anacaballero / ML-Finance
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…
☆31Updated 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
- Financial risk analysis on a stocks portfolio through the VaR (Value at Risk), using Monte Carlo Simulation and Multiple Linear Regressio…☆22Updated 4 years ago
- Quantitative analysis of fundamentals in quarterly reports by Machine Learning☆22Updated 5 years ago
- This project explores stock trading modelling with the use recurrent neural network (RNN) with long-short term memory (LSTM) architecture…☆26Updated 6 years ago
- Stock Prediction with XGBoost: A Technical Indicators' approach☆28Updated 6 years ago
- XGBoost is known to be fast and achieve good prediction results as compared to the regular gradient boosting libraries. This project atte…☆32Updated 6 years ago
- ☆18Updated 8 years ago
- • Conducted a volatility study to develop pairs trading strategy by writing web crawlers that automated extracting 30 equity and ETF spot…☆46Updated 4 years ago
- Stock Price prediction using news data. The datasets used consists news and stock price data from 2008 to 2016. The polarity(Subjectivity…☆48Updated 7 years ago
- Market Risk Management with Time Series Prediction of Stock Market Trends using ARMA, ARIMA, GARCH regression models and RNN for time ser…☆21Updated 7 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…☆60Updated 3 years ago
- Pairs trading strategy that includes a research pipeline for identifying and selecting pairs. Tests all possible pairs in a universe for …☆36Updated last year
- Design your own Trading Strategy☆37Updated last year
- Compilation of technical analysis tools (EMA, Bollinger bands), fundamental analysis, machine learning models (LSTM, Random forest, ARIMA…☆13Updated 3 years ago
- Tracking S&P 500 index with deep learning model☆12Updated last year
- The Implied Volatility Smirk of Individual Option in S&P 500 Shows its Underlying Asset’s Return☆37Updated 4 years ago
- quantitative asset allocation strategy☆26Updated 3 months ago
- Built a smart beta portfolio and compared it to a benchmark index by calculating the tracking error. Built a portfolio using quadratic pr…☆66Updated 6 years ago
- Contains detailed and extensive notes on quantitative trading, leveraging NLP for finance, backtesting, alpha factor research, portfolio …☆44Updated 2 years ago
- keywords - Kmeans Clustering, Tsne, PCA, Indian Stocks, Johansen test☆32Updated 6 years ago
- 【Framework】A Multi Factor Strategy based on XGboost, its my homework project in Tsinghua, the Introduction to Quantitative Finance, 2019 …☆15Updated 2 years ago
- This project is to practice applying Long Short-Term Memory network in deep learning to predict time series financial data. I selected Am…☆15Updated 7 years ago
- Having effective intraday forecast for the level of trading volume is of vital importance to algorithmic trading and portfolio management…☆45Updated 4 years ago
- The NLP News Sentiment Factor Trading Strategy for a Portfolio of S&P 500 Stocks☆12Updated 5 years ago
- A 50ETF Option Volatility Arbitrage Strategy Based on SABR Model☆24Updated 2 years ago
- Multi-Factor model with regression method☆9Updated 6 years ago
- Trend Prediction for High Frequency Trading☆40Updated 2 years ago
- Python Jupyter Notebooks for Financial Portfolio Optimization☆35Updated 6 years ago
- Machine learning trading method using meta-labeling. You can see the details in 'Advances in Financial Machine Learning' by Lopez de Prad…☆13Updated 3 years ago
- Deep Reinforcement Learning Framework for Factor Investing☆26Updated 2 years ago
- applications for risk management through computational portfolio construction methods☆40Updated 4 years ago