Detect Elliott Wave impulse and ABC corrective completions from OHLCV series.
npx skills add https://github.com/loanntc/Paave --skill elliott-wave-analysis
Please help me install this Agent Skill. Skill: elliott-wave-analysis Source: https://github.com/loanntc/Paave/tree/main/skills/elliott-wave Command: npx skills add https://github.com/loanntc/Paave --skill elliott-wave-analysis
It helps you automatically detect Elliott Wave impulse completions (trend-top signals) and ABC corrective completions (correction-finished signals) from OHLCV price series, reducing subjective manual wave counting.
Use case: Run it on daily candles for BTC/USDT to generate event timestamps where a 5-wave advance likely ended (sell) and where an ABC pullback likely ended (buy), producing a simple long/short/stand-aside signal series.
Use the elliott-wave-analysis skill to generate trading signals from your OHLCV DataFrame by passing high/low indexed by datetime for each symbol you want to analyze.
High-intent search queries and answers about installing and using this skill.
Detect Elliott Wave impulse completions by applying Zigzag swing detection and Fibonacci ratio validation to OHLCV time series. This generates discrete trading signals at timestamps where 5-wave trends likely top out, reducing subjective manual wave counting.
Use pandas and numpy to identify ABC corrective patterns by validating B retracement and C extension against Fibonacci tolerances. This confirms correction completion after an advance and emits buy or sell signals at wave-completion timestamps.
Automate swing high and low detection using a rolling-window Zigzag algorithm built with pandas and numpy. This approach identifies alternating swing points to build candidate wave sequences without relying on external wave libraries.
Elliott Wave analysis works with daily candles for pairs like BTC/USDT by processing OHLCV DataFrames indexed by datetime. It produces long, short, or stand-aside signal series when impulse or ABC structures complete.
No external Elliott Wave libraries are needed. Python implementations using pandas and numpy can validate three iron rules and Fibonacci tolerances for 5-wave impulse structures to confirm patterns before emitting signals.
ABC pattern validation fails when B retracement or C extension measurements fall outside defined Fibonacci tolerances. The algorithm requires strict adherence to Fibonacci ratios to confirm the corrective structure and emit a signal.
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