https://github.com/ai4finance-foundation/finrl-trading

For trading. Please star.
https://github.com/ai4finance-foundation/finrl-trading

a2c-algorithm automated-stock-trading ddpg deep-reinforcement-learning ensemble-strategy openai-gym ppo sharpe-ratio stock-trading stock-trading-strategy

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For trading. Please star.

• Host: GitHub
• URL: https://github.com/ai4finance-foundation/finrl-trading
• Owner: AI4Finance-Foundation
• License: mit
• Created: 2020-07-26T13:12:53.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2024-07-01T08:09:06.000Z (6 months ago)
• Last Synced: 2024-11-16T18:01:09.717Z (about 1 month ago)
• Topics: a2c-algorithm, automated-stock-trading, ddpg, deep-reinforcement-learning, ensemble-strategy, openai-gym, ppo, sharpe-ratio, stock-trading, stock-trading-strategy
• Language: Jupyter Notebook
• Homepage: https://ai4finance.org
• Size: 139 MB
• Stars: 2,114
• Watchers: 102
• Forks: 746
• Open Issues: 46
• Metadata Files:
  • Readme: README.md

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README

        






# FinRL for trading

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Purpose: Based on FinRL (https://github.com/AI4Finance-Foundation/FinRL), develop an AI stock-selection and trading strategy using Supervised Learning (SL) and Deep Reinforcement Learning (DRL), and deploy it to an online trading platform.

### Phase I: Financial Data Processing and Technical Indicators

1. Download Dow-30, NASDAQ-100, or S&P 500 data, including Open, High, Low, Close prices, and Volume (OHLCV) and fundamental indicators.

2. Obtain technical indicators and perform feature engineering: technical indicators, such as MACD, RSI; and fundamental indicators, such as EPS, ROI, ROE, P/E, P/S.

### Phase II: Stock Selection and Portfolio Allocation with Backtesting Results

1. Stock Selection: Perform supervised machine learning using classic machine learning algorithms (LSTM, Random Forest, SVM, Linear Regression, Lasso, Ridge) to select stocks based on fundamental multi-factor data, and select the top 25% of stocks every quarter; 

• Reference paper: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3302088

• GitHub Code: https://github.com/AI4Finance-Foundation/Machine-Learning-for-Stock-Recommendation-IEEE-2018

2. Portfolio Allocation: Use DRL Ensemble strategy (including PPO, DDPG, A2C, SAC, and TD3) in FinRL for asset allocation of the selected stocks, trade with daily data, and output positions; 

• Reference paper: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3690996

• GitHub Code: https://github.com/AI4Finance-Foundation/FinRL-Meta/blob/master/tutorials/1-Introduction/FinRL_PortfolioAllocation_NeurIPS_2020.ipynb

### Phase III: Deploy a DRL agent to an online trading platform

1. Deployment: Deploy strategies to online trading platforms such as Alpaca for paper trading

• GitHub Code: https://github.com/AI4Finance-Foundation/FinRL-Meta/blob/master/tutorials/3-Practical/FinRL_PaperTrading_Demo.ipynb

**Disclaimer: Nothing herein is financial advice, and NOT a recommendation to trade real money. Please use common sense and always first consult a professional before trading or investing.**