anacaballero / ML-FinanceLinks
Calculate technical indicators from historical stock data Create features and targets out of the historical stock data. Prepare features for linear models, xgboost models, and neural network models. Use linear models, decision trees, random forests, and neural networks to predict the future price of stocks in the US markets. Evaluate performance…
☆33Updated 6 years ago
Alternatives and similar repositories for ML-Finance
Users that are interested in ML-Finance are comparing it to the libraries listed below
Sorting:
- Quantitative analysis of fundamentals in quarterly reports by Machine Learning☆23Updated 5 years ago
- Market Risk Management with Time Series Prediction of Stock Market Trends using ARMA, ARIMA, GARCH regression models and RNN for time ser…☆22Updated 8 years ago
- Stock Prediction with XGBoost: A Technical Indicators' approach☆31Updated 6 years ago
- Financial risk analysis on a stocks portfolio through the VaR (Value at Risk), using Monte Carlo Simulation and Multiple Linear Regressio…☆22Updated 5 years ago
- Using Python and Tushare financial database☆29Updated last year
- applications for risk management through computational portfolio construction methods☆43Updated 5 years ago
- Testing trading signals of commodity futures☆17Updated 5 years ago
- Stock Market Price Prediction: Used machine learning algorithms such as Linear Regression, Logistics Regression, Naive Bayes, K Nearest N…☆26Updated 5 years ago
- A multi-factor stock selection model based on random forest with an average annualized yield of 33.74% from March 2014 to June 2017 when …☆17Updated 6 years ago
- keywords - Kmeans Clustering, Tsne, PCA, Indian Stocks, Johansen test☆32Updated 7 years ago
- XGBoost is known to be fast and achieve good prediction results as compared to the regular gradient boosting libraries. This project atte…☆46Updated 6 years ago
- Applying Deep Learning and NLP in Quantitative Trading☆110Updated 6 years ago
- The random forest, FFNN, CNN and RNN models are developed to predict the movement of future trading price of Netflix (NFLX) stock using t…☆61Updated 4 years ago
- kennedyCzar / STOCK-RETURN-PREDICTION-USING-KNN-SVM-GUASSIAN-PROCESS-ADABOOST-TREE-REGRESSION-AND-QDAForecast stock prices using machine learning approach. A time series analysis. Employ the Use of Predictive Modeling in Machine Learning …☆136Updated 3 years ago
- This project studies the intrinsic relationship between the stocks’ multiple factors and the investment value of the stocks listed in Chi…☆92Updated 4 years ago
- Reinforcement Learning Script that trades Equities from Yahoo Finance☆78Updated 6 years ago
- This repository represents work in progress for the Worldquant University Capstone Project titled: Asset Portfolio Management using Deep …☆89Updated 3 years ago
- Python Jupyter Notebooks for Financial Portfolio Optimization☆37Updated 7 years ago
- factor return calculation, mean-variance / Black&Litterman portfolio optimization, risk decomposition☆31Updated 6 years ago
- Computing a solution for the optimal mean-variance tradeoff (maximising Sharpe Ratio) of a portfolio according to MPT.☆49Updated 5 years ago
- This project is to apply Copula Function to pair trading strategy both in American stock market.☆30Updated 7 years ago
- Mean-Variance Optimization using DL (pytorch)☆32Updated 3 years ago
- Developing a long/short equity investment portfolio with Machine Learning predictions using data acquired from web-scraping. Flatiron Mod…☆39Updated 5 years ago
- Portfolio Construction Functions under the Basic Mean_Variance Model, the Factor Model and the Black_Litterman Model.☆41Updated 8 years ago
- Having effective intraday forecast for the level of trading volume is of vital importance to algorithmic trading and portfolio management…☆54Updated 5 years ago
- The Implied Volatility Smirk of Individual Option in S&P 500 Shows its Underlying Asset’s Return☆38Updated 5 years ago
- Design your own Trading Strategy☆38Updated last year
- Notebook based on the book "Advances in Financial Machine Learning" by Marcos Lopez de Prado☆126Updated 6 years ago
- ☆20Updated 9 years ago
- Pairs trading strategy that includes a research pipeline for identifying and selecting pairs. Tests all possible pairs in a universe for …☆35Updated last year