metadata

title: Stock Predictions
emoji: 🐢
colorFrom: yellow
colorTo: yellow
sdk: gradio
sdk_version: 5.33.0
app_file: app.py
pinned: false
license: mit
short_description: Use Amazon Chronos To Predict Stock Prices
tags:
  - mcp-server-track

Advanced Stock Prediction Analysis

A comprehensive stock prediction and analysis tool that combines Chronos forecasting with advanced features including regime detection, ensemble methods, and stress testing.

Features

Core Prediction Engine

Chronos Forecasting: State-of-the-art time series forecasting using Amazon's Chronos model
Technical Analysis: Traditional technical indicators (RSI, MACD, Bollinger Bands, SMA)
Multi-timeframe Support: Daily, hourly, and 15-minute analysis
Real-time Data: Live market data via yfinance

Advanced Features

1. Market Regime Detection

Hidden Markov Models (HMM): Automatic detection of market regimes (bull, bear, sideways)
Volatility-based Fallback: Simplified regime detection when HMM is unavailable
Regime-adjusted Signals: Trading signals that adapt to current market conditions

2. Ensemble Methods

Multi-model Combination: Combines Chronos, technical, and statistical predictions
Adaptive Weighting: User-configurable weights for different models
Uncertainty Quantification: Advanced uncertainty estimation with skewness adjustment

3. Advanced Risk Metrics

Tail Risk Analysis: VaR and CVaR calculations
Market Correlation: Beta, alpha, and correlation with market indices
Risk-adjusted Returns: Sharpe, Sortino, and Calmar ratios
Drawdown Analysis: Maximum drawdown and recovery metrics

4. Stress Testing

Scenario Analysis: Market crash, high volatility, bull market scenarios
Interest Rate Shocks: Impact of rate changes on predictions
Custom Scenarios: User-defined stress test parameters

5. Enhanced Uncertainty Quantification

Skewness-aware: Accounts for non-normal return distributions
Adaptive Smoothing: Reduces prediction drift based on uncertainty
Confidence Intervals: Dynamic confidence levels based on market conditions

Installation

Install dependencies:

pip install -r requirements.txt

Run the application:

python app.py

Usage

Basic Analysis

Enter a stock symbol (e.g., AAPL, MSFT, GOOGL)
Select timeframe (Daily, Hourly, or 15-minute)
Choose prediction strategy (Chronos or Technical)
Set prediction days and lookback period
Click "Analyze Stock"

Advanced Settings

Ensemble Methods: Enable/disable multi-model combination
Regime Detection: Enable/disable market regime analysis
Stress Testing: Enable/disable scenario analysis
Risk-free Rate: Set annual risk-free rate for calculations
Market Index: Choose correlation index (S&P 500, Dow Jones, NASDAQ, Russell 2000)
Ensemble Weights: Adjust weights for Chronos, Technical, and Statistical models

Output Sections

Daily Analysis

Structured Product Metrics: Market cap, sector, financial ratios
Advanced Risk Analysis: Comprehensive risk metrics with market correlation
Market Regime Analysis: Current regime and transition probabilities
Trading Signals: Advanced signals with confidence levels
Stress Test Results: Scenario analysis outcomes
Ensemble Analysis: Multi-model combination details

Hourly/15-minute Analysis

Intraday Metrics: High-frequency volatility and momentum indicators
Volume Analysis: Volume-price trends and momentum
Real-time Indicators: Pre/post market data analysis

Technical Details

Regime Detection

Uses Hidden Markov Models with 3 states (low volatility, normal, high volatility)
Falls back to volatility-based detection if HMM unavailable
Regime probabilities influence trading signal thresholds

Ensemble Methods

Chronos: Primary deep learning model (60% default weight)
Technical: Traditional indicators with mean reversion (20% default weight)
Statistical: ARIMA-like models with momentum (20% default weight)

Stress Testing Scenarios

Market Crash: 3x volatility, -15% return shock
High Volatility: 2x volatility, -5% return shock
Low Volatility: 0.5x volatility, +2% return shock
Bull Market: 1.2x volatility, +10% return shock
Interest Rate Shock: 1.5x volatility, -8% return shock

Uncertainty Quantification

Skewness-adjusted confidence intervals
Adaptive smoothing based on prediction uncertainty
Time-varying volatility modeling

Dependencies

Core

torch>=2.1.2: PyTorch for deep learning
chronos-forecasting>=1.0.0: Amazon's Chronos model
yfinance>=0.2.0: Yahoo Finance data
gradio>=4.0.0: Web interface

Advanced Features

hmmlearn>=0.3.0: Hidden Markov Models for regime detection
scipy>=1.10.0: Scientific computing and statistics
scikit-learn>=1.0.0: Machine learning utilities
plotly>=5.0.0: Interactive visualizations

Limitations

Market Hours: Intraday data (hourly/15-minute) only available during market hours
Data Quality: Dependent on yfinance data availability and quality
Model Complexity: Advanced features may increase computation time
GPU Requirements: Chronos model requires CUDA-capable GPU for optimal performance

Disclaimer

This tool is for educational and research purposes only. Stock predictions are inherently uncertain and should not be used as the sole basis for investment decisions. Always conduct thorough research and consider consulting with financial professionals before making investment decisions.

Contributing

Contributions are welcome! Please feel free to submit pull requests or open issues for bugs and feature requests.

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.