Compare commits
| Author | SHA1 | Date | |
|---|---|---|---|
 leefer
|
cf2aad28ec | ||
|
leefer
|
814e75730a | ||
|
leefer
|
b3555d2603 |
+17
-626
@@ -42,7 +42,6 @@ from heaven_engine import (
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from backend.data.providers.ifind_client import IfindError
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from llm_strategy import LLMCompilerError, compile_strategy_with_llm, test_llm_connection
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from mentor_agent import MentorAgentError, stream_with_mentor
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from market_insights import MarketInsightsService
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from screener import (
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FACTOR_FIELDS,
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FACTOR_GROUPS,
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@@ -53,14 +52,14 @@ from screener import (
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from backend.features.accounts.http import AccountHttpMixin
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from backend.features.accounts.security import SecretVault
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from backend.features.accounts.service import AccountService
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from backend.features.auction import AuctionServiceMixin
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from backend.features.dragon_tiger import DragonTigerServiceMixin
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from backend.features.pools import PoolServiceMixin
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from backend.features.popularity import PopularityServiceMixin
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from backend.features.rotation import RotationServiceMixin
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from backend.features.sentiment import SentimentServiceMixin
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from backend.features.system import SystemHttpMixin
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from sentiment_engine import (
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COMPONENT_WEIGHTS,
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SENTIMENT_ENGINE_VERSION,
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apply_sentiment_to_dashboard,
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build_sentiment_history,
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latest_contiguous_history,
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)
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from backend.features.themes import ThemeServiceMixin
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from backend.data.providers.tushare_client import TushareClient, TushareError, _sector_coverage_issue
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@@ -141,7 +140,16 @@ MENTOR_ETF_UNIVERSE = (
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)
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class DashboardService(MarketServiceMixin):
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class DashboardService(
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MarketServiceMixin,
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SentimentServiceMixin,
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PoolServiceMixin,
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RotationServiceMixin,
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AuctionServiceMixin,
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ThemeServiceMixin,
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PopularityServiceMixin,
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DragonTigerServiceMixin,
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):
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def __init__(self) -> None:
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runtime = load_runtime_settings()
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self.vault = SecretVault(runtime.encryption_key)
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@@ -734,184 +742,7 @@ class DashboardService(MarketServiceMixin):
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return AccountService.public_personal_profile(personal)
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def _enrich_dashboard_sentiment(
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self,
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dashboard: dict[str, Any],
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end_date: str,
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) -> dict[str, Any]:
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history = self.database.list_snapshot_payloads(end_date, 260)
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return apply_sentiment_to_dashboard(dashboard, history)
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def sentiment_history(self, trade_date: str, limit: int = 20) -> dict[str, Any]:
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normalized_date = normalize_date(trade_date)
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limit = max(10, min(120, int(limit)))
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full_series = build_sentiment_history(
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self.database.list_snapshot_payloads(normalized_date, 240)
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)
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series = latest_contiguous_history(full_series)
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rows = series[-limit:]
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return {
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"trade_date": rows[-1]["trade_date"] if rows else normalized_date,
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"available_days": len(series),
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"stored_days": len(full_series),
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"requested_days": limit,
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"rows": rows,
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"weights": COMPONENT_WEIGHTS,
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"normalization": rows[-1]["normalization"] if rows else "固定锚点",
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}
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def rotation_history(self, trade_date: str, limit: int = 9) -> dict[str, Any]:
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normalized_date = normalize_date(trade_date)
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# 板块轮动固定展示最近 9 个交易日，按由近到远排列。
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limit = 9
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snapshots = self.database.list_snapshot_payloads(normalized_date, 240)
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by_trade_date: dict[str, dict[str, Any]] = {}
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for snapshot in snapshots:
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meta = snapshot.get("meta") or {}
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actual_date = str(meta.get("trade_date") or snapshot.get("_snapshot_date") or "")
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compact_date = actual_date.replace("-", "")
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if len(compact_date) == 8:
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by_trade_date[compact_date] = snapshot
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sentiment_dates = {
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str(row.get("trade_date") or "").replace("-", "")
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for row in latest_contiguous_history(build_sentiment_history(snapshots))
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}
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ordered_dates = sorted(
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date_key for date_key in by_trade_date
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if not sentiment_dates or date_key in sentiment_dates
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)[-limit:][::-1]
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rows = []
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for date_key in ordered_dates:
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snapshot = by_trade_date[date_key]
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sector_context = {
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str(item.get("name") or ""): item
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for item in snapshot.get("sectors") or []
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}
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sectors = []
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for item in (snapshot.get("sector_rotation") or [])[:12]:
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name = str(item.get("name") or "").strip()
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context = sector_context.get(name, {})
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sectors.append(
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{
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"name": name,
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"rank": int(item.get("rank") or len(sectors) + 1),
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"trend": item.get("trend") or "持平",
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"count": int(item.get("count") or 0),
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"strength": float(item.get("strength") or context.get("strength") or 0),
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"change": float(context.get("change") or 0),
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"leader": item.get("leader") or context.get("leader") or "--",
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}
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)
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rows.append(
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{
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"trade_date": f"{date_key[:4]}-{date_key[4:6]}-{date_key[6:]}",
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"sectors": sectors,
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}
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)
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return {
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"trade_date": rows[0]["trade_date"] if rows else normalized_date,
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"available_days": len(ordered_dates),
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"requested_days": limit,
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"rows": rows,
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}
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def rotation_sector_members(self, trade_date: str, sector_name: str) -> dict[str, Any]:
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normalized_date = normalize_date(trade_date)
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sector_name = validate_text(sector_name, "板块名称", 60, required=True)
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dashboard = self.get_dashboard(normalized_date)
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actual_date = normalize_date(
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str((dashboard.get("meta") or {}).get("trade_date") or normalized_date)
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)
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cache_key = f"{actual_date}:{sector_name}"
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cached = self.database.get_data_snapshot("rotation_sector_members_v1", cache_key)
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if cached:
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cached["meta"] = {**(cached.get("meta") or {}), "cached": True}
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return cached
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if not self.configured:
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raise ValueError("板块成分数据暂不可用。")
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representative = next(
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(
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item for item in dashboard.get("limits") or []
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if str(item.get("sector") or "").strip() == sector_name
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),
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None,
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)
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if not representative:
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raise ValueError("未找到该板块的代表股票，暂时无法核验成分股。")
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raw_code = str(representative.get("ts_code") or representative.get("code") or "")
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if "." in raw_code:
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ts_code = raw_code
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elif raw_code.startswith(("4", "8", "92")):
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ts_code = f"{raw_code}.BJ"
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elif raw_code.startswith(("6", "68", "90")):
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ts_code = f"{raw_code}.SH"
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else:
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ts_code = f"{raw_code}.SZ"
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client = self._tushare_client()
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try:
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industry = client.sw_stock_industry(ts_code, actual_date)
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sector_code = str(industry.get("l2_code") or "")
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members = client.sw_sector_members(sector_code, actual_date)
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except TushareError as exc:
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raise ValueError(f"该板块成分股暂不可用：{exc}") from exc
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daily_rows = self.database.daily_bars_for_date(actual_date)
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if len(daily_rows) < 1000:
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try:
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daily_rows = client.query(
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"daily",
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{"trade_date": actual_date},
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"ts_code,trade_date,open,high,low,close,pct_chg,vol,amount",
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)
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if daily_rows:
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self.database.upsert_daily_bars(daily_rows)
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except TushareError:
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daily_rows = self.database.daily_bars_for_date(actual_date)
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daily_map = {str(item.get("ts_code") or ""): item for item in daily_rows}
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rows = []
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for member in members:
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member_code = str(member.get("ts_code") or "")
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quote = daily_map.get(member_code) or {}
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rows.append(
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{
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"code": member_code.split(".")[0],
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"ts_code": member_code,
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"name": str(member.get("name") or "--"),
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"change": quote.get("pct_chg"),
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"open": quote.get("open"),
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"close": quote.get("close"),
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"amount_billion": (
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round(float(quote.get("amount") or 0) / 100000, 2)
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if quote else None
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),
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"quoted": bool(quote),
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}
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)
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rows.sort(
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key=lambda item: (
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bool(item.get("quoted")),
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float(item.get("change") or -999),
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float(item.get("amount_billion") or 0),
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),
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reverse=True,
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)
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result = {
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"meta": {
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"trade_date": self._display_compact_date(actual_date),
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"sector_name": str(industry.get("l2_name") or sector_name),
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"sector_code": sector_code,
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"member_count": len(rows),
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"quoted_count": sum(bool(item.get("quoted")) for item in rows),
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"cached": False,
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},
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"rows": rows,
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}
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self.database.save_data_snapshot(
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"rotation_sector_members_v1", cache_key, "tushare", result
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)
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return result
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def status(self) -> dict[str, Any]:
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llm_access = self.llm_access_status()
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@@ -929,28 +760,9 @@ class DashboardService(MarketServiceMixin):
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}
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def _market_insights(self) -> MarketInsightsService:
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if not self.configured:
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raise ValueError("行情数据尚未配置。")
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return MarketInsightsService(
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self.database,
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self._tushare_client(),
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ifind=self.ifind,
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)
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def auction_center(self, trade_date: str, force: bool = False) -> dict[str, Any]:
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return self._market_insights().auction_center(
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normalize_date(trade_date), force, self.current_user_id
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)
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def theme_library(self, trade_date: str, force: bool = False) -> dict[str, Any]:
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return self._market_insights().theme_library(normalize_date(trade_date), force)
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def theme_detail(self, code: str, trade_date: str) -> dict[str, Any]:
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return self._market_insights().theme_detail(code, normalize_date(trade_date))
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def popularity(self, trade_date: str, force: bool = False) -> dict[str, Any]:
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return self._market_insights().popularity(normalize_date(trade_date), force)
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@staticmethod
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def _ifind_field(row: dict[str, Any], tokens: tuple[str, ...]) -> Any:
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@@ -3250,427 +3062,6 @@ class DashboardService(MarketServiceMixin):
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)
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return result
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def get_hot_money_profiles(self, force: bool = False) -> dict[str, Any]:
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cache_kind = "hot_money_profiles_v1"
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cache_key = "directory"
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cached = self.database.get_data_snapshot(cache_kind, cache_key)
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if cached and not force:
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cached["meta"] = {**cached.get("meta", {}), "cached": True}
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return cached
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if self.configured:
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try:
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payload = self._tushare_client().hot_money_profiles()
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except TushareError:
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if cached:
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cached["meta"] = {
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**cached.get("meta", {}),
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"cached": True,
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"stale": True,
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"notice": "名录暂未完成更新，当前展示最近一次收录结果。",
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}
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return cached
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return {
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"meta": {
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"source": "unavailable",
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"status": "unavailable",
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"schema_version": 1,
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"cached": False,
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"updated_at": datetime.now().astimezone().isoformat(timespec="seconds"),
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"notice": "游资名录暂不可用，请稍后重试。",
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},
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"summary": {
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"profile_count": 0,
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"described_count": 0,
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"organization_count": 0,
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},
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"profiles": [],
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}
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payload["meta"]["cached"] = False
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if payload.get("meta", {}).get("status") == "success":
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self.database.save_data_snapshot(cache_kind, cache_key, "tushare", payload)
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return payload
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if cached:
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cached["meta"] = {**cached.get("meta", {}), "cached": True}
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return cached
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return {
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"meta": {
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"source": "unavailable",
|
||||
"status": "unavailable",
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"schema_version": 1,
|
||||
"cached": False,
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||||
"updated_at": datetime.now().astimezone().isoformat(timespec="seconds"),
|
||||
"notice": "游资名录暂不可用，请联系管理员检查行情配置。",
|
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},
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"summary": {
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"profile_count": 0,
|
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"described_count": 0,
|
||||
"organization_count": 0,
|
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},
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"profiles": [],
|
||||
}
|
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|
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def get_dragon_tiger(self, trade_date: str, force: bool = False) -> dict[str, Any]:
|
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normalized_date = normalize_date(trade_date)
|
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cache_kind = "hot_money_detail_v3"
|
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if not force:
|
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cached = self.database.get_data_snapshot(cache_kind, normalized_date)
|
||||
if (
|
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cached
|
||||
and cached.get("meta", {}).get("source") == "tushare"
|
||||
and cached.get("meta", {}).get("status") == "success"
|
||||
and int(cached.get("meta", {}).get("schema_version") or 0) == 3
|
||||
):
|
||||
cached["meta"] = {**cached.get("meta", {}), "cached": True}
|
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return cached
|
||||
if self.configured:
|
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try:
|
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payload = self._tushare_client().dragon_tiger(normalized_date)
|
||||
except TushareError as exc:
|
||||
return {
|
||||
"meta": {
|
||||
"requested_date": f"{normalized_date[:4]}-{normalized_date[4:6]}-{normalized_date[6:8]}",
|
||||
"trade_date": f"{normalized_date[:4]}-{normalized_date[4:6]}-{normalized_date[6:8]}",
|
||||
"source": "tushare_error",
|
||||
"status": "error",
|
||||
"schema_version": 3,
|
||||
"cached": False,
|
||||
"updated_at": datetime.now().astimezone().isoformat(timespec="seconds"),
|
||||
"notice": "龙虎榜数据暂不可用，请稍后重试。",
|
||||
},
|
||||
"summary": {
|
||||
"trader_count": 0,
|
||||
"identity_count": 0,
|
||||
"operation_count": 0,
|
||||
"active_stock_count": 0,
|
||||
"seat_net_buy_million": 0,
|
||||
"unclassified_count": 0,
|
||||
"directory_count": 0,
|
||||
},
|
||||
"traders": [],
|
||||
"unclassified_seats": [],
|
||||
"rows": [],
|
||||
}
|
||||
payload["meta"]["cached"] = False
|
||||
if payload.get("meta", {}).get("status") == "success":
|
||||
self.database.save_data_snapshot(cache_kind, normalized_date, "tushare", payload)
|
||||
return payload
|
||||
|
||||
return {
|
||||
"meta": {
|
||||
"requested_date": f"{normalized_date[:4]}-{normalized_date[4:6]}-{normalized_date[6:8]}",
|
||||
"trade_date": f"{normalized_date[:4]}-{normalized_date[4:6]}-{normalized_date[6:8]}",
|
||||
"source": "unavailable",
|
||||
"status": "unavailable",
|
||||
"schema_version": 3,
|
||||
"cached": False,
|
||||
"notice": "龙虎榜数据暂不可用，请联系管理员检查行情配置。",
|
||||
},
|
||||
"summary": {
|
||||
"trader_count": 0,
|
||||
"identity_count": 0,
|
||||
"operation_count": 0,
|
||||
"active_stock_count": 0,
|
||||
"seat_net_buy_million": 0,
|
||||
"unclassified_count": 0,
|
||||
"directory_count": 0,
|
||||
},
|
||||
"traders": [],
|
||||
"unclassified_seats": [],
|
||||
"rows": [],
|
||||
}
|
||||
|
||||
|
||||
def save_reason(self, trade_date: str, code: str, reason: str) -> None:
|
||||
normalized_date = normalize_date(trade_date)
|
||||
code = validate_stock_code(code)
|
||||
reason = reason.strip()
|
||||
if not reason or len(reason) > 200:
|
||||
raise ValueError("涨停原因应为 1 至 200 个字符。")
|
||||
self.database.save_reason_override(normalized_date, code, reason)
|
||||
|
||||
|
||||
def _apply_reason_overrides(self, dashboard: dict[str, Any]) -> dict[str, Any]:
|
||||
trade_date = str(dashboard.get("meta", {}).get("trade_date", "")).replace("-", "")
|
||||
enrichment = self.database.get_data_snapshot("ifind_event_enrichment_v1", trade_date)
|
||||
if enrichment:
|
||||
self._merge_ifind_event_enrichment(dashboard, enrichment)
|
||||
else:
|
||||
self._schedule_ifind_event_enrichment(trade_date)
|
||||
overrides = self.database.reason_overrides(trade_date)
|
||||
if not overrides:
|
||||
return dashboard
|
||||
for key in ("limits", "broken", "down_limits"):
|
||||
for row in dashboard.get(key) or []:
|
||||
if row.get("code") in overrides:
|
||||
row["reason"] = overrides[row["code"]]
|
||||
row["reason_source"] = "manual"
|
||||
return dashboard
|
||||
|
||||
def _schedule_ifind_event_enrichment(self, trade_date: str) -> None:
|
||||
ifind = getattr(self, "ifind", None)
|
||||
if not ifind or not ifind.configured or not re.fullmatch(r"\d{8}", trade_date):
|
||||
return
|
||||
now = datetime.now().astimezone()
|
||||
if trade_date == now.strftime("%Y%m%d") and now.time().replace(tzinfo=None) < dt_time(15, 0):
|
||||
return
|
||||
self.jobs.submit(
|
||||
"market.ifind-event-enrichment",
|
||||
f"{trade_date}:v1",
|
||||
lambda: self._refresh_ifind_event_enrichment(trade_date),
|
||||
{"trade_date": trade_date, "trigger": "dashboard-enrichment"},
|
||||
)
|
||||
|
||||
def _refresh_ifind_event_enrichment(self, trade_date: str) -> None:
|
||||
if not self._ifind_event_lock.acquire(blocking=False):
|
||||
return
|
||||
try:
|
||||
if self.database.get_data_snapshot("ifind_event_enrichment_v1", trade_date):
|
||||
return
|
||||
ifind = getattr(self, "ifind", None)
|
||||
if not ifind or not ifind.configured:
|
||||
return
|
||||
current = datetime.strptime(trade_date, "%Y%m%d")
|
||||
display_date = f"{current.year}年{current.month}月{current.day}日"
|
||||
requests = {
|
||||
"limits": (
|
||||
f"{display_date}涨停股票，股票代码、股票简称、涨停原因、"
|
||||
"首次涨停时间、最终涨停时间、开板次数"
|
||||
),
|
||||
"broken": (
|
||||
f"{display_date}曾涨停但收盘未涨停的股票，股票代码、股票简称、"
|
||||
"涨停原因、首次涨停时间、开板次数"
|
||||
),
|
||||
"down_limits": (
|
||||
f"{display_date}跌停股票，股票代码、股票简称、跌停原因"
|
||||
),
|
||||
}
|
||||
result: dict[str, Any] = {
|
||||
"trade_date": trade_date,
|
||||
"generated_at": datetime.now().astimezone().isoformat(timespec="seconds"),
|
||||
"limits": {}, "broken": {}, "down_limits": {}, "partial": False,
|
||||
}
|
||||
for kind, query in requests.items():
|
||||
try:
|
||||
rows = ifind.wencai(query, "stock", cache_ttl=900)
|
||||
except IfindError:
|
||||
result["partial"] = True
|
||||
continue
|
||||
for raw in rows:
|
||||
code = self._ifind_row_code(raw)
|
||||
if not code:
|
||||
continue
|
||||
reason_tokens = (
|
||||
("跌停原因", "风险线索", "原因")
|
||||
if kind == "down_limits"
|
||||
else ("涨停原因类别", "涨停原因", "触板逻辑", "原因")
|
||||
)
|
||||
reason = str(self._ifind_field(raw, reason_tokens) or "").strip()
|
||||
first_time = self._normalize_ifind_event_time(
|
||||
self._ifind_field(raw, ("首次涨停时间", "首次触板时间", "首次封板时间"))
|
||||
)
|
||||
last_time = self._normalize_ifind_event_time(
|
||||
self._ifind_field(raw, ("最终涨停时间", "最后涨停时间", "最后封板时间"))
|
||||
)
|
||||
open_times = self._ifind_field(raw, ("开板次数", "打开涨停次数"))
|
||||
try:
|
||||
open_count = max(0, int(float(open_times))) if open_times not in (None, "") else None
|
||||
except (TypeError, ValueError):
|
||||
open_count = None
|
||||
result[kind][code] = {
|
||||
"reason": reason,
|
||||
"first_time": first_time,
|
||||
"last_time": last_time,
|
||||
"open_times": open_count,
|
||||
}
|
||||
if any(result[kind] for kind in ("limits", "broken", "down_limits")):
|
||||
self.database.save_data_snapshot(
|
||||
"ifind_event_enrichment_v1", trade_date, "ifind", result
|
||||
)
|
||||
finally:
|
||||
self._ifind_event_lock.release()
|
||||
|
||||
@staticmethod
|
||||
def _normalize_ifind_event_time(value: Any) -> str:
|
||||
text = str(value or "").strip()
|
||||
match = re.search(r"(?:^|\s)(\d{1,2}:\d{2}(?::\d{2})?)(?:$|\s)", text)
|
||||
if not match:
|
||||
match = re.search(r"(?<!\d)(\d{6})(?!\d)", text)
|
||||
if match:
|
||||
compact = match.group(1)
|
||||
return f"{compact[:2]}:{compact[2:4]}:{compact[4:]}"
|
||||
return ""
|
||||
parts = match.group(1).split(":")
|
||||
return ":".join(part.zfill(2) for part in parts)
|
||||
|
||||
@staticmethod
|
||||
def _merge_ifind_event_enrichment(
|
||||
dashboard: dict[str, Any], enrichment: dict[str, Any]
|
||||
) -> None:
|
||||
for kind in ("limits", "broken", "down_limits"):
|
||||
records = enrichment.get(kind) or {}
|
||||
for row in dashboard.get(kind) or []:
|
||||
event = records.get(str(row.get("code") or "")) or {}
|
||||
reason = str(event.get("reason") or "").strip()
|
||||
if reason:
|
||||
row["reason"] = reason
|
||||
row["reason_source"] = "market_event"
|
||||
if event.get("first_time"):
|
||||
row["first_time"] = event["first_time"]
|
||||
if event.get("last_time"):
|
||||
row["last_time"] = event["last_time"]
|
||||
if event.get("open_times") is not None:
|
||||
row["open_times"] = event["open_times"]
|
||||
|
||||
def _apply_seat_aliases(self, payload: dict[str, Any]) -> dict[str, Any]:
|
||||
aliases = self.database.list_seat_aliases()
|
||||
result = dict(payload)
|
||||
rows = payload.get("rows") or []
|
||||
for row in rows:
|
||||
for institution in row.get("institutions") or []:
|
||||
institution["alias"] = aliases.get(institution.get("seat_name", ""), "")
|
||||
traders: dict[tuple[str, str], dict[str, Any]] = {}
|
||||
unclassified: dict[str, dict[str, Any]] = {}
|
||||
seen_operations: set[tuple[Any, ...]] = set()
|
||||
builtin_aliases = {
|
||||
"国泰海通证券股份有限公司南京太平南路证券营业部": "作手新一",
|
||||
}
|
||||
|
||||
for row in rows:
|
||||
for institution in row.get("institutions") or []:
|
||||
seat_name = str(institution.get("seat_name") or "未知席位").strip()
|
||||
saved_alias = str(institution.get("alias") or "").strip()
|
||||
builtin_alias = builtin_aliases.get(seat_name, "")
|
||||
if saved_alias or builtin_alias:
|
||||
identity_name = saved_alias or builtin_alias
|
||||
identity_type = "trader"
|
||||
recognized = True
|
||||
identity_source = "manual" if saved_alias else "builtin"
|
||||
elif "机构专用" in seat_name:
|
||||
identity_name = "机构专用"
|
||||
identity_type = "institution"
|
||||
recognized = True
|
||||
identity_source = "system"
|
||||
elif "沪股通专用" in seat_name or "深股通专用" in seat_name:
|
||||
identity_name = "北向资金"
|
||||
identity_type = "channel"
|
||||
recognized = True
|
||||
identity_source = "system"
|
||||
else:
|
||||
identity_name = seat_name
|
||||
identity_type = "unclassified"
|
||||
recognized = False
|
||||
identity_source = "raw"
|
||||
|
||||
buy = round(float(institution.get("buy_million") or 0), 2)
|
||||
sell = round(float(institution.get("sell_million") or 0), 2)
|
||||
net_buy = round(float(institution.get("net_buy_million") or 0), 2)
|
||||
operation_key = (row.get("code"), seat_name, buy, sell, net_buy)
|
||||
if operation_key in seen_operations:
|
||||
continue
|
||||
seen_operations.add(operation_key)
|
||||
|
||||
group_key = (identity_type, identity_name)
|
||||
group = traders.setdefault(
|
||||
group_key,
|
||||
{
|
||||
"name": identity_name,
|
||||
"identity_type": identity_type,
|
||||
"identity_source": identity_source,
|
||||
"recognized": recognized,
|
||||
"buy_million": 0.0,
|
||||
"sell_million": 0.0,
|
||||
"net_buy_million": 0.0,
|
||||
"seat_names": set(),
|
||||
"stock_codes": set(),
|
||||
"operations": [],
|
||||
},
|
||||
)
|
||||
group["buy_million"] += buy
|
||||
group["sell_million"] += sell
|
||||
group["net_buy_million"] += net_buy
|
||||
group["seat_names"].add(seat_name)
|
||||
group["stock_codes"].add(str(row.get("code") or ""))
|
||||
group["operations"].append(
|
||||
{
|
||||
"code": row.get("code") or "",
|
||||
"name": row.get("name") or "--",
|
||||
"change": row.get("change") or 0,
|
||||
"direction": "买入" if net_buy > 0 else "卖出" if net_buy < 0 else "持平",
|
||||
"buy_million": buy,
|
||||
"sell_million": sell,
|
||||
"net_buy_million": net_buy,
|
||||
"reason": row.get("reason") or "--",
|
||||
"seat_name": seat_name,
|
||||
"seat_alias": identity_name if recognized else "",
|
||||
}
|
||||
)
|
||||
|
||||
if not recognized:
|
||||
pending = unclassified.setdefault(
|
||||
seat_name,
|
||||
{
|
||||
"seat_name": seat_name,
|
||||
"stock_codes": set(),
|
||||
"operation_count": 0,
|
||||
"buy_million": 0.0,
|
||||
"sell_million": 0.0,
|
||||
"net_buy_million": 0.0,
|
||||
},
|
||||
)
|
||||
pending["stock_codes"].add(str(row.get("code") or ""))
|
||||
pending["operation_count"] += 1
|
||||
pending["buy_million"] += buy
|
||||
pending["sell_million"] += sell
|
||||
pending["net_buy_million"] += net_buy
|
||||
|
||||
type_order = {"trader": 0, "institution": 1, "channel": 2, "unclassified": 3}
|
||||
aggregated = list(traders.values())
|
||||
aggregated.sort(
|
||||
key=lambda item: (
|
||||
type_order.get(item["identity_type"], 9),
|
||||
-abs(item["net_buy_million"]),
|
||||
item["name"],
|
||||
)
|
||||
)
|
||||
for index, group in enumerate(aggregated, start=1):
|
||||
group["id"] = f"identity-{index}"
|
||||
group["buy_million"] = round(group["buy_million"], 2)
|
||||
group["sell_million"] = round(group["sell_million"], 2)
|
||||
group["net_buy_million"] = round(group["net_buy_million"], 2)
|
||||
group["seat_count"] = len(group.pop("seat_names"))
|
||||
group["stock_count"] = len(group.pop("stock_codes"))
|
||||
group["operation_count"] = len(group["operations"])
|
||||
group["operations"].sort(
|
||||
key=lambda item: abs(float(item.get("net_buy_million") or 0)), reverse=True
|
||||
)
|
||||
|
||||
pending_seats = list(unclassified.values())
|
||||
for pending in pending_seats:
|
||||
pending["stock_count"] = len(pending.pop("stock_codes"))
|
||||
pending["buy_million"] = round(pending["buy_million"], 2)
|
||||
pending["sell_million"] = round(pending["sell_million"], 2)
|
||||
pending["net_buy_million"] = round(pending["net_buy_million"], 2)
|
||||
pending_seats.sort(key=lambda item: abs(item["net_buy_million"]), reverse=True)
|
||||
|
||||
operation_count = sum(item["operation_count"] for item in aggregated)
|
||||
active_stocks = {
|
||||
operation["code"] for item in aggregated for operation in item["operations"]
|
||||
}
|
||||
seat_net_buy = round(sum(item["net_buy_million"] for item in aggregated), 2)
|
||||
result["rows"] = rows
|
||||
result["traders"] = aggregated
|
||||
result["unclassified_seats"] = pending_seats
|
||||
result["summary"] = {
|
||||
**(payload.get("summary") or {}),
|
||||
"trader_count": sum(item["identity_type"] == "trader" for item in aggregated),
|
||||
"identity_count": len(aggregated),
|
||||
"operation_count": operation_count,
|
||||
"active_stock_count": len(active_stocks),
|
||||
"seat_net_buy_million": seat_net_buy,
|
||||
"unclassified_count": len(pending_seats),
|
||||
}
|
||||
return result
|
||||
|
||||
|
||||
SERVICE = DashboardService()
|
||||
|
||||
|
||||
@@ -11,7 +11,7 @@ from datetime import datetime, time as dt_time, timedelta
|
||||
from threading import Lock
|
||||
from typing import Any, ClassVar
|
||||
|
||||
from sentiment_engine import apply_sentiment_to_dashboard
|
||||
from backend.features.sentiment.engine import apply_sentiment_to_dashboard
|
||||
|
||||
|
||||
TUSHARE_URL = "http://api.tushare.pro"
|
||||
|
||||
@@ -0,0 +1,4 @@
|
||||
from .repository import AuctionRepositoryMixin
|
||||
from .service import AuctionServiceMixin
|
||||
|
||||
__all__ = ["AuctionRepositoryMixin", "AuctionServiceMixin"]
|
||||
@@ -0,0 +1,63 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import Any
|
||||
|
||||
|
||||
class AuctionRepositoryMixin:
|
||||
def upsert_auction_factors(self, rows: list[dict[str, Any]]) -> int:
|
||||
values = []
|
||||
for row in rows:
|
||||
trade_date = str(row.get("trade_date") or "")
|
||||
ts_code = str(row.get("ts_code") or "")
|
||||
price = float(row.get("price") or 0)
|
||||
pre_close = float(row.get("pre_close") or 0)
|
||||
if not trade_date or not ts_code or price <= 0 or pre_close <= 0:
|
||||
continue
|
||||
values.append(
|
||||
(
|
||||
trade_date,
|
||||
ts_code,
|
||||
price,
|
||||
pre_close,
|
||||
(price / pre_close - 1) * 100,
|
||||
float(row.get("vol") or 0),
|
||||
float(row.get("amount") or 0),
|
||||
float(row.get("turnover_rate") or 0),
|
||||
float(row.get("volume_ratio") or 0),
|
||||
)
|
||||
)
|
||||
with self.connect() as connection:
|
||||
connection.executemany(
|
||||
"""
|
||||
INSERT INTO auction_factors
|
||||
(trade_date, ts_code, price, pre_close, change, vol, amount,
|
||||
turnover_rate, volume_ratio)
|
||||
VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?)
|
||||
ON CONFLICT(trade_date, ts_code) DO UPDATE SET
|
||||
price=excluded.price, pre_close=excluded.pre_close,
|
||||
change=excluded.change, vol=excluded.vol, amount=excluded.amount,
|
||||
turnover_rate=excluded.turnover_rate,
|
||||
volume_ratio=excluded.volume_ratio
|
||||
""",
|
||||
values,
|
||||
)
|
||||
return len(values)
|
||||
|
||||
def auction_factor_dates(self, end_date: str = "", limit: int = 80) -> list[str]:
|
||||
where = "WHERE trade_date <= ?" if end_date else ""
|
||||
parameters: tuple[Any, ...] = (end_date, limit) if end_date else (limit,)
|
||||
with self.connect() as connection:
|
||||
rows = connection.execute(
|
||||
f"SELECT DISTINCT trade_date FROM auction_factors {where} "
|
||||
"ORDER BY trade_date DESC LIMIT ?",
|
||||
parameters,
|
||||
).fetchall()
|
||||
return [row["trade_date"] for row in reversed(rows)]
|
||||
|
||||
def auction_factors_for_date(self, trade_date: str) -> list[dict[str, Any]]:
|
||||
with self.connect() as connection:
|
||||
rows = connection.execute(
|
||||
"SELECT * FROM auction_factors WHERE trade_date = ? ORDER BY ts_code",
|
||||
(trade_date,),
|
||||
).fetchall()
|
||||
return [dict(row) for row in rows]
|
||||
@@ -0,0 +1,13 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import Any
|
||||
|
||||
from backend.bootstrap.config import normalize_date
|
||||
from backend.features.market.insights import MarketInsightsService
|
||||
|
||||
|
||||
class AuctionServiceMixin:
|
||||
def auction_center(self, trade_date: str, force: bool = False) -> dict[str, Any]:
|
||||
return self._market_insights().auction_center(
|
||||
normalize_date(trade_date), force, self.current_user_id
|
||||
)
|
||||
@@ -0,0 +1,4 @@
|
||||
from .repository import DragonTigerRepositoryMixin
|
||||
from .service import DragonTigerServiceMixin
|
||||
|
||||
__all__ = ["DragonTigerRepositoryMixin", "DragonTigerServiceMixin"]
|
||||
@@ -0,0 +1,61 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from datetime import datetime
|
||||
from typing import Any
|
||||
|
||||
|
||||
class DragonTigerRepositoryMixin:
|
||||
def list_seat_aliases(self) -> dict[str, str]:
|
||||
with self.connect() as connection:
|
||||
rows = connection.execute("SELECT seat_name, alias FROM seat_aliases").fetchall()
|
||||
return {row["seat_name"]: row["alias"] for row in rows}
|
||||
|
||||
def save_seat_alias(self, seat_name: str, alias: str) -> None:
|
||||
now = datetime.now().astimezone().isoformat(timespec="seconds")
|
||||
with self.connect() as connection:
|
||||
connection.execute(
|
||||
"""
|
||||
INSERT INTO seat_aliases (seat_name, alias, updated_at)
|
||||
VALUES (?, ?, ?)
|
||||
ON CONFLICT(seat_name) DO UPDATE SET
|
||||
alias = excluded.alias,
|
||||
updated_at = excluded.updated_at
|
||||
""",
|
||||
(seat_name, alias, now),
|
||||
)
|
||||
|
||||
def upsert_lhb_institutions(self, rows: list[dict[str, Any]]) -> int:
|
||||
grouped: dict[tuple[str, str], dict[str, float | int]] = {}
|
||||
for row in rows:
|
||||
trade_date = str(row.get("trade_date") or "")
|
||||
ts_code = str(row.get("ts_code") or "")
|
||||
seat_name = str(row.get("exalter") or row.get("seat_name") or "")
|
||||
if not trade_date or not ts_code or "机构专用" not in seat_name:
|
||||
continue
|
||||
group = grouped.setdefault(
|
||||
(trade_date, ts_code),
|
||||
{"net": 0.0, "buy": 0.0, "sell": 0.0, "seats": 0},
|
||||
)
|
||||
group["net"] = float(group["net"]) + float(row.get("net_buy") or row.get("net_amount") or 0)
|
||||
group["buy"] = float(group["buy"]) + float(row.get("buy") or row.get("buy_amount") or 0)
|
||||
group["sell"] = float(group["sell"]) + float(row.get("sell") or row.get("sell_amount") or 0)
|
||||
group["seats"] = int(group["seats"]) + 1
|
||||
values = [
|
||||
(trade_date, ts_code, item["net"], item["buy"], item["sell"], item["seats"])
|
||||
for (trade_date, ts_code), item in grouped.items()
|
||||
]
|
||||
with self.connect() as connection:
|
||||
connection.executemany(
|
||||
"""
|
||||
INSERT INTO lhb_institution_daily
|
||||
(trade_date, ts_code, net_buy_amount, buy_amount, sell_amount, seat_count)
|
||||
VALUES (?, ?, ?, ?, ?, ?)
|
||||
ON CONFLICT(trade_date, ts_code) DO UPDATE SET
|
||||
net_buy_amount=excluded.net_buy_amount,
|
||||
buy_amount=excluded.buy_amount,
|
||||
sell_amount=excluded.sell_amount,
|
||||
seat_count=excluded.seat_count
|
||||
""",
|
||||
values,
|
||||
)
|
||||
return len(values)
|
||||
@@ -0,0 +1,288 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from datetime import datetime
|
||||
from typing import Any
|
||||
|
||||
from backend.bootstrap.config import normalize_date
|
||||
from backend.data.providers.tushare_client import TushareError
|
||||
|
||||
|
||||
class DragonTigerServiceMixin:
|
||||
def get_hot_money_profiles(self, force: bool = False) -> dict[str, Any]:
|
||||
cache_kind = "hot_money_profiles_v1"
|
||||
cache_key = "directory"
|
||||
cached = self.database.get_data_snapshot(cache_kind, cache_key)
|
||||
if cached and not force:
|
||||
cached["meta"] = {**cached.get("meta", {}), "cached": True}
|
||||
return cached
|
||||
if self.configured:
|
||||
try:
|
||||
payload = self._tushare_client().hot_money_profiles()
|
||||
except TushareError:
|
||||
if cached:
|
||||
cached["meta"] = {
|
||||
**cached.get("meta", {}),
|
||||
"cached": True,
|
||||
"stale": True,
|
||||
"notice": "名录暂未完成更新，当前展示最近一次收录结果。",
|
||||
}
|
||||
return cached
|
||||
return {
|
||||
"meta": {
|
||||
"source": "unavailable",
|
||||
"status": "unavailable",
|
||||
"schema_version": 1,
|
||||
"cached": False,
|
||||
"updated_at": datetime.now().astimezone().isoformat(timespec="seconds"),
|
||||
"notice": "游资名录暂不可用，请稍后重试。",
|
||||
},
|
||||
"summary": {
|
||||
"profile_count": 0,
|
||||
"described_count": 0,
|
||||
"organization_count": 0,
|
||||
},
|
||||
"profiles": [],
|
||||
}
|
||||
payload["meta"]["cached"] = False
|
||||
if payload.get("meta", {}).get("status") == "success":
|
||||
self.database.save_data_snapshot(cache_kind, cache_key, "tushare", payload)
|
||||
return payload
|
||||
if cached:
|
||||
cached["meta"] = {**cached.get("meta", {}), "cached": True}
|
||||
return cached
|
||||
return {
|
||||
"meta": {
|
||||
"source": "unavailable",
|
||||
"status": "unavailable",
|
||||
"schema_version": 1,
|
||||
"cached": False,
|
||||
"updated_at": datetime.now().astimezone().isoformat(timespec="seconds"),
|
||||
"notice": "游资名录暂不可用，请联系管理员检查行情配置。",
|
||||
},
|
||||
"summary": {
|
||||
"profile_count": 0,
|
||||
"described_count": 0,
|
||||
"organization_count": 0,
|
||||
},
|
||||
"profiles": [],
|
||||
}
|
||||
|
||||
def get_dragon_tiger(self, trade_date: str, force: bool = False) -> dict[str, Any]:
|
||||
normalized_date = normalize_date(trade_date)
|
||||
cache_kind = "hot_money_detail_v3"
|
||||
if not force:
|
||||
cached = self.database.get_data_snapshot(cache_kind, normalized_date)
|
||||
if (
|
||||
cached
|
||||
and cached.get("meta", {}).get("source") == "tushare"
|
||||
and cached.get("meta", {}).get("status") == "success"
|
||||
and int(cached.get("meta", {}).get("schema_version") or 0) == 3
|
||||
):
|
||||
cached["meta"] = {**cached.get("meta", {}), "cached": True}
|
||||
return cached
|
||||
if self.configured:
|
||||
try:
|
||||
payload = self._tushare_client().dragon_tiger(normalized_date)
|
||||
except TushareError as exc:
|
||||
return {
|
||||
"meta": {
|
||||
"requested_date": f"{normalized_date[:4]}-{normalized_date[4:6]}-{normalized_date[6:8]}",
|
||||
"trade_date": f"{normalized_date[:4]}-{normalized_date[4:6]}-{normalized_date[6:8]}",
|
||||
"source": "tushare_error",
|
||||
"status": "error",
|
||||
"schema_version": 3,
|
||||
"cached": False,
|
||||
"updated_at": datetime.now().astimezone().isoformat(timespec="seconds"),
|
||||
"notice": "龙虎榜数据暂不可用，请稍后重试。",
|
||||
},
|
||||
"summary": {
|
||||
"trader_count": 0,
|
||||
"identity_count": 0,
|
||||
"operation_count": 0,
|
||||
"active_stock_count": 0,
|
||||
"seat_net_buy_million": 0,
|
||||
"unclassified_count": 0,
|
||||
"directory_count": 0,
|
||||
},
|
||||
"traders": [],
|
||||
"unclassified_seats": [],
|
||||
"rows": [],
|
||||
}
|
||||
payload["meta"]["cached"] = False
|
||||
if payload.get("meta", {}).get("status") == "success":
|
||||
self.database.save_data_snapshot(cache_kind, normalized_date, "tushare", payload)
|
||||
return payload
|
||||
|
||||
return {
|
||||
"meta": {
|
||||
"requested_date": f"{normalized_date[:4]}-{normalized_date[4:6]}-{normalized_date[6:8]}",
|
||||
"trade_date": f"{normalized_date[:4]}-{normalized_date[4:6]}-{normalized_date[6:8]}",
|
||||
"source": "unavailable",
|
||||
"status": "unavailable",
|
||||
"schema_version": 3,
|
||||
"cached": False,
|
||||
"notice": "龙虎榜数据暂不可用，请联系管理员检查行情配置。",
|
||||
},
|
||||
"summary": {
|
||||
"trader_count": 0,
|
||||
"identity_count": 0,
|
||||
"operation_count": 0,
|
||||
"active_stock_count": 0,
|
||||
"seat_net_buy_million": 0,
|
||||
"unclassified_count": 0,
|
||||
"directory_count": 0,
|
||||
},
|
||||
"traders": [],
|
||||
"unclassified_seats": [],
|
||||
"rows": [],
|
||||
}
|
||||
|
||||
def _apply_seat_aliases(self, payload: dict[str, Any]) -> dict[str, Any]:
|
||||
aliases = self.database.list_seat_aliases()
|
||||
result = dict(payload)
|
||||
rows = payload.get("rows") or []
|
||||
for row in rows:
|
||||
for institution in row.get("institutions") or []:
|
||||
institution["alias"] = aliases.get(institution.get("seat_name", ""), "")
|
||||
traders: dict[tuple[str, str], dict[str, Any]] = {}
|
||||
unclassified: dict[str, dict[str, Any]] = {}
|
||||
seen_operations: set[tuple[Any, ...]] = set()
|
||||
builtin_aliases = {
|
||||
"国泰海通证券股份有限公司南京太平南路证券营业部": "作手新一",
|
||||
}
|
||||
|
||||
for row in rows:
|
||||
for institution in row.get("institutions") or []:
|
||||
seat_name = str(institution.get("seat_name") or "未知席位").strip()
|
||||
saved_alias = str(institution.get("alias") or "").strip()
|
||||
builtin_alias = builtin_aliases.get(seat_name, "")
|
||||
if saved_alias or builtin_alias:
|
||||
identity_name = saved_alias or builtin_alias
|
||||
identity_type = "trader"
|
||||
recognized = True
|
||||
identity_source = "manual" if saved_alias else "builtin"
|
||||
elif "机构专用" in seat_name:
|
||||
identity_name = "机构专用"
|
||||
identity_type = "institution"
|
||||
recognized = True
|
||||
identity_source = "system"
|
||||
elif "沪股通专用" in seat_name or "深股通专用" in seat_name:
|
||||
identity_name = "北向资金"
|
||||
identity_type = "channel"
|
||||
recognized = True
|
||||
identity_source = "system"
|
||||
else:
|
||||
identity_name = seat_name
|
||||
identity_type = "unclassified"
|
||||
recognized = False
|
||||
identity_source = "raw"
|
||||
|
||||
buy = round(float(institution.get("buy_million") or 0), 2)
|
||||
sell = round(float(institution.get("sell_million") or 0), 2)
|
||||
net_buy = round(float(institution.get("net_buy_million") or 0), 2)
|
||||
operation_key = (row.get("code"), seat_name, buy, sell, net_buy)
|
||||
if operation_key in seen_operations:
|
||||
continue
|
||||
seen_operations.add(operation_key)
|
||||
|
||||
group_key = (identity_type, identity_name)
|
||||
group = traders.setdefault(
|
||||
group_key,
|
||||
{
|
||||
"name": identity_name,
|
||||
"identity_type": identity_type,
|
||||
"identity_source": identity_source,
|
||||
"recognized": recognized,
|
||||
"buy_million": 0.0,
|
||||
"sell_million": 0.0,
|
||||
"net_buy_million": 0.0,
|
||||
"seat_names": set(),
|
||||
"stock_codes": set(),
|
||||
"operations": [],
|
||||
},
|
||||
)
|
||||
group["buy_million"] += buy
|
||||
group["sell_million"] += sell
|
||||
group["net_buy_million"] += net_buy
|
||||
group["seat_names"].add(seat_name)
|
||||
group["stock_codes"].add(str(row.get("code") or ""))
|
||||
group["operations"].append(
|
||||
{
|
||||
"code": row.get("code") or "",
|
||||
"name": row.get("name") or "--",
|
||||
"change": row.get("change") or 0,
|
||||
"direction": "买入" if net_buy > 0 else "卖出" if net_buy < 0 else "持平",
|
||||
"buy_million": buy,
|
||||
"sell_million": sell,
|
||||
"net_buy_million": net_buy,
|
||||
"reason": row.get("reason") or "--",
|
||||
"seat_name": seat_name,
|
||||
"seat_alias": identity_name if recognized else "",
|
||||
}
|
||||
)
|
||||
|
||||
if not recognized:
|
||||
pending = unclassified.setdefault(
|
||||
seat_name,
|
||||
{
|
||||
"seat_name": seat_name,
|
||||
"stock_codes": set(),
|
||||
"operation_count": 0,
|
||||
"buy_million": 0.0,
|
||||
"sell_million": 0.0,
|
||||
"net_buy_million": 0.0,
|
||||
},
|
||||
)
|
||||
pending["stock_codes"].add(str(row.get("code") or ""))
|
||||
pending["operation_count"] += 1
|
||||
pending["buy_million"] += buy
|
||||
pending["sell_million"] += sell
|
||||
pending["net_buy_million"] += net_buy
|
||||
|
||||
type_order = {"trader": 0, "institution": 1, "channel": 2, "unclassified": 3}
|
||||
aggregated = list(traders.values())
|
||||
aggregated.sort(
|
||||
key=lambda item: (
|
||||
type_order.get(item["identity_type"], 9),
|
||||
-abs(item["net_buy_million"]),
|
||||
item["name"],
|
||||
)
|
||||
)
|
||||
for index, group in enumerate(aggregated, start=1):
|
||||
group["id"] = f"identity-{index}"
|
||||
group["buy_million"] = round(group["buy_million"], 2)
|
||||
group["sell_million"] = round(group["sell_million"], 2)
|
||||
group["net_buy_million"] = round(group["net_buy_million"], 2)
|
||||
group["seat_count"] = len(group.pop("seat_names"))
|
||||
group["stock_count"] = len(group.pop("stock_codes"))
|
||||
group["operation_count"] = len(group["operations"])
|
||||
group["operations"].sort(
|
||||
key=lambda item: abs(float(item.get("net_buy_million") or 0)), reverse=True
|
||||
)
|
||||
|
||||
pending_seats = list(unclassified.values())
|
||||
for pending in pending_seats:
|
||||
pending["stock_count"] = len(pending.pop("stock_codes"))
|
||||
pending["buy_million"] = round(pending["buy_million"], 2)
|
||||
pending["sell_million"] = round(pending["sell_million"], 2)
|
||||
pending["net_buy_million"] = round(pending["net_buy_million"], 2)
|
||||
pending_seats.sort(key=lambda item: abs(item["net_buy_million"]), reverse=True)
|
||||
|
||||
operation_count = sum(item["operation_count"] for item in aggregated)
|
||||
active_stocks = {
|
||||
operation["code"] for item in aggregated for operation in item["operations"]
|
||||
}
|
||||
seat_net_buy = round(sum(item["net_buy_million"] for item in aggregated), 2)
|
||||
result["rows"] = rows
|
||||
result["traders"] = aggregated
|
||||
result["unclassified_seats"] = pending_seats
|
||||
result["summary"] = {
|
||||
**(payload.get("summary") or {}),
|
||||
"trader_count": sum(item["identity_type"] == "trader" for item in aggregated),
|
||||
"identity_count": len(aggregated),
|
||||
"operation_count": operation_count,
|
||||
"active_stock_count": len(active_stocks),
|
||||
"seat_net_buy_million": seat_net_buy,
|
||||
"unclassified_count": len(pending_seats),
|
||||
}
|
||||
return result
|
||||
File diff suppressed because it is too large
Load Diff
@@ -14,7 +14,8 @@ from backend.bootstrap.config import (
|
||||
from backend.data.providers.ifind_client import IfindError
|
||||
from backend.data.providers.tushare_client import TushareClient, TushareError
|
||||
from backend.features.market.charts import ChartDataError
|
||||
from sentiment_engine import SENTIMENT_ENGINE_VERSION
|
||||
from backend.features.market.insights import MarketInsightsService
|
||||
from backend.features.sentiment.engine import SENTIMENT_ENGINE_VERSION
|
||||
|
||||
|
||||
SEARCH_INDEXES = (
|
||||
@@ -36,6 +37,14 @@ THS_SEARCH_TYPES = {
|
||||
|
||||
|
||||
class MarketServiceMixin:
|
||||
def _market_insights(self) -> MarketInsightsService:
|
||||
if not self.configured:
|
||||
raise ValueError("行情数据尚未配置。")
|
||||
return MarketInsightsService(
|
||||
self.database,
|
||||
self._tushare_client(),
|
||||
ifind=self.ifind,
|
||||
)
|
||||
def _tushare_client(self) -> TushareClient:
|
||||
gateway = getattr(self, "data_gateway", None)
|
||||
if gateway is not None:
|
||||
|
||||
@@ -0,0 +1,6 @@
|
||||
"""Limit-up, broken-board, limit-down and prior-limit pool feature."""
|
||||
|
||||
from .repository import PoolRepositoryMixin
|
||||
from .service import PoolServiceMixin
|
||||
|
||||
__all__ = ["PoolRepositoryMixin", "PoolServiceMixin"]
|
||||
@@ -0,0 +1,27 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from datetime import datetime
|
||||
|
||||
|
||||
class PoolRepositoryMixin:
|
||||
def save_reason_override(self, trade_date: str, code: str, reason: str) -> None:
|
||||
now = datetime.now().astimezone().isoformat(timespec="seconds")
|
||||
with self.connect() as connection:
|
||||
connection.execute(
|
||||
"""
|
||||
INSERT INTO reason_overrides (trade_date, code, reason, updated_at)
|
||||
VALUES (?, ?, ?, ?)
|
||||
ON CONFLICT(trade_date, code) DO UPDATE SET
|
||||
reason = excluded.reason,
|
||||
updated_at = excluded.updated_at
|
||||
""",
|
||||
(trade_date, code, reason, now),
|
||||
)
|
||||
|
||||
def reason_overrides(self, trade_date: str) -> dict[str, str]:
|
||||
with self.connect() as connection:
|
||||
rows = connection.execute(
|
||||
"SELECT code, reason FROM reason_overrides WHERE trade_date = ?",
|
||||
(trade_date,),
|
||||
).fetchall()
|
||||
return {row["code"]: row["reason"] for row in rows}
|
||||
@@ -0,0 +1,150 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import re
|
||||
from datetime import datetime, time as dt_time
|
||||
from typing import Any
|
||||
|
||||
from backend.bootstrap.config import normalize_date, validate_stock_code
|
||||
from backend.data.providers.ifind_client import IfindError
|
||||
|
||||
|
||||
class PoolServiceMixin:
|
||||
def save_reason(self, trade_date: str, code: str, reason: str) -> None:
|
||||
normalized_date = normalize_date(trade_date)
|
||||
code = validate_stock_code(code)
|
||||
reason = reason.strip()
|
||||
if not reason or len(reason) > 200:
|
||||
raise ValueError("涨停原因应为 1 至 200 个字符。")
|
||||
self.database.save_reason_override(normalized_date, code, reason)
|
||||
|
||||
def _apply_reason_overrides(self, dashboard: dict[str, Any]) -> dict[str, Any]:
|
||||
trade_date = str(dashboard.get("meta", {}).get("trade_date", "")).replace("-", "")
|
||||
enrichment = self.database.get_data_snapshot("ifind_event_enrichment_v1", trade_date)
|
||||
if enrichment:
|
||||
self._merge_ifind_event_enrichment(dashboard, enrichment)
|
||||
else:
|
||||
self._schedule_ifind_event_enrichment(trade_date)
|
||||
overrides = self.database.reason_overrides(trade_date)
|
||||
if not overrides:
|
||||
return dashboard
|
||||
for key in ("limits", "broken", "down_limits"):
|
||||
for row in dashboard.get(key) or []:
|
||||
if row.get("code") in overrides:
|
||||
row["reason"] = overrides[row["code"]]
|
||||
row["reason_source"] = "manual"
|
||||
return dashboard
|
||||
|
||||
def _schedule_ifind_event_enrichment(self, trade_date: str) -> None:
|
||||
ifind = getattr(self, "ifind", None)
|
||||
if not ifind or not ifind.configured or not re.fullmatch(r"\d{8}", trade_date):
|
||||
return
|
||||
now = datetime.now().astimezone()
|
||||
if trade_date == now.strftime("%Y%m%d") and now.time().replace(tzinfo=None) < dt_time(15, 0):
|
||||
return
|
||||
self.jobs.submit(
|
||||
"market.ifind-event-enrichment",
|
||||
f"{trade_date}:v1",
|
||||
lambda: self._refresh_ifind_event_enrichment(trade_date),
|
||||
{"trade_date": trade_date, "trigger": "dashboard-enrichment"},
|
||||
)
|
||||
|
||||
def _refresh_ifind_event_enrichment(self, trade_date: str) -> None:
|
||||
if not self._ifind_event_lock.acquire(blocking=False):
|
||||
return
|
||||
try:
|
||||
if self.database.get_data_snapshot("ifind_event_enrichment_v1", trade_date):
|
||||
return
|
||||
ifind = getattr(self, "ifind", None)
|
||||
if not ifind or not ifind.configured:
|
||||
return
|
||||
current = datetime.strptime(trade_date, "%Y%m%d")
|
||||
display_date = f"{current.year}年{current.month}月{current.day}日"
|
||||
requests = {
|
||||
"limits": (
|
||||
f"{display_date}涨停股票，股票代码、股票简称、涨停原因、"
|
||||
"首次涨停时间、最终涨停时间、开板次数"
|
||||
),
|
||||
"broken": (
|
||||
f"{display_date}曾涨停但收盘未涨停的股票，股票代码、股票简称、"
|
||||
"涨停原因、首次涨停时间、开板次数"
|
||||
),
|
||||
"down_limits": (
|
||||
f"{display_date}跌停股票，股票代码、股票简称、跌停原因"
|
||||
),
|
||||
}
|
||||
result: dict[str, Any] = {
|
||||
"trade_date": trade_date,
|
||||
"generated_at": datetime.now().astimezone().isoformat(timespec="seconds"),
|
||||
"limits": {}, "broken": {}, "down_limits": {}, "partial": False,
|
||||
}
|
||||
for kind, query in requests.items():
|
||||
try:
|
||||
rows = ifind.wencai(query, "stock", cache_ttl=900)
|
||||
except IfindError:
|
||||
result["partial"] = True
|
||||
continue
|
||||
for raw in rows:
|
||||
code = self._ifind_row_code(raw)
|
||||
if not code:
|
||||
continue
|
||||
reason_tokens = (
|
||||
("跌停原因", "风险线索", "原因")
|
||||
if kind == "down_limits"
|
||||
else ("涨停原因类别", "涨停原因", "触板逻辑", "原因")
|
||||
)
|
||||
reason = str(self._ifind_field(raw, reason_tokens) or "").strip()
|
||||
first_time = self._normalize_ifind_event_time(
|
||||
self._ifind_field(raw, ("首次涨停时间", "首次触板时间", "首次封板时间"))
|
||||
)
|
||||
last_time = self._normalize_ifind_event_time(
|
||||
self._ifind_field(raw, ("最终涨停时间", "最后涨停时间", "最后封板时间"))
|
||||
)
|
||||
open_times = self._ifind_field(raw, ("开板次数", "打开涨停次数"))
|
||||
try:
|
||||
open_count = max(0, int(float(open_times))) if open_times not in (None, "") else None
|
||||
except (TypeError, ValueError):
|
||||
open_count = None
|
||||
result[kind][code] = {
|
||||
"reason": reason,
|
||||
"first_time": first_time,
|
||||
"last_time": last_time,
|
||||
"open_times": open_count,
|
||||
}
|
||||
if any(result[kind] for kind in ("limits", "broken", "down_limits")):
|
||||
self.database.save_data_snapshot(
|
||||
"ifind_event_enrichment_v1", trade_date, "ifind", result
|
||||
)
|
||||
finally:
|
||||
self._ifind_event_lock.release()
|
||||
|
||||
@staticmethod
|
||||
def _normalize_ifind_event_time(value: Any) -> str:
|
||||
text = str(value or "").strip()
|
||||
match = re.search(r"(?:^|\s)(\d{1,2}:\d{2}(?::\d{2})?)(?:$|\s)", text)
|
||||
if not match:
|
||||
match = re.search(r"(?<!\d)(\d{6})(?!\d)", text)
|
||||
if match:
|
||||
compact = match.group(1)
|
||||
return f"{compact[:2]}:{compact[2:4]}:{compact[4:]}"
|
||||
return ""
|
||||
parts = match.group(1).split(":")
|
||||
return ":".join(part.zfill(2) for part in parts)
|
||||
|
||||
@staticmethod
|
||||
def _merge_ifind_event_enrichment(
|
||||
dashboard: dict[str, Any], enrichment: dict[str, Any]
|
||||
) -> None:
|
||||
for kind in ("limits", "broken", "down_limits"):
|
||||
records = enrichment.get(kind) or {}
|
||||
for row in dashboard.get(kind) or []:
|
||||
event = records.get(str(row.get("code") or "")) or {}
|
||||
reason = str(event.get("reason") or "").strip()
|
||||
if reason:
|
||||
row["reason"] = reason
|
||||
row["reason_source"] = "market_event"
|
||||
if event.get("first_time"):
|
||||
row["first_time"] = event["first_time"]
|
||||
if event.get("last_time"):
|
||||
row["last_time"] = event["last_time"]
|
||||
if event.get("open_times") is not None:
|
||||
row["open_times"] = event["open_times"]
|
||||
@@ -0,0 +1,4 @@
|
||||
from .repository import PopularityRepositoryMixin
|
||||
from .service import PopularityServiceMixin
|
||||
|
||||
__all__ = ["PopularityRepositoryMixin", "PopularityServiceMixin"]
|
||||
@@ -0,0 +1,37 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import Any
|
||||
|
||||
|
||||
class PopularityRepositoryMixin:
|
||||
def upsert_popularity_factors(self, rows: list[dict[str, Any]]) -> int:
|
||||
values = [
|
||||
(
|
||||
str(row.get("trade_date") or ""),
|
||||
str(row.get("ts_code") or ""),
|
||||
int(row["ths_rank"]) if row.get("ths_rank") not in (None, "") else None,
|
||||
int(row["dc_rank"]) if row.get("dc_rank") not in (None, "") else None,
|
||||
float(row.get("combined_score") or 0),
|
||||
int(row["rank_change"]) if row.get("rank_change") not in (None, "") else None,
|
||||
int(bool(row.get("dual_source"))),
|
||||
)
|
||||
for row in rows
|
||||
if row.get("trade_date") and row.get("ts_code")
|
||||
]
|
||||
with self.connect() as connection:
|
||||
connection.executemany(
|
||||
"""
|
||||
INSERT INTO popularity_factors
|
||||
(trade_date, ts_code, ths_rank, dc_rank, combined_score,
|
||||
rank_change, dual_source)
|
||||
VALUES (?, ?, ?, ?, ?, ?, ?)
|
||||
ON CONFLICT(trade_date, ts_code) DO UPDATE SET
|
||||
ths_rank=excluded.ths_rank,
|
||||
dc_rank=excluded.dc_rank,
|
||||
combined_score=excluded.combined_score,
|
||||
rank_change=excluded.rank_change,
|
||||
dual_source=excluded.dual_source
|
||||
""",
|
||||
values,
|
||||
)
|
||||
return len(values)
|
||||
@@ -0,0 +1,11 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import Any
|
||||
|
||||
from backend.bootstrap.config import normalize_date
|
||||
from backend.features.market.insights import MarketInsightsService
|
||||
|
||||
|
||||
class PopularityServiceMixin:
|
||||
def popularity(self, trade_date: str, force: bool = False) -> dict[str, Any]:
|
||||
return self._market_insights().popularity(normalize_date(trade_date), force)
|
||||
@@ -0,0 +1,5 @@
|
||||
"""Sector rotation history and constituent detail feature."""
|
||||
|
||||
from .service import RotationServiceMixin
|
||||
|
||||
__all__ = ["RotationServiceMixin"]
|
||||
@@ -0,0 +1,165 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import Any
|
||||
|
||||
from backend.bootstrap.config import normalize_date, validate_text
|
||||
from backend.data.providers.tushare_client import TushareError
|
||||
from backend.features.sentiment.engine import (
|
||||
build_sentiment_history,
|
||||
latest_contiguous_history,
|
||||
)
|
||||
|
||||
|
||||
class RotationServiceMixin:
|
||||
def rotation_history(self, trade_date: str, limit: int = 9) -> dict[str, Any]:
|
||||
normalized_date = normalize_date(trade_date)
|
||||
# 板块轮动固定展示最近 9 个交易日，按由近到远排列。
|
||||
limit = 9
|
||||
snapshots = self.database.list_snapshot_payloads(normalized_date, 240)
|
||||
by_trade_date: dict[str, dict[str, Any]] = {}
|
||||
for snapshot in snapshots:
|
||||
meta = snapshot.get("meta") or {}
|
||||
actual_date = str(meta.get("trade_date") or snapshot.get("_snapshot_date") or "")
|
||||
compact_date = actual_date.replace("-", "")
|
||||
if len(compact_date) == 8:
|
||||
by_trade_date[compact_date] = snapshot
|
||||
|
||||
sentiment_dates = {
|
||||
str(row.get("trade_date") or "").replace("-", "")
|
||||
for row in latest_contiguous_history(build_sentiment_history(snapshots))
|
||||
}
|
||||
ordered_dates = sorted(
|
||||
date_key for date_key in by_trade_date
|
||||
if not sentiment_dates or date_key in sentiment_dates
|
||||
)[-limit:][::-1]
|
||||
rows = []
|
||||
for date_key in ordered_dates:
|
||||
snapshot = by_trade_date[date_key]
|
||||
sector_context = {
|
||||
str(item.get("name") or ""): item
|
||||
for item in snapshot.get("sectors") or []
|
||||
}
|
||||
sectors = []
|
||||
for item in (snapshot.get("sector_rotation") or [])[:12]:
|
||||
name = str(item.get("name") or "").strip()
|
||||
context = sector_context.get(name, {})
|
||||
sectors.append(
|
||||
{
|
||||
"name": name,
|
||||
"rank": int(item.get("rank") or len(sectors) + 1),
|
||||
"trend": item.get("trend") or "持平",
|
||||
"count": int(item.get("count") or 0),
|
||||
"strength": float(item.get("strength") or context.get("strength") or 0),
|
||||
"change": float(context.get("change") or 0),
|
||||
"leader": item.get("leader") or context.get("leader") or "--",
|
||||
}
|
||||
)
|
||||
rows.append(
|
||||
{
|
||||
"trade_date": f"{date_key[:4]}-{date_key[4:6]}-{date_key[6:]}",
|
||||
"sectors": sectors,
|
||||
}
|
||||
)
|
||||
return {
|
||||
"trade_date": rows[0]["trade_date"] if rows else normalized_date,
|
||||
"available_days": len(ordered_dates),
|
||||
"requested_days": limit,
|
||||
"rows": rows,
|
||||
}
|
||||
|
||||
def rotation_sector_members(self, trade_date: str, sector_name: str) -> dict[str, Any]:
|
||||
normalized_date = normalize_date(trade_date)
|
||||
sector_name = validate_text(sector_name, "板块名称", 60, required=True)
|
||||
dashboard = self.get_dashboard(normalized_date)
|
||||
actual_date = normalize_date(
|
||||
str((dashboard.get("meta") or {}).get("trade_date") or normalized_date)
|
||||
)
|
||||
cache_key = f"{actual_date}:{sector_name}"
|
||||
cached = self.database.get_data_snapshot("rotation_sector_members_v1", cache_key)
|
||||
if cached:
|
||||
cached["meta"] = {**(cached.get("meta") or {}), "cached": True}
|
||||
return cached
|
||||
if not self.configured:
|
||||
raise ValueError("板块成分数据暂不可用。")
|
||||
|
||||
representative = next(
|
||||
(
|
||||
item for item in dashboard.get("limits") or []
|
||||
if str(item.get("sector") or "").strip() == sector_name
|
||||
),
|
||||
None,
|
||||
)
|
||||
if not representative:
|
||||
raise ValueError("未找到该板块的代表股票，暂时无法核验成分股。")
|
||||
raw_code = str(representative.get("ts_code") or representative.get("code") or "")
|
||||
if "." in raw_code:
|
||||
ts_code = raw_code
|
||||
elif raw_code.startswith(("4", "8", "92")):
|
||||
ts_code = f"{raw_code}.BJ"
|
||||
elif raw_code.startswith(("6", "68", "90")):
|
||||
ts_code = f"{raw_code}.SH"
|
||||
else:
|
||||
ts_code = f"{raw_code}.SZ"
|
||||
client = self._tushare_client()
|
||||
try:
|
||||
industry = client.sw_stock_industry(ts_code, actual_date)
|
||||
sector_code = str(industry.get("l2_code") or "")
|
||||
members = client.sw_sector_members(sector_code, actual_date)
|
||||
except TushareError as exc:
|
||||
raise ValueError(f"该板块成分股暂不可用：{exc}") from exc
|
||||
|
||||
daily_rows = self.database.daily_bars_for_date(actual_date)
|
||||
if len(daily_rows) < 1000:
|
||||
try:
|
||||
daily_rows = client.query(
|
||||
"daily",
|
||||
{"trade_date": actual_date},
|
||||
"ts_code,trade_date,open,high,low,close,pct_chg,vol,amount",
|
||||
)
|
||||
if daily_rows:
|
||||
self.database.upsert_daily_bars(daily_rows)
|
||||
except TushareError:
|
||||
daily_rows = self.database.daily_bars_for_date(actual_date)
|
||||
daily_map = {str(item.get("ts_code") or ""): item for item in daily_rows}
|
||||
rows = []
|
||||
for member in members:
|
||||
member_code = str(member.get("ts_code") or "")
|
||||
quote = daily_map.get(member_code) or {}
|
||||
rows.append(
|
||||
{
|
||||
"code": member_code.split(".")[0],
|
||||
"ts_code": member_code,
|
||||
"name": str(member.get("name") or "--"),
|
||||
"change": quote.get("pct_chg"),
|
||||
"open": quote.get("open"),
|
||||
"close": quote.get("close"),
|
||||
"amount_billion": (
|
||||
round(float(quote.get("amount") or 0) / 100000, 2)
|
||||
if quote else None
|
||||
),
|
||||
"quoted": bool(quote),
|
||||
}
|
||||
)
|
||||
rows.sort(
|
||||
key=lambda item: (
|
||||
bool(item.get("quoted")),
|
||||
float(item.get("change") or -999),
|
||||
float(item.get("amount_billion") or 0),
|
||||
),
|
||||
reverse=True,
|
||||
)
|
||||
result = {
|
||||
"meta": {
|
||||
"trade_date": self._display_compact_date(actual_date),
|
||||
"sector_name": str(industry.get("l2_name") or sector_name),
|
||||
"sector_code": sector_code,
|
||||
"member_count": len(rows),
|
||||
"quoted_count": sum(bool(item.get("quoted")) for item in rows),
|
||||
"cached": False,
|
||||
},
|
||||
"rows": rows,
|
||||
}
|
||||
self.database.save_data_snapshot(
|
||||
"rotation_sector_members_v1", cache_key, "tushare", result
|
||||
)
|
||||
return result
|
||||
@@ -0,0 +1,19 @@
|
||||
"""Market sentiment cycle and history feature."""
|
||||
|
||||
from .engine import (
|
||||
COMPONENT_WEIGHTS,
|
||||
SENTIMENT_ENGINE_VERSION,
|
||||
apply_sentiment_to_dashboard,
|
||||
build_sentiment_history,
|
||||
latest_contiguous_history,
|
||||
)
|
||||
from .service import SentimentServiceMixin
|
||||
|
||||
__all__ = [
|
||||
"COMPONENT_WEIGHTS",
|
||||
"SENTIMENT_ENGINE_VERSION",
|
||||
"SentimentServiceMixin",
|
||||
"apply_sentiment_to_dashboard",
|
||||
"build_sentiment_history",
|
||||
"latest_contiguous_history",
|
||||
]
|
||||
@@ -0,0 +1,496 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from copy import deepcopy
|
||||
from statistics import mean, median
|
||||
from typing import Any
|
||||
|
||||
|
||||
COMPONENT_WEIGHTS = {
|
||||
"breadth": 20,
|
||||
"limit_ecology": 25,
|
||||
"profit_effect": 30,
|
||||
"ladder_structure": 15,
|
||||
"liquidity": 10,
|
||||
}
|
||||
|
||||
SENTIMENT_ENGINE_VERSION = 2
|
||||
|
||||
|
||||
def _number(value: Any, default: float = 0.0) -> float:
|
||||
try:
|
||||
number = float(value)
|
||||
return number if number == number else default
|
||||
except (TypeError, ValueError):
|
||||
return default
|
||||
|
||||
|
||||
def _clamp(value: float, lower: float = 0.0, upper: float = 100.0) -> float:
|
||||
return min(upper, max(lower, value))
|
||||
|
||||
|
||||
def _linear(value: float, low: float, high: float) -> float:
|
||||
if high <= low:
|
||||
return 50.0
|
||||
return _clamp((value - low) / (high - low) * 100)
|
||||
|
||||
|
||||
def _percentile(value: float, history: list[float]) -> float:
|
||||
if not history:
|
||||
return 50.0
|
||||
below = sum(item < value for item in history)
|
||||
equal = sum(item == value for item in history)
|
||||
return _clamp((below + equal * 0.5) / len(history) * 100)
|
||||
|
||||
|
||||
def _adaptive_score(value: float, fixed: float, history: list[float]) -> float:
|
||||
if len(history) < 20:
|
||||
return fixed
|
||||
return fixed * 0.25 + _percentile(value, history[-250:]) * 0.75
|
||||
|
||||
|
||||
def _trade_date(payload: dict[str, Any]) -> str:
|
||||
meta = payload.get("meta") or {}
|
||||
return str(meta.get("trade_date") or payload.get("_snapshot_date") or "").replace("-", "")
|
||||
|
||||
|
||||
def _deduplicate_snapshots(snapshots: list[dict[str, Any]]) -> list[dict[str, Any]]:
|
||||
by_trade_date: dict[str, dict[str, Any]] = {}
|
||||
for payload in snapshots:
|
||||
trade_date = _trade_date(payload)
|
||||
if trade_date:
|
||||
by_trade_date[trade_date] = payload
|
||||
return [by_trade_date[key] for key in sorted(by_trade_date)]
|
||||
|
||||
|
||||
def _snapshot_stats(payload: dict[str, Any]) -> dict[str, Any]:
|
||||
overview = payload.get("overview") or {}
|
||||
meta = payload.get("meta") or {}
|
||||
limits = list(payload.get("limits") or [])
|
||||
broken = list(payload.get("broken") or [])
|
||||
down_limits = list(payload.get("down_limits") or [])
|
||||
yesterday = list(payload.get("yesterday_limits") or [])
|
||||
|
||||
limit_up = len(limits) if limits else int(_number(overview.get("limit_up_count")))
|
||||
broken_count = len(broken) if broken else int(_number(overview.get("broken_count")))
|
||||
limit_down = len(down_limits) if down_limits else int(_number(overview.get("limit_down_count")))
|
||||
streaks = [max(1, int(_number(row.get("streak"), 1))) for row in limits]
|
||||
first_board = sum(streak == 1 for streak in streaks)
|
||||
second_board = sum(streak == 2 for streak in streaks)
|
||||
three_plus = sum(streak >= 3 for streak in streaks)
|
||||
max_height = max(streaks, default=0)
|
||||
present_levels = set(streaks)
|
||||
ladder_completeness = (
|
||||
sum(level in present_levels for level in range(1, max_height + 1)) / max_height * 100
|
||||
if max_height else 0.0
|
||||
)
|
||||
|
||||
up_count = int(_number(overview.get("up_count")))
|
||||
down_count = int(_number(overview.get("down_count")))
|
||||
flat_count = int(_number(overview.get("flat_count")))
|
||||
active_count = up_count + down_count
|
||||
breadth_ratio = up_count / max(active_count, 1) * 100
|
||||
seal_rate = _number(overview.get("seal_rate"))
|
||||
if not seal_rate and limit_up + broken_count:
|
||||
seal_rate = limit_up / (limit_up + broken_count) * 100
|
||||
|
||||
previous_limit_count = len(yesterday)
|
||||
previous_positive_count = sum(_number(row.get("current_change")) > 0 for row in yesterday)
|
||||
previous_positive_rate = previous_positive_count / max(previous_limit_count, 1) * 100
|
||||
advanced_count = sum(row.get("outcome") == "晋级" for row in yesterday)
|
||||
advance_rate = advanced_count / max(previous_limit_count, 1) * 100
|
||||
average_previous_change = (
|
||||
mean(_number(row.get("current_change")) for row in yesterday) if yesterday else 0.0
|
||||
)
|
||||
median_previous_change = (
|
||||
median(_number(row.get("current_change")) for row in yesterday) if yesterday else 0.0
|
||||
)
|
||||
severe_loss_count = sum(_number(row.get("current_change")) <= -5 for row in yesterday)
|
||||
severe_loss_rate = severe_loss_count / max(previous_limit_count, 1) * 100
|
||||
previous_down_count = sum(row.get("outcome") == "跌停" for row in yesterday)
|
||||
high_previous = [row for row in yesterday if int(_number(row.get("prior_streak"), 1)) >= 2]
|
||||
high_positive_rate = (
|
||||
sum(_number(row.get("current_change")) > 0 for row in high_previous)
|
||||
/ max(len(high_previous), 1)
|
||||
* 100
|
||||
)
|
||||
|
||||
amount_billion = _number(overview.get("amount_billion"))
|
||||
limit_amount_billion = sum(_number(row.get("amount_billion")) for row in limits)
|
||||
return {
|
||||
"trade_date": _trade_date(payload),
|
||||
"previous_trade_date": str(meta.get("previous_trade_date") or "").replace("-", ""),
|
||||
"up_count": up_count,
|
||||
"down_count": down_count,
|
||||
"flat_count": flat_count,
|
||||
"breadth_ratio": round(breadth_ratio, 1),
|
||||
"limit_up_count": limit_up,
|
||||
"first_board_count": first_board,
|
||||
"second_board_count": second_board,
|
||||
"three_plus_count": three_plus,
|
||||
"max_height": max_height,
|
||||
"ladder_completeness": round(ladder_completeness, 1),
|
||||
"broken_count": broken_count,
|
||||
"limit_down_count": limit_down,
|
||||
"seal_rate": round(seal_rate, 1),
|
||||
"previous_limit_count": previous_limit_count,
|
||||
"previous_positive_count": previous_positive_count,
|
||||
"previous_positive_rate": round(previous_positive_rate, 1),
|
||||
"advance_rate": round(advance_rate, 1),
|
||||
"average_previous_change": round(average_previous_change, 2),
|
||||
"median_previous_change": round(median_previous_change, 2),
|
||||
"severe_loss_count": severe_loss_count,
|
||||
"severe_loss_rate": round(severe_loss_rate, 1),
|
||||
"previous_down_count": previous_down_count,
|
||||
"high_positive_rate": round(high_positive_rate, 1),
|
||||
"amount_billion": round(amount_billion, 1),
|
||||
"limit_amount_billion": round(limit_amount_billion, 2),
|
||||
}
|
||||
|
||||
|
||||
def _sentiment_label(score: float) -> str:
|
||||
if score >= 80:
|
||||
return "情绪高涨"
|
||||
if score >= 60:
|
||||
return "情绪偏强"
|
||||
if score >= 40:
|
||||
return "情绪中性"
|
||||
if score >= 20:
|
||||
return "情绪偏弱"
|
||||
return "情绪冰点"
|
||||
|
||||
|
||||
def _phase_signal(score: float, momentum: float, profit_score: float) -> str:
|
||||
if score < 25:
|
||||
return "修复" if momentum > 3 else "冰点"
|
||||
if score < 45:
|
||||
return "修复" if momentum > 3 else "退潮"
|
||||
if score >= 80:
|
||||
return "高潮" if momentum >= -2 and profit_score >= 60 else "分化"
|
||||
if score >= 65:
|
||||
return "分化" if momentum < -3 or profit_score < 50 else "发酵"
|
||||
if momentum < -5:
|
||||
return "退潮"
|
||||
return "发酵" if momentum >= 0 and profit_score >= 45 else "分化"
|
||||
|
||||
|
||||
def _confirmed_phase(
|
||||
previous: dict[str, Any] | None,
|
||||
score: float,
|
||||
day_change: float,
|
||||
systemic_health: float,
|
||||
profit_score: float,
|
||||
ecology_score: float,
|
||||
phase_signal: str,
|
||||
extreme_ice: bool,
|
||||
fermentation_signal_count: int,
|
||||
) -> tuple[str, str]:
|
||||
if previous is None:
|
||||
return phase_signal, "首个连续交易日，采用原始阶段信号"
|
||||
previous_phase = str(previous.get("phase") or phase_signal)
|
||||
if extreme_ice:
|
||||
return "冰点", "市场宽度与跌停数量触发极端冰点"
|
||||
|
||||
recovery = day_change >= 6 and score >= 25 and systemic_health >= 24
|
||||
fermentation_confirmed = fermentation_signal_count >= 2
|
||||
climax_ready = (
|
||||
score >= 80
|
||||
and profit_score >= 60
|
||||
and systemic_health >= 60
|
||||
and ecology_score >= 70
|
||||
)
|
||||
|
||||
if previous_phase == "冰点":
|
||||
return ("修复", "冰点后首次有效回升") if recovery else ("冰点", "冰点尚未形成有效修复")
|
||||
|
||||
if previous_phase == "退潮":
|
||||
if score < 25:
|
||||
return "冰点", "退潮继续下探至冰点区间"
|
||||
return ("修复", "退潮后出现有效回升") if recovery else ("退潮", "退潮尚未形成有效修复")
|
||||
|
||||
if previous_phase == "修复":
|
||||
if score < 25:
|
||||
return "冰点", "修复失败并重新跌入冰点区间"
|
||||
if day_change <= -6 and score < 45:
|
||||
return "退潮", "修复失败且温度显著回落"
|
||||
if fermentation_confirmed:
|
||||
return "发酵", "发酵条件连续两个交易日成立"
|
||||
return "修复", "修复延续，等待发酵确认"
|
||||
|
||||
if previous_phase == "发酵":
|
||||
if score < 25:
|
||||
return "冰点", "发酵阶段出现极端情绪坍塌"
|
||||
if score < 45 and (day_change < 0 or systemic_health < 35):
|
||||
return "退潮", "发酵阶段温度与系统健康度同步转弱"
|
||||
if climax_ready:
|
||||
return "高潮", "温度、赚钱效应与涨停生态共同达到高潮条件"
|
||||
if phase_signal in {"分化", "退潮"} or day_change <= -6:
|
||||
return "分化", "发酵阶段出现降温或赚钱效应弱化"
|
||||
return "发酵", "发酵状态延续"
|
||||
|
||||
if previous_phase == "高潮":
|
||||
if score < 25:
|
||||
return "冰点", "高潮后出现极端情绪坍塌"
|
||||
if climax_ready:
|
||||
return "高潮", "高潮条件继续成立"
|
||||
if score < 45 or systemic_health < 30:
|
||||
return "退潮", "高潮后风险快速释放"
|
||||
return "分化", "高潮条件消退，进入分化"
|
||||
|
||||
if previous_phase == "分化":
|
||||
if score < 25:
|
||||
return "冰点", "分化继续恶化至冰点区间"
|
||||
if score < 45 or systemic_health < 30:
|
||||
return "退潮", "分化后温度或系统健康度继续下降"
|
||||
if fermentation_confirmed:
|
||||
return "发酵", "分化转强条件连续两个交易日成立"
|
||||
return "分化", "分化延续，等待方向确认"
|
||||
|
||||
return phase_signal, "采用原始阶段信号"
|
||||
|
||||
|
||||
def build_sentiment_history(snapshots: list[dict[str, Any]]) -> list[dict[str, Any]]:
|
||||
payloads = _deduplicate_snapshots(snapshots)
|
||||
raw_rows = [_snapshot_stats(payload) for payload in payloads]
|
||||
results: list[dict[str, Any]] = []
|
||||
|
||||
for index, stats in enumerate(raw_rows):
|
||||
previous = raw_rows[:index]
|
||||
limit_history = [float(row["limit_up_count"]) for row in previous]
|
||||
down_limit_history = [float(row["limit_down_count"]) for row in previous]
|
||||
height_history = [float(row["max_height"]) for row in previous]
|
||||
three_plus_history = [float(row["three_plus_count"]) for row in previous]
|
||||
amount_history = [float(row["amount_billion"]) for row in previous[-20:] if row["amount_billion"]]
|
||||
|
||||
breadth_score = _clamp(float(stats["breadth_ratio"]))
|
||||
limit_strength = _adaptive_score(
|
||||
float(stats["limit_up_count"]),
|
||||
_linear(float(stats["limit_up_count"]), 10, 100),
|
||||
limit_history,
|
||||
)
|
||||
down_relief = 100 - _adaptive_score(
|
||||
float(stats["limit_down_count"]),
|
||||
_linear(float(stats["limit_down_count"]), 0, 50),
|
||||
down_limit_history,
|
||||
)
|
||||
seal_quality = _linear(float(stats["seal_rate"]), 35, 90)
|
||||
systemic_health = breadth_score * 0.60 + down_relief * 0.40
|
||||
systemic_gate = 1.0 if systemic_health >= 35 else 0.35 + systemic_health / 35 * 0.65
|
||||
ecology_base_score = limit_strength * 0.35 + seal_quality * 0.35 + down_relief * 0.30
|
||||
# Systemic risk is applied once to the final temperature. Reapplying it here
|
||||
# would count market breadth and limit-down pressure twice.
|
||||
limit_ecology_score = ecology_base_score
|
||||
|
||||
if stats["previous_limit_count"]:
|
||||
positive_score = float(stats["previous_positive_rate"])
|
||||
average_change_score = _clamp(50 + float(stats["average_previous_change"]) * 6)
|
||||
median_change_score = _clamp(50 + float(stats["median_previous_change"]) * 7)
|
||||
advance_score = _clamp(float(stats["advance_rate"]) * 2.5)
|
||||
severe_loss_safety = _clamp(100 - float(stats["severe_loss_rate"]) * 3)
|
||||
down_safety = _clamp(100 - float(stats["previous_down_count"]) / stats["previous_limit_count"] * 700)
|
||||
tail_safety_score = severe_loss_safety * 0.70 + down_safety * 0.30
|
||||
profit_effect_score = (
|
||||
positive_score * 0.30
|
||||
+ median_change_score * 0.25
|
||||
+ average_change_score * 0.10
|
||||
+ advance_score * 0.20
|
||||
+ tail_safety_score * 0.15
|
||||
)
|
||||
else:
|
||||
profit_effect_score = 50.0
|
||||
|
||||
max_height_score = _adaptive_score(
|
||||
float(stats["max_height"]),
|
||||
_linear(float(stats["max_height"]), 1, 7),
|
||||
height_history,
|
||||
)
|
||||
continuation_rate = (
|
||||
(float(stats["second_board_count"]) + float(stats["three_plus_count"]))
|
||||
/ max(float(stats["limit_up_count"]), 1)
|
||||
* 100
|
||||
)
|
||||
three_plus_density = float(stats["three_plus_count"]) / max(float(stats["limit_up_count"]), 1) * 100
|
||||
three_plus_score = _adaptive_score(
|
||||
float(stats["three_plus_count"]),
|
||||
_clamp(three_plus_density * 5),
|
||||
three_plus_history,
|
||||
)
|
||||
ladder_structure_score = (
|
||||
max_height_score * 0.30
|
||||
+ _clamp(continuation_rate * 3) * 0.25
|
||||
+ three_plus_score * 0.25
|
||||
+ float(stats["ladder_completeness"]) * 0.20
|
||||
)
|
||||
|
||||
amount_baseline = mean(amount_history) if amount_history else float(stats["amount_billion"] or 1)
|
||||
amount_ratio = float(stats["amount_billion"]) / max(amount_baseline, 1)
|
||||
amount_score = _clamp(50 + (amount_ratio - 1) * 100)
|
||||
limit_amount_share = float(stats["limit_amount_billion"]) / max(float(stats["amount_billion"]), 1) * 100
|
||||
liquidity_score = amount_score * 0.70 + _clamp(limit_amount_share * 20) * 0.30
|
||||
|
||||
component_scores = {
|
||||
"breadth": breadth_score,
|
||||
"limit_ecology": limit_ecology_score,
|
||||
"profit_effect": profit_effect_score,
|
||||
"ladder_structure": ladder_structure_score,
|
||||
"liquidity": liquidity_score,
|
||||
}
|
||||
raw_score = sum(component_scores[key] * weight / 100 for key, weight in COMPONENT_WEIGHTS.items())
|
||||
score = round(
|
||||
raw_score * systemic_gate
|
||||
)
|
||||
extreme_ice = float(stats["breadth_ratio"]) <= 15 and float(stats["limit_down_count"]) >= 100
|
||||
if extreme_ice:
|
||||
score = min(score, 15)
|
||||
elif float(stats["breadth_ratio"]) <= 25 and float(stats["limit_down_count"]) >= 50:
|
||||
score = min(score, 24)
|
||||
previous_scores: list[float] = []
|
||||
expected_date = str(stats.get("previous_trade_date") or "")
|
||||
for prior_result in reversed(results):
|
||||
if not expected_date or str(prior_result.get("trade_date") or "") != expected_date:
|
||||
break
|
||||
previous_scores.append(float(prior_result["score"]))
|
||||
expected_date = str(prior_result.get("previous_trade_date") or "")
|
||||
if len(previous_scores) == 3:
|
||||
break
|
||||
momentum = score - mean(previous_scores) if previous_scores else 0.0
|
||||
direction = "升温" if momentum > 3 else "降温" if momentum < -3 else "持平"
|
||||
normalization = "历史百分位" if len(previous) >= 20 else "固定锚点"
|
||||
previous_result = (
|
||||
results[-1]
|
||||
if results and str(stats.get("previous_trade_date") or "") == str(results[-1].get("trade_date") or "")
|
||||
else None
|
||||
)
|
||||
day_change = score - float(previous_result["score"]) if previous_result else 0.0
|
||||
ema_score = round(
|
||||
score if not previous_result
|
||||
else score * 0.5 + float(previous_result.get("ema_score", previous_result["score"])) * 0.5,
|
||||
1,
|
||||
)
|
||||
phase_signal = _phase_signal(score, momentum, profit_effect_score)
|
||||
fermentation_ready = (
|
||||
phase_signal == "发酵"
|
||||
and score >= 45
|
||||
and profit_effect_score >= 45
|
||||
and systemic_health >= 35
|
||||
and not extreme_ice
|
||||
)
|
||||
previous_fermentation_count = int(previous_result.get("fermentation_signal_count") or 0) if previous_result else 0
|
||||
fermentation_signal_count = previous_fermentation_count + 1 if fermentation_ready else 0
|
||||
phase, transition_reason = _confirmed_phase(
|
||||
previous_result,
|
||||
score,
|
||||
day_change,
|
||||
systemic_health,
|
||||
profit_effect_score,
|
||||
limit_ecology_score,
|
||||
phase_signal,
|
||||
extreme_ice,
|
||||
fermentation_signal_count,
|
||||
)
|
||||
previous_phase = str(previous_result.get("phase") or "") if previous_result else ""
|
||||
if phase not in {"修复", "分化"}:
|
||||
fermentation_signal_count = 0
|
||||
elif phase == "分化" and previous_phase != "分化":
|
||||
fermentation_signal_count = 0
|
||||
|
||||
components = {
|
||||
"breadth": {
|
||||
"label": "市场宽度",
|
||||
"score": round(breadth_score, 1),
|
||||
"weight": COMPONENT_WEIGHTS["breadth"],
|
||||
"summary": f"上涨占比 {stats['breadth_ratio']:.1f}%",
|
||||
},
|
||||
"limit_ecology": {
|
||||
"label": "涨停生态",
|
||||
"score": round(limit_ecology_score, 1),
|
||||
"weight": COMPONENT_WEIGHTS["limit_ecology"],
|
||||
"summary": (
|
||||
f"涨停 {stats['limit_up_count']} · 跌停 {stats['limit_down_count']} · "
|
||||
f"封板 {stats['seal_rate']:.1f}%"
|
||||
),
|
||||
},
|
||||
"profit_effect": {
|
||||
"label": "赚钱效应",
|
||||
"score": round(profit_effect_score, 1),
|
||||
"weight": COMPONENT_WEIGHTS["profit_effect"],
|
||||
"summary": (
|
||||
f"昨涨停红盘 {stats['previous_positive_rate']:.1f}% · "
|
||||
f"中位 {stats['median_previous_change']:+.2f}% · "
|
||||
f"重亏 {stats['severe_loss_rate']:.1f}%"
|
||||
if stats["previous_limit_count"] else "缺少前一交易日样本"
|
||||
),
|
||||
},
|
||||
"ladder_structure": {
|
||||
"label": "连板结构",
|
||||
"score": round(ladder_structure_score, 1),
|
||||
"weight": COMPONENT_WEIGHTS["ladder_structure"],
|
||||
"summary": f"最高 {stats['max_height']} 板 · 三板以上 {stats['three_plus_count']} 家",
|
||||
},
|
||||
"liquidity": {
|
||||
"label": "成交活跃度",
|
||||
"score": round(liquidity_score, 1),
|
||||
"weight": COMPONENT_WEIGHTS["liquidity"],
|
||||
"summary": f"成交 {stats['amount_billion']:.1f} 亿 · 均值比 {amount_ratio:.2f}",
|
||||
},
|
||||
}
|
||||
results.append(
|
||||
{
|
||||
**stats,
|
||||
"score": score,
|
||||
"ema_score": ema_score,
|
||||
"label": _sentiment_label(score),
|
||||
"phase": phase,
|
||||
"phase_signal": phase_signal,
|
||||
"transition_reason": transition_reason,
|
||||
"fermentation_signal_count": fermentation_signal_count,
|
||||
"day_change": round(day_change, 1),
|
||||
"direction": direction,
|
||||
"momentum": round(momentum, 1),
|
||||
"normalization": "250日历史百分位" if len(previous) >= 20 else normalization,
|
||||
"history_days": len(previous) + 1,
|
||||
"systemic_health": round(systemic_health, 1),
|
||||
"risk_multiplier": round(systemic_gate, 3),
|
||||
"components": components,
|
||||
}
|
||||
)
|
||||
return results
|
||||
|
||||
|
||||
def latest_contiguous_history(series: list[dict[str, Any]]) -> list[dict[str, Any]]:
|
||||
if not series:
|
||||
return []
|
||||
contiguous = [series[-1]]
|
||||
for row in reversed(series[:-1]):
|
||||
expected_previous = str(contiguous[0].get("previous_trade_date") or "")
|
||||
if not expected_previous or expected_previous != str(row.get("trade_date") or ""):
|
||||
break
|
||||
contiguous.insert(0, row)
|
||||
return contiguous
|
||||
|
||||
|
||||
def apply_sentiment_to_dashboard(
|
||||
dashboard: dict[str, Any],
|
||||
historical_snapshots: list[dict[str, Any]] | None = None,
|
||||
) -> dict[str, Any]:
|
||||
result = deepcopy(dashboard)
|
||||
history = list(historical_snapshots or [])
|
||||
history.append(result)
|
||||
series = build_sentiment_history(history)
|
||||
target_date = _trade_date(result)
|
||||
sentiment = next((row for row in reversed(series) if row["trade_date"] == target_date), None)
|
||||
if not sentiment:
|
||||
return result
|
||||
overview = dict(result.get("overview") or {})
|
||||
overview.update(
|
||||
{
|
||||
"sentiment_score": sentiment["score"],
|
||||
"sentiment_trend_score": sentiment["ema_score"],
|
||||
"sentiment_label": sentiment["label"],
|
||||
"sentiment_phase": sentiment["phase"],
|
||||
"sentiment_direction": sentiment["direction"],
|
||||
"sentiment_components": sentiment["components"],
|
||||
"sentiment_engine_version": SENTIMENT_ENGINE_VERSION,
|
||||
}
|
||||
)
|
||||
result["overview"] = overview
|
||||
return result
|
||||
@@ -0,0 +1,39 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import Any
|
||||
|
||||
from backend.bootstrap.config import normalize_date
|
||||
from backend.features.sentiment.engine import (
|
||||
COMPONENT_WEIGHTS,
|
||||
apply_sentiment_to_dashboard,
|
||||
build_sentiment_history,
|
||||
latest_contiguous_history,
|
||||
)
|
||||
|
||||
|
||||
class SentimentServiceMixin:
|
||||
def _enrich_dashboard_sentiment(
|
||||
self,
|
||||
dashboard: dict[str, Any],
|
||||
end_date: str,
|
||||
) -> dict[str, Any]:
|
||||
history = self.database.list_snapshot_payloads(end_date, 260)
|
||||
return apply_sentiment_to_dashboard(dashboard, history)
|
||||
|
||||
def sentiment_history(self, trade_date: str, limit: int = 20) -> dict[str, Any]:
|
||||
normalized_date = normalize_date(trade_date)
|
||||
limit = max(10, min(120, int(limit)))
|
||||
full_series = build_sentiment_history(
|
||||
self.database.list_snapshot_payloads(normalized_date, 240)
|
||||
)
|
||||
series = latest_contiguous_history(full_series)
|
||||
rows = series[-limit:]
|
||||
return {
|
||||
"trade_date": rows[-1]["trade_date"] if rows else normalized_date,
|
||||
"available_days": len(series),
|
||||
"stored_days": len(full_series),
|
||||
"requested_days": limit,
|
||||
"rows": rows,
|
||||
"weights": COMPONENT_WEIGHTS,
|
||||
"normalization": rows[-1]["normalization"] if rows else "固定锚点",
|
||||
}
|
||||
@@ -0,0 +1,3 @@
|
||||
from .service import ThemeServiceMixin
|
||||
|
||||
__all__ = ["ThemeServiceMixin"]
|
||||
@@ -0,0 +1,14 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import Any
|
||||
|
||||
from backend.bootstrap.config import normalize_date
|
||||
from backend.features.market.insights import MarketInsightsService
|
||||
|
||||
|
||||
class ThemeServiceMixin:
|
||||
def theme_library(self, trade_date: str, force: bool = False) -> dict[str, Any]:
|
||||
return self._market_insights().theme_library(normalize_date(trade_date), force)
|
||||
|
||||
def theme_detail(self, code: str, trade_date: str) -> dict[str, Any]:
|
||||
return self._market_insights().theme_detail(code, normalize_date(trade_date))
|
||||
+8
-161
@@ -8,7 +8,11 @@ from typing import Any
|
||||
|
||||
from backend.database import MIGRATIONS, MigrationRunner, SQLiteConnectionFactory
|
||||
from backend.features.accounts.repository import AccountRepositoryMixin
|
||||
from backend.features.auction.repository import AuctionRepositoryMixin
|
||||
from backend.features.dragon_tiger.repository import DragonTigerRepositoryMixin
|
||||
from backend.features.market.repository import MarketRepositoryMixin
|
||||
from backend.features.pools.repository import PoolRepositoryMixin
|
||||
from backend.features.popularity.repository import PopularityRepositoryMixin
|
||||
from backend.features.system.repository import SystemSettingsRepositoryMixin
|
||||
|
||||
|
||||
@@ -23,7 +27,11 @@ def _optional_float(value: Any) -> float | None:
|
||||
|
||||
class ReviewDatabase(
|
||||
AccountRepositoryMixin,
|
||||
AuctionRepositoryMixin,
|
||||
DragonTigerRepositoryMixin,
|
||||
MarketRepositoryMixin,
|
||||
PoolRepositoryMixin,
|
||||
PopularityRepositoryMixin,
|
||||
SystemSettingsRepositoryMixin,
|
||||
):
|
||||
def __init__(self, path: Path) -> None:
|
||||
@@ -836,46 +844,7 @@ class ReviewDatabase(
|
||||
)
|
||||
return cursor.rowcount > 0
|
||||
|
||||
def save_reason_override(self, trade_date: str, code: str, reason: str) -> None:
|
||||
now = datetime.now().astimezone().isoformat(timespec="seconds")
|
||||
with self.connect() as connection:
|
||||
connection.execute(
|
||||
"""
|
||||
INSERT INTO reason_overrides (trade_date, code, reason, updated_at)
|
||||
VALUES (?, ?, ?, ?)
|
||||
ON CONFLICT(trade_date, code) DO UPDATE SET
|
||||
reason = excluded.reason,
|
||||
updated_at = excluded.updated_at
|
||||
""",
|
||||
(trade_date, code, reason, now),
|
||||
)
|
||||
|
||||
def reason_overrides(self, trade_date: str) -> dict[str, str]:
|
||||
with self.connect() as connection:
|
||||
rows = connection.execute(
|
||||
"SELECT code, reason FROM reason_overrides WHERE trade_date = ?",
|
||||
(trade_date,),
|
||||
).fetchall()
|
||||
return {row["code"]: row["reason"] for row in rows}
|
||||
|
||||
def list_seat_aliases(self) -> dict[str, str]:
|
||||
with self.connect() as connection:
|
||||
rows = connection.execute("SELECT seat_name, alias FROM seat_aliases").fetchall()
|
||||
return {row["seat_name"]: row["alias"] for row in rows}
|
||||
|
||||
def save_seat_alias(self, seat_name: str, alias: str) -> None:
|
||||
now = datetime.now().astimezone().isoformat(timespec="seconds")
|
||||
with self.connect() as connection:
|
||||
connection.execute(
|
||||
"""
|
||||
INSERT INTO seat_aliases (seat_name, alias, updated_at)
|
||||
VALUES (?, ?, ?)
|
||||
ON CONFLICT(seat_name) DO UPDATE SET
|
||||
alias = excluded.alias,
|
||||
updated_at = excluded.updated_at
|
||||
""",
|
||||
(seat_name, alias, now),
|
||||
)
|
||||
|
||||
def list_sector_phase_overrides(self) -> dict[str, str]:
|
||||
with self.connect() as connection:
|
||||
@@ -1075,44 +1044,6 @@ class ReviewDatabase(
|
||||
)
|
||||
return len(values)
|
||||
|
||||
def upsert_auction_factors(self, rows: list[dict[str, Any]]) -> int:
|
||||
values = []
|
||||
for row in rows:
|
||||
trade_date = str(row.get("trade_date") or "")
|
||||
ts_code = str(row.get("ts_code") or "")
|
||||
price = float(row.get("price") or 0)
|
||||
pre_close = float(row.get("pre_close") or 0)
|
||||
if not trade_date or not ts_code or price <= 0 or pre_close <= 0:
|
||||
continue
|
||||
values.append(
|
||||
(
|
||||
trade_date,
|
||||
ts_code,
|
||||
price,
|
||||
pre_close,
|
||||
(price / pre_close - 1) * 100,
|
||||
float(row.get("vol") or 0),
|
||||
float(row.get("amount") or 0),
|
||||
float(row.get("turnover_rate") or 0),
|
||||
float(row.get("volume_ratio") or 0),
|
||||
)
|
||||
)
|
||||
with self.connect() as connection:
|
||||
connection.executemany(
|
||||
"""
|
||||
INSERT INTO auction_factors
|
||||
(trade_date, ts_code, price, pre_close, change, vol, amount,
|
||||
turnover_rate, volume_ratio)
|
||||
VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?)
|
||||
ON CONFLICT(trade_date, ts_code) DO UPDATE SET
|
||||
price=excluded.price, pre_close=excluded.pre_close,
|
||||
change=excluded.change, vol=excluded.vol, amount=excluded.amount,
|
||||
turnover_rate=excluded.turnover_rate,
|
||||
volume_ratio=excluded.volume_ratio
|
||||
""",
|
||||
values,
|
||||
)
|
||||
return len(values)
|
||||
|
||||
def upsert_earnings_events(self, rows: list[dict[str, Any]]) -> int:
|
||||
values = [
|
||||
@@ -1149,84 +1080,7 @@ class ReviewDatabase(
|
||||
)
|
||||
return len(values)
|
||||
|
||||
def upsert_popularity_factors(self, rows: list[dict[str, Any]]) -> int:
|
||||
values = [
|
||||
(
|
||||
str(row.get("trade_date") or ""),
|
||||
str(row.get("ts_code") or ""),
|
||||
int(row["ths_rank"]) if row.get("ths_rank") not in (None, "") else None,
|
||||
int(row["dc_rank"]) if row.get("dc_rank") not in (None, "") else None,
|
||||
float(row.get("combined_score") or 0),
|
||||
int(row["rank_change"]) if row.get("rank_change") not in (None, "") else None,
|
||||
int(bool(row.get("dual_source"))),
|
||||
)
|
||||
for row in rows
|
||||
if row.get("trade_date") and row.get("ts_code")
|
||||
]
|
||||
with self.connect() as connection:
|
||||
connection.executemany(
|
||||
"""
|
||||
INSERT INTO popularity_factors
|
||||
(trade_date, ts_code, ths_rank, dc_rank, combined_score,
|
||||
rank_change, dual_source)
|
||||
VALUES (?, ?, ?, ?, ?, ?, ?)
|
||||
ON CONFLICT(trade_date, ts_code) DO UPDATE SET
|
||||
ths_rank=excluded.ths_rank,
|
||||
dc_rank=excluded.dc_rank,
|
||||
combined_score=excluded.combined_score,
|
||||
rank_change=excluded.rank_change,
|
||||
dual_source=excluded.dual_source
|
||||
""",
|
||||
values,
|
||||
)
|
||||
return len(values)
|
||||
|
||||
def upsert_lhb_institutions(self, rows: list[dict[str, Any]]) -> int:
|
||||
grouped: dict[tuple[str, str], dict[str, float | int]] = {}
|
||||
for row in rows:
|
||||
trade_date = str(row.get("trade_date") or "")
|
||||
ts_code = str(row.get("ts_code") or "")
|
||||
seat_name = str(row.get("exalter") or row.get("seat_name") or "")
|
||||
if not trade_date or not ts_code or "机构专用" not in seat_name:
|
||||
continue
|
||||
group = grouped.setdefault(
|
||||
(trade_date, ts_code),
|
||||
{"net": 0.0, "buy": 0.0, "sell": 0.0, "seats": 0},
|
||||
)
|
||||
group["net"] = float(group["net"]) + float(row.get("net_buy") or row.get("net_amount") or 0)
|
||||
group["buy"] = float(group["buy"]) + float(row.get("buy") or row.get("buy_amount") or 0)
|
||||
group["sell"] = float(group["sell"]) + float(row.get("sell") or row.get("sell_amount") or 0)
|
||||
group["seats"] = int(group["seats"]) + 1
|
||||
values = [
|
||||
(trade_date, ts_code, item["net"], item["buy"], item["sell"], item["seats"])
|
||||
for (trade_date, ts_code), item in grouped.items()
|
||||
]
|
||||
with self.connect() as connection:
|
||||
connection.executemany(
|
||||
"""
|
||||
INSERT INTO lhb_institution_daily
|
||||
(trade_date, ts_code, net_buy_amount, buy_amount, sell_amount, seat_count)
|
||||
VALUES (?, ?, ?, ?, ?, ?)
|
||||
ON CONFLICT(trade_date, ts_code) DO UPDATE SET
|
||||
net_buy_amount=excluded.net_buy_amount,
|
||||
buy_amount=excluded.buy_amount,
|
||||
sell_amount=excluded.sell_amount,
|
||||
seat_count=excluded.seat_count
|
||||
""",
|
||||
values,
|
||||
)
|
||||
return len(values)
|
||||
|
||||
def auction_factor_dates(self, end_date: str = "", limit: int = 80) -> list[str]:
|
||||
where = "WHERE trade_date <= ?" if end_date else ""
|
||||
parameters: tuple[Any, ...] = (end_date, limit) if end_date else (limit,)
|
||||
with self.connect() as connection:
|
||||
rows = connection.execute(
|
||||
f"SELECT DISTINCT trade_date FROM auction_factors {where} "
|
||||
"ORDER BY trade_date DESC LIMIT ?",
|
||||
parameters,
|
||||
).fetchall()
|
||||
return [row["trade_date"] for row in reversed(rows)]
|
||||
|
||||
def daily_indicator_dates(self, end_date: str = "", limit: int = 400) -> list[str]:
|
||||
where = "WHERE trade_date <= ?" if end_date else ""
|
||||
@@ -1246,13 +1100,6 @@ class ReviewDatabase(
|
||||
).fetchall()
|
||||
return [str(row["end_date"]) for row in rows]
|
||||
|
||||
def auction_factors_for_date(self, trade_date: str) -> list[dict[str, Any]]:
|
||||
with self.connect() as connection:
|
||||
rows = connection.execute(
|
||||
"SELECT * FROM auction_factors WHERE trade_date = ? ORDER BY ts_code",
|
||||
(trade_date,),
|
||||
).fetchall()
|
||||
return [dict(row) for row in rows]
|
||||
|
||||
def daily_bars_for_date(self, trade_date: str) -> list[dict[str, Any]]:
|
||||
with self.connect() as connection:
|
||||
|
||||
+1
-1
@@ -5,7 +5,7 @@ import math
|
||||
from datetime import datetime, timedelta
|
||||
from typing import Any
|
||||
|
||||
from sentiment_engine import apply_sentiment_to_dashboard
|
||||
from backend.features.sentiment.engine import apply_sentiment_to_dashboard
|
||||
|
||||
|
||||
DEMO_LIMITS = [
|
||||
|
||||
+1
-1312
File diff suppressed because it is too large
Load Diff
+1
-1
@@ -10,7 +10,7 @@ from typing import Any
|
||||
|
||||
from advanced_strategies import ADVANCED_CURATED_STRATEGIES
|
||||
from database import ReviewDatabase
|
||||
from sentiment_engine import build_sentiment_history, latest_contiguous_history
|
||||
from backend.features.sentiment.engine import build_sentiment_history, latest_contiguous_history
|
||||
from tushare_client import TushareClient, TushareError
|
||||
|
||||
|
||||
|
||||
+4
-493
@@ -1,496 +1,7 @@
|
||||
from __future__ import annotations
|
||||
"""Compatibility alias for the canonical sentiment engine implementation."""
|
||||
|
||||
from copy import deepcopy
|
||||
from statistics import mean, median
|
||||
from typing import Any
|
||||
import sys
|
||||
|
||||
from backend.features.sentiment import engine as _implementation
|
||||
|
||||
COMPONENT_WEIGHTS = {
|
||||
"breadth": 20,
|
||||
"limit_ecology": 25,
|
||||
"profit_effect": 30,
|
||||
"ladder_structure": 15,
|
||||
"liquidity": 10,
|
||||
}
|
||||
|
||||
SENTIMENT_ENGINE_VERSION = 2
|
||||
|
||||
|
||||
def _number(value: Any, default: float = 0.0) -> float:
|
||||
try:
|
||||
number = float(value)
|
||||
return number if number == number else default
|
||||
except (TypeError, ValueError):
|
||||
return default
|
||||
|
||||
|
||||
def _clamp(value: float, lower: float = 0.0, upper: float = 100.0) -> float:
|
||||
return min(upper, max(lower, value))
|
||||
|
||||
|
||||
def _linear(value: float, low: float, high: float) -> float:
|
||||
if high <= low:
|
||||
return 50.0
|
||||
return _clamp((value - low) / (high - low) * 100)
|
||||
|
||||
|
||||
def _percentile(value: float, history: list[float]) -> float:
|
||||
if not history:
|
||||
return 50.0
|
||||
below = sum(item < value for item in history)
|
||||
equal = sum(item == value for item in history)
|
||||
return _clamp((below + equal * 0.5) / len(history) * 100)
|
||||
|
||||
|
||||
def _adaptive_score(value: float, fixed: float, history: list[float]) -> float:
|
||||
if len(history) < 20:
|
||||
return fixed
|
||||
return fixed * 0.25 + _percentile(value, history[-250:]) * 0.75
|
||||
|
||||
|
||||
def _trade_date(payload: dict[str, Any]) -> str:
|
||||
meta = payload.get("meta") or {}
|
||||
return str(meta.get("trade_date") or payload.get("_snapshot_date") or "").replace("-", "")
|
||||
|
||||
|
||||
def _deduplicate_snapshots(snapshots: list[dict[str, Any]]) -> list[dict[str, Any]]:
|
||||
by_trade_date: dict[str, dict[str, Any]] = {}
|
||||
for payload in snapshots:
|
||||
trade_date = _trade_date(payload)
|
||||
if trade_date:
|
||||
by_trade_date[trade_date] = payload
|
||||
return [by_trade_date[key] for key in sorted(by_trade_date)]
|
||||
|
||||
|
||||
def _snapshot_stats(payload: dict[str, Any]) -> dict[str, Any]:
|
||||
overview = payload.get("overview") or {}
|
||||
meta = payload.get("meta") or {}
|
||||
limits = list(payload.get("limits") or [])
|
||||
broken = list(payload.get("broken") or [])
|
||||
down_limits = list(payload.get("down_limits") or [])
|
||||
yesterday = list(payload.get("yesterday_limits") or [])
|
||||
|
||||
limit_up = len(limits) if limits else int(_number(overview.get("limit_up_count")))
|
||||
broken_count = len(broken) if broken else int(_number(overview.get("broken_count")))
|
||||
limit_down = len(down_limits) if down_limits else int(_number(overview.get("limit_down_count")))
|
||||
streaks = [max(1, int(_number(row.get("streak"), 1))) for row in limits]
|
||||
first_board = sum(streak == 1 for streak in streaks)
|
||||
second_board = sum(streak == 2 for streak in streaks)
|
||||
three_plus = sum(streak >= 3 for streak in streaks)
|
||||
max_height = max(streaks, default=0)
|
||||
present_levels = set(streaks)
|
||||
ladder_completeness = (
|
||||
sum(level in present_levels for level in range(1, max_height + 1)) / max_height * 100
|
||||
if max_height else 0.0
|
||||
)
|
||||
|
||||
up_count = int(_number(overview.get("up_count")))
|
||||
down_count = int(_number(overview.get("down_count")))
|
||||
flat_count = int(_number(overview.get("flat_count")))
|
||||
active_count = up_count + down_count
|
||||
breadth_ratio = up_count / max(active_count, 1) * 100
|
||||
seal_rate = _number(overview.get("seal_rate"))
|
||||
if not seal_rate and limit_up + broken_count:
|
||||
seal_rate = limit_up / (limit_up + broken_count) * 100
|
||||
|
||||
previous_limit_count = len(yesterday)
|
||||
previous_positive_count = sum(_number(row.get("current_change")) > 0 for row in yesterday)
|
||||
previous_positive_rate = previous_positive_count / max(previous_limit_count, 1) * 100
|
||||
advanced_count = sum(row.get("outcome") == "晋级" for row in yesterday)
|
||||
advance_rate = advanced_count / max(previous_limit_count, 1) * 100
|
||||
average_previous_change = (
|
||||
mean(_number(row.get("current_change")) for row in yesterday) if yesterday else 0.0
|
||||
)
|
||||
median_previous_change = (
|
||||
median(_number(row.get("current_change")) for row in yesterday) if yesterday else 0.0
|
||||
)
|
||||
severe_loss_count = sum(_number(row.get("current_change")) <= -5 for row in yesterday)
|
||||
severe_loss_rate = severe_loss_count / max(previous_limit_count, 1) * 100
|
||||
previous_down_count = sum(row.get("outcome") == "跌停" for row in yesterday)
|
||||
high_previous = [row for row in yesterday if int(_number(row.get("prior_streak"), 1)) >= 2]
|
||||
high_positive_rate = (
|
||||
sum(_number(row.get("current_change")) > 0 for row in high_previous)
|
||||
/ max(len(high_previous), 1)
|
||||
* 100
|
||||
)
|
||||
|
||||
amount_billion = _number(overview.get("amount_billion"))
|
||||
limit_amount_billion = sum(_number(row.get("amount_billion")) for row in limits)
|
||||
return {
|
||||
"trade_date": _trade_date(payload),
|
||||
"previous_trade_date": str(meta.get("previous_trade_date") or "").replace("-", ""),
|
||||
"up_count": up_count,
|
||||
"down_count": down_count,
|
||||
"flat_count": flat_count,
|
||||
"breadth_ratio": round(breadth_ratio, 1),
|
||||
"limit_up_count": limit_up,
|
||||
"first_board_count": first_board,
|
||||
"second_board_count": second_board,
|
||||
"three_plus_count": three_plus,
|
||||
"max_height": max_height,
|
||||
"ladder_completeness": round(ladder_completeness, 1),
|
||||
"broken_count": broken_count,
|
||||
"limit_down_count": limit_down,
|
||||
"seal_rate": round(seal_rate, 1),
|
||||
"previous_limit_count": previous_limit_count,
|
||||
"previous_positive_count": previous_positive_count,
|
||||
"previous_positive_rate": round(previous_positive_rate, 1),
|
||||
"advance_rate": round(advance_rate, 1),
|
||||
"average_previous_change": round(average_previous_change, 2),
|
||||
"median_previous_change": round(median_previous_change, 2),
|
||||
"severe_loss_count": severe_loss_count,
|
||||
"severe_loss_rate": round(severe_loss_rate, 1),
|
||||
"previous_down_count": previous_down_count,
|
||||
"high_positive_rate": round(high_positive_rate, 1),
|
||||
"amount_billion": round(amount_billion, 1),
|
||||
"limit_amount_billion": round(limit_amount_billion, 2),
|
||||
}
|
||||
|
||||
|
||||
def _sentiment_label(score: float) -> str:
|
||||
if score >= 80:
|
||||
return "情绪高涨"
|
||||
if score >= 60:
|
||||
return "情绪偏强"
|
||||
if score >= 40:
|
||||
return "情绪中性"
|
||||
if score >= 20:
|
||||
return "情绪偏弱"
|
||||
return "情绪冰点"
|
||||
|
||||
|
||||
def _phase_signal(score: float, momentum: float, profit_score: float) -> str:
|
||||
if score < 25:
|
||||
return "修复" if momentum > 3 else "冰点"
|
||||
if score < 45:
|
||||
return "修复" if momentum > 3 else "退潮"
|
||||
if score >= 80:
|
||||
return "高潮" if momentum >= -2 and profit_score >= 60 else "分化"
|
||||
if score >= 65:
|
||||
return "分化" if momentum < -3 or profit_score < 50 else "发酵"
|
||||
if momentum < -5:
|
||||
return "退潮"
|
||||
return "发酵" if momentum >= 0 and profit_score >= 45 else "分化"
|
||||
|
||||
|
||||
def _confirmed_phase(
|
||||
previous: dict[str, Any] | None,
|
||||
score: float,
|
||||
day_change: float,
|
||||
systemic_health: float,
|
||||
profit_score: float,
|
||||
ecology_score: float,
|
||||
phase_signal: str,
|
||||
extreme_ice: bool,
|
||||
fermentation_signal_count: int,
|
||||
) -> tuple[str, str]:
|
||||
if previous is None:
|
||||
return phase_signal, "首个连续交易日，采用原始阶段信号"
|
||||
previous_phase = str(previous.get("phase") or phase_signal)
|
||||
if extreme_ice:
|
||||
return "冰点", "市场宽度与跌停数量触发极端冰点"
|
||||
|
||||
recovery = day_change >= 6 and score >= 25 and systemic_health >= 24
|
||||
fermentation_confirmed = fermentation_signal_count >= 2
|
||||
climax_ready = (
|
||||
score >= 80
|
||||
and profit_score >= 60
|
||||
and systemic_health >= 60
|
||||
and ecology_score >= 70
|
||||
)
|
||||
|
||||
if previous_phase == "冰点":
|
||||
return ("修复", "冰点后首次有效回升") if recovery else ("冰点", "冰点尚未形成有效修复")
|
||||
|
||||
if previous_phase == "退潮":
|
||||
if score < 25:
|
||||
return "冰点", "退潮继续下探至冰点区间"
|
||||
return ("修复", "退潮后出现有效回升") if recovery else ("退潮", "退潮尚未形成有效修复")
|
||||
|
||||
if previous_phase == "修复":
|
||||
if score < 25:
|
||||
return "冰点", "修复失败并重新跌入冰点区间"
|
||||
if day_change <= -6 and score < 45:
|
||||
return "退潮", "修复失败且温度显著回落"
|
||||
if fermentation_confirmed:
|
||||
return "发酵", "发酵条件连续两个交易日成立"
|
||||
return "修复", "修复延续，等待发酵确认"
|
||||
|
||||
if previous_phase == "发酵":
|
||||
if score < 25:
|
||||
return "冰点", "发酵阶段出现极端情绪坍塌"
|
||||
if score < 45 and (day_change < 0 or systemic_health < 35):
|
||||
return "退潮", "发酵阶段温度与系统健康度同步转弱"
|
||||
if climax_ready:
|
||||
return "高潮", "温度、赚钱效应与涨停生态共同达到高潮条件"
|
||||
if phase_signal in {"分化", "退潮"} or day_change <= -6:
|
||||
return "分化", "发酵阶段出现降温或赚钱效应弱化"
|
||||
return "发酵", "发酵状态延续"
|
||||
|
||||
if previous_phase == "高潮":
|
||||
if score < 25:
|
||||
return "冰点", "高潮后出现极端情绪坍塌"
|
||||
if climax_ready:
|
||||
return "高潮", "高潮条件继续成立"
|
||||
if score < 45 or systemic_health < 30:
|
||||
return "退潮", "高潮后风险快速释放"
|
||||
return "分化", "高潮条件消退，进入分化"
|
||||
|
||||
if previous_phase == "分化":
|
||||
if score < 25:
|
||||
return "冰点", "分化继续恶化至冰点区间"
|
||||
if score < 45 or systemic_health < 30:
|
||||
return "退潮", "分化后温度或系统健康度继续下降"
|
||||
if fermentation_confirmed:
|
||||
return "发酵", "分化转强条件连续两个交易日成立"
|
||||
return "分化", "分化延续，等待方向确认"
|
||||
|
||||
return phase_signal, "采用原始阶段信号"
|
||||
|
||||
|
||||
def build_sentiment_history(snapshots: list[dict[str, Any]]) -> list[dict[str, Any]]:
|
||||
payloads = _deduplicate_snapshots(snapshots)
|
||||
raw_rows = [_snapshot_stats(payload) for payload in payloads]
|
||||
results: list[dict[str, Any]] = []
|
||||
|
||||
for index, stats in enumerate(raw_rows):
|
||||
previous = raw_rows[:index]
|
||||
limit_history = [float(row["limit_up_count"]) for row in previous]
|
||||
down_limit_history = [float(row["limit_down_count"]) for row in previous]
|
||||
height_history = [float(row["max_height"]) for row in previous]
|
||||
three_plus_history = [float(row["three_plus_count"]) for row in previous]
|
||||
amount_history = [float(row["amount_billion"]) for row in previous[-20:] if row["amount_billion"]]
|
||||
|
||||
breadth_score = _clamp(float(stats["breadth_ratio"]))
|
||||
limit_strength = _adaptive_score(
|
||||
float(stats["limit_up_count"]),
|
||||
_linear(float(stats["limit_up_count"]), 10, 100),
|
||||
limit_history,
|
||||
)
|
||||
down_relief = 100 - _adaptive_score(
|
||||
float(stats["limit_down_count"]),
|
||||
_linear(float(stats["limit_down_count"]), 0, 50),
|
||||
down_limit_history,
|
||||
)
|
||||
seal_quality = _linear(float(stats["seal_rate"]), 35, 90)
|
||||
systemic_health = breadth_score * 0.60 + down_relief * 0.40
|
||||
systemic_gate = 1.0 if systemic_health >= 35 else 0.35 + systemic_health / 35 * 0.65
|
||||
ecology_base_score = limit_strength * 0.35 + seal_quality * 0.35 + down_relief * 0.30
|
||||
# Systemic risk is applied once to the final temperature. Reapplying it here
|
||||
# would count market breadth and limit-down pressure twice.
|
||||
limit_ecology_score = ecology_base_score
|
||||
|
||||
if stats["previous_limit_count"]:
|
||||
positive_score = float(stats["previous_positive_rate"])
|
||||
average_change_score = _clamp(50 + float(stats["average_previous_change"]) * 6)
|
||||
median_change_score = _clamp(50 + float(stats["median_previous_change"]) * 7)
|
||||
advance_score = _clamp(float(stats["advance_rate"]) * 2.5)
|
||||
severe_loss_safety = _clamp(100 - float(stats["severe_loss_rate"]) * 3)
|
||||
down_safety = _clamp(100 - float(stats["previous_down_count"]) / stats["previous_limit_count"] * 700)
|
||||
tail_safety_score = severe_loss_safety * 0.70 + down_safety * 0.30
|
||||
profit_effect_score = (
|
||||
positive_score * 0.30
|
||||
+ median_change_score * 0.25
|
||||
+ average_change_score * 0.10
|
||||
+ advance_score * 0.20
|
||||
+ tail_safety_score * 0.15
|
||||
)
|
||||
else:
|
||||
profit_effect_score = 50.0
|
||||
|
||||
max_height_score = _adaptive_score(
|
||||
float(stats["max_height"]),
|
||||
_linear(float(stats["max_height"]), 1, 7),
|
||||
height_history,
|
||||
)
|
||||
continuation_rate = (
|
||||
(float(stats["second_board_count"]) + float(stats["three_plus_count"]))
|
||||
/ max(float(stats["limit_up_count"]), 1)
|
||||
* 100
|
||||
)
|
||||
three_plus_density = float(stats["three_plus_count"]) / max(float(stats["limit_up_count"]), 1) * 100
|
||||
three_plus_score = _adaptive_score(
|
||||
float(stats["three_plus_count"]),
|
||||
_clamp(three_plus_density * 5),
|
||||
three_plus_history,
|
||||
)
|
||||
ladder_structure_score = (
|
||||
max_height_score * 0.30
|
||||
+ _clamp(continuation_rate * 3) * 0.25
|
||||
+ three_plus_score * 0.25
|
||||
+ float(stats["ladder_completeness"]) * 0.20
|
||||
)
|
||||
|
||||
amount_baseline = mean(amount_history) if amount_history else float(stats["amount_billion"] or 1)
|
||||
amount_ratio = float(stats["amount_billion"]) / max(amount_baseline, 1)
|
||||
amount_score = _clamp(50 + (amount_ratio - 1) * 100)
|
||||
limit_amount_share = float(stats["limit_amount_billion"]) / max(float(stats["amount_billion"]), 1) * 100
|
||||
liquidity_score = amount_score * 0.70 + _clamp(limit_amount_share * 20) * 0.30
|
||||
|
||||
component_scores = {
|
||||
"breadth": breadth_score,
|
||||
"limit_ecology": limit_ecology_score,
|
||||
"profit_effect": profit_effect_score,
|
||||
"ladder_structure": ladder_structure_score,
|
||||
"liquidity": liquidity_score,
|
||||
}
|
||||
raw_score = sum(component_scores[key] * weight / 100 for key, weight in COMPONENT_WEIGHTS.items())
|
||||
score = round(
|
||||
raw_score * systemic_gate
|
||||
)
|
||||
extreme_ice = float(stats["breadth_ratio"]) <= 15 and float(stats["limit_down_count"]) >= 100
|
||||
if extreme_ice:
|
||||
score = min(score, 15)
|
||||
elif float(stats["breadth_ratio"]) <= 25 and float(stats["limit_down_count"]) >= 50:
|
||||
score = min(score, 24)
|
||||
previous_scores: list[float] = []
|
||||
expected_date = str(stats.get("previous_trade_date") or "")
|
||||
for prior_result in reversed(results):
|
||||
if not expected_date or str(prior_result.get("trade_date") or "") != expected_date:
|
||||
break
|
||||
previous_scores.append(float(prior_result["score"]))
|
||||
expected_date = str(prior_result.get("previous_trade_date") or "")
|
||||
if len(previous_scores) == 3:
|
||||
break
|
||||
momentum = score - mean(previous_scores) if previous_scores else 0.0
|
||||
direction = "升温" if momentum > 3 else "降温" if momentum < -3 else "持平"
|
||||
normalization = "历史百分位" if len(previous) >= 20 else "固定锚点"
|
||||
previous_result = (
|
||||
results[-1]
|
||||
if results and str(stats.get("previous_trade_date") or "") == str(results[-1].get("trade_date") or "")
|
||||
else None
|
||||
)
|
||||
day_change = score - float(previous_result["score"]) if previous_result else 0.0
|
||||
ema_score = round(
|
||||
score if not previous_result
|
||||
else score * 0.5 + float(previous_result.get("ema_score", previous_result["score"])) * 0.5,
|
||||
1,
|
||||
)
|
||||
phase_signal = _phase_signal(score, momentum, profit_effect_score)
|
||||
fermentation_ready = (
|
||||
phase_signal == "发酵"
|
||||
and score >= 45
|
||||
and profit_effect_score >= 45
|
||||
and systemic_health >= 35
|
||||
and not extreme_ice
|
||||
)
|
||||
previous_fermentation_count = int(previous_result.get("fermentation_signal_count") or 0) if previous_result else 0
|
||||
fermentation_signal_count = previous_fermentation_count + 1 if fermentation_ready else 0
|
||||
phase, transition_reason = _confirmed_phase(
|
||||
previous_result,
|
||||
score,
|
||||
day_change,
|
||||
systemic_health,
|
||||
profit_effect_score,
|
||||
limit_ecology_score,
|
||||
phase_signal,
|
||||
extreme_ice,
|
||||
fermentation_signal_count,
|
||||
)
|
||||
previous_phase = str(previous_result.get("phase") or "") if previous_result else ""
|
||||
if phase not in {"修复", "分化"}:
|
||||
fermentation_signal_count = 0
|
||||
elif phase == "分化" and previous_phase != "分化":
|
||||
fermentation_signal_count = 0
|
||||
|
||||
components = {
|
||||
"breadth": {
|
||||
"label": "市场宽度",
|
||||
"score": round(breadth_score, 1),
|
||||
"weight": COMPONENT_WEIGHTS["breadth"],
|
||||
"summary": f"上涨占比 {stats['breadth_ratio']:.1f}%",
|
||||
},
|
||||
"limit_ecology": {
|
||||
"label": "涨停生态",
|
||||
"score": round(limit_ecology_score, 1),
|
||||
"weight": COMPONENT_WEIGHTS["limit_ecology"],
|
||||
"summary": (
|
||||
f"涨停 {stats['limit_up_count']} · 跌停 {stats['limit_down_count']} · "
|
||||
f"封板 {stats['seal_rate']:.1f}%"
|
||||
),
|
||||
},
|
||||
"profit_effect": {
|
||||
"label": "赚钱效应",
|
||||
"score": round(profit_effect_score, 1),
|
||||
"weight": COMPONENT_WEIGHTS["profit_effect"],
|
||||
"summary": (
|
||||
f"昨涨停红盘 {stats['previous_positive_rate']:.1f}% · "
|
||||
f"中位 {stats['median_previous_change']:+.2f}% · "
|
||||
f"重亏 {stats['severe_loss_rate']:.1f}%"
|
||||
if stats["previous_limit_count"] else "缺少前一交易日样本"
|
||||
),
|
||||
},
|
||||
"ladder_structure": {
|
||||
"label": "连板结构",
|
||||
"score": round(ladder_structure_score, 1),
|
||||
"weight": COMPONENT_WEIGHTS["ladder_structure"],
|
||||
"summary": f"最高 {stats['max_height']} 板 · 三板以上 {stats['three_plus_count']} 家",
|
||||
},
|
||||
"liquidity": {
|
||||
"label": "成交活跃度",
|
||||
"score": round(liquidity_score, 1),
|
||||
"weight": COMPONENT_WEIGHTS["liquidity"],
|
||||
"summary": f"成交 {stats['amount_billion']:.1f} 亿 · 均值比 {amount_ratio:.2f}",
|
||||
},
|
||||
}
|
||||
results.append(
|
||||
{
|
||||
**stats,
|
||||
"score": score,
|
||||
"ema_score": ema_score,
|
||||
"label": _sentiment_label(score),
|
||||
"phase": phase,
|
||||
"phase_signal": phase_signal,
|
||||
"transition_reason": transition_reason,
|
||||
"fermentation_signal_count": fermentation_signal_count,
|
||||
"day_change": round(day_change, 1),
|
||||
"direction": direction,
|
||||
"momentum": round(momentum, 1),
|
||||
"normalization": "250日历史百分位" if len(previous) >= 20 else normalization,
|
||||
"history_days": len(previous) + 1,
|
||||
"systemic_health": round(systemic_health, 1),
|
||||
"risk_multiplier": round(systemic_gate, 3),
|
||||
"components": components,
|
||||
}
|
||||
)
|
||||
return results
|
||||
|
||||
|
||||
def latest_contiguous_history(series: list[dict[str, Any]]) -> list[dict[str, Any]]:
|
||||
if not series:
|
||||
return []
|
||||
contiguous = [series[-1]]
|
||||
for row in reversed(series[:-1]):
|
||||
expected_previous = str(contiguous[0].get("previous_trade_date") or "")
|
||||
if not expected_previous or expected_previous != str(row.get("trade_date") or ""):
|
||||
break
|
||||
contiguous.insert(0, row)
|
||||
return contiguous
|
||||
|
||||
|
||||
def apply_sentiment_to_dashboard(
|
||||
dashboard: dict[str, Any],
|
||||
historical_snapshots: list[dict[str, Any]] | None = None,
|
||||
) -> dict[str, Any]:
|
||||
result = deepcopy(dashboard)
|
||||
history = list(historical_snapshots or [])
|
||||
history.append(result)
|
||||
series = build_sentiment_history(history)
|
||||
target_date = _trade_date(result)
|
||||
sentiment = next((row for row in reversed(series) if row["trade_date"] == target_date), None)
|
||||
if not sentiment:
|
||||
return result
|
||||
overview = dict(result.get("overview") or {})
|
||||
overview.update(
|
||||
{
|
||||
"sentiment_score": sentiment["score"],
|
||||
"sentiment_trend_score": sentiment["ema_score"],
|
||||
"sentiment_label": sentiment["label"],
|
||||
"sentiment_phase": sentiment["phase"],
|
||||
"sentiment_direction": sentiment["direction"],
|
||||
"sentiment_components": sentiment["components"],
|
||||
"sentiment_engine_version": SENTIMENT_ENGINE_VERSION,
|
||||
}
|
||||
)
|
||||
result["overview"] = overview
|
||||
return result
|
||||
sys.modules[__name__] = _implementation
|
||||
|
||||
@@ -0,0 +1,92 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import ast
|
||||
import hashlib
|
||||
import unittest
|
||||
from pathlib import Path
|
||||
|
||||
|
||||
APP_ROOT = Path(__file__).resolve().parents[1]
|
||||
ORIGINAL_ROOT = APP_ROOT.parent
|
||||
|
||||
ROTATION_METHODS = {
|
||||
"rotation_history",
|
||||
"rotation_sector_members",
|
||||
}
|
||||
LADDER_ROTATION_BUILDERS = {
|
||||
"_build_ladders",
|
||||
"_build_sector_rotation",
|
||||
}
|
||||
|
||||
|
||||
def class_methods(path: Path, class_name: str) -> dict[str, str]:
|
||||
tree = ast.parse(path.read_text(encoding="utf-8"), filename=str(path))
|
||||
owner = next(
|
||||
node
|
||||
for node in tree.body
|
||||
if isinstance(node, ast.ClassDef) and node.name == class_name
|
||||
)
|
||||
return {
|
||||
node.name: ast.dump(node, include_attributes=False)
|
||||
for node in owner.body
|
||||
if isinstance(node, (ast.FunctionDef, ast.AsyncFunctionDef))
|
||||
}
|
||||
|
||||
|
||||
def top_level_functions(path: Path) -> dict[str, str]:
|
||||
tree = ast.parse(path.read_text(encoding="utf-8"), filename=str(path))
|
||||
return {
|
||||
node.name: ast.dump(node, include_attributes=False)
|
||||
for node in tree.body
|
||||
if isinstance(node, (ast.FunctionDef, ast.AsyncFunctionDef))
|
||||
and node.name in LADDER_ROTATION_BUILDERS
|
||||
}
|
||||
|
||||
|
||||
def sha256(path: Path) -> str:
|
||||
return hashlib.sha256(path.read_bytes()).hexdigest()
|
||||
|
||||
|
||||
class LadderRotationSliceSourceEquivalenceTests(unittest.TestCase):
|
||||
def test_rotation_service_methods_are_exact_original_ast(self) -> None:
|
||||
original = class_methods(ORIGINAL_ROOT / "server.py", "DashboardService")
|
||||
migrated = class_methods(
|
||||
APP_ROOT / "backend" / "features" / "rotation" / "service.py",
|
||||
"RotationServiceMixin",
|
||||
)
|
||||
self.assertEqual(set(migrated), ROTATION_METHODS)
|
||||
for name in sorted(ROTATION_METHODS):
|
||||
self.assertEqual(migrated[name], original[name], name)
|
||||
|
||||
def test_dashboard_service_no_longer_duplicates_rotation_methods(self) -> None:
|
||||
remaining = class_methods(
|
||||
APP_ROOT / "backend" / "application.py", "DashboardService"
|
||||
)
|
||||
self.assertTrue(ROTATION_METHODS.isdisjoint(remaining))
|
||||
|
||||
def test_ladder_and_rotation_builders_are_exact_original_ast(self) -> None:
|
||||
self.assertEqual(
|
||||
top_level_functions(ORIGINAL_ROOT / "tushare_client.py"),
|
||||
top_level_functions(
|
||||
APP_ROOT / "backend" / "data" / "providers" / "tushare_client.py"
|
||||
),
|
||||
)
|
||||
|
||||
def test_api_and_frontend_assets_are_unchanged(self) -> None:
|
||||
for relative in (
|
||||
"config/api.config.json",
|
||||
"static/index.html",
|
||||
"static/app.js",
|
||||
"static/styles.css",
|
||||
"static/pages/ladder/page.js",
|
||||
"static/pages/rotation/page.js",
|
||||
):
|
||||
self.assertEqual(
|
||||
sha256(APP_ROOT / relative),
|
||||
sha256(ORIGINAL_ROOT / relative),
|
||||
relative,
|
||||
)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
unittest.main()
|
||||
@@ -20,6 +20,7 @@ ORIGINAL_ROOT = APP_ROOT.parent
|
||||
|
||||
MARKET_METHODS = {
|
||||
"_tushare_client",
|
||||
"_market_insights",
|
||||
"get_dashboard",
|
||||
"_dashboard_sentiment_ready",
|
||||
"_display_compact_date",
|
||||
@@ -127,12 +128,15 @@ class MarketSliceSourceEquivalenceTests(unittest.TestCase):
|
||||
|
||||
def test_provider_logic_is_the_original_implementation(self) -> None:
|
||||
exact_moves = (
|
||||
("tushare_client.py", "backend/data/providers/tushare_client.py"),
|
||||
("ifind_client.py", "backend/data/providers/ifind_client.py"),
|
||||
("realtime_aggregator.py", "backend/data/realtime.py"),
|
||||
)
|
||||
for original, migrated in exact_moves:
|
||||
self.assertEqual(sha256(ORIGINAL_ROOT / original), sha256(APP_ROOT / migrated))
|
||||
self.assertEqual(
|
||||
top_level_definitions(ORIGINAL_ROOT / "tushare_client.py"),
|
||||
top_level_definitions(APP_ROOT / "backend/data/providers/tushare_client.py"),
|
||||
)
|
||||
self.assertEqual(
|
||||
top_level_definitions(ORIGINAL_ROOT / "chart_data_provider.py"),
|
||||
top_level_definitions(APP_ROOT / "backend/features/market/charts.py"),
|
||||
|
||||
@@ -0,0 +1,195 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import ast
|
||||
import hashlib
|
||||
import unittest
|
||||
from pathlib import Path
|
||||
|
||||
import market_insights
|
||||
from backend.features.market import insights as canonical_insights
|
||||
|
||||
|
||||
APP_ROOT = Path(__file__).resolve().parents[1]
|
||||
ORIGINAL_ROOT = APP_ROOT.parent
|
||||
|
||||
MARKET_INSIGHT_METHODS = {
|
||||
"__init__",
|
||||
"_trade_context",
|
||||
"_latest_feature_snapshot",
|
||||
"_auction_session",
|
||||
"_stock_master",
|
||||
"_expectation_label",
|
||||
"_auction_confirmation",
|
||||
"_attention_score",
|
||||
"_auction_candidates",
|
||||
"_auction_theme_evidence",
|
||||
"_auction_amount_history",
|
||||
"_ensure_auction_amount_history",
|
||||
"_with_auction_watchlist",
|
||||
"_dynamic_auction_rows",
|
||||
"auction_center",
|
||||
"_theme_directory",
|
||||
"theme_library",
|
||||
"theme_detail",
|
||||
"_parse_concepts",
|
||||
"popularity",
|
||||
"_hot_rows",
|
||||
"_normalize_hot",
|
||||
}
|
||||
MARKET_SERVICE_METHODS = {"_market_insights"}
|
||||
AUCTION_SERVICE_METHODS = {"auction_center"}
|
||||
THEME_SERVICE_METHODS = {"theme_library", "theme_detail"}
|
||||
POPULARITY_SERVICE_METHODS = {"popularity"}
|
||||
DRAGON_TIGER_SERVICE_METHODS = {
|
||||
"get_hot_money_profiles",
|
||||
"get_dragon_tiger",
|
||||
"_apply_seat_aliases",
|
||||
}
|
||||
AUCTION_REPOSITORY_METHODS = {
|
||||
"upsert_auction_factors",
|
||||
"auction_factor_dates",
|
||||
"auction_factors_for_date",
|
||||
}
|
||||
POPULARITY_REPOSITORY_METHODS = {"upsert_popularity_factors"}
|
||||
DRAGON_TIGER_REPOSITORY_METHODS = {
|
||||
"list_seat_aliases",
|
||||
"save_seat_alias",
|
||||
"upsert_lhb_institutions",
|
||||
}
|
||||
TUSHARE_METHODS = {"hot_money_profiles", "dragon_tiger"}
|
||||
|
||||
|
||||
def class_methods(path: Path, class_name: str) -> dict[str, str]:
|
||||
tree = ast.parse(path.read_text(encoding="utf-8"), filename=str(path))
|
||||
owner = next(
|
||||
node
|
||||
for node in tree.body
|
||||
if isinstance(node, ast.ClassDef) and node.name == class_name
|
||||
)
|
||||
return {
|
||||
node.name: ast.dump(node, include_attributes=False)
|
||||
for node in owner.body
|
||||
if isinstance(node, (ast.FunctionDef, ast.AsyncFunctionDef))
|
||||
}
|
||||
|
||||
|
||||
def sha256(path: Path) -> str:
|
||||
return hashlib.sha256(path.read_bytes()).hexdigest()
|
||||
|
||||
|
||||
class MarketInsightsSliceSourceEquivalenceTests(unittest.TestCase):
|
||||
def assert_methods_equal(
|
||||
self,
|
||||
original_path: Path,
|
||||
original_class: str,
|
||||
migrated_path: Path,
|
||||
migrated_class: str,
|
||||
names: set[str],
|
||||
) -> None:
|
||||
original = class_methods(original_path, original_class)
|
||||
migrated = class_methods(migrated_path, migrated_class)
|
||||
self.assertEqual(set(migrated), names)
|
||||
for name in sorted(names):
|
||||
self.assertEqual(migrated[name], original[name], name)
|
||||
|
||||
def test_shared_market_insight_service_is_exact_original_ast(self) -> None:
|
||||
self.assert_methods_equal(
|
||||
ORIGINAL_ROOT / "market_insights.py",
|
||||
"MarketInsightsService",
|
||||
APP_ROOT / "backend" / "features" / "market" / "insights.py",
|
||||
"MarketInsightsService",
|
||||
MARKET_INSIGHT_METHODS,
|
||||
)
|
||||
self.assertIs(market_insights.MarketInsightsService, canonical_insights.MarketInsightsService)
|
||||
|
||||
def test_dashboard_service_methods_are_exact_original_ast(self) -> None:
|
||||
original = ORIGINAL_ROOT / "server.py"
|
||||
mappings = (
|
||||
("auction/service.py", "AuctionServiceMixin", AUCTION_SERVICE_METHODS),
|
||||
("themes/service.py", "ThemeServiceMixin", THEME_SERVICE_METHODS),
|
||||
("popularity/service.py", "PopularityServiceMixin", POPULARITY_SERVICE_METHODS),
|
||||
("dragon_tiger/service.py", "DragonTigerServiceMixin", DRAGON_TIGER_SERVICE_METHODS),
|
||||
)
|
||||
for relative, class_name, names in mappings:
|
||||
with self.subTest(relative=relative):
|
||||
self.assert_methods_equal(
|
||||
original,
|
||||
"DashboardService",
|
||||
APP_ROOT / "backend" / "features" / relative,
|
||||
class_name,
|
||||
names,
|
||||
)
|
||||
original_methods = class_methods(original, "DashboardService")
|
||||
market_methods = class_methods(
|
||||
APP_ROOT / "backend" / "features" / "market" / "service.py",
|
||||
"MarketServiceMixin",
|
||||
)
|
||||
for name in MARKET_SERVICE_METHODS:
|
||||
self.assertEqual(market_methods[name], original_methods[name], name)
|
||||
|
||||
def test_repository_methods_are_exact_original_ast(self) -> None:
|
||||
original = ORIGINAL_ROOT / "database.py"
|
||||
mappings = (
|
||||
("auction/repository.py", "AuctionRepositoryMixin", AUCTION_REPOSITORY_METHODS),
|
||||
("popularity/repository.py", "PopularityRepositoryMixin", POPULARITY_REPOSITORY_METHODS),
|
||||
("dragon_tiger/repository.py", "DragonTigerRepositoryMixin", DRAGON_TIGER_REPOSITORY_METHODS),
|
||||
)
|
||||
for relative, class_name, names in mappings:
|
||||
with self.subTest(relative=relative):
|
||||
self.assert_methods_equal(
|
||||
original,
|
||||
"ReviewDatabase",
|
||||
APP_ROOT / "backend" / "features" / relative,
|
||||
class_name,
|
||||
names,
|
||||
)
|
||||
|
||||
def test_original_classes_no_longer_duplicate_moved_methods(self) -> None:
|
||||
remaining_service = class_methods(
|
||||
APP_ROOT / "backend" / "application.py", "DashboardService"
|
||||
)
|
||||
remaining_database = class_methods(APP_ROOT / "database.py", "ReviewDatabase")
|
||||
moved_service = (
|
||||
MARKET_SERVICE_METHODS
|
||||
| AUCTION_SERVICE_METHODS
|
||||
| THEME_SERVICE_METHODS
|
||||
| POPULARITY_SERVICE_METHODS
|
||||
| DRAGON_TIGER_SERVICE_METHODS
|
||||
)
|
||||
moved_repository = (
|
||||
AUCTION_REPOSITORY_METHODS
|
||||
| POPULARITY_REPOSITORY_METHODS
|
||||
| DRAGON_TIGER_REPOSITORY_METHODS
|
||||
)
|
||||
self.assertTrue(moved_service.isdisjoint(remaining_service))
|
||||
self.assertTrue(moved_repository.isdisjoint(remaining_database))
|
||||
|
||||
def test_tushare_dragon_tiger_implementations_are_exact_original_ast(self) -> None:
|
||||
original = class_methods(ORIGINAL_ROOT / "tushare_client.py", "TushareClient")
|
||||
migrated = class_methods(
|
||||
APP_ROOT / "backend" / "data" / "providers" / "tushare_client.py",
|
||||
"TushareClient",
|
||||
)
|
||||
for name in sorted(TUSHARE_METHODS):
|
||||
self.assertEqual(migrated[name], original[name], name)
|
||||
|
||||
def test_api_and_frontend_assets_are_unchanged(self) -> None:
|
||||
for relative in (
|
||||
"config/api.config.json",
|
||||
"static/index.html",
|
||||
"static/app.js",
|
||||
"static/styles.css",
|
||||
"static/pages/auction/page.js",
|
||||
"static/pages/themes/page.js",
|
||||
"static/pages/popularity/page.js",
|
||||
"static/pages/dragon-tiger/page.js",
|
||||
):
|
||||
self.assertEqual(
|
||||
sha256(APP_ROOT / relative),
|
||||
sha256(ORIGINAL_ROOT / relative),
|
||||
relative,
|
||||
)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
unittest.main()
|
||||
@@ -0,0 +1,114 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import ast
|
||||
import hashlib
|
||||
import unittest
|
||||
from pathlib import Path
|
||||
|
||||
import sentiment_engine
|
||||
from backend.features.sentiment import engine as canonical_engine
|
||||
|
||||
|
||||
APP_ROOT = Path(__file__).resolve().parents[1]
|
||||
ORIGINAL_ROOT = APP_ROOT.parent
|
||||
|
||||
SENTIMENT_METHODS = {
|
||||
"_enrich_dashboard_sentiment",
|
||||
"sentiment_history",
|
||||
}
|
||||
POOL_METHODS = {
|
||||
"save_reason",
|
||||
"_apply_reason_overrides",
|
||||
"_schedule_ifind_event_enrichment",
|
||||
"_refresh_ifind_event_enrichment",
|
||||
"_normalize_ifind_event_time",
|
||||
"_merge_ifind_event_enrichment",
|
||||
}
|
||||
POOL_REPOSITORY_METHODS = {
|
||||
"save_reason_override",
|
||||
"reason_overrides",
|
||||
}
|
||||
|
||||
|
||||
def class_methods(path: Path, class_name: str) -> dict[str, str]:
|
||||
tree = ast.parse(path.read_text(encoding="utf-8"), filename=str(path))
|
||||
owner = next(
|
||||
node
|
||||
for node in tree.body
|
||||
if isinstance(node, ast.ClassDef) and node.name == class_name
|
||||
)
|
||||
return {
|
||||
node.name: ast.dump(node, include_attributes=False)
|
||||
for node in owner.body
|
||||
if isinstance(node, (ast.FunctionDef, ast.AsyncFunctionDef))
|
||||
}
|
||||
|
||||
|
||||
def sha256(path: Path) -> str:
|
||||
return hashlib.sha256(path.read_bytes()).hexdigest()
|
||||
|
||||
|
||||
class SentimentPoolSliceSourceEquivalenceTests(unittest.TestCase):
|
||||
def test_sentiment_service_methods_are_exact_original_ast(self) -> None:
|
||||
original = class_methods(ORIGINAL_ROOT / "server.py", "DashboardService")
|
||||
migrated = class_methods(
|
||||
APP_ROOT / "backend" / "features" / "sentiment" / "service.py",
|
||||
"SentimentServiceMixin",
|
||||
)
|
||||
self.assertEqual(set(migrated), SENTIMENT_METHODS)
|
||||
for name in sorted(SENTIMENT_METHODS):
|
||||
self.assertEqual(migrated[name], original[name], name)
|
||||
|
||||
def test_pool_service_methods_are_exact_original_ast(self) -> None:
|
||||
original = class_methods(ORIGINAL_ROOT / "server.py", "DashboardService")
|
||||
migrated = class_methods(
|
||||
APP_ROOT / "backend" / "features" / "pools" / "service.py",
|
||||
"PoolServiceMixin",
|
||||
)
|
||||
self.assertEqual(set(migrated), POOL_METHODS)
|
||||
for name in sorted(POOL_METHODS):
|
||||
self.assertEqual(migrated[name], original[name], name)
|
||||
|
||||
def test_pool_repository_methods_are_exact_original_ast(self) -> None:
|
||||
original = class_methods(ORIGINAL_ROOT / "database.py", "ReviewDatabase")
|
||||
migrated = class_methods(
|
||||
APP_ROOT / "backend" / "features" / "pools" / "repository.py",
|
||||
"PoolRepositoryMixin",
|
||||
)
|
||||
self.assertEqual(set(migrated), POOL_REPOSITORY_METHODS)
|
||||
for name in sorted(POOL_REPOSITORY_METHODS):
|
||||
self.assertEqual(migrated[name], original[name], name)
|
||||
|
||||
def test_original_classes_no_longer_duplicate_moved_methods(self) -> None:
|
||||
remaining_service = class_methods(
|
||||
APP_ROOT / "backend" / "application.py", "DashboardService"
|
||||
)
|
||||
remaining_database = class_methods(APP_ROOT / "database.py", "ReviewDatabase")
|
||||
self.assertTrue((SENTIMENT_METHODS | POOL_METHODS).isdisjoint(remaining_service))
|
||||
self.assertTrue(POOL_REPOSITORY_METHODS.isdisjoint(remaining_database))
|
||||
|
||||
def test_sentiment_engine_is_exact_original_with_legacy_alias(self) -> None:
|
||||
self.assertEqual(
|
||||
sha256(ORIGINAL_ROOT / "sentiment_engine.py"),
|
||||
sha256(APP_ROOT / "backend" / "features" / "sentiment" / "engine.py"),
|
||||
)
|
||||
self.assertIs(sentiment_engine, canonical_engine)
|
||||
|
||||
def test_api_and_frontend_assets_are_unchanged(self) -> None:
|
||||
for relative in (
|
||||
"config/api.config.json",
|
||||
"static/index.html",
|
||||
"static/app.js",
|
||||
"static/styles.css",
|
||||
"static/pages/sentiment/page.js",
|
||||
"static/pages/pools/page.js",
|
||||
):
|
||||
self.assertEqual(
|
||||
sha256(APP_ROOT / relative),
|
||||
sha256(ORIGINAL_ROOT / relative),
|
||||
relative,
|
||||
)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
unittest.main()
|
||||
@@ -0,0 +1,111 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import argparse
|
||||
import hashlib
|
||||
import http.cookiejar
|
||||
import json
|
||||
import urllib.error
|
||||
import urllib.request
|
||||
from pathlib import Path
|
||||
from typing import Any
|
||||
|
||||
|
||||
def request_json(
|
||||
opener: urllib.request.OpenerDirector,
|
||||
url: str,
|
||||
payload: dict[str, Any] | None = None,
|
||||
) -> tuple[int, Any]:
|
||||
data = None
|
||||
headers = {"Accept": "application/json"}
|
||||
if payload is not None:
|
||||
data = json.dumps(payload, ensure_ascii=False).encode("utf-8")
|
||||
headers["Content-Type"] = "application/json"
|
||||
request = urllib.request.Request(url, data=data, headers=headers)
|
||||
try:
|
||||
with opener.open(request, timeout=90) as response:
|
||||
return response.status, json.loads(response.read().decode("utf-8"))
|
||||
except urllib.error.HTTPError as exc:
|
||||
return exc.code, json.loads(exc.read().decode("utf-8"))
|
||||
|
||||
|
||||
def session(base_url: str, username: str, password: str) -> urllib.request.OpenerDirector:
|
||||
opener = urllib.request.build_opener(
|
||||
urllib.request.HTTPCookieProcessor(http.cookiejar.CookieJar())
|
||||
)
|
||||
status, body = request_json(
|
||||
opener,
|
||||
f"{base_url.rstrip('/')}/api/auth/login",
|
||||
{"username": username, "password": password},
|
||||
)
|
||||
if status != 200 or not body.get("ok"):
|
||||
raise RuntimeError(f"Login failed for {base_url}: HTTP {status} {body}")
|
||||
return opener
|
||||
|
||||
|
||||
def digest(value: Any) -> str:
|
||||
content = json.dumps(
|
||||
value, ensure_ascii=False, sort_keys=True, separators=(",", ":")
|
||||
).encode("utf-8")
|
||||
return hashlib.sha256(content).hexdigest()
|
||||
|
||||
|
||||
def comparable(value: Any) -> Any:
|
||||
if isinstance(value, dict):
|
||||
return {
|
||||
key: comparable(item)
|
||||
for key, item in value.items()
|
||||
if key != "request_id"
|
||||
}
|
||||
if isinstance(value, list):
|
||||
return [comparable(item) for item in value]
|
||||
return value
|
||||
|
||||
|
||||
def main() -> None:
|
||||
parser = argparse.ArgumentParser(description="Compare authenticated preservation APIs")
|
||||
parser.add_argument("--original", required=True)
|
||||
parser.add_argument("--migrated", required=True)
|
||||
parser.add_argument("--username", required=True)
|
||||
parser.add_argument("--password", required=True)
|
||||
parser.add_argument("--output", type=Path, required=True)
|
||||
parser.add_argument("endpoints", nargs="+")
|
||||
args = parser.parse_args()
|
||||
|
||||
original = session(args.original, args.username, args.password)
|
||||
migrated = session(args.migrated, args.username, args.password)
|
||||
rows = []
|
||||
all_equal = True
|
||||
for endpoint in args.endpoints:
|
||||
original_status, original_body = request_json(
|
||||
original, f"{args.original.rstrip('/')}{endpoint}"
|
||||
)
|
||||
migrated_status, migrated_body = request_json(
|
||||
migrated, f"{args.migrated.rstrip('/')}{endpoint}"
|
||||
)
|
||||
original_comparable = comparable(original_body)
|
||||
migrated_comparable = comparable(migrated_body)
|
||||
equal = original_status == migrated_status and original_comparable == migrated_comparable
|
||||
all_equal = all_equal and equal
|
||||
rows.append(
|
||||
{
|
||||
"endpoint": endpoint,
|
||||
"original_status": original_status,
|
||||
"migrated_status": migrated_status,
|
||||
"original_sha256": digest(original_comparable),
|
||||
"migrated_sha256": digest(migrated_comparable),
|
||||
"equal": equal,
|
||||
}
|
||||
)
|
||||
|
||||
result = {"all_equal": all_equal, "endpoints": rows}
|
||||
args.output.parent.mkdir(parents=True, exist_ok=True)
|
||||
args.output.write_text(
|
||||
json.dumps(result, ensure_ascii=False, indent=2) + "\n", encoding="utf-8"
|
||||
)
|
||||
print(json.dumps(result, ensure_ascii=False, indent=2))
|
||||
if not all_equal:
|
||||
raise SystemExit(1)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -0,0 +1,92 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import argparse
|
||||
import hashlib
|
||||
import json
|
||||
import sqlite3
|
||||
from pathlib import Path
|
||||
from typing import Any
|
||||
|
||||
|
||||
def digest(value: Any) -> str:
|
||||
content = json.dumps(
|
||||
value, ensure_ascii=False, sort_keys=True, separators=(",", ":"), default=str
|
||||
).encode("utf-8")
|
||||
return hashlib.sha256(content).hexdigest()
|
||||
|
||||
|
||||
def schema(connection: sqlite3.Connection) -> list[dict[str, Any]]:
|
||||
rows = connection.execute(
|
||||
"""
|
||||
SELECT type, name, tbl_name, sql
|
||||
FROM sqlite_master
|
||||
WHERE name NOT LIKE 'sqlite_%'
|
||||
ORDER BY type, name
|
||||
"""
|
||||
).fetchall()
|
||||
return [dict(row) for row in rows]
|
||||
|
||||
|
||||
def table_rows(connection: sqlite3.Connection, table: str) -> list[dict[str, Any]]:
|
||||
quoted = '"' + table.replace('"', '""') + '"'
|
||||
rows = [dict(row) for row in connection.execute(f"SELECT * FROM {quoted}").fetchall()]
|
||||
return sorted(rows, key=lambda row: json.dumps(row, ensure_ascii=False, sort_keys=True, default=str))
|
||||
|
||||
|
||||
def main() -> None:
|
||||
parser = argparse.ArgumentParser(description="Compare preservation SQLite databases")
|
||||
parser.add_argument("--original", type=Path, required=True)
|
||||
parser.add_argument("--migrated", type=Path, required=True)
|
||||
parser.add_argument("--output", type=Path, required=True)
|
||||
parser.add_argument("tables", nargs="+")
|
||||
args = parser.parse_args()
|
||||
|
||||
original = sqlite3.connect(args.original)
|
||||
migrated = sqlite3.connect(args.migrated)
|
||||
original.row_factory = sqlite3.Row
|
||||
migrated.row_factory = sqlite3.Row
|
||||
try:
|
||||
original_schema = schema(original)
|
||||
migrated_schema = schema(migrated)
|
||||
tables = []
|
||||
all_equal = original_schema == migrated_schema
|
||||
for table in args.tables:
|
||||
original_rows = table_rows(original, table)
|
||||
migrated_rows = table_rows(migrated, table)
|
||||
equal = original_rows == migrated_rows
|
||||
all_equal = all_equal and equal
|
||||
tables.append(
|
||||
{
|
||||
"table": table,
|
||||
"original_count": len(original_rows),
|
||||
"migrated_count": len(migrated_rows),
|
||||
"original_sha256": digest(original_rows),
|
||||
"migrated_sha256": digest(migrated_rows),
|
||||
"equal": equal,
|
||||
}
|
||||
)
|
||||
result = {
|
||||
"all_equal": all_equal,
|
||||
"schema": {
|
||||
"object_count": len(original_schema),
|
||||
"original_sha256": digest(original_schema),
|
||||
"migrated_sha256": digest(migrated_schema),
|
||||
"equal": original_schema == migrated_schema,
|
||||
},
|
||||
"tables": tables,
|
||||
}
|
||||
finally:
|
||||
original.close()
|
||||
migrated.close()
|
||||
|
||||
args.output.parent.mkdir(parents=True, exist_ok=True)
|
||||
args.output.write_text(
|
||||
json.dumps(result, ensure_ascii=False, indent=2) + "\n", encoding="utf-8"
|
||||
)
|
||||
print(json.dumps(result, ensure_ascii=False, indent=2))
|
||||
if not all_equal:
|
||||
raise SystemExit(1)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -0,0 +1,46 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import argparse
|
||||
import sys
|
||||
from http.server import ThreadingHTTPServer
|
||||
from pathlib import Path
|
||||
|
||||
|
||||
def main() -> None:
|
||||
parser = argparse.ArgumentParser(description="Run an isolated preservation runtime")
|
||||
parser.add_argument("--runtime-root", type=Path, required=True)
|
||||
parser.add_argument("--data-dir", type=Path, required=True)
|
||||
parser.add_argument("--port", type=int, required=True)
|
||||
args = parser.parse_args()
|
||||
|
||||
runtime_root = args.runtime_root.resolve()
|
||||
data_dir = args.data_dir.resolve()
|
||||
data_dir.mkdir(parents=True, exist_ok=True)
|
||||
sys.path.insert(0, str(runtime_root))
|
||||
|
||||
if (runtime_root / "backend" / "bootstrap" / "config.py").is_file():
|
||||
from backend.bootstrap import config
|
||||
|
||||
config.DATA_DIR = data_dir
|
||||
config.PRIVATE_MENTOR_SKILLS_DIR = data_dir / "private-mentor-skills"
|
||||
else:
|
||||
import app_config as config
|
||||
|
||||
config.DATA_DIR = data_dir
|
||||
config.PRIVATE_MENTOR_SKILLS_DIR = data_dir / "private-mentor-skills"
|
||||
|
||||
from server import RequestHandler, SERVICE
|
||||
|
||||
server = ThreadingHTTPServer(("127.0.0.1", args.port), RequestHandler)
|
||||
print(f"Preservation runtime is running at http://127.0.0.1:{args.port}", flush=True)
|
||||
try:
|
||||
server.serve_forever()
|
||||
except KeyboardInterrupt:
|
||||
pass
|
||||
finally:
|
||||
SERVICE._background_stop.set()
|
||||
server.server_close()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -0,0 +1,66 @@
|
||||
# 切片 03：情绪周期、五类股池与涨停表现
|
||||
|
||||
> 基线：`a426432`（切片 02）
|
||||
> 回档标签：`xiaobai-preservation-slice-03-20260731`
|
||||
> 结论：源码、API、数据库、真实页面和浏览器回归通过；最终视觉仍等待全站人工验收
|
||||
|
||||
## 1. 原实现归位
|
||||
|
||||
本切片只移动原版副本中的真实实现，没有从 `next/` 取用代码，也没有改写情绪公式、股池数据、
|
||||
原因补全、表格、样式或交互。
|
||||
|
||||
| 原位置 | 新的唯一实现位置 | 原位置兼容 |
|
||||
|---|---|---|
|
||||
| `app/backend/application.py` 的 2 个情绪服务方法 | `app/backend/features/sentiment/service.py` | `DashboardService` 继承 `SentimentServiceMixin` |
|
||||
| `app/backend/application.py` 的 6 个股池原因及事件补全方法 | `app/backend/features/pools/service.py` | `DashboardService` 继承 `PoolServiceMixin` |
|
||||
| `app/database.py` 的 2 个原因覆盖方法 | `app/backend/features/pools/repository.py` | `ReviewDatabase` 继承 `PoolRepositoryMixin` |
|
||||
| `app/sentiment_engine.py` | `app/backend/features/sentiment/engine.py` | 根模块为同一模块对象的兼容别名 |
|
||||
|
||||
五类股池、涨停梯队和涨停表现仍由切片 02 已归位的原 Tushare 总览实现生成，本切片没有建立第二套
|
||||
计算或数据来源。
|
||||
|
||||
## 2. 等价证据
|
||||
|
||||
- `test_preservation_slice_sentiment_pools.py` 对 8 个业务方法和 2 个 Repository 方法逐项执行无位置
|
||||
信息 AST 比较，全部与根目录原版 `server.py`、`database.py` 完全相同。
|
||||
- 新的情绪引擎文件与原版 `sentiment_engine.py` SHA-256 完全相同；根级兼容模块与新模块是同一模块对象。
|
||||
- 已归位的应用、行情服务、Tushare Provider、演示数据和选股模块直接导入新的唯一实现；Tushare
|
||||
Provider 仅调整该导入，其全部类和函数 AST 继续与原版一致。
|
||||
- 原版 `8784` 与迁移版 `8785` 在相同账号、日期和数据库副本上请求 `/api/dashboard` 与
|
||||
`/api/sentiment/history`，JSON 状态、字段、值和顺序完全相同。
|
||||
- 2026-07-30 的同请求结果均为：涨停 56、炸板 23、跌停 83、昨日涨停 81、情绪历史 20 日。
|
||||
- 原版和迁移版数据库均为 62 个 schema 对象，schema 哈希均为
|
||||
`17918327f8b919496e6630458293f9f777c7c24662625bb3fc0b64ff0a8fbeef`。
|
||||
- `config/api.config.json`、API 路径、鉴权和 `app/static/` 未修改。
|
||||
- `app-light-1920x1080.png` 是真实迁移服务载入完成后的情绪周期页面，SHA-256 为
|
||||
`e387417abbe0667e00875a8d4061b5546748ecf2452a692d06d078d516330dab`。
|
||||
|
||||
## 3. 真实运行检查
|
||||
|
||||
- 迁移副本：`http://127.0.0.1:8785/`，管理员会话与缓存行情载入正常。
|
||||
- 情绪周期：20 个连续交易日、当前阶段、评分构成和交易日明细均完整显示。
|
||||
- 股池：涨停池 56 行、炸板池 23 行、跌停池 83 行、昨日涨停 81 行。
|
||||
- 涨停表现：四档晋级率、市场宽度和今日结论均显示原版结果。
|
||||
- 1920×1080 下六个页面横向溢出均为 0；日间、夜间背景与面板状态正常；浏览器控制台无迁移错误。
|
||||
|
||||
## 4. 自动验证
|
||||
|
||||
| 验证 | 结果 |
|
||||
|---|---:|
|
||||
| `python -m unittest discover -s tests -q` | 248 项通过 |
|
||||
| `python -m unittest tests.test_preservation_slice_sentiment_pools -q` | 6 项通过 |
|
||||
| 情绪、总览、缓存、iFinD 与前端契约专项集合 | 48 项通过 |
|
||||
| `npx.cmd playwright test --reporter=dot` | 45 项通过 |
|
||||
| `python -m compileall -q ...` | 通过 |
|
||||
| `git diff --check` | 通过 |
|
||||
|
||||
Windows 下由 Playwright 自行创建临时静态服务器时，45 项完成后子进程无法回收；改为预先启动同一个
|
||||
`8876` 静态服务器并让 Playwright 复用后，测试以零退出码正常结束，结果为 `45 passed (2.1m)`。
|
||||
|
||||
## 5. 保留边界
|
||||
|
||||
- 板块轮动仍调用情绪历史公共函数，待切片 04 与市场天梯一并归位。
|
||||
- 竞价、题材、人气和龙虎榜对股池数据的消费保持原调用路径，待切片 05 迁移。
|
||||
- 根级情绪引擎兼容模块、`DashboardService` 与 `ReviewDatabase` 兼容面继续保留；数据库内尚未迁移的
|
||||
选股统计方法仍走兼容别名，待切片 06 随完整方法一并归位。
|
||||
- 没有删除待定代码、没有改动根目录正式数据库、没有切换 Docker/NAS。
|
||||
Binary file not shown.
|
After Width: | Height: | Size: 151 KiB |
@@ -0,0 +1,58 @@
|
||||
# 切片 04：市场天梯与板块轮动
|
||||
|
||||
> 基线：`b3555d2`（切片 03）
|
||||
> 回档标签：`xiaobai-preservation-slice-04-20260731`
|
||||
> 结论：源码、API、真实页面和浏览器回归通过；最终视觉仍等待全站人工验收
|
||||
|
||||
## 1. 原实现归位
|
||||
|
||||
本切片从原版副本机械移动板块轮动服务，没有从 `next/` 取用代码，也没有修改天梯、轮动的计算、
|
||||
排序、展开、配色、页面结构或交互。
|
||||
|
||||
| 原位置 | 新的唯一实现位置 | 原位置兼容 |
|
||||
|---|---|---|
|
||||
| `app/backend/application.py` 的 2 个轮动方法 | `app/backend/features/rotation/service.py` | `DashboardService` 继承 `RotationServiceMixin` |
|
||||
| Tushare Provider 的天梯与轮动构造函数 | 保持 `app/backend/data/providers/tushare_client.py` | 切片 02 已归位的公共数据实现 |
|
||||
|
||||
市场天梯没有独立后端 API 或第二套计算，直接展示 `/api/dashboard` 中原 Tushare 实现生成的
|
||||
`ladders`；因此没有为目录形式建立空的天梯服务。
|
||||
|
||||
## 2. 等价证据
|
||||
|
||||
- `test_preservation_slice_ladder_rotation.py` 对 2 个轮动服务方法逐项执行无位置信息 AST 比较，
|
||||
全部与根目录原版 `server.py` 完全相同。
|
||||
- `_build_ladders` 与 `_build_sector_rotation` 两个原数据构造函数的 AST 与根目录原版完全相同。
|
||||
- 原版 `8784` 与迁移版 `8785` 在相同账号、日期和数据库副本上返回的天梯数据及 9 日轮动历史
|
||||
JSON 逐字段完全相同。
|
||||
- 成分股接口在当前外部网络状态下两版均返回 HTTP 400、`bad_request` 和相同的
|
||||
`该板块成分股暂不可用：Tushare request failed:`，没有改变错误或增加静默降级。
|
||||
- `config/api.config.json`、API 路径、鉴权、数据库 schema 和 `app/static/` 未修改。
|
||||
|
||||
## 3. 真实运行检查
|
||||
|
||||
- 市场天梯：8 个层级（含断层）、18 个首屏股票单元格、3 个结构分析模块正常；1920×1080 下
|
||||
页面宽度无溢出，首板展开入口保留。
|
||||
- 板块轮动：9 个交易日、每日 Top 12 共 108 个板块单元格、由远到近/由近到远两个排序入口正常；
|
||||
1920×1080 下页面宽度无溢出并保持全页滚动。
|
||||
- 日间模式页面控制台没有错误或警告。
|
||||
- `app-light-ladder-1920x1080.png` SHA-256：
|
||||
`9e57d18d92e745fd92131f7bf08f21faaaa745476dd942cdaa2a703b9a7a303a`。
|
||||
- `app-light-rotation-1920x1080.png` SHA-256：
|
||||
`03c092bb40bd0eb672136dff853abffc87e9790840107c2f22e6cf31d5f83c09`。
|
||||
|
||||
## 4. 自动验证
|
||||
|
||||
| 验证 | 结果 |
|
||||
|---|---:|
|
||||
| `python -m unittest discover -s tests -q` | 252 项通过 |
|
||||
| `python -m unittest tests.test_preservation_slice_ladder_rotation -q` | 4 项通过 |
|
||||
| 切片 02 至 04 与总览缓存专项集合 | 24 项通过 |
|
||||
| `npx.cmd playwright test --reporter=dot` | 45 项通过 |
|
||||
| `git diff --check` | 通过 |
|
||||
|
||||
## 5. 保留边界
|
||||
|
||||
- 成分股接口依赖的日行情与因子持久化方法仍由原 `ReviewDatabase` 提供，因其同时服务智能选股，
|
||||
待切片 06 随完整共享职责归位。
|
||||
- 天梯和轮动前端资产保持原位，切片 10 再按页面职责归档；当前没有复制或改写。
|
||||
- 没有删除待定代码、没有改动根目录正式数据库、没有切换 Docker/NAS。
|
||||
Binary file not shown.
|
After Width: | Height: | Size: 139 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 198 KiB |
@@ -0,0 +1,69 @@
|
||||
# 切片 05：集合竞价、题材库、人气热榜与龙虎榜
|
||||
|
||||
> 基线：`814e757`（切片 04）
|
||||
> 回档标签：`xiaobai-preservation-slice-05-20260731`
|
||||
> 结论：源码、API、数据库、真实页面和全量回归通过；最终视觉仍等待全站人工验收
|
||||
|
||||
## 1. 原实现归位
|
||||
|
||||
本切片没有从`next/`取用代码，也没有重写计算、页面或接口。集合竞价、题材库和人气热榜原本
|
||||
共享`MarketInsightsService`，其中竞价候选会直接调用人气榜热度数据，因此整体移动到公共行情
|
||||
领域，避免拆出互相复制的实现；各页面入口仍按功能目录归位。
|
||||
|
||||
| 原位置 | 新的唯一实现位置 | 兼容方式 |
|
||||
|---|---|---|
|
||||
| `app/market_insights.py` | `app/backend/features/market/insights.py` | 根级模块导出同一类对象 |
|
||||
| `DashboardService`竞价入口 | `app/backend/features/auction/service.py` | `AuctionServiceMixin` |
|
||||
| `DashboardService`题材入口 | `app/backend/features/themes/service.py` | `ThemeServiceMixin` |
|
||||
| `DashboardService`人气入口 | `app/backend/features/popularity/service.py` | `PopularityServiceMixin` |
|
||||
| `DashboardService`龙虎榜及游资档案 | `app/backend/features/dragon_tiger/service.py` | `DragonTigerServiceMixin` |
|
||||
| 竞价、人气、龙虎榜持久化方法 | 对应功能目录的`repository.py` | `ReviewDatabase`继承原接口 |
|
||||
|
||||
Tushare Provider 中`hot_money_profiles`与`dragon_tiger`继续保持切片02归位的唯一实现，没有为目录
|
||||
形式再制造一套数据构造逻辑。
|
||||
|
||||
## 2. 源码与接口等价
|
||||
|
||||
- `test_preservation_slice_market_insights.py`逐项比较22个市场洞察方法、8个页面服务方法、7个
|
||||
Repository方法和2个Tushare方法，全部与根目录原版无位置信息AST一致。
|
||||
- `DashboardService`与`ReviewDatabase`不再重复保留已移动方法；根级`market_insights`与新模块
|
||||
暴露同一个`MarketInsightsService`类对象。
|
||||
- 原版`8784`和迁移版`8785`使用同一数据库的独立副本，集合竞价、题材库、题材详情、人气热榜、
|
||||
龙虎榜、游资档案和席位别名共7个真实API状态码及JSON一致。
|
||||
- 题材详情在当前外部网络条件下两版均返回HTTP 400；差分只排除每次请求随机生成的
|
||||
`request_id`，错误码与错误内容仍完全一致。
|
||||
- 完整接口摘要见`api-diff.json`。
|
||||
|
||||
## 3. 数据库差分
|
||||
|
||||
- 两个副本均为62个schema对象，哈希均为
|
||||
`60a4e044f0ddb6502e44b45f65bedc1a0a31d4bd596f386d4ccfe153a6c8ddd1`。
|
||||
- `auction_factors` 511914行、`popularity_factors` 232行、`lhb_institution_daily` 47行、
|
||||
`seat_aliases` 0行、`stock_master` 5535行均逐行一致。
|
||||
- 完整表计数与哈希见`database-diff.json`；运行数据库副本已在验收后删除，未提交凭据或正式数据。
|
||||
|
||||
## 4. 真实浏览器检查
|
||||
|
||||
- 1920×1080日间模式检查集合竞价、题材库、人气热榜和龙虎榜四页；均无横向溢出，控制台无
|
||||
错误或警告。
|
||||
- 集合竞价载入30行重点候选；题材库载入394个题材及选中题材成分股；人气热榜载入3个摘要
|
||||
模块和200行综合榜；龙虎榜按当前缓存显示既有不可用空态。
|
||||
- 四页HTML、主JS、CSS及各自页面JS与根目录原版字节哈希一致。
|
||||
- 截图SHA-256：
|
||||
- `app-light-auction-1920x1080.png`：`8065086c8f2b360aeb1004bd60429f3e2d7f1b8c872ec4a965e9a5d0c016b915`
|
||||
- `app-light-themes-1920x1080.png`：`80e1e41101497ee7213e1dadfaa4c9572a1c47b3495edd09e36745ccdb639402`
|
||||
- `app-light-popularity-1920x1080.png`：`614d6b7770f4e9ec72c059ab8a4129486df9eff5c4dd7e8279cc68ebcb80a36b`
|
||||
- `app-light-dragon-tiger-1920x1080.png`：`2f1e2ad3a9bc3874c73bae884fc8744177cef354c7176559da1453fab1f85993`
|
||||
|
||||
## 5. 自动验证与保留边界
|
||||
|
||||
| 验证 | 结果 |
|
||||
|---|---:|
|
||||
| `python -m unittest discover -s tests -q` | 258项通过 |
|
||||
| `python -m unittest tests.test_preservation_slice_market_insights -q` | 6项通过 |
|
||||
| `npx.cmd playwright test --reporter=dot` | 45项通过 |
|
||||
| `git diff --check` | 通过 |
|
||||
|
||||
- 竞价、人气和龙虎榜因子同时服务切片06智能选股，迁移后仍由`ReviewDatabase`原方法名暴露。
|
||||
- 前端资产保持原位置，切片10再按页面职责归档；本切片没有改DOM、CSS、动画或交互。
|
||||
- 没有删除待定代码、没有修改根目录正式数据库、没有切换Docker/NAS。
|
||||
@@ -0,0 +1,61 @@
|
||||
{
|
||||
"all_equal": true,
|
||||
"endpoints": [
|
||||
{
|
||||
"endpoint": "/api/auction?trade_date=2026-07-29",
|
||||
"original_status": 200,
|
||||
"migrated_status": 200,
|
||||
"original_sha256": "523144cc14d876577b7d518cdf38fd2722b13a8f01ed5d2e20dcc38f6a2624ce",
|
||||
"migrated_sha256": "523144cc14d876577b7d518cdf38fd2722b13a8f01ed5d2e20dcc38f6a2624ce",
|
||||
"equal": true
|
||||
},
|
||||
{
|
||||
"endpoint": "/api/themes?trade_date=2026-07-29",
|
||||
"original_status": 200,
|
||||
"migrated_status": 200,
|
||||
"original_sha256": "95c2ad418f18d877d94ec2a71fe6fafd5e329b069d87a9300f7fcac92d4ba5d1",
|
||||
"migrated_sha256": "95c2ad418f18d877d94ec2a71fe6fafd5e329b069d87a9300f7fcac92d4ba5d1",
|
||||
"equal": true
|
||||
},
|
||||
{
|
||||
"endpoint": "/api/themes/detail?code=885001.TI&trade_date=2026-07-29",
|
||||
"original_status": 400,
|
||||
"migrated_status": 400,
|
||||
"original_sha256": "b11a3314b172d3ad6ba28d969fcd6a9a2a4b49e7d29ed804496a4dfecd2a364a",
|
||||
"migrated_sha256": "b11a3314b172d3ad6ba28d969fcd6a9a2a4b49e7d29ed804496a4dfecd2a364a",
|
||||
"equal": true
|
||||
},
|
||||
{
|
||||
"endpoint": "/api/popularity?trade_date=2026-07-29",
|
||||
"original_status": 200,
|
||||
"migrated_status": 200,
|
||||
"original_sha256": "e62a93c41f7c3f95c3d47f8ccaedaa809563c8dd65c04dd54b164f73e6014e94",
|
||||
"migrated_sha256": "e62a93c41f7c3f95c3d47f8ccaedaa809563c8dd65c04dd54b164f73e6014e94",
|
||||
"equal": true
|
||||
},
|
||||
{
|
||||
"endpoint": "/api/dragon-tiger?trade_date=2026-07-29",
|
||||
"original_status": 200,
|
||||
"migrated_status": 200,
|
||||
"original_sha256": "a3998b935377d5fd0673ec5b9214d1b0a64680d155c61e6cff49d0d5fcf0e843",
|
||||
"migrated_sha256": "a3998b935377d5fd0673ec5b9214d1b0a64680d155c61e6cff49d0d5fcf0e843",
|
||||
"equal": true
|
||||
},
|
||||
{
|
||||
"endpoint": "/api/dragon-tiger/profiles",
|
||||
"original_status": 200,
|
||||
"migrated_status": 200,
|
||||
"original_sha256": "dfb1e534c018ec12fd8d8ee0e7fe0f234e73dc7715d7f0a948f74ef8d12d779a",
|
||||
"migrated_sha256": "dfb1e534c018ec12fd8d8ee0e7fe0f234e73dc7715d7f0a948f74ef8d12d779a",
|
||||
"equal": true
|
||||
},
|
||||
{
|
||||
"endpoint": "/api/seat-aliases",
|
||||
"original_status": 200,
|
||||
"migrated_status": 200,
|
||||
"original_sha256": "2b0fb0a6b3e353c69158d61221c2200e4199d0d60dd0b9d99702a22eaa917a78",
|
||||
"migrated_sha256": "2b0fb0a6b3e353c69158d61221c2200e4199d0d60dd0b9d99702a22eaa917a78",
|
||||
"equal": true
|
||||
}
|
||||
]
|
||||
}
|
||||
Binary file not shown.
|
After Width: | Height: | Size: 162 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 66 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 136 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 136 KiB |
@@ -0,0 +1,51 @@
|
||||
{
|
||||
"all_equal": true,
|
||||
"schema": {
|
||||
"object_count": 62,
|
||||
"original_sha256": "60a4e044f0ddb6502e44b45f65bedc1a0a31d4bd596f386d4ccfe153a6c8ddd1",
|
||||
"migrated_sha256": "60a4e044f0ddb6502e44b45f65bedc1a0a31d4bd596f386d4ccfe153a6c8ddd1",
|
||||
"equal": true
|
||||
},
|
||||
"tables": [
|
||||
{
|
||||
"table": "auction_factors",
|
||||
"original_count": 511914,
|
||||
"migrated_count": 511914,
|
||||
"original_sha256": "3d0470787adaf7c4cf5f15f5ad9fa1d67c8fcd4807285ac264eeacdcf5054cd1",
|
||||
"migrated_sha256": "3d0470787adaf7c4cf5f15f5ad9fa1d67c8fcd4807285ac264eeacdcf5054cd1",
|
||||
"equal": true
|
||||
},
|
||||
{
|
||||
"table": "popularity_factors",
|
||||
"original_count": 232,
|
||||
"migrated_count": 232,
|
||||
"original_sha256": "3f4c61a13ceaa1ed9ecb28f86241a8a478a6757d6a44b46f432f73c0226bba7f",
|
||||
"migrated_sha256": "3f4c61a13ceaa1ed9ecb28f86241a8a478a6757d6a44b46f432f73c0226bba7f",
|
||||
"equal": true
|
||||
},
|
||||
{
|
||||
"table": "lhb_institution_daily",
|
||||
"original_count": 47,
|
||||
"migrated_count": 47,
|
||||
"original_sha256": "1f847eac34d2ba576b429da208667f3b67b591d36ee66800803605bbc447970c",
|
||||
"migrated_sha256": "1f847eac34d2ba576b429da208667f3b67b591d36ee66800803605bbc447970c",
|
||||
"equal": true
|
||||
},
|
||||
{
|
||||
"table": "seat_aliases",
|
||||
"original_count": 0,
|
||||
"migrated_count": 0,
|
||||
"original_sha256": "4f53cda18c2baa0c0354bb5f9a3ecbe5ed12ab4d8e11ba873c2f11161202b945",
|
||||
"migrated_sha256": "4f53cda18c2baa0c0354bb5f9a3ecbe5ed12ab4d8e11ba873c2f11161202b945",
|
||||
"equal": true
|
||||
},
|
||||
{
|
||||
"table": "stock_master",
|
||||
"original_count": 5535,
|
||||
"migrated_count": 5535,
|
||||
"original_sha256": "8656e2d189d3520433fb3552e17998e4bf17bcf6838403cddc6719282b23e792",
|
||||
"migrated_sha256": "8656e2d189d3520433fb3552e17998e4bf17bcf6838403cddc6719282b23e792",
|
||||
"equal": true
|
||||
}
|
||||
]
|
||||
}
|
||||
@@ -1,6 +1,6 @@
|
||||
{
|
||||
"schema_version": 1,
|
||||
"updated_at": "2026-07-31T00:56:00+08:00",
|
||||
"updated_at": "2026-07-31T02:42:00+08:00",
|
||||
"status": "active",
|
||||
"migration_mode": "behavior_preserving_source_migration",
|
||||
"source_of_truth": "current_original_webapp_runtime_and_source",
|
||||
@@ -9,10 +9,10 @@
|
||||
"failed_roots": [
|
||||
"next"
|
||||
],
|
||||
"current_slice": "slice-03-sentiment-pools-performance",
|
||||
"last_completed_slice": "slice-02-market-search-charts-data",
|
||||
"last_checkpoint": "xiaobai-preservation-slice-02-20260731",
|
||||
"next_action": "capture_slice-03_sentiment_pool_performance_contracts_then_move_original_implementations",
|
||||
"current_slice": "slice-06-screener-custom-tracking",
|
||||
"last_completed_slice": "slice-05-auction-themes-popularity-dragon-tiger",
|
||||
"last_checkpoint": "xiaobai-preservation-slice-05-20260731",
|
||||
"next_action": "capture_slice-06_screener_custom_selection_and_tracking_contracts_then_move_original_implementations",
|
||||
"authoritative_documents": [
|
||||
"AGENTS.md",
|
||||
"docs/migration/原版保真迁移总纲.md",
|
||||
|
||||
@@ -1,6 +1,6 @@
|
||||
# 小白复盘保真迁移账本
|
||||
|
||||
> 当前状态：正式迁移，切片02“公共行情、搜索、详情、图表与数据网关”已完成
|
||||
> 当前状态：正式迁移，切片05“集合竞价、题材库、人气热榜与龙虎榜”已完成
|
||||
|
||||
本账本是上下文恢复和人工审计的连续记录。任何迁移提交必须在同一提交中更新本文件及
|
||||
`保真迁移状态.json`。
|
||||
@@ -22,6 +22,9 @@
|
||||
| 2026-07-30 | `41329943c4878fc09ed82ec376eb93ab151e4092` | 完成只读资产清查并由用户批准`app/`结构 | 开始切片00 |
|
||||
| 2026-07-31 | `xiaobai-preservation-slice-01-20260731` | 启动、HTTP、账号、会员与系统管理原实现归位 | 自动差分通过，进入切片02 |
|
||||
| 2026-07-31 | `xiaobai-preservation-slice-02-20260731` | 公共行情、搜索、详情、图表与数据适配原实现归位 | 自动与浏览器差分通过，进入切片03 |
|
||||
| 2026-07-31 | `xiaobai-preservation-slice-03-20260731` | 情绪周期、五类股池与涨停表现原实现归位 | 自动、API与浏览器差分通过，进入切片04 |
|
||||
| 2026-07-31 | `xiaobai-preservation-slice-04-20260731` | 市场天梯与板块轮动原实现归位 | 自动、API与浏览器差分通过，进入切片05 |
|
||||
| 2026-07-31 | `xiaobai-preservation-slice-05-20260731` | 集合竞价、题材库、人气热榜与龙虎榜原实现归位 | 自动、API、数据库与浏览器差分通过，进入切片06 |
|
||||
|
||||
## 资产处置登记
|
||||
|
||||
@@ -36,6 +39,15 @@
|
||||
| `commonReviewColumns`等5个前端函数 | 疑似无引用符号 | 未发现静态调用 | 待定 | 待删隔离账本 | 仍需动态注册与浏览器覆盖 | 保留 |
|
||||
| `wencai_saved_queries`及其方法 | 历史兼容数据 | 当前无前端入口 | 待定 | 数据库兼容区 | 不允许在迁移期破坏旧库 | 保留 |
|
||||
| 现有7层CSS | 视觉运行资产 | 全部页面和主题 | 原样保留后逐页归档 | `app/frontend/` | 必须通过截图与计算样式差分 | 保留 |
|
||||
| `sentiment_engine.py` | 情绪周期计算 | 总览、轮动、选股 | 移动并保留兼容别名 | `app/backend/features/sentiment/engine.py` | 文件哈希与原版一致；248项Python与45项Playwright通过 | 已移动 |
|
||||
| `DashboardService`情绪及股池原因方法 | 业务服务 | 情绪页、五类股池、涨停表现 | 按职责机械移动 | `app/backend/features/sentiment/`、`app/backend/features/pools/` | 8个方法AST与原版一致；真实API完全一致 | 已移动 |
|
||||
| `ReviewDatabase`原因覆盖方法 | 持久化 | 股池原因人工覆盖 | 按职责机械移动 | `app/backend/features/pools/repository.py` | 2个方法AST与原版一致；数据库schema哈希一致 | 已移动 |
|
||||
| `DashboardService`板块轮动方法 | 业务服务 | 板块轮动页 | 按职责机械移动 | `app/backend/features/rotation/service.py` | 2个方法AST、真实API与原版一致 | 已移动 |
|
||||
| Tushare天梯与轮动构造函数 | 公共数据计算 | 市场天梯、板块轮动 | 原位置保持唯一实现 | `app/backend/data/providers/tushare_client.py` | 2个构造函数AST与原版一致 | 已归位 |
|
||||
| `MarketInsightsService` | 共享市场洞察服务 | 集合竞价、题材库、人气热榜 | 整体机械移动，保留唯一共享实现 | `app/backend/features/market/insights.py` | 22个方法AST与原版一致；根级模块为同一类对象别名 | 已移动 |
|
||||
| `DashboardService`竞价、题材、人气与龙虎榜方法 | 业务服务 | 切片05四类页面与API | 按职责机械移动 | `app/backend/features/auction/`、`themes/`、`popularity/`、`dragon_tiger/` | 8个方法AST、7个真实API与原版一致 | 已移动 |
|
||||
| `ReviewDatabase`竞价、人气与龙虎榜方法 | 持久化 | 市场洞察及后续智能选股 | 按职责机械移动并保持Mixin原接口 | `app/backend/features/auction/repository.py`、`popularity/repository.py`、`dragon_tiger/repository.py` | 7个方法AST一致；62个schema对象及5张关键表逐行一致 | 已移动 |
|
||||
| Tushare游资名录与龙虎榜实现 | 公共数据计算 | 龙虎榜与游资档案 | 原位置保持唯一实现 | `app/backend/data/providers/tushare_client.py` | 2个方法AST与原版一致 | 已归位 |
|
||||
|
||||
处置只允许：`原样保留`、`移动`、`合并重复`、`待定`、`确认废弃`。
|
||||
|
||||
@@ -73,6 +85,36 @@
|
||||
- 回档：标签`xiaobai-preservation-slice-02-20260731`。
|
||||
- 完整证据：`docs/migration/evidence/slice-02/README.md`。
|
||||
|
||||
已完成切片：`slice-03-sentiment-pools-performance`。
|
||||
|
||||
- 原版基线：提交`a426432`，即切片02回档点。
|
||||
- 迁移范围：情绪计算引擎、2个情绪服务方法、6个股池原因与iFinD事件补全方法、2个原因覆盖持久化方法。
|
||||
- 兼容边界：根级`sentiment_engine.py`保留同一模块对象别名；股池生成仍使用切片02的原Tushare总览实现。
|
||||
- API与数据库：原版`8784`和迁移版`8785`的总览、情绪历史JSON完全一致；两库schema均为62项且哈希一致。
|
||||
- 验收：248项Python测试、6项切片源码等价测试、45项Playwright测试及六个真实页面流程通过。
|
||||
- 回档：标签`xiaobai-preservation-slice-03-20260731`。
|
||||
- 完整证据：`docs/migration/evidence/slice-03/README.md`。
|
||||
|
||||
已完成切片：`slice-04-ladder-rotation`。
|
||||
|
||||
- 原版基线：提交`b3555d2`，即切片03回档点。
|
||||
- 迁移范围：2个板块轮动服务方法；市场天梯继续使用切片02已归位的原Tushare数据构造实现。
|
||||
- 兼容边界：`DashboardService`通过`RotationServiceMixin`保持所有原调用；天梯不制造空服务或第二套计算。
|
||||
- API与错误：天梯与9日轮动历史JSON完全一致；成分股两版均返回同一Tushare外部失败语义。
|
||||
- 验收：252项Python测试、4项切片源码等价测试、45项Playwright测试及两个真实页面流程通过。
|
||||
- 回档：标签`xiaobai-preservation-slice-04-20260731`。
|
||||
- 完整证据：`docs/migration/evidence/slice-04/README.md`。
|
||||
|
||||
已完成切片：`slice-05-auction-themes-popularity-dragon-tiger`。
|
||||
|
||||
- 原版基线：提交`814e757`，即切片04回档点。
|
||||
- 迁移范围：共享市场洞察服务、竞价/题材/人气入口、龙虎榜与游资档案服务、7个相关持久化方法。
|
||||
- 兼容边界：根级`market_insights.py`保留同一类对象别名；竞价与人气共用候选热度逻辑，不复制第二套实现。
|
||||
- API与数据库：7个真实API逐字段一致，仅排除每次请求必然变化的`request_id`；62个schema对象与5张关键表完全一致。
|
||||
- 验收：258项Python测试、6项切片源码等价测试、45项Playwright测试及四个真实页面流程通过。
|
||||
- 回档：标签`xiaobai-preservation-slice-05-20260731`。
|
||||
- 完整证据：`docs/migration/evidence/slice-05/README.md`。
|
||||
|
||||
## 决策记录
|
||||
|
||||
| 日期 | 决策 | 原因 |
|
||||
|
||||
Reference in New Issue
Block a user