start experiment 39 (exp/39-q07-weekly-rebalance-recompute-topkdropo)
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zhaoli
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"""HMM-regime overlay TopkDropout strategy.
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Regime-gate overlay on ``qlib.contrib.strategy.signal_strategy.TopkDropoutStrategy``:
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selection and sizing are identical to the reference, but a name is only BOUGHT
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(entry gate) when its per-symbol HMM regime posterior ``sp_hmm_p_regime1`` on
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the signal date is >= ``regime_threshold``; otherwise it is held in cash instead
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of being opened.
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The regime posterior is read from the lake feature provider on the fly via
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``qlib.data.D.features`` (field ``$sp_hmm_p_regime1``) for the signal window, so
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no regime column needs to enter the model's ``feature_fields`` — the gate is a
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pure overlay (book ch.01: regime flags regressed as model features, survived
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only as an overlay). The HMM itself was fit with ``fit_end=<train end>`` when
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the lake features were backfilled, so there is no lookahead.
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Names already held are NOT force-sold when the regime turns unfavourable
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(entry gate only, matching the queue-10 design).
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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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try:
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from qlib.data import D
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except ImportError: # pragma: no cover - qlib always present in this stack
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D = None
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__all__ = ["RegimeGateDropoutStrategy"]
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DEFAULT_REGIME_THRESHOLD = 0.5
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REGIME_FIELD = "$sp_hmm_p_regime1"
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class RegimeGateDropoutStrategy(TopkDropoutStrategy):
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"""TopkDropout with an HMM-regime entry gate on buy candidates.
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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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regime_threshold : minimum ``sp_hmm_p_regime1`` posterior required to open a
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new position (default 0.5).
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"""
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def __init__(self, *, topk, n_drop, regime_threshold: float = DEFAULT_REGIME_THRESHOLD, **kwargs):
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super().__init__(topk=topk, n_drop=n_drop, **kwargs)
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self.regime_threshold = regime_threshold
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def _regime_for(self, codes, pred_start, pred_end) -> pd.Series:
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"""Return {code: sp_hmm_p_regime1} for the signal window (last day)."""
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if D is None:
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return pd.Series(dtype=float)
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try:
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df = D.features(list(codes), [REGIME_FIELD], start_time=pred_start, end_time=pred_end, freq="day")
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except Exception: # noqa: BLE001 - a regime read failure should gate open, not crash
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return pd.Series(dtype=float)
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if df is None or len(df) == 0:
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return pd.Series(dtype=float)
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# df index is MultiIndex (datetime, instrument); take the last day's values
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df = df.reset_index()
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ts_col = "datetime" if "datetime" in df.columns else df.columns[0]
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sym_col = "instrument" if "instrument" in df.columns else df.columns[1]
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last_ts = df[ts_col].max()
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last = df[df[ts_col] == last_ts]
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out = {}
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for _, row in last.iterrows():
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sym = str(row[sym_col]).split("/")[-1].upper()
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val = row.iloc[-1]
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out[sym] = float(val) if val == val else np.nan
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return pd.Series(out)
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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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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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# ---- regime gate -----------------------------------------------------
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if buy:
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regime = self._regime_for(buy, pred_start_time, pred_end_time)
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gated = [c for c in buy if regime.get(c, np.nan) >= self.regime_threshold]
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else:
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gated = []
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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(gated) == 0:
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return TradeDecisionWO(sell_order_list, self)
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value = cash * self.risk_degree / len(gated)
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for code in gated:
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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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