An AI-orchestrated pipeline that turns a stock ticker and a date into a fully working TradingView indicator — no coding required.
Elliott Wave analysis requires simultaneous mastery of wave rules, Fibonacci math, and charting code — a rare combination that keeps it out of reach for most traders.
A Claude AI prompt system orchestrates specialized subagents — each an expert in one domain — to handle the full pipeline automatically.
Each domain has exactly one authoritative source file. No rule duplication. Clear ownership prevents contradictions.
Six mandatory validation checks on every data point — ensures only real, confirmed market pivots enter the analysis. No interpolation. No model memory. Yahoo Finance data only.
Wave analysis results are persisted to [TICKER] [DATE].wave. Re-runs skip the expensive data fetch and jump straight to PineScript generation.
The AI fetches live OHLC data from Yahoo Finance and applies the full Elliott Wave ruleset — including subwave decomposition at one level deep.
The generated indicator includes 8 interactive controls and handles all TradingView v6 quirks automatically.
Primary count (55% confidence) — 5-wave motive impulse from the October 2022 lows. Subwaves, Fibonacci labels, projected correction targets, and invalidation level all generated automatically.
Verifies PineScript v6 syntax. Checks //@version=6 header, balanced brackets, no v4/v5 deprecated constructs.
Validates that label style constants are declared as string, not as type keywords. Prevents silent runtime failures.
Confirms wave ordering is chronologically correct, primary vs alternate drawing separation, and that alternate opacity applies in "Both" mode.
Ensures xloc.bar_time is used (never bar index), timestamps are in milliseconds, and zoom stability is preserved.
Every array access guarded by array.size() check. Y-offset collision detection verified for dense pivot regions.
Scripts compile and execute on first paste into TradingView — no manual debugging needed after generation.
A structured optimization report identified 14 weak spots across the prompt system, categorized by severity and impact.
Decomposing a complex task into specialized subagents — each with focused context — dramatically improves output quality and reliability over a single monolithic prompt.
Modular skill files with explicit rule ownership, a cache layer, and a validation pipeline mirrors software engineering best practices applied to AI system design.
Embedding expert-level financial analysis rules (Elliott Wave theory, Fibonacci ratios, PineScript v6 constraints) is what makes AI output actually useful in a professional context.
Smart caching means the expensive data-fetch and wave-counting step runs once. Subsequent visual iterations are near-instant — a real productivity multiplier.
5-pass automated validation isn't an afterthought — it's a core phase of the pipeline. The system self-corrects before handing output to the user.
A structured optimization report treats prompts as living artifacts — bugs fixed in source, contradictions documented, and weak spots prioritized by severity.
This project shows that when AI is architected thoughtfully — with domain expertise, modular design, and built-in QA — it can automate genuinely complex professional workflows.
Built with Claude Code · github.com/fede_/ElliotWavePineScriptGenerator