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Stock Market Analysis MCP Server

by CalvinLiuu
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# Stock Market MCP Server - Enhancement Roadmap **Current Version:** 0.3.0 **Target Version:** 1.0.0 **Last Updated:** January 2025 --- ## πŸ“Š Progress Overview | Phase | Status | Completion | Items | |-------|--------|------------|-------| | **Quick Wins** | 🟑 Not Started | 0/10 | High-impact, low-effort improvements | | **Phase 1: Foundation** | 🟑 Not Started | 0/8 | Infrastructure & quality basics | | **Phase 2: Performance** | 🟑 Not Started | 0/6 | Speed & scalability improvements | | **Phase 3: Core Features** | 🟑 Not Started | 0/12 | New analysis capabilities | | **Phase 4: Advanced Features** | 🟑 Not Started | 0/10 | Sophisticated trading tools | | **Phase 5: Nice-to-Have** | 🟑 Not Started | 0/8 | Polish & convenience features | **Total Progress:** 0/54 items completed (0%) --- ## πŸ“‘ Table of Contents 1. [Quick Wins](#-quick-wins-priority-1) 2. [Phase 1: Foundation](#-phase-1-foundation) 3. [Phase 2: Performance](#-phase-2-performance) 4. [Phase 3: Core Features](#-phase-3-core-features) 5. [Phase 4: Advanced Features](#-phase-4-advanced-features) 6. [Phase 5: Nice-to-Have](#-phase-5-nice-to-have) 7. [Category Index](#-category-index) 8. [Implementation Notes](#-implementation-notes) --- ## πŸš€ Quick Wins (Priority 1) **High-value, low-effort improvements to implement first** ### Infrastructure Quick Wins - [ ] **Add Simple Caching** [Priority: High] [Complexity: Easy] [Effort: 2-4 hours] - Description: Cache stock prices and historical data to reduce API calls - Implementation: Use dictionary-based in-memory cache with TTL - Files: Create `cache.py`, update `price_data.py` - Benefits: 80-90% reduction in API calls, much faster responses - Dependencies: None ```python # Add to cache.py from datetime import datetime, timedelta cache = {} def get_cached(key, ttl_seconds=60): if key in cache: value, timestamp = cache[key] if datetime.now() - timestamp < timedelta(seconds=ttl_seconds): return value return None ``` - [ ] **Add Logging System** [Priority: High] [Complexity: Easy] [Effort: 1-2 hours] - Description: Structured logging for debugging and monitoring - Implementation: Use Python's logging module or loguru - Files: Create `logger.py`, add to all modules - Benefits: Better debugging, error tracking, audit trail - Dependencies: `pip install loguru` (optional) - [ ] **Configuration File** [Priority: High] [Complexity: Easy] [Effort: 1-2 hours] - Description: Centralized configuration management - Implementation: YAML or JSON config file - Files: Create `config.yaml`, update `utils.py` - Benefits: Easy customization, environment-specific settings - Dependencies: `pip install pyyaml` ```yaml # config.yaml api: rate_limit: 100 cache_ttl: 60 portfolio: auto_save: true backup_enabled: true alerts: check_interval: 300 ``` ### Feature Quick Wins - [ ] **Earnings Calendar** [Priority: High] [Complexity: Easy] [Effort: 2-3 hours] - Description: Show upcoming earnings dates for stocks - Implementation: Use yfinance `stock.calendar` - Files: Create `earnings.py`, add tool function - Benefits: Plan trades around earnings, avoid surprises - API: `stock.calendar` returns earnings dates and estimates - [ ] **Financial Ratios** [Priority: High] [Complexity: Easy] [Effort: 2-3 hours] - Description: Additional fundamental metrics (P/B, ROE, ROA, debt/equity) - Implementation: Extract from yfinance `stock.info` - Files: Update `price_data.py` or create `fundamentals.py` - Benefits: Better fundamental analysis capabilities - Ratios: P/B, ROE, ROA, Debt/Equity, Current Ratio, Quick Ratio - [ ] **News Headlines** [Priority: Medium] [Complexity: Easy] [Effort: 2-3 hours] - Description: Fetch recent news for stocks - Implementation: Use yfinance `stock.news` - Files: Create `news.py`, add tool function - Benefits: Stay informed about holdings, sentiment analysis - API: Returns title, publisher, link, publish time - [ ] **Batch Price Updates** [Priority: Medium] [Complexity: Easy] [Effort: 2-3 hours] - Description: Update all portfolio prices at once - Implementation: Loop through holdings, fetch prices - Files: Update `portfolio.py` - Benefits: Faster portfolio refresh - Function: `refresh_all_prices()` - [ ] **Correlation Matrix** [Priority: Medium] [Complexity: Easy] [Effort: 2-3 hours] - Description: Show correlation between portfolio holdings - Implementation: Calculate correlation using pandas - Files: Update `portfolio.py` or `risk.py` - Benefits: Better diversification insights - Uses: `pandas.DataFrame.corr()` - [ ] **Export to CSV** [Priority: Medium] [Complexity: Easy] [Effort: 1-2 hours] - Description: Export portfolio and transactions to CSV - Implementation: Use pandas `to_csv()` - Files: Update `portfolio.py` - Benefits: Use data in Excel/Google Sheets, backup - Functions: `export_portfolio_csv()`, `export_transactions_csv()` - [ ] **Bollinger Bands** [Priority: Medium] [Complexity: Easy] [Effort: 2-3 hours] - Description: Calculate Bollinger Bands for volatility analysis - Implementation: 20-day SMA Β± 2 standard deviations - Files: Update `analysis.py` - Benefits: Identify overbought/oversold conditions - Formula: Middle Band = 20-day SMA, Upper/Lower = SMA Β± (2 Γ— std dev) --- ## πŸ—οΈ Phase 1: Foundation **Build solid infrastructure and quality foundations** ### Testing & Quality - [ ] **Unit Test Framework** [Priority: High] [Complexity: Medium] [Effort: 1-2 days] - Description: Comprehensive unit tests for all modules - Implementation: pytest with fixtures and mocks - Files: Create `tests/` directory with test files for each module - Coverage Target: 80%+ - Dependencies: `pip install pytest pytest-cov pytest-mock` - Tests needed: Portfolio operations, indicators, risk calculations, alerts ```bash tests/ test_portfolio.py test_analysis.py test_risk.py test_alerts.py test_dividends.py test_sector.py ``` - [ ] **Mock API Responses** [Priority: High] [Complexity: Medium] [Effort: 4-6 hours] - Description: Mock yfinance responses for testing - Implementation: Use pytest-mock or responses library - Files: Create `tests/fixtures/` with sample data - Benefits: Fast tests, no API dependency, deterministic results - [ ] **Type Checking with mypy** [Priority: Medium] [Complexity: Easy] [Effort: 2-3 hours] - Description: Static type checking - Implementation: Add mypy configuration, fix type issues - Files: Create `mypy.ini`, add type hints where missing - Dependencies: `pip install mypy` - Config: Strict mode, no implicit optional - [ ] **Code Formatting & Linting** [Priority: Medium] [Complexity: Easy] [Effort: 1-2 hours] - Description: Consistent code style - Implementation: black, flake8, isort - Files: Create `.flake8`, `pyproject.toml` - Dependencies: `pip install black flake8 isort` ### Infrastructure - [ ] **Enhanced Error Handling** [Priority: High] [Complexity: Medium] [Effort: 3-4 hours] - Description: Custom exception classes, better error messages - Implementation: Create exception hierarchy - Files: Create `exceptions.py`, update all modules - Exceptions: `TickerNotFoundError`, `InsufficientDataError`, `APILimitError` ```python class StockAnalyzerError(Exception): """Base exception""" class TickerNotFoundError(StockAnalyzerError): """Invalid ticker""" class APILimitError(StockAnalyzerError): """Rate limit exceeded""" ``` - [ ] **Retry Logic & Circuit Breaker** [Priority: High] [Complexity: Medium] [Effort: 3-4 hours] - Description: Graceful handling of API failures - Implementation: Exponential backoff, circuit breaker pattern - Files: Create `retry.py`, update API calls - Dependencies: `pip install tenacity` - Features: Max retries, exponential backoff, circuit breaker - [ ] **Data Validation with Pydantic** [Priority: Medium] [Complexity: Medium] [Effort: 4-6 hours] - Description: Validate all inputs and data structures - Implementation: Pydantic models for holdings, transactions, alerts - Files: Create `models.py`, update all modules - Dependencies: `pip install pydantic` - Benefits: Type safety, automatic validation, better errors - [ ] **Environment Variables** [Priority: Low] [Complexity: Easy] [Effort: 1 hour] - Description: Use .env file for configuration - Implementation: python-dotenv - Files: Create `.env.example`, update `utils.py` - Dependencies: `pip install python-dotenv` --- ## ⚑ Phase 2: Performance **Optimize for speed and scalability** ### Database & Storage - [ ] **Migrate to SQLite** [Priority: High] [Complexity: Medium] [Effort: 1-2 days] - Description: Replace JSON files with SQLite database - Implementation: SQLAlchemy ORM - Files: Create `database.py`, `schema.sql`, update all data access - Benefits: Better performance, transactions, complex queries - Dependencies: `pip install sqlalchemy` - Tables: holdings, transactions, alerts, cache ```sql CREATE TABLE holdings ( ticker TEXT PRIMARY KEY, shares REAL NOT NULL, avg_price REAL NOT NULL, last_updated TEXT NOT NULL ); CREATE TABLE transactions ( id INTEGER PRIMARY KEY AUTOINCREMENT, type TEXT NOT NULL, ticker TEXT NOT NULL, shares REAL NOT NULL, price REAL NOT NULL, date TEXT NOT NULL, total REAL NOT NULL ); ``` - [ ] **Data Migration Script** [Priority: High] [Complexity: Easy] [Effort: 2-3 hours] - Description: Migrate existing JSON data to SQLite - Implementation: Read JSON, insert into database - Files: Create `migrate.py` - Dependencies: SQLite migration must be completed first ### Async & Concurrency - [ ] **Async API Calls** [Priority: High] [Complexity: Hard] [Effort: 2-3 days] - Description: Concurrent stock data fetching - Implementation: asyncio + aiohttp, async yfinance wrapper - Files: Create `async_data.py`, refactor API calls - Benefits: 5-10x faster for multi-stock operations - Dependencies: `pip install aiohttp asyncio` - Impact: portfolio_view, compare_stocks, sector_analysis - [ ] **Background Job Scheduler** [Priority: Medium] [Complexity: Medium] [Effort: 4-6 hours] - Description: Scheduled tasks (alert checks, price updates) - Implementation: APScheduler or celery - Files: Create `scheduler.py` - Dependencies: `pip install apscheduler` - Jobs: Alert checking (5 min), portfolio refresh (15 min), data cleanup (daily) ### Caching - [ ] **Redis Cache** [Priority: Medium] [Complexity: Medium] [Effort: 4-6 hours] - Description: Distributed caching with Redis - Implementation: Redis for shared cache, TTL management - Files: Update `cache.py` - Dependencies: `pip install redis` - Keys: `price:{ticker}`, `hist:{ticker}:{period}`, `info:{ticker}` - [ ] **Request Batching** [Priority: Medium] [Complexity: Medium] [Effort: 3-4 hours] - Description: Batch multiple API requests - Implementation: Queue requests, fetch in batches - Files: Create `batch.py` - Benefits: Reduce API calls, respect rate limits - Function: `batch_get_prices(tickers)` --- ## πŸ“ˆ Phase 3: Core Features **Expand analysis capabilities** ### Technical Analysis - [ ] **Stochastic Oscillator** [Priority: Medium] [Complexity: Medium] [Effort: 3-4 hours] - Description: %K and %D momentum indicator - Implementation: Calculate using high/low/close - Files: Update `analysis.py` - Formula: %K = ((Close - Low14) / (High14 - Low14)) Γ— 100 - Interpretation: >80 overbought, <20 oversold - [ ] **On-Balance Volume (OBV)** [Priority: Medium] [Complexity: Easy] [Effort: 2-3 hours] - Description: Volume-based momentum indicator - Implementation: Cumulative volume flow - Files: Update `analysis.py` - Formula: OBV = Previous OBV + (Volume if up day, -Volume if down day) - [ ] **Volume-Weighted Average Price (VWAP)** [Priority: Medium] [Complexity: Medium] [Effort: 2-3 hours] - Description: Average price weighted by volume - Implementation: Cumulative (Price Γ— Volume) / Cumulative Volume - Files: Update `analysis.py` - Use case: Intraday trading reference point - [ ] **Fibonacci Retracement** [Priority: Medium] [Complexity: Medium] [Effort: 3-4 hours] - Description: Calculate Fibonacci levels - Implementation: Find high/low, calculate 23.6%, 38.2%, 50%, 61.8%, 78.6% - Files: Update `analysis.py` - Returns: Support/resistance levels - [ ] **Ichimoku Cloud** [Priority: Low] [Complexity: Hard] [Effort: 6-8 hours] - Description: Complex trend indicator - Implementation: Tenkan-sen, Kijun-sen, Senkou Span A/B, Chikou Span - Files: Update `analysis.py` - Complexity: Multiple components, advanced visualization - [ ] **Support/Resistance Levels** [Priority: Medium] [Complexity: Hard] [Effort: 6-8 hours] - Description: Automatically identify key price levels - Implementation: Find local minima/maxima, clustering - Files: Create `levels.py` - Algorithm: Peak detection + clustering ### Fundamental Analysis - [ ] **Financial Statements** [Priority: High] [Complexity: Medium] [Effort: 4-6 hours] - Description: Income statement, balance sheet, cash flow - Implementation: Use yfinance financials, quarterly/annual - Files: Create `fundamentals.py` - API: `stock.financials`, `stock.balance_sheet`, `stock.cashflow` - Returns: Last 4 quarters or years - [ ] **Financial Ratios Extended** [Priority: High] [Complexity: Medium] [Effort: 4-6 hours] - Description: Comprehensive ratio analysis - Implementation: Calculate from financial statements - Files: Update `fundamentals.py` - Ratios: Profit margin, asset turnover, interest coverage, ROIC - Categories: Profitability, Liquidity, Leverage, Efficiency - [ ] **DCF Valuation Model** [Priority: Medium] [Complexity: Hard] [Effort: 1-2 days] - Description: Discounted cash flow intrinsic value - Implementation: Project FCF, calculate terminal value, discount - Files: Update `fundamentals.py` - Inputs: Growth rate, discount rate, terminal growth - Output: Estimated fair value - [ ] **Peer Comparison** [Priority: Medium] [Complexity: Medium] [Effort: 4-6 hours] - Description: Compare stock to industry peers - Implementation: Find similar companies, compare metrics - Files: Update `fundamentals.py` or `sector.py` - Metrics: P/E, P/B, ROE, margins vs sector average ### Screeners - [ ] **Custom Stock Screener** [Priority: High] [Complexity: Hard] [Effort: 1-2 days] - Description: Filter stocks by custom criteria - Implementation: Multi-parameter filtering - Files: Create `screener.py` - Filters: Price range, market cap, P/E, dividend yield, volume, sector - Database: Requires stock universe database ```python def screen_stocks( min_price=None, max_price=None, min_market_cap=None, max_pe=None, min_dividend_yield=None, sectors=None ): # Filter logic ``` - [ ] **Unusual Volume Detector** [Priority: Medium] [Complexity: Medium] [Effort: 3-4 hours] - Description: Find stocks with abnormal volume - Implementation: Compare current volume to average - Files: Update `screener.py` - Threshold: Volume > 2x average --- ## 🎯 Phase 4: Advanced Features **Sophisticated trading and analysis tools** ### Options Analysis - [ ] **Options Chain Data** [Priority: High] [Complexity: Medium] [Effort: 4-6 hours] - Description: Fetch options data for stocks - Implementation: Use yfinance `stock.option_chain()` - Files: Create `options.py` - Returns: Calls and puts with strike, premium, volume, OI - Note: Not all stocks have options data - [ ] **Greeks Calculator** [Priority: High] [Complexity: Hard] [Effort: 1-2 days] - Description: Calculate Delta, Gamma, Theta, Vega, Rho - Implementation: Black-Scholes model - Files: Update `options.py` - Dependencies: `pip install scipy` - Formulas: Use Black-Scholes formulas for each Greek - [ ] **Implied Volatility** [Priority: Medium] [Complexity: Hard] [Effort: 6-8 hours] - Description: Calculate IV from option prices - Implementation: Newton-Raphson method - Files: Update `options.py` - Use: Option pricing, volatility analysis - [ ] **Options Strategies Analyzer** [Priority: Medium] [Complexity: Hard] [Effort: 1-2 days] - Description: Analyze covered calls, spreads, etc. - Implementation: Calculate P&L profiles - Files: Update `options.py` - Strategies: Covered call, protective put, iron condor, vertical spreads - Output: Max profit, max loss, breakeven points ### Portfolio Optimization - [ ] **Efficient Frontier** [Priority: High] [Complexity: Hard] [Effort: 2-3 days] - Description: Portfolio optimization for max return/min risk - Implementation: Modern Portfolio Theory, optimization - Files: Create `optimization.py` - Dependencies: `pip install scipy cvxpy` - Algorithm: Mean-variance optimization - Output: Optimal weights for holdings - [ ] **Monte Carlo Simulation** [Priority: Medium] [Complexity: Hard] [Effort: 1-2 days] - Description: Simulate future portfolio values - Implementation: Random walk with historical stats - Files: Update `portfolio.py` or `optimization.py` - Parameters: Simulations (1000+), time horizon, confidence intervals - Output: Distribution of future values, probability of goals - [ ] **Rebalancing Recommendations** [Priority: High] [Complexity: Medium] [Effort: 4-6 hours] - Description: Suggest trades to maintain target allocation - Implementation: Compare current vs target weights - Files: Update `portfolio.py` - Input: Target allocation by ticker or sector - Output: Buy/sell recommendations with quantities - [ ] **Tax Loss Harvesting** [Priority: Medium] [Complexity: Medium] [Effort: 4-6 hours] - Description: Identify opportunities to harvest losses - Implementation: Find positions with losses, suggest replacements - Files: Update `portfolio.py` - Logic: Loss > threshold, suggest similar ticker - Output: Sell recommendations with tax savings ### Backtesting - [ ] **Backtesting Framework** [Priority: High] [Complexity: Hard] [Effort: 3-5 days] - Description: Test trading strategies on historical data - Implementation: Event-driven backtester - Files: Create `backtesting/` module - Dependencies: `pip install backtrader` or build custom - Features: Multiple strategies, transaction costs, slippage - Metrics: Total return, Sharpe, max DD, win rate - [ ] **Strategy Library** [Priority: Medium] [Complexity: Medium] [Effort: 2-3 days] - Description: Pre-built strategies to test - Implementation: Strategy classes for common approaches - Files: Create `strategies/` directory - Strategies: SMA crossover, RSI mean reversion, momentum, trend following - Interface: Consistent strategy API --- ## 🌟 Phase 5: Nice-to-Have **Polish and convenience features** ### Market Intelligence - [ ] **Economic Calendar** [Priority: Medium] [Complexity: Medium] [Effort: 4-6 hours] - Description: Track economic events (Fed meetings, GDP, etc.) - Implementation: Scrape economic calendar or use API - Files: Create `economic.py` - Data: Event name, date, impact level, forecast/actual - Source: Investing.com API or web scraping - [ ] **Insider Trading Tracker** [Priority: Low] [Complexity: Medium] [Effort: 4-6 hours] - Description: Track insider buys/sells - Implementation: SEC EDGAR API or yfinance - Files: Create `insider.py` - Data: Name, position, transaction type, shares, date - [ ] **Analyst Ratings** [Priority: Medium] [Complexity: Medium] [Effort: 4-6 hours] - Description: Aggregate analyst recommendations - Implementation: Web scraping or financial API - Files: Update `fundamentals.py` - Data: Buy/Hold/Sell ratings, price targets, changes - [ ] **IPO Calendar** [Priority: Low] [Complexity: Medium] [Effort: 3-4 hours] - Description: Upcoming IPOs - Implementation: Web scraping - Files: Create `ipo.py` - Data: Company, symbol, date, price range ### Visualization & Export - [ ] **Chart Generation** [Priority: Medium] [Complexity: Medium] [Effort: 1-2 days] - Description: Generate price charts, indicators - Implementation: matplotlib or plotly - Files: Create `charts.py` - Dependencies: `pip install matplotlib plotly` - Charts: Candlestick, line, volume, indicators overlay - [ ] **PDF Reports** [Priority: Low] [Complexity: Medium] [Effort: 1-2 days] - Description: Generate PDF portfolio reports - Implementation: ReportLab or WeasyPrint - Files: Create `reports.py` - Dependencies: `pip install reportlab` - Includes: Portfolio summary, performance, charts - [ ] **Export to Excel** [Priority: Medium] [Complexity: Easy] [Effort: 2-3 hours] - Description: Export data to Excel with formatting - Implementation: openpyxl - Files: Update portfolio.py - Dependencies: `pip install openpyxl` - Features: Multiple sheets, formatting, formulas - [ ] **Email Alerts** [Priority: Low] [Complexity: Medium] [Effort: 3-4 hours] - Description: Send email when alerts trigger - Implementation: SMTP or SendGrid - Files: Update `alerts.py` - Config: Email settings in config file --- ## πŸ“š Category Index ### By Feature Area **Performance & Infrastructure** - Caching (Quick Win, Phase 2) - Logging (Quick Win) - Configuration (Quick Win) - Database Migration (Phase 2) - Async Processing (Phase 2) - Testing (Phase 1) - Error Handling (Phase 1) **Technical Analysis** - Bollinger Bands (Quick Win) - Stochastic Oscillator (Phase 3) - OBV (Phase 3) - VWAP (Phase 3) - Fibonacci (Phase 3) - Ichimoku (Phase 3) - Support/Resistance (Phase 3) **Fundamental Analysis** - Financial Ratios (Quick Win) - Financial Statements (Phase 3) - DCF Valuation (Phase 3) - Peer Comparison (Phase 3) **Portfolio Management** - Correlation Matrix (Quick Win) - Batch Updates (Quick Win) - Export CSV (Quick Win) - Efficient Frontier (Phase 4) - Monte Carlo (Phase 4) - Rebalancing (Phase 4) - Tax Loss Harvesting (Phase 4) **Market Intelligence** - News (Quick Win) - Earnings Calendar (Quick Win) - Economic Calendar (Phase 5) - Insider Trading (Phase 5) - Analyst Ratings (Phase 5) - IPO Calendar (Phase 5) **Options Trading** - Options Chain (Phase 4) - Greeks (Phase 4) - Implied Volatility (Phase 4) - Strategy Analyzer (Phase 4) **Trading & Strategy** - Backtesting (Phase 4) - Strategy Library (Phase 4) - Stock Screener (Phase 3) - Unusual Volume (Phase 3) **Reporting & UI** - Charts (Phase 5) - PDF Reports (Phase 5) - Excel Export (Phase 5) - Email Alerts (Phase 5) ### By Priority **High Priority** (15 items) - Caching, Logging, Configuration - Testing, Error Handling, Retry Logic - SQLite Migration, Async Calls - Earnings Calendar, Financial Ratios - Stock Screener, Financial Statements - Options Chain, Greeks - Efficient Frontier, Rebalancing, Backtesting **Medium Priority** (25 items) - News, Correlation, Batch Updates, Export CSV, Bollinger Bands - Type Checking, Data Validation - Redis Cache, Request Batching, Background Jobs - All technical indicators - DCF Valuation, Peer Comparison - Options strategies, Monte Carlo, Tax Loss Harvesting - Economic Calendar, Analyst Ratings, Charts **Low Priority** (14 items) - Environment Variables - Ichimoku Cloud - Insider Trading, IPO Calendar - PDF Reports, Email Alerts ### By Complexity **Easy** (13 items) - Logging, Configuration, Export CSV - Financial Ratios Extended, News, Earnings Calendar - Batch Updates, Bollinger Bands, OBV - Code Formatting, Environment Variables - Excel Export **Medium** (26 items) - Caching, Testing, Error Handling, Retry Logic - Type Checking, Data Validation - SQLite Migration, Redis Cache, Batching, Background Jobs - All fundamental analysis features - Most technical indicators - Rebalancing, Tax Loss Harvesting - Options Chain, Unusual Volume - Most market intelligence features - Charts, PDF Reports **Hard** (15 items) - Async Processing - Support/Resistance, Ichimoku - DCF Valuation, Stock Screener - Greeks, IV, Options Strategies - Efficient Frontier, Monte Carlo - Backtesting Framework, Strategy Library --- ## πŸ’‘ Implementation Notes ### Getting Started **Recommended Order:** 1. Start with Quick Wins (immediate value) 2. Build Foundation (Phase 1) for quality 3. Optimize Performance (Phase 2) for scale 4. Add Core Features (Phase 3) for value 5. Advanced Features (Phase 4) as needed 6. Nice-to-Have (Phase 5) for polish ### Dependencies to Add ```txt # Phase 1 pytest>=7.0.0 pytest-cov>=4.0.0 pytest-mock>=3.10.0 mypy>=1.0.0 black>=23.0.0 flake8>=6.0.0 isort>=5.12.0 pydantic>=2.0.0 tenacity>=8.2.0 python-dotenv>=1.0.0 pyyaml>=6.0 # Phase 2 sqlalchemy>=2.0.0 aiohttp>=3.8.0 apscheduler>=3.10.0 redis>=4.5.0 # Phase 3 scipy>=1.10.0 # Phase 4 cvxpy>=1.3.0 backtrader>=1.9.0 # or custom # Phase 5 matplotlib>=3.7.0 plotly>=5.14.0 reportlab>=4.0.0 openpyxl>=3.1.0 ``` ### File Structure After Implementation ``` stock_mcp_server/ β”œβ”€β”€ stock.server.py β”œβ”€β”€ utils.py β”œβ”€β”€ config.yaml # NEW β”œβ”€β”€ logger.py # NEW β”œβ”€β”€ cache.py # NEW β”œβ”€β”€ database.py # NEW β”œβ”€β”€ models.py # NEW β”œβ”€β”€ exceptions.py # NEW β”œβ”€β”€ retry.py # NEW β”œβ”€β”€ price_data.py β”œβ”€β”€ portfolio.py β”œβ”€β”€ analysis.py β”œβ”€β”€ alerts.py β”œβ”€β”€ dividends.py β”œβ”€β”€ sector.py β”œβ”€β”€ risk.py β”œβ”€β”€ fundamentals.py # NEW β”œβ”€β”€ earnings.py # NEW β”œβ”€β”€ news.py # NEW β”œβ”€β”€ screener.py # NEW β”œβ”€β”€ options.py # NEW β”œβ”€β”€ optimization.py # NEW β”œβ”€β”€ charts.py # NEW β”œβ”€β”€ reports.py # NEW β”œβ”€β”€ backtesting/ # NEW β”‚ β”œβ”€β”€ __init__.py β”‚ β”œβ”€β”€ engine.py β”‚ └── metrics.py β”œβ”€β”€ strategies/ # NEW β”‚ β”œβ”€β”€ __init__.py β”‚ β”œβ”€β”€ sma_crossover.py β”‚ └── rsi_mean_reversion.py β”œβ”€β”€ tests/ # NEW β”‚ β”œβ”€β”€ __init__.py β”‚ β”œβ”€β”€ test_portfolio.py β”‚ β”œβ”€β”€ test_analysis.py β”‚ β”œβ”€β”€ test_risk.py β”‚ └── fixtures/ β”œβ”€β”€ portfolio.json β”œβ”€β”€ alerts.json β”œβ”€β”€ stock_data.db # NEW (SQLite) β”œβ”€β”€ requirements.txt β”œβ”€β”€ .env.example # NEW β”œβ”€β”€ mypy.ini # NEW β”œβ”€β”€ pyproject.toml # NEW β”œβ”€β”€ README.md β”œβ”€β”€ ROADMAP.md └── TOOLS_REFERENCE.md ``` ### Testing Strategy **Unit Tests (Phase 1):** - Mock all external API calls - Test edge cases and error conditions - Aim for 80%+ coverage - Fast execution (<10 seconds total) **Integration Tests:** - Test with real API calls (marked as slow) - Use test portfolio data - Run nightly or on-demand **Performance Tests:** - Benchmark critical operations - Track regression - Measure cache effectiveness ### API Considerations **yfinance Limitations:** - Rate limiting (avoid 2000+ requests/hour) - Data delays (15 minutes for free tier) - Some tickers have limited data - Options data not available for all stocks - Historical data limited to ~10 years **Best Practices:** - Always cache responses - Implement exponential backoff - Handle missing data gracefully - Batch requests when possible - Consider premium APIs for production (Polygon, Alpha Vantage) ### Performance Targets **After Phase 2 Optimizations:** - Portfolio view: <1 second (with cache) - Multi-stock comparison: <2 seconds - Sector analysis: <3 seconds - Portfolio risk calculation: <2 seconds - Alert checking: <30 seconds - Cold start (no cache): <5 seconds ### Security Considerations **Data Protection:** - Encrypt sensitive data at rest (if multi-user) - Secure API keys in environment variables - Validate all inputs - Sanitize user data **API Security:** - Rate limiting - Request validation - Error message sanitization (don't leak internals) --- ## 🎯 Next Steps 1. **Review this roadmap** and prioritize based on your needs 2. **Start with Quick Wins** for immediate value 3. **Set up testing** before major refactoring 4. **Implement caching** to reduce API load 5. **Add logging** for better debugging 6. **Create configuration system** for flexibility 7. **Pick features** from Phase 3+ based on use case --- ## πŸ“ Progress Tracking **How to Use This Document:** 1. Check off items as you complete them: `- [x]` 2. Update completion percentages in the Progress Overview 3. Update phase status: 🟑 Not Started β†’ πŸ”΅ In Progress β†’ 🟒 Complete 4. Add notes in implementation sections 5. Update "Last Updated" date at top **Status Indicators:** - 🟑 Not Started - πŸ”΅ In Progress - 🟒 Complete - πŸ”΄ Blocked - ⏸️ Paused --- **Remember:** This is a living document. Adjust priorities based on your needs, feedback, and evolving requirements. Not every feature needs to be implementedβ€”focus on what adds value to your use case! **Happy Coding! πŸš€**

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