Merge pull request #73 from hummingbot/feat/simplify_strategies

Feat/simplify strategies

quants_lab/controllers/bollinger.py → quants_lab/controllers/bollinger_v1.py

@@ -1,3 +1,4 @@
+import time
 from typing import Optional
 
 import pandas as pd
@@ -9,18 +10,17 @@
 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 BollingerV1Conf(DirectionalTradingControllerConfigBase):
+    strategy_name = "bollinger_v1"
+    bb_length: int = Field(default=100, ge=100, le=400)
+    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)
 
 
-class Bollinger(DirectionalTradingControllerBase):
+class BollingerV1(DirectionalTradingControllerBase):
 
-    def __init__(self, config: BollingerConf):
+    def __init__(self, config: BollingerV1Conf):
         super().__init__(config)
         self.config = config
 
@@ -54,8 +54,7 @@ def get_processed_data(self) -> pd.DataFrame:
         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
+
+    def extra_columns_to_show(self):
+        return [f"BBP_{self.config.bb_length}_{self.config.bb_std}"]

quants_lab/controllers/macd_bb_v1.py

@@ -0,0 +1,69 @@
+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}"]

quants_lab/controllers/trend_follower_v1.py

@@ -0,0 +1,66 @@
+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 TrendFollowerV1Config(DirectionalTradingControllerConfigBase):
+    strategy_name: str = "trend_follower_v1"
+    sma_fast: int = Field(default=20, ge=10, le=150)
+    sma_slow: int = Field(default=100, ge=50, le=400)
+    bb_length: int = Field(default=100, ge=100, le=200)
+    bb_std: float = Field(default=2.0, ge=2.0, le=3.0)
+    bb_threshold: float = Field(default=0.2, ge=0.1, le=0.5)
+
+
+class TrendFollowerV1(DirectionalTradingControllerBase):
+
+    def __init__(self, config: TrendFollowerV1Config):
+        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.sma(length=self.config.sma_fast, append=True)
+        df.ta.sma(length=self.config.sma_slow, append=True)
+        df.ta.bbands(length=self.config.bb_length, std=2.0, append=True)
+
+
+        # Generate long and short conditions
+        bbp = df[f"BBP_{self.config.bb_length}_2.0"]
+        inside_bounds_condition = (bbp < 0.5 + self.config.bb_threshold) & (bbp > 0.5 - self.config.bb_threshold)
+
+        long_cond = (df[f'SMA_{self.config.sma_fast}'] > df[f'SMA_{self.config.sma_slow}'])
+        short_cond = (df[f'SMA_{self.config.sma_fast}'] < df[f'SMA_{self.config.sma_slow}'])
+
+        # Choose side
+        df['signal'] = 0
+        df.loc[long_cond & inside_bounds_condition, 'signal'] = 1
+        df.loc[short_cond & inside_bounds_condition, 'signal'] = -1
+        return df
+
+    def extra_columns_to_show(self):
+        return [f"BBP_{self.config.bb_length}_{self.config.bb_std}",
+                f"SMA_{self.config.sma_fast}",
+                f"SMA_{self.config.sma_slow}"]

utils/file_templates.py

@@ -66,7 +66,7 @@ def get_optuna_suggest_str(field_name: str, properties: Dict):
     if field_name == "candles_config":
         return f"""{field_name}=[
                         CandlesConfig(connector=exchange, trading_pair=trading_pair, 
-                                      interval="3m", max_records=1000000)  # Max number of candles for the real-time bot,
+                                      interval="1h", max_records=1000000)
                     ]"""
     if field_name == "strategy_name":
         return f"{field_name}='{properties.get('default', '_')}'"
@@ -111,8 +111,8 @@ def strategy_optimization_template(strategy_info: dict):
 def objective(trial):
     try:
         # General configuration for the backtesting
-        exchange = trial.suggest_categorical('exchange', ['binance_perpetual', ])
-        trading_pair = trial.suggest_categorical('trading_pair', ['BTC-USDT', ])
+        exchange = "binance_perpetual"
+        trading_pair = "BTC-USDT"
         start = "2023-01-01"
         end = "2023-08-01"
         initial_portfolio_usd = 1000.0