Compare commits
5
Commits
| Author | SHA1 | Date | |
|---|---|---|---|
 zhaoli
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7cd2d91218 | ||
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zhaoli
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e1781f606b | ||
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zhaoli
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336fa676b6 | ||
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zhaoli
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01292ff21e | ||
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zhaoli
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875222a85a |
+13
-16
@@ -1,31 +1,28 @@
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# TradeAC custom-qlib-code snapshot (auto-generated)
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# parent repo HEAD : 507846cee16eeee11daf33c4176e8aec79b985b2
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# parent repo HEAD : e1781f606b8d0e1df4ce06fd272e97eb9435b975
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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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1b6298c4a5652f2e863cbdc385a1014a570fcd59 tac-qlib/tac_qlib/contrib/__init__.py
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b419ee55ed455a1c45423d1c9025ca5cc0a98576 tac-qlib/tac_qlib/contrib/__pycache__/__init__.cpython-312.pyc
|
||||
861592c63edd6a0853a9cb174b5970435b135fc8 tac-qlib/tac_qlib/contrib/__pycache__/__init__.cpython-312.pyc
|
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c76a9f17f680e74eea766eff27f7624359749ed6 tac-qlib/tac_qlib/contrib/data/__init__.py
|
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2f6c67620aa2f9e6aaaef3369361d9b3eac3d6ca tac-qlib/tac_qlib/contrib/data/__pycache__/__init__.cpython-312.pyc
|
||||
fdd5923a70a399e8680913593ff111641947898e tac-qlib/tac_qlib/contrib/data/__pycache__/handler.cpython-312.pyc
|
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5c547a2ef92e075e550fe6d01508a2f1d3f536bc tac-qlib/tac_qlib/contrib/data/__pycache__/__init__.cpython-312.pyc
|
||||
4f656130d167e79dcaaeb7783a121f0b36852374 tac-qlib/tac_qlib/contrib/data/__pycache__/handler.cpython-312.pyc
|
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0dd25ef161c6e0f15eafc84886e7e1381deb38c3 tac-qlib/tac_qlib/contrib/data/handler.py
|
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b151d139a0dcde87d74b21e7c4b729176ba5c39b tac-qlib/tac_qlib/contrib/model/__init__.py
|
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08dec87ccdf6bb5d2cf611ca3032a4280aaab8cf tac-qlib/tac_qlib/contrib/model/__pycache__/__init__.cpython-312.pyc
|
||||
6fb61946ea9a83dfb560de3717f5fbf482c4c00e tac-qlib/tac_qlib/contrib/model/__pycache__/rank_ensemble.cpython-312.pyc
|
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3e80f2e08b661ddd2f58ffe5a6196063fa41ae51 tac-qlib/tac_qlib/contrib/model/__pycache__/rank_gbdt.cpython-312.pyc
|
||||
b1489f2fc0dee85f0a4f90b2e6ad545ed9c8967b tac-qlib/tac_qlib/contrib/model/__pycache__/__init__.cpython-312.pyc
|
||||
121ef237da1df1b8e21a561c3ad0db200b901339 tac-qlib/tac_qlib/contrib/model/__pycache__/rank_ensemble.cpython-312.pyc
|
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74d0da348cbcc3700c96b6f4fe4391488e61efc5 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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6ad10c2ebe37c16417e67c7aeb731ad1fcb6da2f tac-qlib/tac_qlib/contrib/strategy/__pycache__/__init__.cpython-312.pyc
|
||||
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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4afcf9058231111c412925f4c4b84e81d656db87 tac-qlib/tac_qlib/contrib/strategy/__init__.py
|
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c29e45e562256bf786c36f91a971b097467276e9 tac-qlib/tac_qlib/contrib/strategy/__pycache__/__init__.cpython-312.pyc
|
||||
6dd1c568a2961842793674390d5abffd1a0e71b8 tac-qlib/tac_qlib/contrib/strategy/__pycache__/optimal_stop.cpython-312.pyc
|
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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
|
||||
020dcdcf288e4832c8cf2386351f78d5ceb4fe13 tac-qlib/tac_qlib/data/__pycache__/providers.cpython-312.pyc
|
||||
316bf4aa160cc8d15929ea648be03f4b4999667d tac-qlib/tac_qlib/data/__pycache__/__init__.cpython-312.pyc
|
||||
554a3f29d181b64effbf49a8161b32e7f93d8d3e tac-qlib/tac_qlib/data/__pycache__/config.cpython-312.pyc
|
||||
8b47f6d78ac046b6b7b2fb07bd7f3382773ffb73 tac-qlib/tac_qlib/data/__pycache__/providers.cpython-312.pyc
|
||||
53c9007a928841fd3c3b08450f9a6520ce1ac091 tac-qlib/tac_qlib/data/config.py
|
||||
8d0644f6f0d1efb94798ed444cc73e63b643459b tac-qlib/tac_qlib/data/providers.py
|
||||
|
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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(
|
||||
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(
|
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stock_id=code, start_time=trade_start_time, end_time=trade_end_time, direction=OrderDir.BUY
|
||||
)
|
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notional = eq_notional * min(self.cap_frac, float(w.get(code, 0.0)))
|
||||
buy_amount = notional / 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)
|
||||
@@ -1,231 +0,0 @@
|
||||
"""HMM-regime overlay TopkDropout strategy.
|
||||
|
||||
Regime-gate overlay on ``qlib.contrib.strategy.signal_strategy.TopkDropoutStrategy``:
|
||||
selection and sizing are identical to the reference, but a name is only BOUGHT
|
||||
(entry gate) when its per-symbol HMM regime posterior ``sp_hmm_p_regime1`` on
|
||||
the signal date is >= ``regime_threshold``; otherwise it is held in cash instead
|
||||
of being opened.
|
||||
|
||||
The regime posterior is read from the lake feature provider on the fly via
|
||||
``qlib.data.D.features`` (field ``$sp_hmm_p_regime1``) for the signal window, so
|
||||
no regime column needs to enter the model's ``feature_fields`` — the gate is a
|
||||
pure overlay (book ch.01: regime flags regressed as model features, survived
|
||||
only as an overlay). The HMM itself was fit with ``fit_end=<train end>`` when
|
||||
the lake features were backfilled, so there is no lookahead.
|
||||
|
||||
Names already held are NOT force-sold when the regime turns unfavourable
|
||||
(entry gate only, matching the queue-10 design).
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import List
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
|
||||
from qlib.backtest import Order
|
||||
from qlib.backtest.decision import OrderDir, TradeDecisionWO
|
||||
from qlib.contrib.strategy.signal_strategy import TopkDropoutStrategy
|
||||
|
||||
try:
|
||||
from qlib.data import D
|
||||
except ImportError: # pragma: no cover - qlib always present in this stack
|
||||
D = None
|
||||
|
||||
__all__ = ["RegimeGateDropoutStrategy"]
|
||||
|
||||
DEFAULT_REGIME_THRESHOLD = 0.5
|
||||
REGIME_FIELD = "$sp_hmm_p_regime1"
|
||||
|
||||
|
||||
class RegimeGateDropoutStrategy(TopkDropoutStrategy):
|
||||
"""TopkDropout with an HMM-regime entry gate on buy candidates.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
topk, n_drop, method_sell, method_buy, hold_thresh, only_tradable,
|
||||
forbid_all_trade_at_limit : same as ``TopkDropoutStrategy``.
|
||||
regime_threshold : minimum ``sp_hmm_p_regime1`` posterior required to open a
|
||||
new position (default 0.5).
|
||||
"""
|
||||
|
||||
def __init__(self, *, topk, n_drop, regime_threshold: float = DEFAULT_REGIME_THRESHOLD, **kwargs):
|
||||
super().__init__(topk=topk, n_drop=n_drop, **kwargs)
|
||||
self.regime_threshold = regime_threshold
|
||||
|
||||
def _regime_for(self, codes, pred_start, pred_end) -> pd.Series:
|
||||
"""Return {code: sp_hmm_p_regime1} for the signal window (last day)."""
|
||||
if D is None:
|
||||
return pd.Series(dtype=float)
|
||||
try:
|
||||
df = D.features(list(codes), [REGIME_FIELD], start_time=pred_start, end_time=pred_end, freq="day")
|
||||
except Exception: # noqa: BLE001 - a regime read failure should gate open, not crash
|
||||
return pd.Series(dtype=float)
|
||||
if df is None or len(df) == 0:
|
||||
return pd.Series(dtype=float)
|
||||
# df index is MultiIndex (datetime, instrument); take the last day's values
|
||||
df = df.reset_index()
|
||||
ts_col = "datetime" if "datetime" in df.columns else df.columns[0]
|
||||
sym_col = "instrument" if "instrument" in df.columns else df.columns[1]
|
||||
last_ts = df[ts_col].max()
|
||||
last = df[df[ts_col] == last_ts]
|
||||
out = {}
|
||||
for _, row in last.iterrows():
|
||||
sym = str(row[sym_col]).split("/")[-1].upper()
|
||||
val = row.iloc[-1]
|
||||
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)
|
||||
Binary file not shown.
Binary file not shown.
Binary file not shown.
@@ -1,35 +0,0 @@
|
||||
# Q08 — Risk-limit A/B re-validation (trace 40)
|
||||
|
||||
**Status:** DONE (verdict: REFUTED as an IR edge; safety-net value retained)
|
||||
|
||||
## Input
|
||||
- Reference signal: exp-26 pred, run `21afc6afdb674a399b59dd76c97628ce` (mlflow exp 25)
|
||||
- Window: 2026-01-04 → 2026-08-10, Topk10 n_drop1, SPY benchmark, $1M, 5/15bp/$5
|
||||
- Tool: `rd_risk_calibrate` (A/B + sensitivity grid). Full JSON: `risk_calibration.json`
|
||||
|
||||
## Candidate spec (round-3 live spec)
|
||||
`{"liquidity_floor_adv": 5000000, "size_cap_pct": 0.12, "concentration_cap_pct": 0.95, "drawdown_pause_pct": 0.10}`
|
||||
|
||||
## Results (net, with cost)
|
||||
| Config | IR | Ann. return | Max DD |
|
||||
|---|---|---|---|
|
||||
| baseline (no limits) | 1.5804 | +27.50% | −6.91% |
|
||||
| **candidate (5M floor + caps)** | **1.5121** | +2.20% | **−0.65%** |
|
||||
| liquidity $10M | 1.5457 | +2.25% | −0.64% |
|
||||
|
||||
## Findings
|
||||
- **Floor binds, not a no-op**: $5M liquidity floor dropped 8 symbols —
|
||||
`DBA, DBC, ESPO, FDN, REM, TAN, UNG, XAR`.
|
||||
- **No IR edge from the gate**: candidate IR (1.512) is BELOW baseline (1.580).
|
||||
The exp-18 direction (floor IR 0.81→0.98) does NOT reproduce on the clean-lake
|
||||
reference signal.
|
||||
- **Drawdown cut is pure defunding**: size_cap 0.12 × concentration 0.95 fold
|
||||
the effective risk_degree to ~0.0095 → ~$9.5k deployed of $1M (~100x less).
|
||||
Sensitivity grid shows both caps are no-ops (conc 20–50% identical,
|
||||
size_cap 5–20% identical); only the liquidity floor moves returns, marginally.
|
||||
- **Conclusion**: keep the live spec as a safety net; there is no risk-limit
|
||||
gate IR edge to harvest when the signal is the bottleneck (exp-20 pattern).
|
||||
|
||||
## Artifacts on this branch
|
||||
- `evidence/q08-risklimit/risk_calibration.json` — full calibration dump
|
||||
- `queue/designs/q08_risk_limit_ab.md` — the pre-registered design doc
|
||||
@@ -1,401 +0,0 @@
|
||||
{
|
||||
"rows": [
|
||||
{
|
||||
"label": "baseline (no limits)",
|
||||
"mean": 0.001155,
|
||||
"std": 0.011279,
|
||||
"annualized_return": 0.274989,
|
||||
"information_ratio": 1.580427,
|
||||
"max_drawdown": -0.069145
|
||||
},
|
||||
{
|
||||
"label": "liquidity $10,000,000",
|
||||
"mean": 9.4e-05,
|
||||
"std": 0.000942,
|
||||
"annualized_return": 0.022464,
|
||||
"information_ratio": 1.545736,
|
||||
"max_drawdown": -0.006389
|
||||
},
|
||||
{
|
||||
"label": "conc 20%",
|
||||
"mean": 0.000115,
|
||||
"std": 0.001168,
|
||||
"annualized_return": 0.027285,
|
||||
"information_ratio": 1.513718,
|
||||
"max_drawdown": -0.008104
|
||||
},
|
||||
{
|
||||
"label": "conc 30%",
|
||||
"mean": 0.000115,
|
||||
"std": 0.001168,
|
||||
"annualized_return": 0.027285,
|
||||
"information_ratio": 1.513718,
|
||||
"max_drawdown": -0.008104
|
||||
},
|
||||
{
|
||||
"label": "conc 40%",
|
||||
"mean": 0.000115,
|
||||
"std": 0.001168,
|
||||
"annualized_return": 0.027285,
|
||||
"information_ratio": 1.513718,
|
||||
"max_drawdown": -0.008104
|
||||
},
|
||||
{
|
||||
"label": "conc 50%",
|
||||
"mean": 0.000115,
|
||||
"std": 0.001168,
|
||||
"annualized_return": 0.027285,
|
||||
"information_ratio": 1.513718,
|
||||
"max_drawdown": -0.008104
|
||||
},
|
||||
{
|
||||
"label": "candidate {\"liquidity_floor_adv\": 5000000.0, \"size_cap_pct\": 0.12, \"concentration_cap_pct\": 0.95, \"drawdown_pause_pct\": 0.1}",
|
||||
"mean": 9.2e-05,
|
||||
"std": 0.000943,
|
||||
"annualized_return": 0.021991,
|
||||
"information_ratio": 1.512051,
|
||||
"max_drawdown": -0.00653
|
||||
},
|
||||
{
|
||||
"label": "size_cap 5%",
|
||||
"mean": 9.2e-05,
|
||||
"std": 0.000943,
|
||||
"annualized_return": 0.021991,
|
||||
"information_ratio": 1.512051,
|
||||
"max_drawdown": -0.00653
|
||||
},
|
||||
{
|
||||
"label": "size_cap 10%",
|
||||
"mean": 9.2e-05,
|
||||
"std": 0.000943,
|
||||
"annualized_return": 0.021991,
|
||||
"information_ratio": 1.512051,
|
||||
"max_drawdown": -0.00653
|
||||
},
|
||||
{
|
||||
"label": "size_cap 15%",
|
||||
"mean": 9.2e-05,
|
||||
"std": 0.000943,
|
||||
"annualized_return": 0.021991,
|
||||
"information_ratio": 1.512051,
|
||||
"max_drawdown": -0.00653
|
||||
},
|
||||
{
|
||||
"label": "size_cap 20%",
|
||||
"mean": 9.2e-05,
|
||||
"std": 0.000943,
|
||||
"annualized_return": 0.021991,
|
||||
"information_ratio": 1.512051,
|
||||
"max_drawdown": -0.00653
|
||||
},
|
||||
{
|
||||
"label": "liquidity $5,000,000",
|
||||
"mean": 9.2e-05,
|
||||
"std": 0.000943,
|
||||
"annualized_return": 0.021991,
|
||||
"information_ratio": 1.512051,
|
||||
"max_drawdown": -0.00653
|
||||
},
|
||||
{
|
||||
"label": "liquidity $1,000,000",
|
||||
"mean": 9.1e-05,
|
||||
"std": 0.000929,
|
||||
"annualized_return": 0.021625,
|
||||
"information_ratio": 1.508748,
|
||||
"max_drawdown": -0.006376
|
||||
},
|
||||
{
|
||||
"label": "liquidity $2,500,000",
|
||||
"mean": 7.1e-05,
|
||||
"std": 0.000918,
|
||||
"annualized_return": 0.017,
|
||||
"information_ratio": 1.199721,
|
||||
"max_drawdown": -0.007158
|
||||
}
|
||||
],
|
||||
"runs": {
|
||||
"baseline": {
|
||||
"risk": {
|
||||
"mean": 0.0011554172081987572,
|
||||
"std": 0.01127853762493476,
|
||||
"annualized_return": 0.27498929555130425,
|
||||
"information_ratio": 1.5804272791471323,
|
||||
"max_drawdown": -0.06914515336341577
|
||||
},
|
||||
"applied": {}
|
||||
},
|
||||
"candidate": {
|
||||
"risk": {
|
||||
"mean": 9.239707947451976e-05,
|
||||
"std": 0.0009427144352738658,
|
||||
"annualized_return": 0.0219905049149357,
|
||||
"information_ratio": 1.5120514373488407,
|
||||
"max_drawdown": -0.006530482262119444
|
||||
},
|
||||
"applied": {
|
||||
"dropped_liquidity": [
|
||||
"DBA",
|
||||
"DBC",
|
||||
"ESPO",
|
||||
"FDN",
|
||||
"REM",
|
||||
"TAN",
|
||||
"UNG",
|
||||
"XAR"
|
||||
]
|
||||
}
|
||||
},
|
||||
"size_cap 5%": {
|
||||
"risk": {
|
||||
"mean": 9.239707947451976e-05,
|
||||
"std": 0.0009427144352738658,
|
||||
"annualized_return": 0.0219905049149357,
|
||||
"information_ratio": 1.5120514373488407,
|
||||
"max_drawdown": -0.006530482262119444
|
||||
},
|
||||
"applied": {
|
||||
"dropped_liquidity": [
|
||||
"DBA",
|
||||
"DBC",
|
||||
"ESPO",
|
||||
"FDN",
|
||||
"REM",
|
||||
"TAN",
|
||||
"UNG",
|
||||
"XAR"
|
||||
]
|
||||
}
|
||||
},
|
||||
"size_cap 10%": {
|
||||
"risk": {
|
||||
"mean": 9.239707947451976e-05,
|
||||
"std": 0.0009427144352738658,
|
||||
"annualized_return": 0.0219905049149357,
|
||||
"information_ratio": 1.5120514373488407,
|
||||
"max_drawdown": -0.006530482262119444
|
||||
},
|
||||
"applied": {
|
||||
"dropped_liquidity": [
|
||||
"DBA",
|
||||
"DBC",
|
||||
"ESPO",
|
||||
"FDN",
|
||||
"REM",
|
||||
"TAN",
|
||||
"UNG",
|
||||
"XAR"
|
||||
]
|
||||
}
|
||||
},
|
||||
"size_cap 15%": {
|
||||
"risk": {
|
||||
"mean": 9.239707947451976e-05,
|
||||
"std": 0.0009427144352738658,
|
||||
"annualized_return": 0.0219905049149357,
|
||||
"information_ratio": 1.5120514373488407,
|
||||
"max_drawdown": -0.006530482262119444
|
||||
},
|
||||
"applied": {
|
||||
"dropped_liquidity": [
|
||||
"DBA",
|
||||
"DBC",
|
||||
"ESPO",
|
||||
"FDN",
|
||||
"REM",
|
||||
"TAN",
|
||||
"UNG",
|
||||
"XAR"
|
||||
]
|
||||
}
|
||||
},
|
||||
"size_cap 20%": {
|
||||
"risk": {
|
||||
"mean": 9.239707947451976e-05,
|
||||
"std": 0.0009427144352738658,
|
||||
"annualized_return": 0.0219905049149357,
|
||||
"information_ratio": 1.5120514373488407,
|
||||
"max_drawdown": -0.006530482262119444
|
||||
},
|
||||
"applied": {
|
||||
"dropped_liquidity": [
|
||||
"DBA",
|
||||
"DBC",
|
||||
"ESPO",
|
||||
"FDN",
|
||||
"REM",
|
||||
"TAN",
|
||||
"UNG",
|
||||
"XAR"
|
||||
]
|
||||
}
|
||||
},
|
||||
"conc 20%": {
|
||||
"risk": {
|
||||
"mean": 0.00011464156491316718,
|
||||
"std": 0.0011683839517000441,
|
||||
"annualized_return": 0.027284692449333788,
|
||||
"information_ratio": 1.5137180903503433,
|
||||
"max_drawdown": -0.008103887185240407
|
||||
},
|
||||
"applied": {
|
||||
"dropped_liquidity": [
|
||||
"DBA",
|
||||
"DBC",
|
||||
"ESPO",
|
||||
"FDN",
|
||||
"REM",
|
||||
"TAN",
|
||||
"UNG",
|
||||
"XAR"
|
||||
]
|
||||
}
|
||||
},
|
||||
"conc 30%": {
|
||||
"risk": {
|
||||
"mean": 0.00011464156491316718,
|
||||
"std": 0.0011683839517000441,
|
||||
"annualized_return": 0.027284692449333788,
|
||||
"information_ratio": 1.5137180903503433,
|
||||
"max_drawdown": -0.008103887185240407
|
||||
},
|
||||
"applied": {
|
||||
"dropped_liquidity": [
|
||||
"DBA",
|
||||
"DBC",
|
||||
"ESPO",
|
||||
"FDN",
|
||||
"REM",
|
||||
"TAN",
|
||||
"UNG",
|
||||
"XAR"
|
||||
]
|
||||
}
|
||||
},
|
||||
"conc 40%": {
|
||||
"risk": {
|
||||
"mean": 0.00011464156491316718,
|
||||
"std": 0.0011683839517000441,
|
||||
"annualized_return": 0.027284692449333788,
|
||||
"information_ratio": 1.5137180903503433,
|
||||
"max_drawdown": -0.008103887185240407
|
||||
},
|
||||
"applied": {
|
||||
"dropped_liquidity": [
|
||||
"DBA",
|
||||
"DBC",
|
||||
"ESPO",
|
||||
"FDN",
|
||||
"REM",
|
||||
"TAN",
|
||||
"UNG",
|
||||
"XAR"
|
||||
]
|
||||
}
|
||||
},
|
||||
"conc 50%": {
|
||||
"risk": {
|
||||
"mean": 0.00011464156491316718,
|
||||
"std": 0.0011683839517000441,
|
||||
"annualized_return": 0.027284692449333788,
|
||||
"information_ratio": 1.5137180903503433,
|
||||
"max_drawdown": -0.008103887185240407
|
||||
},
|
||||
"applied": {
|
||||
"dropped_liquidity": [
|
||||
"DBA",
|
||||
"DBC",
|
||||
"ESPO",
|
||||
"FDN",
|
||||
"REM",
|
||||
"TAN",
|
||||
"UNG",
|
||||
"XAR"
|
||||
]
|
||||
}
|
||||
},
|
||||
"liquidity $1,000,000": {
|
||||
"risk": {
|
||||
"mean": 9.086210454881382e-05,
|
||||
"std": 0.0009290831160004576,
|
||||
"annualized_return": 0.021625180882617688,
|
||||
"information_ratio": 1.508747982736451,
|
||||
"max_drawdown": -0.006376134679664126
|
||||
},
|
||||
"applied": {
|
||||
"dropped_liquidity": [
|
||||
"ESPO"
|
||||
]
|
||||
}
|
||||
},
|
||||
"liquidity $2,500,000": {
|
||||
"risk": {
|
||||
"mean": 7.142665167642606e-05,
|
||||
"std": 0.0009184775632266332,
|
||||
"annualized_return": 0.016999543098989402,
|
||||
"information_ratio": 1.1997208834083914,
|
||||
"max_drawdown": -0.0071582979845040825
|
||||
},
|
||||
"applied": {
|
||||
"dropped_liquidity": [
|
||||
"DBA",
|
||||
"DBC",
|
||||
"ESPO",
|
||||
"REM",
|
||||
"XAR"
|
||||
]
|
||||
}
|
||||
},
|
||||
"liquidity $5,000,000": {
|
||||
"risk": {
|
||||
"mean": 9.239707947451976e-05,
|
||||
"std": 0.0009427144352738658,
|
||||
"annualized_return": 0.0219905049149357,
|
||||
"information_ratio": 1.5120514373488407,
|
||||
"max_drawdown": -0.006530482262119444
|
||||
},
|
||||
"applied": {
|
||||
"dropped_liquidity": [
|
||||
"DBA",
|
||||
"DBC",
|
||||
"ESPO",
|
||||
"FDN",
|
||||
"REM",
|
||||
"TAN",
|
||||
"UNG",
|
||||
"XAR"
|
||||
]
|
||||
}
|
||||
},
|
||||
"liquidity $10,000,000": {
|
||||
"risk": {
|
||||
"mean": 9.438545151345752e-05,
|
||||
"std": 0.0009420158170657147,
|
||||
"annualized_return": 0.02246373746020289,
|
||||
"information_ratio": 1.5457360696934006,
|
||||
"max_drawdown": -0.006388809561209335
|
||||
},
|
||||
"applied": {
|
||||
"dropped_liquidity": [
|
||||
"DBA",
|
||||
"DBC",
|
||||
"ESPO",
|
||||
"FDN",
|
||||
"ICLN",
|
||||
"ITA",
|
||||
"MDY",
|
||||
"REM",
|
||||
"SHY",
|
||||
"TAN",
|
||||
"UNG",
|
||||
"XAR"
|
||||
]
|
||||
}
|
||||
}
|
||||
},
|
||||
"candidate": {
|
||||
"liquidity_floor_adv": 5000000.0,
|
||||
"size_cap_pct": 0.12,
|
||||
"concentration_cap_pct": 0.95,
|
||||
"drawdown_pause_pct": 0.1
|
||||
}
|
||||
}
|
||||
@@ -1,34 +0,0 @@
|
||||
# QUEUE-08 — Risk-limit A/B re-validation: $5M liquidity floor on the exp-26 reference
|
||||
|
||||
**Status:** QUEUED · **Priority:** P1 · **Effort:** tool-only (no new code)
|
||||
|
||||
## Hypothesis (prove)
|
||||
The $5M liquidity floor improves net IR and cuts drawdown on the **post-reset**
|
||||
reference signal (pre-reset exp 18, EVIDENCE#008: net IR 0.81→0.98, cumDD
|
||||
7.93%→5.44%), while size/concentration caps hurt by cutting deployed capital.
|
||||
Needs re-validation on the exp-26 lineage because exp 18 is pre-clean-lake and
|
||||
not comparable (EVIDENCE#009/010). Source: `book/CLAIMS.md` open question +
|
||||
`book/README.md` `TODO(evidence-needed: reconciliation of exp 18 risk-limit spec
|
||||
on the post-reset reference signal)`.
|
||||
|
||||
## Change vs exp-26 reference (ONE variable)
|
||||
- Reference: the saved exp-26 prediction (run `21afc6af…`, mlflow exp 25).
|
||||
- A/B via `rd_risk_calibrate` (runs limit-vs-no-limit A/B + sensitivity grid
|
||||
over size_cap_pct, concentration_cap_pct, liquidity_floor_adv) and/or
|
||||
`rd_backtest` with `risk_limits` on the SAME saved `pred.pkl`:
|
||||
- baseline: no limits (this must reproduce the exp-26 net +2.13% / IR 0.21);
|
||||
- candidate: `{"liquidity_floor_adv": 5000000, "size_cap_pct": 0.12,
|
||||
"concentration_cap_pct": 0.95, "drawdown_pause_pct": 0.10}` (round-3 spec).
|
||||
- Pick the spec (B2 calibration) that keeps live ≈ backtest.
|
||||
|
||||
## Acceptance
|
||||
- Candidate spec: `net_IR > 0.21` AND `net_max_drawdown < 7.69%` vs no-limit on
|
||||
the same pred. Size/concentration caps expected to REDUCE deployed capital
|
||||
(record the direction as confirmation of exp 18).
|
||||
- If the floor is a no-op (gates don't bind at this signal) → report that gates
|
||||
are no-ops when the signal is the bottleneck (exp 20 pattern) as a PROVEN
|
||||
clean-lake result.
|
||||
|
||||
## Execution prerequisites
|
||||
- None (uses saved pred + `rd_risk_calibrate`/`rd_backtest`). Trace the A/B as
|
||||
an experiment; record the spec chosen for the next live round.
|
||||
@@ -0,0 +1,95 @@
|
||||
# Walk-forward: A-weekly / test 2024
|
||||
# 3x3 re-validation (trace exp 52). Strategy WeeklyRebalanceDropoutStrategy n_drop=1, parallel=5.
|
||||
{% set LAKE = TAC_LAKE_DIR %}
|
||||
qlib_init:
|
||||
provider_uri: "{{ LAKE }}"
|
||||
region: us
|
||||
expression_cache: null
|
||||
dataset_cache: null
|
||||
calendar_provider:
|
||||
class: tac_qlib.data.providers.LakeCalendarProvider
|
||||
kwargs: { lake_root: "{{ LAKE }}", market: US }
|
||||
instrument_provider:
|
||||
class: tac_qlib.data.providers.LakeInstrumentProvider
|
||||
kwargs: { lake_root: "{{ LAKE }}", market: US, markets: {} }
|
||||
feature_provider:
|
||||
class: tac_qlib.data.providers.LakeFeatureProvider
|
||||
kwargs: { lake_root: "{{ LAKE }}", market: US }
|
||||
exp_manager:
|
||||
class: MLflowExpManager
|
||||
module_path: qlib.workflow.expm
|
||||
kwargs: { uri: "sqlite:///mlruns.db", default_exp_name: "tac-rd-bt-3x3-windows" }
|
||||
|
||||
task:
|
||||
model:
|
||||
class: RankICEnsembleLGBModel
|
||||
module_path: tac_qlib.contrib.model.rank_ensemble
|
||||
kwargs:
|
||||
loss: mse
|
||||
learning_rate: 0.02
|
||||
num_leaves: 31
|
||||
n_estimators: 3000
|
||||
num_boost_round: 3000
|
||||
early_stopping_rounds: 200
|
||||
min_data_in_leaf: 20
|
||||
lambda_l2: 0.5
|
||||
colsample_bytree: 0.8
|
||||
subsample: 0.8
|
||||
subsample_freq: 1
|
||||
reg_alpha: 0.1
|
||||
reg_lambda: 1.0
|
||||
seeds: "42,7,2026,99,123"
|
||||
parallel: 5
|
||||
dataset:
|
||||
class: DatasetH
|
||||
module_path: qlib.data.dataset
|
||||
kwargs:
|
||||
handler:
|
||||
class: TACHandler
|
||||
module_path: tac_qlib.contrib.data.handler
|
||||
kwargs:
|
||||
instruments: "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM"
|
||||
start_time: "2015-01-03"
|
||||
end_time: "2025-01-07"
|
||||
fit_start_time: "2016-01-04"
|
||||
fit_end_time: "2023-08-31"
|
||||
freq: day
|
||||
lake_root: "{{ LAKE }}"
|
||||
market: US
|
||||
label: "Ref($close,-6)/Ref($close,-1)-1"
|
||||
feature_fields: "$open,$high,$low,$close,$vwap,$volume,sp_ret,sp_jump_ratio,sp_jump_flag,sp_jump_tail,sp_max_move,sp_rv1,sp_rv5,sp_rv22,sp_vol_ratio_5_22,sp_vol_ratio_1_22,sp_trend_slope_5,sp_trend_slope_20,sp_trend_slope_60,sp_logp,sp_hurst_exponent,sp_sig_level1_lead,sp_sig_level1_lag,sp_sig_level2_lead_lag,sp_sig_level2_lag_lead"
|
||||
infer_processors:
|
||||
- { class: DropAllNaN, kwargs: { fit_start_time: "2016-01-04", fit_end_time: "2023-08-31" } }
|
||||
- { class: ProcessInf, kwargs: {} }
|
||||
- { class: CSRankNorm, kwargs: {} }
|
||||
- { class: ZScoreNorm, kwargs: { fit_start_time: "2016-01-04", fit_end_time: "2023-08-31" } }
|
||||
- { class: Fillna, kwargs: {} }
|
||||
segments:
|
||||
train: ["2016-01-04", "2023-08-31"]
|
||||
valid: ["2023-09-01", "2023-12-29"]
|
||||
test: ["2024-01-02", "2024-12-31"]
|
||||
|
||||
record:
|
||||
- { class: SignalRecord, module_path: qlib.workflow.record_temp, kwargs: {} }
|
||||
- { class: SigAnaRecord, module_path: qlib.workflow.record_temp, kwargs: { ana_long_short: true, ann_scaler: 252 } }
|
||||
- class: PortAnaRecord
|
||||
module_path: qlib.workflow.record_temp
|
||||
kwargs:
|
||||
config:
|
||||
strategy:
|
||||
class: WeeklyRebalanceDropoutStrategy
|
||||
module_path: tac_qlib.contrib.strategy.weekly_rebalance
|
||||
kwargs: { signal: "<PRED>", topk: 10, n_drop: 1, only_tradable: true, risk_degree: 0.95 }
|
||||
backtest:
|
||||
start_time: "2024-01-02"
|
||||
end_time: "2024-12-31"
|
||||
account: 1000000
|
||||
benchmark: SPY
|
||||
exchange_kwargs:
|
||||
codes: "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM"
|
||||
deal_price: $close
|
||||
freq: day
|
||||
open_cost: 0.0005
|
||||
close_cost: 0.0015
|
||||
min_cost: 5.0
|
||||
risk_analysis_freq: 1d
|
||||
@@ -0,0 +1,95 @@
|
||||
# Walk-forward: A-weekly / test 2025
|
||||
# 3x3 re-validation (trace exp 52). Strategy WeeklyRebalanceDropoutStrategy n_drop=1, parallel=5.
|
||||
{% set LAKE = TAC_LAKE_DIR %}
|
||||
qlib_init:
|
||||
provider_uri: "{{ LAKE }}"
|
||||
region: us
|
||||
expression_cache: null
|
||||
dataset_cache: null
|
||||
calendar_provider:
|
||||
class: tac_qlib.data.providers.LakeCalendarProvider
|
||||
kwargs: { lake_root: "{{ LAKE }}", market: US }
|
||||
instrument_provider:
|
||||
class: tac_qlib.data.providers.LakeInstrumentProvider
|
||||
kwargs: { lake_root: "{{ LAKE }}", market: US, markets: {} }
|
||||
feature_provider:
|
||||
class: tac_qlib.data.providers.LakeFeatureProvider
|
||||
kwargs: { lake_root: "{{ LAKE }}", market: US }
|
||||
exp_manager:
|
||||
class: MLflowExpManager
|
||||
module_path: qlib.workflow.expm
|
||||
kwargs: { uri: "sqlite:///mlruns.db", default_exp_name: "tac-rd-bt-3x3-windows" }
|
||||
|
||||
task:
|
||||
model:
|
||||
class: RankICEnsembleLGBModel
|
||||
module_path: tac_qlib.contrib.model.rank_ensemble
|
||||
kwargs:
|
||||
loss: mse
|
||||
learning_rate: 0.02
|
||||
num_leaves: 31
|
||||
n_estimators: 3000
|
||||
num_boost_round: 3000
|
||||
early_stopping_rounds: 200
|
||||
min_data_in_leaf: 20
|
||||
lambda_l2: 0.5
|
||||
colsample_bytree: 0.8
|
||||
subsample: 0.8
|
||||
subsample_freq: 1
|
||||
reg_alpha: 0.1
|
||||
reg_lambda: 1.0
|
||||
seeds: "42,7,2026,99,123"
|
||||
parallel: 5
|
||||
dataset:
|
||||
class: DatasetH
|
||||
module_path: qlib.data.dataset
|
||||
kwargs:
|
||||
handler:
|
||||
class: TACHandler
|
||||
module_path: tac_qlib.contrib.data.handler
|
||||
kwargs:
|
||||
instruments: "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM"
|
||||
start_time: "2015-01-03"
|
||||
end_time: "2026-01-07"
|
||||
fit_start_time: "2016-01-04"
|
||||
fit_end_time: "2024-08-30"
|
||||
freq: day
|
||||
lake_root: "{{ LAKE }}"
|
||||
market: US
|
||||
label: "Ref($close,-6)/Ref($close,-1)-1"
|
||||
feature_fields: "$open,$high,$low,$close,$vwap,$volume,sp_ret,sp_jump_ratio,sp_jump_flag,sp_jump_tail,sp_max_move,sp_rv1,sp_rv5,sp_rv22,sp_vol_ratio_5_22,sp_vol_ratio_1_22,sp_trend_slope_5,sp_trend_slope_20,sp_trend_slope_60,sp_logp,sp_hurst_exponent,sp_sig_level1_lead,sp_sig_level1_lag,sp_sig_level2_lead_lag,sp_sig_level2_lag_lead"
|
||||
infer_processors:
|
||||
- { class: DropAllNaN, kwargs: { fit_start_time: "2016-01-04", fit_end_time: "2024-08-30" } }
|
||||
- { class: ProcessInf, kwargs: {} }
|
||||
- { class: CSRankNorm, kwargs: {} }
|
||||
- { class: ZScoreNorm, kwargs: { fit_start_time: "2016-01-04", fit_end_time: "2024-08-30" } }
|
||||
- { class: Fillna, kwargs: {} }
|
||||
segments:
|
||||
train: ["2016-01-04", "2024-08-30"]
|
||||
valid: ["2024-09-03", "2024-12-31"]
|
||||
test: ["2025-01-02", "2025-12-31"]
|
||||
|
||||
record:
|
||||
- { class: SignalRecord, module_path: qlib.workflow.record_temp, kwargs: {} }
|
||||
- { class: SigAnaRecord, module_path: qlib.workflow.record_temp, kwargs: { ana_long_short: true, ann_scaler: 252 } }
|
||||
- class: PortAnaRecord
|
||||
module_path: qlib.workflow.record_temp
|
||||
kwargs:
|
||||
config:
|
||||
strategy:
|
||||
class: WeeklyRebalanceDropoutStrategy
|
||||
module_path: tac_qlib.contrib.strategy.weekly_rebalance
|
||||
kwargs: { signal: "<PRED>", topk: 10, n_drop: 1, only_tradable: true, risk_degree: 0.95 }
|
||||
backtest:
|
||||
start_time: "2025-01-02"
|
||||
end_time: "2025-12-31"
|
||||
account: 1000000
|
||||
benchmark: SPY
|
||||
exchange_kwargs:
|
||||
codes: "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM"
|
||||
deal_price: $close
|
||||
freq: day
|
||||
open_cost: 0.0005
|
||||
close_cost: 0.0015
|
||||
min_cost: 5.0
|
||||
risk_analysis_freq: 1d
|
||||
@@ -0,0 +1,95 @@
|
||||
# Walk-forward: A-weekly / test 2026
|
||||
# 3x3 re-validation (trace exp 52). Strategy WeeklyRebalanceDropoutStrategy n_drop=1, parallel=5.
|
||||
{% set LAKE = TAC_LAKE_DIR %}
|
||||
qlib_init:
|
||||
provider_uri: "{{ LAKE }}"
|
||||
region: us
|
||||
expression_cache: null
|
||||
dataset_cache: null
|
||||
calendar_provider:
|
||||
class: tac_qlib.data.providers.LakeCalendarProvider
|
||||
kwargs: { lake_root: "{{ LAKE }}", market: US }
|
||||
instrument_provider:
|
||||
class: tac_qlib.data.providers.LakeInstrumentProvider
|
||||
kwargs: { lake_root: "{{ LAKE }}", market: US, markets: {} }
|
||||
feature_provider:
|
||||
class: tac_qlib.data.providers.LakeFeatureProvider
|
||||
kwargs: { lake_root: "{{ LAKE }}", market: US }
|
||||
exp_manager:
|
||||
class: MLflowExpManager
|
||||
module_path: qlib.workflow.expm
|
||||
kwargs: { uri: "sqlite:///mlruns.db", default_exp_name: "tac-rd-bt-3x3-windows" }
|
||||
|
||||
task:
|
||||
model:
|
||||
class: RankICEnsembleLGBModel
|
||||
module_path: tac_qlib.contrib.model.rank_ensemble
|
||||
kwargs:
|
||||
loss: mse
|
||||
learning_rate: 0.02
|
||||
num_leaves: 31
|
||||
n_estimators: 3000
|
||||
num_boost_round: 3000
|
||||
early_stopping_rounds: 200
|
||||
min_data_in_leaf: 20
|
||||
lambda_l2: 0.5
|
||||
colsample_bytree: 0.8
|
||||
subsample: 0.8
|
||||
subsample_freq: 1
|
||||
reg_alpha: 0.1
|
||||
reg_lambda: 1.0
|
||||
seeds: "42,7,2026,99,123"
|
||||
parallel: 5
|
||||
dataset:
|
||||
class: DatasetH
|
||||
module_path: qlib.data.dataset
|
||||
kwargs:
|
||||
handler:
|
||||
class: TACHandler
|
||||
module_path: tac_qlib.contrib.data.handler
|
||||
kwargs:
|
||||
instruments: "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM"
|
||||
start_time: "2015-01-03"
|
||||
end_time: "2026-08-19"
|
||||
fit_start_time: "2016-01-04"
|
||||
fit_end_time: "2025-09-01"
|
||||
freq: day
|
||||
lake_root: "{{ LAKE }}"
|
||||
market: US
|
||||
label: "Ref($close,-6)/Ref($close,-1)-1"
|
||||
feature_fields: "$open,$high,$low,$close,$vwap,$volume,sp_ret,sp_jump_ratio,sp_jump_flag,sp_jump_tail,sp_max_move,sp_rv1,sp_rv5,sp_rv22,sp_vol_ratio_5_22,sp_vol_ratio_1_22,sp_trend_slope_5,sp_trend_slope_20,sp_trend_slope_60,sp_logp,sp_hurst_exponent,sp_sig_level1_lead,sp_sig_level1_lag,sp_sig_level2_lead_lag,sp_sig_level2_lag_lead"
|
||||
infer_processors:
|
||||
- { class: DropAllNaN, kwargs: { fit_start_time: "2016-01-04", fit_end_time: "2025-09-01" } }
|
||||
- { class: ProcessInf, kwargs: {} }
|
||||
- { class: CSRankNorm, kwargs: {} }
|
||||
- { class: ZScoreNorm, kwargs: { fit_start_time: "2016-01-04", fit_end_time: "2025-09-01" } }
|
||||
- { class: Fillna, kwargs: {} }
|
||||
segments:
|
||||
train: ["2016-01-04", "2025-09-01"]
|
||||
valid: ["2025-09-03", "2026-01-03"]
|
||||
test: ["2026-01-04", "2026-08-19"]
|
||||
|
||||
record:
|
||||
- { class: SignalRecord, module_path: qlib.workflow.record_temp, kwargs: {} }
|
||||
- { class: SigAnaRecord, module_path: qlib.workflow.record_temp, kwargs: { ana_long_short: true, ann_scaler: 252 } }
|
||||
- class: PortAnaRecord
|
||||
module_path: qlib.workflow.record_temp
|
||||
kwargs:
|
||||
config:
|
||||
strategy:
|
||||
class: WeeklyRebalanceDropoutStrategy
|
||||
module_path: tac_qlib.contrib.strategy.weekly_rebalance
|
||||
kwargs: { signal: "<PRED>", topk: 10, n_drop: 1, only_tradable: true, risk_degree: 0.95 }
|
||||
backtest:
|
||||
start_time: "2026-01-04"
|
||||
end_time: "2026-08-19"
|
||||
account: 1000000
|
||||
benchmark: SPY
|
||||
exchange_kwargs:
|
||||
codes: "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM"
|
||||
deal_price: $close
|
||||
freq: day
|
||||
open_cost: 0.0005
|
||||
close_cost: 0.0015
|
||||
min_cost: 5.0
|
||||
risk_analysis_freq: 1d
|
||||
@@ -0,0 +1,95 @@
|
||||
# Walk-forward: B-moments / test 2024
|
||||
# 3x3 re-validation (trace exp 52). Strategy TopkDropoutStrategy n_drop=1, parallel=default(auto).
|
||||
{% set LAKE = TAC_LAKE_DIR %}
|
||||
qlib_init:
|
||||
provider_uri: "{{ LAKE }}"
|
||||
region: us
|
||||
expression_cache: null
|
||||
dataset_cache: null
|
||||
calendar_provider:
|
||||
class: tac_qlib.data.providers.LakeCalendarProvider
|
||||
kwargs: { lake_root: "{{ LAKE }}", market: US }
|
||||
instrument_provider:
|
||||
class: tac_qlib.data.providers.LakeInstrumentProvider
|
||||
kwargs: { lake_root: "{{ LAKE }}", market: US, markets: {} }
|
||||
feature_provider:
|
||||
class: tac_qlib.data.providers.LakeFeatureProvider
|
||||
kwargs: { lake_root: "{{ LAKE }}", market: US }
|
||||
exp_manager:
|
||||
class: MLflowExpManager
|
||||
module_path: qlib.workflow.expm
|
||||
kwargs: { uri: "sqlite:///mlruns.db", default_exp_name: "tac-rd-bt-3x3-windows" }
|
||||
|
||||
task:
|
||||
model:
|
||||
class: RankICEnsembleLGBModel
|
||||
module_path: tac_qlib.contrib.model.rank_ensemble
|
||||
kwargs:
|
||||
loss: mse
|
||||
learning_rate: 0.02
|
||||
num_leaves: 31
|
||||
n_estimators: 3000
|
||||
num_boost_round: 3000
|
||||
early_stopping_rounds: 200
|
||||
min_data_in_leaf: 20
|
||||
lambda_l2: 0.5
|
||||
colsample_bytree: 0.8
|
||||
subsample: 0.8
|
||||
subsample_freq: 1
|
||||
reg_alpha: 0.1
|
||||
reg_lambda: 1.0
|
||||
seeds: "42,7,2026,99,123"
|
||||
|
||||
dataset:
|
||||
class: DatasetH
|
||||
module_path: qlib.data.dataset
|
||||
kwargs:
|
||||
handler:
|
||||
class: TACHandler
|
||||
module_path: tac_qlib.contrib.data.handler
|
||||
kwargs:
|
||||
instruments: "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM"
|
||||
start_time: "2015-01-03"
|
||||
end_time: "2025-01-07"
|
||||
fit_start_time: "2016-01-04"
|
||||
fit_end_time: "2023-08-31"
|
||||
freq: day
|
||||
lake_root: "{{ LAKE }}"
|
||||
market: US
|
||||
label: "Ref($close,-6)/Ref($close,-1)-1"
|
||||
feature_fields: "$open,$high,$low,$close,$vwap,$volume,sp_ret,sp_jump_ratio,sp_jump_flag,sp_jump_tail,sp_max_move,sp_rv1,sp_rv5,sp_rv22,sp_vol_ratio_5_22,sp_vol_ratio_1_22,sp_trend_slope_5,sp_trend_slope_20,sp_trend_slope_60,sp_logp,sp_hurst_exponent,sp_sig_level1_lead,sp_sig_level1_lag,sp_sig_level2_lead_lag,sp_sig_level2_lag_lead,sp_rskew_5,sp_rskew_22,sp_rkurt_5,sp_rkurt_22,sp_dsv_5,sp_dsv_22"
|
||||
infer_processors:
|
||||
- { class: DropAllNaN, kwargs: { fit_start_time: "2016-01-04", fit_end_time: "2023-08-31" } }
|
||||
- { class: ProcessInf, kwargs: {} }
|
||||
- { class: CSRankNorm, kwargs: {} }
|
||||
- { class: ZScoreNorm, kwargs: { fit_start_time: "2016-01-04", fit_end_time: "2023-08-31" } }
|
||||
- { class: Fillna, kwargs: {} }
|
||||
segments:
|
||||
train: ["2016-01-04", "2023-08-31"]
|
||||
valid: ["2023-09-01", "2023-12-29"]
|
||||
test: ["2024-01-02", "2024-12-31"]
|
||||
|
||||
record:
|
||||
- { class: SignalRecord, module_path: qlib.workflow.record_temp, kwargs: {} }
|
||||
- { class: SigAnaRecord, module_path: qlib.workflow.record_temp, kwargs: { ana_long_short: true, ann_scaler: 252 } }
|
||||
- class: PortAnaRecord
|
||||
module_path: qlib.workflow.record_temp
|
||||
kwargs:
|
||||
config:
|
||||
strategy:
|
||||
class: TopkDropoutStrategy
|
||||
module_path: qlib.contrib.strategy
|
||||
kwargs: { signal: "<PRED>", topk: 10, n_drop: 1, only_tradable: true, risk_degree: 0.95 }
|
||||
backtest:
|
||||
start_time: "2024-01-02"
|
||||
end_time: "2024-12-31"
|
||||
account: 1000000
|
||||
benchmark: SPY
|
||||
exchange_kwargs:
|
||||
codes: "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM"
|
||||
deal_price: $close
|
||||
freq: day
|
||||
open_cost: 0.0005
|
||||
close_cost: 0.0015
|
||||
min_cost: 5.0
|
||||
risk_analysis_freq: 1d
|
||||
@@ -0,0 +1,95 @@
|
||||
# Walk-forward: B-moments / test 2025
|
||||
# 3x3 re-validation (trace exp 52). Strategy TopkDropoutStrategy n_drop=1, parallel=default(auto).
|
||||
{% set LAKE = TAC_LAKE_DIR %}
|
||||
qlib_init:
|
||||
provider_uri: "{{ LAKE }}"
|
||||
region: us
|
||||
expression_cache: null
|
||||
dataset_cache: null
|
||||
calendar_provider:
|
||||
class: tac_qlib.data.providers.LakeCalendarProvider
|
||||
kwargs: { lake_root: "{{ LAKE }}", market: US }
|
||||
instrument_provider:
|
||||
class: tac_qlib.data.providers.LakeInstrumentProvider
|
||||
kwargs: { lake_root: "{{ LAKE }}", market: US, markets: {} }
|
||||
feature_provider:
|
||||
class: tac_qlib.data.providers.LakeFeatureProvider
|
||||
kwargs: { lake_root: "{{ LAKE }}", market: US }
|
||||
exp_manager:
|
||||
class: MLflowExpManager
|
||||
module_path: qlib.workflow.expm
|
||||
kwargs: { uri: "sqlite:///mlruns.db", default_exp_name: "tac-rd-bt-3x3-windows" }
|
||||
|
||||
task:
|
||||
model:
|
||||
class: RankICEnsembleLGBModel
|
||||
module_path: tac_qlib.contrib.model.rank_ensemble
|
||||
kwargs:
|
||||
loss: mse
|
||||
learning_rate: 0.02
|
||||
num_leaves: 31
|
||||
n_estimators: 3000
|
||||
num_boost_round: 3000
|
||||
early_stopping_rounds: 200
|
||||
min_data_in_leaf: 20
|
||||
lambda_l2: 0.5
|
||||
colsample_bytree: 0.8
|
||||
subsample: 0.8
|
||||
subsample_freq: 1
|
||||
reg_alpha: 0.1
|
||||
reg_lambda: 1.0
|
||||
seeds: "42,7,2026,99,123"
|
||||
|
||||
dataset:
|
||||
class: DatasetH
|
||||
module_path: qlib.data.dataset
|
||||
kwargs:
|
||||
handler:
|
||||
class: TACHandler
|
||||
module_path: tac_qlib.contrib.data.handler
|
||||
kwargs:
|
||||
instruments: "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM"
|
||||
start_time: "2015-01-03"
|
||||
end_time: "2026-01-07"
|
||||
fit_start_time: "2016-01-04"
|
||||
fit_end_time: "2024-08-30"
|
||||
freq: day
|
||||
lake_root: "{{ LAKE }}"
|
||||
market: US
|
||||
label: "Ref($close,-6)/Ref($close,-1)-1"
|
||||
feature_fields: "$open,$high,$low,$close,$vwap,$volume,sp_ret,sp_jump_ratio,sp_jump_flag,sp_jump_tail,sp_max_move,sp_rv1,sp_rv5,sp_rv22,sp_vol_ratio_5_22,sp_vol_ratio_1_22,sp_trend_slope_5,sp_trend_slope_20,sp_trend_slope_60,sp_logp,sp_hurst_exponent,sp_sig_level1_lead,sp_sig_level1_lag,sp_sig_level2_lead_lag,sp_sig_level2_lag_lead,sp_rskew_5,sp_rskew_22,sp_rkurt_5,sp_rkurt_22,sp_dsv_5,sp_dsv_22"
|
||||
infer_processors:
|
||||
- { class: DropAllNaN, kwargs: { fit_start_time: "2016-01-04", fit_end_time: "2024-08-30" } }
|
||||
- { class: ProcessInf, kwargs: {} }
|
||||
- { class: CSRankNorm, kwargs: {} }
|
||||
- { class: ZScoreNorm, kwargs: { fit_start_time: "2016-01-04", fit_end_time: "2024-08-30" } }
|
||||
- { class: Fillna, kwargs: {} }
|
||||
segments:
|
||||
train: ["2016-01-04", "2024-08-30"]
|
||||
valid: ["2024-09-03", "2024-12-31"]
|
||||
test: ["2025-01-02", "2025-12-31"]
|
||||
|
||||
record:
|
||||
- { class: SignalRecord, module_path: qlib.workflow.record_temp, kwargs: {} }
|
||||
- { class: SigAnaRecord, module_path: qlib.workflow.record_temp, kwargs: { ana_long_short: true, ann_scaler: 252 } }
|
||||
- class: PortAnaRecord
|
||||
module_path: qlib.workflow.record_temp
|
||||
kwargs:
|
||||
config:
|
||||
strategy:
|
||||
class: TopkDropoutStrategy
|
||||
module_path: qlib.contrib.strategy
|
||||
kwargs: { signal: "<PRED>", topk: 10, n_drop: 1, only_tradable: true, risk_degree: 0.95 }
|
||||
backtest:
|
||||
start_time: "2025-01-02"
|
||||
end_time: "2025-12-31"
|
||||
account: 1000000
|
||||
benchmark: SPY
|
||||
exchange_kwargs:
|
||||
codes: "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM"
|
||||
deal_price: $close
|
||||
freq: day
|
||||
open_cost: 0.0005
|
||||
close_cost: 0.0015
|
||||
min_cost: 5.0
|
||||
risk_analysis_freq: 1d
|
||||
@@ -0,0 +1,95 @@
|
||||
# Walk-forward: B-moments / test 2026
|
||||
# 3x3 re-validation (trace exp 52). Strategy TopkDropoutStrategy n_drop=1, parallel=default(auto).
|
||||
{% set LAKE = TAC_LAKE_DIR %}
|
||||
qlib_init:
|
||||
provider_uri: "{{ LAKE }}"
|
||||
region: us
|
||||
expression_cache: null
|
||||
dataset_cache: null
|
||||
calendar_provider:
|
||||
class: tac_qlib.data.providers.LakeCalendarProvider
|
||||
kwargs: { lake_root: "{{ LAKE }}", market: US }
|
||||
instrument_provider:
|
||||
class: tac_qlib.data.providers.LakeInstrumentProvider
|
||||
kwargs: { lake_root: "{{ LAKE }}", market: US, markets: {} }
|
||||
feature_provider:
|
||||
class: tac_qlib.data.providers.LakeFeatureProvider
|
||||
kwargs: { lake_root: "{{ LAKE }}", market: US }
|
||||
exp_manager:
|
||||
class: MLflowExpManager
|
||||
module_path: qlib.workflow.expm
|
||||
kwargs: { uri: "sqlite:///mlruns.db", default_exp_name: "tac-rd-bt-3x3-windows" }
|
||||
|
||||
task:
|
||||
model:
|
||||
class: RankICEnsembleLGBModel
|
||||
module_path: tac_qlib.contrib.model.rank_ensemble
|
||||
kwargs:
|
||||
loss: mse
|
||||
learning_rate: 0.02
|
||||
num_leaves: 31
|
||||
n_estimators: 3000
|
||||
num_boost_round: 3000
|
||||
early_stopping_rounds: 200
|
||||
min_data_in_leaf: 20
|
||||
lambda_l2: 0.5
|
||||
colsample_bytree: 0.8
|
||||
subsample: 0.8
|
||||
subsample_freq: 1
|
||||
reg_alpha: 0.1
|
||||
reg_lambda: 1.0
|
||||
seeds: "42,7,2026,99,123"
|
||||
|
||||
dataset:
|
||||
class: DatasetH
|
||||
module_path: qlib.data.dataset
|
||||
kwargs:
|
||||
handler:
|
||||
class: TACHandler
|
||||
module_path: tac_qlib.contrib.data.handler
|
||||
kwargs:
|
||||
instruments: "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM"
|
||||
start_time: "2015-01-03"
|
||||
end_time: "2026-08-19"
|
||||
fit_start_time: "2016-01-04"
|
||||
fit_end_time: "2025-09-01"
|
||||
freq: day
|
||||
lake_root: "{{ LAKE }}"
|
||||
market: US
|
||||
label: "Ref($close,-6)/Ref($close,-1)-1"
|
||||
feature_fields: "$open,$high,$low,$close,$vwap,$volume,sp_ret,sp_jump_ratio,sp_jump_flag,sp_jump_tail,sp_max_move,sp_rv1,sp_rv5,sp_rv22,sp_vol_ratio_5_22,sp_vol_ratio_1_22,sp_trend_slope_5,sp_trend_slope_20,sp_trend_slope_60,sp_logp,sp_hurst_exponent,sp_sig_level1_lead,sp_sig_level1_lag,sp_sig_level2_lead_lag,sp_sig_level2_lag_lead,sp_rskew_5,sp_rskew_22,sp_rkurt_5,sp_rkurt_22,sp_dsv_5,sp_dsv_22"
|
||||
infer_processors:
|
||||
- { class: DropAllNaN, kwargs: { fit_start_time: "2016-01-04", fit_end_time: "2025-09-01" } }
|
||||
- { class: ProcessInf, kwargs: {} }
|
||||
- { class: CSRankNorm, kwargs: {} }
|
||||
- { class: ZScoreNorm, kwargs: { fit_start_time: "2016-01-04", fit_end_time: "2025-09-01" } }
|
||||
- { class: Fillna, kwargs: {} }
|
||||
segments:
|
||||
train: ["2016-01-04", "2025-09-01"]
|
||||
valid: ["2025-09-03", "2026-01-03"]
|
||||
test: ["2026-01-04", "2026-08-19"]
|
||||
|
||||
record:
|
||||
- { class: SignalRecord, module_path: qlib.workflow.record_temp, kwargs: {} }
|
||||
- { class: SigAnaRecord, module_path: qlib.workflow.record_temp, kwargs: { ana_long_short: true, ann_scaler: 252 } }
|
||||
- class: PortAnaRecord
|
||||
module_path: qlib.workflow.record_temp
|
||||
kwargs:
|
||||
config:
|
||||
strategy:
|
||||
class: TopkDropoutStrategy
|
||||
module_path: qlib.contrib.strategy
|
||||
kwargs: { signal: "<PRED>", topk: 10, n_drop: 1, only_tradable: true, risk_degree: 0.95 }
|
||||
backtest:
|
||||
start_time: "2026-01-04"
|
||||
end_time: "2026-08-19"
|
||||
account: 1000000
|
||||
benchmark: SPY
|
||||
exchange_kwargs:
|
||||
codes: "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM"
|
||||
deal_price: $close
|
||||
freq: day
|
||||
open_cost: 0.0005
|
||||
close_cost: 0.0015
|
||||
min_cost: 5.0
|
||||
risk_analysis_freq: 1d
|
||||
@@ -0,0 +1,95 @@
|
||||
# Walk-forward: C-ndrop2 / test 2024
|
||||
# 3x3 re-validation (trace exp 52). Strategy TopkDropoutStrategy n_drop=2, parallel=5.
|
||||
{% set LAKE = TAC_LAKE_DIR %}
|
||||
qlib_init:
|
||||
provider_uri: "{{ LAKE }}"
|
||||
region: us
|
||||
expression_cache: null
|
||||
dataset_cache: null
|
||||
calendar_provider:
|
||||
class: tac_qlib.data.providers.LakeCalendarProvider
|
||||
kwargs: { lake_root: "{{ LAKE }}", market: US }
|
||||
instrument_provider:
|
||||
class: tac_qlib.data.providers.LakeInstrumentProvider
|
||||
kwargs: { lake_root: "{{ LAKE }}", market: US, markets: {} }
|
||||
feature_provider:
|
||||
class: tac_qlib.data.providers.LakeFeatureProvider
|
||||
kwargs: { lake_root: "{{ LAKE }}", market: US }
|
||||
exp_manager:
|
||||
class: MLflowExpManager
|
||||
module_path: qlib.workflow.expm
|
||||
kwargs: { uri: "sqlite:///mlruns.db", default_exp_name: "tac-rd-bt-3x3-windows" }
|
||||
|
||||
task:
|
||||
model:
|
||||
class: RankICEnsembleLGBModel
|
||||
module_path: tac_qlib.contrib.model.rank_ensemble
|
||||
kwargs:
|
||||
loss: mse
|
||||
learning_rate: 0.02
|
||||
num_leaves: 31
|
||||
n_estimators: 3000
|
||||
num_boost_round: 3000
|
||||
early_stopping_rounds: 200
|
||||
min_data_in_leaf: 20
|
||||
lambda_l2: 0.5
|
||||
colsample_bytree: 0.8
|
||||
subsample: 0.8
|
||||
subsample_freq: 1
|
||||
reg_alpha: 0.1
|
||||
reg_lambda: 1.0
|
||||
seeds: "42,7,2026,99,123"
|
||||
parallel: 5
|
||||
dataset:
|
||||
class: DatasetH
|
||||
module_path: qlib.data.dataset
|
||||
kwargs:
|
||||
handler:
|
||||
class: TACHandler
|
||||
module_path: tac_qlib.contrib.data.handler
|
||||
kwargs:
|
||||
instruments: "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM"
|
||||
start_time: "2015-01-03"
|
||||
end_time: "2025-01-07"
|
||||
fit_start_time: "2016-01-04"
|
||||
fit_end_time: "2023-08-31"
|
||||
freq: day
|
||||
lake_root: "{{ LAKE }}"
|
||||
market: US
|
||||
label: "Ref($close,-6)/Ref($close,-1)-1"
|
||||
feature_fields: "$open,$high,$low,$close,$vwap,$volume,sp_ret,sp_jump_ratio,sp_jump_flag,sp_jump_tail,sp_max_move,sp_rv1,sp_rv5,sp_rv22,sp_vol_ratio_5_22,sp_vol_ratio_1_22,sp_trend_slope_5,sp_trend_slope_20,sp_trend_slope_60,sp_logp,sp_hurst_exponent,sp_sig_level1_lead,sp_sig_level1_lag,sp_sig_level2_lead_lag,sp_sig_level2_lag_lead"
|
||||
infer_processors:
|
||||
- { class: DropAllNaN, kwargs: { fit_start_time: "2016-01-04", fit_end_time: "2023-08-31" } }
|
||||
- { class: ProcessInf, kwargs: {} }
|
||||
- { class: CSRankNorm, kwargs: {} }
|
||||
- { class: ZScoreNorm, kwargs: { fit_start_time: "2016-01-04", fit_end_time: "2023-08-31" } }
|
||||
- { class: Fillna, kwargs: {} }
|
||||
segments:
|
||||
train: ["2016-01-04", "2023-08-31"]
|
||||
valid: ["2023-09-01", "2023-12-29"]
|
||||
test: ["2024-01-02", "2024-12-31"]
|
||||
|
||||
record:
|
||||
- { class: SignalRecord, module_path: qlib.workflow.record_temp, kwargs: {} }
|
||||
- { class: SigAnaRecord, module_path: qlib.workflow.record_temp, kwargs: { ana_long_short: true, ann_scaler: 252 } }
|
||||
- class: PortAnaRecord
|
||||
module_path: qlib.workflow.record_temp
|
||||
kwargs:
|
||||
config:
|
||||
strategy:
|
||||
class: TopkDropoutStrategy
|
||||
module_path: qlib.contrib.strategy
|
||||
kwargs: { signal: "<PRED>", topk: 10, n_drop: 2, only_tradable: true, risk_degree: 0.95 }
|
||||
backtest:
|
||||
start_time: "2024-01-02"
|
||||
end_time: "2024-12-31"
|
||||
account: 1000000
|
||||
benchmark: SPY
|
||||
exchange_kwargs:
|
||||
codes: "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM"
|
||||
deal_price: $close
|
||||
freq: day
|
||||
open_cost: 0.0005
|
||||
close_cost: 0.0015
|
||||
min_cost: 5.0
|
||||
risk_analysis_freq: 1d
|
||||
@@ -0,0 +1,95 @@
|
||||
# Walk-forward: C-ndrop2 / test 2025
|
||||
# 3x3 re-validation (trace exp 52). Strategy TopkDropoutStrategy n_drop=2, parallel=5.
|
||||
{% set LAKE = TAC_LAKE_DIR %}
|
||||
qlib_init:
|
||||
provider_uri: "{{ LAKE }}"
|
||||
region: us
|
||||
expression_cache: null
|
||||
dataset_cache: null
|
||||
calendar_provider:
|
||||
class: tac_qlib.data.providers.LakeCalendarProvider
|
||||
kwargs: { lake_root: "{{ LAKE }}", market: US }
|
||||
instrument_provider:
|
||||
class: tac_qlib.data.providers.LakeInstrumentProvider
|
||||
kwargs: { lake_root: "{{ LAKE }}", market: US, markets: {} }
|
||||
feature_provider:
|
||||
class: tac_qlib.data.providers.LakeFeatureProvider
|
||||
kwargs: { lake_root: "{{ LAKE }}", market: US }
|
||||
exp_manager:
|
||||
class: MLflowExpManager
|
||||
module_path: qlib.workflow.expm
|
||||
kwargs: { uri: "sqlite:///mlruns.db", default_exp_name: "tac-rd-bt-3x3-windows" }
|
||||
|
||||
task:
|
||||
model:
|
||||
class: RankICEnsembleLGBModel
|
||||
module_path: tac_qlib.contrib.model.rank_ensemble
|
||||
kwargs:
|
||||
loss: mse
|
||||
learning_rate: 0.02
|
||||
num_leaves: 31
|
||||
n_estimators: 3000
|
||||
num_boost_round: 3000
|
||||
early_stopping_rounds: 200
|
||||
min_data_in_leaf: 20
|
||||
lambda_l2: 0.5
|
||||
colsample_bytree: 0.8
|
||||
subsample: 0.8
|
||||
subsample_freq: 1
|
||||
reg_alpha: 0.1
|
||||
reg_lambda: 1.0
|
||||
seeds: "42,7,2026,99,123"
|
||||
parallel: 5
|
||||
dataset:
|
||||
class: DatasetH
|
||||
module_path: qlib.data.dataset
|
||||
kwargs:
|
||||
handler:
|
||||
class: TACHandler
|
||||
module_path: tac_qlib.contrib.data.handler
|
||||
kwargs:
|
||||
instruments: "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM"
|
||||
start_time: "2015-01-03"
|
||||
end_time: "2026-01-07"
|
||||
fit_start_time: "2016-01-04"
|
||||
fit_end_time: "2024-08-30"
|
||||
freq: day
|
||||
lake_root: "{{ LAKE }}"
|
||||
market: US
|
||||
label: "Ref($close,-6)/Ref($close,-1)-1"
|
||||
feature_fields: "$open,$high,$low,$close,$vwap,$volume,sp_ret,sp_jump_ratio,sp_jump_flag,sp_jump_tail,sp_max_move,sp_rv1,sp_rv5,sp_rv22,sp_vol_ratio_5_22,sp_vol_ratio_1_22,sp_trend_slope_5,sp_trend_slope_20,sp_trend_slope_60,sp_logp,sp_hurst_exponent,sp_sig_level1_lead,sp_sig_level1_lag,sp_sig_level2_lead_lag,sp_sig_level2_lag_lead"
|
||||
infer_processors:
|
||||
- { class: DropAllNaN, kwargs: { fit_start_time: "2016-01-04", fit_end_time: "2024-08-30" } }
|
||||
- { class: ProcessInf, kwargs: {} }
|
||||
- { class: CSRankNorm, kwargs: {} }
|
||||
- { class: ZScoreNorm, kwargs: { fit_start_time: "2016-01-04", fit_end_time: "2024-08-30" } }
|
||||
- { class: Fillna, kwargs: {} }
|
||||
segments:
|
||||
train: ["2016-01-04", "2024-08-30"]
|
||||
valid: ["2024-09-03", "2024-12-31"]
|
||||
test: ["2025-01-02", "2025-12-31"]
|
||||
|
||||
record:
|
||||
- { class: SignalRecord, module_path: qlib.workflow.record_temp, kwargs: {} }
|
||||
- { class: SigAnaRecord, module_path: qlib.workflow.record_temp, kwargs: { ana_long_short: true, ann_scaler: 252 } }
|
||||
- class: PortAnaRecord
|
||||
module_path: qlib.workflow.record_temp
|
||||
kwargs:
|
||||
config:
|
||||
strategy:
|
||||
class: TopkDropoutStrategy
|
||||
module_path: qlib.contrib.strategy
|
||||
kwargs: { signal: "<PRED>", topk: 10, n_drop: 2, only_tradable: true, risk_degree: 0.95 }
|
||||
backtest:
|
||||
start_time: "2025-01-02"
|
||||
end_time: "2025-12-31"
|
||||
account: 1000000
|
||||
benchmark: SPY
|
||||
exchange_kwargs:
|
||||
codes: "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM"
|
||||
deal_price: $close
|
||||
freq: day
|
||||
open_cost: 0.0005
|
||||
close_cost: 0.0015
|
||||
min_cost: 5.0
|
||||
risk_analysis_freq: 1d
|
||||
@@ -0,0 +1,95 @@
|
||||
# Walk-forward: C-ndrop2 / test 2026
|
||||
# 3x3 re-validation (trace exp 52). Strategy TopkDropoutStrategy n_drop=2, parallel=5.
|
||||
{% set LAKE = TAC_LAKE_DIR %}
|
||||
qlib_init:
|
||||
provider_uri: "{{ LAKE }}"
|
||||
region: us
|
||||
expression_cache: null
|
||||
dataset_cache: null
|
||||
calendar_provider:
|
||||
class: tac_qlib.data.providers.LakeCalendarProvider
|
||||
kwargs: { lake_root: "{{ LAKE }}", market: US }
|
||||
instrument_provider:
|
||||
class: tac_qlib.data.providers.LakeInstrumentProvider
|
||||
kwargs: { lake_root: "{{ LAKE }}", market: US, markets: {} }
|
||||
feature_provider:
|
||||
class: tac_qlib.data.providers.LakeFeatureProvider
|
||||
kwargs: { lake_root: "{{ LAKE }}", market: US }
|
||||
exp_manager:
|
||||
class: MLflowExpManager
|
||||
module_path: qlib.workflow.expm
|
||||
kwargs: { uri: "sqlite:///mlruns.db", default_exp_name: "tac-rd-bt-3x3-windows" }
|
||||
|
||||
task:
|
||||
model:
|
||||
class: RankICEnsembleLGBModel
|
||||
module_path: tac_qlib.contrib.model.rank_ensemble
|
||||
kwargs:
|
||||
loss: mse
|
||||
learning_rate: 0.02
|
||||
num_leaves: 31
|
||||
n_estimators: 3000
|
||||
num_boost_round: 3000
|
||||
early_stopping_rounds: 200
|
||||
min_data_in_leaf: 20
|
||||
lambda_l2: 0.5
|
||||
colsample_bytree: 0.8
|
||||
subsample: 0.8
|
||||
subsample_freq: 1
|
||||
reg_alpha: 0.1
|
||||
reg_lambda: 1.0
|
||||
seeds: "42,7,2026,99,123"
|
||||
parallel: 5
|
||||
dataset:
|
||||
class: DatasetH
|
||||
module_path: qlib.data.dataset
|
||||
kwargs:
|
||||
handler:
|
||||
class: TACHandler
|
||||
module_path: tac_qlib.contrib.data.handler
|
||||
kwargs:
|
||||
instruments: "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM"
|
||||
start_time: "2015-01-03"
|
||||
end_time: "2026-08-19"
|
||||
fit_start_time: "2016-01-04"
|
||||
fit_end_time: "2025-09-01"
|
||||
freq: day
|
||||
lake_root: "{{ LAKE }}"
|
||||
market: US
|
||||
label: "Ref($close,-6)/Ref($close,-1)-1"
|
||||
feature_fields: "$open,$high,$low,$close,$vwap,$volume,sp_ret,sp_jump_ratio,sp_jump_flag,sp_jump_tail,sp_max_move,sp_rv1,sp_rv5,sp_rv22,sp_vol_ratio_5_22,sp_vol_ratio_1_22,sp_trend_slope_5,sp_trend_slope_20,sp_trend_slope_60,sp_logp,sp_hurst_exponent,sp_sig_level1_lead,sp_sig_level1_lag,sp_sig_level2_lead_lag,sp_sig_level2_lag_lead"
|
||||
infer_processors:
|
||||
- { class: DropAllNaN, kwargs: { fit_start_time: "2016-01-04", fit_end_time: "2025-09-01" } }
|
||||
- { class: ProcessInf, kwargs: {} }
|
||||
- { class: CSRankNorm, kwargs: {} }
|
||||
- { class: ZScoreNorm, kwargs: { fit_start_time: "2016-01-04", fit_end_time: "2025-09-01" } }
|
||||
- { class: Fillna, kwargs: {} }
|
||||
segments:
|
||||
train: ["2016-01-04", "2025-09-01"]
|
||||
valid: ["2025-09-03", "2026-01-03"]
|
||||
test: ["2026-01-04", "2026-08-19"]
|
||||
|
||||
record:
|
||||
- { class: SignalRecord, module_path: qlib.workflow.record_temp, kwargs: {} }
|
||||
- { class: SigAnaRecord, module_path: qlib.workflow.record_temp, kwargs: { ana_long_short: true, ann_scaler: 252 } }
|
||||
- class: PortAnaRecord
|
||||
module_path: qlib.workflow.record_temp
|
||||
kwargs:
|
||||
config:
|
||||
strategy:
|
||||
class: TopkDropoutStrategy
|
||||
module_path: qlib.contrib.strategy
|
||||
kwargs: { signal: "<PRED>", topk: 10, n_drop: 2, only_tradable: true, risk_degree: 0.95 }
|
||||
backtest:
|
||||
start_time: "2026-01-04"
|
||||
end_time: "2026-08-19"
|
||||
account: 1000000
|
||||
benchmark: SPY
|
||||
exchange_kwargs:
|
||||
codes: "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM"
|
||||
deal_price: $close
|
||||
freq: day
|
||||
open_cost: 0.0005
|
||||
close_cost: 0.0015
|
||||
min_cost: 5.0
|
||||
risk_analysis_freq: 1d
|
||||
+9
-14
@@ -1,19 +1,15 @@
|
||||
# -----------------------------------------------------------------------------
|
||||
# EXP 18 - Risk-limit control: reference model + TopkDropout baseline (A).
|
||||
# EXP 14 - Strategy A (baseline): reference model + TopkDropout.
|
||||
#
|
||||
# Signal/model identical to the reference (tac-rd-rank-ensemble-isolated,
|
||||
# run 0cea66d9...): RankICEnsembleLGBModel (parallel, 5 seeds) on the 50-ETF
|
||||
# SP-5d panel, test 2026-01-04..2026-08-10. This workflow reproduces the
|
||||
# unconstrained TopkDropout baseline net-of-cost so the risk-limited variant
|
||||
# (same pred, liquidity/size/concentration caps) can be compared 1:1.
|
||||
#
|
||||
# The risk_limits spec itself is applied via rd_backtest / rd_strategy_targets
|
||||
# (tool-level param, not a YAML key); this run records the unconstrained
|
||||
# baseline that the limit A/B is measured against.
|
||||
# Model = RankICEnsembleLGBModel (5-seed RankIC-early-stopped LGB), the class
|
||||
# wired by the tac-rd-rank-ensemble-isolated reference (run 0cea66d9...).
|
||||
# Strategy = TopkDropout topk=10 n_drop=2 risk_degree=0.95 (the reference's own
|
||||
# recorded backtest strategy), so this run reproduces the reference baseline on
|
||||
# the same 50-ETF SP-5d panel.
|
||||
#
|
||||
# Run:
|
||||
# rd_run_workflow config_path=experiments/workflows/exp18-risk-limit/a_baseline.yaml \
|
||||
# experiment_name=tac-rd-risk-limit
|
||||
# rd_run_workflow config_path=experiments/workflows/exp14-optstop-v2/a_topk_baseline.yaml \
|
||||
# experiment_name=tac-rd-optstop-v2
|
||||
# -----------------------------------------------------------------------------
|
||||
{%- set LAKE = TAC_LAKE_DIR %}
|
||||
{%- set UNIVERSE = "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM" %}
|
||||
@@ -47,7 +43,7 @@ qlib_init:
|
||||
module_path: qlib.workflow.expm
|
||||
kwargs:
|
||||
uri: "sqlite:///{{ LAKE }}/mlruns.db"
|
||||
default_exp_name: "tac-rd-risk-limit"
|
||||
default_exp_name: "tac-rd-optstop-v2"
|
||||
|
||||
task:
|
||||
model:
|
||||
@@ -68,7 +64,6 @@ task:
|
||||
reg_alpha: 0.1
|
||||
reg_lambda: 1.0
|
||||
seeds: "42,7,2026,99,123"
|
||||
parallel: 5
|
||||
|
||||
dataset:
|
||||
class: DatasetH
|
||||
@@ -0,0 +1,149 @@
|
||||
# -----------------------------------------------------------------------------
|
||||
# EXP 14 - Strategy B (enhanced): reference model + OptimalStopControlV2.
|
||||
#
|
||||
# Model = RankICEnsembleLGBModel (5-seed RankIC-early-stopped LGB), identical to
|
||||
# Strategy A. Strategy = OptimalStopControlV2 (tac_qlib.contrib.strategy.
|
||||
# optimal_stop_v2) with the controls that address OptimalStopControl's documented
|
||||
# weaknesses:
|
||||
# - turnover / cost control: rebalance_band=0.05 (skip small rebalances),
|
||||
# cooldown_days=3 (no whipsaw re-entries), max_turnover=0.30 (cap daily
|
||||
# traded notional, priority exits > opens > rebalances)
|
||||
# - robust thresholds (no valid-window overfit): entry 0.85 / exit 0.70 /
|
||||
# max_hold 10 / min_hold 2 / sl -0.08
|
||||
# Sizing = equal-weight control (risk_degree fraction of total value split
|
||||
# across targets) - the "proper allocation" that replaces cash-heuristic sizing.
|
||||
#
|
||||
# Run:
|
||||
# rd_run_workflow config_path=experiments/workflows/exp14-optstop-v2/b_optstop_v2.yaml \
|
||||
# experiment_name=tac-rd-optstop-v2
|
||||
# -----------------------------------------------------------------------------
|
||||
{%- set LAKE = TAC_LAKE_DIR %}
|
||||
{%- set UNIVERSE = "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM" %}
|
||||
{%- set SP_FIELDS = "sp_ret,sp_jump_ratio,sp_jump_flag,sp_jump_tail,sp_max_move,sp_rv1,sp_rv5,sp_rv22,sp_vol_ratio_5_22,sp_vol_ratio_1_22,sp_trend_slope_5,sp_trend_slope_20,sp_trend_slope_60,sp_logp,sp_hurst_exponent,sp_sig_level1_lead,sp_sig_level1_lag,sp_sig_level2_lead_lag,sp_sig_level2_lag_lead" %}
|
||||
|
||||
qlib_init:
|
||||
provider_uri: "{{ LAKE }}"
|
||||
region: us
|
||||
expression_cache: null
|
||||
dataset_cache: null
|
||||
|
||||
calendar_provider:
|
||||
class: tac_qlib.data.providers.LakeCalendarProvider
|
||||
kwargs:
|
||||
lake_root: "{{ LAKE }}"
|
||||
market: US
|
||||
instrument_provider:
|
||||
class: tac_qlib.data.providers.LakeInstrumentProvider
|
||||
kwargs:
|
||||
lake_root: "{{ LAKE }}"
|
||||
market: US
|
||||
markets: {}
|
||||
feature_provider:
|
||||
class: tac_qlib.data.providers.LakeFeatureProvider
|
||||
kwargs:
|
||||
lake_root: "{{ LAKE }}"
|
||||
market: US
|
||||
|
||||
exp_manager:
|
||||
class: MLflowExpManager
|
||||
module_path: qlib.workflow.expm
|
||||
kwargs:
|
||||
uri: "sqlite:///{{ LAKE }}/mlruns.db"
|
||||
default_exp_name: "tac-rd-optstop-v2"
|
||||
|
||||
task:
|
||||
model:
|
||||
class: RankICEnsembleLGBModel
|
||||
module_path: tac_qlib.contrib.model.rank_ensemble
|
||||
kwargs:
|
||||
loss: mse
|
||||
learning_rate: 0.02
|
||||
num_leaves: 31
|
||||
n_estimators: 3000
|
||||
num_boost_round: 3000
|
||||
early_stopping_rounds: 200
|
||||
min_data_in_leaf: 20
|
||||
lambda_l2: 0.5
|
||||
colsample_bytree: 0.8
|
||||
subsample: 0.8
|
||||
subsample_freq: 1
|
||||
reg_alpha: 0.1
|
||||
reg_lambda: 1.0
|
||||
seeds: "42,7,2026,99,123"
|
||||
|
||||
dataset:
|
||||
class: DatasetH
|
||||
module_path: qlib.data.dataset
|
||||
kwargs:
|
||||
handler:
|
||||
class: TACHandler
|
||||
module_path: tac_qlib.contrib.data.handler
|
||||
kwargs:
|
||||
instruments: "{{ UNIVERSE }}"
|
||||
start_time: 2015-01-03
|
||||
end_time: 2026-08-14
|
||||
fit_start_time: 2016-01-04
|
||||
fit_end_time: 2025-09-01
|
||||
freq: day
|
||||
lake_root: "{{ LAKE }}"
|
||||
market: US
|
||||
label: "Ref($close,-6)/Ref($close,-1)-1"
|
||||
feature_fields: "$open,$high,$low,$close,$vwap,$volume,{{ SP_FIELDS }}"
|
||||
infer_processors:
|
||||
- class: DropAllNaN
|
||||
kwargs: {}
|
||||
- class: ProcessInf
|
||||
kwargs: {}
|
||||
- class: CSRankNorm
|
||||
kwargs: {}
|
||||
- class: ZScoreNorm
|
||||
kwargs: {}
|
||||
- class: Fillna
|
||||
kwargs: {}
|
||||
segments:
|
||||
train: [2016-01-04, 2025-09-01]
|
||||
valid: [2025-09-03, 2026-01-03]
|
||||
test: [2026-01-04, 2026-08-10]
|
||||
|
||||
record:
|
||||
- class: SignalRecord
|
||||
module_path: qlib.workflow.record_temp
|
||||
kwargs: {}
|
||||
- class: SigAnaRecord
|
||||
module_path: qlib.workflow.record_temp
|
||||
kwargs:
|
||||
ana_long_short: true
|
||||
ann_scaler: 252
|
||||
- class: PortAnaRecord
|
||||
module_path: qlib.workflow.record_temp
|
||||
kwargs:
|
||||
config:
|
||||
strategy:
|
||||
class: OptimalStopControlV2
|
||||
module_path: tac_qlib.contrib.strategy.optimal_stop_v2
|
||||
kwargs:
|
||||
signal: "<PRED>"
|
||||
topk: 10
|
||||
entry_pct: 0.85
|
||||
exit_pct: 0.70
|
||||
max_hold_days: 10
|
||||
min_hold_days: 2
|
||||
sl: -0.08
|
||||
risk_degree: 0.95
|
||||
notional: 20000
|
||||
rebalance_band: 0.05
|
||||
cooldown_days: 3
|
||||
max_turnover: 0.30
|
||||
backtest:
|
||||
start_time: 2026-01-04
|
||||
end_time: 2026-08-10
|
||||
account: 1000000
|
||||
benchmark: SPY
|
||||
exchange_kwargs:
|
||||
codes: "{{ UNIVERSE }}"
|
||||
deal_price: $close
|
||||
freq: day
|
||||
open_cost: 0.0005
|
||||
close_cost: 0.0015
|
||||
min_cost: 5.0
|
||||
risk_analysis_freq: 1d
|
||||
Reference in New Issue
Block a user