SentimentGPT: create trading signals using unstructured data with sentiment analysis

**SentimentGPT** is a cutting-edge platform designed to leverage sentiment analysis in trading. Sentiment analysis involves analyzing and interpreting emotions, opinions, and attitudes expressed in text data. Market sentiment refers to market participants' overall attitude and emotions towards a particular financial instrument or market, which can influence buying and selling decisions.

SentimentGPT Overview
Components
Installation
Usage
File Structure
License

SentimentGPT Overview

SentimentGPT utilizes natural language processing and machine learning techniques to determine the sentiment behind text data, whether positive, negative, or neutral. It provides valuable insights into how traders and investors feel about certain assets, which can influence their decision-making process.

Components

Data Collection

Collect relevant text data from various sources, such as news articles, social media, and financial reports, to understand market sentiment.

Data Preprocessing

Prepare the collected data for analysis by cleaning, tokenizing, and transforming it into a suitable format for sentiment analysis.

Sentiment Analysis

Apply natural language processing and machine learning techniques to determine the sentiment behind the preprocessed text data.

Trading Strategy Integration

Integrate the sentiment analysis results into trading strategies to gauge market sentiment, identify trends, and make informed trading decisions.

Installation

1. (Recommended) Create a new virtual environment

conda create --name sentimentgpt python=3.10
conda activate sentimentgpt

2. Download the SentimentGPT repository

git clone https://github.com/YourUsername/SentimentGPT.git
cd SentimentGPT

3. Install SentimentGPT & dependencies from source or PyPI

pip install -U sentimentgpt

pip install -e .

4. Configure API keys

1. Rename `OAI_CONFIG_LIST_sample` to `OAI_CONFIG_LIST` and add your OpenAI API key.
2. Rename `config_api_keys_sample` to `config_api_keys` and add your financial data API keys.

Usage

Data Collection Usage

To collect relevant text data:

from sentimentgpt.data_collection import DataCollector

collector = DataCollector(config)
data = collector.collect()

Data Preprocessing Usage

To preprocess the collected data:

from sentimentgpt.data_preprocessing import DataPreprocessor

preprocessor = DataPreprocessor(config)
processed_data = preprocessor.preprocess(data)

Sentiment Analysis Usage

To perform sentiment analysis:

from sentimentgpt.sentiment_analysis import SentimentAnalyzer

analyzer = SentimentAnalyzer(config)
sentiment = analyzer.analyze(processed_data)

Trading Strategy Integration Usage

To integrate sentiment analysis into trading strategies:

from sentimentgpt.trading_strategy import StrategyIntegrator

integrator = StrategyIntegrator(config)
integrated_strategy = integrator.integrate(sentiment)

File Structure

The main folder sentimentgpt has four subfolders data_collection, data_preprocessing, sentiment_analysis, trading_strategy.

SentimentGPT
├── sentimentgpt (main folder)
│   ├── data_collection
│   	├── data_collector.py
│   ├── data_preprocessing
│   	├── data_preprocessor.py
│   ├── sentiment_analysis
│   	├── sentiment_analyzer.py
│   ├── trading_strategy
│   	├── strategy_integrator.py
│   ├── utils.py
│
├── configs
├── experiments
├── tutorials (hands-on tutorial)
│   ├── data_collection_tutorial.ipynb
│   ├── data_preprocessing_tutorial.ipynb 
│   └── sentiment_analysis_tutorial.ipynb
├── setup.py
├── config_api_keys_sample
├── requirements.txt
└── README.md

License

This project is licensed under the Apache-2.0 License. See the LICENSE file for more details.

Disclaimer: The information provided in this repository is for educational purposes only and should not be construed as financial advice. Always consult with a qualified financial advisor before making any investment decisions.

LLMQuant/SentimentGPT