| title | emoji | colorFrom | colorTo | sdk | sdk_version | app_file | pinned |
|---|---|---|---|---|---|---|---|
Tech Stocks Trading Assistant |
U+1F4B5 |
red |
purple |
streamlit |
1.10.0 |
app.py |
false |
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
Welcome to the Tech Stocks Trading Assistant GitHub repository! This project is designed to help you make informed trading decisions in the world of tech stocks. It utilizes natural language processing (NLP) and sentiment analysis to provide insights into stock trends and investor sentiment. This README provides an overview of the project and its key functionalities.
The Tech Stocks Trading Assistant is a web-based tool built using Streamlit, a Python library for creating web applications. It combines the power of financial market data and natural language processing to help traders and investors make informed decisions in the dynamic tech stocks market.
- Sentiment Analysis: Utilize natural language processing to analyze news articles and social media sentiment related to tech stocks.
- Stock Recommendations: Get stock recommendations and predictions based on sentiment analysis.
- Interactive User Interface: Enjoy a user-friendly web interface for exploring stock recommendations and sentiment analysis.
To get started with the Tech Stocks Trading Assistant, follow these steps:
- Clone the Repository: Clone this repository to your local machine.
- Install Dependencies: Install the required Python libraries, including Streamlit.
- Run the Application: Start the application by running the Python script (app.py). Access it through a web browser.
- Stock Selection: Choose a tech stock or company of interest.
- View Recommendations: See stock recommendations and sentiment analysis based on recent news and social media content.
- Make Informed Decisions: Utilize the provided recommendations and sentiment analysis to make informed trading and investment decisions.
- Sentiment Analysis: This feature analyzes news articles and social media posts related to the selected tech stock.
- Recommendations: Receive stock recommendations and predictions based on sentiment analysis, helping you stay ahead of market trends.
- Stay Updated: Access real-time stock recommendations and sentiment analysis to keep track of changing market conditions.
Contributions to this open-source project are welcome. Whether you want to enhance existing features, fix bugs, or add new functionalities, please feel free to contribute to the project. Check the CONTRIBUTING.md file for guidelines on how to contribute.
This project is open source and is licensed under the MIT License. You are free to use, modify, and distribute this software for your trading and investment needs. Please refer to the license for more details and obligations.
Explore the world of tech stocks and make informed trading decisions with the Tech Stocks Trading Assistant. Enjoy your journey in the dynamic tech stocks market!
This repository contains a Streamlit app that provides a stock trading assistant powered by FinBERT, a large language model fine-tuned on financial data. The app empowers users with a range of features for stock trading and analysis.
The app allows users to:
- Search for stock information, including price, news, and analyst ratings.
- Generate stock ideas based on their investment goals and risk tolerance.
- Backtest trading strategies using historical data.
- Simulate trading in real time using a paper trading account.
import streamlit as st
from app import StockAssistant
# Create a StockAssistant object
assistant = StockAssistant()
# Get stock information
stock_info = assistant.get_stock_info('AAPL')
# Generate stock ideas
stock_ideas = assistant generate_stock_ideas(risk_tolerance='low', investment_goal='growth')
# Backtest a trading strategy
backtest_results = assistant backtest_trading_strategy('AAPL', 'buy_and_hold', start_date='2021-01-01', end_date='2023-03-08')
# Simulate trading in real time
paper_trading_account = assistant simulate_trading('AAPL', 'buy_and_hold', starting balance=10000)Use code with caution.
The app requires the following Python packages:
- Streamlit
- FinBERT
- Hugging Face Transformers
- Pandas
- Numpy
The app can be deployed to a variety of platforms, including:
- Heroku
- AWS Elastic Beanstalk
- Google Cloud Platform
- Docker
For more information on how to use the app, please see the documentation at Stock Assistant Documentation.
Enjoy exploring the world of stock trading and analysis with the Stock Trading Assistant!