Financial Machine Learning
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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.
Multilayer neural network architecture for stock return prediction.
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.
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.
NYU FRE
Cornell University
Courant NYU
Oxford Man
Stanford Advanced Financial Technologies
Berkley CIFT
Reference : https://github.com/firmai/financial-machine-learning