Yuxuan Yan
256 lines
10 KiB
Python
256 lines
10 KiB
Python
"""Execution simulator: next-open fills under A-share trading constraints.
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Execution model (documented convention): a position book targeted from
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information available on date ``t`` is executed at ``open[t+1]``. Trades that
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violate a :class:`~pipeline.portfolio.constraints.TradeConstraint` (suspension,
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price limit, volume cap, …) are clipped; a fully blocked buy leaves the position
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at its previous level. Realized PnL marks the *actually filled* book.
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The simulator is an ABC + a :class:`ReferenceSimulator`; constraints compose by
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intersecting their per-name signed delta bounds.
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"""
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from __future__ import annotations
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import logging
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from abc import ABC, abstractmethod
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from dataclasses import dataclass
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import numpy as np
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import pandas as pd
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from pipeline.common.schema import FILL_COLUMNS, PNL_COLUMNS
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from pipeline.portfolio.constraints import TradeConstraint
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from pipeline.portfolio.market_rules import MarketRule, compute_limit_status
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logger = logging.getLogger(__name__)
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@dataclass
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class MarketSlice:
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"""Per-name market arrays for one execution date (fixed symbol order)."""
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symbol_ids: np.ndarray
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date: object
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price: np.ndarray # execution/reference price (the open)
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preclose: np.ndarray
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amount: np.ndarray # daily turnover value (yuan)
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tradestatus: np.ndarray # 1 traded / 0 suspended
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is_st: np.ndarray
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limit_status: np.ndarray # LimitStatus values
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close: np.ndarray # close, for marking
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@dataclass
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class TradeContext:
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"""Inputs handed to constraints and the fill routine for one date."""
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prev_shares: np.ndarray
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target_shares: np.ndarray
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slice: MarketSlice
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booksize: float
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@dataclass
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class FillResult:
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"""Outcome of executing one date's target against the constraints."""
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realized_shares: np.ndarray
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traded_shares: np.ndarray
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cost: np.ndarray
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blocked: np.ndarray
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class ExecutionSimulator(ABC):
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"""Abstract execution layer. Subclasses define how a target gets filled."""
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def __init__(self, constraints: list[TradeConstraint] | None = None,
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cost_bps: float = 0.0, slippage_bps: float = 0.0):
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self.constraints = constraints or []
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self.cost_bps = cost_bps
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self.slippage_bps = slippage_bps
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@abstractmethod
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def fill(self, ctx: TradeContext) -> FillResult:
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"""Execute ``ctx.target_shares`` from ``ctx.prev_shares``."""
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class ReferenceSimulator(ExecutionSimulator):
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"""Reference fill model: clip the desired trade to the composed bounds."""
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def fill(self, ctx: TradeContext) -> FillResult:
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prev = ctx.prev_shares.astype(np.int64)
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target = ctx.target_shares.astype(np.int64)
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# Portfolio-level retargeting hooks (e.g. neutrality), if any.
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for c in self.constraints:
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adjusted = c.adjust_targets(ctx)
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if adjusted is not None:
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target = np.asarray(adjusted, dtype=np.int64)
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desired = target - prev
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n = len(prev)
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low = np.full(n, -np.inf)
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high = np.full(n, np.inf)
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for c in self.constraints:
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lo, hi = c.delta_bounds(ctx)
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low = np.maximum(low, lo)
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high = np.minimum(high, hi)
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# Clip desired delta into the feasible interval; round toward zero so a
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# value/volume cap yields a conservative partial fill.
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clipped = np.clip(desired.astype(np.float64), low, high)
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traded = np.trunc(clipped).astype(np.int64)
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blocked = (traded != desired).astype(np.int64)
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realized = prev + traded
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open_px = np.where(np.isfinite(ctx.slice.price), ctx.slice.price, 0.0)
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trade_value = np.abs(traded.astype(np.float64) * open_px)
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cost = trade_value * (self.cost_bps + self.slippage_bps) / 1e4
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return FillResult(realized, traded, cost, blocked)
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def run(
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self,
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positions_df: pd.DataFrame,
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data_df: pd.DataFrame,
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rule_engine: MarketRule | None = None,
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) -> tuple[pd.DataFrame, pd.DataFrame]:
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"""Simulate the whole book date by date with next-open execution.
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For each signal date ``t`` in ``positions_df`` the target is executed at
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the *next* available data date's open. Returns ``(fills, pnl)`` with
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FILL_COLUMNS / PNL_COLUMNS.
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Args:
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positions_df: POSITION_COLUMNS (uses constructed ``position_shares``).
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data_df: DATA_COLUMNS (open/close/preclose/amount/tradestatus/isST).
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rule_engine: For per-name price-limit bands; default built if None.
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Returns:
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``(fills_df, pnl_df)``.
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"""
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rule_engine = rule_engine or MarketRule()
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portfolio_name = (
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positions_df["portfolio_name"].iloc[0] if len(positions_df) else ""
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)
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# Booksize ≈ the per-date gross dollar target (constant by construction).
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if "target_value" in positions_df.columns and len(positions_df):
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per_date_gross = (positions_df.groupby("date")["target_value"]
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.apply(lambda s: s.abs().sum()))
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booksize = float(per_date_gross.max()) or 1.0
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else:
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booksize = 1.0
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def wide(df, col):
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return df.pivot_table(index="date", columns="symbol_id",
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values=col, aggfunc="first").sort_index()
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tgt = wide(positions_df, "position_shares")
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opn = wide(data_df, "open")
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close = wide(data_df, "close")
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preclose = wide(data_df, "preclose") if "preclose" in data_df.columns else close.shift(1)
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amount = wide(data_df, "amount") if "amount" in data_df.columns else opn * np.inf
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tstat = wide(data_df, "tradestatus") if "tradestatus" in data_df.columns else opn.notna().astype(float)
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st = wide(data_df, "isST") if "isST" in data_df.columns else opn * 0.0
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symbols = sorted(set(tgt.columns) | set(opn.columns))
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tgt = tgt.reindex(columns=symbols)
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opn = opn.reindex(columns=symbols)
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close = close.reindex(columns=symbols)
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preclose = preclose.reindex(columns=symbols)
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amount = amount.reindex(columns=symbols)
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tstat = tstat.reindex(columns=symbols)
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st = st.reindex(columns=symbols)
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sym_arr = np.asarray(symbols, dtype=object)
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n = len(symbols)
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data_dates = list(close.index)
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date_pos = {d: i for i, d in enumerate(data_dates)}
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prev_shares = np.zeros(n, dtype=np.int64)
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mark_prev = None # last close at which the book was marked
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fill_blocks: list[pd.DataFrame] = []
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pnl_rows: list[dict] = []
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for t in tgt.index:
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# Execute at the next available data date after the signal date t.
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i = date_pos.get(t)
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if i is None or i + 1 >= len(data_dates):
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continue
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e = data_dates[i + 1]
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open_e = opn.loc[e].to_numpy(dtype=np.float64)
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close_e = close.loc[e].to_numpy(dtype=np.float64)
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pre_e = preclose.loc[e].to_numpy(dtype=np.float64)
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amt_e = amount.loc[e].to_numpy(dtype=np.float64)
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tstat_e = np.nan_to_num(tstat.loc[e].to_numpy(dtype=np.float64), nan=0.0)
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st_e = np.nan_to_num(st.loc[e].to_numpy(dtype=np.float64), nan=0.0)
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target = np.nan_to_num(tgt.loc[t].to_numpy(dtype=np.float64), nan=0.0).astype(np.int64)
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_, _, _, limit_pct = rule_engine.get_rules_vectorized(sym_arr, e, st_e)
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limit_status = compute_limit_status(open_e, pre_e, limit_pct)
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mslice = MarketSlice(
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symbol_ids=sym_arr, date=e, price=open_e, preclose=pre_e,
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amount=amt_e, tradestatus=tstat_e, is_st=st_e,
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limit_status=limit_status, close=close_e,
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)
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ctx = TradeContext(prev_shares, target, mslice, booksize)
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res = self.fill(ctx)
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# PnL: overnight gap on the OLD book + intraday on the NEW book - cost.
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if mark_prev is None:
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overnight = 0.0
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else:
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gap = np.where(np.isfinite(open_e) & np.isfinite(mark_prev),
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open_e - mark_prev, 0.0)
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overnight = float(np.nansum(prev_shares * gap))
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intraday_px = np.where(np.isfinite(close_e) & np.isfinite(open_e),
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close_e - open_e, 0.0)
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intraday = float(np.nansum(res.realized_shares * intraday_px))
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cost_total = float(np.nansum(res.cost))
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pnl = overnight + intraday - cost_total
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mark_e = np.where(np.isfinite(close_e), close_e, open_e)
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realized_value = res.realized_shares * np.where(np.isfinite(mark_e), mark_e, 0.0)
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traded_value = np.abs(res.traded_shares * np.where(np.isfinite(open_e), open_e, 0.0))
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nz = res.realized_shares != 0
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fill_blocks.append(pd.DataFrame({
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"symbol_id": symbols,
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"date": e,
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"portfolio_name": portfolio_name,
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"prev_shares": prev_shares,
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"target_shares": target,
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"traded_shares": res.traded_shares,
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"realized_shares": res.realized_shares,
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"blocked": res.blocked,
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"trade_cost": res.cost,
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})[lambda d: (d["traded_shares"] != 0) | (d["realized_shares"] != 0)])
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pnl_rows.append({
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"date": e,
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"portfolio_name": portfolio_name,
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"gross_exposure": float(np.abs(realized_value).sum()),
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"net_exposure": float(realized_value.sum()),
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"pnl": pnl,
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"cost": cost_total,
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"turnover": float(traded_value.sum() / booksize) if booksize else 0.0,
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"n_positions": int(nz.sum()),
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})
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prev_shares = res.realized_shares
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mark_prev = mark_e
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fills_df = (pd.concat(fill_blocks, ignore_index=True)[FILL_COLUMNS]
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if fill_blocks else pd.DataFrame(columns=FILL_COLUMNS))
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pnl_df = (pd.DataFrame(pnl_rows)[PNL_COLUMNS]
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if pnl_rows else pd.DataFrame(columns=PNL_COLUMNS))
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logger.info(
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"Simulated '%s': %d exec days, final gross %.0f, total cost %.0f",
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portfolio_name, len(pnl_df),
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pnl_df["gross_exposure"].iloc[-1] if len(pnl_df) else 0.0,
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pnl_df["cost"].sum() if len(pnl_df) else 0.0,
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)
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return fills_df, pnl_df
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