Initial commit of integrated agent system
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cillin
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backend/explain/similarity_service.py
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202
backend/explain/similarity_service.py
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# -*- coding: utf-8 -*-
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"""Same-ticker historical similar day search for explain view."""
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from __future__ import annotations
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from math import sqrt
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from typing import Any
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from backend.data.market_store import MarketStore
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def _safe_float(value: Any, default: float = 0.0) -> float:
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try:
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parsed = float(value)
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except (TypeError, ValueError):
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return default
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return parsed
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def build_daily_feature_rows(
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*,
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symbol: str,
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ohlc_rows: list[dict[str, Any]],
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news_rows: list[dict[str, Any]],
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) -> list[dict[str, Any]]:
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"""Aggregate price/news context into daily feature rows."""
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price_by_date = {str(row.get("date")): row for row in ohlc_rows if row.get("date")}
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ordered_dates = [str(row.get("date")) for row in ohlc_rows if row.get("date")]
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news_by_date: dict[str, list[dict[str, Any]]] = {}
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for row in news_rows:
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trade_date = str(row.get("trade_date") or "")[:10] or str(row.get("date") or "")[:10]
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if not trade_date:
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continue
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news_by_date.setdefault(trade_date, []).append(row)
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features: list[dict[str, Any]] = []
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previous_close: float | None = None
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for idx, date in enumerate(ordered_dates):
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price_row = price_by_date[date]
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close_price = _safe_float(price_row.get("close"))
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open_price = _safe_float(price_row.get("open"), close_price)
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day_news = news_by_date.get(date, [])
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positive_count = sum(1 for item in day_news if str(item.get("sentiment") or "").lower() == "positive")
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negative_count = sum(1 for item in day_news if str(item.get("sentiment") or "").lower() == "negative")
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high_relevance_count = sum(
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1 for item in day_news if str(item.get("relevance") or "").lower() in {"high", "relevant"}
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)
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ret_1d = (
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((close_price - previous_close) / previous_close)
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if previous_close not in (None, 0)
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else 0.0
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)
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intraday_ret = ((close_price - open_price) / open_price) if open_price else 0.0
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sentiment_score = (
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(positive_count - negative_count) / max(len(day_news), 1)
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if day_news
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else 0.0
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)
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future_t1 = None
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future_t3 = None
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if idx + 1 < len(ordered_dates) and close_price:
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next_close = _safe_float(price_by_date[ordered_dates[idx + 1]].get("close"))
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future_t1 = ((next_close - close_price) / close_price) if next_close else None
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if idx + 3 < len(ordered_dates) and close_price:
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next_close = _safe_float(price_by_date[ordered_dates[idx + 3]].get("close"))
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future_t3 = ((next_close - close_price) / close_price) if next_close else None
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features.append(
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{
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"date": date,
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"symbol": symbol,
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"n_articles": len(day_news),
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"positive_count": positive_count,
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"negative_count": negative_count,
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"high_relevance_count": high_relevance_count,
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"sentiment_score": sentiment_score,
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"ret_1d": ret_1d,
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"intraday_ret": intraday_ret,
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"close": close_price,
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"ret_t1_after": future_t1,
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"ret_t3_after": future_t3,
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"news": [
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{
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"title": row.get("title") or "",
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"sentiment": row.get("sentiment") or "neutral",
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}
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for row in day_news[:3]
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],
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}
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)
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previous_close = close_price
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return features
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def compute_similarity_scores(
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target_vector: list[float],
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candidate_vectors: list[tuple[str, list[float], dict[str, Any]]],
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) -> list[dict[str, Any]]:
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"""Return sorted similarity matches based on normalized Euclidean distance."""
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if not candidate_vectors:
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return []
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dimensions = len(target_vector)
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ranges = []
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for dimension in range(dimensions):
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values = [vector[1][dimension] for vector in candidate_vectors] + [target_vector[dimension]]
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min_value = min(values)
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max_value = max(values)
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ranges.append(max(max_value - min_value, 1e-9))
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scored = []
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for date, vector, payload in candidate_vectors:
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distance = sqrt(
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sum(
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((target_vector[i] - vector[i]) / ranges[i]) ** 2
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for i in range(dimensions)
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)
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)
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similarity = 1.0 / (1.0 + distance)
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scored.append(
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{
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"date": date,
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"score": round(similarity, 4),
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**payload,
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}
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)
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return sorted(scored, key=lambda item: item["score"], reverse=True)
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def find_similar_days(
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store: MarketStore,
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*,
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symbol: str,
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target_date: str,
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top_k: int = 10,
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) -> dict[str, Any]:
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"""Find same-ticker historical days most similar to a target day."""
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cached = store.get_similar_day_cache(symbol, target_date=target_date)
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if cached and cached.get("payload"):
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return cached["payload"]
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ohlc_rows = store.get_ohlc(symbol, "1900-01-01", target_date)
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news_rows = store.get_news_items_enriched(symbol, end_date=target_date, limit=500)
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daily_rows = build_daily_feature_rows(symbol=symbol, ohlc_rows=ohlc_rows, news_rows=news_rows)
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feature_map = {row["date"]: row for row in daily_rows}
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target_row = feature_map.get(target_date)
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if not target_row:
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return {
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"symbol": symbol,
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"target_date": target_date,
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"items": [],
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"error": "No feature row for target date",
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}
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vector_keys = [
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"sentiment_score",
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"n_articles",
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"positive_count",
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"negative_count",
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"high_relevance_count",
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"ret_1d",
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"intraday_ret",
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]
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target_vector = [_safe_float(target_row.get(key)) for key in vector_keys]
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candidates = []
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for row in daily_rows:
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date = row["date"]
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if date == target_date:
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continue
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payload = {
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"n_articles": row["n_articles"],
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"sentiment_score": round(row["sentiment_score"], 4),
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"ret_1d": round(row["ret_1d"] * 100, 2),
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"intraday_ret": round(row["intraday_ret"] * 100, 2),
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"ret_t1_after": round(row["ret_t1_after"] * 100, 2) if row["ret_t1_after"] is not None else None,
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"ret_t3_after": round(row["ret_t3_after"] * 100, 2) if row["ret_t3_after"] is not None else None,
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"top_reasons": [item["title"] for item in row["news"][:2] if item.get("title")],
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"news": row["news"],
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}
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candidates.append(
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(
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date,
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[_safe_float(row.get(key)) for key in vector_keys],
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payload,
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)
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)
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items = compute_similarity_scores(target_vector, candidates)[: max(1, min(int(top_k), 20))]
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result = {
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"symbol": symbol,
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"target_date": target_date,
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"target_features": {
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"sentiment_score": round(target_row["sentiment_score"], 4),
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"n_articles": target_row["n_articles"],
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"ret_1d": round(target_row["ret_1d"] * 100, 2),
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"intraday_ret": round(target_row["intraday_ret"] * 100, 2),
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"high_relevance_count": target_row["high_relevance_count"],
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},
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"items": items,
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}
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store.upsert_similar_day_cache(symbol, target_date=target_date, payload=result, source="local")
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return result
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