algotrading sentimentanalysis genai

AlgoTrading with Sentiment Analysis and Backtesting using GenAI

This project provides a versatile framework for sentiment analysis on stock market data, conducting backtesting with Backtrader and leveraging generative AI models such as OpenAI, Llama, and Transformers pipeline for sentiment data analysis. It encompasses functionalities for downloading stock data from Yahoo Finance, fetching stock market news data using Alpaca APIs, preprocessing sentiment data, and running backtests with customizable strategies. Integrating advanced language models enhances the sentiment analysis process, allowing for a more nuanced understanding of market sentiments.

Table of Contents

• Introduction
• Features
• Requirements
• Installation
• Usage
  • Configuration
  • Run Backtest
• Project Structure
• Contributing
• License

Introduction

This project aims to provide a streamlined workflow for analyzing stock market sentiment and backtesting trading strategies. It leverages Backtrader for backtesting and yfinance for downloading stock data.

Features

• Download stock data from Yahoo Finance.
• Preprocess sentiment data and merge it with stock data.
• Run backtests with customizable strategies using Backtrader.
• Analyze backtest results with various performance metrics.

Requirements

• Python 3.6+
• Dependencies listed in requirements.txt

Installation

1. Clone the repository:

   git clone https://github.com/your-username/stock-sentiment-backtesting.git
   cd stock-sentiment-backtesting

2. Create a virtual environment (optional but recommended):

   python -m venv venv

3. Activate the virtual environment:

   • On Windows:

     venvScriptsactivate

   • On macOS/Linux:

     source venv/bin/activate

4. Install dependencies:

   pip install -r requirements.txt

Usage

Configuration

Edit the configuration parameters in main.py to customize the stock, date range, and other settings for your analysis.

STOCK_TICKER = 'AAPL'
START_DATE = '2022-03-21'
END_DATE = '2022-12-31'
SENTIMENT_DATA_PATH = 'data/stock_sentiment_data.csv'

Run Backtest

Execute the main script to run the backtest:

python main.py

The backtest results, including performance metrics, will be displayed in the console.

Project Structure

algotrading-sentimentanalysis-genai/
├── alpaca/
│   └── client.py
├── data/
│   └── stock_sentiment_data.csv
│   └── ...
├── llms/
│   └── llama_llm.py
│   └── openai_llm.py
├── processor/
│   └── stock_data_processor.py
├── runner/
│   └── backtest_runner.py
├── sentiment_analysis/
│   └── sentiment_analysis_pipeline.py
├── strategies/
│   └── technical_only_strategy/
│   └── technical_with_sentiment_strategy/
├── output/
│   └── ...
├── .gitignore
├── README.md
├── requirements.txt
├── main.py
└── venv/
    └── ...

Getting Client Secrets and Configurations

1. Alpaca REST Client

Requirements:

• Python 3.7 or later
• Alpaca account
• API key ID and secret (available in your Alpaca account dashboard)

Configuration:

1. Install the Alpaca Python library:

   pip install alpaca-trade-api

2. Use the API key in your code:

   from alpaca_trade_api import REST
   
   alpaca_api_key = "YOUR_API_KEY"
   alpaca_secret_key = "YOUR_SECRET_KEY"
   
   rest_client = REST(alpaca_api_key, alpaca_secret_key)

2. OpenAI LLM Client

Requirements:

• Python 3.6 or later
• OpenAI account
• API key (available in your OpenAI account dashboard)

Installation:

1. Install the official OpenAI library:

   pip install openai

2. Set your API key as an environment variable:

   export OPENAI_API_KEY="YOUR_API_KEY"

   Alternatively, provide it directly in your code:

   import openai
   
   openai.api_key = "YOUR_API_KEY"

3. Llama LLM Client

Requirements:

• Python 3.7 or later
• Hugging Face account with an access token
• Token with access to the desired Llama model

Installation:

1. Install necessary libraries:

   pip install transformers

2. Set your Hugging Face token as an environment variable:

   export HF_ACCESS_TOKEN="YOUR_TOKEN"

Important:

Use caution when handling API keys and tokens. Avoid exposing them in public repositories or sharing them without proper security measures.

Adding to README:

Feel free to include this information in your README for comprehensive setup instructions.

• main.py: Contains the main script for running backtests and strategy definition.

• data: Directory for storing data files, including stock and sentiment data.

• output: Directory for saving backtest results and plots.

• llms: Contains OpenAI and Llama clients for sentiment analysis.

• processor: Contains stock data processor for preprocessing stock news and sentiment data.

• runner: Contains backtest runner class for backtesting using cerebro and backtrader.

• sentiment_analysis: Contains transformer pipeline for sentiment analysis on news data.

• strategies: Contains code for technical only strategy and technical with sentiment analysis strategy.

• .gitignore: Specifies files and directories to be ignored by version control.

• README.md: Project documentation.

• requirements.txt: List of Python dependencies.

Contributing

Contributions are welcome! Please follow the Contribution Guidelines.

License

This project is licensed under the MIT License - see the LICENSE file for details.