Initial commit of integrated agent system
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cillin
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backend/explain/range_explainer.py
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214
backend/explain/range_explainer.py
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# -*- coding: utf-8 -*-
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"""Local range explanation built from price and persisted news."""
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from __future__ import annotations
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from typing import Any, Dict
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from backend.enrich.llm_enricher import analyze_range_with_llm
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from backend.explain.category_engine import categorize_news_rows
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from backend.tools.data_tools import get_prices
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def _rank_event_score(row: Dict[str, Any]) -> float:
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relevance = str(row.get("relevance") or "").strip().lower()
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relevance_score = {"high": 3.0, "relevant": 3.0, "medium": 2.0, "low": 1.0}.get(
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relevance,
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0.5,
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)
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impact_score = abs(float(row.get("ret_t0") or 0.0)) * 100
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return relevance_score + impact_score
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def summarize_bullish_factors(
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news_rows: list[Dict[str, Any]],
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*,
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limit: int = 5,
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) -> list[str]:
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factors = []
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for row in news_rows:
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if str(row.get("sentiment") or "").strip().lower() != "positive":
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continue
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candidate = row.get("reason_growth") or row.get("key_discussion") or row.get("summary") or row.get("title")
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if candidate:
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factors.append(str(candidate).strip())
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seen = set()
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output = []
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for factor in factors:
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if factor in seen:
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continue
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seen.add(factor)
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output.append(factor[:200])
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if len(output) >= limit:
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break
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return output
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def summarize_bearish_factors(
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news_rows: list[Dict[str, Any]],
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*,
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limit: int = 5,
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) -> list[str]:
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factors = []
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for row in news_rows:
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if str(row.get("sentiment") or "").strip().lower() != "negative":
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continue
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candidate = row.get("reason_decrease") or row.get("key_discussion") or row.get("summary") or row.get("title")
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if candidate:
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factors.append(str(candidate).strip())
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seen = set()
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output = []
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for factor in factors:
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if factor in seen:
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continue
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seen.add(factor)
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output.append(factor[:200])
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if len(output) >= limit:
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break
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return output
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def build_trend_analysis(prices: list[Any]) -> str:
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if len(prices) < 2:
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return "区间样本较短，暂不具备足够趋势信息。"
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if len(prices) < 3:
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open_price = float(prices[0].open)
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close_price = float(prices[-1].close)
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change = ((close_price - open_price) / open_price) * 100 if open_price else 0.0
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return f"短区间内价格变动 {change:+.2f}%，趋势信息有限。"
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mid = len(prices) // 2
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first_open = float(prices[0].open)
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first_close = float(prices[mid].close)
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second_open = float(prices[mid].open)
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second_close = float(prices[-1].close)
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first_half = ((first_close - first_open) / first_open) * 100 if first_open else 0.0
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second_half = ((second_close - second_open) / second_open) * 100 if second_open else 0.0
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return (
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f"前半段{'上涨' if first_half >= 0 else '下跌'} {abs(first_half):.2f}%，"
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f"后半段{'上涨' if second_half >= 0 else '下跌'} {abs(second_half):.2f}%，"
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"说明价格驱动在区间内部出现了阶段性切换。"
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)
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def build_range_explanation(
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*,
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ticker: str,
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start_date: str,
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end_date: str,
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news_rows: list[Dict[str, Any]],
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) -> Dict[str, Any]:
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"""Explain a price range with local price and news heuristics."""
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prices = get_prices(ticker, start_date, end_date)
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if not prices:
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return {
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"symbol": ticker,
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"start_date": start_date,
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"end_date": end_date,
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"error": "No OHLC data for this range",
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}
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open_price = float(prices[0].open)
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close_price = float(prices[-1].close)
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high_price = max(float(price.high) for price in prices)
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low_price = min(float(price.low) for price in prices)
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total_volume = sum(int(price.volume) for price in prices)
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price_change_pct = (
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((close_price - open_price) / open_price) * 100 if open_price else 0.0
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)
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categories = categorize_news_rows(news_rows)
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news_count = len(news_rows)
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dominant_categories = sorted(
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(
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{"category": key, "count": value["count"]}
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for key, value in categories.items()
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if value["count"] > 0
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),
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key=lambda item: item["count"],
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reverse=True,
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)
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direction = "上涨" if price_change_pct > 0 else "下跌" if price_change_pct < 0 else "横盘"
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category_text = (
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f"主要主题集中在 {', '.join(item['category'] for item in dominant_categories[:3])}。"
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if dominant_categories
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else "区间内未识别出明显的主题聚类。"
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)
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summary = (
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f"{ticker} 在 {start_date} 至 {end_date} 区间内{direction} {abs(price_change_pct):.2f}%，"
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f"区间覆盖 {len(prices)} 个交易日，关联新闻 {news_count} 条。{category_text}"
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)
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bullish_factors = summarize_bullish_factors(news_rows)
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bearish_factors = summarize_bearish_factors(news_rows)
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trend_analysis = build_trend_analysis(prices)
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llm_source = "local"
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range_payload = {
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"ticker": ticker,
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"start_date": start_date,
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"end_date": end_date,
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"price_change_pct": round(price_change_pct, 2),
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"trading_days": len(prices),
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"news_count": news_count,
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"dominant_categories": dominant_categories[:5],
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"bullish_factors": bullish_factors[:3],
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"bearish_factors": bearish_factors[:3],
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"trend_analysis": trend_analysis,
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"top_news": [
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{
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"date": row.get("trade_date") or str(row.get("date") or "")[:10],
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"title": row.get("title") or "",
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"summary": row.get("summary") or "",
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"sentiment": row.get("sentiment") or "",
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"relevance": row.get("relevance") or "",
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"ret_t0": row.get("ret_t0"),
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}
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for row in sorted(news_rows, key=_rank_event_score, reverse=True)[:5]
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],
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}
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llm_analysis = analyze_range_with_llm(range_payload)
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if isinstance(llm_analysis, dict):
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summary = llm_analysis.get("summary") or summary
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trend_analysis = llm_analysis.get("trend_analysis") or trend_analysis
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bullish_factors = llm_analysis.get("bullish_factors") or bullish_factors
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bearish_factors = llm_analysis.get("bearish_factors") or bearish_factors
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llm_source = "llm"
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key_events = [
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{
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"date": row.get("trade_date") or str(row.get("date") or "")[:10],
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"title": row.get("title") or "Untitled news",
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"summary": row.get("summary") or "",
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"category": row.get("category") or "",
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"id": row.get("id"),
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"sentiment": row.get("sentiment"),
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"ret_t0": row.get("ret_t0"),
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}
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for row in sorted(news_rows, key=_rank_event_score, reverse=True)[:8]
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]
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return {
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"symbol": ticker,
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"start_date": start_date,
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"end_date": end_date,
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"price_change_pct": round(price_change_pct, 2),
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"open_price": open_price,
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"close_price": close_price,
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"high_price": high_price,
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"low_price": low_price,
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"total_volume": total_volume,
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"trading_days": len(prices),
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"news_count": news_count,
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"dominant_categories": dominant_categories[:5],
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"analysis": {
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"summary": summary,
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"key_events": key_events,
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"bullish_factors": bullish_factors,
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"bearish_factors": bearish_factors,
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"trend_analysis": trend_analysis,
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"analysis_source": llm_source,
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"analysis_model_label": llm_analysis.get("model_label") if isinstance(llm_analysis, dict) else None,
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},
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}
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