Please contact me to take over and revamp this repo (it gets around 100k views and 650k clicks per year), I don't have time to update or maintain it - message 15/03/2021

A curated list of practical financial machine learning (FinML) tools and applications. This collection is primarily in Python.

If you want to contribute to this list (please do), send me a pull request or contact me @dereknow or on linkedin. Also, a listed repository should be deprecated if:

• Repository's owner explicitly say that "this library is not maintained".
• Not committed for long time (2~3 years).

• Deep Learning - Technical experimentations to beat the stock market using deep learning.
• Deep Learning II - Tensorflow Regression.
• Deep Learning III - Algorithmic trading with deep learning experiments.
• Deep Learning IV - Bulbea: Deep Learning based Python Library.
• LTSM GRU - Stock Market Forecasting using LSTM\GRU.
• LTSM Recurrent - OHLC Average Prediction of Apple Inc. Using LSTM Recurrent Neural Network.
• ARIMA-LTSM Hybrid - Hybrid model to predict future price correlation coefficients of two assets.
• Neural Network - Neural networks to predict stock prices.
• AI Trading - AI to predict stock market movements.

• RL Trading - A collection of 25+ Reinforcement Learning Trading Strategies - Google Colab.
• RL - OpenGym with Deep Q-learning and Policy Gradient.
• RL II - reinforcement learning on stock market and agent tries to learn trading.
• RL III - Github - Deep Reinforcement Learning based Trading Agent for Bitcoin.
• RL IV - Reinforcement Learning for finance.
• RL V - Building an Agent to Trade with Reinforcement Learning.
• Pair Trading RL - Using deep actor-critic model to learn best strategies in pair trading.

• Mixture Models I - Mixture models to predict market bottoms.
• Mixture Models II - Mixture models and stock trading.
• Scikit-learn Stock Prediction - Using python and scikit-learn to make stock predictions.
• Fundamental LT Forecasts - Research in investment finance for long term forecasts.
• Short-Term Movement Cues - Identify social/historical cues for short term stock movement.
• Trend Following - A futures trend following portfolio investment strategy.

• Advanced ML - Exercises too Financial Machine Learning (De Prado).
• Advanced ML II - More implementations of Financial Machine Learning (De Prado).

• Distribution Characteristic Optimisation - Extends classical portfolio optimisation to take the skewness and kurtosis of the distribution of market invariants into account.
• Reinforcement Learning - Reinforcement Learning for Portfolio Management.
• Efficient Frontier - Modern Portfolio Theory.
• PyPortfolioOpt - Financial portfolio optimisation, including classical efficient frontier and advanced methods.
• Policy Gradient Portfolio - A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem.
• Deep Portfolio Theory - Autoencoder framework for portfolio selection.
• 401K Portfolio Optimisation - Portfolio analyses and optimisation for 401K.
• Online Portfolio Selection - ****Comparing OLPS algorithms on a diversified set of ETFs.
• OLMAR Algorithm - Relative importance of each component of the OLMAR algorithm.
• Modern Portfolio Theory - Universal portfolios; modern portfolio theory.
• DeepDow - Portfolio optimization with deep learning.

• Various Risk Measures - Risk measures and factors for alternative and responsible investments.
• Pyfolio - Portfolio and risk analytics in Python.
• Risk Basic - Active portfolio risk management .
• CAPM - Expected returns using CAPM.
• Factor Analysis - Factor analysis for mutual funds.
• VaR GaN - Estimate Value-at-Risk for market risk management using Keras and TensorFlow.
• VaR - Value-at-risk calculations.
• Python for Finance - Various financial notebooks.
• Performance Analysis - Performance analysis of predictive (alpha) stock factors.
• Quant Finance - General quant repository.
• Risk and Return - Riskiness of portfolios and assets.
• Convex Optimisation - Convex Optimization for Finance.
• Factor Analysis - Factor strategy notebooks.
• Statistical Finance - Various financial experiments.

• PCA Pairs Trading - PCA, Factor Returns, and trading strategies.
• Fund Clusters - Data exploration of fund clusters.
• VRA Stock Embedding - Variational Reccurrent Autoencoder for Embedding stocks to vectors based on the price history.
• Industry Clustering - Clustering of industries.
• Pairs Trading - Finding pairs with cluster analysis.
• Industry Clustering - Project to cluster industries according to financial attributes.

• NLP - This project assembles a lot of NLP operations needed for finance domain.
• Earning call transcripts - Correlation between mutual fund investment decision and earning call transcripts.
• Buzzwords - Return performance and mutual fund selection.
• Fund classification - Fund classification using text mining and NLP.
• NLP Event - Applying Deep Learning and NLP in Quantitative Trading.
• Financial Sentiment Analysis - Sentiment, distance and proportion analysis for trading signals.
• Financial Statement Sentiment - Extracting sentiment from financial statements using neural networks.
• Extensive NLP - Comprehensive NLP techniques for accounting research.
• Accounting Anomalies - Using deep-learning frameworks to identify accounting anomalies.

• Options - Introduction to options.
• Derivative Markets - The economics of futures, futures, options, and swaps.
• Black Scholes - Options pricing.
• Computational Derivatives - Projects focusing on investigating simulations and computational techniques applied in finance.
• Reinforcement Learning - Hedging portfolios with reinforcement learning.
• Delta Hedging - Advanced derivatives.
• Options Risk Measures - Efficient financial risk estimation via computer experiment design (regression + variance-reduced sampling).
• Derivatives Python - Derivative analytics with Python.
• Volatility and Variance Derivatives - Volatility derivatives analytics.
• Options - Black Scholes and Copula.
• Option Strategies - Valuation of Vanilla and Exotic option strategies (Butterfly, Risk Reversal etc.) with widget animations.
• Derman - Binomial tree for American call.
• Hull White - Callable Bond, Hull White.

• Vasicek - Bootstrapping and interpolation.
• Binomial Tree - Utility functions in fixed income securities.
• Corporate Bonds - Predicting the buying and selling volume of the corporate bonds.

• Kiva Crowdfunding - Exploratory data analysis.
• Venture Capital - Insight into a new founder to make data-driven investment decisions.
• Venture Capital NN - Cox-PH neural network predictions for VC/innovations finance research.
• Private Equity - Valuation models.
• VC OLS - VC regression.
• Watch Valuation - Analysis of luxury watch data to classify whether a certain model is likely to be over- or undervalued.
• Art Valuation - Art evaluation analytics.
• Blockchain - Repository for distributed autonomous investment banking.

• HFT - High frequency trading.
• Deep Portfolio - Deep learning for finance Predict volume of bonds.
• Mathematical Finance - Notebooks for math and financial tutorials.
• NLP Finance Papers - Curating quantitative finance papers using machine learning.
• Simulation - Investigating simulations as part of computational finance.
• Market Crash Prediction - Predicting market crashes using an LPPL model.
• Commodity - Commodity influence over Brazilian stocks.
• Finance Graph Theory - Modelling Contentedness of Firms in Financial Markets with Heterogeneous Agents.
• Real Estate Property Fraud - Unsupervised fraud detection model that can identify likely candidates of fraud.
• Behavioural Economics - Behavioural Economics and Finance Python Notebooks.
• Bayesian Finance - Notebook PyMC3 implementation.
• Bayesian Finance I - Stochastic Process Calibration using Bayesian Inference & Probabilistic Programs.
• Currency PCA - Forex spots PCA.
• Backtests - Trading data and algorithms.
• High Frequency - A Python toolkit for high-frequency trade research.
• Financial Economics - Financial Economics Models.
• Critical Transitions - Detecting critical transitions in financial networks with topological data analysis.
• Economic Foundations - Basic economic models.
• Corporate Finance - Basic corporate finance.
• Applied Corporate Finance - Studies the empirical behaviours in stock market.
• M&A - Mergers and Acquisitions.
• Life-cycle - Company life cycle.
• Computational Finance - Applied Computational Economics and Finance.
• Liquidity and Momentum - Various factors and portfolio constructions.

• Mathematical Finance - NYU Math-GA 2048: Scientific Computing in Finance.
• Algo Trading - Intro to algo trading.
• Python for Finance - CEU python for finance course material.
• Handson Python for Finance - Hands-on Python for Finance published by Packt.
• Machine Learning for Trading - Notebooks, resources and references accompanying the book Machine Learning for Algorithmic Trading.
• ML Specialisation - Machine Learning in Finance.
• Risk Management - Finance risk engagement course resources.
• Basic Investments - Basic investment tools in python.
• Basic Derivatives - Basic forward contracts and hedging.
• Basic Finance - Source code notebooks basic finance applications.

• Capital Markets Data (and chartpack)
• Employee Count SEC Filings
• SEC Parsing
• Open Edgar
• EDGAR
• IRS
• Rating Industries
• Web Scraping (FirmAI)
• Financial Corporate
• Non-financial Corporate
• http://finance.yahoo.com/
• https://fred.stlouisfed.org/
• https://stooq.com
• https://github.com/timestocome/StockMarketData

• Financial Event Prediction using Machine Learning
• Machine Learning in Asset Management—Part 1: Portfolio Construction—Trading Strategies
• Machine Learning in Asset Management—Part 2: Portfolio Construction—Weight Optimization
• Machine Learning in Asset Management

• NYU FRE - Finance and Risk Engineering (NYU Tandon)
• Cornell University
• NYU Courant - Courant Institute of Mathematical Sciences, New York University
• Oxford Man - Oxford-Man Institute of Quantitative Finance
• Stanford Advanced Financial Technologies - Stanford Advanced Financial Technologies Laboratory
• Berkley CIFT