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--- |
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title: AI Stock Predictor |
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sdk: streamlit |
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emoji: 🏆 |
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colorFrom: green |
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colorTo: blue |
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pinned: true |
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thumbnail: >- |
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https://cdn-uploads.huggingface.co/production/uploads/65ad293914e6582c30c688cc/qQkQV_-VsWMgfevm1H9RV.jpeg |
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short_description: Advanced AI predictions for the next week's stock prices |
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--- |
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# 📈 AI Stock Predictor |
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This app provides: |
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✅ Advanced AI predictions for the next week's stock prices |
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✅ Buy/Sell recommendations with confidence levels |
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✅ Stop Loss, Target Price, and Volatility Analysis |
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✅ Key Support/Resistance Levels |
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### How to Use |
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1. Enter a **Stock Ticker** (e.g., `TATAMOTORS.NS`). |
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2. Click **Submit** to get insights and predictions. |
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# Stock_Analytics |
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Stock Prediction and Analysis Script Overview This script is designed to predict stock prices using various machine learning and statistical models. It fetches historical stock data, processes it, and then applies several predictive models. The results, including forecasts and model coefficients, are saved to an Excel file for further analysis. |
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1. Data Ingestion and Preprocessing |
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Data Source: Historical stock data is fetched using the yfinance library, which provides access to financial data directly from Yahoo Finance. |
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Preprocessing: The data is then cleaned and processed using pandas and numpy for further analysis. This includes handling missing values, calculating moving averages, and other necessary data transformations. |
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2. Technical Analysis and Machine Learning |
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Technical Indicators: Using libraries like pandas and numpy, the project calculates various technical indicators such as moving averages, RSI (Relative Strength Index), Bollinger Bands, etc. |
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Feature Engineering: Features are created and selected for training machine learning models. These features may include technical indicators and other stock-related metrics. |
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Machine Learning Models: The scikit-learn library is used to build predictive models. These models might include Linear Regression, Random Forest, or other algorithms to predict future stock prices. |
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Risk Assessment: The project assesses risk levels associated with each stock, possibly by analyzing volatility, technical indicators, or other metrics. |
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3. Visualization |
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Matplotlib and Seaborn: These libraries are used to create static visualizations such as line plots for stock prices, candlestick charts, and bar plots for feature importance. |
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Plotly: An optional tool for creating interactive visualizations, especially within the Streamlit app. |
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Candlestick Charts: mplfinance is used to generate candlestick charts that visualize open, high, low, and close prices. |
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4. Web Application with Streamlit |
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User Interface: The entire analysis and visualization can be wrapped in a web-based UI using Streamlit. Users can input stock tickers and get visualized results, including price predictions, technical indicators, and risk levels. |
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Custom Styling: The Streamlit app is styled according to user preferences, including setting backgrounds, coloring text and numeric values based on risk levels, and displaying buy signals. |
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Tabs and Layout: Multiple tabs or sections can be created in Streamlit for different types of visualizations like technical indicators, feature importance, and future predictions. |
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5. LLM Integration |
