diff --git a/quants_lab/controllers/supertrend.py b/quants_lab/controllers/supertrend.py
new file mode 100644
index 0000000..6da96c4
--- /dev/null
+++ b/quants_lab/controllers/supertrend.py
@@ -0,0 +1,52 @@
+import time
+
+import pandas as pd
+from pydantic import Field
+
+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.directional_trading_controller_base import (
+    DirectionalTradingControllerBase,
+    DirectionalTradingControllerConfigBase,
+)
+
+
+class SuperTrendConfig(DirectionalTradingControllerConfigBase):
+    strategy_name: str = "supertrend"
+    length: int = Field(default=20, ge=5, le=200)
+    multiplier: float = Field(default=4.0, ge=2.0, le=7.0)
+    percentage_threshold: float = Field(default=0.01, ge=0.005, le=0.05)
+
+
+class SuperTrend(DirectionalTradingControllerBase):
+    def __init__(self, config: SuperTrendConfig):
+        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. This feature is not available
+        # for the backtesting yet
+        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
+        df.ta.supertrend(length=self.config.length, multiplier=self.config.multiplier, append=True)
+        df["percentage_distance"] = abs(df["close"] - df[f"SUPERT_{self.config.length}_{self.config.multiplier}"]) / df["close"]
+
+        # Generate long and short conditions
+        long_condition = (df[f"SUPERTd_{self.config.length}_{self.config.multiplier}"] == 1) & (df["percentage_distance"] < self.config.percentage_threshold)
+        short_condition = (df[f"SUPERTd_{self.config.length}_{self.config.multiplier}"] == -1) & (df["percentage_distance"] < self.config.percentage_threshold)
+
+        # Choose side
+        df['signal'] = 0
+        df.loc[long_condition, 'signal'] = 1
+        df.loc[short_condition, 'signal'] = -1
+        return df