feat: Find Similar + Merge in Pattern Library
Backend (patterns.py): - POST /api/patterns/find-similar — GPT-4o-mini compares a library pattern against all others; returns merge_as_instance | counter_scenario | new_pattern - POST /api/patterns/merge — full transactional merge: remaps pattern_id in pattern_score_history, trade_entry_prices, ai_reasoning_traces, ai_call_logs, skipped_trades; unions historical_instances (dedup); sums backtest counters; deletes the discarded pattern Frontend (PatternExplorer.tsx + useApi.ts): - ScanSearch button on each non-builtin card triggers find-similar - Inline result panel: duplicate → merge CTA with confirmation + destructive warning counter_scenario → apply regime_tag CTA; new_pattern → green "unique" badge - useFindSimilarPattern + useMergePatterns hooks invalidate all-patterns on success Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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OpenSquared
@@ -1,11 +1,15 @@
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import json
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import logging
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from fastapi import APIRouter, HTTPException, Query
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from pydantic import BaseModel
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from typing import Optional, List, Dict, Any
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from services.database import (
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save_custom_pattern, get_custom_patterns, delete_custom_pattern, toggle_pattern_active
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save_custom_pattern, get_custom_patterns, delete_custom_pattern, toggle_pattern_active,
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get_config,
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)
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from services.geo_analyzer import PATTERN_TAXONOMY
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logger = logging.getLogger(__name__)
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router = APIRouter(prefix="/api/patterns", tags=["patterns"])
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@@ -135,3 +139,211 @@ def get_by_instrument(ticker: str = Query(..., description="Ticker / underlying
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if matching:
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result.append({**p, "matching_trades": matching})
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return result
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# ── Find Similar ──────────────────────────────────────────────────────────────
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class FindSimilarRequest(BaseModel):
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pattern_id: str
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@router.post("/find-similar")
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async def find_similar_pattern(req: FindSimilarRequest):
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"""Ask GPT-4o-mini if a library pattern duplicates or counter-scenarios another."""
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from openai import AsyncOpenAI
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openai_key = get_config("openai_api_key") or ""
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if not openai_key:
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raise HTTPException(400, "OpenAI API key not configured")
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library = get_custom_patterns()
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source = next((p for p in library if p["id"] == req.pattern_id), None)
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if not source:
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raise HTTPException(404, "Pattern not found")
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others = [p for p in library if p["id"] != req.pattern_id]
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if not others:
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return {
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"recommendation": "new_pattern", "match_id": None, "match_name": None,
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"confidence": 100, "reasoning": "No other patterns in library to compare.",
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"suggested_regime_tag": source.get("regime_tag") or source.get("category", ""),
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}
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def _compact(p: dict) -> dict:
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trades = p.get("suggested_trades") or []
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underlying = trades[0].get("underlying", "") if trades else ""
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return {
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"id": p["id"],
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"name": p["name"],
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"regime_tag": p.get("regime_tag") or "",
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"category": p.get("category") or "",
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"underlying": underlying,
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"signal_direction": p.get("signal_direction") or "",
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"description": (p.get("description") or "")[:150],
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}
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trades_src = source.get("suggested_trades") or []
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underlying_src = trades_src[0].get("underlying", "") if trades_src else ""
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prompt = f"""You are a quantitative macro pattern analyst. Determine if this library pattern duplicates or counter-scenarios another existing pattern.
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## SOURCE PATTERN
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Name: {source.get('name')}
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Category: {source.get('category')}
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Description: {source.get('description')}
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Underlying: {underlying_src}
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Signal direction: {source.get('signal_direction')}
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Regime tag: {source.get('regime_tag') or 'none'}
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## EXISTING LIBRARY PATTERNS (excluding source)
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{json.dumps([_compact(p) for p in others], ensure_ascii=False)}
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## DECISION RULES
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**merge_as_instance**: Same macro REGIME + same INSTRUMENT + same DIRECTION. The source pattern is a redundant instance of an existing one — safe to merge.
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**counter_scenario**: Same macro REGIME + same INSTRUMENT + OPPOSITE DIRECTION. Both are valid distinct patterns — do NOT merge, just mark the relationship.
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**new_pattern**: Different regime, instrument, or no meaningful structural match. Keep as-is.
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CRITICAL: Never merge opposite directions — use counter_scenario instead.
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Suggest a short generic regime_tag (2-5 words) capturing the macro theme.
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Return ONLY valid JSON:
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{{
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"recommendation": "merge_as_instance" | "counter_scenario" | "new_pattern",
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"match_id": "matched pattern id or null",
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"match_name": "matched pattern name or null",
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"confidence": 0-100,
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"reasoning": "2-3 sentences explaining the decision",
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"suggested_regime_tag": "short generic label"
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}}"""
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client = AsyncOpenAI(api_key=openai_key)
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resp = await client.chat.completions.create(
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model="gpt-4o-mini",
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messages=[{"role": "user", "content": prompt}],
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temperature=0.1,
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response_format={"type": "json_object"},
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)
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raw = resp.choices[0].message.content or "{}"
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try:
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return json.loads(raw)
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except Exception:
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raise HTTPException(500, f"AI returned non-JSON: {raw[:200]}")
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# ── Merge Patterns ────────────────────────────────────────────────────────────
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class MergeRequest(BaseModel):
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keep_id: str
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discard_id: str
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@router.post("/merge")
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def merge_patterns(req: MergeRequest):
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"""Merge discard_id into keep_id.
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- Migrates all child-table rows (scores, trades, traces, logs, embeddings)
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- Merges historical_instances (union, dedup by date+event)
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- Sums backtest_hits + backtest_runs_count
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- Deletes the discarded pattern
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"""
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from services.database import get_conn
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if req.keep_id == req.discard_id:
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raise HTTPException(400, "keep_id and discard_id must be different")
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conn = get_conn()
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try:
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keep_row = conn.execute("SELECT * FROM custom_patterns WHERE id=?", (req.keep_id,)).fetchone()
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discard_row = conn.execute("SELECT * FROM custom_patterns WHERE id=?", (req.discard_id,)).fetchone()
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if not keep_row:
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raise HTTPException(404, f"Pattern {req.keep_id} not found")
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if not discard_row:
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raise HTTPException(404, f"Pattern {req.discard_id} not found")
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keep = dict(keep_row)
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discard = dict(discard_row)
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# ── Merge historical_instances ────────────────────────────────────────
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def _load_instances(raw) -> list:
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if not raw:
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return []
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try:
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v = json.loads(raw)
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return v if isinstance(v, list) else []
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except Exception:
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return []
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keep_inst = _load_instances(keep.get("historical_instances"))
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discard_inst = _load_instances(discard.get("historical_instances"))
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seen = {(i.get("date", ""), i.get("event", "")) for i in keep_inst}
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for inst in discard_inst:
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key = (inst.get("date", ""), inst.get("event", ""))
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if key not in seen:
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keep_inst.append(inst)
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seen.add(key)
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keep_inst.sort(key=lambda x: x.get("date", ""), reverse=True)
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# ── Merge numeric counters ────────────────────────────────────────────
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merged_hits = (keep.get("backtest_hits") or 0) + (discard.get("backtest_hits") or 0)
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merged_runs = (keep.get("backtest_runs_count") or 0) + (discard.get("backtest_runs_count") or 0)
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# Prefer keep's regime_tag; fall back to discard's
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merged_regime = keep.get("regime_tag") or discard.get("regime_tag")
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# ── Remap child tables ────────────────────────────────────────────────
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_CHILD_TABLES = [
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"pattern_score_history",
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"trade_entry_prices",
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"ai_reasoning_traces",
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"ai_call_logs",
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"skipped_trades",
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]
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for tbl in _CHILD_TABLES:
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try:
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conn.execute(
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f"UPDATE {tbl} SET pattern_id=? WHERE pattern_id=?",
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(req.keep_id, req.discard_id),
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)
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except Exception as e:
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logger.warning(f"[merge] {tbl} remap failed (may not exist): {e}")
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# pattern_embeddings: unique constraint on pattern_id — delete discard's embedding
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# (keep's embedding stays; it'll be regenerated on next cycle if needed)
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try:
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conn.execute("DELETE FROM pattern_embeddings WHERE pattern_id=?", (req.discard_id,))
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except Exception:
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pass
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# ── Update keep pattern ───────────────────────────────────────────────
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conn.execute(
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"""UPDATE custom_patterns SET
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historical_instances=?,
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backtest_hits=?,
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backtest_runs_count=?,
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regime_tag=?,
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updated_at=datetime('now')
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WHERE id=?""",
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(
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json.dumps(keep_inst, ensure_ascii=False),
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merged_hits,
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merged_runs,
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merged_regime,
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req.keep_id,
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),
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)
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# ── Delete discarded pattern ──────────────────────────────────────────
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conn.execute("DELETE FROM custom_patterns WHERE id=?", (req.discard_id,))
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conn.commit()
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logger.info(f"[Merge] Kept {req.keep_id}, discarded {req.discard_id} — {len(keep_inst)} instances, {merged_hits}/{merged_runs} backtest hits")
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updated = conn.execute("SELECT * FROM custom_patterns WHERE id=?", (req.keep_id,)).fetchone()
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return {"status": "merged", "kept_id": req.keep_id, "discarded_id": req.discard_id,
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"merged_instances": len(keep_inst), "pattern": dict(updated)}
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finally:
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conn.close()
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