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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).
Advances in Financial Machine Learning
Answers to the questions at the back of the chapters of Advances in Financial Machine Learning.
This git repository is based on the work of J.Heaton, N.Polson and J.Witte and their articleDeep Learning for Finance: Deep Portfolios. This paper let us explore the use of deeplearning models for problems in financial prediction and classification. Our goal isto show how applying deep learning methods to these problems can produce betteroutcomes than standard methods in finance or in Machine Learning
Stock for Deep Learning and Machine Learning
Autoencoder framework for portfolio selection (paper published by J. B. Heaton, N. G. Polson, J. H. Witte.)
Deep Reinforcement Learning Framework for Factor Investing
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3350138
Fama French 3 Factor Model
Machine Learning in Asset Management
A curated list of practical financial machine learning (FinML) tools and applications in Python.
Implementing a Generative Adversarial Network on the Stock Market
Notes and exercises for Machine Learning for Trading Specialization Offered by Google Cloud and New York Institute of Finance on Coursera
Hedging portfolios with reinforcement learning.
Reinforce Your Career: Machine Learning in Finance. Extend your expertise of algorithms and tools needed to predict financial markets.
Package based on the work of Dr Marcos Lopez de Prado regarding his research with respect to Advances in Financial Machine Learning
Experimental solutions to selected exercises from the book [Advances in Financial Machine Learning by Marcos Lopez De Prado]
My codework for my economics undergraduate thesis titled "Empirical Asset Pricing via Deep Learning"
My first Machine Learning project
Project 1 of Udacity/WorldQuant trading course
Using python and scikit-learn to make stock predictions
Python: Basic Fama French model with subset data
The "Python Machine Learning (2nd edition)" book code repository and info resource
Stanford Project: Artificial Intelligence is changing virtually every aspect of our lives. Today’s algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is an exciting time to adopt a disruptive technology that will transform how everyone invests for generations. Models that explain the returns of individual stocks generally use company and stock characteristics, e.g., the market prices of financial instruments and companies’ accounting data. These characteristics can also be used to predict expected stock returns out-of-sample. Most studies use simple linear models to form these predictions [1] or [2]. An increasing body of academic literature documents that more sophisticated tools from the Machine Learning (ML) and Deep Learning (DL) repertoire, which allow for nonlinear predictor interactions, can improve the stock return forecasts [3], [4] or [5]. The main goal of this project is to investigate whether modern DL techniques can be utilized to more efficiently predict the movements of the stock market. Specifically, we train a LSTM neural network with time series price-volume data and compare its out-of-sample return predictability with the performance of a simple logistic regression (our baseline model).
Stock Fundamental Analysis using Machine Learning Classification Models
Gathers machine learning and deep learning models for Stock forecasting including trading bots and simulations
Stock Price Prediction using Machine Learning Techniques
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.