MVP Goals & Requirements Definition

Version: 1.0 Date: January 2, 2026 Status: Active

Executive Summary

The Minimum Viable Product (MVP) for AlphaTwin focuses on establishing a solid foundation for quantitative trading with core data processing, strategy implementation, and backtesting capabilities. The MVP will demonstrate the system's ability to acquire market data, generate trading signals, and evaluate performance through systematic backtesting.

MVP Scope Definition

Must Have Features (Core Requirements)

1. Data Acquisition & Processing ✅

Goal: Reliable access to historical market data with automated processing Requirements: - Fetch EUR/USD forex data from Yahoo Finance API (2 years historical) - Implement data validation and cleaning pipeline - Calculate basic technical indicators (SMA, returns, volatility) - Store processed data in efficient format (Parquet/CSV)

Acceptance Criteria: - Successfully download and process EUR/USD data - Handle API errors and rate limits gracefully - Data quality: >99% completeness, no duplicate entries - Processing time: <5 minutes for 2-year dataset

2. Trading Strategy Implementation ✅

Goal: Implement proven quantitative trading strategies Requirements: - Moving Average Crossover strategy (SMA 20/50) - RSI-based mean reversion strategy - Basic momentum strategy - Modular strategy framework for easy extension

Acceptance Criteria: - Generate clear buy/sell/hold signals - Strategy parameters configurable - Signal generation time: <30 seconds for 500 trading days - Strategy logic documented and testable

3. Backtesting Engine ✅

Goal: Simulate trading performance with realistic constraints Requirements: - Event-driven backtesting framework - Transaction cost modeling (0.1% commission) - Position sizing (fixed amount per trade) - Performance metrics calculation (Sharpe ratio, max drawdown, total return)

Acceptance Criteria: - Accurate trade execution simulation - Realistic P&L calculations - Performance report generation - Backtest execution time: <2 minutes

4. Performance Visualization ✅

Goal: Clear presentation of backtesting results Requirements: - Portfolio value over time chart - Drawdown visualization - Returns distribution histogram - Key performance metrics table

Acceptance Criteria: - Charts render correctly in Jupyter notebooks - Metrics clearly displayed and explained - Visualizations exportable as images - Results reproducible across runs

5. Documentation Portal ✅

Goal: Professional documentation with Mermaid diagrams Requirements: - MkDocs portal with dark theme - Architecture documentation with diagrams - Development logs and setup instructions - Interactive navigation and search

Acceptance Criteria: - Portal accessible at localhost:8001 - All documentation pages load correctly - Mermaid diagrams render properly - Mobile-responsive design

Nice to Have Features (Enhanced Requirements)

Interactive Web Dashboard

Goal: Web-based interface for strategy exploration Requirements: - Streamlit application for parameter tuning - Real-time chart updates - Strategy comparison interface - Basic portfolio analytics

Acceptance Criteria: - Dashboard loads in web browser - Parameter changes update results immediately - Multiple strategies comparable side-by-side - Clean, professional UI

Advanced Risk Management

Goal: Enhanced risk controls beyond basic position sizing Requirements: - Stop-loss mechanisms - Maximum drawdown limits - Position size adjustment based on volatility - Risk parity allocation

Acceptance Criteria: - Risk controls prevent excessive losses - Parameters configurable per strategy - Risk metrics calculated and displayed - Backtest results reflect risk management impact

Multi-Asset Support

Goal: Extend beyond EUR/USD to multiple assets Requirements: - Support for major currency pairs (GBP/USD, USD/JPY, etc.) - Stock market data integration - Asset class diversification - Correlation analysis

Acceptance Criteria: - Multiple assets processed simultaneously - Cross-asset strategy implementation - Performance comparison across assets - Data consistency maintained

Technical Requirements

Infrastructure

Python Version: 3.13
Containerization: Docker with docker-compose
Version Control: Git with GitHub repository
Documentation: MkDocs with Material theme

Dependencies

Data Processing: pandas, numpy
Financial Data: yfinance
Visualization: matplotlib, plotly
Documentation: mkdocs, mkdocs-material, pymdown-extensions

Performance Targets

Data Processing: <5 minutes for 2-year dataset
Signal Generation: <30 seconds for 500 trading days
Backtesting: <2 minutes for complete simulation
Memory Usage: <2GB RAM for typical operations

MVP Success Criteria

Functional Success

Data Pipeline: EUR/USD data successfully acquired, processed, and stored
Strategy Execution: At least 3 trading strategies implemented and generating signals
Backtesting: Complete backtest simulation with realistic trading conditions
Results Analysis: Clear performance metrics and visualizations produced
Documentation: Professional portal with comprehensive system documentation

Quality Success

Code Quality: Well-structured, documented, and testable code
Error Handling: Graceful handling of API failures, data issues, and edge cases
Reproducibility: Results consistent across multiple runs with same parameters
Maintainability: Clear separation of concerns and modular architecture

User Experience Success

Ease of Use: Simple commands to run data pipeline and backtests
Clear Outputs: Intuitive charts and metrics presentation
Documentation: Complete setup and usage instructions
Extensibility: Framework ready for additional strategies and features

MVP Limitations & Assumptions

Current Limitations

Single asset focus (EUR/USD only for MVP)
Simplified transaction cost model
No live trading integration
Limited risk management features
Basic visualization capabilities

Assumptions

Reliable internet connection for data acquisition
Yahoo Finance API availability and rate limits
Local development environment with Docker
Sufficient computational resources for backtesting

Post-MVP Roadmap

Phase 2: Enhanced Strategies

Machine learning-based strategies
Multi-asset portfolio construction
Advanced risk management
Live trading integration

Phase 3: Production Features

Real-time data processing
High-frequency trading capabilities
Advanced analytics and reporting
Cloud deployment infrastructure

Phase 4: Enterprise Features

Multi-user support
Advanced backtesting scenarios
Compliance and audit features
API integrations with brokers

Risk Assessment

Technical Risks

API Dependency: Yahoo Finance API changes or outages
Data Quality: Inaccurate or incomplete market data
Performance: Computational complexity for large datasets
Compatibility: Python package version conflicts

Mitigation Strategies

API Redundancy: Implement fallback data sources
Data Validation: Comprehensive quality checks and error handling
Optimization: Efficient algorithms and data structures
Testing: Automated testing suite with CI/CD integration

Business Risks

Market Conditions: Strategy performance in different market environments
Regulatory Changes: Impact of financial regulations
Competition: Market saturation with similar tools

Mitigation Strategies

Robust Testing: Multiple market conditions and time periods
Compliance: Built-in regulatory considerations
Differentiation: Focus on ease of use and educational value

Testing Strategy

Unit Testing

Individual function and class testing
Mock data for API-dependent functions
Edge case coverage

Integration Testing

End-to-end data pipeline testing
Strategy signal generation validation
Backtesting engine verification

Performance Testing

Large dataset processing benchmarks
Memory usage monitoring
Execution time measurements

User Acceptance Testing

Manual testing of key workflows
Documentation completeness review
Setup process validation

Success Metrics

Quantitative Metrics

Data Accuracy: >99.5% data completeness
Strategy Performance: Positive Sharpe ratio for at least one strategy
Execution Speed: All operations complete within time targets
Code Coverage: >80% test coverage

Qualitative Metrics

Code Quality: Maintainable, well-documented codebase
User Experience: Intuitive interfaces and clear documentation
Extensibility: Framework easily accommodates new features
Reliability: System handles errors gracefully

This MVP definition provides a focused, achievable foundation for AlphaTwin while maintaining flexibility for future expansion and enhancement.