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About Algorithmic Trading Using Sentiment Analysis and Reinforcement Learning
This document proposes an algorithmic trading system using reinforcement learning that incorporates sentiment analysis and trend analysis. It formulates the trading problem as a Markov Decision Process (MDP) that is solved using Q-learning. The MDP states are augmented with estimates of market trend from technical indicators and sentiment scores from news articles, estimated using neural networks. Testing with $10,000 achieved a Sharpe Ratio of 2.4, compared to a baseline of -0.2 and estimated optimal of 3.0. The system learns an optimal trading strategy based not just on historical prices but also sentiment and trend information to make more informed decisions.
Detailed Information
| Author: | ['Simerjot Kaur'] |
|---|---|
| Publication Year: | 2019 |
| Pages: | 6 |
| Language: | English |
| Format: | |
| Price: | FREE |
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