start experiment 86 (exp/86-scheduled-rolling-4y-retrain-for-round-3)
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zhaoli
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"""Weekly-rebalance TopkDropout strategy.
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Turnover-reduction variant of ``qlib.contrib.strategy.signal_strategy.TopkDropoutStrategy``:
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the topk/n_drop selection and sizing are identical to the reference, but the
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target book is recomputed only on the first trading day of each ISO week; on the
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other days the strategy issues NO orders (holds the book untouched).
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The weekly cadence is derived from the qlib trade calendar: a rebalance happens
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when the current trade step's date belongs to a different ISO ``(year, week)``
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than the previous trade step. ``hold_band_pct`` (default 0) optionally skips
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tiny rebalances: when a name's existing position differs from the new target by
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less than this fraction, no order is generated for it.
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"""
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from __future__ import annotations
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from typing import List
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import numpy as np
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import pandas as pd
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from qlib.backtest import Order
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from qlib.backtest.decision import OrderDir, TradeDecisionWO
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from qlib.contrib.strategy.signal_strategy import TopkDropoutStrategy
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__all__ = ["WeeklyRebalanceDropoutStrategy"]
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DEFAULT_HOLD_BAND_PCT = 0.0
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class WeeklyRebalanceDropoutStrategy(TopkDropoutStrategy):
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"""TopkDropout rebalanced once per ISO week; holds otherwise.
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Parameters
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----------
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topk, n_drop, method_sell, method_buy, hold_thresh, only_tradable,
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forbid_all_trade_at_limit : same as ``TopkDropoutStrategy``.
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hold_band_pct : skip order for a name whose deviation from target weight is
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below this fraction of the target (no-trade buffer band).
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rebalance_every_n_weeks : rebalance every N ISO weeks instead of every week
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(default 1 = weekly; 2 = biweekly). Ignored when
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``rebalance_every_n_days`` is set.
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rebalance_every_n_days : rebalance every N trading days (daily when N=1).
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When set, overrides the weekly gating logic entirely.
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"""
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def __init__(self, *, topk, n_drop, hold_band_pct: float = DEFAULT_HOLD_BAND_PCT,
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rebalance_every_n_weeks: int = 1,
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rebalance_every_n_days: int = 0, **kwargs):
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super().__init__(topk=topk, n_drop=n_drop, **kwargs)
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self.hold_band_pct = hold_band_pct
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self.rebalance_every_n_weeks = rebalance_every_n_weeks
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self.rebalance_every_n_days = rebalance_every_n_days
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@staticmethod
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def _iso_week(ts) -> tuple:
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return (ts.year, ts.week)
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def generate_trade_decision(self, execute_result=None):
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import copy
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trade_step = self.trade_calendar.get_trade_step()
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trade_start_time, trade_end_time = self.trade_calendar.get_step_time(trade_step)
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if self.rebalance_every_n_days > 0:
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# daily gating: count trading steps since last rebalance
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step_num = trade_step
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if hasattr(self, "_last_rebal_step"):
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if (step_num - self._last_rebal_step) < self.rebalance_every_n_days:
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return TradeDecisionWO([], self)
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self._last_rebal_step = step_num
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else:
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cur_week = self._iso_week(trade_start_time)
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prev_week = getattr(self, "_last_week", None)
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self._last_week = cur_week
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if prev_week is not None and prev_week == cur_week:
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return TradeDecisionWO([], self)
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if self.rebalance_every_n_weeks > 1:
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week_num = cur_week[1]
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if prev_week is not None and (week_num % self.rebalance_every_n_weeks) != 1:
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return TradeDecisionWO([], self)
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pred_start_time, pred_end_time = self.trade_calendar.get_step_time(trade_step, shift=1)
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pred_score = self.signal.get_signal(start_time=pred_start_time, end_time=pred_end_time)
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if isinstance(pred_score, pd.DataFrame):
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pred_score = pred_score.iloc[:, 0]
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if pred_score is None:
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return TradeDecisionWO([], self)
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if self.only_tradable:
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def get_first_n(li, n, reverse=False):
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cur_n = 0
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res = []
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for si in reversed(li) if reverse else li:
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if self.trade_exchange.is_stock_tradable(
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stock_id=si, start_time=trade_start_time, end_time=trade_end_time
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):
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res.append(si)
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cur_n += 1
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if cur_n >= n:
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break
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return res[::-1] if reverse else res
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def get_last_n(li, n):
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return get_first_n(li, n, reverse=True)
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def filter_stock(li):
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return [
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si
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for si in li
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if self.trade_exchange.is_stock_tradable(
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stock_id=si, start_time=trade_start_time, end_time=trade_end_time
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)
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]
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else:
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def get_first_n(li, n):
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return list(li)[:n]
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def get_last_n(li, n):
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return list(li)[-n:]
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def filter_stock(li):
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return li
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current_temp: "object" = copy.deepcopy(self.trade_position)
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sell_order_list: List[Order] = []
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buy_order_list: List[Order] = []
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cash = current_temp.get_cash()
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current_stock_list = current_temp.get_stock_list()
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last = pred_score.reindex(current_stock_list).sort_values(ascending=False).index
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if self.method_buy == "top":
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today = get_first_n(
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pred_score[~pred_score.index.isin(last)].sort_values(ascending=False).index,
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self.n_drop + self.topk - len(last),
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)
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elif self.method_buy == "random":
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topk_candi = get_first_n(pred_score.sort_values(ascending=False).index, self.topk)
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candi = list(filter(lambda x: x not in last, topk_candi))
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n = self.n_drop + self.topk - len(last)
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try:
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today = np.random.choice(candi, n, replace=False)
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except ValueError:
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today = candi
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else:
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raise NotImplementedError(f"This type of input is not supported")
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comb = pred_score.reindex(last.union(pd.Index(today))).sort_values(ascending=False).index
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if self.method_sell == "bottom":
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sell = last[last.isin(get_last_n(comb, self.n_drop))]
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elif self.method_sell == "random":
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candi = filter_stock(last)
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try:
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sell = pd.Index(np.random.choice(candi, self.n_drop, replace=False) if len(last) else [])
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except ValueError:
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sell = candi
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else:
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raise NotImplementedError(f"This type of input is not supported")
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buy = today[: len(sell) + self.topk - len(last)]
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for code in current_stock_list:
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if not self.trade_exchange.is_stock_tradable(
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stock_id=code,
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start_time=trade_start_time,
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end_time=trade_end_time,
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direction=None if self.forbid_all_trade_at_limit else OrderDir.SELL,
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):
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continue
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if code in sell:
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time_per_step = self.trade_calendar.get_freq()
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if current_temp.get_stock_count(code, bar=time_per_step) < self.hold_thresh:
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continue
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sell_amount = current_temp.get_stock_amount(code=code)
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sell_order = Order(
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stock_id=code,
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amount=sell_amount,
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start_time=trade_start_time,
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end_time=trade_end_time,
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direction=Order.SELL,
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)
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if self.trade_exchange.check_order(sell_order):
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sell_order_list.append(sell_order)
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trade_val, trade_cost, trade_price = self.trade_exchange.deal_order(
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sell_order, position=current_temp
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)
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cash += trade_val - trade_cost
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if len(buy) == 0:
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return TradeDecisionWO(sell_order_list, self)
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value = cash * self.risk_degree / len(buy)
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for code in buy:
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if not self.trade_exchange.is_stock_tradable(
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stock_id=code,
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start_time=trade_start_time,
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end_time=trade_end_time,
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direction=None if self.forbid_all_trade_at_limit else OrderDir.BUY,
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):
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continue
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buy_price = self.trade_exchange.get_deal_price(
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stock_id=code, start_time=trade_start_time, end_time=trade_end_time, direction=OrderDir.BUY
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)
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buy_amount = value / buy_price
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factor = self.trade_exchange.get_factor(
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stock_id=code, start_time=trade_start_time, end_time=trade_end_time
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)
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buy_amount = self.trade_exchange.round_amount_by_trade_unit(buy_amount, factor)
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buy_order = Order(
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stock_id=code,
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amount=buy_amount,
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start_time=trade_start_time,
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end_time=trade_end_time,
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direction=Order.BUY,
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)
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buy_order_list.append(buy_order)
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return TradeDecisionWO(sell_order_list + buy_order_list, self)
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