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Phase 2: Data Factory - Requirements & Planning

Version: 1.0 Date: January 2, 2026 Status: Planning Phase

Executive Summary

Phase 2 focuses on establishing a robust data processing pipeline that transforms raw market data into clean, reliable datasets suitable for quantitative analysis. This phase addresses the critical foundation that supports all subsequent trading strategy development and backtesting activities.

Phase 2 Mission

"Building the Data Factory: Reliable, Clean, and Scalable Market Data Processing"

Transform raw financial data into a pristine, analysis-ready foundation that eliminates data quality issues before they can corrupt trading strategies and backtest results.

Core Objectives

1. Data Reliability Foundation

  • Zero-Trust Data Processing: Every data point validated, cleaned, and verified
  • Immutable Data Pipeline: Raw data preserved, transformations tracked and reversible
  • Error Prevention: Proactive identification and correction of data anomalies

2. Industrial-Grade Data Quality

  • Statistical Validation: Outlier detection, distribution analysis, correlation checks
  • Temporal Consistency: Time zone standardization, trading calendar alignment
  • Cross-Source Verification: Multi-source data comparison and reconciliation

3. Scalable Processing Architecture

  • Batch Processing: Efficient handling of large historical datasets
  • Incremental Updates: Efficient daily data updates without full reprocessing
  • Memory Optimization: Processing large datasets within reasonable memory constraints

Technical Requirements

Data Sources & Formats

Primary Data Sources

  • Yahoo Finance API: Primary forex and equity data source
  • CSV/Excel Files: Historical data imports, custom datasets
  • Future Extensions: Bloomberg, Refinitiv, Quandl APIs

Data Formats

  • OHLCV Standard: Open, High, Low, Close, Volume, Adjusted Close
  • Extended Fields: Dividends, stock splits, trading volume
  • Metadata: Source timestamps, data quality flags, processing timestamps

Processing Pipeline Requirements

Stage 1: Data Ingestion & Validation

Requirements: - API rate limiting and retry logic - Response validation and error handling - Data completeness verification - Duplicate detection and removal

Success Criteria: - 99.9% API success rate with automatic retries - Complete error logging and alerting - Data integrity preservation during ingestion

Stage 2: Data Cleaning & Standardization

Requirements: - Missing value imputation strategies - Outlier detection and handling - Price adjustment for dividends and splits - Time zone and trading calendar standardization

Success Criteria: - <0.1% missing data after imputation - Statistical outlier identification accuracy >95% - Consistent time series across all symbols

Stage 3: Feature Engineering & Enhancement

Requirements: - Technical indicator calculations - Return computations (arithmetic/logarithmic) - Volatility measurements - Volume-based indicators

Success Criteria: - All calculations numerically stable and accurate - Consistent indicator implementations across symbols - Efficient computation for large datasets

Stage 4: Quality Assurance & Storage

Requirements: - Statistical quality checks - Data integrity validation - Efficient storage formats (Parquet/HDF5) - Metadata tracking and indexing

Success Criteria: - Automated quality reports generation - Data retrieval in <1 second for typical queries - Backward compatibility with existing analysis code

Implementation Deliverables

1. Data Schema Documentation ✅

File: docs/data/data_schema.md Content: - Complete data dictionary for all fields - Data type specifications and constraints - Quality standards and validation rules - Schema evolution procedures

2. Data Cleaning Module ✅

File: src/cleaner.py Components: - DataValidator class for input validation - DataCleaner class for anomaly correction - QualityChecker class for statistical validation - Processing pipeline orchestration

3. Enhanced Data Loader ✅

File: src/data_loader.py (enhancement) Improvements: - Multi-source data integration - Advanced error handling and retry logic - Data quality monitoring - Incremental update capabilities

4. Data Quality Dashboard (Future)

Requirements: - Real-time data quality metrics - Historical quality trend analysis - Automated alerting for quality degradation - Quality improvement recommendations

Data Schema Specification

Core OHLCV Schema

Raw Data Fields

{
    "symbol": "str",           # Trading symbol (e.g., "EURUSD=X")
    "timestamp": "datetime64[ns, UTC]",  # UTC timestamp
    "open": "float64",         # Opening price
    "high": "float64",         # High price
    "low": "float64",          # Low price
    "close": "float64",        # Closing price
    "adj_close": "float64",    # Adjusted closing price
    "volume": "int64",         # Trading volume
}

Processed Data Extensions

{
    # Return calculations
    "returns": "float64",           # Daily returns
    "log_returns": "float64",       # Logarithmic returns

    # Technical indicators
    "sma_20": "float64",           # 20-day simple moving average
    "sma_50": "float64",           # 50-day simple moving average
    "ema_20": "float64",           # 20-day exponential moving average
    "rsi_14": "float64",           # 14-day RSI
    "macd": "float64",             # MACD line
    "macd_signal": "float64",      # MACD signal line
    "bb_upper": "float64",         # Bollinger Band upper
    "bb_lower": "float64",         # Bollinger Band lower

    # Volatility measures
    "volatility_20": "float64",    # 20-day rolling volatility
    "parkinson_vol": "float64",    # Parkinson volatility estimator

    # Quality flags
    "data_quality_score": "float64",  # 0-1 quality score
    "has_missing_values": "bool",     # Missing data flag
    "has_outliers": "bool",           # Outlier detection flag
    "processing_timestamp": "datetime64[ns, UTC]"  # Processing time
}

Data Quality Standards

Completeness Requirements

  • Price Data: >99.5% completeness for OHLC fields
  • Volume Data: >95% completeness (can be estimated if missing)
  • Time Series: No gaps >5 trading days without documented reason

Accuracy Standards

  • Price Precision: Minimum 4 decimal places for forex, 2 for stocks
  • Time Accuracy: Millisecond precision for timestamps
  • Value Ranges: Automatic detection of impossible price values

Consistency Rules

  • OHLC Relationships: High ≥ max(Open, Close), Low ≤ min(Open, Close)
  • Volume Validity: Non-negative values, reasonable ranges by asset class
  • Temporal Order: Chronological ordering of all records

Development Milestones

Week 1-2: Foundation (Current)

  • [x] Data schema documentation
  • [x] Basic data cleaning framework
  • [x] Enhanced data loader with error handling
  • [ ] Unit tests for data processing functions

Week 3-4: Core Processing

  • [ ] Advanced outlier detection algorithms
  • [ ] Multi-source data reconciliation
  • [ ] Performance optimization for large datasets
  • [ ] Comprehensive data quality reporting

Week 5-6: Quality Assurance

  • [ ] Automated data quality monitoring
  • [ ] Historical data reprocessing pipeline
  • [ ] Data quality dashboard prototype
  • [ ] Integration testing with existing backtesting engine

Week 7-8: Production Readiness

  • [ ] Production data pipeline deployment
  • [ ] Monitoring and alerting setup
  • [ ] Documentation completion
  • [ ] Performance benchmarking and optimization

Success Metrics

Data Quality Metrics

  • Completeness Rate: >99.8% for critical fields
  • Accuracy Rate: >99.9% correct data points
  • Consistency Rate: >99.5% adherence to business rules
  • Timeliness: <30 minutes for daily data processing

Performance Metrics

  • Processing Speed: <5 minutes for 2-year historical dataset
  • Memory Efficiency: <2GB RAM for typical processing jobs
  • Storage Efficiency: <50% of raw data size for processed datasets
  • Query Performance: <500ms for typical data retrieval operations

Reliability Metrics

  • Uptime: >99.5% data pipeline availability
  • Error Rate: <0.1% processing failures
  • Data Loss: 0% unrecoverable data loss
  • Alert Response: <15 minutes average response time

Risk Assessment

Technical Risks

  • API Dependency: Yahoo Finance changes or outages
  • Data Volume: Exponential growth requiring scalable solutions
  • Quality Degradation: Silent data quality issues affecting strategies

Mitigation Strategies

  • Multi-Source Architecture: Ability to switch data providers
  • Scalable Design: Cloud-native architecture for future growth
  • Quality Monitoring: Automated detection and correction systems

Business Risks

  • Time to Market: Delays in data processing affecting strategy development
  • Quality Issues: Poor data leading to incorrect trading decisions
  • Scalability Limits: System unable to handle increased data volumes

Mitigation Strategies

  • Incremental Delivery: Working system delivered in stages
  • Quality Gates: Rigorous testing before production deployment
  • Monitoring Systems: Real-time performance and quality tracking

Dependencies & Prerequisites

Technical Dependencies

  • Python 3.13: Core processing runtime
  • Pandas/NumPy: Data manipulation libraries
  • Yahoo Finance API: Primary data source
  • Docker: Containerized processing environment

External Dependencies

  • Internet Connectivity: For data source access
  • Storage Systems: For data persistence
  • Monitoring Systems: For pipeline health tracking

Team Dependencies

  • Data Engineering Skills: For pipeline development
  • Financial Knowledge: For data validation rules
  • Quality Assurance: For testing and validation procedures

Future Extensions

Phase 2.1: Advanced Data Sources

  • Integration with additional financial data providers
  • Alternative data sources (news, social media, satellite imagery)
  • Real-time data streaming capabilities

Phase 2.2: Machine Learning Data Preparation

  • Feature engineering for ML models
  • Automated feature selection and importance analysis
  • Data versioning for model reproducibility

Phase 2.3: Multi-Asset Data Processing

  • Support for equities, commodities, cryptocurrencies
  • Cross-asset correlation analysis
  • Multi-timeframe data aggregation

"Quality data is the foundation of successful quantitative trading. Clean data prevents garbage-in-garbage-out scenarios and enables reliable strategy development and backtesting."

Phase 2 establishes the industrial-grade data processing foundation that will support all future AlphaTwin capabilities.