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macd_bb_v1.py
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macd_bb_v1.py
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from typing import List
import pandas_ta as ta # noqa: F401
from pydantic import Field, validator
from hummingbot.client.config.config_data_types import ClientFieldData
from hummingbot.data_feed.candles_feed.data_types import CandlesConfig
from hummingbot.strategy_v2.controllers.directional_trading_controller_base import (
DirectionalTradingControllerBase,
DirectionalTradingControllerConfigBase,
)
class MACDBBV1ControllerConfig(DirectionalTradingControllerConfigBase):
controller_name = "macd_bb_v1"
candles_config: List[CandlesConfig] = []
candles_connector: str = Field(
default=None,
client_data=ClientFieldData(
prompt_on_new=True,
prompt=lambda mi: "Enter the connector for the candles data, leave empty to use the same exchange as the connector: ", )
)
candles_trading_pair: str = Field(
default=None,
client_data=ClientFieldData(
prompt_on_new=True,
prompt=lambda mi: "Enter the trading pair for the candles data, leave empty to use the same trading pair as the connector: ", )
)
interval: str = Field(
default="3m",
client_data=ClientFieldData(
prompt=lambda mi: "Enter the candle interval (e.g., 1m, 5m, 1h, 1d): ",
prompt_on_new=False))
bb_length: int = Field(
default=100,
client_data=ClientFieldData(
prompt=lambda mi: "Enter the Bollinger Bands length: ",
prompt_on_new=True))
bb_std: float = Field(
default=2.0,
client_data=ClientFieldData(
prompt=lambda mi: "Enter the Bollinger Bands standard deviation: ",
prompt_on_new=False))
bb_long_threshold: float = Field(
default=0.0,
client_data=ClientFieldData(
prompt=lambda mi: "Enter the Bollinger Bands long threshold: ",
prompt_on_new=True))
bb_short_threshold: float = Field(
default=1.0,
client_data=ClientFieldData(
prompt=lambda mi: "Enter the Bollinger Bands short threshold: ",
prompt_on_new=True))
macd_fast: int = Field(
default=21,
client_data=ClientFieldData(
prompt=lambda mi: "Enter the MACD fast period: ",
prompt_on_new=True))
macd_slow: int = Field(
default=42,
client_data=ClientFieldData(
prompt=lambda mi: "Enter the MACD slow period: ",
prompt_on_new=True))
macd_signal: int = Field(
default=9,
client_data=ClientFieldData(
prompt=lambda mi: "Enter the MACD signal period: ",
prompt_on_new=True))
@validator("candles_connector", pre=True, always=True)
def set_candles_connector(cls, v, values):
if v is None or v == "":
return values.get("connector_name")
return v
@validator("candles_trading_pair", pre=True, always=True)
def set_candles_trading_pair(cls, v, values):
if v is None or v == "":
return values.get("trading_pair")
return v
class MACDBBV1Controller(DirectionalTradingControllerBase):
def __init__(self, config: MACDBBV1ControllerConfig, *args, **kwargs):
self.config = config
self.max_records = max(config.macd_slow, config.macd_fast, config.macd_signal, config.bb_length)
if len(self.config.candles_config) == 0:
self.config.candles_config = [CandlesConfig(
connector=config.candles_connector,
trading_pair=config.candles_trading_pair,
interval=config.interval,
max_records=self.max_records
)]
super().__init__(config, *args, **kwargs)
async def update_processed_data(self):
df = self.market_data_provider.get_candles_df(connector_name=self.config.candles_connector,
trading_pair=self.config.candles_trading_pair,
interval=self.config.interval,
max_records=self.max_records)
# 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
# Update processed data
self.processed_data["signal"] = df["signal"].iloc[-1]
self.processed_data["features"] = df