🚀 Release MeridianAlgo v4.0.0 - Quantum Edition

Author: MeridianAlgo

.kiro/specs/ultimate-quant-platform/design.md

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+# Requirements Document
+
+## Introduction
+
+Transform MeridianAlgo into the ultimate quantitative development platform in Python by integrating the best features from leading quantitative finance libraries (QuantLib, Zipline, PyPortfolioOpt, TA-Lib, Backtrader, etc.) while maintaining superior performance, usability, and extensibility. The platform should become the go-to solution for quantitative analysts, portfolio managers, algorithmic traders, and financial researchers worldwide.
+
+## Requirements
+
+### Requirement 1: Comprehensive Data Infrastructure
+
+**User Story:** As a quantitative analyst, I want access to multiple data sources and formats, so that I can work with any financial dataset without data pipeline limitations.
+
+#### Acceptance Criteria
+
+1. WHEN a user requests market data THEN the system SHALL support at least 10 different data providers (Yahoo Finance, Alpha Vantage, Quandl, IEX Cloud, Polygon, FRED, Bloomberg API, etc.)
+2. WHEN a user imports data THEN the system SHALL automatically detect and handle different data formats (CSV, JSON, Parquet, HDF5, SQL databases)
+3. WHEN data is missing or corrupted THEN the system SHALL provide intelligent data cleaning and interpolation methods
+4. WHEN a user needs real-time data THEN the system SHALL support streaming data feeds with WebSocket connections
+5. WHEN working with alternative data THEN the system SHALL support news sentiment, social media data, and economic indicators
+
+### Requirement 2: Advanced Technical Analysis Suite
+
+**User Story:** As a technical analyst, I want access to 200+ technical indicators with customizable parameters, so that I can perform comprehensive technical analysis beyond basic indicators.
+
+#### Acceptance Criteria
+
+1. WHEN a user calculates indicators THEN the system SHALL provide all indicators from TA-Lib (150+) plus custom advanced indicators
+2. WHEN indicators are computed THEN the system SHALL optimize calculations using vectorized operations and Numba JIT compilation
+3. WHEN custom indicators are needed THEN the system SHALL provide a framework for creating custom indicators with automatic optimization
+4. WHEN pattern recognition is required THEN the system SHALL detect 50+ candlestick patterns and chart patterns
+5. WHEN indicators are visualized THEN the system SHALL integrate with Plotly, Matplotlib, and Bokeh for interactive charts
+
+### Requirement 3: Institutional-Grade Portfolio Management
+
+**User Story:** As a portfolio manager, I want advanced portfolio optimization and risk management tools, so that I can manage institutional-scale portfolios with sophisticated strategies.
+
+#### Acceptance Criteria
+
+1. WHEN optimizing portfolios THEN the system SHALL support Modern Portfolio Theory, Black-Litterman, Risk Parity, Factor Models, and Hierarchical Risk Parity
+2. WHEN managing risk THEN the system SHALL calculate VaR, CVaR, Maximum Drawdown, Tail Risk, and stress testing scenarios
+3. WHEN rebalancing portfolios THEN the system SHALL support transaction cost optimization and tax-loss harvesting
+4. WHEN analyzing performance THEN the system SHALL provide attribution analysis, factor decomposition, and benchmark comparison
+5. WHEN handling constraints THEN the system SHALL support position limits, sector constraints, ESG constraints, and regulatory requirements
+
+### Requirement 4: Production-Ready Backtesting Engine
+
+**User Story:** As an algorithmic trader, I want a high-performance backtesting engine with realistic market simulation, so that I can validate strategies before live deployment.
+
+#### Acceptance Criteria
+
+1. WHEN backtesting strategies THEN the system SHALL simulate realistic market conditions including slippage, transaction costs, and market impact
+2. WHEN handling orders THEN the system SHALL support all order types (market, limit, stop, bracket, OCO, etc.)
+3. WHEN processing data THEN the system SHALL handle tick-level, minute-level, and daily data with proper timestamp handling
+4. WHEN running backtests THEN the system SHALL utilize parallel processing and GPU acceleration for large-scale testing
+5. WHEN analyzing results THEN the system SHALL provide comprehensive performance metrics and risk analytics
+
+### Requirement 5: Machine Learning and AI Integration
+
+**User Story:** As a quantitative researcher, I want state-of-the-art machine learning tools specifically designed for finance, so that I can build predictive models and automated trading systems.
+
+#### Acceptance Criteria
+
+1. WHEN building models THEN the system SHALL provide pre-built financial ML models (LSTM, Transformer, GAN, Reinforcement Learning)
+2. WHEN engineering features THEN the system SHALL automatically generate 500+ financial features with proper time-series handling
+3. WHEN training models THEN the system SHALL support walk-forward analysis, purged cross-validation, and combinatorial purged cross-validation
+4. WHEN deploying models THEN the system SHALL provide model versioning, A/B testing, and performance monitoring
+5. WHEN handling alternative data THEN the system SHALL process news sentiment, satellite imagery, and social media data
+
+### Requirement 6: Fixed Income and Derivatives Pricing
+
+**User Story:** As a fixed income analyst, I want comprehensive bond pricing and derivatives valuation tools, so that I can analyze complex financial instruments beyond equities.
+
+#### Acceptance Criteria
+
+1. WHEN pricing bonds THEN the system SHALL calculate yield curves, duration, convexity, and credit spreads
+2. WHEN valuing options THEN the system SHALL support Black-Scholes, Binomial, Monte Carlo, and finite difference methods
+3. WHEN analyzing derivatives THEN the system SHALL calculate Greeks (Delta, Gamma, Theta, Vega, Rho) with sensitivity analysis
+4. WHEN modeling interest rates THEN the system SHALL support Vasicek, CIR, Hull-White, and HJM models
+5. WHEN handling exotic instruments THEN the system SHALL price barrier options, Asian options, and structured products
+
+### Requirement 7: Risk Management and Compliance
+
+**User Story:** As a risk manager, I want comprehensive risk monitoring and regulatory compliance tools, so that I can ensure portfolios meet risk limits and regulatory requirements.
+
+#### Acceptance Criteria
+
+1. WHEN monitoring risk THEN the system SHALL provide real-time risk dashboards with customizable alerts
+2. WHEN calculating regulatory metrics THEN the system SHALL support Basel III, Solvency II, and CFTC requirements
+3. WHEN stress testing THEN the system SHALL run historical scenarios, Monte Carlo simulations, and custom stress tests
+4. WHEN reporting THEN the system SHALL generate automated compliance reports in multiple formats
+5. WHEN managing limits THEN the system SHALL enforce position limits, concentration limits, and leverage constraints
+
+### Requirement 8: High-Performance Computing Architecture
+
+**User Story:** As a quantitative developer, I want a scalable, high-performance platform, so that I can handle large datasets and complex calculations efficiently.
+
+#### Acceptance Criteria
+
+1. WHEN processing large datasets THEN the system SHALL utilize Dask, Ray, or Spark for distributed computing
+2. WHEN performing calculations THEN the system SHALL leverage GPU acceleration with CuPy and RAPIDS
+3. WHEN optimizing code THEN the system SHALL use Numba JIT compilation and Cython for critical paths
+4. WHEN scaling workloads THEN the system SHALL support cloud deployment on AWS, GCP, and Azure
+5. WHEN caching results THEN the system SHALL implement intelligent caching with Redis and memory mapping
+
+### Requirement 9: Interactive Development Environment
+
+**User Story:** As a quantitative analyst, I want an integrated development environment with visualization and collaboration tools, so that I can efficiently develop and share quantitative research.
+
+#### Acceptance Criteria
+
+1. WHEN developing strategies THEN the system SHALL provide Jupyter notebook integration with custom widgets
+2. WHEN visualizing data THEN the system SHALL create interactive dashboards with Plotly Dash and Streamlit
+3. WHEN collaborating THEN the system SHALL support version control integration and shared workspaces
+4. WHEN documenting research THEN the system SHALL generate automated reports with LaTeX and HTML output
+5. WHEN sharing results THEN the system SHALL export to multiple formats (PDF, Excel, PowerPoint, web apps)
+
+### Requirement 10: Extensible Plugin Architecture
+
+**User Story:** As a quantitative developer, I want a modular plugin system, so that I can extend the platform with custom functionality and integrate third-party tools.
+
+#### Acceptance Criteria
+
+1. WHEN adding functionality THEN the system SHALL support plugin development with standardized APIs
+2. WHEN integrating tools THEN the system SHALL connect with popular platforms (QuantConnect, Quantopian alternatives, TradingView)
+3. WHEN customizing workflows THEN the system SHALL provide configuration management and environment isolation
+4. WHEN deploying plugins THEN the system SHALL support package management and dependency resolution
+5. WHEN maintaining compatibility THEN the system SHALL provide backward compatibility and migration tools