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103 lines
4.5 KiB
Python
103 lines
4.5 KiB
Python
import time
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from decimal import Decimal
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import pandas_ta as ta # noqa: F401
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from hummingbot.core.data_type.common import TradeType
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from hummingbot.smart_components.executors.position_executor.data_types import PositionConfig, TrailingStop
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from hummingbot.smart_components.executors.position_executor.position_executor import PositionExecutor
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from hummingbot.smart_components.strategy_frameworks.data_types import OrderLevel
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from hummingbot.smart_components.strategy_frameworks.market_making.market_making_controller_base import (
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MarketMakingControllerBase,
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MarketMakingControllerConfigBase,
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)
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class DManV3Config(MarketMakingControllerConfigBase):
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strategy_name: str = "dman_v3"
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bb_length: int = 100
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bb_std: float = 2.0
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class DManV3(MarketMakingControllerBase):
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"""
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Directional Market Making Strategy making use of NATR indicator to make spreads dynamic and shift the mid price.
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"""
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def __init__(self, config: DManV3Config):
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super().__init__(config)
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self.config = config
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def refresh_order_condition(self, executor: PositionExecutor, order_level: OrderLevel) -> bool:
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"""
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Checks if the order needs to be refreshed.
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You can reimplement this method to add more conditions.
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"""
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if executor.position_config.timestamp + order_level.order_refresh_time > time.time():
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return False
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return True
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def early_stop_condition(self, executor: PositionExecutor, order_level: OrderLevel) -> bool:
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"""
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If an executor has an active position, should we close it based on a condition.
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"""
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return False
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def cooldown_condition(self, executor: PositionExecutor, order_level: OrderLevel) -> bool:
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"""
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After finishing an order, the executor will be in cooldown for a certain amount of time.
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This prevents the executor from creating a new order immediately after finishing one and execute a lot
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of orders in a short period of time from the same side.
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"""
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if executor.close_timestamp and executor.close_timestamp + order_level.cooldown_time > time.time():
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return True
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return False
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def get_processed_data(self):
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"""
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Gets the price and spread multiplier from the last candlestick.
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"""
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candles_df = self.candles[0].candles_df
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bbp = ta.bbands(candles_df["close"], length=self.config.bb_length, std=self.config.bb_std)
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candles_df["spread_multiplier"] = bbp[f"BBB_{self.config.bb_length}_{self.config.bb_std}"] / 200
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candles_df["price_multiplier"] = bbp[f"BBM_{self.config.bb_length}_{self.config.bb_std}"]
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return candles_df
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def get_position_config(self, order_level: OrderLevel) -> PositionConfig:
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"""
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Creates a PositionConfig object from an OrderLevel object.
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Here you can use technical indicators to determine the parameters of the position config.
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"""
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close_price = self.get_close_price(self.config.exchange, self.config.trading_pair)
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amount = order_level.order_amount_usd / close_price
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price_multiplier, spread_multiplier = self.get_price_and_spread_multiplier()
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side_multiplier = -1 if order_level.side == TradeType.BUY else 1
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order_spread_multiplier = order_level.spread_factor * spread_multiplier * side_multiplier
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order_price = price_multiplier * (1 + order_spread_multiplier)
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if order_level.triple_barrier_conf.trailing_stop_trailing_delta and order_level.triple_barrier_conf.trailing_stop_trailing_delta:
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trailing_stop = TrailingStop(
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activation_price_delta=order_level.triple_barrier_conf.trailing_stop_activation_price_delta,
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trailing_delta=order_level.triple_barrier_conf.trailing_stop_trailing_delta,
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)
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else:
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trailing_stop = None
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position_config = PositionConfig(
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timestamp=time.time(),
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trading_pair=self.config.trading_pair,
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exchange=self.config.exchange,
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side=order_level.side,
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amount=amount,
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take_profit=order_level.triple_barrier_conf.take_profit,
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stop_loss=order_level.triple_barrier_conf.stop_loss,
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time_limit=order_level.triple_barrier_conf.time_limit,
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entry_price=Decimal(order_price),
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open_order_type=order_level.triple_barrier_conf.open_order_type,
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take_profit_order_type=order_level.triple_barrier_conf.take_profit_order_type,
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trailing_stop=trailing_stop,
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leverage=self.config.leverage
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)
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return position_config
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