely fail if you've never installed any scientific Python packages before.

catalyst - An Algorithmic Trading Library for Crypto-Assets in Python

  •    Python

Catalyst is an algorithmic trading library for crypto-assets written in Python. It allows trading strategies to be easily expressed and backtested against historical data (with daily and minute resolution), providing analytics and insights regarding a particular strategy's performance. Catalyst also supports live-trading of crypto-assets starting with four exchanges (Binance, Bitfinex, Bittrex, and Poloniex) with more being added over time. Catalyst empowers users to share and curate data and build profitable, data-driven investment strategies. Please visit catalystcrypto.io to learn more about Catalyst. Catalyst builds on top of the well-established Zipline project. We did our best to minimize structural changes to the general API to maximize compatibility with existing trading algorithms, developer knowledge, and tutorials. Join us on the Catalyst Forum for questions around Catalyst, algorithmic trading and technical support. We also have a Discord group with the #catalyst_dev and #catalyst_setup dedicated channels.

Robinhood - Unofficial Documentation of Robinhood Trade's Private API

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Robinhood is a commission-free, online securities brokerage. As you would expect, being an online service means everything is handled through a request that is made to a specific URL. Before I go too far, I must say that this is a big, messy work in progress. I'll continue to update this as I figure more out. Sections marked TODO are in my head but I haven't found the time to describe them yet. Work in progress and all.

alphalens - Performance analysis of predictive (alpha) stock factors

  •    Jupyter

Alphalens is a Python Library for performance analysis of predictive (alpha) stock factors. Alphalens works great with the Zipline open source backtesting library, and Pyfolio which provides performance and risk analysis of financial portfolios.Check out the example notebooks for more on how to read and use the factor tear sheet.




quantmod - Quantitative Financial Modelling Framework

  •    R

quantmod is an R package that provides a framework for quantitative financial modeling and trading. It provides a rapid prototyping environment that makes modeling easier by removing the repetitive workflow issues surrounding data management and visualization. Ask your question on Stack Overflow or the R-SIG-Finance mailing list (you must subscribe to post).

IBCSharp

  •    DotNet

IBCSharp consists of an improved version of Karl Schulze's Interactive Brokers C# API, a WinForms C# algorithmic daytrading program, and a WinForms C# long term trading program.

StockSharp

  •    CSharp

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algo-coin - Algorithmic trading cryptocurrencies across multiple exchanges

  •    Javascript

Algorithmic Trading Bitcoin. Lightweight, extensible program for algorithmically trading cryptocurrencies and derivatives across multiple exchanges.


automated-trading - Automated Trading: Trading View Strategies => Bitfinex, itBit, DriveWealth

  •    Javascript

That is to say, this is already a working solution for placing automated trades with various exchanges via Trading View strategies. Resources. Email. Meeting.

PyTrendFollow - PyTrendFollow - systematic futures trading using trend following

  •    Python

This program trades futures using a systematic trend following strategy, similar to most managed futures hedge funds. It produces returns of around ~20% per year, based on a volatility of 25%. You can read more about trend following in the /docs folder. Start with introduction to trend following. If you just want to play with futures data, see working with prices. It is recommended (though not required) to have data subscriptions for both Quandl and IB. Quandl has more historical contracts and works well for backtesting, while IB data is usually updated more frequently and is better for live trading.

marketplace - Data Marketplace documentation: https://enigmampc.github.io/marketplace

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The Enigma Data Marketplace is the platform layer that lays in between the protocol and application layers of the Enigma network. It provides the decentralized and secure data infrastructure on top of which applications can be built, like Catalyst. The Data Marketplace is currently in Phase 1 of its development. The current implementation includes the on-chain portion, dealing with contextual information about data-sets, namespaces, and subscriptions. The on-chain logic is coded in smart contracts deployed on the Ethereum network, and is operated directly with Enigma tokens (ENG). In this first implementation, all data sets are provided off-chain by several providers, and their storage is managed independently from the logic embedded in the smart contract.

gobacktest - event-driven backtesting framework written in golang

  •    Go

Heads up: This is a framework in development, with only basic functionality. An event-driven backtesting framework to test stock trading strategies based on fundamental analysis. Preferably this package will be the core of a backend service exposed via a REST API.

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.

alpaca-zipline - Alpaca riding on a zipline

  •    Python

This is the Alpaca official docker container packages zipline. With this container image, you can easily run your Quantopian algorithm with live trading. This is to run your algorithm in your computer, or server by yourself, and Alpaca is also desiging a "one-click" solution to run your algorithm without having your server. All you need is docker installed in your system.

pylivetrader - Python live trade execution library with zipline interface.

  •    Python

pylivetrader is a simple python live trading framework with zipline interface. The main purpose is to run algorithms developed in the Quantopian platform in live trading via broker API. In order to convert your algorithm for pylivetrader, please read the migration document. Here is the example dual moving average algorithm (by quantopian/zipline). We provide mostly the same API interfaces with zipline.

opentrade - An open source OEMS, and intraday algorithmic trading platform in modern C++ for professional quant

  •    C++

OpenTrade is an open source OEMS, and algorithmic trading platform, designed for simplicity, flexibility and performance. we prepared Dockfile-dev for you.