Compare commits
2
Commits
| Author | SHA1 | Date | |
|---|---|---|---|
 zhaoli
|
9915a82d38 | ||
|
zhaoli
|
c2edfe6671 |
+2
-6
@@ -1,5 +1,5 @@
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# TradeAC custom-qlib-code snapshot (auto-generated)
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# parent repo HEAD : fccac3e792e2a6c5e72e63ca20a0eb4b8713d561
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# parent repo HEAD : c2edfe6671f17a364a27ecf9619df25f194f43e6
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# tac-qlib/tac_qlib/contrib
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# tac-qlib/tac_qlib/data
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# per-file hashes (git hash-object):
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@@ -15,14 +15,10 @@
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3e80f2e08b661ddd2f58ffe5a6196063fa41ae51 tac-qlib/tac_qlib/contrib/model/__pycache__/rank_gbdt.cpython-312.pyc
|
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d3f051f3a8650c42fedc7b367b966f7c74fb5789 tac-qlib/tac_qlib/contrib/model/rank_ensemble.py
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d03e6611338918d4aac5eea4adf26f85a3763652 tac-qlib/tac_qlib/contrib/model/rank_gbdt.py
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c4ef84ffda2a611262412fe1127689c667f3d0c1 tac-qlib/tac_qlib/contrib/strategy/__init__.py
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4afcf9058231111c412925f4c4b84e81d656db87 tac-qlib/tac_qlib/contrib/strategy/__init__.py
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6ad10c2ebe37c16417e67c7aeb731ad1fcb6da2f tac-qlib/tac_qlib/contrib/strategy/__pycache__/__init__.cpython-312.pyc
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8d684b3216b040071d9ee4fa920a0e0c7486d278 tac-qlib/tac_qlib/contrib/strategy/__pycache__/optimal_stop.cpython-312.pyc
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896ef74ae47bcd1ed388e1e5d9c8d70c28097fe9 tac-qlib/tac_qlib/contrib/strategy/kelly_dropout.py
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79aaad9e39fcc740a773f4f63c512ce1086cfde0 tac-qlib/tac_qlib/contrib/strategy/optimal_stop.py
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5b9acfb4340111b204249add7760bd53c6ae03f1 tac-qlib/tac_qlib/contrib/strategy/regime_gate.py
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aa1ee880d52ceb5821d65973962099c2254f710a tac-qlib/tac_qlib/contrib/strategy/top_bottom.py
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fe60bacdfedd48617863be31f24b7c7daebfac5a tac-qlib/tac_qlib/contrib/strategy/weekly_rebalance.py
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92e6e90eb0cd0a25142034560f27adb6b705b1a8 tac-qlib/tac_qlib/data/__init__.py
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7c4e6c345fad1978efe8860c0d977d0c02d6f8d9 tac-qlib/tac_qlib/data/__pycache__/__init__.cpython-312.pyc
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99e602392d51663cb06d5c425000b1ed1e5a916b tac-qlib/tac_qlib/data/__pycache__/config.cpython-312.pyc
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@@ -1,13 +1,3 @@
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from .kelly_dropout import FractionalKellyDropoutStrategy # noqa: F401
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from .optimal_stop import OptimalStopControl # noqa: F401
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from .regime_gate import RegimeGateDropoutStrategy # noqa: F401
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from .top_bottom import TopBottomDropoutStrategy # noqa: F401
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from .weekly_rebalance import WeeklyRebalanceDropoutStrategy # noqa: F401
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__all__ = [
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"OptimalStopControl",
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"FractionalKellyDropoutStrategy",
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"WeeklyRebalanceDropoutStrategy",
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"TopBottomDropoutStrategy",
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"RegimeGateDropoutStrategy",
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]
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__all__ = ["OptimalStopControl"]
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@@ -1,201 +0,0 @@
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"""Fractional-Kelly dropout strategy for cross-sectional signals.
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Sizing rule variant of ``qlib.contrib.strategy.signal_strategy.TopkDropoutStrategy``:
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the topk/n_drop SELECTION is identical to the reference, but the buy size is
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proportional to the score MAGNITUDE (edge) instead of equal-weight, capped at a
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fraction ``cap_frac`` of the equal-weight notional so a single name cannot
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over-concentrate the book.
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``cap_frac`` is the fraction of the equal-weight per-name notional that a top
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signal can deploy at most (e.g. 0.5 = at most half the equal-weight size).
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Names whose score is below the median of the buy set get a proportionally
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smaller slice; the residual stays in cash (that is the point of the rule:
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throw away less edge per name, deploy less capital when conviction is low).
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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__ = ["FractionalKellyDropoutStrategy"]
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DEFAULT_CAP_FRAC = 0.5
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class FractionalKellyDropoutStrategy(TopkDropoutStrategy):
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"""TopkDropout selection with score-magnitude (fractional-Kelly) sizing.
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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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cap_frac : max buy notional as a fraction of the equal-weight notional.
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"""
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def __init__(self, *, topk, n_drop, cap_frac: float = DEFAULT_CAP_FRAC, **kwargs):
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super().__init__(topk=topk, n_drop=n_drop, **kwargs)
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self.cap_frac = cap_frac
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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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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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# ---- fractional-Kelly sizing --------------------------------------
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# equal-weight notional (reference baseline)
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eq_notional = cash * self.risk_degree / len(buy)
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buy_scores = pred_score.reindex(buy).astype(float)
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lo, hi = buy_scores.min(), buy_scores.max()
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if hi == lo:
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w = pd.Series(1.0, index=buy_scores.index)
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else:
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w = (buy_scores - lo) / (hi - lo) # [0,1] edge magnitude
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w = w.clip(lower=0.0)
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w_max = w.max()
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w = w / w_max if w_max > 0 else w # max == 1.0
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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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notional = eq_notional * min(self.cap_frac, float(w.get(code, 0.0)))
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buy_amount = notional / 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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@@ -1,231 +0,0 @@
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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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|
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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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|
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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():
|
||||
sym = str(row[sym_col]).split("/")[-1].upper()
|
||||
val = row.iloc[-1]
|
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out[sym] = float(val) if val == val else np.nan
|
||||
return pd.Series(out)
|
||||
|
||||
def generate_trade_decision(self, execute_result=None):
|
||||
import copy
|
||||
|
||||
trade_step = self.trade_calendar.get_trade_step()
|
||||
trade_start_time, trade_end_time = self.trade_calendar.get_step_time(trade_step)
|
||||
pred_start_time, pred_end_time = self.trade_calendar.get_step_time(trade_step, shift=1)
|
||||
pred_score = self.signal.get_signal(start_time=pred_start_time, end_time=pred_end_time)
|
||||
if isinstance(pred_score, pd.DataFrame):
|
||||
pred_score = pred_score.iloc[:, 0]
|
||||
if pred_score is None:
|
||||
return TradeDecisionWO([], self)
|
||||
|
||||
if self.only_tradable:
|
||||
|
||||
def get_first_n(li, n, reverse=False):
|
||||
cur_n = 0
|
||||
res = []
|
||||
for si in reversed(li) if reverse else li:
|
||||
if self.trade_exchange.is_stock_tradable(
|
||||
stock_id=si, start_time=trade_start_time, end_time=trade_end_time
|
||||
):
|
||||
res.append(si)
|
||||
cur_n += 1
|
||||
if cur_n >= n:
|
||||
break
|
||||
return res[::-1] if reverse else res
|
||||
|
||||
def get_last_n(li, n):
|
||||
return get_first_n(li, n, reverse=True)
|
||||
|
||||
def filter_stock(li):
|
||||
return [
|
||||
si
|
||||
for si in li
|
||||
if self.trade_exchange.is_stock_tradable(
|
||||
stock_id=si, start_time=trade_start_time, end_time=trade_end_time
|
||||
)
|
||||
]
|
||||
|
||||
else:
|
||||
|
||||
def get_first_n(li, n):
|
||||
return list(li)[:n]
|
||||
|
||||
def get_last_n(li, n):
|
||||
return list(li)[-n:]
|
||||
|
||||
def filter_stock(li):
|
||||
return li
|
||||
|
||||
current_temp: "object" = copy.deepcopy(self.trade_position)
|
||||
sell_order_list: List[Order] = []
|
||||
buy_order_list: List[Order] = []
|
||||
cash = current_temp.get_cash()
|
||||
current_stock_list = current_temp.get_stock_list()
|
||||
last = pred_score.reindex(current_stock_list).sort_values(ascending=False).index
|
||||
|
||||
if self.method_buy == "top":
|
||||
today = get_first_n(
|
||||
pred_score[~pred_score.index.isin(last)].sort_values(ascending=False).index,
|
||||
self.n_drop + self.topk - len(last),
|
||||
)
|
||||
elif self.method_buy == "random":
|
||||
topk_candi = get_first_n(pred_score.sort_values(ascending=False).index, self.topk)
|
||||
candi = list(filter(lambda x: x not in last, topk_candi))
|
||||
n = self.n_drop + self.topk - len(last)
|
||||
try:
|
||||
today = np.random.choice(candi, n, replace=False)
|
||||
except ValueError:
|
||||
today = candi
|
||||
else:
|
||||
raise NotImplementedError(f"This type of input is not supported")
|
||||
|
||||
comb = pred_score.reindex(last.union(pd.Index(today))).sort_values(ascending=False).index
|
||||
|
||||
if self.method_sell == "bottom":
|
||||
sell = last[last.isin(get_last_n(comb, self.n_drop))]
|
||||
elif self.method_sell == "random":
|
||||
candi = filter_stock(last)
|
||||
try:
|
||||
sell = pd.Index(np.random.choice(candi, self.n_drop, replace=False) if len(last) else [])
|
||||
except ValueError:
|
||||
sell = candi
|
||||
else:
|
||||
raise NotImplementedError(f"This type of input is not supported")
|
||||
|
||||
buy = today[: len(sell) + self.topk - len(last)]
|
||||
|
||||
# ---- regime gate -----------------------------------------------------
|
||||
if buy:
|
||||
regime = self._regime_for(buy, pred_start_time, pred_end_time)
|
||||
gated = [c for c in buy if regime.get(c, np.nan) >= self.regime_threshold]
|
||||
else:
|
||||
gated = []
|
||||
|
||||
for code in current_stock_list:
|
||||
if not self.trade_exchange.is_stock_tradable(
|
||||
stock_id=code,
|
||||
start_time=trade_start_time,
|
||||
end_time=trade_end_time,
|
||||
direction=None if self.forbid_all_trade_at_limit else OrderDir.SELL,
|
||||
):
|
||||
continue
|
||||
if code in sell:
|
||||
time_per_step = self.trade_calendar.get_freq()
|
||||
if current_temp.get_stock_count(code, bar=time_per_step) < self.hold_thresh:
|
||||
continue
|
||||
sell_amount = current_temp.get_stock_amount(code=code)
|
||||
sell_order = Order(
|
||||
stock_id=code,
|
||||
amount=sell_amount,
|
||||
start_time=trade_start_time,
|
||||
end_time=trade_end_time,
|
||||
direction=Order.SELL,
|
||||
)
|
||||
if self.trade_exchange.check_order(sell_order):
|
||||
sell_order_list.append(sell_order)
|
||||
trade_val, trade_cost, trade_price = self.trade_exchange.deal_order(
|
||||
sell_order, position=current_temp
|
||||
)
|
||||
cash += trade_val - trade_cost
|
||||
|
||||
if len(gated) == 0:
|
||||
return TradeDecisionWO(sell_order_list, self)
|
||||
|
||||
value = cash * self.risk_degree / len(gated)
|
||||
for code in gated:
|
||||
if not self.trade_exchange.is_stock_tradable(
|
||||
stock_id=code,
|
||||
start_time=trade_start_time,
|
||||
end_time=trade_end_time,
|
||||
direction=None if self.forbid_all_trade_at_limit else OrderDir.BUY,
|
||||
):
|
||||
continue
|
||||
buy_price = self.trade_exchange.get_deal_price(
|
||||
stock_id=code, start_time=trade_start_time, end_time=trade_end_time, direction=OrderDir.BUY
|
||||
)
|
||||
buy_amount = value / buy_price
|
||||
factor = self.trade_exchange.get_factor(
|
||||
stock_id=code, start_time=trade_start_time, end_time=trade_end_time
|
||||
)
|
||||
buy_amount = self.trade_exchange.round_amount_by_trade_unit(buy_amount, factor)
|
||||
buy_order = Order(
|
||||
stock_id=code,
|
||||
amount=buy_amount,
|
||||
start_time=trade_start_time,
|
||||
end_time=trade_end_time,
|
||||
direction=Order.BUY,
|
||||
)
|
||||
buy_order_list.append(buy_order)
|
||||
|
||||
return TradeDecisionWO(sell_order_list + buy_order_list, self)
|
||||
@@ -1,169 +0,0 @@
|
||||
"""Market-neutral top/bottom long-short strategy for cross-sectional signals.
|
||||
|
||||
Captures the cross-sectional long-short spread net of costs: buys the top-ranked
|
||||
``topk`` names and shorts the bottom-ranked ``topk`` names, equal-weight per
|
||||
side, sized to ``risk_degree`` of total value per side. Rebalances daily to the
|
||||
current rank (dropout-free: the book converges to the latest top/bottom sets).
|
||||
|
||||
The long and short legs use equal notional per side (gross exposure ~2x
|
||||
``risk_degree`` of NAV, i.e. approximately market neutral before transaction
|
||||
costs). Benchmark neutrality (SPY beta ~ 0) is the secondary sanity metric.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import List
|
||||
|
||||
import copy
|
||||
|
||||
import pandas as pd
|
||||
|
||||
from qlib.backtest import Order
|
||||
from qlib.backtest.decision import OrderDir, TradeDecisionWO
|
||||
from qlib.contrib.strategy.signal_strategy import BaseSignalStrategy
|
||||
|
||||
__all__ = ["TopBottomDropoutStrategy"]
|
||||
|
||||
DEFAULT_SHORT_LEG = True
|
||||
DEFAULT_REBALANCE_DAILY = True
|
||||
|
||||
|
||||
class TopBottomDropoutStrategy(BaseSignalStrategy):
|
||||
"""Long top-k / short bottom-k equal-weight market-neutral book.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
topk : number of names on each side (long top-k and short bottom-k).
|
||||
short_leg : whether to open the short side (if False, long-only topk).
|
||||
rebalance_daily : if True rebalance to current rank every day; else keep
|
||||
positions and only refresh on score changes (dropout-style).
|
||||
risk_degree : fraction of total value deployed per side.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
*,
|
||||
topk: int = 10,
|
||||
short_leg: bool = DEFAULT_SHORT_LEG,
|
||||
rebalance_daily: bool = DEFAULT_REBALANCE_DAILY,
|
||||
**kwargs,
|
||||
):
|
||||
super().__init__(**kwargs)
|
||||
self.topk = topk
|
||||
self.short_leg = short_leg
|
||||
self.rebalance_daily = rebalance_daily
|
||||
self._prev_longs = set()
|
||||
self._prev_shorts = set()
|
||||
|
||||
def generate_trade_decision(self, execute_result=None):
|
||||
trade_step = self.trade_calendar.get_trade_step()
|
||||
trade_start_time, trade_end_time = self.trade_calendar.get_step_time(trade_step)
|
||||
pred_start_time, pred_end_time = self.trade_calendar.get_step_time(trade_step, shift=1)
|
||||
pred_score = self.signal.get_signal(start_time=pred_start_time, end_time=pred_end_time)
|
||||
if isinstance(pred_score, pd.DataFrame):
|
||||
pred_score = pred_score.iloc[:, 0]
|
||||
if pred_score is None or len(pred_score) == 0:
|
||||
return TradeDecisionWO([], self)
|
||||
|
||||
# rank all names; topk longs and topk shorts
|
||||
ranked = pred_score.sort_values(ascending=False)
|
||||
longs = list(ranked.index[: self.topk])
|
||||
shorts = list(ranked.index[-self.topk :]) if self.short_leg else []
|
||||
|
||||
current_temp: "object" = copy.deepcopy(self.trade_position)
|
||||
current_codes = set(current_temp.get_stock_list())
|
||||
holdings = {c: current_temp for c in current_codes if abs(current_temp.get_stock_amount(c)) > 1e-6}
|
||||
|
||||
sell_orders: List[Order] = []
|
||||
buy_orders: List[Order] = []
|
||||
|
||||
def _tradable(code, direction):
|
||||
try:
|
||||
return self.trade_exchange.is_stock_tradable(
|
||||
stock_id=code, start_time=trade_start_time, end_time=trade_end_time, direction=direction
|
||||
)
|
||||
except TypeError:
|
||||
return self.trade_exchange.is_stock_tradable(
|
||||
stock_id=code, start_time=trade_start_time, end_time=trade_end_time
|
||||
)
|
||||
|
||||
# determine target set (long/short)
|
||||
target_longs = set(longs)
|
||||
target_shorts = set(shorts)
|
||||
|
||||
# close positions not in the target book
|
||||
for code in list(holdings):
|
||||
if code in target_longs or code in target_shorts:
|
||||
continue
|
||||
amt = abs(current_temp.get_stock_amount(code))
|
||||
o = Order(
|
||||
stock_id=code,
|
||||
amount=amt,
|
||||
start_time=trade_start_time,
|
||||
end_time=trade_end_time,
|
||||
direction=Order.SELL if code in target_longs else Order.SELL,
|
||||
)
|
||||
if self.trade_exchange.check_order(o):
|
||||
sell_orders.append(o)
|
||||
self.trade_exchange.deal_order(o, position=current_temp)
|
||||
|
||||
# equal-weight notional per side
|
||||
total_value = current_temp.get_cash()
|
||||
for code, pos in holdings.items():
|
||||
if code in target_longs or code in target_shorts:
|
||||
mark = self.trade_exchange.get_deal_price(
|
||||
stock_id=code, start_time=trade_start_time, end_time=trade_end_time, direction=Order.SELL
|
||||
)
|
||||
if mark is not None and mark == mark:
|
||||
total_value += abs(current_temp.get_stock_amount(code)) * mark
|
||||
|
||||
side_notional = total_value * self.risk_degree / max(1, self.topk)
|
||||
|
||||
for code in longs:
|
||||
if code in holdings and abs(current_temp.get_stock_amount(code)) > 1e-6:
|
||||
continue
|
||||
px = self.trade_exchange.get_deal_price(
|
||||
stock_id=code, start_time=trade_start_time, end_time=trade_end_time, direction=Order.BUY
|
||||
)
|
||||
if px is None or px != px or px <= 0:
|
||||
continue
|
||||
amount = side_notional / px
|
||||
factor = self.trade_exchange.get_factor(
|
||||
stock_id=code, start_time=trade_start_time, end_time=trade_end_time
|
||||
)
|
||||
amount = self.trade_exchange.round_amount_by_trade_unit(amount, factor)
|
||||
o = Order(
|
||||
stock_id=code,
|
||||
amount=amount,
|
||||
start_time=trade_start_time,
|
||||
end_time=trade_end_time,
|
||||
direction=Order.BUY,
|
||||
)
|
||||
if self.trade_exchange.check_order(o):
|
||||
buy_orders.append(o)
|
||||
|
||||
if self.short_leg:
|
||||
for code in shorts:
|
||||
if code in holdings and abs(current_temp.get_stock_amount(code)) > 1e-6:
|
||||
continue
|
||||
px = self.trade_exchange.get_deal_price(
|
||||
stock_id=code, start_time=trade_start_time, end_time=trade_end_time, direction=Order.SELL
|
||||
)
|
||||
if px is None or px != px or px <= 0:
|
||||
continue
|
||||
amount = side_notional / px
|
||||
factor = self.trade_exchange.get_factor(
|
||||
stock_id=code, start_time=trade_start_time, end_time=trade_end_time
|
||||
)
|
||||
amount = self.trade_exchange.round_amount_by_trade_unit(amount, factor)
|
||||
o = Order(
|
||||
stock_id=code,
|
||||
amount=amount,
|
||||
start_time=trade_start_time,
|
||||
end_time=trade_end_time,
|
||||
direction=Order.SELL,
|
||||
)
|
||||
if self.trade_exchange.check_order(o):
|
||||
sell_orders.append(o)
|
||||
|
||||
return TradeDecisionWO(sell_orders + buy_orders, self)
|
||||
@@ -1,202 +0,0 @@
|
||||
"""Weekly-rebalance TopkDropout strategy.
|
||||
|
||||
Turnover-reduction variant of ``qlib.contrib.strategy.signal_strategy.TopkDropoutStrategy``:
|
||||
the topk/n_drop selection and sizing are identical to the reference, but the
|
||||
target book is recomputed only on the first trading day of each ISO week; on the
|
||||
other days the strategy issues NO orders (holds the book untouched).
|
||||
|
||||
The weekly cadence is derived from the qlib trade calendar: a rebalance happens
|
||||
when the current trade step's date belongs to a different ISO ``(year, week)``
|
||||
than the previous trade step. ``hold_band_pct`` (default 0) optionally skips
|
||||
tiny rebalances: when a name's existing position differs from the new target by
|
||||
less than this fraction, no order is generated for it.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import List
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
|
||||
from qlib.backtest import Order
|
||||
from qlib.backtest.decision import OrderDir, TradeDecisionWO
|
||||
from qlib.contrib.strategy.signal_strategy import TopkDropoutStrategy
|
||||
|
||||
__all__ = ["WeeklyRebalanceDropoutStrategy"]
|
||||
|
||||
DEFAULT_HOLD_BAND_PCT = 0.0
|
||||
|
||||
|
||||
class WeeklyRebalanceDropoutStrategy(TopkDropoutStrategy):
|
||||
"""TopkDropout rebalanced once per ISO week; holds otherwise.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
topk, n_drop, method_sell, method_buy, hold_thresh, only_tradable,
|
||||
forbid_all_trade_at_limit : same as ``TopkDropoutStrategy``.
|
||||
hold_band_pct : skip order for a name whose deviation from target weight is
|
||||
below this fraction of the target (no-trade buffer band).
|
||||
"""
|
||||
|
||||
def __init__(self, *, topk, n_drop, hold_band_pct: float = DEFAULT_HOLD_BAND_PCT, **kwargs):
|
||||
super().__init__(topk=topk, n_drop=n_drop, **kwargs)
|
||||
self.hold_band_pct = hold_band_pct
|
||||
|
||||
@staticmethod
|
||||
def _iso_week(ts) -> tuple:
|
||||
return (ts.year, ts.week)
|
||||
|
||||
def generate_trade_decision(self, execute_result=None):
|
||||
import copy
|
||||
|
||||
trade_step = self.trade_calendar.get_trade_step()
|
||||
trade_start_time, trade_end_time = self.trade_calendar.get_step_time(trade_step)
|
||||
|
||||
cur_week = self._iso_week(trade_start_time)
|
||||
prev_week = getattr(self, "_last_week", None)
|
||||
self._last_week = cur_week
|
||||
|
||||
if prev_week is not None and prev_week == cur_week:
|
||||
# not the first trading day of this ISO week -> hold
|
||||
return TradeDecisionWO([], self)
|
||||
|
||||
pred_start_time, pred_end_time = self.trade_calendar.get_step_time(trade_step, shift=1)
|
||||
pred_score = self.signal.get_signal(start_time=pred_start_time, end_time=pred_end_time)
|
||||
if isinstance(pred_score, pd.DataFrame):
|
||||
pred_score = pred_score.iloc[:, 0]
|
||||
if pred_score is None:
|
||||
return TradeDecisionWO([], self)
|
||||
|
||||
if self.only_tradable:
|
||||
|
||||
def get_first_n(li, n, reverse=False):
|
||||
cur_n = 0
|
||||
res = []
|
||||
for si in reversed(li) if reverse else li:
|
||||
if self.trade_exchange.is_stock_tradable(
|
||||
stock_id=si, start_time=trade_start_time, end_time=trade_end_time
|
||||
):
|
||||
res.append(si)
|
||||
cur_n += 1
|
||||
if cur_n >= n:
|
||||
break
|
||||
return res[::-1] if reverse else res
|
||||
|
||||
def get_last_n(li, n):
|
||||
return get_first_n(li, n, reverse=True)
|
||||
|
||||
def filter_stock(li):
|
||||
return [
|
||||
si
|
||||
for si in li
|
||||
if self.trade_exchange.is_stock_tradable(
|
||||
stock_id=si, start_time=trade_start_time, end_time=trade_end_time
|
||||
)
|
||||
]
|
||||
|
||||
else:
|
||||
|
||||
def get_first_n(li, n):
|
||||
return list(li)[:n]
|
||||
|
||||
def get_last_n(li, n):
|
||||
return list(li)[-n:]
|
||||
|
||||
def filter_stock(li):
|
||||
return li
|
||||
|
||||
current_temp: "object" = copy.deepcopy(self.trade_position)
|
||||
sell_order_list: List[Order] = []
|
||||
buy_order_list: List[Order] = []
|
||||
cash = current_temp.get_cash()
|
||||
current_stock_list = current_temp.get_stock_list()
|
||||
last = pred_score.reindex(current_stock_list).sort_values(ascending=False).index
|
||||
|
||||
if self.method_buy == "top":
|
||||
today = get_first_n(
|
||||
pred_score[~pred_score.index.isin(last)].sort_values(ascending=False).index,
|
||||
self.n_drop + self.topk - len(last),
|
||||
)
|
||||
elif self.method_buy == "random":
|
||||
topk_candi = get_first_n(pred_score.sort_values(ascending=False).index, self.topk)
|
||||
candi = list(filter(lambda x: x not in last, topk_candi))
|
||||
n = self.n_drop + self.topk - len(last)
|
||||
try:
|
||||
today = np.random.choice(candi, n, replace=False)
|
||||
except ValueError:
|
||||
today = candi
|
||||
else:
|
||||
raise NotImplementedError(f"This type of input is not supported")
|
||||
|
||||
comb = pred_score.reindex(last.union(pd.Index(today))).sort_values(ascending=False).index
|
||||
|
||||
if self.method_sell == "bottom":
|
||||
sell = last[last.isin(get_last_n(comb, self.n_drop))]
|
||||
elif self.method_sell == "random":
|
||||
candi = filter_stock(last)
|
||||
try:
|
||||
sell = pd.Index(np.random.choice(candi, self.n_drop, replace=False) if len(last) else [])
|
||||
except ValueError:
|
||||
sell = candi
|
||||
else:
|
||||
raise NotImplementedError(f"This type of input is not supported")
|
||||
|
||||
buy = today[: len(sell) + self.topk - len(last)]
|
||||
for code in current_stock_list:
|
||||
if not self.trade_exchange.is_stock_tradable(
|
||||
stock_id=code,
|
||||
start_time=trade_start_time,
|
||||
end_time=trade_end_time,
|
||||
direction=None if self.forbid_all_trade_at_limit else OrderDir.SELL,
|
||||
):
|
||||
continue
|
||||
if code in sell:
|
||||
time_per_step = self.trade_calendar.get_freq()
|
||||
if current_temp.get_stock_count(code, bar=time_per_step) < self.hold_thresh:
|
||||
continue
|
||||
sell_amount = current_temp.get_stock_amount(code=code)
|
||||
sell_order = Order(
|
||||
stock_id=code,
|
||||
amount=sell_amount,
|
||||
start_time=trade_start_time,
|
||||
end_time=trade_end_time,
|
||||
direction=Order.SELL,
|
||||
)
|
||||
if self.trade_exchange.check_order(sell_order):
|
||||
sell_order_list.append(sell_order)
|
||||
trade_val, trade_cost, trade_price = self.trade_exchange.deal_order(
|
||||
sell_order, position=current_temp
|
||||
)
|
||||
cash += trade_val - trade_cost
|
||||
|
||||
if len(buy) == 0:
|
||||
return TradeDecisionWO(sell_order_list, self)
|
||||
|
||||
value = cash * self.risk_degree / len(buy)
|
||||
for code in buy:
|
||||
if not self.trade_exchange.is_stock_tradable(
|
||||
stock_id=code,
|
||||
start_time=trade_start_time,
|
||||
end_time=trade_end_time,
|
||||
direction=None if self.forbid_all_trade_at_limit else OrderDir.BUY,
|
||||
):
|
||||
continue
|
||||
buy_price = self.trade_exchange.get_deal_price(
|
||||
stock_id=code, start_time=trade_start_time, end_time=trade_end_time, direction=OrderDir.BUY
|
||||
)
|
||||
buy_amount = value / buy_price
|
||||
factor = self.trade_exchange.get_factor(
|
||||
stock_id=code, start_time=trade_start_time, end_time=trade_end_time
|
||||
)
|
||||
buy_amount = self.trade_exchange.round_amount_by_trade_unit(buy_amount, factor)
|
||||
buy_order = Order(
|
||||
stock_id=code,
|
||||
amount=buy_amount,
|
||||
start_time=trade_start_time,
|
||||
end_time=trade_end_time,
|
||||
direction=Order.BUY,
|
||||
)
|
||||
buy_order_list.append(buy_order)
|
||||
|
||||
return TradeDecisionWO(sell_order_list + buy_order_list, self)
|
||||
Reference in New Issue
Block a user