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https://github.com/sam120204/stock_trading_reddit
TrendSage, the stock prediction reddit tool, aims to develop a correlation between stock predictions based on the posts or comments on Reddit and the actual stock trends.
https://github.com/sam120204/stock_trading_reddit
reddit stock-market stock-price-prediction
Last synced: 14 days ago
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TrendSage, the stock prediction reddit tool, aims to develop a correlation between stock predictions based on the posts or comments on Reddit and the actual stock trends.
- Host: GitHub
- URL: https://github.com/sam120204/stock_trading_reddit
- Owner: Sam120204
- Created: 2024-05-12T17:49:35.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2024-05-23T04:53:24.000Z (6 months ago)
- Last Synced: 2024-05-23T04:55:11.327Z (6 months ago)
- Topics: reddit, stock-market, stock-price-prediction
- Language: Python
- Homepage: https://github.com/Sam120204/Stock_Trading_Reddit
- Size: 14.6 KB
- Stars: 1
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Security: SECURITY.md
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README
# TrendSage - Stock and Crypto Prediction Reddit Tool
## Overview
TrendSage aims to develop a correlation between stock and cryptocurrency predictions based on the posts or comments on Reddit and the actual market trends. The project consists of three phases, each focusing on different aspects of data collection, analysis, and prediction using social media sentiment. The data is fetched from the Apewisdom API, Reddit, and other sources, and is used to analyze sentiment and potential market movements.
## Phases
### Phase 1: Testing API (testing directory)
**Objective**: Build an API similar to Apewisdom that predicts stock trends based on social media sentiment, particularly from Reddit posts and comments. Develop a stock prediction tool that predicts stock trends based on social media sentiment, particularly from Reddit posts and comments. This involves using the Random Forest Regressor machine learning algorithm to analyze sentiment and other features extracted from social media data.
**Tools and Technologies**:
- Python
- PRAW (Python Reddit API Wrapper) for fetching Reddit data
- Requests library for making HTTP requests
- NLTK and VADER for sentiment analysis
- Scikit-learn for machine learning (Random Forest Regressor)
- Pandas for data manipulation
- Matplotlib and Seaborn for data visualization
### Phase 2: Apewisdom API Integration (apewisdom directory)
**Objective**: Use the Apewisdom API to gather trending stock tickers' mentions and store this data in MongoDB. Fetch real-time stock prices based on MongoDB stock ticker names and compare the real trending stock tickers on Reddit posts/comments through Apewisdom with the real-time prices to show their correlation. Integrate GPT-4 for a chatbot that provides information on these stock tickers.
**Tools and Technologies**:
- Python
- PRAW (Python Reddit API Wrapper)
- Requests library
- MongoDB (for data storage)
- Matplotlib and Seaborn (for data visualization)
- GPT-4 (for chatbot)
- Streamlit (for frontend)
- Docker and Google Cloud (for deployment and scheduling)
### Phase 3: Stock/Crypto Analysis (stock/crypto directories)
**Objective**: Analyze the correlation between social media sentiment and stock/cryptocurrency prices. Use embeddings (tokenized with BERT and GPT models) to analyze the sentiments of Reddit posts and comments about cryptocurrencies. Store these embeddings in a vector database (FAISS) for future implementation.
**Tools and Technologies**:
- Python
- Anaconda (Virtual Environment)
- PRAW (Python Reddit API Wrapper)
- Requests library
- Matplotlib and Seaborn (for data visualization)
- BERT and GPT models (for embedding and sentiment analysis)
- FAISS (for vector database storage)
**Subreddits Analyzed**:
- `wallstreetbets`
- `stocks`
- `pennystocks`
- `StockMarket`
- `EducatedInvesting`
- `Wallstreetbetsnew`
- `bitcoin`
- `btc`
- `CryptoCurrency`
- `BitcoinBeginners`
- `binance`
- `coinbase`
- `CryptoMoonShots`
- `CryptoMarkets`
- `CryptoTechnology`
**Next Steps**:
- Continue training the model to better fit and provide more accurate sentiment scores focused on cryptocurrency discussions.
- Use embeddings and store this information in the FAISS vector database for future implementation.
- Use LLM (Large Language Models) to analyze the correlation between social media sentiment and real cryptocurrency prices in detail.
## Installation and Setup Instructions
### Prerequisites
- Python 3.6 or higher
- Conda (recommended for virtual environment management)
- MongoDB (for data storage)
### Clone the Repository
```sh
git clone https://github.com/Sam120204/Stock_Trading_Reddit.git
cd Stock_Trading_Reddit
```
## Example Output
```plaintext
Trending Stocks on Reddit in the past 24 hours
Rank Ticker Name Mentions 24h Change (%)
======================================================================
1 BB BlackBerry 1420 -8.00
2 AMC AMC Entertainment 1278 -46.00
3 SPY SPDR S&P 500 ETF Trust 617 51.00
4 NVDA NVIDIA 298 121.00
5 GME GameStop 228 -52.00
6 TSLA Tesla 132 47.00
7 SOFI SoFi 119 -40.00
8 QQQ Invesco QQQ ETF 88 72.00
9 FFIE Faraday Future 82 204.00
10 SNDL Sundial Growers 79 -65.00
Trending Cryptocurrencies on Reddit in the past 24 hours
Rank Ticker Name Mentions 24h Change (%)
======================================================================
1 BTC Bitcoin 2200 10.00
2 ETH Ethereum 1800 -5.00
3 DOGE Dogecoin 1200 20.00
4 ADA Cardano 1000 15.00
5 XRP Ripple 800 12.00
Most mentioned Stocks & Cryptos on 4Chan /biz in the last 24h (BETA)
Rank Ticker Name Mentions 24h Change (%)
======================================================================
1 GME GameStop 500 12.00
2 AMC AMC Entertainment 450 -8.00
3 TSLA Tesla 400 7.00
4 BTC Bitcoin 350 15.00
5 ETH Ethereum 300 -5.00
```
## Contributing
Contributions are welcome! Please fork the repository and submit a pull request for any enhancements or bug fixes.
## License
This project is licensed under the MIT License.
## Contact
If you have any questions or feedback, feel free to reach out at [email protected].