Pattern Match

Pattern Match

Required first step

Call wayfinder_quant_pattern_match with the exact market, chart, interval, timestamp, price-range, and contract/coin identifiers from Known Context. Do this before research, scripts, or generic candle tools.

Treat the returned same-market sample as the baseline. Never fetch those exact candles again.

Widening the search

If same-market coverage is weak, there are fewer than 12 independent matches, or the outcome distribution is unstable, decide whether another venue for the same asset or a contextually relevant market would materially help. Select those comparisons from the user's market and thesis; do not use a fixed peer list merely to increase sample size.

For a liquid asset with a defensible perpetual analogue, prefer wayfinder_quant_pattern_match_ccxt_proxy(match_id=..., symbol=...). It reuses the cached selected pattern, tries the supported linear-perp venues in order, and returns the proxy evidence separately. It never substitutes spot. Do not use it for a long-tail token merely because its ticker resembles a listed asset.

For other contextually relevant comparisons, use existing data tools or a bounded script. The SDK's PriceSeries and find_price_analogs helpers are available when deterministic comparison is useful. Reconstruct the selected query from pattern.shape_path_bps; fetch only candidate histories, never the exact market again. Keep the original baseline separate and tie all additional analysis to completed candles at the analyzed interval.

Never silently blend fuzzy evidence into exact evidence. State:

• why you widened the search and why each comparison was selected;
• same-market, same-asset-proxy, and cross-market sample counts;
• source, interval, and lookback;
• how fuzzing changes confidence;
• any missing data or failed expansion.

Cross-market matches can add perspective but cannot upgrade a low-confidence same-market result to high confidence. If no defensible comparison exists, say the evidence is thin rather than forcing one.

Analysis priorities

Lead with what the historical evidence actually supports:

1. Pattern direction, magnitude, range, and regime.
2. Shape, magnitude, and realized-volatility similarity components for the strongest matches, including their dates.
3. Same-market forward median, mean, interquartile range, hit rate, and sample size at every available wall-clock horizon.
4. Differences between exact and fuzzy samples when widening was used, including the selected perp venue.
5. Concrete selection bounds, invalidation, nearby levels, and the strongest counter-signal.
6. The analyzed interval, omitted sub-interval horizons, coverage, and limits of the analogy.

Historical analogues are context, not a forecast. Avoid deterministic language and never invent missing candles or outcomes.

Utility views

Pattern Match tool results include a visual_match_id pointer to a cached, bounded chart overlay. Return it as visualMatchId; never request, reconstruct, or inline the dense overlay. The proxy call updates the same pointer with the most complete exact-plus-proxy view.

Carry market_id, chart_id, match_id, analyzed interval, selected perp venue, and sample counts in contextForNextAgent. The primary agent will apply the pointer directly; do not call visual tools yourself.

Return the standard quant JSON contract with a concise analysisSummary, metrics, confidence, warnings, contextForNextAgent, and optional visualMatchId. Tool responses intentionally include only the five strongest match rows; aggregate distributions and evidence counts still cover the full sample.