754 lines
28 KiB
Python
754 lines
28 KiB
Python
"""Backtest engine skeleton for daily bar simulation."""
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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 statistics import mean, pstdev
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from typing import Any, Callable, Dict, Iterable, List, Mapping, Optional
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from app.agents.base import AgentAction, 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.llm.metrics import record_decision as metrics_record_decision
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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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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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name: str
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start_date: date
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end_date: date
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universe: List[str]
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params: Dict[str, float]
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method: str = "nash"
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@dataclass
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class PortfolioState:
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cash: float = 1_000_000.0
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holdings: Dict[str, float] = field(default_factory=dict)
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cost_basis: Dict[str, float] = field(default_factory=dict)
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opened_dates: Dict[str, str] = field(default_factory=dict)
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realized_pnl: float = 0.0
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@dataclass
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class BacktestResult:
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nav_series: List[Dict[str, float]] = field(default_factory=list)
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trades: List[Dict[str, str]] = field(default_factory=list)
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class BacktestEngine:
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"""Runs the multi-agent game inside a daily event-driven loop."""
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def __init__(self, cfg: BtConfig) -> None:
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self.cfg = cfg
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self.agents = default_agents()
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app_cfg = get_config()
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weight_config = app_cfg.agent_weights.as_dict() if app_cfg.agent_weights else {}
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if weight_config:
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self.weights = weight_config
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else:
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self.weights = {agent.name: 1.0 for agent in self.agents}
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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, Any]]:
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"""Load per-stock feature vectors and context slices for the trade date."""
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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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is_suspended = self.data_broker.fetch_flags(
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"suspend",
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ts_code,
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trade_date_str,
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"suspend_date <= ? AND (resume_date IS NULL OR resume_date > ?)",
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(trade_date_str, trade_date_str),
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)
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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": is_suspended,
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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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"required_fields": self.required_fields,
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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(
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self,
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trade_date: date,
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state: PortfolioState,
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decision_callback: Optional[Callable[[str, date, AgentContext, Decision], None]] = None,
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) -> List[tuple[str, AgentContext, Decision]]:
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feature_map = self.load_market_data(trade_date)
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records: List[tuple[str, AgentContext, Decision]] = []
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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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self.weights,
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method=self.cfg.method,
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department_manager=self.department_manager,
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)
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try:
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metrics_record_decision(
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ts_code=ts_code,
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trade_date=context.trade_date,
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action=decision.action.value,
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confidence=decision.confidence,
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summary=_extract_summary(decision),
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source="backtest",
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departments={
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code: dept.to_dict()
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for code, dept in decision.department_decisions.items()
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},
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)
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except Exception: # noqa: BLE001
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LOGGER.debug("记录决策指标失败", extra=LOG_EXTRA)
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records.append((ts_code, context, decision))
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self.record_agent_state(context, decision)
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if decision_callback:
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try:
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decision_callback(ts_code, trade_date, context, decision)
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except Exception: # noqa: BLE001
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LOGGER.exception("决策回调执行失败", extra=LOG_EXTRA)
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# TODO: translate decisions into fills, holdings, and NAV updates.
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_ = state
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return records
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def record_agent_state(self, context: AgentContext, decision: Decision) -> None:
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payload = {
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"trade_date": context.trade_date,
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"ts_code": context.ts_code,
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"action": decision.action.value,
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"confidence": decision.confidence,
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"department_votes": decision.department_votes,
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"requires_review": decision.requires_review,
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"departments": {
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code: dept.to_dict()
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for code, dept in decision.department_decisions.items()
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},
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}
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combined_weights = dict(self.weights)
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if self.department_manager:
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for code, agent in self.department_manager.agents.items():
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key = f"dept_{code}"
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combined_weights[key] = agent.settings.weight
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feasible_json = json.dumps(
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[action.value for action in decision.feasible_actions],
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ensure_ascii=False,
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)
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rows = []
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for agent_name, weight in combined_weights.items():
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action_scores = {
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action.value: float(decision.utilities.get(action, {}).get(agent_name, 0.0))
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for action in decision.utilities.keys()
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}
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best_action = decision.action.value
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if action_scores:
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best_action = max(action_scores.items(), key=lambda item: item[1])[0]
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metadata: Dict[str, object] = {}
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if agent_name.startswith("dept_"):
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dept_code = agent_name.split("dept_", 1)[-1]
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dept_decision = decision.department_decisions.get(dept_code)
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if dept_decision:
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metadata = {
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"_summary": dept_decision.summary,
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"_signals": dept_decision.signals,
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"_risks": dept_decision.risks,
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"_confidence": dept_decision.confidence,
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}
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if dept_decision.supplements:
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metadata["_supplements"] = dept_decision.supplements
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if dept_decision.dialogue:
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metadata["_dialogue"] = dept_decision.dialogue
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if dept_decision.telemetry:
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metadata["_telemetry"] = dept_decision.telemetry
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payload_json = {**action_scores, **metadata}
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rows.append(
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(
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context.trade_date,
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context.ts_code,
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agent_name,
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best_action,
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json.dumps(payload_json, ensure_ascii=False),
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feasible_json,
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float(weight),
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)
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)
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global_payload = {
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"_confidence": decision.confidence,
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"_target_weight": decision.target_weight,
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"_department_votes": decision.department_votes,
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"_requires_review": decision.requires_review,
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"_scope_values": context.raw.get("scope_values", {}),
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"_close_series": context.raw.get("close_series", []),
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"_turnover_series": context.raw.get("turnover_series", []),
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"_department_supplements": {
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code: dept.supplements
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for code, dept in decision.department_decisions.items()
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if dept.supplements
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},
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"_department_dialogue": {
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code: dept.dialogue
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for code, dept in decision.department_decisions.items()
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if dept.dialogue
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},
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"_department_telemetry": {
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code: dept.telemetry
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for code, dept in decision.department_decisions.items()
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if dept.telemetry
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},
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}
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rows.append(
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(
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context.trade_date,
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context.ts_code,
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"global",
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decision.action.value,
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json.dumps(global_payload, ensure_ascii=False),
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feasible_json,
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1.0,
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)
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)
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try:
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with db_session() as conn:
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conn.executemany(
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"""
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INSERT OR REPLACE INTO agent_utils
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(trade_date, ts_code, agent, action, utils, feasible, weight)
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VALUES (?, ?, ?, ?, ?, ?, ?)
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""",
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rows,
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)
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except Exception:
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LOGGER.exception("写入 agent_utils 失败", extra=LOG_EXTRA)
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_ = payload
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# TODO: persist payload into bt_trades / audit tables when schema is ready.
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try:
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self._record_investment_candidate(context, decision)
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except Exception: # noqa: BLE001
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LOGGER.exception("写入 investment_pool 失败", extra=LOG_EXTRA)
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def _apply_portfolio_updates(
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self,
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trade_date: date,
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state: PortfolioState,
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records: List[tuple[str, AgentContext, Decision]],
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result: BacktestResult,
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) -> None:
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trade_date_str = trade_date.isoformat()
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price_map: Dict[str, float] = {}
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decisions_map: Dict[str, Decision] = {}
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for ts_code, context, decision in records:
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scope_values = context.raw.get("scope_values") if context.raw else {}
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if not isinstance(scope_values, Mapping):
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scope_values = {}
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price = scope_values.get("daily.close") or scope_values.get("close")
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if price is None:
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continue
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try:
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price = float(price)
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except (TypeError, ValueError):
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continue
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price_map[ts_code] = price
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decisions_map[ts_code] = decision
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if not price_map and state.holdings:
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trade_date_compact = trade_date.strftime("%Y%m%d")
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for ts_code in state.holdings.keys():
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fetched = self.data_broker.fetch_latest(ts_code, trade_date_compact, ["daily.close"])
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price = fetched.get("daily.close")
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if price:
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price_map[ts_code] = float(price)
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portfolio_value_before = state.cash
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for ts_code, qty in state.holdings.items():
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price = price_map.get(ts_code)
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if price is None:
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continue
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portfolio_value_before += qty * price
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if portfolio_value_before <= 0:
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portfolio_value_before = state.cash or 1.0
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trades_records: List[Dict[str, Any]] = []
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for ts_code, decision in decisions_map.items():
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price = price_map.get(ts_code)
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if price is None or price <= 0:
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continue
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current_qty = state.holdings.get(ts_code, 0.0)
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desired_qty = current_qty
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if decision.action is AgentAction.SELL:
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desired_qty = 0.0
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elif decision.action is AgentAction.HOLD:
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desired_qty = current_qty
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else:
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target_weight = max(decision.target_weight, 0.0)
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desired_value = target_weight * portfolio_value_before
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if desired_value > 0:
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desired_qty = desired_value / price
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else:
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desired_qty = current_qty
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delta = desired_qty - current_qty
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if abs(delta) < 1e-6:
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continue
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if delta > 0:
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cost = delta * price
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if cost > state.cash:
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affordable_qty = state.cash / price if price > 0 else 0.0
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delta = max(0.0, affordable_qty)
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cost = delta * price
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desired_qty = current_qty + delta
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if delta <= 0:
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continue
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total_cost = state.cost_basis.get(ts_code, 0.0) * current_qty + cost
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new_qty = current_qty + delta
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state.cost_basis[ts_code] = total_cost / new_qty if new_qty > 0 else 0.0
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state.cash -= cost
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state.holdings[ts_code] = new_qty
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state.opened_dates.setdefault(ts_code, trade_date_str)
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trades_records.append(
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{
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"trade_date": trade_date_str,
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"ts_code": ts_code,
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"action": "buy",
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"quantity": float(delta),
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"price": price,
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"value": cost,
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"confidence": decision.confidence,
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"target_weight": decision.target_weight,
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}
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)
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else:
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sell_qty = abs(delta)
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if sell_qty > current_qty:
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sell_qty = current_qty
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delta = -sell_qty
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proceeds = sell_qty * price
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cost_basis = state.cost_basis.get(ts_code, 0.0)
|
||
realized = (price - cost_basis) * sell_qty
|
||
state.cash += proceeds
|
||
state.realized_pnl += realized
|
||
new_qty = current_qty + delta
|
||
if new_qty <= 1e-6:
|
||
state.holdings.pop(ts_code, None)
|
||
state.cost_basis.pop(ts_code, None)
|
||
state.opened_dates.pop(ts_code, None)
|
||
else:
|
||
state.holdings[ts_code] = new_qty
|
||
trades_records.append(
|
||
{
|
||
"trade_date": trade_date_str,
|
||
"ts_code": ts_code,
|
||
"action": "sell",
|
||
"quantity": float(sell_qty),
|
||
"price": price,
|
||
"value": proceeds,
|
||
"confidence": decision.confidence,
|
||
"target_weight": decision.target_weight,
|
||
"realized_pnl": realized,
|
||
}
|
||
)
|
||
|
||
market_value = 0.0
|
||
unrealized_pnl = 0.0
|
||
for ts_code, qty in state.holdings.items():
|
||
price = price_map.get(ts_code)
|
||
if price is None:
|
||
continue
|
||
market_value += qty * price
|
||
cost_basis = state.cost_basis.get(ts_code, 0.0)
|
||
unrealized_pnl += (price - cost_basis) * qty
|
||
|
||
nav = state.cash + market_value
|
||
result.nav_series.append(
|
||
{
|
||
"trade_date": trade_date_str,
|
||
"nav": nav,
|
||
"cash": state.cash,
|
||
"market_value": market_value,
|
||
"realized_pnl": state.realized_pnl,
|
||
"unrealized_pnl": unrealized_pnl,
|
||
}
|
||
)
|
||
if trades_records:
|
||
result.trades.extend(trades_records)
|
||
|
||
try:
|
||
self._persist_portfolio(
|
||
trade_date_str,
|
||
state,
|
||
market_value,
|
||
unrealized_pnl,
|
||
trades_records,
|
||
price_map,
|
||
decisions_map,
|
||
)
|
||
except Exception: # noqa: BLE001
|
||
LOGGER.exception("持仓数据写入失败", extra=LOG_EXTRA)
|
||
|
||
def _record_investment_candidate(
|
||
self, context: AgentContext, decision: Decision
|
||
) -> None:
|
||
status = _candidate_status(decision.action, decision.requires_review)
|
||
summary = _extract_summary(decision)
|
||
if not summary:
|
||
collected_signals: List[str] = []
|
||
for dept in decision.department_decisions.values():
|
||
collected_signals.extend(dept.signals)
|
||
summary = "；".join(str(sig) for sig in collected_signals[:3])
|
||
|
||
metadata = {
|
||
"target_weight": decision.target_weight,
|
||
"feasible_actions": [action.value for action in decision.feasible_actions],
|
||
"department_votes": decision.department_votes,
|
||
"requires_review": decision.requires_review,
|
||
"confidence": decision.confidence,
|
||
}
|
||
if decision.department_decisions:
|
||
metadata["departments"] = {
|
||
code: dept.to_dict()
|
||
for code, dept in decision.department_decisions.items()
|
||
}
|
||
|
||
with db_session() as conn:
|
||
conn.execute(
|
||
"""
|
||
INSERT OR REPLACE INTO investment_pool
|
||
(trade_date, ts_code, score, status, rationale, tags, metadata)
|
||
VALUES (?, ?, ?, ?, ?, ?, ?)
|
||
""",
|
||
(
|
||
context.trade_date,
|
||
context.ts_code,
|
||
float(decision.confidence or 0.0),
|
||
status,
|
||
summary or None,
|
||
json.dumps(_department_tags(decision), ensure_ascii=False),
|
||
json.dumps(metadata, ensure_ascii=False),
|
||
),
|
||
)
|
||
|
||
def _persist_portfolio(
|
||
self,
|
||
trade_date: str,
|
||
state: PortfolioState,
|
||
market_value: float,
|
||
unrealized_pnl: float,
|
||
trades: List[Dict[str, Any]],
|
||
price_map: Dict[str, float],
|
||
decisions_map: Dict[str, Decision],
|
||
) -> None:
|
||
holdings_rows: List[tuple] = []
|
||
for ts_code, qty in state.holdings.items():
|
||
price = price_map.get(ts_code)
|
||
market_val = qty * price if price is not None else None
|
||
cost_basis = state.cost_basis.get(ts_code, 0.0)
|
||
unrealized = (price - cost_basis) * qty if price is not None else None
|
||
decision = decisions_map.get(ts_code)
|
||
target_weight = decision.target_weight if decision else None
|
||
metadata = {
|
||
"last_action": decision.action.value if decision else None,
|
||
"confidence": decision.confidence if decision else None,
|
||
}
|
||
holdings_rows.append(
|
||
(
|
||
ts_code,
|
||
state.opened_dates.get(ts_code, trade_date),
|
||
None,
|
||
qty,
|
||
cost_basis,
|
||
price,
|
||
market_val,
|
||
state.realized_pnl,
|
||
unrealized,
|
||
target_weight,
|
||
"open",
|
||
None,
|
||
json.dumps(metadata, ensure_ascii=False),
|
||
)
|
||
)
|
||
|
||
snapshot_metadata = {
|
||
"holdings": len(state.holdings),
|
||
}
|
||
|
||
with db_session() as conn:
|
||
conn.execute(
|
||
"""
|
||
INSERT OR REPLACE INTO portfolio_snapshots
|
||
(trade_date, total_value, cash, invested_value, unrealized_pnl, realized_pnl, net_flow, exposure, notes, metadata)
|
||
VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
|
||
""",
|
||
(
|
||
trade_date,
|
||
market_value + state.cash,
|
||
state.cash,
|
||
market_value,
|
||
unrealized_pnl,
|
||
state.realized_pnl,
|
||
None,
|
||
None,
|
||
None,
|
||
json.dumps(snapshot_metadata, ensure_ascii=False),
|
||
),
|
||
)
|
||
|
||
conn.execute("DELETE FROM portfolio_positions")
|
||
if holdings_rows:
|
||
conn.executemany(
|
||
"""
|
||
INSERT INTO portfolio_positions
|
||
(ts_code, opened_date, closed_date, quantity, cost_price, market_price, market_value, realized_pnl, unrealized_pnl, target_weight, status, notes, metadata)
|
||
VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
|
||
""",
|
||
holdings_rows,
|
||
)
|
||
|
||
if trades:
|
||
conn.executemany(
|
||
"""
|
||
INSERT INTO portfolio_trades
|
||
(trade_date, ts_code, action, quantity, price, fee, order_id, source, notes, metadata)
|
||
VALUES (?, ?, ?, ?, ?, 0, NULL, 'backtest', NULL, ?)
|
||
""",
|
||
[
|
||
(
|
||
trade["trade_date"],
|
||
trade["ts_code"],
|
||
trade["action"],
|
||
trade["quantity"],
|
||
trade["price"],
|
||
json.dumps(trade, ensure_ascii=False),
|
||
)
|
||
for trade in trades
|
||
],
|
||
)
|
||
|
||
def run(
|
||
self,
|
||
decision_callback: Optional[Callable[[str, date, AgentContext, Decision], None]] = None,
|
||
) -> BacktestResult:
|
||
state = PortfolioState()
|
||
result = BacktestResult()
|
||
current = self.cfg.start_date
|
||
while current <= self.cfg.end_date:
|
||
records = self.simulate_day(current, state, decision_callback)
|
||
self._apply_portfolio_updates(current, state, records, result)
|
||
current = date.fromordinal(current.toordinal() + 1)
|
||
return result
|
||
|
||
|
||
def run_backtest(
|
||
cfg: BtConfig,
|
||
*,
|
||
decision_callback: Optional[Callable[[str, date, AgentContext, Decision], None]] = None,
|
||
) -> BacktestResult:
|
||
engine = BacktestEngine(cfg)
|
||
result = engine.run(decision_callback=decision_callback)
|
||
with db_session() as conn:
|
||
_ = conn
|
||
# Implementation should persist bt_nav, bt_trades, and bt_report rows.
|
||
return result
|
||
|
||
|
||
def _candidate_status(action: AgentAction, requires_review: bool) -> str:
|
||
mapping = {
|
||
AgentAction.SELL: "exit",
|
||
AgentAction.HOLD: "watch",
|
||
AgentAction.BUY_S: "buy_s",
|
||
AgentAction.BUY_M: "buy_m",
|
||
AgentAction.BUY_L: "buy_l",
|
||
}
|
||
base = mapping.get(action, "candidate")
|
||
if requires_review:
|
||
return f"{base}_review"
|
||
return base
|
||
def _extract_summary(decision: Decision) -> str:
|
||
for dept_decision in decision.department_decisions.values():
|
||
summary = getattr(dept_decision, "summary", "")
|
||
if summary:
|
||
return str(summary)
|
||
return ""
|
||
|
||
|
||
def _department_tags(decision: Decision) -> List[str]:
|
||
tags: List[str] = []
|
||
for code, dept in decision.department_decisions.items():
|
||
action = getattr(dept, "action", None)
|
||
if action is None:
|
||
continue
|
||
tags.append(f"{code}:{action.value}")
|
||
return sorted(set(tags))
|