MaxMA2000 / Research-on-Stock-Prediction-based-Portfolio-Optimization
An Empirical Study of Optimal Combination of Algorithms for Prediction-Based Portfolio Optimization Model using Machine Learning over Covid-19 Period using HK stock market
☆11Updated 2 years ago
Alternatives and similar repositories for Research-on-Stock-Prediction-based-Portfolio-Optimization:
Users that are interested in Research-on-Stock-Prediction-based-Portfolio-Optimization are comparing it to the libraries listed below
- 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 …☆14Updated 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…☆31Updated 5 years ago
- Compilation of technical analysis tools (EMA, Bollinger bands), fundamental analysis, machine learning models (LSTM, Random forest, ARIMA…☆13Updated 3 years ago
- https://arxiv.org/abs/2006.04992☆19Updated 3 years ago
- Deep Reinforcement Learning Framework for Factor Investing☆25Updated last year
- This project analysed financial data and designed trading strategies by machine learning models.☆10Updated 6 years ago
- Quantitative analysis with deep learning prediction and reinforcement learning transactions.☆13Updated 4 years ago
- ☆17Updated 8 years ago
- An investment portfolio of stocks is created using Long Short-Term Memory (LSTM) stock price prediction and optimized weights. The perfor…☆34Updated last year
- 实行gamma scalping策略时的期权组合选择工具☆15Updated 6 years ago
- Apply Box&Tiao to generate stationary price spread series in steel industry commodity futures market for pair trading☆12Updated 2 years ago
- Deep Reinforcement Learning for Stock trading task☆20Updated 4 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
- Momentum following strategies and optimal execution cost upon Implement Shortfall algorithm☆15Updated 5 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
- Firstly, multiple effective factors are discovered through IC value, IR value, and correlation analysis and back-testing. Then, XGBoost c…☆17Updated 4 years ago
- A simply framework of researching stock data through LSTM by Tensorflow☆17Updated 6 years ago
- 多因子模型相关☆21Updated 3 years ago
- Contains detailed and extensive notes on quantitative trading, leveraging NLP for finance, backtesting, alpha factor research, portfolio …☆43Updated 2 years ago
- Stock Prediction with XGBoost: A Technical Indicators' approach☆28Updated 6 years ago
- This is for the capstone project "Optimal Execution of a VWAP order".☆33Updated 5 years ago
- Using past price data and sentiment analysis from news and other documents to predict the S&P500 index using a LSTM RNN. Idea replicated …☆32Updated 10 months ago
- In this project, we implement and compare the performance of several machine learning and deep learning algorithms in predicting the US s…☆54Updated 4 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
- This project explores stock trading modelling with the use recurrent neural network (RNN) with long-short term memory (LSTM) architecture…☆27Updated 5 years ago
- Reproduction of code described in the paper "Stock Market Prediction Based on Generative Adversarial Network" by Kang Zhang et al.☆25Updated 4 years ago
- TensorFlow implementation of Z. Hu et al. "Listening to Chaotic Whispers: A Deep Learning Framework for News-oriented Stock Trend Predict…☆29Updated 6 months ago
- Implementation of algorithmic trading using reinforcement learning.☆26Updated 4 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
- Stock Market Prediction on High-Frequency Data Using soft computing based AI models☆20Updated 6 months ago