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feat(backtest): add TWAPExecution + VWAPExecution
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@@ -185,3 +185,339 @@ class ImmediateExecution(ExecutionModel):
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]
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return []
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class TWAPExecution(ExecutionModel):
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"""时间加权平均价格执行。
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将订单均匀拆分为 n_bars 份，在连续 n_bars 根 K 线上执行。
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"""
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def __init__(self, n_bars: int = 5) -> None:
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self._n_bars = max(1, n_bars)
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def execute(
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self,
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signal: Signal,
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df: pd.DataFrame,
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bar_idx: int,
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cash: float,
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position: float,
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position_mode: str,
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commission: float,
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min_commission: float,
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stamp_tax: float,
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slippage_model: SlippageModel | None,
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) -> list[Trade]:
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if signal.direction == "BUY":
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return self._execute_buy(
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signal,
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df,
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bar_idx,
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cash,
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position_mode,
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commission,
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min_commission,
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stamp_tax,
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slippage_model,
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)
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return self._execute_sell(
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signal,
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df,
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bar_idx,
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position,
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commission,
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min_commission,
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stamp_tax,
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slippage_model,
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)
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def _execute_buy(
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self,
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signal: Signal,
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df: pd.DataFrame,
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bar_idx: int,
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cash: float,
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position_mode: str,
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commission: float,
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min_commission: float,
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stamp_tax: float,
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slippage_model: SlippageModel | None,
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) -> list[Trade]:
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first_price = float(df["open"].iloc[bar_idx + 1]) if bar_idx + 1 < len(df) else 0
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if first_price <= 0:
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return []
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total_size = self._calc_buy_size(
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signal.size,
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first_price,
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cash,
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position_mode,
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commission,
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)
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if total_size <= 0:
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return []
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sub_size = int(total_size / self._n_bars / 100) * 100
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if sub_size <= 0:
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sub_size = 100
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trades: list[Trade] = []
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for i in range(self._n_bars):
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exec_idx = bar_idx + 1 + i
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if exec_idx >= len(df):
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break
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price = float(df["close"].iloc[exec_idx])
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remaining = total_size - sum(t.size for t in trades)
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actual_size = min(sub_size, remaining)
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actual_size = int(actual_size / 100) * 100
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if actual_size <= 0:
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break
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comm = self._calc_commission(
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actual_size,
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price,
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False,
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commission,
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min_commission,
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stamp_tax,
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)
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slip = self._calc_slippage(actual_size, price, False, slippage_model, df)
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trades.append(
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Trade(
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datetime=self._get_datetime_int(df, exec_idx),
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direction="BUY",
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size=float(actual_size),
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price=price,
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commission=comm,
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slippage=slip,
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)
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)
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return trades
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def _execute_sell(
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self,
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signal: Signal,
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df: pd.DataFrame,
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bar_idx: int,
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position: float,
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commission: float,
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min_commission: float,
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stamp_tax: float,
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slippage_model: SlippageModel | None,
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) -> list[Trade]:
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total_size = signal.size if signal.size > 0 else position
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if total_size <= 0:
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return []
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sub_size = int(total_size / self._n_bars / 100) * 100
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if sub_size <= 0:
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sub_size = 100
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trades: list[Trade] = []
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for i in range(self._n_bars):
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exec_idx = bar_idx + 1 + i
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if exec_idx >= len(df):
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break
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price = float(df["close"].iloc[exec_idx])
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remaining = total_size - sum(t.size for t in trades)
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actual_size = min(sub_size, remaining)
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actual_size = int(actual_size / 100) * 100
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if actual_size <= 0:
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break
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comm = self._calc_commission(
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actual_size,
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price,
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True,
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commission,
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min_commission,
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stamp_tax,
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)
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slip = self._calc_slippage(actual_size, price, True, slippage_model, df)
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trades.append(
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Trade(
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datetime=self._get_datetime_int(df, exec_idx),
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direction="SELL",
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size=float(actual_size),
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price=price,
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commission=comm,
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slippage=slip,
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)
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)
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return trades
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class VWAPExecution(ExecutionModel):
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"""成交量加权平均价格执行。
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按历史成交量分布比例拆分订单。
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"""
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def __init__(self, n_bars: int = 5, volume_lookback: int = 20) -> None:
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self._n_bars = max(1, n_bars)
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self._volume_lookback = max(1, volume_lookback)
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def execute(
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self,
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signal: Signal,
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df: pd.DataFrame,
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bar_idx: int,
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cash: float,
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position: float,
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position_mode: str,
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commission: float,
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min_commission: float,
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stamp_tax: float,
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slippage_model: SlippageModel | None,
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) -> list[Trade]:
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if signal.direction == "BUY":
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return self._execute_buy(
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signal,
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df,
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bar_idx,
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cash,
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position_mode,
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commission,
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min_commission,
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stamp_tax,
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slippage_model,
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)
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return self._execute_sell(
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signal,
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df,
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bar_idx,
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position,
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commission,
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min_commission,
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stamp_tax,
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slippage_model,
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)
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def _get_volume_weights(self, df: pd.DataFrame, bar_idx: int) -> list[float]:
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"""获取成交量权重分布。"""
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start = max(0, bar_idx - self._volume_lookback + 1)
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lookback = df.iloc[start : bar_idx + 1]
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if "volume" not in lookback.columns or len(lookback) == 0:
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return [1.0 / self._n_bars] * self._n_bars
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volumes = lookback["volume"].to_numpy()
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total_vol = float(volumes.sum())
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if total_vol <= 0:
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return [1.0 / self._n_bars] * self._n_bars
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weights: list[float] = []
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for i in range(self._n_bars):
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idx = max(0, len(volumes) - 1 - (i % max(1, len(volumes))))
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weights.append(float(volumes[idx]) / total_vol)
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total_w = sum(weights)
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if total_w <= 0:
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return [1.0 / self._n_bars] * self._n_bars
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return [w / total_w for w in weights]
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def _execute_buy(
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self,
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signal: Signal,
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df: pd.DataFrame,
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bar_idx: int,
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cash: float,
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position_mode: str,
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commission: float,
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min_commission: float,
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stamp_tax: float,
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slippage_model: SlippageModel | None,
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) -> list[Trade]:
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first_price = float(df["open"].iloc[bar_idx + 1]) if bar_idx + 1 < len(df) else 0
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if first_price <= 0:
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return []
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total_size = self._calc_buy_size(
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signal.size,
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first_price,
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cash,
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position_mode,
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commission,
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)
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if total_size <= 0:
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return []
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weights = self._get_volume_weights(df, bar_idx)
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trades: list[Trade] = []
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for i in range(self._n_bars):
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exec_idx = bar_idx + 1 + i
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if exec_idx >= len(df):
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break
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price = float(df["close"].iloc[exec_idx])
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w = weights[i] if i < len(weights) else 1.0 / self._n_bars
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target = int(total_size * w / 100) * 100
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remaining = total_size - sum(t.size for t in trades)
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actual_size = min(target, remaining)
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actual_size = int(actual_size / 100) * 100
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if actual_size <= 0:
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continue
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comm = self._calc_commission(
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actual_size,
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price,
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False,
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commission,
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min_commission,
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stamp_tax,
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)
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slip = self._calc_slippage(actual_size, price, False, slippage_model, df)
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trades.append(
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Trade(
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datetime=self._get_datetime_int(df, exec_idx),
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direction="BUY",
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size=float(actual_size),
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price=price,
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commission=comm,
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slippage=slip,
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)
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)
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return trades
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def _execute_sell(
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self,
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signal: Signal,
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df: pd.DataFrame,
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bar_idx: int,
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position: float,
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commission: float,
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min_commission: float,
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stamp_tax: float,
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slippage_model: SlippageModel | None,
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) -> list[Trade]:
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total_size = signal.size if signal.size > 0 else position
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if total_size <= 0:
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return []
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weights = self._get_volume_weights(df, bar_idx)
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trades: list[Trade] = []
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for i in range(self._n_bars):
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exec_idx = bar_idx + 1 + i
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if exec_idx >= len(df):
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break
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price = float(df["close"].iloc[exec_idx])
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w = weights[i] if i < len(weights) else 1.0 / self._n_bars
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target = int(total_size * w / 100) * 100
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remaining = total_size - sum(t.size for t in trades)
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actual_size = min(target, remaining)
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actual_size = int(actual_size / 100) * 100
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if actual_size <= 0:
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continue
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comm = self._calc_commission(
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actual_size,
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price,
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True,
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commission,
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min_commission,
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stamp_tax,
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)
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slip = self._calc_slippage(actual_size, price, True, slippage_model, df)
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trades.append(
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Trade(
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datetime=self._get_datetime_int(df, exec_idx),
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direction="SELL",
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size=float(actual_size),
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price=price,
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commission=comm,
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slippage=slip,
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)
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)
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return trades
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@@ -5,7 +5,12 @@ from __future__ import annotations
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import pandas as pd
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import pytest
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from easy_tdx.backtest.execution import ExecutionModel, ImmediateExecution
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from easy_tdx.backtest.execution import (
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ExecutionModel,
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ImmediateExecution,
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TWAPExecution,
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VWAPExecution,
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)
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from easy_tdx.backtest.types import Signal
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@@ -167,3 +172,150 @@ class TestImmediateExecution:
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)
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assert len(trades) == 1
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assert trades[0].size == 100
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class TestTWAPExecution:
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"""时间加权平均价格执行。"""
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def test_split_buy_into_3_bars(self) -> None:
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df = _make_df(20)
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model = TWAPExecution(n_bars=3)
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signal = Signal(datetime=20240101, direction="BUY", size=300)
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trades = model.execute(
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signal=signal,
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df=df,
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bar_idx=0,
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cash=100000,
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position=0,
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position_mode="fixed",
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commission=0.0003,
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min_commission=5.0,
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stamp_tax=0.001,
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slippage_model=None,
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)
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assert len(trades) == 3
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total_size = sum(t.size for t in trades)
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assert total_size <= 300
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prices = [t.price for t in trades]
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assert prices[0] != prices[1]
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def test_split_sell_into_2_bars(self) -> None:
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df = _make_df(20)
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model = TWAPExecution(n_bars=2)
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signal = Signal(datetime=20240101, direction="SELL", size=200)
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trades = model.execute(
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signal=signal,
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df=df,
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bar_idx=0,
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cash=0,
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position=500,
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position_mode="fixed",
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commission=0.0003,
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min_commission=5.0,
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stamp_tax=0.001,
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slippage_model=None,
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)
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assert len(trades) == 2
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assert sum(t.size for t in trades) == 200.0
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def test_truncates_at_data_end(self) -> None:
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df = _make_df(5)
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model = TWAPExecution(n_bars=10)
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signal = Signal(datetime=20240101, direction="BUY", size=1000)
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trades = model.execute(
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signal=signal,
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df=df,
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bar_idx=0,
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cash=100000,
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position=0,
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position_mode="fixed",
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commission=0.0003,
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min_commission=5.0,
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stamp_tax=0.001,
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slippage_model=None,
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)
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assert len(trades) <= 4
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def test_full_position_mode(self) -> None:
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df = _make_df(20)
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model = TWAPExecution(n_bars=3)
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signal = Signal(datetime=20240101, direction="BUY", size=0)
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trades = model.execute(
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signal=signal,
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df=df,
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bar_idx=0,
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cash=60000,
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position=0,
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position_mode="full",
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commission=0.0003,
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min_commission=5.0,
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stamp_tax=0.001,
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slippage_model=None,
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)
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assert len(trades) == 3
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assert all(t.size > 0 for t in trades)
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class TestVWAPExecution:
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"""成交量加权平均价格执行。"""
|
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def test_basic_buy(self) -> None:
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df = _make_df(20)
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model = VWAPExecution(n_bars=3, volume_lookback=10)
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signal = Signal(datetime=20240101, direction="BUY", size=300)
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trades = model.execute(
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signal=signal,
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df=df,
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bar_idx=5,
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cash=100000,
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position=0,
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position_mode="fixed",
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commission=0.0003,
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min_commission=5.0,
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stamp_tax=0.001,
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slippage_model=None,
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)
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assert len(trades) == 3
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total_size = sum(t.size for t in trades)
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assert total_size <= 300
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def test_volume_weighted_split(self) -> None:
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df = _make_df(20)
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df.loc[6, "volume"] = 50000
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df.loc[7, "volume"] = 50000
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df.loc[8, "volume"] = 50000
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model = VWAPExecution(n_bars=3, volume_lookback=5)
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signal = Signal(datetime=20240105, direction="BUY", size=300)
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trades = model.execute(
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signal=signal,
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df=df,
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bar_idx=5,
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cash=100000,
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position=0,
|
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position_mode="fixed",
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commission=0.0003,
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min_commission=5.0,
|
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stamp_tax=0.001,
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slippage_model=None,
|
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)
|
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assert len(trades) == 3
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sizes = [t.size for t in trades]
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assert sum(sizes) <= 300
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def test_truncates_at_data_end(self) -> None:
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df = _make_df(5)
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model = VWAPExecution(n_bars=10, volume_lookback=3)
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signal = Signal(datetime=20240101, direction="BUY", size=1000)
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trades = model.execute(
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signal=signal,
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df=df,
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bar_idx=0,
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cash=100000,
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position=0,
|
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position_mode="fixed",
|
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commission=0.0003,
|
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min_commission=5.0,
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stamp_tax=0.001,
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slippage_model=None,
|
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)
|
||||
assert len(trades) <= 4
|
||||
|
||||
Reference in New Issue
Block a user