diff --git a/quants_lab/controllers/bollinger.py b/quants_lab/controllers/bollinger.py
deleted file mode 100644
index de207fa..0000000
--- a/quants_lab/controllers/bollinger.py
+++ /dev/null
@@ -1,61 +0,0 @@
-from typing import Optional
-
-import pandas as pd
-import pandas_ta as ta
-from hummingbot.smart_components.executors.position_executor.position_executor import PositionExecutor
-from hummingbot.smart_components.strategy_frameworks.data_types import OrderLevel
-from hummingbot.smart_components.strategy_frameworks.directional_trading import DirectionalTradingControllerConfigBase, \
-    DirectionalTradingControllerBase
-from pydantic import Field
-
-
-class BollingerConf(DirectionalTradingControllerConfigBase):
-    strategy_name = "bollinger"
-    bb_length: int = Field(default=100, ge=2, le=1000)
-    bb_std: float = Field(default=2.0, ge=0.5, le=4.0)
-    bb_long_threshold: float = Field(default=0.0, ge=-3.0, le=0.5)
-    bb_short_threshold: float = Field(default=1.0, ge=0.5, le=3.0)
-    std_span: Optional[int] = Field(default=100, ge=100, le=400)
-
-
-class Bollinger(DirectionalTradingControllerBase):
-
-    def __init__(self, config: BollingerConf):
-        super().__init__(config)
-        self.config = config
-
-    def early_stop_condition(self, executor: PositionExecutor, order_level: OrderLevel) -> bool:
-        """
-        If an executor has an active position, should we close it based on a condition.
-        """
-        return False
-
-    def cooldown_condition(self, executor: PositionExecutor, order_level: OrderLevel) -> bool:
-        """
-        After finishing an order, the executor will be in cooldown for a certain amount of time.
-        This prevents the executor from creating a new order immediately after finishing one and execute a lot
-        of orders in a short period of time from the same side.
-        """
-        if executor.close_timestamp and executor.close_timestamp + order_level.cooldown_time > time.time():
-            return True
-        return False
-
-    def get_processed_data(self) -> pd.DataFrame:
-        df = self.candles[0].candles_df
-
-        # Add indicators
-        df.ta.bbands(length=self.config.bb_length, std=self.config.bb_std, append=True)
-
-        # Generate signal
-        long_condition = df[f"BBP_{self.config.bb_length}_{self.config.bb_std}"] < self.config.bb_long_threshold
-        short_condition = df[f"BBP_{self.config.bb_length}_{self.config.bb_std}"] > self.config.bb_short_threshold
-
-        # Generate signal
-        df["signal"] = 0
-        df.loc[long_condition, "signal"] = 1
-        df.loc[short_condition, "signal"] = -1
-
-        # Optional: Generate spread multiplier
-        if self.config.std_span:
-            df["target"] = df["close"].rolling(self.config.std_span).std() / df["close"]
-        return df