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Data & Analytics · By agiprolabs
Skills
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| # | Skill | Repository | Stars | Used in | Tokens | Auto-check | Licence | Updated |
|---|---|---|---|---|---|---|---|---|
| 1 | Feature construction from market data for ML trading models including price, volume, on-chain, and microstructure features | agiprolabs/ | 410 | — | ~2.7k | Automated safety check: Pass | MIT | 1 mo ago |
| 2 | Market data preparation including OHLCV resampling, gap handling, anomaly detection, normalization, and multi-source merging | agiprolabs/ | 410 | — | ~3.1k | Automated safety check: Pass | MIT | 1 mo ago |
| 3 | Technical analysis with 130+ indicators using pandas-ta for crypto market data | agiprolabs/ | 410 | — | ~2.3k | Automated safety check: Pass | MIT | 1 mo ago |
| 4 | ML trading signal classifiers using XGBoost and LightGBM with walk-forward validation, SHAP feature importance, and threshold optimization | agiprolabs/ | 410 | — | ~2.8k | Automated safety check: Pass | MIT | 1 mo ago |
| 5 | 5.Ta Lib C-optimized technical analysis with 150+ functions and 61 candlestick pattern recognition functions via TA-Lib | agiprolabs/ | 410 | — | ~2.4k | Automated safety check: Notes | MIT | 1 mo ago |
| 6 | Professional trading charts including candlesticks, equity curves, drawdowns, correlation heatmaps, and return distributions | agiprolabs/ | 410 | — | ~2.8k | Automated safety check: Pass | MIT | 1 mo ago |
| 7 | Volatility estimation, forecasting, and regime classification using GARCH, EWMA, realized volatility, and volatility cones | agiprolabs/ | 410 | — | ~2.1k | Automated safety check: Pass | MIT | 1 mo ago |
| 8 | Walk-forward validation framework for trading strategies and ML models with time-series-aware splits, overfit detection, and regime-aware validation | agiprolabs/ | 410 | — | ~2.2k | Automated safety check: Pass | MIT | 1 mo ago |
