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Data & Analytics · pandas · HKUDS/Vibe-Trading
Skills
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| # | Skill | Repository | Stars | Used in | Tokens | Auto-check | Licence | Updated |
|---|---|---|---|---|---|---|---|---|
| 1 | Detects Chan theory price structures (fractals, strokes, pivots) and first, second and third buy and sell points from OHLCV bars using the czsc library, in Chinese. | HKUDS/ | 35k | — | ~658 | Automated safety check: Pass | MIT | yesterday |
| 2 | Detects Elliott Wave structures in price data with a Zigzag swing finder and Fibonacci checks, and turns completed waves into long, short or flat signals. | HKUDS/ | 35k | — | ~482 | Automated safety check: Pass | MIT | yesterday |
| 3 | Detects 15 classic candlestick patterns with vectorized pandas code and combines bullish and bearish scores into a long, short or flat trading signal. | HKUDS/ | 35k | — | ~468 | Automated safety check: Pass | MIT | yesterday |
| 4 | Generates trading signals from the Ichimoku five-line system using Tenkan and Kijun crossovers, cloud position and cloud direction, implemented in pandas. | HKUDS/ | 35k | — | ~417 | Automated safety check: Pass | MIT | yesterday |
| 5 | Fetches minute candlesticks from OKX, Tushare or yfinance, computes intraday VWAP, TWAP and volume distribution, and runs minute-level backtests by setting an interval in config.json. | HKUDS/ | 35k | — | ~868 | Automated safety check: Pass | MIT | yesterday |
| 6 | Trains scikit-learn models with walk-forward validation on features from OHLCV data to predict return direction and turn the predictions into trading signals. | HKUDS/ | 35k | — | ~3.2k | Automated safety check: Pass | MIT | yesterday |
| 7 | Ranks stocks by standardized factor scores (momentum, reversal, volatility, volume and valuation) and builds an equal-weight TopN portfolio with periodic rebalancing. | HKUDS/ | 35k | — | ~1k | Automated safety check: Pass | MIT | yesterday |
| 8 | Builds a composite trading signal from three groups of classic indicators (trend, mean reversion, volume) voting long, short or flat, in plain pandas for OHLCV data. | HKUDS/ | 35k | — | ~492 | Automated safety check: Pass | MIT | yesterday |
| 9 | Finds co-moving assets and tests them for cointegration, with workflows for correlation studies, sector clustering, hedge ratios and pair-trading signals. | HKUDS/ | 35k | — | ~10k | Automated safety check: Pass | MIT | yesterday |
