added window size optimization classes

lib/pt_strategy/trading_pair.py

@@ -3,11 +3,10 @@ from __future__ import annotations
 from abc import ABC, abstractmethod
 from datetime import datetime
 from enum import Enum
-from typing import Any, Dict, Optional, Type, cast, Generator, List
+from typing import Any, Dict, Generator, List, Optional, Type, cast
 
 import pandas as pd
-
-from pt_strategy.model_data_policy import DataParams
+from pt_strategy.model_data_policy import DataWindowParams
 
 
 class PairState(Enum):
@@ -26,7 +25,6 @@ class TradingPair:
     
     stat_model_price_: str
     model_: PairsTradingModel # type: ignore[assignment]
-    model_tdp_: ModelDataPolicy # type: ignore[assignment]
     
     user_data_: Dict[str, Any]
 
@@ -44,7 +42,6 @@ class TradingPair:
         self.symbol_a_ = instruments[0]["symbol"]
         self.symbol_b_ = instruments[1]["symbol"]
         self.model_ = PairsTradingModel.create(config)
-        self.model_tdp_ = ModelDataPolicy.create(config)
         self.stat_model_price_ = config["stat_model_price"]
         self.user_data_ = {
             "state": PairState.INITIAL,
@@ -159,12 +156,9 @@ class TradingPair:
         })
 
 
-    def run(self, market_data: pd.DataFrame, data_params: DataParams) -> Prediction: # type: ignore[assignment]
+    def run(self, market_data: pd.DataFrame, data_params: DataWindowParams) -> Prediction: # type: ignore[assignment]
         self.market_data_ = market_data[data_params.training_start_index:data_params.training_start_index + data_params.training_size]
         return self.model_.predict(pair=self)
 
-        while self.model_tdp_.has_next_training_data():
-            training_data = self.model_tdp_.get_next_training_data()
-