Infrastructure-Based ML Deployment: The Model Factory Approach

Abstract

The Model Factory automatically generates 486 distinct model configurations from 25+ base architectures, enabling performance-based selection and continuous adaptation without manual intervention.

Introduction

Traditional algorithmic trading systems require manual implementation of each model variant—a time-consuming process that limits scalability and adaptability. Our Model Factory takes a fundamentally different approach: we define base architectures and let the system automatically generate and evaluate variants.

Base Architectures

Our Model Factory starts with 25+ carefully selected base architectures across multiple categories:

Deep Learning Models

• LSTM Networks: Long Short-Term Memory for sequence modeling
• GRU-LSTM Hybrids: Combining GRU efficiency with LSTM memory
• Transformers: Self-attention mechanisms for pattern recognition
• Temporal Convolutional Networks (TCN): Causal convolutions for time series
• Temporal Fusion Transformers: Combining attention with interpretable features
• Bidirectional LSTM: Processing sequences in both directions

Classical Machine Learning

• Random Forest: Ensemble of decision trees
• Gradient Boosting (XGBoost, LightGBM): Sequential tree boosting
• Support Vector Machines: Hyperplane classification with kernel tricks
• Logistic Regression: Baseline probabilistic classification

Econometric Models

• ARIMA: Autoregressive Integrated Moving Average
• GARCH: Generalized Autoregressive Conditional Heteroskedasticity
• Holt-Winters: Exponential smoothing with trends
• VAR Models: Vector Autoregression for multivariate series

Reinforcement Learning

• Deep Q-Networks (DQN): Value function approximation
• Proximal Policy Optimization (PPO): Policy gradient with constraints
• A3C/A2C Agents: Asynchronous advantage actor-critic

Automatic Expansion

Each base architecture is automatically expanded across multiple dimensions:

1. Lookback Windows

• 7-day (short-term patterns)
• 14-day (two-week cycles)
• 30-day (monthly patterns)
• 60-day (quarterly trends)
• 90-day (seasonal effects)

2. Feature Engineering Approaches

We apply 8 distinct feature engineering pipelines:

• Raw OHLCV with volume normalization
• Technical indicators (RSI, MACD, Bollinger Bands, etc.)
• Price momentum and acceleration
• Volatility features (ATR, historical volatility)
• Order flow features (OFI, VPIN)
• Cross-asset correlations
• Sentiment aggregates
• On-chain metrics (for crypto assets)

3. Hyperparameter Configurations

Each architecture undergoes hyperparameter exploration:

• Learning rates: 1e-4, 5e-4, 1e-3
• Hidden dimensions: 64, 128, 256
• Dropout rates: 0.1, 0.2, 0.3
• Regularization: L1, L2, combined

The 486 Configuration Count

The total configuration count is calculated as:

25 architectures × 5 lookbacks × 8 feature sets × ~3-5 hyperparameters
= 486 distinct configurations

Note: Some combinations are excluded based on computational feasibility and prior performance analysis.

Performance-Based Selection

All 486 configurations run continuously in evaluation mode. A rating engine evaluates each based on:

Metrics Tracked

• Rolling Sharpe Ratio: 30/60/90 day windows
• Win Rate: Percentage of profitable predictions
• Profit Factor: Gross profit / gross loss
• Maximum Drawdown: Peak-to-trough decline
• Recovery Time: Days to recover from drawdown
• Correlation: With other active configurations
• Computational Cost: Resources per prediction

Selection Algorithm

Every 24 hours, the system:

1. Ranks all configurations by composite score
2. Selects top 10-15 performers
3. Activates selected configurations for live signals
4. Demotes underperformers to evaluation-only mode

Continuous Adaptation

The Model Factory doesn't just select once—it continuously adapts:

• 5-minute performance snapshots: Real-time metric updates
• 24-hour rebalancing cycles: Full configuration review
• Automatic promotion: Rising configurations gain allocation
• Automatic demotion: Declining configurations lose allocation
• Zero manual intervention: System operates autonomously

Results

Our backtesting shows the Model Factory approach delivers:

Conclusion

The Model Factory represents a paradigm shift from manual model development to infrastructure-based deployment. By automating variant generation and selection, we achieve:

• Scalability: Hundreds of configurations without additional engineering
• Adaptability: Rapid response to market regime changes
• Objectivity: Performance-based selection removes human bias
• Efficiency: Computational resources focused on proven performers

This approach forms the foundation of HyperTrader's machine learning infrastructure.

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For implementation details, see our blog post on building transparent trading systems.