diff --git a/utils/file_templates.py b/utils/file_templates.py
index 8815006..ff8be84 100644
--- a/utils/file_templates.py
+++ b/utils/file_templates.py
@@ -1,60 +1,51 @@
 from typing import Dict
 
 
-def directional_strategy_template(strategy_name: str,
-                                  exchange: str,
-                                  trading_pair: str,
-                                  interval: str) -> str:
+def directional_strategy_template(strategy_cls_name: str) -> str:
+    strategy_config_cls_name = f"{strategy_cls_name}Config"
+    sma_config_text = "{self.config.sma_length}"
     return f"""import pandas_ta as ta
-import pandas as pd
-import numpy as np
+from pydantic import BaseModel, Field
 
 from quants_lab.strategy.directional_strategy_base import DirectionalStrategyBase
 
 
+class {strategy_config_cls_name}(BaseModel):
+    exchange: str = Field(default="binance_perpetual")
+    trading_pair: str = Field(default="ETH-USDT")
+    interval: str = Field(default="1h")
+    sma_length: int = Field(default=20, ge=10, le=200)
+    # ... Add more fields here
 
-class {strategy_name}(DirectionalStrategyBase):
-    # Define the attributes of the strategy
-    def __init__(self,
-                 exchange="{exchange}",
-                 trading_pair="{trading_pair}",
-                 interval="{interval}"):
-        self.exchange = exchange
-        self.trading_pair = trading_pair
-        self.interval = interval
+
+class {strategy_cls_name}(DirectionalStrategyBase[{strategy_config_cls_name}]):
 
     def get_raw_data(self):
         # The method get candles will search for the data in the folder data/candles
         # If the data is not there, you can use the candles downloader to get the data
         df = self.get_candles(
-            exchange=self.exchange,
-            trading_pair=self.trading_pair,
-            interval=self.interval,
+            exchange=self.config.exchange,
+            trading_pair=self.config.trading_pair,
+            interval=self.config.interval,
         )
         return df
 
-    def add_indicators(self, df):
-        df.ta.sma(length=20, append=True)
+    def preprocessing(self, df):
+        df.ta.sma(length=self.config.sma_length, append=True)
         # ... Add more indicators here
         # ... Check https://github.com/twopirllc/pandas-ta#indicators-by-category for more indicators
         # ... Use help(ta.indicator_name) to get more info
         return df
 
-    def add_signals(self, df):
-        # ... Do your own logic
-        random_series = pd.Series(np.random.randint(low=0, high=101, size=100))
-        random_series_2 = pd.Series(np.random.randint(low=0, high=101, size=100))
-        random_thold = np.random.randint(low=45, high=65)
-        random_thold_2 = np.random.randint(low=45, high=65)
-
+    def predict(self, df):
         # Generate long and short conditions
-        macd_long_cond = (random_series > random_thold) & (random_series_2 > random_thold_2)
-        macd_short_cond = (random_series < random_thold) & (random_series_2 > random_thold_2)
+        long_cond = (df['close'] > df[f'SMA_{sma_config_text}'])
+        short_cond = (df['close'] < df[f'SMA_{sma_config_text}'])
 
         # Choose side
         df['side'] = 0
-        df.loc[macd_long_cond, 'side'] = 1
-        df.loc[macd_short_cond, 'side'] = -1
+        df.loc[long_cond, 'side'] = 1
+        df.loc[short_cond, 'side'] = -1
         return df
 """
 
@@ -122,7 +113,6 @@ def objective(trial):
         trial.set_user_attr("avg_trading_time_in_hours", strategy_analysis.avg_trading_time_in_minutes() / 60)
         return strategy_analysis.net_profit_pct()
     except Exception as e:
-        # TODO: Log error
         traceback.print_exc()
         raise TrialPruned()
     """