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(feat) add mm controllers
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cardosofede
0
bots/controllers/market_making/__init__.py
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0
bots/controllers/market_making/__init__.py
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138
bots/controllers/market_making/dman_maker_v2.py
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bots/controllers/market_making/dman_maker_v2.py
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from decimal import Decimal
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from typing import List, Optional
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import pandas_ta as ta # noqa: F401
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from pydantic import Field, validator
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from hummingbot.client.config.config_data_types import ClientFieldData
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from hummingbot.core.data_type.common import TradeType
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from hummingbot.data_feed.candles_feed.data_types import CandlesConfig
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from hummingbot.strategy_v2.controllers.market_making_controller_base import (
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MarketMakingControllerBase,
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MarketMakingControllerConfigBase,
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)
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from hummingbot.strategy_v2.executors.dca_executor.data_types import DCAExecutorConfig, DCAMode
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from hummingbot.strategy_v2.models.executor_actions import ExecutorAction, StopExecutorAction
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class DManMakerV2Config(MarketMakingControllerConfigBase):
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"""
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Configuration required to run the D-Man Maker V2 strategy.
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"""
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controller_name: str = "dman_maker_v2"
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candles_config: List[CandlesConfig] = []
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# DCA configuration
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dca_spreads: List[Decimal] = Field(
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default="0.01,0.02,0.04,0.08",
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client_data=ClientFieldData(
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prompt_on_new=True,
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prompt=lambda mi: "Enter a comma-separated list of spreads for each DCA level: "))
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dca_amounts: List[Decimal] = Field(
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default="0.1,0.2,0.4,0.8",
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client_data=ClientFieldData(
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prompt_on_new=True,
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prompt=lambda mi: "Enter a comma-separated list of amounts for each DCA level: "))
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time_limit: int = Field(
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default=60 * 60 * 24 * 7, gt=0,
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client_data=ClientFieldData(
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prompt=lambda mi: "Enter the time limit for each DCA level: ",
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prompt_on_new=False))
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stop_loss: Decimal = Field(
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default=Decimal("0.03"), gt=0,
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client_data=ClientFieldData(
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prompt=lambda mi: "Enter the stop loss (as a decimal, e.g., 0.03 for 3%): ",
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prompt_on_new=True))
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top_executor_refresh_time: Optional[float] = Field(
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default=None,
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client_data=ClientFieldData(
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is_updatable=True,
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prompt_on_new=False))
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executor_activation_bounds: Optional[List[Decimal]] = Field(
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default=None,
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client_data=ClientFieldData(
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is_updatable=True,
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prompt=lambda mi: "Enter the activation bounds for the orders "
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"(e.g., 0.01 activates the next order when the price is closer than 1%): ",
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prompt_on_new=False))
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@validator("executor_activation_bounds", pre=True, always=True)
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def parse_activation_bounds(cls, v):
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if isinstance(v, list):
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return [Decimal(val) for val in v]
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elif isinstance(v, str):
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if v == "":
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return None
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return [Decimal(val) for val in v.split(",")]
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return v
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@validator('dca_spreads', pre=True, always=True)
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def parse_spreads(cls, v):
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if v is None:
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return []
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if isinstance(v, str):
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if v == "":
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return []
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return [float(x.strip()) for x in v.split(',')]
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return v
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@validator('dca_amounts', pre=True, always=True)
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def parse_and_validate_amounts(cls, v, values, field):
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if v is None or v == "":
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return [1 for _ in values[values['dca_spreads']]]
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if isinstance(v, str):
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return [float(x.strip()) for x in v.split(',')]
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elif isinstance(v, list) and len(v) != len(values['dca_spreads']):
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raise ValueError(
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f"The number of {field.name} must match the number of {values['dca_spreads']}.")
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return v
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class DManMakerV2(MarketMakingControllerBase):
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def __init__(self, config: DManMakerV2Config, *args, **kwargs):
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super().__init__(config, *args, **kwargs)
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self.config = config
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self.dca_amounts_pct = [Decimal(amount) / sum(self.config.dca_amounts) for amount in self.config.dca_amounts]
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self.spreads = self.config.dca_spreads
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def first_level_refresh_condition(self, executor):
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if self.config.top_executor_refresh_time is not None:
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if self.get_level_from_level_id(executor.custom_info["level_id"]) == 0:
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return self.market_data_provider.time() - executor.timestamp > self.config.top_executor_refresh_time * 1000
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return False
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def order_level_refresh_condition(self, executor):
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return self.market_data_provider.time() - executor.timestamp > self.config.executor_refresh_time * 1000
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def executors_to_refresh(self) -> List[ExecutorAction]:
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executors_to_refresh = self.filter_executors(
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executors=self.executors_info,
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filter_func=lambda x: not x.is_trading and x.is_active and (self.order_level_refresh_condition(x) or self.first_level_refresh_condition(x)))
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return [StopExecutorAction(
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controller_id=self.config.id,
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executor_id=executor.id) for executor in executors_to_refresh]
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def get_executor_config(self, level_id: str, price: Decimal, amount: Decimal):
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trade_type = self.get_trade_type_from_level_id(level_id)
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if trade_type == TradeType.BUY:
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prices = [price * (1 - spread) for spread in self.spreads]
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else:
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prices = [price * (1 + spread) for spread in self.spreads]
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amounts = [amount * pct for pct in self.dca_amounts_pct]
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amounts_quote = [amount * price for amount, price in zip(amounts, prices)]
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return DCAExecutorConfig(
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timestamp=self.market_data_provider.time(),
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connector_name=self.config.connector_name,
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trading_pair=self.config.trading_pair,
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mode=DCAMode.MAKER,
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side=trade_type,
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prices=prices,
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amounts_quote=amounts_quote,
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level_id=level_id,
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time_limit=self.config.time_limit,
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stop_loss=self.config.stop_loss,
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take_profit=self.config.take_profit,
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trailing_stop=self.config.trailing_stop,
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activation_bounds=self.config.executor_activation_bounds,
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leverage=self.config.leverage,
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)
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134
bots/controllers/market_making/pmm_dynamic.py
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134
bots/controllers/market_making/pmm_dynamic.py
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from decimal import Decimal
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from typing import List
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import pandas_ta as ta # noqa: F401
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from pydantic import Field, validator
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from hummingbot.client.config.config_data_types import ClientFieldData
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from hummingbot.data_feed.candles_feed.data_types import CandlesConfig
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from hummingbot.strategy_v2.controllers.market_making_controller_base import (
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MarketMakingControllerBase,
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MarketMakingControllerConfigBase,
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)
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from hummingbot.strategy_v2.executors.position_executor.data_types import PositionExecutorConfig
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class PMMDynamicControllerConfig(MarketMakingControllerConfigBase):
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controller_name = "pmm_dynamic"
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candles_config: List[CandlesConfig] = []
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buy_spreads: List[float] = Field(
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default="1,2,4",
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client_data=ClientFieldData(
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is_updatable=True,
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prompt_on_new=True,
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prompt=lambda mi: "Enter a comma-separated list of buy spreads (e.g., '0.01, 0.02'):"))
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sell_spreads: List[float] = Field(
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default="1,2,4",
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client_data=ClientFieldData(
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is_updatable=True,
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prompt_on_new=True,
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prompt=lambda mi: "Enter a comma-separated list of sell spreads (e.g., '0.01, 0.02'):"))
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candles_connector: str = Field(
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default=None,
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client_data=ClientFieldData(
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prompt_on_new=True,
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prompt=lambda mi: "Enter the connector for the candles data, leave empty to use the same exchange as the connector: ", )
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)
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candles_trading_pair: str = Field(
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default=None,
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client_data=ClientFieldData(
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prompt_on_new=True,
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prompt=lambda mi: "Enter the trading pair for the candles data, leave empty to use the same trading pair as the connector: ", )
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)
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interval: str = Field(
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default="3m",
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client_data=ClientFieldData(
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prompt=lambda mi: "Enter the candle interval (e.g., 1m, 5m, 1h, 1d): ",
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prompt_on_new=False))
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macd_fast: int = Field(
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default=12,
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client_data=ClientFieldData(
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prompt=lambda mi: "Enter the MACD fast length: ",
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prompt_on_new=True))
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macd_slow: int = Field(
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default=26,
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client_data=ClientFieldData(
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prompt=lambda mi: "Enter the MACD slow length: ",
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prompt_on_new=True))
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macd_signal: int = Field(
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default=9,
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client_data=ClientFieldData(
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prompt=lambda mi: "Enter the MACD signal length: ",
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prompt_on_new=True))
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natr_length: int = Field(
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default=14,
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client_data=ClientFieldData(
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prompt=lambda mi: "Enter the NATR length: ",
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prompt_on_new=True))
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@validator("candles_connector", pre=True, always=True)
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def set_candles_connector(cls, v, values):
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if v is None or v == "":
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return values.get("connector_name")
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return v
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@validator("candles_trading_pair", pre=True, always=True)
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def set_candles_trading_pair(cls, v, values):
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if v is None or v == "":
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return values.get("trading_pair")
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return v
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class PMMDynamicController(MarketMakingControllerBase):
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"""
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This is a dynamic version of the PMM controller.It uses the MACD to shift the mid-price and the NATR
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to make the spreads dynamic. It also uses the Triple Barrier Strategy to manage the risk.
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"""
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def __init__(self, config: PMMDynamicControllerConfig, *args, **kwargs):
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self.config = config
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self.max_records = max(config.macd_slow, config.macd_fast, config.macd_signal, config.natr_length) + 10
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if len(self.config.candles_config) == 0:
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self.config.candles_config = [CandlesConfig(
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connector=config.candles_connector,
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trading_pair=config.candles_trading_pair,
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interval=config.interval,
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max_records=self.max_records
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)]
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super().__init__(config, *args, **kwargs)
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async def update_processed_data(self):
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candles = self.market_data_provider.get_candles_df(connector_name=self.config.candles_connector,
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trading_pair=self.config.candles_trading_pair,
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interval=self.config.interval,
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max_records=self.max_records)
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natr = ta.natr(candles["high"], candles["low"], candles["close"], length=self.config.natr_length) / 100
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macd_output = ta.macd(candles["close"], fast=self.config.macd_fast,
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slow=self.config.macd_slow, signal=self.config.macd_signal)
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macd = macd_output[f"MACD_{self.config.macd_fast}_{self.config.macd_slow}_{self.config.macd_signal}"]
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macd_signal = - (macd - macd.mean()) / macd.std()
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macdh = macd_output[f"MACDh_{self.config.macd_fast}_{self.config.macd_slow}_{self.config.macd_signal}"]
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macdh_signal = macdh.apply(lambda x: 1 if x > 0 else -1)
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max_price_shift = natr / 2
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price_multiplier = ((0.5 * macd_signal + 0.5 * macdh_signal) * max_price_shift).iloc[-1]
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candles["spread_multiplier"] = natr
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candles["reference_price"] = candles["close"] * (1 + price_multiplier)
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self.processed_data = {
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"reference_price": Decimal(candles["reference_price"].iloc[-1]),
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"spread_multiplier": Decimal(candles["spread_multiplier"].iloc[-1]),
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"features": candles
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}
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def get_executor_config(self, level_id: str, price: Decimal, amount: Decimal):
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trade_type = self.get_trade_type_from_level_id(level_id)
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return PositionExecutorConfig(
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timestamp=self.market_data_provider.time(),
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level_id=level_id,
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connector_name=self.config.connector_name,
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trading_pair=self.config.trading_pair,
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entry_price=price,
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amount=amount,
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triple_barrier_config=self.config.triple_barrier_config,
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leverage=self.config.leverage,
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side=trade_type,
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)
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38
bots/controllers/market_making/pmm_simple.py
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38
bots/controllers/market_making/pmm_simple.py
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from decimal import Decimal
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from typing import List
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from pydantic import Field
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from hummingbot.client.config.config_data_types import ClientFieldData
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from hummingbot.data_feed.candles_feed.data_types import CandlesConfig
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from hummingbot.strategy_v2.controllers.market_making_controller_base import (
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MarketMakingControllerBase,
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MarketMakingControllerConfigBase,
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)
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from hummingbot.strategy_v2.executors.position_executor.data_types import PositionExecutorConfig
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class PMMSimpleConfig(MarketMakingControllerConfigBase):
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controller_name = "pmm_simple"
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# As this controller is a simple version of the PMM, we are not using the candles feed
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candles_config: List[CandlesConfig] = Field(default=[], client_data=ClientFieldData(prompt_on_new=False))
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class PMMSimpleController(MarketMakingControllerBase):
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def __init__(self, config: PMMSimpleConfig, *args, **kwargs):
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super().__init__(config, *args, **kwargs)
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self.config = config
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def get_executor_config(self, level_id: str, price: Decimal, amount: Decimal):
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trade_type = self.get_trade_type_from_level_id(level_id)
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return PositionExecutorConfig(
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timestamp=self.market_data_provider.time(),
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level_id=level_id,
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connector_name=self.config.connector_name,
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trading_pair=self.config.trading_pair,
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entry_price=price,
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amount=amount,
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triple_barrier_config=self.config.triple_barrier_config,
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leverage=self.config.leverage,
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side=trade_type,
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)
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