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All Projects → AI4Finance-Foundation → Deep-Reinforcement-Learning-for-Automated-Stock-Trading-Ensemble-Strategy-ICAIF-2020

AI4Finance-Foundation / Deep-Reinforcement-Learning-for-Automated-Stock-Trading-Ensemble-Strategy-ICAIF-2020

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Live Trading. Please star.

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FinRL Live trading

Title: FinRL for Live Trading

GitHub: https://github.com/AI4Finance-Foundation/FinRL Purpose: Based on the open-source financial reinforcement learning framework-FinRL, develop an AI stock-selection and trading strategy based on Supervised Machine Learning (Supervised ML) and Deep Reinforcement Learning (DRL) algorithms and deploy it to an online trading platform for paper trading.

Phase I: Data Processing and Feature Engineering

  1. Download and install FinRL, download S&P 500 Open, High, Low, Close, and Volume (OHLCV) and Fundamental Indicators (company fundamental data), and convert the data into a daily format

  2. Perform feature engineering: based on OHLCV data to make company technical analysis indicators such as MACD, RSI; based on company fundamental data to make fundamental indicators such as EPS, ROI, ROE, P/E, P/S; and convert it into machine learning data format

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 Deep reinforcement learning Ensemble strategy (PPO, DDPG, A2C, SAC, 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 the DRL trading strategy 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.

Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].