Make static prediction easier

qlib/contrib/strategy/__init__.py

@@ -2,7 +2,7 @@
 # Licensed under the MIT License.
 
 
-from .model_strategy import (
+from .signal_strategy import (
     TopkDropoutStrategy,
     WeightStrategyBase,
 )

qlib/contrib/strategy/cost_control.py

@@ -6,7 +6,7 @@ This strategy is not well maintained
 
 
 from .order_generator import OrderGenWInteract
-from .model_strategy import WeightStrategyBase
+from .signal_strategy import WeightStrategyBase
 import copy
 
 

qlib/contrib/strategy/rule_strategy.py

@@ -1,3 +1,5 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
 from pathlib import Path
 import warnings
 import numpy as np

qlib/contrib/strategy/signal_strategy.py

@@ -1,27 +1,33 @@
 import copy
+from qlib.backtest.signal import ModelSignal, Signal, SignalWCache
+from typing import Union
+from qlib.data.dataset import Dataset
+from qlib.model.base import BaseModel
 from qlib.backtest.position import Position
 import warnings
 import numpy as np
 import pandas as pd
 
 from ...utils.resam import resam_ts_data
-from ...strategy.base import ModelStrategy
+from ...strategy.base import BaseStrategy
 from ...backtest.decision import Order, BaseTradeDecision, OrderDir, TradeDecisionWO
 
 from .order_generator import OrderGenWInteract
 
 
-class TopkDropoutStrategy(ModelStrategy):
+class TopkDropoutStrategy(BaseStrategy):
     # TODO:
     # 1. Supporting leverage the get_range_limit result from the decision
     # 2. Supporting alter_outer_trade_decision
     # 3. Supporting checking the availability of trade decision
     def __init__(
         self,
-        model,
-        dataset,
+        *,
         topk,
         n_drop,
+        model: BaseModel = None,
+        dataset: Dataset = None,
+        signal: Union[pd.DataFrame, pd.Series] = None,
         method_sell="bottom",
         method_buy="top",
         risk_degree=0.95,
@@ -64,7 +70,7 @@ class TopkDropoutStrategy(ModelStrategy):
 
         """
         super(TopkDropoutStrategy, self).__init__(
-            model, dataset, level_infra=level_infra, common_infra=common_infra, trade_exchange=trade_exchange, **kwargs
+            level_infra=level_infra, common_infra=common_infra, trade_exchange=trade_exchange, **kwargs
         )
         self.topk = topk
         self.n_drop = n_drop
@@ -73,6 +79,8 @@ class TopkDropoutStrategy(ModelStrategy):
         self.risk_degree = risk_degree
         self.hold_thresh = hold_thresh
         self.only_tradable = only_tradable
+        assert signal is not None or dataset is not None and model is not None
+        self.signal: Signal = ModelSignal(model=model, dataset=dataset) if signal is None else SignalWCache(signal)
 
     def get_risk_degree(self, trade_step=None):
         """get_risk_degree
@@ -87,7 +95,7 @@ class TopkDropoutStrategy(ModelStrategy):
         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 = resam_ts_data(self.pred_scores, start_time=pred_start_time, end_time=pred_end_time, method="last")
+        pred_score = self.signal.get_signal(start_time=pred_start_time, end_time=pred_end_time)
         if pred_score is None:
             return TradeDecisionWO([], self)
         if self.only_tradable:
@@ -235,15 +243,17 @@ class TopkDropoutStrategy(ModelStrategy):
         return TradeDecisionWO(sell_order_list + buy_order_list, self)
 
 
-class WeightStrategyBase(ModelStrategy):
+class WeightStrategyBase(BaseStrategy):
     # TODO:
     # 1. Supporting leverage the get_range_limit result from the decision
     # 2. Supporting alter_outer_trade_decision
     # 3. Supporting checking the availability of trade decision
     def __init__(
         self,
-        model,
-        dataset,
+        *,
+        model: BaseModel = None,
+        dataset: Dataset = None,
+        signal: Union[pd.DataFrame, pd.Series] = None,
         order_generator_cls_or_obj=OrderGenWInteract,
         trade_exchange=None,
         level_infra=None,
@@ -260,12 +270,14 @@ class WeightStrategyBase(ModelStrategy):
                 - In minutely execution, the daily exchange is not usable, only the minutely exchange is recommended.
         """
         super(WeightStrategyBase, self).__init__(
-            model, dataset, level_infra=level_infra, common_infra=common_infra, trade_exchange=trade_exchange, **kwargs
+            level_infra=level_infra, common_infra=common_infra, trade_exchange=trade_exchange, **kwargs
         )
         if isinstance(order_generator_cls_or_obj, type):
             self.order_generator = order_generator_cls_or_obj()
         else:
             self.order_generator = order_generator_cls_or_obj
+        assert signal is not None or dataset is not None and model is not None
+        self.signal: Signal = ModelSignal(model=model, dataset=dataset) if signal is None else SignalWCache(signal)
 
     def get_risk_degree(self, trade_step=None):
         """get_risk_degree
@@ -298,7 +310,7 @@ class WeightStrategyBase(ModelStrategy):
         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 = resam_ts_data(self.pred_scores, start_time=pred_start_time, end_time=pred_end_time, method="last")
+        pred_score = self.signal.get_signal(start_time=pred_start_time, end_time=pred_end_time)
         if pred_score is None:
             return TradeDecisionWO([], self)
         current_temp = copy.deepcopy(self.trade_position)