elliott-wave
The Elliott Wave skill detects swing points and automatically counts impulse (5-wave) and corrective (3-wave) structures in price data, validating them against Fibonacci ratios and Elliott Wave rules. Use it to generate buy signals when ABC corrections complete and sell signals when five-wave advances finish, helping traders identify trend reversals and continuation points based on classical wave theory patterns.
git clone --depth 1 https://github.com/HKUDS/Vibe-Trading /tmp/elliott-wave && cp -r /tmp/elliott-wave/agent/src/skills/elliott-wave ~/.claude/skills/elliott-waveSKILL.md
# Elliott Wave Theory ## Purpose Classic wave theory based on the core assumption that markets move in fractal wave structures: | Structure | Wave Count | Direction | Meaning | |------|------|------|------| | Impulse wave | 5 waves (1-2-3-4-5) | Trend-following | Main trend direction | | Corrective wave | 3 waves (A-B-C) | Counter-trend | Pullback correction | ## Core Rules ### Three Iron Rules for Impulse Waves 1. Wave 2 cannot retrace beyond the start of wave 1 2. Wave 3 cannot be the shortest impulse wave 3. Wave 4 cannot enter the price territory of wave 1 ### Fibonacci Relationships Between Waves - Wave 2 retraces 0.5-0.618 of wave 1 - Wave 3 = wave 1 × 1.618 (most common) - Wave 4 retraces 0.382 of wave 3 - Wave 5 ≈ the length of wave 1 ## Signal Logic - **5-wave advance completed** → sell (trend top) - **ABC pullback completed** → buy (correction finished) - **Wave 3 in progress** → stay with the trend (no reversal signal is generated) ## Parameters | Parameter | Default | Description | |------|--------|------| | swing_window | 10 | Rolling window for swing-point detection | | fib_tolerance | 0.15 | Tolerance for Fibonacci ratios | | min_wave_bars | 5 | Minimum number of candles per wave | ## Notes Wave theory is highly subjective, and automatic counting can yield multiple interpretations. This implementation uses a "simplest effective single interpretation" strategy and would rather miss signals than misclassify them. ## Dependencies ```bash pip install pandas numpy requests ``` ## Signal Convention - `1` = long, `-1` = short, `0` = stand aside
Professional finance research toolkit — backtesting (7 engines + benchmark comparison panel), factor analysis, Alpha Zoo (452 pre-built alphas across qlib158/alpha101/gtja191/academic), options pricing, 77 finance skills, 29 multi-agent swarm teams, Trade Journal analyzer, and Shadow Account (extract → backtest → render) across 7 data sources (tushare, yfinance, okx, akshare, mootdx, ccxt, futu).
ADR/H-share/A-share cross-listing premium analysis — track pricing gaps between US-listed ADRs, HK-listed H-shares, and A-shares for arbitrage signals, dual-listing valuation, and delisting risk assessment.
AKShare financial data aggregator (18k+ stars). Free, no API key. Covers A-shares, US, HK, futures, macro, forex. Primary fallback for tushare and yfinance.
Browse and bench the bundled alpha zoos — prebuilt cross-sectional factor libraries (Kakushadze 101, GTJA 191, Qlib 158, Fama-French / Carhart). Use when the user asks "which alphas exist", wants metadata on a named alpha, or wants to run IC/IR on a whole zoo over a universe.
A 股 ST/*ST 风险预测框架 — 基于最新中报/三季报或业绩预告/快报,预测下一财年是否会因营收、利润、净资产、分红不达标而被风险警示,并将新浪监管处罚记录作为独立证据面纳入风险等级。仅适用于 A 股,不预测财务造假。
Asset allocation theory and optimizer usage — MPT / Black-Litterman / risk budgeting / all-weather strategy, including guides for 4 optimizers and rebalancing rules.
Diagnose failed or underperforming backtests, locate the root cause, and fix the issue
Behavioral finance applications: theories of overreaction and underreaction, behavioral explanations for momentum and reversal, investor sentiment cycles, cognitive-bias checklists, and debiasing quantitative strategies.