update
This commit is contained in:
sam
parent
15bd9d06a7
commit
8b3c6fe690
@ -4,12 +4,14 @@ from __future__ import annotations
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import json
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from dataclasses import dataclass, field
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from datetime import date
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from typing import Dict, Iterable, List, Mapping
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from statistics import mean, pstdev
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from typing import Any, Dict, Iterable, List, Mapping
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from app.agents.base import AgentContext
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from app.agents.departments import DepartmentManager
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from app.agents.game import Decision, decide
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from app.agents.registry import default_agents
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from app.utils.data_access import DataBroker
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from app.utils.config import get_config
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from app.utils.db import db_session
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from app.utils.logging import get_logger
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@ -19,6 +21,45 @@ LOGGER = get_logger(__name__)
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LOG_EXTRA = {"stage": "backtest"}
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def _compute_momentum(values: List[float], window: int) -> float:
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if window <= 0 or len(values) < window:
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return 0.0
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latest = values[0]
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past = values[window - 1]
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if past is None or past == 0:
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return 0.0
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try:
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return (latest / past) - 1.0
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except ZeroDivisionError:
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return 0.0
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def _compute_volatility(values: List[float], window: int) -> float:
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if len(values) < 2 or window <= 1:
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return 0.0
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limit = min(window, len(values) - 1)
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returns: List[float] = []
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for idx in range(limit):
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current = values[idx]
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previous = values[idx + 1]
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if previous is None or previous == 0:
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continue
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returns.append((current / previous) - 1.0)
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if len(returns) < 2:
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return 0.0
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return float(pstdev(returns))
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def _normalize(value: Any, factor: float) -> float:
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try:
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numeric = float(value)
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except (TypeError, ValueError):
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return 0.0
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if factor <= 0:
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return max(0.0, min(1.0, numeric))
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return max(0.0, min(1.0, numeric / factor))
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@dataclass
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class BtConfig:
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id: str
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@ -57,18 +98,137 @@ class BacktestEngine:
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self.department_manager = (
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DepartmentManager(app_cfg) if app_cfg.departments else None
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)
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self.data_broker = DataBroker()
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department_scope: set[str] = set()
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for settings in app_cfg.departments.values():
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department_scope.update(settings.data_scope)
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base_scope = {
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"daily.close",
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"daily.open",
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"daily.high",
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"daily.low",
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"daily.pct_chg",
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"daily.vol",
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"daily.amount",
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"daily_basic.turnover_rate",
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"daily_basic.turnover_rate_f",
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"daily_basic.volume_ratio",
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"stk_limit.up_limit",
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"stk_limit.down_limit",
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}
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self.required_fields = sorted(base_scope | department_scope)
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def load_market_data(self, trade_date: date) -> Mapping[str, Dict[str, float]]:
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"""Load per-stock feature vectors. Replace with real data access."""
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def load_market_data(self, trade_date: date) -> Mapping[str, Dict[str, Any]]:
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"""Load per-stock feature vectors and context slices for the trade date."""
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_ = trade_date
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return {}
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trade_date_str = trade_date.strftime("%Y%m%d")
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feature_map: Dict[str, Dict[str, Any]] = {}
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universe = self.cfg.universe or []
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for ts_code in universe:
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scope_values = self.data_broker.fetch_latest(
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ts_code,
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trade_date_str,
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self.required_fields,
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)
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closes = self.data_broker.fetch_series(
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"daily",
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"close",
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ts_code,
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trade_date_str,
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window=60,
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)
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close_values = [value for _date, value in closes]
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mom20 = _compute_momentum(close_values, 20)
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mom60 = _compute_momentum(close_values, 60)
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volat20 = _compute_volatility(close_values, 20)
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turnover_series = self.data_broker.fetch_series(
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"daily_basic",
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"turnover_rate",
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ts_code,
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trade_date_str,
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window=20,
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)
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turnover_values = [value for _date, value in turnover_series]
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turn20 = mean(turnover_values) if turnover_values else 0.0
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liquidity_score = _normalize(turn20, factor=20.0)
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cost_penalty = _normalize(scope_values.get("daily_basic.volume_ratio", 0.0), factor=50.0)
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latest_close = scope_values.get("daily.close", 0.0)
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latest_pct = scope_values.get("daily.pct_chg", 0.0)
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latest_turnover = scope_values.get("daily_basic.turnover_rate", 0.0)
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up_limit = scope_values.get("stk_limit.up_limit")
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limit_up = False
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if up_limit and latest_close:
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limit_up = latest_close >= up_limit * 0.999
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down_limit = scope_values.get("stk_limit.down_limit")
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limit_down = False
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if down_limit and latest_close:
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limit_down = latest_close <= down_limit * 1.001
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features = {
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"mom_20": mom20,
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"mom_60": mom60,
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"volat_20": volat20,
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"turn_20": turn20,
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"liquidity_score": liquidity_score,
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"cost_penalty": cost_penalty,
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"news_heat": scope_values.get("news.heat_score", 0.0),
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"news_sentiment": scope_values.get("news.sentiment_index", 0.0),
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"industry_heat": scope_values.get("macro.industry_heat", 0.0),
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"industry_relative_mom": scope_values.get(
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"macro.relative_strength",
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scope_values.get("index.performance_peers", 0.0),
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),
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"risk_penalty": min(1.0, volat20 * 5.0),
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"is_suspended": False,
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"limit_up": limit_up,
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"limit_down": limit_down,
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"position_limit": False,
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}
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market_snapshot = {
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"close": latest_close,
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"pct_chg": latest_pct,
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"turnover_rate": latest_turnover,
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"volume": scope_values.get("daily.vol", 0.0),
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"amount": scope_values.get("daily.amount", 0.0),
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"up_limit": up_limit,
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"down_limit": down_limit,
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}
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raw_payload = {
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"scope_values": scope_values,
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"close_series": closes,
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"turnover_series": turnover_series,
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}
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feature_map[ts_code] = {
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"features": features,
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"market_snapshot": market_snapshot,
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"raw": raw_payload,
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}
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return feature_map
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def simulate_day(self, trade_date: date, state: PortfolioState) -> List[Decision]:
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feature_map = self.load_market_data(trade_date)
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decisions: List[Decision] = []
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for ts_code, features in feature_map.items():
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context = AgentContext(ts_code=ts_code, trade_date=trade_date.isoformat(), features=features)
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for ts_code, payload in feature_map.items():
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features = payload.get("features", {})
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market_snapshot = payload.get("market_snapshot", {})
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raw = payload.get("raw", {})
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context = AgentContext(
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ts_code=ts_code,
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trade_date=trade_date.isoformat(),
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features=features,
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market_snapshot=market_snapshot,
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raw=raw,
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)
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decision = decide(
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context,
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self.agents,
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159
app/utils/data_access.py
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159
app/utils/data_access.py
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@ -0,0 +1,159 @@
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"""Utility helpers to retrieve structured data slices for agents and departments."""
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from __future__ import annotations
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import re
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from dataclasses import dataclass
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from typing import Dict, Iterable, List, Sequence, Tuple
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from .db import db_session
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from .logging import get_logger
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LOGGER = get_logger(__name__)
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LOG_EXTRA = {"stage": "data_broker"}
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_IDENTIFIER_RE = re.compile(r"^[A-Za-z_][A-Za-z0-9_]*$")
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def _is_safe_identifier(name: str) -> bool:
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return bool(_IDENTIFIER_RE.match(name))
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def _safe_split(path: str) -> Tuple[str, str] | None:
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if "." not in path:
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return None
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table, column = path.split(".", 1)
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table = table.strip()
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column = column.strip()
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if not table or not column:
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return None
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if not (_is_safe_identifier(table) and _is_safe_identifier(column)):
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LOGGER.debug("忽略非法字段：%s", path, extra=LOG_EXTRA)
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return None
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return table, column
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@dataclass
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class DataBroker:
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"""Lightweight data access helper for agent/LLM consumption."""
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def fetch_latest(
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self,
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ts_code: str,
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trade_date: str,
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fields: Iterable[str],
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) -> Dict[str, float]:
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"""Fetch the latest value (<= trade_date) for each requested field."""
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grouped: Dict[str, List[str]] = {}
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for item in fields:
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if not item:
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continue
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normalized = _safe_split(str(item))
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if not normalized:
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continue
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table, column = normalized
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grouped.setdefault(table, [])
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if column not in grouped[table]:
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grouped[table].append(column)
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if not grouped:
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return {}
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results: Dict[str, float] = {}
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with db_session(read_only=True) as conn:
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for table, columns in grouped.items():
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joined_cols = ", ".join(columns)
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query = (
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f"SELECT trade_date, {joined_cols} FROM {table} "
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"WHERE ts_code = ? AND trade_date <= ? "
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"ORDER BY trade_date DESC LIMIT 1"
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)
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try:
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row = conn.execute(query, (ts_code, trade_date)).fetchone()
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except Exception as exc: # noqa: BLE001
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LOGGER.debug(
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"查询失败 table=%s fields=%s err=%s",
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table,
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columns,
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exc,
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extra=LOG_EXTRA,
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)
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continue
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if not row:
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continue
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for column in columns:
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value = row[column]
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if value is None:
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continue
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key = f"{table}.{column}"
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results[key] = float(value)
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return results
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def fetch_series(
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self,
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table: str,
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column: str,
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ts_code: str,
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end_date: str,
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window: int,
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) -> List[Tuple[str, float]]:
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"""Return descending time series tuples within the specified window."""
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if window <= 0:
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return []
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if not (_is_safe_identifier(table) and _is_safe_identifier(column)):
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return []
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query = (
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f"SELECT trade_date, {column} FROM {table} "
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"WHERE ts_code = ? AND trade_date <= ? "
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"ORDER BY trade_date DESC LIMIT ?"
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)
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with db_session(read_only=True) as conn:
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try:
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rows = conn.execute(query, (ts_code, end_date, window)).fetchall()
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except Exception as exc: # noqa: BLE001
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LOGGER.debug(
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"时间序列查询失败 table=%s column=%s err=%s",
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table,
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column,
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exc,
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extra=LOG_EXTRA,
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)
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return []
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series: List[Tuple[str, float]] = []
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for row in rows:
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value = row[column]
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if value is None:
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continue
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series.append((row["trade_date"], float(value)))
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return series
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def fetch_flags(
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self,
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table: str,
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ts_code: str,
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trade_date: str,
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where_clause: str,
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params: Sequence[object],
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) -> bool:
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"""Generic helper to test if a record exists (used for limit/suspend lookups)."""
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if not _is_safe_identifier(table):
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return False
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query = (
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f"SELECT 1 FROM {table} WHERE ts_code = ? AND {where_clause} LIMIT 1"
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)
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bind_params = (ts_code, *params)
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with db_session(read_only=True) as conn:
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try:
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row = conn.execute(query, bind_params).fetchone()
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except Exception as exc: # noqa: BLE001
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LOGGER.debug(
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"flag 查询失败 table=%s where=%s err=%s",
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table,
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where_clause,
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exc,
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extra=LOG_EXTRA,
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)
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return False
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return row is not None
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