added window size optimization classes

configuration/ols-opt.cfg

@@ -0,0 +1,48 @@
+{
+    "market_data_loading": {
+        "CRYPTO": {
+            "data_directory": "./data/crypto",
+            "db_table_name": "md_1min_bars",
+            "instrument_id_pfx": "PAIR-",
+        },
+        "EQUITY": {
+            "data_directory": "./data/equity",
+            "db_table_name": "md_1min_bars",
+            "instrument_id_pfx": "STOCK-",
+        }
+    },
+
+    # ====== Funding ======
+    "funding_per_pair": 2000.0,
+    # ====== Trading Parameters ======
+    "stat_model_price": "close",
+    "execution_price": {
+        "column": "vwap",
+        "shift": 1,
+    },
+    "dis-equilibrium_open_trshld": 1.75,
+    "dis-equilibrium_close_trshld": 0.9,
+    "model_class": "pt_strategy.models.OLSModel",
+
+    # "training_size": 120,
+    # "model_data_policy_class": "pt_strategy.model_data_policy.RollingWindowDataPolicy",
+    # "model_data_policy_class": "pt_strategy.model_data_policy.ADFOptimizedWndDataPolicy",
+    "model_data_policy_class": "pt_strategy.model_data_policy.JohansenOptdWndDataPolicy",
+    "min_training_size": 60,
+    "max_training_size": 150,
+
+    # ====== Stop Conditions ======
+    "stop_close_conditions": {
+        "profit": 2.0,
+        "loss": -0.5
+    }
+
+    # ====== End of Session Closeout ======
+    "close_outstanding_positions": true,
+    # "close_outstanding_positions": false,
+    "trading_hours": {
+        "timezone": "America/New_York",
+        "begin_session": "7:30:00",
+        "end_session": "18:30:00",
+    }
+}

configuration/ols.cfg

@@ -20,11 +20,15 @@
         "column": "vwap",
         "shift": 1,
     },
-    "dis-equilibrium_open_trshld": 2.0,
+    "dis-equilibrium_open_trshld": 1.75,
-    "dis-equilibrium_close_trshld": 0.5,
+    "dis-equilibrium_close_trshld": 0.9,
-    "training_size": 120,
     "model_class": "pt_strategy.models.OLSModel",
+
+    "training_size": 120,
     "model_data_policy_class": "pt_strategy.model_data_policy.RollingWindowDataPolicy",
+    # "model_data_policy_class": "pt_strategy.model_data_policy.OptimizedWindowDataPolicy",
+    # "min_training_size": 60,
+    # "max_training_size": 150,
 
     # ====== Stop Conditions ======
     "stop_close_conditions": {

configuration/vecm-opt.cfg

@@ -21,11 +21,16 @@
         "column": "vwap",
         "shift": 1,
     },
-    "dis-equilibrium_open_trshld": 2.0,
+    "dis-equilibrium_open_trshld": 1.75,
     "dis-equilibrium_close_trshld": 1.0,
-    "training_size": 120,
+
     "model_class": "pt_strategy.models.VECMModel",
-    "model_data_policy_class": "pt_strategy.model_data_policy.ExpandingWindowDataPolicy",
+
+    # "training_size": 120,
+    # "model_data_policy_class": "pt_strategy.model_data_policy.RollingWindowDataPolicy",
+    "model_data_policy_class": "pt_strategy.model_data_policy.ADFOptimizedWndDataPolicy",
+    "min_training_size": 60,
+    "max_training_size": 150,
 
     # ====== Stop Conditions ======
     "stop_close_conditions": {

configuration/vecm.cfg

@@ -21,11 +21,15 @@
         "column": "vwap",
         "shift": 1,
     },
-    "dis-equilibrium_open_trshld": 2.0,
+    "dis-equilibrium_open_trshld": 1.75,
     "dis-equilibrium_close_trshld": 1.0,
-    "training_size": 120,
     "model_class": "pt_strategy.models.VECMModel",
+
+    "training_size": 120,
     "model_data_policy_class": "pt_strategy.model_data_policy.RollingWindowDataPolicy",
+    # "model_data_policy_class": "pt_strategy.model_data_policy.OptimizedWindowDataPolicy",
+    # "min_training_size": 60,
+    # "max_training_size": 150,
 
     # ====== Stop Conditions ======
     "stop_close_conditions": {

lib/pt_strategy/model_data_policy.py

@@ -1,64 +1,235 @@
 from __future__ import annotations
 
+import copy
 from abc import ABC, abstractmethod
 from dataclasses import dataclass
-from enum import Enum
+from typing import Any, Dict, cast
-from typing import Any, Dict, Optional, cast, Generator, List
+
+import numpy as np
+import pandas as pd
 
 
 @dataclass
-class DataParams:
+class DataWindowParams:
     training_size: int
     training_start_index: int
-        
+
+
 class ModelDataPolicy(ABC):
     config_: Dict[str, Any]
-    current_data_params_: DataParams
+    current_data_params_: DataWindowParams
-    count_:int
+    count_: int
 
-    
     def __init__(self, config: Dict[str, Any]):
         self.config_ = config
-        self.current_data_params_ = DataParams(
+        self.current_data_params_ = DataWindowParams(
             training_size=config.get("training_size", 120),
             training_start_index=0,
         )
         self.count_ = 0
-    
+
     @abstractmethod
-    def advance(self) -> DataParams:
+    def advance(self) -> DataWindowParams:
         self.count_ += 1
-        print(self.count_, end='\r')
+        print(self.count_, end="\r")
-    
+        return self.current_data_params_
+
     @staticmethod
-    def create(config: Dict[str, Any]) -> ModelDataPolicy:
+    def create(config: Dict[str, Any], *args: Any, **kwargs: Any) -> ModelDataPolicy:
         import importlib
-        
+
         model_data_policy_class_name = config.get("model_data_policy_class", None)
         assert model_data_policy_class_name is not None
         module_name, class_name = model_data_policy_class_name.rsplit(".", 1)
         module = importlib.import_module(module_name)
-        model_training_data_policy_object = getattr(module, class_name)(config=config)
+        model_training_data_policy_object = getattr(module, class_name)(
+            config=config, *args, **kwargs
+        )
         return cast(ModelDataPolicy, model_training_data_policy_object)
 
+
 class RollingWindowDataPolicy(ModelDataPolicy):
-    def __init__(self, config: Dict[str, Any]):
+    def __init__(self, config: Dict[str, Any], *args: Any, **kwargs: Any):
         super().__init__(config)
         self.count_ = 1
-        
+
-    def advance(self) -> DataParams:
+    def advance(self) -> DataWindowParams:
         super().advance()
         self.current_data_params_.training_start_index += 1
         return self.current_data_params_
-    
+
-    
+
 class ExpandingWindowDataPolicy(ModelDataPolicy):
-    def __init__(self, config: Dict[str, Any]):
+    def __init__(self, config: Dict[str, Any], *args: Any, **kwargs: Any):
         super().__init__(config)
-        
+
-    def advance(self) -> DataParams:
+    def advance(self) -> DataWindowParams:
         super().advance()
         self.current_data_params_.training_size += 1
         return self.current_data_params_
-    
 
+
+class OptimizedWndDataPolicy(ModelDataPolicy, ABC):
+    mkt_data_df_: pd.DataFrame
+    pair_: TradingPair  # type: ignore
+    min_training_size_: int
+    max_training_size_: int
+    end_index_: int
+    prices_a_: np.ndarray
+    prices_b_: np.ndarray
+
+    def __init__(self, config: Dict[str, Any], *args: Any, **kwargs: Any):
+        super().__init__(config)
+        assert (
+            kwargs.get("mkt_data") is not None and kwargs.get("pair") is not None
+        ), "mkt_data and/or pair must be provided"
+        assert (
+            "min_training_size" in config and "max_training_size" in config
+        ), "min_training_size and max_training_size must be provided"
+        self.min_training_size_ = cast(int, config.get("min_training_size"))
+        self.max_training_size_ = cast(int, config.get("max_training_size"))
+        assert self.min_training_size_ < self.max_training_size_
+
+        from pt_strategy.trading_pair import TradingPair
+
+        self.mkt_data_df_ = cast(pd.DataFrame, kwargs.get("mkt_data"))
+        self.pair_ = cast(TradingPair, kwargs.get("pair"))
+
+        self.end_index_ = (
+            self.current_data_params_.training_start_index + self.max_training_size_
+        )
+        col_a, col_b = self.pair_.colnames()
+        self.prices_a_ = np.array(self.mkt_data_df_[col_a])
+        self.prices_b_ = np.array(self.mkt_data_df_[col_b])
+        
+
+    def advance(self) -> DataWindowParams:
+        super().advance()
+        self.current_data_params_ = self.optimize_window_size()
+        self.end_index_ += 1
+        return self.current_data_params_
+
+    @abstractmethod
+    def optimize_window_size(self) -> DataWindowParams:
+        ...
+
+class EGOptimizedWndDataPolicy(OptimizedWndDataPolicy):
+    '''
+    # Engle-Granger cointegration test
+    *** VERY SLOW ***
+    '''
+    def __init__(self, config: Dict[str, Any], *args: Any, **kwargs: Any):
+        super().__init__(config, *args, **kwargs)
+
+    def optimize_window_size(self) -> DataWindowParams:
+        # Run Engle-Granger cointegration test
+        last_pvalue = 1.0
+        result = copy.copy(self.current_data_params_)
+        for trn_size in range(self.min_training_size_, self.max_training_size_):
+            from statsmodels.tsa.stattools import coint  # type: ignore
+
+            start_index = self.end_index_ - trn_size
+            series_a = self.prices_a_[start_index : self.end_index_]
+            series_b = self.prices_b_[start_index : self.end_index_]
+            eg_pvalue = float(coint(series_a, series_b)[1])
+            if eg_pvalue < last_pvalue:
+                last_pvalue = eg_pvalue
+                result.training_size = trn_size
+                result.training_start_index = start_index
+
+        # print(
+        #     f"*** DEBUG *** end_index={self.end_index_}, best_trn_size={self.current_data_params_.training_size}, {last_pvalue=}"
+        # )
+        return result
+
+class ADFOptimizedWndDataPolicy(OptimizedWndDataPolicy):
+    # Augmented Dickey-Fuller test
+    def __init__(self, config: Dict[str, Any], *args: Any, **kwargs: Any):
+        super().__init__(config, *args, **kwargs)
+
+    def optimize_window_size(self) -> DataWindowParams:
+        from statsmodels.regression.linear_model import OLS
+        from statsmodels.tools.tools import add_constant
+        from statsmodels.tsa.stattools import adfuller
+
+        last_pvalue = 1.0
+        result = copy.copy(self.current_data_params_)
+        for trn_size in range(self.min_training_size_, self.max_training_size_):
+            start_index = self.end_index_ - trn_size
+            y = self.prices_a_[start_index : self.end_index_]
+            x = self.prices_b_[start_index : self.end_index_]
+
+            # Add constant to x for intercept
+            x_with_const = add_constant(x)
+
+            # OLS regression: y = a + b*x + e
+            model = OLS(y, x_with_const).fit()
+            residuals = y - model.predict(x_with_const)
+
+            # ADF test on residuals
+            try:
+                adf_result = adfuller(residuals, maxlag=1, regression="c")
+                adf_pvalue = float(adf_result[1])
+            except Exception as e:
+                # Handle edge cases with exception (e.g., constant series, etc.)
+                adf_pvalue = 1.0
+
+            if adf_pvalue < last_pvalue:
+                last_pvalue = adf_pvalue
+                result.training_size = trn_size
+                result.training_start_index = start_index
+
+        # print(
+        #     f"*** DEBUG *** end_index={self.end_index_},"
+        #     f" best_trn_size={self.current_data_params_.training_size},"
+        #     f" {last_pvalue=}"
+        # )
+        return result
+    
+class JohansenOptdWndDataPolicy(OptimizedWndDataPolicy):
+    # Johansen test
+    def __init__(self, config: Dict[str, Any], *args: Any, **kwargs: Any):
+        super().__init__(config, *args, **kwargs)
+
+    def optimize_window_size(self) -> DataWindowParams:
+        from statsmodels.tsa.vector_ar.vecm import coint_johansen
+        import numpy as np
+
+        best_stat = -np.inf
+        best_trn_size = 0
+        best_start_index = -1
+
+        result = copy.copy(self.current_data_params_)
+        for trn_size in range(self.min_training_size_, self.max_training_size_):
+            start_index = self.end_index_ - trn_size
+            series_a = self.prices_a_[start_index:self.end_index_]
+            series_b = self.prices_b_[start_index:self.end_index_]
+
+            # Combine into 2D matrix for Johansen test
+            try:
+                data = np.column_stack([series_a, series_b])
+
+                # Johansen test: det_order=0 (no deterministic trend), k_ar_diff=1 (lag)
+                res = coint_johansen(data, det_order=0, k_ar_diff=1)
+
+                # Trace statistic for cointegration rank 1
+                trace_stat = res.lr1[0]  # test stat for rank=0 vs >=1
+                critical_value = res.cvt[0, 1]  # 5% critical value
+
+                if trace_stat > best_stat:
+                    best_stat = trace_stat
+                    best_trn_size = trn_size
+                    best_start_index = start_index
+            except Exception:
+                continue
+
+        if best_trn_size > 0:
+            result.training_size = best_trn_size
+            result.training_start_index = best_start_index
+        else:
+            print("*** WARNING: No valid cointegration window found.")
+
+        # print(
+        #     f"*** DEBUG *** end_index={self.end_index_}, best_trn_size={best_trn_size}, trace_stat={best_stat}"
+        # )
+        return result

lib/pt_strategy/results.py

@@ -439,9 +439,12 @@ class PairResearchResult:
         summary = self.get_return_summary()
         
         print(f"\n====== PAIR RESEARCH GRAND TOTALS ======")
+        print('---')
         print(f"Total Return: {summary['total_return']:+.2f}%")
+        print('---')
         print(f"Total Days Traded: {summary['total_days']}")
-        print(f"Total Pair Trades: {summary['total_pairs']}")
+        print(f"Total Open-Close Actions: {summary['total_pairs']}")
+        print(f"Total Trades:  4 * {summary['total_pairs']} = {4 * summary['total_pairs']}")
         
         if summary['total_days'] > 0:
             print(f"Average Daily Return: {summary['average_daily_return']:+.2f}%")
@@ -469,8 +472,8 @@ class PairResearchResult:
         self.calculate_returns()
         self.print_returns_by_day()
         self.print_outstanding_positions()
-        self.print_grand_totals()
         self._print_additional_metrics()
+        self.print_grand_totals()
     
     def _print_additional_metrics(self) -> None:
         """Print additional performance metrics."""

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 DataWindowParams
-from pt_strategy.model_data_policy import DataParams
 
 
 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()
-
 
         

lib/pt_strategy/trading_strategy.py

@@ -1,22 +1,12 @@
 from __future__ import annotations
 
-import os
+from typing import Any, Dict, List, Optional
-from abc import ABC, abstractmethod
-from enum import Enum
-from typing import Any, Dict, Generator, List, Optional, Type, cast
 
 import pandas as pd
 from pt_strategy.model_data_policy import ModelDataPolicy
 from pt_strategy.pt_market_data import PtMarketData
 from pt_strategy.pt_model import Prediction
-from pt_strategy.results import (
-    PairResearchResult,
-    create_result_database,
-    store_config_in_database,
-)
 from pt_strategy.trading_pair import PairState, TradingPair
-from tools.filetools import resolve_datafiles
-from tools.instruments import get_instruments
 
 
 class PtResearchStrategy:
@@ -41,7 +31,6 @@ class PtResearchStrategy:
         self.config_ = config
         self.trades_ = []
         self.trading_pair_ = TradingPair(config=config, instruments=instruments)
-        self.model_data_policy_ = ModelDataPolicy.create(config)
         self.predictions_ = pd.DataFrame()
 
         import copy
@@ -54,6 +43,9 @@ class PtResearchStrategy:
             config=config_copy, md_class=ResearchMarketData
         )
         self.pt_mkt_data_.load()
+        self.model_data_policy_ = ModelDataPolicy.create(
+            config, mkt_data=self.pt_mkt_data_.market_data_df_, pair=self.trading_pair_
+        )
 
     def outstanding_positions(self) -> List[Dict[str, Any]]:
         return list(self.trading_pair_.user_data_.get("outstanding_positions", []))
@@ -67,9 +59,7 @@ class PtResearchStrategy:
         while self.pt_mkt_data_.has_next():
             market_data_series = self.pt_mkt_data_.get_next()
             new_row = pd.DataFrame([market_data_series])
-            market_data_df = pd.concat(
+            market_data_df = pd.concat([market_data_df, new_row], ignore_index=True)
-                [market_data_df, new_row], ignore_index=True
-            )
             if idx >= training_minutes:
                 break
             idx += 1
@@ -85,7 +75,9 @@ class PtResearchStrategy:
             prediction = self.trading_pair_.run(
                 market_data_df, self.model_data_policy_.advance()
             )
-            self.predictions_ = pd.concat([self.predictions_, prediction.to_df()], ignore_index=True)
+            self.predictions_ = pd.concat(
+                [self.predictions_, prediction.to_df()], ignore_index=True
+            )
             assert prediction is not None
 
             trades = self._create_trades(
@@ -223,13 +215,13 @@ class PtResearchStrategy:
             side_b = "SELL"
 
         # save closing sides
-        pair.user_data_["open_side_a"] = side_a # used in oustanding positions
+        pair.user_data_["open_side_a"] = side_a  # used in oustanding positions
         pair.user_data_["open_side_b"] = side_b
         pair.user_data_["open_px_a"] = px_a
         pair.user_data_["open_px_b"] = px_b
         pair.user_data_["open_tstamp"] = tstamp
-        
+
-        pair.user_data_["close_side_a"] = side_b # used for closing trades
+        pair.user_data_["close_side_a"] = side_b  # used for closing trades
         pair.user_data_["close_side_b"] = side_a
 
         # create opening trades