diff --git a/src/backtest_configs.py b/src/backtest_configs.py
new file mode 100644
index 0000000..704d6a4
--- /dev/null
+++ b/src/backtest_configs.py
@@ -0,0 +1,95 @@
+from typing import Any, Dict, List, Optional
+
+
+# ------------------------ Configuration ------------------------
+# Default configuration
+CRYPTO_CONFIG: Dict = {
+    "security_type": "CRYPTO",
+    # --- Data retrieval
+    "data_directory": "./data/crypto",
+    "datafiles": [
+        # "20250519.mktdata.ohlcv.db",
+        # "20250520.mktdata.ohlcv.db",
+        # "20250521.mktdata.ohlcv.db",
+        # "20250522.mktdata.ohlcv.db",
+        # "20250523.mktdata.ohlcv.db",
+        # "20250524.mktdata.ohlcv.db",
+        "20250525.mktdata.ohlcv.db",
+    ],
+    "db_table_name": "bnbspot_ohlcv_1min",
+    # ----- Instruments
+    "exchange_id": "BNBSPOT",
+    "instrument_id_pfx": "PAIR-",
+    "instruments": [
+        "BTC-USDT",
+        "BCH-USDT",
+        "ETH-USDT",
+        "LTC-USDT",
+        "XRP-USDT",
+        "ADA-USDT",
+        "SOL-USDT",
+        "DOT-USDT",
+    ],
+    "trading_hours": {
+        "begin_session": "00:00:00",
+        "end_session": "23:59:00",
+        "timezone": "UTC",
+    },
+    # ----- Model Settings
+    "price_column": "close",
+    "min_required_points": 30,
+    "zero_threshold": 1e-10,
+
+    "dis-equilibrium_open_trshld": 2.0,
+    "dis-equilibrium_close_trshld": 0.5,
+
+    # "training_minutes": 120,
+    "training_minutes": 60,
+    # ----- Validation
+    "funding_per_pair": 2000.0,  # USD
+}
+
+# ========================== EQUITIES
+EQT_CONFIG: Dict = {
+    # --- Data retrieval
+    "security_type": "EQUITY",
+    "data_directory": "./data/equity",
+    "datafiles": [
+        # "20250508.alpaca_sim_md.db",
+        # "20250509.alpaca_sim_md.db",
+        "20250512.alpaca_sim_md.db",
+        # "20250513.alpaca_sim_md.db",
+        # "20250514.alpaca_sim_md.db",
+        # "20250515.alpaca_sim_md.db",
+        # "20250516.alpaca_sim_md.db",
+        # "20250519.alpaca_sim_md.db",
+        # "20250520.alpaca_sim_md.db"
+    ],
+    "db_table_name": "md_1min_bars",
+    # ----- Instruments
+    "exchange_id": "ALPACA",
+    "instrument_id_pfx": "STOCK-",
+    "instruments": [
+        "COIN",
+        "GBTC",
+        "HOOD",
+        "MSTR",
+        "PYPL",
+    ],
+    "trading_hours": {
+        "begin_session": "9:30:00",
+        "end_session": "16:00:00",
+        "timezone": "America/New_York",
+    },
+    # ----- Model Settings
+    "price_column": "close",
+    "min_required_points": 30,
+    "zero_threshold": 1e-10,
+    "dis-equilibrium_open_trshld": 2.0,
+    "dis-equilibrium_close_trshld": 0.5,
+    "training_minutes": 120,
+    # ----- Validation
+    "funding_per_pair": 2000.0,
+}
+
+
diff --git a/src/pt_backtest.py b/src/pt_backtest.py
index c1167e7..a2257bc 100644
--- a/src/pt_backtest.py
+++ b/src/pt_backtest.py
@@ -8,6 +8,7 @@ import numpy as np
 # ============= statsmodels ===================
 from statsmodels.tsa.vector_ar.vecm import VECM
 
+from backtest_configs import CRYPTO_CONFIG
 from tools.data_loader import load_market_data, transform_dataframe
 from tools.trading_pair import TradingPair
 from results import BacktestResult
@@ -16,90 +17,11 @@ NanoPerMin = 1e9
 UNSET_FLOAT: float = sys.float_info.max
 UNSET_INT: int = sys.maxsize
 
-# ------------------------ Configuration ------------------------
-# Default configuration
-CRYPTO_CONFIG: Dict = {
-    "security_type": "CRYPTO",
-    # --- Data retrieval
-    "data_directory": "./data/crypto",
-    "datafiles": [
-        "20250519.mktdata.ohlcv.db",
-        # "20250519.mktdata.ohlcv.db",
-    ],
-    "db_table_name": "bnbspot_ohlcv_1min",
-    # ----- Instruments
-    "exchange_id": "BNBSPOT",
-    "instrument_id_pfx": "PAIR-",
-    "instruments": [
-        "BTC-USDT",
-        # "ETH-USDT",
-        "LTC-USDT",
-    ],
-    "trading_hours": {
-        "begin_session": "00:00:00",
-        "end_session": "23:59:00",
-        "timezone": "UTC",
-    },
-    # ----- Model Settings
-    "price_column": "close",
-    "min_required_points": 30,
-    "zero_threshold": 1e-10,
 
-    "disequilibrium_open_trshld": 2,
-    "disequilibrium_close_trshld": 0.5,
+# # ==========================================================================
 
-    "training_minutes": 120,
-    # ----- Validation
-    "funding_per_pair": 2000.0,  # USD
-}
-# ========================== EQUITIES
-EQT_CONFIG: Dict = {
-    # --- Data retrieval
-    "security_type": "EQUITY",
-    "data_directory": "./data/equity",
-    "datafiles": [
-        "20250508.alpaca_sim_md.db",
-        # "20250509.alpaca_sim_md.db",
-        # "20250512.alpaca_sim_md.db",
-        # "20250513.alpaca_sim_md.db",
-        # "20250514.alpaca_sim_md.db",
-        # "20250515.alpaca_sim_md.db",
-        # "20250516.alpaca_sim_md.db",
-        # "20250519.alpaca_sim_md.db",
-        # "20250520.alpaca_sim_md.db"
-    ],
-    "db_table_name": "md_1min_bars",
-    # ----- Instruments
-    "exchange_id": "ALPACA",
-    "instrument_id_pfx": "STOCK-",
-    "instruments": [
-        "COIN",
-        "GBTC",
-        "HOOD",
-        "MSTR",
-        "PYPL",
-    ],
-    "trading_hours": {
-        "begin_session": "9:30:00",
-        "end_session": "16:00:00",
-        "timezone": "America/New_York",
-    },
-    # ----- Model Settings
-    "price_column": "close",
-    "min_required_points": 30,
-    "zero_threshold": 1e-10,
-    "disequilibrium_open_trshld": 2.0,
-    "disequilibrium_close_trshld": 0.5,
-    "training_minutes": 120,
-    # ----- Validation
-    "funding_per_pair": 2000.0,
-}
-
-
-# ==========================================================================
-
-# CONFIG = CRYPTO_CONFIG
-CONFIG = EQT_CONFIG
+CONFIG = CRYPTO_CONFIG
+# CONFIG = EQT_CONFIG
 
 BacktestResults = BacktestResult(config=CONFIG)
 
@@ -146,8 +68,8 @@ def create_trading_signals(pair: TradingPair) -> pd.DataFrame:
     open_row_index = None
     initial_abs_term = None
 
-    open_threshold = CONFIG["disequilibrium_open_trshld"]
-    close_threshold = CONFIG["disequilibrium_close_trshld"]
+    open_threshold = CONFIG["dis-equilibrium_open_trshld"]
+    close_threshold = CONFIG["dis-equilibrium_close_trshld"]
     for row_idx in range(len(pair_result_df)):
         curr_disequilibrium = pair_result_df["scaled_disequilibrium"][row_idx]
 
@@ -299,7 +221,10 @@ def run_single_pair(
         market_data=market_data, training_minutes=CONFIG["training_minutes"]
     )
     try:
-        pair.train_pair()
+        is_cointegrated = pair.train_pair()
+        if not is_cointegrated:
+            print(f"{pair} IS NOT COINTEGRATED")
+            return None
     except Exception as e:
         print(f"{pair}: Training failed: {str(e)}")
         return None
@@ -387,8 +312,6 @@ if __name__ == "__main__":
 
     # BacktestResults.print_results_summary(all_results)
     BacktestResults.calculate_returns(all_results)
-
     # Print grand totals
     BacktestResults.print_grand_totals()
-
     BacktestResults.print_outstanding_positions()
diff --git a/src/results.py b/src/results.py
index 7398eb7..5d55450 100644
--- a/src/results.py
+++ b/src/results.py
@@ -300,8 +300,8 @@ class BacktestResult:
                 "current_abs_term": current_scaled_disequilibrium,
                 "current_disequilibrium": current_disequilibrium,
                 "current_scaled_disequilibrium": current_scaled_disequilibrium,
-                "closing_threshold": initial_abs_term / self.config["disequilibrium_close_trshld"],
-                "disequilibrium_ratio": current_scaled_disequilibrium / (initial_abs_term / self.config["disequilibrium_close_trshld"]),
+                "closing_threshold": initial_abs_term / self.config["dis-equilibrium_close_trshld"],
+                "disequilibrium_ratio": current_scaled_disequilibrium / (initial_abs_term / self.config["dis-equilibrium_close_trshld"]),
             }
         )
 
diff --git a/src/tools/data_loader.py b/src/tools/data_loader.py
index da89693..b40bcb2 100644
--- a/src/tools/data_loader.py
+++ b/src/tools/data_loader.py
@@ -118,23 +118,8 @@ def transform_dataframe(df: pd.DataFrame, price_column: str):
 
     return result_df
 
-# def get_datasets(df: pd.DataFrame, training_minutes: int, pair: TradingPair) -> Tuple[pd.DataFrame, pd.DataFrame]:
-#     # Training dataset
-#     colname_a, colname_b = pair.colnames()
-#     df = df[["tstamp", colname_a, colname_b]]
-#     df = df.dropna()
 
-#     training_df = df.iloc[:training_minutes - 1, :].copy()
-#     training_df.reset_index(drop=True).dropna().reset_index(drop=True)
+# if __name__ == "__main__":
+#     df1 = load_sqlite_to_dataframe(sys.argv[1], table_name="md_1min_bars")
 
-#     # Testing dataset
-#     testing_df = df.iloc[training_minutes:, :].copy()
-#     testing_df.reset_index(drop=True).dropna().reset_index(drop=True)
-
-#     return (training_df, testing_df)
-
-
-if __name__ == "__main__":
-    df1 = load_sqlite_to_dataframe(sys.argv[1], table_name="md_1min_bars")
-
-    print(df1)
+#     print(df1)
diff --git a/src/tools/trading_pair.py b/src/tools/trading_pair.py
index 80b3b7a..2c749e2 100644
--- a/src/tools/trading_pair.py
+++ b/src/tools/trading_pair.py
@@ -51,28 +51,38 @@ class TradingPair:
             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 check_cointegration(self):
+        from statsmodels.tsa.vector_ar.vecm import coint_johansen
+        df = self.training_df_[self.colnames()].reset_index(drop=True)
+        result = coint_johansen(df, det_order=0, k_ar_diff=1)
+        # print(f"{self}: lr1={result.lr1[0]} cvt={result.cvt[0, 1]}.")
+        is_cointegrated = result.lr1[0] > result.cvt[0, 1]
 
-    def train_pair(self):
+        return is_cointegrated
+
+    def train_pair(self) -> bool:
+        is_cointegrated = self.check_cointegration()
+        if not is_cointegrated:
+            return False
+            pass
+
+        print(f"*****\n**************** {self} IS COINTEGRATED ****************\n*****")
         self.fit_VECM()
         diseq_series = self.training_df_[self.colnames()] @ self.vecm_fit_.beta
         self.training_mu_ = diseq_series.mean().iloc[0]
         self.training_std_ = diseq_series.std().iloc[0]
 
-        self.training_df_["disequilibrium"] = self.training_df_[self.colnames()] @ self.vecm_fit_.beta
-        # Normalize the disequilibrium
-        self.training_df_["scaled_disequilibrium"] = (
+        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 mu(self) -> float:
-    #     assert self.training_mu_ is not None
-    #     return self.training_mu_
-
-    # def std(self) -> float:
-    #     assert self.training_std_ is not None
-    #     return self.training_std_
+        return True
 
     def __repr__(self) ->str:
         return f"{self.symbol_a_} & {self.symbol_b_}"