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.
Repository's owner explicitly say that "this library is not maintained".
Not committed for long time (2~3 years).
Multilayer neural network architecture for stock return prediction.
NYU FRE
Cornell University
Courant NYU
Oxford Man
Stanford Advanced Financial Technologies
Berkley CIFT
If you want to contribute to this list (please do), send me a pull request or contact me or on . Also, a listed repository should be deprecated if:
- Technical experimentations to beat the stock market using deep learning.
- Tensorflow Regression.
- Algorithmic trading with deep learning experiments.
- Bulbea: Deep Learning based Python Library.
- Stock Market Forecasting using LSTM\GRU.
- OHLC Average Prediction of Apple Inc. Using LSTM Recurrent Neural Network.
- Hybrid model to predict future price correlation coefficients of two assets.
- Neural networks to predict stock prices.
- AI to predict stock market movements.
- A collection of 25+ Reinforcement Learning Trading Strategies - Google Colab.
- OpenGym with Deep Q-learning and Policy Gradient.
- reinforcement learning on stock market and agent tries to learn trading.
- Github - Deep Reinforcement Learning based Trading Agent for Bitcoin.
- Reinforcement Learning for finance.
- Building an Agent to Trade with Reinforcement Learning.
- Using deep actor-critic model to learn best strategies in pair trading.
- Mixture models to predict market bottoms.
- Mixture models and stock trading.
- Using python and scikit-learn to make stock predictions.
- Research in investment finance for long term forecasts.
- Identify social/historical cues for short term stock movement.
- A futures trend following portfolio investment strategy.
- Exercises too Financial Machine Learning (De Prado).
- More implementations of Financial Machine Learning (De Prado).
- Extends classical portfolio optimisation to take the skewness and kurtosis of the distribution of market invariants into account.
- Reinforcement Learning for Portfolio Management.
- Modern Portfolio Theory.
- A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem.
- Autoencoder framework for portfolio selection.
- Portfolio analyses and optimisation for 401K.
- **Comparing OLPS algorithms on a diversified set of ETFs.
- Relative importance of each component of the OLMAR algorithm.
- Universal portfolios; modern portfolio theory.
- Portfolio optimization with deep learning.
- Risk measures and factors for alternative and responsible investments.
- Portfolio and risk analytics in Python.
- Active portfolio risk management .
- Expected returns using CAPM.
- Factor analysis for mutual funds.
- Estimate Value-at-Risk for market risk management using Keras and TensorFlow.
- Value-at-risk calculations.
- Various financial notebooks.
- Performance analysis of predictive (alpha) stock factors.
- General quant repository.
- Riskiness of portfolios and assets.
- Convex Optimization for Finance.
- Factor strategy notebooks.
- Various financial experiments.
- PCA, Factor Returns, and trading strategies.
- Data exploration of fund clusters.
- Variational Reccurrent Autoencoder for Embedding stocks to vectors based on the price history.
- Clustering of industries.
- Finding pairs with cluster analysis.
- Project to cluster industries according to financial attributes.
- This project assembles a lot of NLP operations needed for finance domain.
- Correlation between mutual fund investment decision and earning call transcripts.
- Return performance and mutual fund selection.
- Fund classification using text mining and NLP.
- Applying Deep Learning and NLP in Quantitative Trading.
- Sentiment, distance and proportion analysis for trading signals.
- Extracting sentiment from financial statements using neural networks.
- Comprehensive NLP techniques for accounting research.
- Using deep-learning frameworks to identify accounting anomalies.
- Introduction to options.
- The economics of futures, futures, options, and swaps.
- Options pricing.
- Projects focusing on investigating simulations and computational techniques applied in finance.
- Hedging portfolios with reinforcement learning.
- Advanced derivatives.
- Efficient financial risk estimation via computer experiment design (regression + variance-reduced sampling).
- Derivative analytics with Python.
- Volatility derivatives analytics.
- Black Scholes and Copula.
- Valuation of Vanilla and Exotic option strategies (Butterfly, Risk Reversal etc.) with widget animations.
- Binomial tree for American call.
- Callable Bond, Hull White.
- Bootstrapping and interpolation.
- Utility functions in fixed income securities.
- Predicting the buying and selling volume of the corporate bonds.
- Exploratory data analysis.
- Insight into a new founder to make data-driven investment decisions.
- Cox-PH neural network predictions for VC/innovations finance research.
- Valuation models.
- VC regression.
- Analysis of luxury watch data to classify whether a certain model is likely to be over- or undervalued.
- Art evaluation analytics.
- Repository for distributed autonomous investment banking.
- High frequency trading.
- Deep learning for finance Predict volume of bonds.
- Notebooks for math and financial tutorials.
- Curating quantitative finance papers using machine learning.
- Investigating simulations as part of computational finance.
- Predicting market crashes using an LPPL model.
- Commodity influence over Brazilian stocks.
- Modelling Contentedness of Firms in Financial Markets with Heterogeneous Agents.
- Unsupervised fraud detection model that can identify likely candidates of fraud.
- Behavioural Economics and Finance Python Notebooks.
- Notebook PyMC3 implementation.
- Stochastic Process Calibration using Bayesian Inference & Probabilistic Programs.
- Forex spots PCA.
- Trading data and algorithms.
- A Python toolkit for high-frequency trade research.
- Financial Economics Models.
- Detecting critical transitions in financial networks with topological data analysis.
- Basic economic models.
- Basic corporate finance.
- Studies the empirical behaviours in stock market.
- Mergers and Acquisitions.
- Company life cycle.
- Applied Computational Economics and Finance.
- Various factors and portfolio constructions.
- NYU Math-GA 2048: Scientific Computing in Finance.
- Intro to algo trading.
- CEU python for finance course material.
- Hands-on Python for Finance published by Packt.
- Notebooks, resources and references accompanying the book Machine Learning for Algorithmic Trading.
- Machine Learning in Finance.
- Finance risk engagement course resources.
- Basic investment tools in python.
- Basic forward contracts and hedging.
- Source code notebooks basic finance applications.
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