Initial commit of integrated agent system
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cillin
362
backend/enrich/news_enricher.py
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362
backend/enrich/news_enricher.py
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# -*- coding: utf-8 -*-
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"""Lightweight news enrichment for explain-oriented market analysis."""
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from __future__ import annotations
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import hashlib
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from typing import Any
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from backend.config.env_config import get_env_int
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from backend.enrich.llm_enricher import (
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analyze_news_row_with_llm,
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analyze_news_rows_with_llm,
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llm_enrichment_enabled,
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)
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from backend.data.market_store import MarketStore
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POSITIVE_KEYWORDS = (
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"beat", "surge", "gain", "growth", "record", "upgrade", "strong",
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"partnership", "approved", "launch", "expands", "profit",
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)
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NEGATIVE_KEYWORDS = (
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"miss", "drop", "fall", "cut", "downgrade", "weak", "warning",
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"delay", "lawsuit", "probe", "tariff", "decline", "layoff",
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)
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HIGH_RELEVANCE_KEYWORDS = (
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"earnings", "guidance", "profit", "revenue", "ceo", "fda", "tariff",
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"regulation", "acquisition", "buyback", "forecast", "launch",
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)
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def _dedupe_key(row: dict[str, Any]) -> str:
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trade_date = str(row.get("trade_date") or row.get("date") or "")[:10]
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title = str(row.get("title") or "").strip().lower()
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summary = str(row.get("summary") or "").strip().lower()[:160]
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raw = f"{trade_date}::{title}::{summary}"
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return hashlib.sha1(raw.encode("utf-8")).hexdigest()
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def _chunk_rows(rows: list[dict[str, Any]], size: int) -> list[list[dict[str, Any]]]:
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chunk_size = max(1, int(size))
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return [rows[index:index + chunk_size] for index in range(0, len(rows), chunk_size)]
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def classify_news_row(row: dict[str, Any]) -> dict[str, Any]:
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"""Return a lightweight explain-oriented analysis for one article."""
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llm_result = analyze_news_row_with_llm(row)
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if isinstance(llm_result, dict):
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merged = dict(llm_result)
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merged.setdefault("summary", str(row.get("summary") or row.get("title") or "")[:280])
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merged.setdefault("raw_json", row)
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merged["analysis_source"] = "llm"
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return merged
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title = str(row.get("title") or "").strip()
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summary = str(row.get("summary") or "").strip()
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text = f"{title} {summary}".lower()
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positive_hits = [keyword for keyword in POSITIVE_KEYWORDS if keyword in text]
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negative_hits = [keyword for keyword in NEGATIVE_KEYWORDS if keyword in text]
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relevance_hits = [keyword for keyword in HIGH_RELEVANCE_KEYWORDS if keyword in text]
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if len(positive_hits) > len(negative_hits):
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sentiment = "positive"
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elif len(negative_hits) > len(positive_hits):
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sentiment = "negative"
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else:
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sentiment = "neutral"
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relevance = "high" if relevance_hits else "medium" if title else "low"
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summary_text = summary or title
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key_discussion = ""
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if relevance_hits:
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key_discussion = f"核心主题集中在 {', '.join(relevance_hits[:3])}"
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elif summary_text:
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key_discussion = summary_text[:160]
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reason_growth = ""
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reason_decrease = ""
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if sentiment == "positive":
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reason_growth = summary_text[:200]
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elif sentiment == "negative":
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reason_decrease = summary_text[:200]
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return {
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"relevance": relevance,
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"sentiment": sentiment,
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"key_discussion": key_discussion,
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"summary": summary_text[:280],
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"reason_growth": reason_growth,
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"reason_decrease": reason_decrease,
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"analysis_source": "local",
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"raw_json": row,
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}
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def attach_forward_returns(
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*,
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news_rows: list[dict[str, Any]],
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ohlc_rows: list[dict[str, Any]],
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) -> list[dict[str, Any]]:
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"""Attach forward-return labels to each analyzed row."""
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if not ohlc_rows:
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return news_rows
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closes_by_date = {
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str(row.get("date")): float(row.get("close"))
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for row in ohlc_rows
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if row.get("date") is not None and row.get("close") is not None
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}
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ordered_dates = [str(row.get("date")) for row in ohlc_rows if row.get("date") is not None]
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date_index = {date: idx for idx, date in enumerate(ordered_dates)}
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horizons = {
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"ret_t0": 0,
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"ret_t1": 1,
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"ret_t3": 3,
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"ret_t5": 5,
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"ret_t10": 10,
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}
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enriched: 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]
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base_close = closes_by_date.get(trade_date)
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if not trade_date or base_close in (None, 0):
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enriched.append(row)
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continue
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next_row = dict(row)
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base_index = date_index.get(trade_date)
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if base_index is None:
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enriched.append(next_row)
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continue
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for field, offset in horizons.items():
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target_index = base_index + offset
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if target_index >= len(ordered_dates):
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next_row[field] = None
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continue
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target_close = closes_by_date.get(ordered_dates[target_index])
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next_row[field] = (
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(float(target_close) - float(base_close)) / float(base_close)
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if target_close not in (None, 0)
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else None
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)
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enriched.append(next_row)
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return enriched
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def build_analysis_rows(
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*,
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symbol: str,
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news_rows: list[dict[str, Any]],
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ohlc_rows: list[dict[str, Any]],
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) -> tuple[list[dict[str, Any]], dict[str, int]]:
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"""Transform raw news rows into market_store news_analysis payloads plus stats."""
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llm_results: dict[str, dict[str, Any]] = {}
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if llm_enrichment_enabled():
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batch_size = get_env_int("EXPLAIN_ENRICH_BATCH_SIZE", 8)
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for chunk in _chunk_rows(news_rows, batch_size):
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llm_results.update(analyze_news_rows_with_llm(chunk))
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staged_rows: list[dict[str, Any]] = []
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seen_dedupe_keys: set[str] = set()
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deduped_count = 0
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llm_count = 0
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local_count = 0
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for row in news_rows:
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news_id = str(row.get("id") or "").strip()
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if not news_id:
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continue
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dedupe_key = _dedupe_key(row)
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if dedupe_key in seen_dedupe_keys:
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deduped_count += 1
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continue
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seen_dedupe_keys.add(dedupe_key)
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batch_result = llm_results.get(news_id)
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if isinstance(batch_result, dict):
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analysis = dict(batch_result)
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analysis.setdefault("summary", str(row.get("summary") or row.get("title") or "")[:280])
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analysis.setdefault("raw_json", row)
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analysis["analysis_source"] = "llm"
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llm_count += 1
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else:
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analysis = classify_news_row(row)
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if analysis.get("analysis_source") == "llm":
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llm_count += 1
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else:
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local_count += 1
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staged_rows.append(
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{
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"news_id": news_id,
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"trade_date": str(row.get("trade_date") or "")[:10] or None,
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**analysis,
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}
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)
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return (
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attach_forward_returns(news_rows=staged_rows, ohlc_rows=ohlc_rows),
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{
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"deduped_count": deduped_count,
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"llm_count": llm_count,
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"local_count": local_count,
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},
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)
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def enrich_news_for_symbol(
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store: MarketStore,
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symbol: str,
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*,
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start_date: str | None = None,
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end_date: str | None = None,
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limit: int = 200,
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analysis_source: str = "local",
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skip_existing: bool = True,
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only_reanalyze_local: bool = False,
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) -> dict[str, Any]:
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"""Read raw market news, compute explain fields, and persist them."""
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normalized_symbol = str(symbol or "").strip().upper()
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if not normalized_symbol:
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return {"symbol": "", "analyzed": 0}
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news_rows = store.get_news_items(
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normalized_symbol,
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start_date=start_date,
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end_date=end_date,
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limit=limit,
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)
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total_news_count = len(news_rows)
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skipped_existing_count = 0
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analyzed_sources: dict[str, str] = {}
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skipped_missing_analysis_count = 0
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skipped_non_local_count = 0
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if news_rows and only_reanalyze_local:
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analyzed_sources = store.get_analyzed_news_sources(
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normalized_symbol,
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start_date=start_date,
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end_date=end_date,
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)
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skipped_missing_analysis_count = sum(
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1
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for row in news_rows
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if str(row.get("id") or "").strip() not in analyzed_sources
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)
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skipped_non_local_count = sum(
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1
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for row in news_rows
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if str(row.get("id") or "").strip() in analyzed_sources
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and analyzed_sources.get(str(row.get("id") or "").strip()) != "local"
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)
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skipped_existing_count = sum(
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1
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for row in news_rows
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if str(row.get("id") or "").strip() not in analyzed_sources
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or analyzed_sources.get(str(row.get("id") or "").strip()) != "local"
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)
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news_rows = [
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row for row in news_rows
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if analyzed_sources.get(str(row.get("id") or "").strip()) == "local"
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]
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elif skip_existing and news_rows:
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analyzed_ids = store.get_analyzed_news_ids(
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normalized_symbol,
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start_date=start_date,
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end_date=end_date,
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)
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skipped_existing_count = sum(
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1
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for row in news_rows
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if str(row.get("id") or "").strip() in analyzed_ids
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)
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news_rows = [
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row for row in news_rows
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if str(row.get("id") or "").strip() not in analyzed_ids
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]
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ohlc_start = start_date or (news_rows[-1]["trade_date"] if news_rows and news_rows[-1].get("trade_date") else None)
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ohlc_end = end_date or (news_rows[0]["trade_date"] if news_rows and news_rows[0].get("trade_date") else None)
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ohlc_rows = (
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store.get_ohlc(normalized_symbol, ohlc_start, ohlc_end)
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if ohlc_start and ohlc_end
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else []
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)
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analysis_rows, stats = build_analysis_rows(
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symbol=normalized_symbol,
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news_rows=news_rows,
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ohlc_rows=ohlc_rows,
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)
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analyzed = store.upsert_news_analysis(
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normalized_symbol,
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analysis_rows,
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analysis_source=analysis_source,
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)
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upgraded_dates = sorted(
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{
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str(row.get("trade_date") or "")[:10]
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for row in analysis_rows
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if str(row.get("analysis_source") or "").strip().lower() == "llm"
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and str(row.get("trade_date") or "").strip()
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}
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)
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remaining_local_titles = [
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str(row.get("title") or row.get("news_id") or "").strip()
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for row in news_rows
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for analyzed_row in analysis_rows
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if str(analyzed_row.get("news_id") or "").strip() == str(row.get("id") or "").strip()
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and str(analyzed_row.get("analysis_source") or "").strip().lower() == "local"
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][:5]
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return {
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"symbol": normalized_symbol,
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"analyzed": analyzed,
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"news_count": total_news_count,
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"queued_count": len(news_rows),
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"skipped_existing_count": skipped_existing_count,
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"deduped_count": stats["deduped_count"],
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"llm_count": stats["llm_count"],
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"local_count": stats["local_count"],
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"only_reanalyze_local": only_reanalyze_local,
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"upgraded_local_to_llm_count": (
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stats["llm_count"]
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if only_reanalyze_local
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else 0
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),
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"execution_summary": {
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"upgraded_dates": upgraded_dates[:5],
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"remaining_local_titles": remaining_local_titles,
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"skipped_missing_analysis_count": skipped_missing_analysis_count,
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"skipped_non_local_count": skipped_non_local_count,
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},
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}
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def enrich_symbols(
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store: MarketStore,
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symbols: list[str],
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*,
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start_date: str | None = None,
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end_date: str | None = None,
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limit: int = 200,
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analysis_source: str = "local",
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skip_existing: bool = True,
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only_reanalyze_local: bool = False,
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) -> list[dict[str, Any]]:
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"""Batch enrich multiple symbols for explain-oriented news analysis."""
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results = []
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for symbol in symbols:
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normalized_symbol = str(symbol or "").strip().upper()
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if not normalized_symbol:
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continue
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results.append(
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enrich_news_for_symbol(
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store,
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normalized_symbol,
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start_date=start_date,
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end_date=end_date,
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limit=limit,
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analysis_source=analysis_source,
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skip_existing=skip_existing,
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only_reanalyze_local=only_reanalyze_local,
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)
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)
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return results
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