(feat) add macd bb strategy v1

quants_lab/controllers/macd_bb_v1.py

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+import time
+from typing import Optional
+
+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 MACDBBV1Config(DirectionalTradingControllerConfigBase):
+    strategy_name: str = "macd_bb_v1"
+    bb_length: int = Field(default=24, ge=100, le=200)
+    bb_std: float = Field(default=2.0, ge=2.0, le=3.0)
+    bb_long_threshold: float = Field(default=0.0, ge=-1.0, le=0.2)
+    bb_short_threshold: float = Field(default=1.0, ge=0.8, le=2.0)
+    macd_fast: int = Field(default=21, ge=12, le=60)
+    macd_slow: int = Field(default=42, ge=26, le=200)
+    macd_signal: int = Field(default=9, ge=8, le=20)
+
+
+class MACDBBV1(DirectionalTradingControllerBase):
+
+    def __init__(self, config: MACDBBV1Config):
+        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)
+        df.ta.macd(fast=self.config.macd_fast, slow=self.config.macd_slow, signal=self.config.macd_signal, append=True)
+        bbp = df[f"BBP_{self.config.bb_length}_{self.config.bb_std}"]
+        macdh = df[f"MACDh_{self.config.macd_fast}_{self.config.macd_slow}_{self.config.macd_signal}"]
+        macd = df[f"MACD_{self.config.macd_fast}_{self.config.macd_slow}_{self.config.macd_signal}"]
+
+        # Generate signal
+        long_condition = (bbp < self.config.bb_long_threshold) & (macdh > 0) & (macd < 0)
+        short_condition = (bbp > self.config.bb_short_threshold) & (macdh < 0) & (macd > 0)
+        df["signal"] = 0
+        df.loc[long_condition, "signal"] = 1
+        df.loc[short_condition, "signal"] = -1
+        return df
+
+    def extra_columns_to_show(self):
+        return [f"BBP_{self.config.bb_length}_{self.config.bb_std}",
+                f"MACDh_{self.config.macd_fast}_{self.config.macd_slow}_{self.config.macd_signal}",
+                f"MACD_{self.config.macd_fast}_{self.config.macd_slow}_{self.config.macd_signal}"]