XGBoost is known to be fast and achieve good prediction results as compared to the regular gradient boosting libraries. This project attempts to predict stock price direction by using the stock's daily data and indicators derived from its daily data as predictors. As such this is a classification problem.
☆50Apr 12, 2019Updated 7 years ago
Alternatives and similar repositories for XGBoost_stock_prediction
Users that are interested in XGBoost_stock_prediction are comparing it to the libraries listed below. We may earn a commission when you buy through links labeled 'Ad' on this page.
Sorting:
- Stock Prediction with XGBoost: A Technical Indicators' approach☆31Jan 27, 2019Updated 7 years ago
- This project uses XGBoost and LSTM to forecast stock market performance.☆20Jan 24, 2021Updated 5 years ago
- Apply different deep learning models to limit order book.☆12Mar 6, 2018Updated 8 years ago
- This script loads desired stock price training data, trains an XGBoost Regressor for Time Series Forecasting (allowing fine-tuning) and d…☆13Jul 6, 2022Updated 3 years ago
- Pytorch implementation of deep learning models for financial time series forecasting using LOB☆21May 25, 2023Updated 3 years ago
- GPUs on demand by Runpod - Special Offer Available • AdRun AI, ML, and HPC workloads on powerful cloud GPUs—without limits or wasted spend. Deploy GPUs in under a minute and pay by the second.
- Use total, upper, down, relative volatility factors to find Alpha. Implement whole trading process & back-test with visualization.☆13May 30, 2021Updated 5 years ago
- This project studies the intrinsic relationship between the stocks’ multiple factors and the investment value of the stocks listed in Chi…☆93Jul 1, 2021Updated 4 years ago
- A machine learning pipeline that ingest and process a 20-year historical stock price dataset and try to predict future prices using Light…☆17Nov 20, 2020Updated 5 years ago
- Modelling for price change forecast using High-frequency Trading limit order book dynamics using ML algorithms☆26Mar 10, 2018Updated 8 years ago
- Quantitative finance research tools in Python☆12Mar 8, 2019Updated 7 years ago
- Loose collection of Jupyter notebooks, mostly for my blog☆28Nov 10, 2024Updated last year
- Trading Strategy on S&P500 with different method (Linear Regression, XGBOOST, LSTM, HMM☆10May 11, 2020Updated 6 years ago
- Code implementations of my studies on the book Advances in Financial Machine Learning☆12May 18, 2020Updated 6 years ago
- 多因子模型相关☆23Jun 16, 2021Updated 4 years ago
- Deploy on Railway without the complexity - Free Credits Offer • AdConnect your repo and Railway handles the rest with instant previews. Quickly provision container image services, databases, and storage volumes.
- Stock Price prediction using news data. The datasets used consists news and stock price data from 2008 to 2016. The polarity(Subjectivity…☆49Nov 30, 2017Updated 8 years ago
- Blaze☆17Jun 19, 2021Updated 4 years ago
- Firstly, multiple effective factors are discovered through IC value, IR value, and correlation analysis and back-testing. Then, XGBoost c…☆21Aug 4, 2020Updated 5 years ago
- Channel break out strategy for High Frequency Trading.☆15Jun 26, 2018Updated 7 years ago
- ☆28May 3, 2022Updated 4 years ago
- Learn how to research fundamental factors using Pipeline, Alphalens, and Sharadar price and fundamental data.☆16Apr 23, 2024Updated 2 years ago
- ☆11Oct 24, 2025Updated 7 months ago
- Momentum following strategies and optimal execution cost upon Implement Shortfall algorithm☆16May 2, 2019Updated 7 years ago
- ☆16Aug 22, 2017Updated 8 years ago
- Deploy on Railway without the complexity - Free Credits Offer • AdConnect your repo and Railway handles the rest with instant previews. Quickly provision container image services, databases, and storage volumes.
- 支持 WPF和 Avalonia 的Chromium嵌入框架☆13Dec 29, 2021Updated 4 years ago
- 📈 如何用深度强化学习自动炒股☆13Mar 31, 2020Updated 6 years ago
- ☆19Jun 24, 2019Updated 6 years ago
- kdb+/q kalman beta matlab python☆11Sep 11, 2019Updated 6 years ago
- select stock automatically, trade manually☆12Jul 26, 2020Updated 5 years ago
- In the high-frequency era of trading, orders of stocks can be executed under a millsecond. The information about the thousands of orders …☆10Mar 30, 2016Updated 10 years ago
- Vpin caculation and backtesting☆14Aug 16, 2019Updated 6 years ago
- A Deep Reinforcement Learning model for high volume and frequency Forex Portfolio Management☆13Jan 11, 2023Updated 3 years ago
- Alpaca-based Order Book Inbalace Algorithm.☆12Jul 23, 2020Updated 5 years ago
- Deploy to Railway using AI coding agents - Free Credits Offer • AdUse Claude Code, Codex, OpenCode, and more. Autonomous software development now has the infrastructure to match with Railway.
- This repo contains backtesting scripts for various models(mainly LSTM) using different type of datasets to predict bitcoin price. Upto 9…☆21Dec 15, 2019Updated 6 years ago
- A 3 part series of Jupyter notebooks to help one find alpha in the stock market with AI☆19Jun 10, 2023Updated 2 years ago
- This is a Python implementation of the Heston model for option pricing using Monte Carlo simulation. The code takes in parameters and gen…☆18Mar 7, 2023Updated 3 years ago
- Price Prediction with Machine Learning Models (practicum project at CME group)☆74May 9, 2016Updated 10 years ago
- Implementation of option pricing models using Numba that performs better. This entire project has utilized as little libraries as possibl…☆20Sep 15, 2022Updated 3 years ago
- Limit Order Book for high-frequency trading (HFT) strategies using data science approaches☆24Dec 12, 2021Updated 4 years ago
- Low latency high throughput GDAX orderbook analysis engine and trading bot☆13Mar 24, 2018Updated 8 years ago