diff --git a/quants_lab/controllers/bollinger.py b/quants_lab/controllers/bollinger_v1.py similarity index 74% rename from quants_lab/controllers/bollinger.py rename to quants_lab/controllers/bollinger_v1.py index de207faf..a0f0566d 100644 --- a/quants_lab/controllers/bollinger.py +++ b/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}"] \ No newline at end of file diff --git a/quants_lab/controllers/macd_bb_v1.py b/quants_lab/controllers/macd_bb_v1.py new file mode 100644 index 00000000..c709cf8f --- /dev/null +++ b/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}"] diff --git a/quants_lab/controllers/trend_follower_v1.py b/quants_lab/controllers/trend_follower_v1.py new file mode 100644 index 00000000..167de8f6 --- /dev/null +++ b/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}"] diff --git a/utils/file_templates.py b/utils/file_templates.py index bee0ac16..ad288294 100644 --- a/utils/file_templates.py +++ b/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