Analyze price swings to detect Elliott Wave structures: five waves following a trend and three waves correcting it. Check wave rules and Fibonacci ratios, then generate signals for completed advances or pullbacks using a pandas implementation.
Elliott Wave Theory signal engine. Detects swing points through Zigzag, matches 5-wave impulse and 3-wave corrective structures, validates them with Fibonacci wave relationships, and generates trend-top / correction-complete signals. Pure in-house pandas implementation.
Review the instructions the agent will receive before you install.
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 |
| 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 |
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.
pip install pandas numpy requests
1 = long, -1 = short, 0 = stand asideswing_window bars after it forms, so the signal is stamped on that confirmation bar, not on the swing bar itselfNo reviews yet. Tell others what you thought.
No login needed. Share how it worked for you, ask a question, or note where it did not run.
No comments yet. Be the first to write one.
Compare a Chinese company's US depositary receipts, Hong Kong shares, and mainland shares after adjusting for exchange rates and share ratios. Analyze pricing gaps, valuation differences, and delisting risks to support research into its different listings.
Retrieve stock prices, futures quotes, exchange rates, and economic indicators with the free Python library AKShare. The skill explains market-specific functions, symbol formats, and data preparation for analysis and testing strategies against historical data.
Explore bundled libraries of investment factors, signals used to compare securities, and inspect individual formulas and metadata. Evaluate a factor or an entire library across a chosen universe and period, producing aggregate performance statistics and an HTML report.
Assess whether a Chinese A-share company may receive ST or *ST risk warnings next financial year. Review financial forecasts, dividends, audit opinions, and regulatory penalties, then report risk severity separately from confidence in the prediction.
Plan portfolio allocations across assets using risk, correlations, return forecasts, and trading costs. Compare five optimization methods, produce settings for config.json, and define rebalancing rules that adjust holdings back toward their target weights.
Diagnose failed or abnormal backtests, which test trading strategies on historical data. Inspect result files, strategy code, and configuration to identify bugs, make targeted repairs, rerun tests, and verify the updated metrics.