From b87b40a6edcdda62f6c0423ed9dee836bdf66dd8 Mon Sep 17 00:00:00 2001
From: Oleg Sheynin <osheynin@cryptovaltrading.com>
Date: Mon, 21 Jul 2025 05:15:33 +0000
Subject: [PATCH] progress

---
 configuration/crypto.cfg                      |   2 +-
 configuration/equity.cfg                      |   2 +-
 lib/pt_trading/{ => __DELETE__}/static_fit.py |   0
 lib/pt_trading/fit_method.py                  |   6 +-
 .../{sliding_fit.py => rolling_window_fit.py} | 157 +------
 lib/pt_trading/trading_pair.py                |  93 +----
 lib/pt_trading/vecm_rolling_fit.py            | 111 +++++
 research/notebooks/pt_sliding.ipynb           | 382 +++++-------------
 research/pt_backtest.py                       |   2 +-
 research/research_tools.py                    |   6 +-
 strategy/pair_strategy.py                     |   2 +-
 11 files changed, 253 insertions(+), 510 deletions(-)
 rename lib/pt_trading/{ => __DELETE__}/static_fit.py (100%)
 rename lib/pt_trading/{sliding_fit.py => rolling_window_fit.py} (65%)
 create mode 100644 lib/pt_trading/vecm_rolling_fit.py

diff --git a/configuration/crypto.cfg b/configuration/crypto.cfg
index 00d0861..fdadd81 100644
--- a/configuration/crypto.cfg
+++ b/configuration/crypto.cfg
@@ -14,7 +14,7 @@
     "dis-equilibrium_open_trshld": 2.0,
     "dis-equilibrium_close_trshld": 1.0,
     "training_minutes": 120,
-    "fit_method_class": "pt_trading.sliding_fit.SlidingFit",
+    "fit_method_class": "pt_trading.vecm_rolling_fit.VECMRollingFit",
 
     # ====== Stop Conditions ======
     "stop_close_conditions": {
diff --git a/configuration/equity.cfg b/configuration/equity.cfg
index d572676..7f4ac03 100644
--- a/configuration/equity.cfg
+++ b/configuration/equity.cfg
@@ -16,7 +16,7 @@
     "dis-equilibrium_open_trshld": 2.0,
     "dis-equilibrium_close_trshld": 1.0,
     "training_minutes": 120,
-    "fit_method_class": "pt_trading.sliding_fit.SlidingFit",
+    "fit_method_class": "pt_trading.vecm_rolling_fit.VECMRollingFit",
 
     # ====== Stop Conditions ======
     "stop_close_conditions": {
diff --git a/lib/pt_trading/static_fit.py b/lib/pt_trading/__DELETE__/static_fit.py
similarity index 100%
rename from lib/pt_trading/static_fit.py
rename to lib/pt_trading/__DELETE__/static_fit.py
diff --git a/lib/pt_trading/fit_method.py b/lib/pt_trading/fit_method.py
index 9375ab2..6742f1d 100644
--- a/lib/pt_trading/fit_method.py
+++ b/lib/pt_trading/fit_method.py
@@ -2,7 +2,7 @@ from abc import ABC, abstractmethod
 from enum import Enum
 from typing import Dict, Optional, cast
 
-import pandas as pd  # type: ignore[import]
+import pandas as pd 
 from pt_trading.results import BacktestResult
 from pt_trading.trading_pair import TradingPair
 
@@ -28,4 +28,8 @@ class PairsTradingFitMethod(ABC):
     @abstractmethod
     def reset(self) -> None: ...
 
+    @abstractmethod
+    def create_trading_pair(
+        self, config: Dict, market_data: pd.DataFrame, symbol_a: str, symbol_b: str, price_column: str
+    ) -> TradingPair: ...
 
diff --git a/lib/pt_trading/sliding_fit.py b/lib/pt_trading/rolling_window_fit.py
similarity index 65%
rename from lib/pt_trading/sliding_fit.py
rename to lib/pt_trading/rolling_window_fit.py
index f940d3c..843fe81 100644
--- a/lib/pt_trading/sliding_fit.py
+++ b/lib/pt_trading/rolling_window_fit.py
@@ -1,15 +1,16 @@
 from abc import ABC, abstractmethod
 from enum import Enum
-from typing import Dict, Optional, cast
+from typing import Any, Dict, Optional, cast
 
 import pandas as pd  # type: ignore[import]
 from pt_trading.fit_method import PairsTradingFitMethod
 from pt_trading.results import BacktestResult
-from pt_trading.trading_pair import CointegrationData, TradingPair, PairState
+from pt_trading.trading_pair import PairState, TradingPair
+from statsmodels.tsa.vector_ar.vecm import VECM, VECMResults
 
 NanoPerMin = 1e9
-
-class SlidingFit(PairsTradingFitMethod):
+    
+class RollingFit(PairsTradingFitMethod):
     def __init__(self) -> None:
         super().__init__()
 
@@ -52,17 +53,11 @@ class SlidingFit(PairsTradingFitMethod):
                 )
                 break
 
-            try:
-                # ================================ TRAINING ================================
-                pair.train_pair()
-            except Exception as e:
-                raise RuntimeError(f"{pair}: Training failed: {str(e)}") from e
-
             try:
                 # ================================ PREDICTION ================================
-                pair.predict()
+                self.pair_predict_result_ = pair.predict()
             except Exception as e:
-                raise RuntimeError(f"{pair}: Prediction failed: {str(e)}") from e
+                raise RuntimeError(f"{pair}: TrainingPrediction failed: {str(e)}") from e
 
                 # break
 
@@ -72,20 +67,21 @@ class SlidingFit(PairsTradingFitMethod):
             curr_predicted_row_idx += 1
 
         self._create_trading_signals(pair, config, bt_result)
-        print(f"***{pair}*** FINISHED *** Num Trades:{len(pair.user_data_['trades'])}")
+        print(f"***{pair}*** FINISHED *** Num Trades:{len(pair.user_data_['trades'])}") 
+        
         return pair.get_trades()
 
     def _create_trading_signals(
         self, pair: TradingPair, config: Dict, bt_result: BacktestResult
     ) -> None:
-        if pair.predicted_df_ is None:
-            print(f"{pair.market_data_.iloc[0]['tstamp']} {pair}: No predicted data")
-            return
+
+        predicted_df = self.pair_predict_result_
+        assert predicted_df is not None
 
         open_threshold = config["dis-equilibrium_open_trshld"]
         close_threshold = config["dis-equilibrium_close_trshld"]
-        for curr_predicted_row_idx in range(len(pair.predicted_df_)):
-            pred_row = pair.predicted_df_.iloc[curr_predicted_row_idx]
+        for curr_predicted_row_idx in range(len(predicted_df)):
+            pred_row = predicted_df.iloc[curr_predicted_row_idx]
             scaled_disequilibrium = pred_row["scaled_disequilibrium"]
 
             if pair.user_data_["state"] in [PairState.INITIAL, PairState.CLOSE, PairState.CLOSE_POSITION]:
@@ -141,10 +137,10 @@ class SlidingFit(PairsTradingFitMethod):
                     pair.user_data_["state"] = PairState.CLOSE_POSITION
                     pair.on_close_trades(close_position_trades)
             else:
-                if pair.predicted_df_ is not None:
+                if predicted_df is not None:
                     bt_result.handle_outstanding_position(
                         pair=pair,
-                        pair_result_df=pair.predicted_df_,
+                        pair_result_df=predicted_df,
                         last_row_index=0,
                         open_side_a=pair.user_data_["open_side_a"],
                         open_side_b=pair.user_data_["open_side_b"],
@@ -158,13 +154,6 @@ class SlidingFit(PairsTradingFitMethod):
     ) -> Optional[pd.DataFrame]:
         colname_a, colname_b = pair.colnames()
 
-        assert pair.predicted_df_ is not None
-        predicted_df = pair.predicted_df_
-
-        # Check if we have any data to work with
-        if len(predicted_df) == 0:
-            return None
-
         open_row = row
         open_tstamp = open_row["tstamp"]
         open_disequilibrium = open_row["disequilibrium"]
@@ -238,10 +227,6 @@ class SlidingFit(PairsTradingFitMethod):
     ) -> Optional[pd.DataFrame]:
         colname_a, colname_b = pair.colnames()
 
-        assert pair.predicted_df_ is not None
-        if len(pair.predicted_df_) == 0:
-            return None
-
         close_row = row
         close_tstamp = close_row["tstamp"]
         close_disequilibrium = close_row["disequilibrium"]
@@ -289,114 +274,6 @@ class SlidingFit(PairsTradingFitMethod):
             "pair": "object"
         })
 
-    # def _get_stop_close_trades(
-    #     self, pair: TradingPair, row: pd.Series, close_threshold: float
-    # ) -> Optional[pd.DataFrame]:
-    #     colname_a, colname_b = pair.colnames()
-    #     assert pair.predicted_df_ is not None
-    #     if len(pair.predicted_df_) == 0:
-    #         return None
-        
-    #     stop_close_row = row
-    #     stop_close_tstamp = stop_close_row["tstamp"]    
-    #     stop_close_disequilibrium = stop_close_row["disequilibrium"]
-    #     stop_close_scaled_disequilibrium = stop_close_row["scaled_disequilibrium"]
-    #     stop_close_px_a = stop_close_row[f"{colname_a}"]
-    #     stop_close_px_b = stop_close_row[f"{colname_b}"]
-        
-    #     stop_close_side_a = pair.user_data_["close_side_a"]
-    #     stop_close_side_b = pair.user_data_["close_side_b"]
-        
-    #     trd_signal_tuples = [
-    #         (
-    #             stop_close_tstamp,
-    #             stop_close_side_a,  
-    #             pair.symbol_a_,
-    #             stop_close_px_a,
-    #             stop_close_disequilibrium,
-    #             stop_close_scaled_disequilibrium,
-    #             pair,
-    #         ),  
-    #         (
-    #             stop_close_tstamp,
-    #             stop_close_side_b,
-    #             pair.symbol_b_,
-    #             stop_close_px_b,
-    #             stop_close_disequilibrium,  
-    #             stop_close_scaled_disequilibrium,
-    #             pair,
-    #         ),
-    #     ]
-    #     df = pd.DataFrame(
-    #         trd_signal_tuples,
-    #         columns=self.TRADES_COLUMNS,
-    #     )
-    #     # Ensure consistent dtypes
-    #     return df.astype({
-    #         "time": "datetime64[ns]",
-    #         "action": "string", 
-    #         "symbol": "string", 
-    #         "price": "float64",
-    #         "disequilibrium": "float64",
-    #         "scaled_disequilibrium": "float64",
-    #         "pair": "object"
-    #     })  
-
-    # def _get_close_position_trades(
-    #     self, pair: TradingPair, row: pd.Series, close_threshold: float
-    # ) -> Optional[pd.DataFrame]:
-    #     colname_a, colname_b = pair.colnames()
-
-    #     assert pair.predicted_df_ is not None
-    #     if len(pair.predicted_df_) == 0:
-    #         return None
-
-    #     close_position_row = row
-    #     close_position_tstamp = close_position_row["tstamp"]
-    #     close_position_disequilibrium = close_position_row["disequilibrium"]
-    #     close_position_scaled_disequilibrium = close_position_row["scaled_disequilibrium"]
-    #     close_position_px_a = close_position_row[f"{colname_a}"]
-    #     close_position_px_b = close_position_row[f"{colname_b}"]
-
-    #     close_position_side_a = pair.user_data_["close_side_a"]
-    #     close_position_side_b = pair.user_data_["close_side_b"]
-
-    #     trd_signal_tuples = [
-    #         (
-    #             close_position_tstamp,
-    #             close_position_side_a,
-    #             pair.symbol_a_,
-    #             close_position_px_a,
-    #             close_position_disequilibrium,
-    #             close_position_scaled_disequilibrium,
-    #             pair,
-    #         ),
-    #         (
-    #             close_position_tstamp,
-    #             close_position_side_b,
-    #             pair.symbol_b_,
-    #             close_position_px_b,
-    #             close_position_disequilibrium,
-    #             close_position_scaled_disequilibrium,
-    #             pair,
-    #         ),
-    #     ]
-
-    #     # Add tuples to data frame with explicit dtypes to avoid concatenation warnings
-    #     df = pd.DataFrame(
-    #         trd_signal_tuples,
-    #         columns=self.TRADES_COLUMNS,
-    #     )
-    #     # Ensure consistent dtypes
-    #     return df.astype({
-    #         "time": "datetime64[ns]",
-    #         "action": "string",
-    #         "symbol": "string", 
-    #         "price": "float64",
-    #         "disequilibrium": "float64",
-    #         "scaled_disequilibrium": "float64",
-    #         "pair": "object"
-    #     })
-
     def reset(self) -> None:
         curr_training_start_idx = 0
+
diff --git a/lib/pt_trading/trading_pair.py b/lib/pt_trading/trading_pair.py
index 3578800..543719c 100644
--- a/lib/pt_trading/trading_pair.py
+++ b/lib/pt_trading/trading_pair.py
@@ -1,10 +1,11 @@
 from __future__ import annotations
 
+from abc import ABC, abstractmethod
 from enum import Enum
 from typing import Any, Dict, List, Optional
 
 import pandas as pd  # type:ignore
-from statsmodels.tsa.vector_ar.vecm import VECM, VECMResults
+
 
 class PairState(Enum):
     INITIAL = 1
@@ -40,7 +41,7 @@ class CointegrationData:
         self.johansen_is_cointegrated_ = self.johansen_lr1_ > self.johansen_cvt_
 
         # Run Engle-Granger cointegration test
-        from statsmodels.tsa.stattools import coint #type: ignore
+        from statsmodels.tsa.stattools import coint  # type: ignore
 
         col1, col2 = pair.colnames()
         assert training_df is not None
@@ -68,7 +69,7 @@ class CointegrationData:
         return f"CointegrationData(tstamp={self.tstamp_}, pair={self.pair_}, eg_pvalue={self.eg_pvalue_}, johansen_lr1={self.johansen_lr1_}, johansen_cvt={self.johansen_cvt_}, eg_is_cointegrated={self.eg_is_cointegrated_}, johansen_is_cointegrated={self.johansen_is_cointegrated_})"
 
 
-class TradingPair:
+class TradingPair(ABC):
     market_data_: pd.DataFrame
     symbol_a_: str
     symbol_b_: str
@@ -80,11 +81,9 @@ class TradingPair:
     training_df_: pd.DataFrame
     testing_df_: pd.DataFrame
 
-    vecm_fit_: VECMResults
-
     user_data_: Dict[str, Any]
 
-    predicted_df_: Optional[pd.DataFrame]
+    # predicted_df_: Optional[pd.DataFrame]
 
     def __init__(
         self, config: Dict[str, Any], market_data: pd.DataFrame, symbol_a: str, symbol_b: str, price_column: str
@@ -193,42 +192,6 @@ class TradingPair:
             f"{self.price_column_}_{self.symbol_b_}",
         ]
 
-    def fit_VECM(self) -> None:
-        assert self.training_df_ is not None
-        vecm_df = self.training_df_[self.colnames()].reset_index(drop=True)
-        vecm_model = VECM(vecm_df, coint_rank=1)
-        vecm_fit = vecm_model.fit()
-
-        assert vecm_fit is not None
-
-        # URGENT check beta and alpha
-
-        # Check if the model converged properly
-        if not hasattr(vecm_fit, "beta") or vecm_fit.beta is None:
-            print(f"{self}: VECM model failed to converge properly")
-
-        self.vecm_fit_ = vecm_fit
-        # print(f"{self}: beta={self.vecm_fit_.beta} alpha={self.vecm_fit_.alpha}"  )
-        # print(f"{self}: {self.vecm_fit_.summary()}")
-        pass
-
-    def train_pair(self) -> None:
-        # print('*' * 80 + '\n' + f"**************** {self} IS COINTEGRATED ****************\n" + '*' * 80)
-        self.fit_VECM()
-        assert self.training_df_ is not None and self.vecm_fit_ is not None
-        diseq_series = self.training_df_[self.colnames()] @ self.vecm_fit_.beta
-        # print(diseq_series.shape)
-        self.training_mu_ = float(diseq_series[0].mean())
-        self.training_std_ = float(diseq_series[0].std())
-
-        self.training_df_["dis-equilibrium"] = (
-            self.training_df_[self.colnames()] @ self.vecm_fit_.beta
-        )
-        # Normalize the dis-equilibrium
-        self.training_df_["scaled_dis-equilibrium"] = (
-            diseq_series - self.training_mu_
-        ) / self.training_std_
-
     def add_trades(self, trades: pd.DataFrame) -> None:
         if self.user_data_["trades"] is None or len(self.user_data_["trades"]) == 0:
             # If trades is empty or None, just assign the new trades directly
@@ -267,45 +230,6 @@ class TradingPair:
     def get_trades(self) -> pd.DataFrame:
         return self.user_data_["trades"] if "trades" in self.user_data_ else pd.DataFrame()
 
-    def predict(self) -> pd.DataFrame:
-        assert self.testing_df_ is not None
-        assert self.vecm_fit_ is not None
-        predicted_prices = self.vecm_fit_.predict(steps=len(self.testing_df_))
-
-        # Convert prediction to a DataFrame for readability
-        predicted_df = pd.DataFrame(
-            predicted_prices, columns=pd.Index(self.colnames()), dtype=float
-        )
-
-
-        predicted_df = pd.merge(
-            self.testing_df_.reset_index(drop=True),
-            pd.DataFrame(
-                predicted_prices, columns=pd.Index(self.colnames()), dtype=float
-            ),
-            left_index=True,
-            right_index=True,
-            suffixes=("", "_pred"),
-        ).dropna()
-
-        predicted_df["disequilibrium"] = (
-            predicted_df[self.colnames()] @ self.vecm_fit_.beta
-        )
-
-        predicted_df["scaled_disequilibrium"] = (
-            abs(predicted_df["disequilibrium"] - self.training_mu_)
-            / self.training_std_
-        )
-        
-        predicted_df = predicted_df.reset_index(drop=True)
-        if self.predicted_df_ is None:
-            self.predicted_df_ = predicted_df
-        else:
-            self.predicted_df_ = pd.concat([self.predicted_df_, predicted_df], ignore_index=True)
-        # Reset index to ensure proper indexing 
-        self.predicted_df_ = self.predicted_df_.reset_index(drop=True)
-        return self.predicted_df_
-
     def cointegration_check(self) -> Optional[pd.DataFrame]:
         print(f"***{self}*** STARTING....")
         config = self.config_
@@ -394,3 +318,10 @@ class TradingPair:
     def name(self) -> str:
         return f"{self.symbol_a_} & {self.symbol_b_}"
         # return f"{self.symbol_a_} & {self.symbol_b_}"
+
+    @abstractmethod
+    def predict(self) -> pd.DataFrame: ...
+
+    # @abstractmethod
+    # def predicted_df(self) -> Optional[pd.DataFrame]: ...
+
diff --git a/lib/pt_trading/vecm_rolling_fit.py b/lib/pt_trading/vecm_rolling_fit.py
new file mode 100644
index 0000000..a8d5d2f
--- /dev/null
+++ b/lib/pt_trading/vecm_rolling_fit.py
@@ -0,0 +1,111 @@
+from typing import Any, Dict, Optional, cast
+
+import pandas as pd
+from pt_trading.results import BacktestResult
+from pt_trading.rolling_window_fit import RollingFit
+from pt_trading.trading_pair import TradingPair
+from statsmodels.tsa.vector_ar.vecm import VECM, VECMResults
+
+NanoPerMin = 1e9
+class VECMTradingPair(TradingPair):
+    vecm_fit_: Optional[VECMResults]
+    pair_predict_result_: Optional[pd.DataFrame]
+    
+    def __init__(self, config: Dict[str, Any], market_data: pd.DataFrame, symbol_a: str, symbol_b: str, price_column: str):
+        super().__init__(config, market_data, symbol_a, symbol_b, price_column)
+        self.vecm_fit_ = None
+        self.pair_predict_result_ = None
+        
+    def _train_pair(self) -> None:
+        # print('*' * 80 + '\n' + f"**************** {self} IS COINTEGRATED ****************\n" + '*' * 80)
+        self._fit_VECM()
+        assert self.training_df_ is not None and self.vecm_fit_ is not None
+        diseq_series = self.training_df_[self.colnames()] @ self.vecm_fit_.beta
+        # print(diseq_series.shape)
+        self.training_mu_ = float(diseq_series[0].mean())
+        self.training_std_ = float(diseq_series[0].std())
+
+        self.training_df_["dis-equilibrium"] = (
+            self.training_df_[self.colnames()] @ self.vecm_fit_.beta
+        )
+        # Normalize the dis-equilibrium
+        self.training_df_["scaled_dis-equilibrium"] = (
+            diseq_series - self.training_mu_
+        ) / self.training_std_
+
+    def _fit_VECM(self) -> None:
+        assert self.training_df_ is not None
+        vecm_df = self.training_df_[self.colnames()].reset_index(drop=True)
+        vecm_model = VECM(vecm_df, coint_rank=1)
+        vecm_fit = vecm_model.fit()
+
+        assert vecm_fit is not None
+
+        # URGENT check beta and alpha
+
+        # Check if the model converged properly
+        if not hasattr(vecm_fit, "beta") or vecm_fit.beta is None:
+            print(f"{self}: VECM model failed to converge properly")
+
+        self.vecm_fit_ = vecm_fit
+        pass
+
+    def predict(self) -> pd.DataFrame:
+        self._train_pair()
+        
+        assert self.testing_df_ is not None
+        assert self.vecm_fit_ is not None
+        predicted_prices = self.vecm_fit_.predict(steps=len(self.testing_df_))
+
+        # Convert prediction to a DataFrame for readability
+        predicted_df = pd.DataFrame(
+            predicted_prices, columns=pd.Index(self.colnames()), dtype=float
+        )
+
+        predicted_df = pd.merge(
+            self.testing_df_.reset_index(drop=True),
+            pd.DataFrame(
+                predicted_prices, columns=pd.Index(self.colnames()), dtype=float
+            ),
+            left_index=True,
+            right_index=True,
+            suffixes=("", "_pred"),
+        ).dropna()
+
+        predicted_df["disequilibrium"] = (
+            predicted_df[self.colnames()] @ self.vecm_fit_.beta
+        )
+
+        predicted_df["scaled_disequilibrium"] = (
+            abs(predicted_df["disequilibrium"] - self.training_mu_)
+            / self.training_std_
+        )
+        
+        predicted_df = predicted_df.reset_index(drop=True)
+        if self.pair_predict_result_ is None:
+            self.pair_predict_result_ = predicted_df
+        else:
+            self.pair_predict_result_ = pd.concat([self.pair_predict_result_, predicted_df], ignore_index=True)
+        # Reset index to ensure proper indexing 
+        self.pair_predict_result_ = self.pair_predict_result_.reset_index(drop=True)
+        return self.pair_predict_result_
+
+    
+class VECMRollingFit(RollingFit):
+    def __init__(self) -> None:
+        super().__init__()
+
+    def run_pair(
+        self, pair: TradingPair, bt_result: BacktestResult
+    ) -> Optional[pd.DataFrame]:
+        return super().run_pair(pair, bt_result)
+    def create_trading_pair(
+        self, config: Dict, market_data: pd.DataFrame, symbol_a: str, symbol_b: str, price_column: str
+    ) -> TradingPair:
+        return VECMTradingPair(
+            config=config,
+            market_data=market_data,
+            symbol_a=symbol_a,
+            symbol_b=symbol_b,
+            price_column=price_column
+        )
diff --git a/research/notebooks/pt_sliding.ipynb b/research/notebooks/pt_sliding.ipynb
index 9411563..f8400e6 100644
--- a/research/notebooks/pt_sliding.ipynb
+++ b/research/notebooks/pt_sliding.ipynb
@@ -1,34 +1,5 @@
 {
   "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "vscode": {
-          "languageId": "raw"
-        }
-      },
-      "source": [
-        "# Pairs Trading Backtest Notebook\n",
-        "\n",
-        "This comprehensive notebook supports both StaticFit and SlidingFit.\n",
-        "It automatically adapts its analysis and visualization based on the strategy specified in the configuration file.\n",
-        "\n",
-        "## Key Features:\n",
-        "\n",
-        "1. **Configuration-Driven**: Loads strategy and parameters from HJSON configuration files\n",
-        "2. **Dual Model Support**: Works with both StaticFit and SlidingFit\n",
-        "3. **Adaptive Visualization**: Different visualizations based on selected strategy\n",
-        "4. **Comprehensive Analysis**: Deep analysis of trading pairs and dis-equilibrium\n",
-        "5. **Interactive Configuration**: Easy parameter adjustment and re-running\n",
-        "\n",
-        "## Usage:\n",
-        "\n",
-        "1. **Configure Parameters**: Set CONFIG_FILE, SYMBOL_A, SYMBOL_B, and TRADING_DATE\n",
-        "2. **Run Analysis**: Execute cells step by step\n",
-        "3. **View Results**: Comprehensive visualizations and trading signals\n",
-        "4. **Experiment**: Modify parameters and re-run for different scenarios\n"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -85,7 +56,7 @@
         "# SYMBOL_B = \"ETH-USDT\"   # Change this to your desired symbol B\n",
         "# ================================ C R Y P T O ================================\n",
         "\n",
-        "FIT_METHOD_TYPE = \"SlidingFit\"\n",
+        "FIT_METHOD_TYPE = \"RollingFit\"\n",
         "TRD_DATE = f\"{TRADING_DATE[0:4]}-{TRADING_DATE[4:6]}-{TRADING_DATE[6:8]}\"\n"
       ]
     },
@@ -122,7 +93,7 @@
         "    from IPython.display import clear_output\n",
         "\n",
         "    # Import our modules\n",
-        "    from pt_trading.sliding_fit import SlidingFit\n",
+        "    from pt_trading.rolling_window_fit import RollingFit\n",
         "    from pt_trading.trading_pair import TradingPair, PairState\n",
         "    # from pt_trading.results import BacktestResult\n",
         "\n",
@@ -188,8 +159,7 @@
         "def instantiate_fit_method_from_config(config: Dict):\n",
         "    \"\"\"Dynamically instantiate strategy from config\"\"\"\n",
         "    fit_method_class_name = config.get(\"fit_method_class\", None)\n",
-        "    if fit_method_class_name is None or fit_method_class_name[-10:] != \"SlidingFit\":\n",
-        "        raise ValueError(f\"Only SidingFit is supported, got {fit_method_class_name}\")\n",
+        "    print(f\"Fit Model: {fit_method_class_name}\")\n",
         "    \n",
         "    try:\n",
         "        # Split module and class name\n",
@@ -339,7 +309,7 @@
         "    print(f\"Time range: {market_data_df['tstamp'].min()} to {market_data_df['tstamp'].max()}\")\n",
         "\n",
         "    # Create trading pair\n",
-        "    pair = TradingPair(\n",
+        "    pair = FIT_MODEL.create_trading_pair(\n",
         "        config=PT_BT_CONFIG,\n",
         "        market_data=market_data_df,\n",
         "        symbol_a=SYMBOL_A,\n",
@@ -383,7 +353,7 @@
         "global pair\n",
         "\n",
         "def print_strategy_specifics() -> None:     # Determine analysis approach based on strategy type\n",
-        "    print(f\"Analysis for SlidingFit ...\")\n",
+        "    print(f\"Analysis for RollingFit ...\")\n",
         "\n",
         "    print(\"\\n=== SLIDING FIT FIT_MODEL ANALYSIS ===\")\n",
         "    print(\"This strategy:\")\n",
@@ -394,7 +364,7 @@
         "    # Calculate maximum possible iterations for sliding window\n",
         "    training_minutes = PT_BT_CONFIG[\"training_minutes\"]\n",
         "    max_iterations = len(pair.market_data_) - training_minutes\n",
-        "    print(f\"\\nSliding window analysis parameters:\")\n",
+        "    print(f\"\\nRolling window analysis parameters:\")\n",
         "    print(f\"  Training window size: {training_minutes} minutes\")\n",
         "    print(f\"  Maximum iterations: {max_iterations}\")\n",
         "    print(f\"  Total analysis time: ~{max_iterations} minutes\")\n",
@@ -523,6 +493,7 @@
         "    global FIT_MODEL\n",
         "    global bt_result\n",
         "    global pair_trades\n",
+        "    global PREDICTED_RESULT\n",
         "\n",
         "    import pandas as pd\n",
         "    from pt_trading.results import BacktestResult\n",
@@ -553,6 +524,7 @@
         "    # Run the sliding fit method\n",
         "    # ==========================================================================\n",
         "    pair_trades = FIT_MODEL.run_pair(pair=pair, bt_result=bt_result)\n",
+        "    PREDICTED_RESULT = pair.pair_predict_result_ # TODO make abstract function\n",
         "    # ==========================================================================\n",
         "\n",
         "    if pair_trades is not None and len(pair_trades) > 0:\n",
@@ -567,7 +539,6 @@
         "    print(\"BACKTEST RESULTS\")\n",
         "    print(\"=\"*80)\n",
         "\n",
-        "    assert pair.predicted_df_ is not None\n",
         "\n",
         "# run_analysis()"
       ]
@@ -593,6 +564,7 @@
         "    global SYMBOL_A\n",
         "    global SYMBOL_B\n",
         "    global TRD_DATE\n",
+        "    global PREDICTED_RESULT\n",
         "\n",
         "    import plotly.graph_objects as go\n",
         "    from plotly.subplots import make_subplots\n",
@@ -606,14 +578,13 @@
         "\n",
         "    # Strategy-specific interactive visualization\n",
         "    assert PT_BT_CONFIG is not None\n",
-        "    assert pair.predicted_df_ is not None\n",
         "\n",
         "    print(\"=== SLIDING FIT INTERACTIVE VISUALIZATION ===\")\n",
-        "    print(\"Note: Sliding strategy visualization with interactive plotly charts\")\n",
+        "    print(\"Note: Rolling Fit strategy visualization with interactive plotly charts\")\n",
         "\n",
         "    # Create consistent timeline - superset of timestamps from both dataframes\n",
         "    market_timestamps = set(pair.market_data_['tstamp'])\n",
-        "    predicted_timestamps = set(pair.predicted_df_['tstamp'])\n",
+        "    predicted_timestamps = set(PREDICTED_RESULT['tstamp'])\n",
         "\n",
         "    # Create superset of all timestamps\n",
         "    all_timestamps = sorted(market_timestamps.union(predicted_timestamps))\n",
@@ -622,7 +593,7 @@
         "    timeline_df = pd.DataFrame({'tstamp': all_timestamps})\n",
         "\n",
         "    # Merge with predicted data to get dis-equilibrium values\n",
-        "    timeline_df = timeline_df.merge(pair.predicted_df_[['tstamp', 'disequilibrium', 'scaled_disequilibrium']], \n",
+        "    timeline_df = timeline_df.merge(PREDICTED_RESULT[['tstamp', 'disequilibrium', 'scaled_disequilibrium']], \n",
         "                                    on='tstamp', how='left')\n",
         "\n",
         "    # Get Symbol_A and Symbol_B market data\n",
@@ -926,7 +897,7 @@
         "        # Update layout\n",
         "        fig.update_layout(\n",
         "            height=1200,\n",
-        "            title_text=f\"Sliding Fit Strategy Analysis - {SYMBOL_A} & {SYMBOL_B} ({TRD_DATE})\",\n",
+        "            title_text=f\"Strategy Analysis - {SYMBOL_A} & {SYMBOL_B} ({TRD_DATE})\",\n",
         "            showlegend=True,\n",
         "            template=\"plotly_white\",\n",
         "            plot_bgcolor='lightgray',\n",
@@ -1152,12 +1123,12 @@
         "    print(f\"  Funding per pair: ${PT_BT_CONFIG['funding_per_pair']}\")\n",
         "\n",
         "    # Strategy-specific summary\n",
-        "    print(f\"\\nSliding Window Analysis:\")\n",
+        "    print(f\"\\nRolling Window Analysis:\")\n",
         "    training_minutes = PT_BT_CONFIG['training_minutes']\n",
         "    max_iterations = len(pair.market_data_) - training_minutes\n",
         "    print(f\"  Total data points: {len(pair.market_data_)}\")\n",
         "    print(f\"  Maximum iterations: {max_iterations}\")\n",
-        "    print(f\"  Analysis type: Dynamic sliding window\")\n",
+        "    print(f\"  Analysis type: Dynamic rolling window\")\n",
         "\n",
         "    # Trading signals summary\n",
         "    if pair_trades is not None and len(pair_trades) > 0:\n",
@@ -1300,11 +1271,12 @@
             "  Data directory: /home/oleg/develop/pairs_trading/data/equity\n",
             "  Database table: md_1min_bars\n",
             "  Exchange: ALPACA\n",
-            "  Training window: 150 minutes\n",
+            "  Training window: 120 minutes\n",
             "  Open threshold: 2\n",
             "  Close threshold: 1\n",
+            "Fit Model: pt_trading.vecm_rolling_fit.VECMRollingFit\n",
             "Load configuration SUCCESS\n",
-            "  Fit Method: SlidingFit\n",
+            "  Fit Method: VECMRollingFit\n",
             "\n",
             "Data Configuration:\n",
             "  Data File: 20250618.mktdata.ohlcv.db\n",
@@ -1472,7 +1444,7 @@
           "name": "stdout",
           "output_type": "stream",
           "text": [
-            "Analysis for SlidingFit ...\n",
+            "Analysis for RollingFit ...\n",
             "\n",
             "=== SLIDING FIT FIT_MODEL ANALYSIS ===\n",
             "This strategy:\n",
@@ -1480,15 +1452,15 @@
             "  - Adapts to changing market conditions\n",
             "  - Dynamic parameter updates every minute\n",
             "\n",
-            "Sliding window analysis parameters:\n",
-            "  Training window size: 150 minutes\n",
-            "  Maximum iterations: 211\n",
-            "  Total analysis time: ~211 minutes\n",
+            "Rolling window analysis parameters:\n",
+            "  Training window size: 120 minutes\n",
+            "  Maximum iterations: 241\n",
+            "  Total analysis time: ~241 minutes\n",
             "\n",
             "Strategy Configuration:\n",
             "  Open threshold: 2\n",
             "  Close threshold: 1\n",
-            "  Training minutes: 150\n",
+            "  Training minutes: 120\n",
             "  Funding per pair: $2000\n"
           ]
         },
@@ -1522,31 +1494,22 @@
             "  MSTR: Mean=$370.87, Std=$1.52\n",
             "  Price Ratio: Mean=0.75, Std=0.04\n",
             "  Correlation: -0.0929\n",
-            "Running SlidingFit analysis...\n",
+            "Running RollingFit analysis...\n",
             "\n",
             "=== SLIDING FIT ANALYSIS ===\n",
             "Processing first 200 iterations for demonstration...\n",
             "***COIN & MSTR*** STARTING....\n",
-            "OPEN_TRADES: 2025-06-18 16:00:00 open_scaled_disequilibrium=np.float64(2.8605299147981675)\n",
+            "OPEN_TRADES: 2025-06-18 15:30:00 open_scaled_disequilibrium=np.float64(4.435087777514003)\n",
             "OPEN TRADES:\n",
             "                 time action symbol     price  disequilibrium  scaled_disequilibrium         pair status\n",
-            "0 2025-06-18 16:00:00   SELL   COIN  278.5026       26.172385                2.86053  COIN & MSTR   OPEN\n",
-            "1 2025-06-18 16:00:00    BUY   MSTR  372.2725       26.172385                2.86053  COIN & MSTR   OPEN\n",
-            "CLOSE TRADES:\n",
-            "                 time action symbol   price  disequilibrium  scaled_disequilibrium         pair status\n",
-            "0 2025-06-18 16:59:00    BUY   COIN  285.70   -153123.39816               0.369634  COIN & MSTR  CLOSE\n",
-            "1 2025-06-18 16:59:00   SELL   MSTR  371.35   -153123.39816               0.369634  COIN & MSTR  CLOSE\n",
-            "OPEN_TRADES: 2025-06-18 17:07:00 open_scaled_disequilibrium=np.float64(2.1726379189346643)\n",
-            "OPEN TRADES:\n",
-            "                 time action symbol    price  disequilibrium  scaled_disequilibrium         pair status\n",
-            "0 2025-06-18 17:07:00    BUY   COIN  288.749     -885.043609               2.172638  COIN & MSTR   OPEN\n",
-            "1 2025-06-18 17:07:00   SELL   MSTR  370.660     -885.043609               2.172638  COIN & MSTR   OPEN\n",
-            "CLOSE TRADES:\n",
-            "                 time action symbol  price  disequilibrium  scaled_disequilibrium         pair status\n",
-            "0 2025-06-18 17:45:00   SELL   COIN  288.3       -1.436136               0.950544  COIN & MSTR  CLOSE\n",
-            "1 2025-06-18 17:45:00    BUY   MSTR  369.6       -1.436136               0.950544  COIN & MSTR  CLOSE\n",
-            "***COIN & MSTR*** FINISHED ... 8\n",
-            "Generated 8 trading signals\n",
+            "0 2025-06-18 15:30:00   SELL   COIN  265.7900       10.226451               4.435088  COIN & MSTR   OPEN\n",
+            "1 2025-06-18 15:30:00    BUY   MSTR  372.9349       10.226451               4.435088  COIN & MSTR   OPEN\n",
+            "STOP CLOSE TRADES:\n",
+            "                 time action symbol   price  disequilibrium  scaled_disequilibrium         pair           status\n",
+            "0 2025-06-18 15:34:00    BUY   COIN  268.47       13.457921               4.608004  COIN & MSTR  CLOSE_STOP_LOSS\n",
+            "1 2025-06-18 15:34:00   SELL   MSTR  373.08       13.457921               4.608004  COIN & MSTR  CLOSE_STOP_LOSS\n",
+            "***COIN & MSTR*** FINISHED *** Num Trades:4\n",
+            "Generated 4 trading signals\n",
             "\n",
             "Strategy execution completed!\n",
             "\n",
@@ -1574,23 +1537,19 @@
           "output_type": "stream",
           "text": [
             "=== SLIDING FIT INTERACTIVE VISUALIZATION ===\n",
-            "Note: Sliding strategy visualization with interactive plotly charts\n",
+            "Note: Rolling Fit strategy visualization with interactive plotly charts\n",
             "Using consistent timeline with 361 timestamps\n",
             "Timeline range: 2025-06-18 13:30:00 to 2025-06-18 19:30:00\n",
             "\n",
             "Symbol_A trades:\n",
-            "                 time action symbol     price  disequilibrium  scaled_disequilibrium         pair status\n",
-            "0 2025-06-18 16:00:00   SELL   COIN  278.5026       26.172385               2.860530  COIN & MSTR   OPEN\n",
-            "2 2025-06-18 16:59:00    BUY   COIN  285.7000  -153123.398160               0.369634  COIN & MSTR  CLOSE\n",
-            "4 2025-06-18 17:07:00    BUY   COIN  288.7490     -885.043609               2.172638  COIN & MSTR   OPEN\n",
-            "6 2025-06-18 17:45:00   SELL   COIN  288.3000       -1.436136               0.950544  COIN & MSTR  CLOSE\n",
+            "                 time action symbol   price  disequilibrium  scaled_disequilibrium         pair           status\n",
+            "0 2025-06-18 15:30:00   SELL   COIN  265.79       10.226451               4.435088  COIN & MSTR             OPEN\n",
+            "2 2025-06-18 15:34:00    BUY   COIN  268.47       13.457921               4.608004  COIN & MSTR  CLOSE_STOP_LOSS\n",
             "\n",
             "Symbol_B trades:\n",
-            "                 time action symbol     price  disequilibrium  scaled_disequilibrium         pair status\n",
-            "1 2025-06-18 16:00:00    BUY   MSTR  372.2725       26.172385               2.860530  COIN & MSTR   OPEN\n",
-            "3 2025-06-18 16:59:00   SELL   MSTR  371.3500  -153123.398160               0.369634  COIN & MSTR  CLOSE\n",
-            "5 2025-06-18 17:07:00   SELL   MSTR  370.6600     -885.043609               2.172638  COIN & MSTR   OPEN\n",
-            "7 2025-06-18 17:45:00    BUY   MSTR  369.6000       -1.436136               0.950544  COIN & MSTR  CLOSE\n"
+            "                 time action symbol     price  disequilibrium  scaled_disequilibrium         pair           status\n",
+            "1 2025-06-18 15:30:00    BUY   MSTR  372.9349       10.226451               4.435088  COIN & MSTR             OPEN\n",
+            "3 2025-06-18 15:34:00   SELL   MSTR  373.0800       13.457921               4.608004  COIN & MSTR  CLOSE_STOP_LOSS\n"
           ]
         },
         {
@@ -1975,7 +1934,7 @@
                   ],
                   "xaxis": "x",
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Price Comparison with BUY\\u002fSELL Signals - COIN&MSTR (2025-06-18)\"},\"xaxis\":{\"title\":{\"text\":\"Time\"}},\"yaxis\":{\"title\":{\"text\":\"Normalized Price (Base = 1.0)\"}},\"height\":600,\"showlegend\":true,\"hovermode\":\"x unified\",\"plot_bgcolor\":\"lightgray\",\"shapes\":[{\"line\":{\"color\":\"gray\",\"dash\":\"dash\"},\"opacity\":0.5,\"type\":\"line\",\"x0\":0,\"x1\":1,\"xref\":\"x domain\",\"y0\":1.0,\"y1\":1.0,\"yref\":\"y\"}],\"annotations\":[{\"showarrow\":false,\"text\":\"Baseline (1.0)\",\"x\":1,\"xanchor\":\"right\",\"xref\":\"x domain\",\"y\":1.0,\"yanchor\":\"bottom\",\"yref\":\"y\"}]},                        {\"responsive\": true}                    ).then(function(){\n",
               "                            \n",
-              "var gd = document.getElementById('52a27e8b-1ab6-4b83-81ef-846e86b0f467');\n",
+              "var gd = document.getElementById('c8afdedf-b2d9-4bd2-8882-418c7fef58ed');\n",
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@@ -5727,85 +5560,70 @@
             "- SELL signals shown as orange triangles pointing down\n",
             "- All BUY signals per symbol grouped together, all SELL signals per symbol grouped together\n",
             "- Hover over markers to see individual trade details (OPEN/CLOSE status)\n",
-            "- Total signals displayed: 8\n",
-            "- COIN signals: 4\n",
-            "- MSTR signals: 4\n",
+            "- Total signals displayed: 4\n",
+            "- COIN signals: 2\n",
+            "- MSTR signals: 2\n",
             "================================================================================\n",
             "PAIRS TRADING BACKTEST SUMMARY\n",
             "================================================================================\n",
             "\n",
             "Pair: COIN & MSTR\n",
-            "Fit Method: SlidingFit\n",
+            "Fit Method: RollingFit\n",
             "Configuration: equity\n",
             "Data file: 20250618.mktdata.ohlcv.db\n",
             "Trading date: 2025-06-18\n",
             "\n",
             "Strategy Parameters:\n",
-            "  Training window: 150 minutes\n",
+            "  Training window: 120 minutes\n",
             "  Open threshold: 2\n",
             "  Close threshold: 1\n",
             "  Funding per pair: $2000\n",
             "\n",
-            "Sliding Window Analysis:\n",
+            "Rolling Window Analysis:\n",
             "  Total data points: 361\n",
-            "  Maximum iterations: 211\n",
-            "  Analysis type: Dynamic sliding window\n",
+            "  Maximum iterations: 241\n",
+            "  Analysis type: Dynamic rolling window\n",
             "\n",
-            "Trading Signals: 8 generated\n",
-            "  Unique trade times: 4\n",
-            "  BUY signals: 4\n",
-            "  SELL signals: 4\n",
+            "Trading Signals: 4 generated\n",
+            "  Unique trade times: 2\n",
+            "  BUY signals: 2\n",
+            "  SELL signals: 2\n",
             "\n",
             "First few trading signals:\n",
-            "  1. SELL COIN @ $278.50 at 2025-06-18 16:00:00\n",
-            "  2. BUY MSTR @ $372.27 at 2025-06-18 16:00:00\n",
-            "  3. BUY COIN @ $285.70 at 2025-06-18 16:59:00\n",
-            "  4. SELL MSTR @ $371.35 at 2025-06-18 16:59:00\n",
-            "  5. BUY COIN @ $288.75 at 2025-06-18 17:07:00\n",
-            "  6. SELL MSTR @ $370.66 at 2025-06-18 17:07:00\n",
-            "  ... and 2 more signals\n",
+            "  1. SELL COIN @ $265.79 at 2025-06-18 15:30:00\n",
+            "  2. BUY MSTR @ $372.93 at 2025-06-18 15:30:00\n",
+            "  3. BUY COIN @ $268.47 at 2025-06-18 15:34:00\n",
+            "  4. SELL MSTR @ $373.08 at 2025-06-18 15:34:00\n",
             "\n",
             "================================================================================\n",
             "\n",
             "Detailed Trading Signals:\n",
             "Time                 Action          Symbol     Price        Scaled Dis-eq   Status    \n",
             "------------------------------------------------------------------------------------------\n",
-            "2025-06-18 16:00:00  SELL            COIN       $278.50      2.861           OPEN      \n",
-            "2025-06-18 16:00:00  BUY             MSTR       $372.27      2.861           OPEN      \n",
-            "2025-06-18 16:59:00  BUY             COIN       $285.70      0.370           CLOSE     \n",
-            "2025-06-18 16:59:00  SELL            MSTR       $371.35      0.370           CLOSE     \n",
-            "2025-06-18 17:07:00  BUY             COIN       $288.75      2.173           OPEN      \n",
-            "2025-06-18 17:07:00  SELL            MSTR       $370.66      2.173           OPEN      \n",
-            "2025-06-18 17:45:00  SELL            COIN       $288.30      0.951           CLOSE     \n",
-            "2025-06-18 17:45:00  BUY             MSTR       $369.60      0.951           CLOSE     \n",
+            "2025-06-18 15:30:00  SELL            COIN       $265.79      4.435           OPEN      \n",
+            "2025-06-18 15:30:00  BUY             MSTR       $372.93      4.435           OPEN      \n",
+            "2025-06-18 15:34:00  BUY             COIN       $268.47      4.608           CLOSE_STOP_LOSS\n",
+            "2025-06-18 15:34:00  SELL            MSTR       $373.08      4.608           CLOSE_STOP_LOSS\n",
             "\n",
             " --------------  Suggested Trades \n",
-            "                    action symbol     price  disequilibrium  scaled_disequilibrium         pair status\n",
-            "time                                                                                                  \n",
-            "2025-06-18 16:00:00   SELL   COIN  278.5026       26.172385               2.860530  COIN & MSTR   OPEN\n",
-            "2025-06-18 16:00:00    BUY   MSTR  372.2725       26.172385               2.860530  COIN & MSTR   OPEN\n",
-            "2025-06-18 16:59:00    BUY   COIN  285.7000  -153123.398160               0.369634  COIN & MSTR  CLOSE\n",
-            "2025-06-18 16:59:00   SELL   MSTR  371.3500  -153123.398160               0.369634  COIN & MSTR  CLOSE\n",
-            "2025-06-18 17:07:00    BUY   COIN  288.7490     -885.043609               2.172638  COIN & MSTR   OPEN\n",
-            "2025-06-18 17:07:00   SELL   MSTR  370.6600     -885.043609               2.172638  COIN & MSTR   OPEN\n",
-            "2025-06-18 17:45:00   SELL   COIN  288.3000       -1.436136               0.950544  COIN & MSTR  CLOSE\n",
-            "2025-06-18 17:45:00    BUY   MSTR  369.6000       -1.436136               0.950544  COIN & MSTR  CLOSE\n",
+            "                    action symbol     price  disequilibrium  scaled_disequilibrium         pair           status\n",
+            "time                                                                                                            \n",
+            "2025-06-18 15:30:00   SELL   COIN  265.7900       10.226451               4.435088  COIN & MSTR             OPEN\n",
+            "2025-06-18 15:30:00    BUY   MSTR  372.9349       10.226451               4.435088  COIN & MSTR             OPEN\n",
+            "2025-06-18 15:34:00    BUY   COIN  268.4700       13.457921               4.608004  COIN & MSTR  CLOSE_STOP_LOSS\n",
+            "2025-06-18 15:34:00   SELL   MSTR  373.0800       13.457921               4.608004  COIN & MSTR  CLOSE_STOP_LOSS\n",
             "\n",
             "====== Returns By Day and Pair ======\n",
             "\n",
             "--- 20250618-COIN & MSTR ---\n",
             "  COIN & MSTR:\n",
-            "    COIN (Trade #1): SELL @ $278.50, BUY @ $285.70, Return: -2.58% | Open Dis-eq: 2.86, Close Dis-eq: 0.37\n",
-            "    COIN (Trade #2): BUY @ $285.70, BUY @ $288.75, Return: 0.00% | Open Dis-eq: 0.37, Close Dis-eq: 2.17\n",
-            "    COIN (Trade #3): BUY @ $288.75, SELL @ $288.30, Return: -0.16% | Open Dis-eq: 2.17, Close Dis-eq: 0.95\n",
-            "    MSTR (Trade #1): BUY @ $372.27, SELL @ $371.35, Return: -0.25% | Open Dis-eq: 2.86, Close Dis-eq: 0.37\n",
-            "    MSTR (Trade #2): SELL @ $371.35, SELL @ $370.66, Return: 0.00% | Open Dis-eq: 0.37, Close Dis-eq: 2.17\n",
-            "    MSTR (Trade #3): SELL @ $370.66, BUY @ $369.60, Return: 0.29% | Open Dis-eq: 2.17, Close Dis-eq: 0.95\n",
-            "    Pair Total Return: -2.70%\n",
-            "  Day Total Return: -2.70%\n",
+            "    COIN (Trade #1): SELL @ $265.79, BUY @ $268.47, Return: -1.01% | Open Dis-eq: 4.44, Close Dis-eq: 4.61\n",
+            "    MSTR (Trade #1): BUY @ $372.93, SELL @ $373.08, Return: 0.04% | Open Dis-eq: 4.44, Close Dis-eq: 4.61\n",
+            "    Pair Total Return: -0.97%\n",
+            "  Day Total Return: -0.97%\n",
             "\n",
             "====== GRAND TOTALS ACROSS ALL PAIRS ======\n",
-            "Total Realized PnL: -2.70%\n",
+            "Total Realized PnL: -0.97%\n",
             "\n",
             "====== NO OUTSTANDING POSITIONS ======\n",
             "\n",
diff --git a/research/pt_backtest.py b/research/pt_backtest.py
index 04d7f96..bd40924 100644
--- a/research/pt_backtest.py
+++ b/research/pt_backtest.py
@@ -72,7 +72,7 @@ def run_backtest(
     bt_result: BacktestResult = BacktestResult(config=config)
 
     pairs_trades = []
-    for pair in create_pairs(datafile, price_column, config, instruments):
+    for pair in create_pairs(datafile=datafile, fit_method=fit_method, price_column=price_column, config=config, instruments=instruments):
         single_pair_trades = fit_method.run_pair(
             pair=pair, bt_result=bt_result
         )
diff --git a/research/research_tools.py b/research/research_tools.py
index 944d856..356fbb2 100644
--- a/research/research_tools.py
+++ b/research/research_tools.py
@@ -2,6 +2,8 @@ import glob
 import os
 from typing import Dict, List, Optional
 
+from pt_trading.fit_method import PairsTradingFitMethod
+
 
 
 def resolve_datafiles(config: Dict, cli_datafiles: Optional[str] = None) -> List[str]:
@@ -42,7 +44,7 @@ def resolve_datafiles(config: Dict, cli_datafiles: Optional[str] = None) -> List
 
     return sorted(list(set(resolved_files)))  # Remove duplicates and sort
 
-def create_pairs(datafile: str, price_column: str, config: Dict, instruments: List[str]) -> List:
+def create_pairs(datafile: str, fit_method: PairsTradingFitMethod, price_column: str, config: Dict, instruments: List[str]) -> List:
         from tools.data_loader import load_market_data
         from pt_trading.trading_pair import TradingPair
         all_indexes = range(len(instruments))
@@ -57,7 +59,7 @@ def create_pairs(datafile: str, price_column: str, config: Dict, instruments: Li
 
         for a_index, b_index in unique_index_pairs:
             from research.pt_backtest import TradingPair
-            pair = TradingPair(
+            pair = fit_method.create_trading_pair(
                 config=config_copy,
                 market_data=market_data_df,
                 symbol_a=instruments[a_index],
diff --git a/strategy/pair_strategy.py b/strategy/pair_strategy.py
index 7407115..57aa23f 100644
--- a/strategy/pair_strategy.py
+++ b/strategy/pair_strategy.py
@@ -47,7 +47,7 @@ def run_strategy(
         market_data_df = load_market_data(datafile, config=config_copy)
 
         for a_index, b_index in unique_index_pairs:
-            pair = TradingPair(
+            pair = fit_method.create_trading_pair(
                 market_data=market_data_df,
                 symbol_a=instruments[a_index],
                 symbol_b=instruments[b_index],