anxxos / sp500-prediction-sentiment-xgboostLinks
In this work an application of the Triple-Barrier Method and Meta-Labeling techniques is explored with XGBoost for the creation of a sentiment-based trading signal on the S&P 500 stock market index. The results confirm that sentiment data have predictive power, but a lot of work is to be carried out prior to implementing a strategy.
☆20Updated last year
Alternatives and similar repositories for sp500-prediction-sentiment-xgboost
Users that are interested in sp500-prediction-sentiment-xgboost are comparing it to the libraries listed below
Sorting:
- 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
- Machine learning-driven financial trading strategy: momentum prediction, regime detection, and enhanced trading decisions.☆64Updated 2 years ago
- ☆19Updated 5 years ago
- A low frequency statistical arbitrage strategy☆20Updated 6 years ago
- The notebook with the experiments to replicate and enhance the stock clustering proposed by Han(2022) for alogtrading, with KMeans Optimi…☆16Updated last year
- Deep q learning on determining buy/sell signal and placing orders☆49Updated 6 years ago
- Regime detection in historical markets using Hidden Markov Models (HMM) and Support Vector Machines (SVM).☆18Updated 3 years ago
- Trend Prediction for High Frequency Trading☆41Updated 2 years ago
- Mean Reversion Trading Strategy☆25Updated 4 years ago
- Exercises in 'Advances in Financial Machine Learning' by Lopez de Prado☆3Updated 2 years ago
- A Practical Application of Hidden Markov Model to Kalman Filter-Based Pairs Trading☆17Updated 4 years ago
- Implementation for "Statistical arbitrage in the US equities market" by Marco Avellaneda and Jeong-hyun Lee☆21Updated 6 years ago
- Backtesting a simple Buy Low Sell High Strategy☆9Updated 3 years ago
- This repository stores the implementation of the paper "DETECTING DATA-DRIVEN ROBUST STATISTICAL ARBITRAGE STRATEGIES WITH DEEP NEURAL NE…☆64Updated last year
- Apply Box&Tiao to generate stationary price spread series in steel industry commodity futures market for pair trading☆12Updated 2 years ago
- Intraday momentum strategy that buys (sells) leveraged ETFs late in the trading session following a significant intraday gain (loss) and …☆26Updated last year
- • Conducted a volatility study to develop pairs trading strategy by writing web crawlers that automated extracting 30 equity and ETF spot…☆47Updated 4 years ago
- Dynamic algorithmic trading systems in Python using Interactive Broker's Python API☆22Updated 4 years ago
- Trading Strategy on S&P500 with different method (Linear Regression, XGBOOST, LSTM, HMM☆10Updated 5 years ago
- Different trading strategies based on technical analysis using Ethereum/USD 5-minute bars data☆18Updated 4 years ago
- ☆40Updated 4 years ago
- Repository containing code for article: Quantconnect – A Complete Guide on https://algotrading101.com/☆17Updated 4 years ago
- This project is to apply Copula Function to pair trading strategy both in American stock market.☆28Updated 6 years ago
- Find trading pairs with Machine Learning☆41Updated 4 years ago
- Limit Order Book for high-frequency trading (HFT) strategies using data science approaches☆22Updated 3 years ago
- ☆22Updated 5 years ago
- A 50ETF Option Volatility Arbitrage Strategy Based on SABR Model☆24Updated 2 years ago
- Implementation of "OPTIMAL MARKET MAKING BY REINFORCEMENT LEARNING"☆27Updated 4 years ago
- High Frequency Trading (HFT) done using the Alpaca Trade API and Python.☆25Updated 5 years ago
- ☆22Updated 3 years ago