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.

machine-learning-asset-management - Machine Learning in Asset Management (by @firmai)

•    Jupyter

Follow this link for SSRN paper. Animated Investment Management Research at Sov.ai — Sponsoring open source AI, Machine learning, and Data Science initiatives.

stock-analysis-engine - Backtest 1000s of minute-by-minute trading algorithms for training AI with automated pricing data from: IEX, Tradier and FinViz

•    Jupyter

Build and tune investment algorithms for use with artificial intelligence (deep neural networks) with a distributed stack for running backtests using live pricing data on publicly traded companies with automated datafeeds from: IEX Cloud, Tradier and FinViz (includes: pricing, options, news, dividends, daily, intraday, screeners, statistics, financials, earnings, and more). This will pull Redis and Minio docker images.

Robinhood - Unofficial Documentation of Robinhood Trade's Private API

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.

PyAlgoTrade - Python Algorithmic Trading Library

•    Python

PyAlgoTrade is a Python Algorithmic Trading Library with focus on backtesting and support for paper-trading and live-trading. Let’s say you have an idea for a trading strategy and you’d like to evaluate it with historical data and see how it behaves. PyAlgoTrade allows you to do so with minimal effort.

pandapy - PandaPy has the speed of NumPy and the usability of Pandas 10x to 50x faster (by @firmai)

•    Python

Animated investment research at Sov.ai, sponsoring open source initiatives. PandaPy software, similar to the original Pandas project, is developed to improve the usability of python for finance. Structured datatypes are designed to be able to mimic ‘structs’ in the C language, and share a similar memory layout. PandaPy currently houses more than 30 functions. Structured NumPy are meant for interfacing with C code and for low-level manipulation of structured buffers, for example for interpreting binary blobs. For these purposes they support specialized features such as subarrays, nested datatypes, and unions, and allow control over the memory layout of the structure.

tdameritrade - Python interface to TD Ameritrade (https://developer.tdameritrade.com)

•    Python

Major changes in the v0.1.0 update to the way tokens are handled. You will still need the original authentication instructions, but the TDClient now takes the refresh token and client id, not the access token. A new session class handles token expiration and will automatically call a new token as needed. It is recommended that you store these as environmental variables.

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).

NowTrade - Algorithmic trading library with a focus on creating powerful strategies

•    Python

NowTrade is an algorithmic trading library with a focus on creating powerful strategies using easily-readable and simple Python code. With the help of NowTrade, full blown stock/currency trading strategies, harnessing the power of machine learning, can be implemented with few lines of code. NowTrade strategies are not event driven like most other algorithmic trading libraries available. The strategies are implemented in a sequential manner (one line at a time) without worrying about events, callbacks, or object overloading.

algotrading - Algorithmic trading framework for cryptocurrencies.

•    Python

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.

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.

Sequence-to-Sequence-Learning-of-Financial-Time-Series-in-Algorithmic-Trading - My bachelor's thesis—analyzing the application of LSTM-based RNNs on financial markets

•    TeX

This is my bachelor's thesis that I wrote over the course of two months during my final year of studies, earning my Bachelor of Science in Computer Science degree. The thesis was co-authored by my good friend Tobias Ånhed. Click here for revised edition on DiVA.

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

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.

DarwinexLabs - Datasets, tools and more from Darwinex Labs - Prop Investing Arm & Quant Team @ Darwinex (www

•    MQL4

Datasets, tools and more from Darwinex Labs - Prop Investing Arm & Quant Team @ Darwinex (www.darwinex.com)