metadata

title: Stock Predictions
emoji: 📈
colorFrom: red
colorTo: purple
sdk: gradio
sdk_version: 5.33.1
app_file: app.py
pinned: true
license: mit
short_description: Use Amazon Chronos To Predict Stock Prices
tags:
  - mcp-server-track

youtube : https://youtu.be/pfEEMukXVjw

for a fast and functional deployment using ZerGPU please use : https://huggingface.co/spaces/Tonic/stock-predictions

Stock Analysis and Prediction Demo

A comprehensive stock analysis and prediction tool built with Gradio, featuring multiple prediction strategies and technical analysis indicators. The application is particularly suited for structured financial product creation and analysis.

Features

Multiple Prediction Strategies:
- Chronos ML-based prediction
- Technical analysis-based prediction
Technical Indicators:
- RSI (Relative Strength Index)
- MACD (Moving Average Convergence Divergence)
- Bollinger Bands
- Simple Moving Averages (20, 50, and 200-day)
Trading Signals:
- Buy/Sell recommendations based on multiple indicators
- Overall trading signal combining all indicators
- Confidence intervals for predictions
Interactive Visualizations:
- Price prediction with confidence intervals
- Technical indicators overlay
- Volume analysis
- Historical price trends
Structured Product Analysis:
- Extended prediction horizons (up to 1 year)
- Historical analysis up to 10 years
- Comprehensive risk metrics
- Sector and industry analysis
- Liquidity assessment

Structured Product Features

Extended Time Horizons

Prediction window up to 365 days
Historical data analysis up to 10 years
Long-term trend analysis
Extended technical indicators

Risk Analysis

Annualized volatility
Maximum drawdown analysis
Current drawdown tracking
Sharpe and Sortino ratios
Risk-adjusted return metrics

Product Metrics

Market capitalization
Sector and industry classification
Dividend yield analysis
Volume metrics
Liquidity scoring

Sector Analysis

Market cap ranking (Large/Mid/Small)
Sector exposure
Industry classification
Liquidity assessment

Installation

Clone the repository:

git clone <repository-url>
cd stock-prediction

Create and activate a virtual environment:

python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

Install dependencies:

pip install -r requirements.txt

Usage

Start the Gradio demo:

python app.py

Open your web browser and navigate to the URL shown in the terminal (typically http://localhost:7860)
Enter a stock symbol (e.g., AAPL, GOOGL, MSFT) and select your desired parameters:
- Timeframe (1d, 1h, 15m)
- Number of days to predict (up to 365 days)
- Historical lookback period (up to 10 years)
- Prediction strategy (Chronos or Technical)
Click "Analyze Stock" to get:
- Price predictions and trading signals
- Structured product metrics
- Risk analysis
- Sector analysis

Using for Structured Products

Initial Screening

Use extended lookback period (up to 10 years) for long-term performance analysis
Look for stocks with stable volatility and good risk-adjusted returns
Check liquidity scores for trading feasibility

Risk Assessment

Review risk metrics to match client risk profile
Analyze maximum drawdowns for worst-case scenarios
Compare risk-adjusted returns using Sharpe and Sortino ratios

Product Structuring

Use prediction horizon (up to 1 year) for product maturity design
Consider dividend yields for income-generating products
Use sector analysis for proper diversification

Portfolio Construction

Analyze multiple stocks for diversified bundles
Use sector metrics to avoid overexposure
Consider market cap rankings for appropriate sizing

Prediction Strategies

Chronos Strategy

Uses Amazon's Chronos model for ML-based price prediction. This strategy:

Analyzes historical price patterns
Generates probabilistic forecasts
Provides confidence intervals

Technical Strategy

Uses traditional technical analysis indicators to generate predictions:

RSI for overbought/oversold conditions
MACD for trend direction
Bollinger Bands for volatility
Moving Averages for trend confirmation

Trading Signals

The demo provides trading signals based on multiple technical indicators:

RSI: Oversold (<30), Overbought (>70), Neutral
MACD: Buy (MACD > Signal), Sell (MACD < Signal)
Bollinger Bands: Buy (price < lower band), Sell (price > upper band)
SMA: Buy (20-day > 50-day), Sell (20-day < 50-day)

An overall trading signal is calculated by combining all individual signals.

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Practical Example: Creating a 6-Month 8% Yield Structured Product

Scenario

A bank needs to create a structured product that offers an 8% yield over 6 months while maintaining profitability for the institution.

Step-by-Step Implementation

Initial Stock Screening
- Use the application to analyze stocks with:
  - High liquidity (for easy hedging)
  - Stable volatility (for predictable risk)
  - Strong technical indicators
  - Positive long-term trends
- Recommended stocks: AAPL, MSFT, GOOGL (high liquidity, stable volatility)
Product Structure Design
- Use the 6-month prediction horizon
- Analyze historical volatility for barrier setting
- Set participation rate based on risk metrics
- Structure: Reverse Convertible with 8% coupon
Risk Analysis
- Use the application's risk metrics:
  - Check maximum drawdown (should be < 15% for 6 months)
  - Verify liquidity scores (should be > 80%)
  - Analyze Sharpe ratio (should be > 1.5)
Business Case Example

Product Parameters:
- Notional Amount: $1,000,000
- Term: 6 months
- Coupon: 8% p.a. (4% for 6 months)
- Underlying: AAPL
- Barrier: 85% of initial price
- Participation: 100%
Revenue Structure:
- Client receives: 8% p.a. (4% for 6 months)
- Bank's hedging cost: ~5% p.a.
- Bank's profit margin: ~3% p.a.
- Total client payout: $40,000 (4% of $1M)
- Bank's profit: $15,000 (1.5% of $1M)
Implementation Steps
- Use the application's extended prediction horizon (180 days)
- Set technical indicators to monitor barrier risk
- Implement dynamic delta hedging based on predictions
- Monitor risk metrics daily using the application
Risk Management
- Use the application's volatility predictions for dynamic hedging
- Monitor technical indicators for early warning signals
- Set up automated alerts for barrier proximity
- Regular rebalancing based on prediction updates

Key Success Factors

Regular monitoring of prediction accuracy
Dynamic adjustment of hedging strategy
Clear communication of product risks to clients
Proper documentation of all assumptions and methodologies

This example demonstrates how the application can be used to create profitable structured products while managing risk effectively. The bank can use this framework to create similar products with different underlying assets, terms, and yield targets.