Feat/supertrend multitimeframe example

quants_lab/controllers/supertrend_multitimeframe.py

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+import time
+from typing import Optional, Callable
+
+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 SuperTrendMTConfig(DirectionalTradingControllerConfigBase):
+    strategy_name: str = "supertrend_multitimeframe"
+    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 SuperTrendMT(DirectionalTradingControllerBase):
+    def __init__(self, config: SuperTrendMTConfig):
+        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
+
+    @staticmethod
+    def get_minutes_from_interval(interval: str):
+        unit = interval[-1]
+        quantity = int(interval[:-1])
+        conversion = {"m": 1, "h": 60, "d": 1440}
+        return conversion[unit] * quantity
+
+    def ordered_market_data_dfs(self):
+        market_data = {f"{candles.name}_{candles.interval}": candles.candles_df for candles in self.candles}
+        return sorted(market_data.items(), key=lambda x: self.get_minutes_from_interval(x[0].split("_")[-1]))
+
+    def get_dataframes_merged_by_min_resolution(self, add_indicators_func: Optional[Callable] = None):
+        ordered_data = self.ordered_market_data_dfs()
+        if add_indicators_func:
+            processed_data = []
+            for interval, df in ordered_data:
+                processed_df = add_indicators_func(df)
+                processed_data.append((interval, processed_df))
+        else:
+            processed_data = ordered_data
+        interval_suffixes = {key: f'_{key.split("_")[-1]}' for key, _ in processed_data}
+        merged_df = None
+        for interval, df in processed_data:
+            if merged_df is None:
+                merged_df = df.copy()
+            else:
+                merged_df = pd.merge_asof(merged_df, df.add_suffix(interval_suffixes[interval]),
+                                          left_on=f"timestamp", right_on=f"timestamp{interval_suffixes[interval]}",
+                                          direction="backward")
+        return merged_df
+
+    def add_indicators(self, df):
+        df.ta.supertrend(length=self.config.length, multiplier=self.config.multiplier, append=True)
+        return df
+
+    def get_processed_data(self) -> pd.DataFrame:
+        df = self.get_dataframes_merged_by_min_resolution(self.add_indicators)
+        df["percentage_distance"] = abs(df["close"] - df[f"SUPERT_{self.config.length}_{self.config.multiplier}"]) / df["close"]
+
+        columns_with_supertrend = [col for col in df.columns if "SUPERTd" in col]
+
+        # Conditions for long and short signals
+        long_condition = df[columns_with_supertrend].apply(lambda x: all(item == 1 for item in x), axis=1)
+        short_condition = df[columns_with_supertrend].apply(lambda x: all(item == -1 for item in x), axis=1)
+
+        # Choose side
+        df['signal'] = 0
+        df.loc[long_condition, 'signal'] = 1
+        df.loc[short_condition, 'signal'] = -1
+        return df