A professional machine learning system featuring model versioning, experiment tracking, drift monitoring, and statistical validation. This is NOT a trading bot—it's a research-grade analytical platform.

This application demonstrates production-ready ML engineering practices applied to financial time-series analysis. It identifies market behavioral regimes using unsupervised learning, tracks model performance, monitors data drift, and presents insights through a premium dark-mode dashboard.

Core Capabilities:

• ✅ Automated model selection (K=3 to K=8)
• ✅ Clustering stability validation
• ✅ Real-time data integration (Yahoo Finance)
• ✅ Statistical validation (ADF tests, distributions)
• ✅ Experiment tracking with full reproducibility
• ✅ Model versioning with artifact management
• ✅ Drift monitoring between training runs
• ✅ Interpretable regime insights
• ✅ Professional visualization dashboard

What This System Does NOT Do:

• ❌ Predict future prices
• ❌ Provide trading signals
• ❌ Make investment recommendations
• ❌ Guarantee financial outcomes

┌──────────────────────────────────────────────────────────────┐
│                    Flask Web Application                      │
├──────────────────────────────────────────────────────────────┤
│  Landing  │  Dashboard  │  Upload  │  Real-time  │  API      │
└─────┬────────────────────────────────────────────────────────┘
      │
      ▼
┌──────────────────────────────────────────────────────────────┐
│                     Analysis Pipeline                         │
├────────────┬────────────┬────────────┬──────────────────────┤
│ Features   │ Clustering │ Validation │ Drift Monitoring     │
│ Trends     │ Regimes    │ Stability  │ Experiment Tracking  │
└────────────┴────────────┴────────────┴──────────────────────┘
      │
      ▼
┌──────────────────────────────────────────────────────────────┐
│                      Data Layer                               │
├────────────┬────────────┬────────────────────────────────────┤
│ data/raw/  │ data/processed/ │ data/features/               │
└────────────┴────────────┴────────────────────────────────────┘
      │
      ▼
┌──────────────────────────────────────────────────────────────┐
│                    ML Pipeline                                │
├────────────┬────────────┬────────────────────────────────────┤
│ Training   │ Versioning │ Monitoring                         │
│ Evaluation │ Registry   │ Drift Detection                    │
└────────────┴────────────┴────────────────────────────────────┘

• Feature Engineering: 20 engineered features (returns, volatility, momentum, MA ratios)
• Data Validation: Schema validation, type checking, quality reports
• Real Data Integration: Yahoo Finance API integration
• Preprocessing: Standardization, missing value handling

• Model Selection: Systematic K comparison (K=3 to K=8)
• Clustering: K-Means with optimal parameter selection
• Stability Analysis: Multiple runs with ARI/NMI metrics
• Evaluation: Silhouette, Davies-Bouldin, Calinski-Harabasz scores

• Drift Monitoring: Feature drift detection between runs
• Experiment Tracking: Full parameter and metric logging
• Model Versioning: Timestamp-based version management
• Audit Trail: Complete reproducibility

• Premium Dark Theme: Bloomberg-inspired professional interface
• Interactive Dashboard: Real-time KPIs and charts
• Regime Analysis: Interpretable cluster insights
• Model Performance: Accuracy gauges and metrics

Python 3.11+
pip (Python package manager)

1. Clone repository

git clone https://github.com/Prajwal0422/ai-financial-time-series-learning.git
cd ai-financial-time-series-learning

2. Install dependencies

pip install -r requirements.txt

3. Run the application

python app.py

4. Open browser

http://127.0.0.1:5000

Basic Training:

python train_model.py

Advanced Training (with all features):

python train_model_advanced.py

This executes:

• Statistical validation
• Model selection (K=3 to K=8)
• Stability analysis
• Regime interpretation
• Model versioning
• Experiment tracking
• Drift monitoring

ai-financial-time-series-learning/
│
├── app.py                          # Flask application
├── config.py                       # Configuration
├── pipeline.py                     # Feature engineering
├── train_model.py                  # Basic training
├── train_model_advanced.py         # Advanced training
├── requirements.txt                # Dependencies
│
├── analysis/                       # Core modules
│   ├── data_loader.py             # Data management
│   ├── features.py                # Feature engineering
│   ├── clustering.py              # K-Means clustering
│   ├── regime_labels.py           # Regime interpretation
│   ├── model_selection.py         # Model comparison
│   ├── stability.py               # Stability analysis
│   ├── statistical_validation.py  # Statistical tests
│   ├── experiment_tracker.py      # Experiment logging
│   ├── model_versioning.py        # Version management
│   ├── drift_monitoring.py        # Drift detection (NEW)
│   ├── charts.py                  # Visualization
│   └── ...
│
├── data/                          # Data storage
│   ├── raw/                       # Original datasets
│   ├── processed/                 # Engineered features
│   └── features/                  # Feature storage
│
├── models/                        # Model artifacts
│   ├── real_data/
│   │   ├── latest/               # Current model
│   │   ├── drift_report.json    # Drift monitoring (NEW)
│   │   └── versions.json        # Version registry
│   ├── kmeans.pkl                # Model
│   ├── scaler.pkl                # Scaler
│   └── metrics.pkl               # Metrics
│
├── templates/                     # HTML templates
│   ├── index.html                # Landing page
│   ├── dashboard.html            # Dashboard
│   ├── upload.html               # Upload interface
│   └── realtime.html             # Real-time data
│
├── static/                        # Static assets
│   ├── css/
│   │   ├── dark-theme.css        # Main theme
│   │   └── modern-ui.css         # UI components
│   ├── js/
│   │   ├── effects.js            # Interactions
│   │   └── modern-ui.js          # UI logic
│   └── charts/                   # Generated charts
│
└── tests/                         # Test suite
    └── test_features.py          # Feature tests

Why K-Means?

• Discovers natural groupings without labels
• Interpretable cluster centers
• Efficient for large datasets
• Well-suited for regime detection

Features Used (7 selected):

1. Log_Return - Price movement direction/magnitude
2. Volatility_10 - Short-term risk
3. Volatility_30 - Long-term risk
4. Momentum_5 - Recent momentum
5. Price_to_MA10 - Short-term trend position
6. Price_to_MA30 - Long-term trend position
7. HL_Range - Intraday volatility

Silhouette Score: Measures cluster separation (-1 to 1, higher better) Davies-Bouldin Index: Measures cluster compactness (lower better) Calinski-Harabasz: Variance ratio criterion (higher better) ARI/NMI: Stability metrics across runs

Clusters mapped to business-friendly labels:

Data drift occurs when the statistical properties of features change over time, potentially degrading model performance.

1. Feature Comparison: Compares mean values between current and previous training runs
2. Threshold Detection: Flags features with >15% change (configurable)
3. Report Generation: Saves detailed drift report to models/real_data/drift_report.json
4. Logging: Warns if drift detected

from analysis.drift_monitoring import monitor_drift_between_runs

drift_report = monitor_drift_between_runs(
    current_data_path='data/processed/current.csv',
    previous_data_path='data/processed/previous.csv',
    threshold=0.15  # 15% threshold
)

if drift_report['overall_drift']:
    print(f"⚠️ Drift detected in {drift_report['num_drifted_features']} features")

{
  "timestamp": "2026-03-02T10:30:00",
  "threshold": 0.15,
  "overall_drift": true,
  "num_drifted_features": 2,
  "total_features": 20,
  "drifted_features": ["Volatility_10", "Momentum_5"],
  "features": {
    "Volatility_10": {
      "current_mean": 0.0234,
      "previous_mean": 0.0198,
      "drift_percentage": 0.1818,
      "is_drifted": true
    }
  }
}

# Run all tests
pytest tests/ -v

# Run with coverage
pytest tests/ --cov=analysis --cov-report=html

# Run specific test
pytest tests/test_features.py -v

Backend:

• Python 3.11+
• Flask 3.1.0
• scikit-learn 1.5.2
• pandas 2.2.3
• NumPy 2.1.3

Frontend:

• HTML5/CSS3
• JavaScript (ES6+)
• Chart.js (via matplotlib)
• Custom dark theme

ML/Data:

• K-Means clustering
• StandardScaler normalization
• Feature engineering pipeline
• Statistical validation

DevOps:

• Git version control
• pytest testing framework
• Modular architecture
• Configuration management

• Unsupervised learning (K-Means)
• Hyperparameter selection
• Model evaluation (multiple metrics)
• Stability analysis
• Feature engineering (20 features)
• Model persistence

• Experiment tracking
• Model versioning
• Drift monitoring (NEW)
• Artifact management
• Reproducibility

• Modular architecture (30+ modules)
• Configuration management
• Error handling
• Logging and monitoring
• Unit testing
• Documentation

• ETL pipelines
• Data validation
• Quality checks
• Real data integration
• Efficient processing

This application is designed for:

• Learning ML engineering practices
• Portfolio demonstration
• Understanding market behavior analysis
• Exploring unsupervised learning

This application is NOT:

• A trading system
• Investment advice
• A prediction tool
• Guaranteed to be accurate

• Financial markets involve risk
• Past performance ≠ future results
• Consult qualified professionals
• Use at your own risk

✅ Production-Ready: Model versioning, experiment tracking, drift monitoring
✅ Well-Tested: Comprehensive test suite with high coverage
✅ Documented: Clear documentation and code comments
✅ Modular: Clean architecture with separation of concerns
✅ Scalable: Designed for extension and maintenance
✅ Professional: Industry-standard practices and patterns

Current Limitations:

• Single algorithm (K-Means only)
• No hyperparameter tuning
• Limited to historical analysis
• No real-time predictions

Planned Improvements:

• Multiple clustering algorithms (DBSCAN, Hierarchical)
• Hyperparameter optimization (GridSearch, Bayesian)
• Cross-validation framework
• MLflow integration
• Docker containerization
• CI/CD pipeline
• API documentation (Swagger)
• Advanced drift detection (KS test, PSI)

MIT License - Educational purposes only

Prajwal Y Jain
Production-ready ML engineering portfolio project

• Inspired by professional analytical platforms
• Built with industry-standard tools
• Designed for educational purposes

Version: 4.0.0
Status: ✅ Production Ready
Last Updated: March 2, 2026

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