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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 | Builds event-driven analysis for A-share companies: merger arbitrage spreads, shareholder buying and selling signals, equity incentives, placements and ST or delisting warnings; Chinese text. | HKUDS/ | 35k | — | ~1.1k | Automated safety check: Pass | MIT | yesterday |
| 5 | Scores news, announcements and macro events with the LLM, stores them in an event CSV and blends the decaying event signal with technical signals in signal_engine.py. | HKUDS/ | 35k | — | ~2.1k | Automated safety check: Pass | MIT | yesterday |
| 6 | Evaluates mutual funds, private funds and ETFs by return, risk and risk-adjusted metrics, style box and drift, and manager quality, then builds FOF portfolios; Chinese text. | HKUDS/ | 35k | — | ~1.2k | Automated safety check: Pass | MIT | yesterday |
| 7 | 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 |
| 8 | 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 |
| 9 | 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 |
| 10 | 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 |
| 11 | Trades mean reversion between two correlated instruments using the Z-score of their price ratio, going long one leg and short the other when the ratio stretches. | HKUDS/ | 35k | — | ~651 | Automated safety check: Pass | MIT | yesterday |
| 12 | Reference of market rules for quant strategies: A-share price limits and T+1, Hong Kong and US trading rules, crypto policy and cross-border tax basics. | HKUDS/ | 35k | — | ~1.3k | Automated safety check: Pass | MIT | yesterday |
| 13 | Generates long, short or flat trading signals from calendar patterns such as month-of-year and day-of-week effects, for any OHLCV price data. | HKUDS/ | 35k | — | ~573 | Automated safety check: Pass | MIT | yesterday |
| 14 | 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 |
| 15 | Mean-reversion signal engine that ranks historical volatility against its own recent history, going long in quiet regimes and exiting or shorting when volatility is high. | HKUDS/ | 35k | — | ~528 | Automated safety check: Pass | MIT | yesterday |
| 16 | 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 |
