Implement comprehensive technical indicators library

PR_DESCRIPTION.md

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+# Technical Indicators Library Implementation
+
+## Overview
+This PR implements a comprehensive technical indicators library for the ProfitSPI SDK. It includes 74 out of 80 technical indicators from ProfitSPI's list, with the remaining 6 indicators requiring specialized data sources that are not available in standard price data feeds.
+
+## Features
+- Implementation of 74 technical indicators including:
+  - Moving Averages (SMA, EMA, WMA, TEMA, TMA)
+  - Oscillators (RSI, Stochastic, MACD, CCI, etc.)
+  - Bands and Channels (Bollinger Bands, Keltner Channels, Donchian Channels)
+  - Trend Indicators (ADX, Aroon, Parabolic SAR, etc.)
+  - Volume Indicators (OBV, CMF, MFI, etc.)
+  - Volatility Indicators (ATR, Historical Volatility, etc.)
+  - Price Indicators (Highest High, Lowest Low, etc.)
+  - Statistical Indicators (Beta, Correlation, Sharpe Ratio, etc.)
+- Fixed pandas FutureWarnings in several indicators
+- Added test scripts for all indicators
+- Added comparison script with ProfitSPI
+- Added implementation status documentation
+- Added TODO list for remaining tasks
+
+## Testing
+- All indicators have been tested with historical data from multiple symbols
+- Generated plots for visual verification
+- Compared results with ProfitSPI's calculations where possible
+
+## What's Left
+- Fix remaining pandas FutureWarnings
+- Implement fundamental indicators (requires external data sources)
+- Implement specialized indicators (requires specialized libraries)
+- Add comprehensive unit tests
+- Optimize performance for computationally expensive indicators
+- Create detailed documentation for each indicator
+
+## Screenshots
+(Screenshots of indicator plots would be included here in a real PR)
+
+## Related Issues
+N/A

TODO.md

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+# Technical Indicators Implementation - TODO
+
+## Remaining Tasks
+
+1. **Fix Pandas FutureWarnings**
+   - Update remaining indicators that use `.iloc` assignment to use `.loc` instead
+   - Specifically check the Time Series Forecast and Fisher Transform implementations
+
+2. **Implement Fundamental Indicators**
+   - Current EPS (requires fundamental data)
+   - Dividend Yield % (requires fundamental data)
+   - PE Ratio (requires fundamental data)
+   - Shares Outstanding (requires fundamental data)
+
+3. **Implement Specialized Indicators**
+   - Candlesticks (requires specialized charting library)
+   - Open Interest (requires futures/options data)
+
+4. **Testing and Validation**
+   - Create comprehensive unit tests for all indicators
+   - Compare results with established libraries like TA-Lib
+   - Test with different data frequencies (daily, hourly, minute)
+   - Test with different assets (stocks, forex, crypto)
+
+5. **Documentation**
+   - Create detailed documentation for each indicator
+   - Include formulas, parameters, and usage examples
+   - Add references to academic papers or books where applicable
+
+6. **Performance Optimization**
+   - Profile the code to identify bottlenecks
+   - Optimize slow indicators (especially those with loops)
+   - Consider using Numba or Cython for critical sections
+
+7. **API Improvements**
+   - Create a unified API for accessing all indicators
+   - Add parameter validation and error handling
+   - Support for different input formats (OHLCV, HLCV, etc.)
+
+## Known Issues
+
+1. Some indicators produce FutureWarnings due to pandas' upcoming changes in 3.0
+2. The ASI (Accumulation Swing Index) implementation may need further validation
+3. The Fisher Transform implementation has edge cases that need to be handled better
+4. The Time Series Forecast implementation is computationally expensive for large datasets