Add comprehensive cryptocurrency statistical analysis tool

[devin-ai-integration]

Add comprehensive cryptocurrency statistical analysis tool

Summary

This PR implements a comprehensive Python script (crypto_stats.py) for generating statistical insights for cryptocurrency tickers, building on the existing TickerDataLoader infrastructure from PR #2. The tool provides flexible ticker selection (single, multiple, or 'all'), optional date range filtering, and calculates a comprehensive set of statistical indicators including basic statistics, return analysis, volume metrics, and technical indicators.

Key Features:

• Flexible input: Single ticker, comma-separated multiple tickers, or 'all' keyword
• Date filtering: Optional --start-date and --end-date parameters (YYYY-MM-DD format)
• Comprehensive statistics: 15+ indicators including mean/median/std dev, returns, volume analysis, SMA/EMA, RSI, MACD, Bollinger Bands
• Robust error handling: Validates tickers, handles missing data, clear error messages
• Compatibility: Fallback to manual technical indicator calculations when pandas_ta has compatibility issues

Review & Testing Checklist for Human

⚠️ HIGH PRIORITY (3 items)

• Verify statistical calculation accuracy: The script implements manual fallback calculations for RSI, MACD, and Bollinger Bands due to pandas_ta/numpy compatibility issues. Please verify these calculations are mathematically correct by comparing outputs with known reference implementations or financial data sources.

• Test performance with large datasets: Run python crypto_stats.py all --start-date 2018-04-07 --end-date 2018-04-09 to verify the tool can handle processing all tickers over multiple days without memory issues or excessive runtime.

• Validate edge case handling: Test scenarios like insufficient data for technical indicators (python crypto_stats.py QTUMUSDT --start-date 2018-04-06 --end-date 2018-04-06), invalid date ranges, and verify error messages are clear and the tool doesn't crash.

Recommended End-to-End Test Plan:

# Test single ticker with date range
python crypto_stats.py BTCUSDT --start-date 2018-04-07 --end-date 2018-04-07

# Test multiple tickers
python crypto_stats.py BTCUSDT,ETHBTC --start-date 2018-04-07 --end-date 2018-04-09  

# Test all tickers (check performance)
python crypto_stats.py all --start-date 2018-04-07 --end-date 2018-04-07

# Test error handling
python crypto_stats.py INVALID
python crypto_stats.py BCCUSDT --start-date 2018-04-07 --end-date 2018-04-07

# Run test suite
python test_crypto_stats.py

---

Diagram

%%{ init : { "theme" : "default" }}%%
graph TD
    CLI["crypto_stats.py<br/>(Main Script)"]:::major-edit
    TestSuite["test_crypto_stats.py<br/>(Test Suite)"]:::major-edit
    DataLoader["reading-src/read_ticker.py<br/>(TickerDataLoader)"]:::context
    DataDir["data/<br/>(Ticker Data)"]:::context
    
    CLI --> DataLoader
    DataLoader --> DataDir
    TestSuite --> CLI
    
    CLI --> StatsCalc["Statistical Calculations<br/>(Basic + Technical)"]:::major-edit
    CLI --> ErrorHandling["Error Handling<br/>(Validation + Fallbacks)"]:::major-edit
    CLI --> OutputFormat["Formatted Output<br/>(Structured Reports)"]:::major-edit
    
    subgraph Legend
        L1[Major Edit]:::major-edit
        L2[Minor Edit]:::minor-edit  
        L3[Context/No Edit]:::context
    end
    
    classDef major-edit fill:#90EE90
    classDef minor-edit fill:#87CEEB
    classDef context fill:#FFFFFF

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Notes

• pandas_ta compatibility: The script gracefully handles pandas_ta import failures due to numpy version conflicts by falling back to manual technical indicator calculations
• Data source: Leverages existing TickerDataLoader from PR Add cryptocurrency tick data reader with pandas DataFrame support #2 for consistent data access patterns
• Output format: Professional tabular output with clear section headers for easy interpretation
• Test coverage: Comprehensive test suite covers all major functionality including error scenarios

Link to Devin run: https://app.devin.ai/sessions/851e775359d94b339a340c39d2f85b96
Requested by: @Nucs

[devin-ai-integration]

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