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Awesome AI in Finance
🔬 A curated list of awesome machine learning strategies & tools in financial market.
Here you can see meta information about this topic like the time we last updated this page, the original creator of the awesome list and a link to the original GitHub repository.
Thank you georgezouq & contributors
View Topic on GitHub:
georgezouq/awesome-ai-in-finance
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Papers
The influences which determine the movements of the Stock Exchange are.
The common-stock prices can be regarded as an ensemble of decisions in statistical equilibrium.
The power laws in the distributions of event sizes under a broad range of conditions in a large variety of systems.
Deep reinforcement learning provides a framework toward end-to-end training of such trading agent.
With an appropriate choice of the reward function, reinforcement learning techniques can successfully handle the risk-averse case.
Slides review few important financial ML applications.
Courses & Books
Mostly experiments based on "Advances in financial machine learning" book
Sources codes for: Mastering Python for Finance, Second Edition
Using advanced ML solutions to overcome real-world investment problems.
Time Series Data
In this noteboook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results. For that purpose we will use a Generative Adversarial Network (GAN) with LSTM, a type of Recurrent Neural Network, as generator, and a Convolutional Neural Network, CNN, as a discriminator. We use LSTM for the obvious reason that we are trying to predict time series data. Why we use GAN and specifically CNN as a discriminator? That is a good question: there are special sections on that later.
📈 Personae is a repo of implements and environment of Deep Reinforcement Learning & Supervised Learning for Quantitative Trading.
Deep Reinforcement Learning for Automated Stock Trading: An Ensemble Strategy. ICAIF 2020.
A Deep Reinforcement Learning Library for Automated Stock Trading in Quantitative Finance, NeurIPS 2020 DRL workshop.
Every day, millions of traders around the world are trying to make money by trading stocks. These days, physical traders are also being replaced by automated trading robots. Algorithmic trading market has experienced significant growth rate and large number of firms are using it. I have tried to build a Deep Q-learning reinforcement agent model to do automated stock trading.
Trading Environment(OpenAI Gym) + PPO(TensorForce)
This trading-gym is the first trading for agent to train with episode of short term trading itself.
Deep Reinforcement Learning for Financial Trading using Price Trailing @ ICASSP 2019
Trading Environment(OpenAI Gym) + DDQN (Keras-RL)
💸 Papers and Code Implements for Quantitative-Trading
Environment for reinforcement-learning algorithmic trading models
A framework for machine-learning bots
机器学习和量化分析学习进行中
This project provides a stock market environment using OpenGym with Deep Q-learning and Policy Gradient.
Portfolio Management
A light-weight deep reinforcement learning framework for portfolio management. This project explores the possibility of applying deep reinforcement learning algorithms to stock trading in a highly modular and scalable framework.
Reinforcement Learning for Portfolio Management
PGPortfolio: Policy Gradient Portfolio, the source code of "A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem"(https://arxiv.org/pdf/1706.10059.pdf).
Portfolio optimization with deep learning.
High Frequency Trading
A high-frequency trading model using Interactive Brokers API with pairs and mean-reversion in Python
Providing the solutions for high-frequency trading (HFT) strategies using data science approaches (Machine Learning) on Full Orderbook Tick Data.
Analysis of High Frequency Trading on Bitcoin exchanges
Event Drive
In this noteboook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results. For that purpose we will use a Generative Adversarial Network (GAN) with LSTM, a type of Recurrent Neural Network, as generator, and a Convolutional Neural Network, CNN, as a discriminator. We use LSTM for the obvious reason that we are trying to predict time series data. Why we use GAN and specifically CNN as a discriminator? That is a good question: there are special sections on that later.
A stock trading bot powered by Trump tweets
Crypto Currencies Strategies
Predicting price trends in cryptomarkets using an lstm-RNN for the use of a trading bot
TensorForce Bitcoin Trading Bot
Using tensorflow to build a population of models that trade crypto and breed/mutate iteratively
Genetic Algorithm for solving optimization of trading strategies using Gekko
My algorithmic trading strategies with the Gekko cryptocurrency trading bot.
Neural network strategy for Gekko
This is the code for "Bitcoin Prediction" by Siraj Raval on Youtube
Technical Analysis
Python quantitative trading strategies including VIX Calculator, Pattern Recognition, Commodity Trading Advisor, Monte Carlo, Options Straddle, London Breakout, Heikin-Ashi, Pair Trading, RSI, Bollinger Bands, Parabolic SAR, Dual Thrust, Awesome, MACD
Gekko bot resources.
Gekko strategies
calculate down peak and trade on
Ethereum trading algorithm using Python 3.5 and the library ZipLine
A dumping ground for my files I use with this awesome crypto currency trading platform https://github.com/askmike/gekko
Node.js native library performing technical analysis over an OHLC dataset with use of genetic algorithm
Bitcoin - MACD Crossover Trading Strategy Backtest
Github.com/CryptoSignal - #1 Quant Trading & Technical Analysis Bot - 2,100 + stars, 580 + forks
Strategies to Gekko trading bot with backtests results and some useful tools.
Gann's Swing trade strategy for Gekko trade bot
Lottery & Gamble
TensorFlow实战，使用LSTM预测彩票
Arbitrage
Arbitrage bot that currently works on bittrex & poloniex
R2 Bitcoin Arbitrager is an automatic arbitrage trading system powered by Node.js + TypeScript.
A cryptocurrency arbitrage opportunity calculator. Over 800 currencies and 50 markets.
Bitcoin arbitrage - opportunity detector
Blackbird Bitcoin Arbitrage: a long/short market-neutral strategy
Traditional Markets
Python module to get stock data from Yahoo! Finance
TuShare is a utility for crawling historical data of China stocks
Get millions of financial and economic dataset from hundreds of publishers via a single free API.
Crypto Currencies
A live cryptocurrency historical trade data blotter. Download live historical trade data from any cryptoexchange, be it for machine learning, backtesting/visualizing trading strategies or for Quantopian/Zipline.
Gekko Trading Bot dataset dumps. Ready to use and download history files in SQLite format.
Research Tools
An open source reinforcement learning framework for training, evaluating, and deploying robust trading agents.
Portfolio and risk analytics in Python
Performance analysis of predictive (alpha) stock factors
Common financial risk and performance metrics. Used by zipline and pyfolio.
modular quant framework.
Traditional Market
Zipline, a Pythonic Algorithmic Trading Library
A extendable, replaceable Python algorithmic backtest && trading framework supporting multiple securities
Python Backtesting library for trading strategies
Kungfu Trader
Lean Algorithmic Trading Engine by QuantConnect (C#, Python)
Python live trade execution library with zipline interface.
This project tests bt(http://pmorissette.github.io/bt) and Quantopian Zipline(https://github.com/quantopian/zipline) as backtesting frameworks for coin trading strategy.
Crypto Currencies
Zenbot is a command-line cryptocurrency trading bot using Node.js and MongoDB.
Bot18 is a high-frequency cryptocurrency trading bot developed by Zenbot creator @carlos8f
Magic8bot is a cryptocurrency trading bot using Node.js and MongoDB.
An Algorithmic Trading Library for Crypto-Assets in Python
Quantitative crypto bot framework
阿布量化交易系统(股票，期权，期货，比特币，机器学习) 基于python的开源量化交易，量化投资架构
Plugins
This project tests bt(http://pmorissette.github.io/bt) and Quantopian Zipline(https://github.com/quantopian/zipline) as backtesting frameworks for coin trading strategy.
Batch backtest, import and strategy params optimalization for Gekko Trading Bot. With one command you will run any number of backtests.
TA Lib
A Python Pandas implementation of technical analysis indicators
Common financial technical indicators implemented in Pandas.
Tulip Node is the official node.js wrapper for Tulip Indicators. It provides over 100 technical analysis overlay and indicator functions.
A visual, technical analysis and charting (Candlestick, OHLC, indicators) library built on D3.
Exchange API
Python API for the Interactive Brokers on-line trading system.
【停止维护】新版本更新已迁移到 IBATS 项目组对应名称项目中。连接火币交易所，获取火币实时行情、火币历史行情，保存到mysql数据库同时redis广播，供 ABAT 交易平台进行策略回测、分析，交易使用
上海期货交易所CTP接口 Shanghai Future CTP Interface CTP Python API Wrapper
Framework
High-performance TensorFlow library for quantitative finance.
Visualizing
Play with neural networks!
Visualizer for neural network, deep learning, and machine learning models
GYM Environment
Trading and Backtesting environment for training reinforcement learning agent or simple rule base algo.
Trading environnement for RL agents, backtesting and training.
Scalable, event-driven, deep-learning-friendly backtesting library
Articles
The Economist 经济学人，持续更新
NYU 金融机器学习 中文笔记
Others
TensorBoard as a Zipline dashboard
An UI port for gekko trading bot using Quasar framework.
Other Resource
Quant/Algorithm trading resources with an emphasis on Machine Learning
A curated list of insanely awesome libraries, packages and resources for Quants (Quantitative Finance)