https://github.com/datarohit/stockastic
Stockastic is an ML-powered stock price prediction app built with Python and Streamlit. It utilizes machine learning models to forecast stock prices and help investors make data-driven decisions.
https://github.com/datarohit/stockastic
machine-learning plotly stock-price-prediction streamlit yfinance
Last synced: about 2 months ago
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Stockastic is an ML-powered stock price prediction app built with Python and Streamlit. It utilizes machine learning models to forecast stock prices and help investors make data-driven decisions.
- Host: GitHub
- URL: https://github.com/datarohit/stockastic
- Owner: DataRohit
- License: mit
- Created: 2023-08-26T19:40:34.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2023-11-03T03:45:46.000Z (over 2 years ago)
- Last Synced: 2023-12-16T16:13:09.442Z (over 2 years ago)
- Topics: machine-learning, plotly, stock-price-prediction, streamlit, yfinance
- Language: Jupyter Notebook
- Homepage: https://stockastic.streamlit.app/
- Size: 230 KB
- Stars: 2
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE.txt
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README
# 📈 **Stockastic**
### **Predicting Stocks with ML**
**Stockastic is an ML-powered stock price prediction app built with Python and Streamlit. It utilizes machine learning models to forecast stock prices and help investors make data-driven decisions.**
## 🏗️ **How It's Built**
Stockastic is built with these core frameworks and modules:
- **Streamlit** - To create the web app UI and interactivity
- **YFinance** - To fetch financial data from Yahoo Finance API
- **StatsModels** - To build the ARIMA time series forecasting model
- **Plotly** - To create interactive financial charts
The app workflow is:
1. User selects a stock ticker
2. Historical data is fetched with YFinance
3. ARIMA model is trained on the data
4. Model makes multi-day price forecasts
5. Results are plotted with Plotly
## 🎯 **Key Features**
- **Real-time data** - Fetch latest prices and fundamentals
- **Financial charts** - Interactive historical and forecast charts
- **ARIMA forecasting** - Make statistically robust predictions
- **Backtesting** - Evaluate model performance
- **Responsive design** - Works on all devices
## 🚀 **Getting Started**
### **Local Installation**
1. Clone the repo
```bash
git clone https://github.com/user/stockastic.git
```
2. Install requirements
```bash
pip install -r requirements.txt
```
3. Change directory
```bash
cd streamlit_app
```
4. Run the app
```bash
streamlit run 00_😎_Main.py
```
The app will be live at ```http://localhost:8501```
## 📈 **Future Roadmap**
Some potential features for future releases:
- **More advanced forecasting models like LSTM**
- **Quantitative trading strategies**
- **Portfolio optimization and tracking**
- **Additional fundamental data**
- **User account system**
## **⚖️ Disclaimer**
**This is not financial advice! Use forecast data to inform your own investment research. No guarantee of trading performance.**