HFTHaidra / Deep-Reinforcement-Learning-for-Automated-Stock-Trading-Strategy
Stock trading strategies play a critical role in investment. However, it is challenging to design a profitable strategy in a complex and dynamic stock market. In this paper, we propose a deep ensemble reinforcement learning scheme that automatically learns a stock trading strategy by maximizing investment return. We train a deep reinforcement le…
☆36Updated 2 years ago
Related projects ⓘ
Alternatives and complementary repositories for Deep-Reinforcement-Learning-for-Automated-Stock-Trading-Strategy
- Deep Direct Recurrent Reinforcement Learning to learn trading system☆26Updated 6 years ago
- Pair Trading Strategy using Machine Learning written in Python☆112Updated 2 years ago
- FinML: A Practical Machine Learning Framework for Dynamic Stock Selection☆109Updated 8 months ago
- This project is to apply Copula Function to pair trading strategy both in American stock market.☆23Updated 6 years ago
- Modeling the S&P500 index as a hidden markov model for regime identification and creating a trading algorithm to capitalize on hidden sta…☆28Updated 4 years ago
- Trend Prediction for High Frequency Trading☆38Updated last year
- ☆31Updated 4 years ago
- This repository represents work in progress for the Worldquant University Capstone Project titled: Asset Portfolio Management using Deep …☆73Updated 2 years ago
- The repository contains the code for project for DS 5500 course at Northeastern.☆35Updated 4 years ago
- Deep Reinforcement Learning Framework for Factor Investing☆21Updated last year
- High Frequency Pairs Trading Based on Statistical Arbitrage (Python)☆99Updated 5 years ago
- Contains detailed and extensive notes on quantitative trading, leveraging NLP for finance, backtesting, alpha factor research, portfolio …☆41Updated 2 years ago
- ☆185Updated 2 months ago
- An implementation of DDPG using PyTorch for algorithmic trading on Chinese SH50 stock market.☆28Updated 4 years ago
- 复现华泰证券《强化学习初探与DQN择时》研报中的DQN模型与效果☆26Updated 2 years ago
- A financial trading method using machine learning.☆58Updated last year
- This repository contains the main code used in the paper "Deep Reinforcement Learning for Market Making Under a Hawkes Process-Based Limi…☆51Updated last year
- The random forest, FFNN, CNN and RNN models are developed to predict the movement of future trading price of Netflix (NFLX) stock using t…☆58Updated 3 years ago
- Momentum following strategies and optimal execution cost upon Implement Shortfall algorithm☆15Updated 5 years ago
- Artificial-Intelligence-Big-Data-Lab / A-Multi-Layer-and-Multi-Ensembled-Stock-Trader-Using-Deep-Learning-and-Deep-Reinforcement-Learning☆51Updated 4 years ago
- A low frequency statistical arbitrage strategy☆18Updated 5 years ago
- CS7641 Team project☆87Updated 4 years ago
- ☆22Updated last year
- Optimizing the Pairs-Trading Strategy using Deep Reinforcement Learning with Trading and Stop-loss Boundaries☆13Updated 2 years ago
- apolanco3225 / Deep-Reinforcement-Learning-for-Optimal-Execution-of-Portfolio-Transactions-using-DDPGPerforming a trading strategy using deep deterministic policy gradients to know when to buy, hold or sell stocks in a virtual environment…☆53Updated 5 years ago
- This repository stores the implementation of the paper "DETECTING DATA-DRIVEN ROBUST STATISTICAL ARBITRAGE STRATEGIES WITH DEEP NEURAL NE…☆49Updated 8 months ago
- This repo contains some codes and outputs of my implementation of DeepLOB model.☆78Updated 3 years ago
- Deep Reinforcement Learning Robot Advisor☆21Updated 3 years ago
- Pair Trading - Reinforcement Learning - with Oanda Trading API☆64Updated 4 years ago
- This project used GARCH type models to estimate volatility and used delta hedging method to make a profit.☆60Updated 4 years ago