RiskAnalyzer

RiskAnalyzer is a Python-based quantitative market analysis and risk assessment tool. It processes historical financial data to provide statistical insights, conditional probabilities, volatility analysis, and actionable trade signals. Inspired by behavioral finance and quantitative methods, it combines techniques like Monte Carlo simulations, GARCH models, and Markov Chain analysis.

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✨ Features

• 📈 Market Data Fetching

  • Uses Yahoo Finance via yfinance to download OHLCV data.
  • Supports multiple timeframes (1d, 1h, intraday).

• 📊 Descriptive & Statistical Analysis

  • Daily returns, volatility, skewness, kurtosis.
  • Adaptive outlier filtering.

• 🔗 Conditional Probabilities & Continuation Chains

  • Computes historical state chains (Bullish, Bearish, Neutral).
  • Continuation and reversal probabilities.
  • Markov Chain Transition Probability Matrix (TPM).

• ⚡ Volatility Models

  • GARCH model fitting and forecasting.
  • Hidden Markov Models (HMM) for volatility regimes.

• 🎲 Monte Carlo Simulations

  • Simple and regime-aware simulations.
  • Value at Risk (VaR) and Conditional VaR (CVaR) calculations.

• 🚀 Trading Signals

  • Regime-based strategy (Momentum / Mean Reversion).
  • Entry/Exit price suggestions, Stop Loss, Take Profit.

• 📄 Detailed Reports

  • Auto-generated TXT reports.
  • Continuation probability chain snapshots.
  • Volatility and risk metrics.

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📂 Project Structure

RiskAnalyzer/
├── main.py
├── src/
│   ├── cli.py
│   ├── data_fetcher.py
│   ├── descriptive_analysis.py
│   ├── distribution_analysis.py
│   ├── filters.py
│   ├── label_candlesticks.py
│   ├── monte_carlo.py
│   ├── monte_carlo_v2.py
│   ├── conditional_probabilities.py
│   ├── report_generator.py
│   ├── statistical_analysis.py
│   ├── trading_rules.py
│   ├── volatility_analysis.py
│   ├── volatility_regimes.py
│   └── __init__.py
├── reports/
├── data_raw.csv (generated)
├── data_filtered.csv (generated)
├── data_with_stats.csv (generated)
├── data_excluded_outliers.csv (generated)
├── .gitignore
└── README.md 

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✅ How to Run

1. Install Requirements---

pip install -r requirements.txt

Main dependencies include:

• pandas
• numpy
• yfinance
• matplotlib
• seaborn
• statsmodels
• hmmlearn

2. Run the Analyzer

run the main.py file

`python main.py`

3. Follow CLI Prompts

=== Risk Analyzer CLI ===

Enter symbol to analyze [default CL=F]: 
Enter timeframe (1d / 1h / 1m / etc) [default 1d]: 
Enter end date (YYYY-MM-DD) [default today]: 

4. Review the Report

• Auto-saves to: reports/
• Example: reports/CL_F_1d_2025-03-24_report.txt

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🧠 Concepts Used

• Statistical Analysis: Descriptive stats, Z-score filtering.
• Behavioral Finance: Continuation probabilities, cognitive market perspectives.
• Monte Carlo Methods: Simulating price paths for risk metrics.
• Volatility Forecasting: GARCH models, regime switching via HMM.
• Markov Chain Models: Transition probabilities and market state prediction.
• Trading Strategy Design: Bias-driven actions (Momentum / Mean Reversion).

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🛠️ Roadmap Ideas

• Integrate Real-time Data Streams.
• Add More Volatility Models (e.g., EGARCH, FIGARCH).
• Refine Trade Execution Module.
• Export Report to PDF or HTML.
• Web Interface (Flask/FastAPI).

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👨‍💻 Author

Designed and built by Marcelo Mazzariol

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📜 License

MIT License
© 2025 Marcelo Mazzariol

Permission is hereby granted, free of charge, to any person obtaining a copy of this software...