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Elliott Wave Signal Engine

Flags possible trend tops and correction ends by counting Elliott waves in price data

Source Download skill
Repository
HKUDS/Vibe-Trading
Stars
37,433
Licence
MIT
Files
4 files

What it does

This skill teaches an agent to apply Elliott Wave Theory, a method that treats market prices as repeating five-wave trends and three-wave pullbacks. When asked, the agent can run a bundled Python engine over price candles to find swing points, match wave patterns, check them against Fibonacci ratios, and output simple signals: 1 for long, -1 for short, 0 for stand aside. Signals are only stamped once a swing point is confirmed, so they are not look-ahead predictions.

Great for

  • Quant or trading researchers who want a rule-based Elliott Wave signal to backtest
  • Developers exploring how wave-counting logic can be coded with pandas
  • Analysts who want a second opinion on whether a trend looks mature or a pullback looks complete

Skip it if

  • Live trading decisions with real money: wave counting is highly subjective, the skill itself notes multiple interpretations exist, and it is not a validated profit tool
  • Users who expect financial advice: the skill provides a technical signal engine, not investment guidance
  • Non-finance uses: the logic is designed for price series such as OHLC candles

Try asking

  • “Run the Elliott Wave signal engine on my BTC-USDT daily OHLC data and count the signals”
  • “Explain the three iron rules for impulse waves and check whether my price series violates any”
  • “Use the Fibonacci wave relationships to validate this 5-wave structure I identified”
  • “Adjust swing_window and fib_tolerance to see how signal counts change on my dataset”
Step-by-step guide: how to use Elliott Wave Signal EngineInstall, first run, examples, tips and FAQs

Install in 3 steps

  1. 1

    Download the skill

    The ZIP contains the 4 original files, the MIT licence and an attribution note.

    elliott-wave.zip
  2. 2

    Add it to your agent

    Claude Code (all projects)

    mkdir -p ~/.claude/skills && unzip elliott-wave.zip -d ~/.claude/skills/

    Claude Code / Agent SDK (this project only)

    mkdir -p .claude/skills && unzip elliott-wave.zip -d .claude/skills/

    Claude apps (web/desktop): Settings → Capabilities → Skills → Upload skill, and choose the ZIP.

  3. 3

    Ask for the task

    No special command needed — the agent reads the skill's description and loads it when your request matches. Try one of the prompts above.

Get better results

  • Test on historical data first and compare signals against a simple benchmark before trusting any output
  • Review the script before running it, since the agent will execute Python code on your machine
  • Keep the default parameters initially; the README-style notes say the engine prefers missing signals over misclassifying them

What's inside

  • example_signal_engine.py
  • references/Fibonacci浪间关系.md
  • references/波浪结构.md
  • SKILL.md

Redistributed unmodified from HKUDS/Vibe-Trading under the MIT licence. The assessment was drafted with AI from the skill's files on 2026-10-29. Skills can instruct your agent to run code — read SKILL.md and any scripts before installing.