# source: https://raw.githubusercontent.com/remiotore/freqtrade/44beaeb6a420cd8e9f2e4ea93e11d6cfa192ee03/strategies/hacimlifit1.py import numpy as np # noqa import pandas as pd # noqa from pandas import DataFrame from technical.indicators import fibma,vwma from functools import reduce from freqtrade.optimize.space import Categorical, Dimension, Integer, SKDecimal from freqtrade.strategy import BooleanParameter,DecimalParameter,IStrategy,IntParameter import talib.abstract as ta from typing import List def get_whale_volume_threshold(dataframe:DataFrame,mean_multiplier=3,volmean=200): data = dataframe.copy() data["mean_volume"] = data['volume'].rolling(volmean).mean() data["std_volume"] = data['volume'].rolling(volmean).std() data["std_threshold"] = round(data["mean_volume"] + mean_multiplier * data["std_volume"],2) return data["std_threshold"] class Github_remiotore_freqtrade__hacimlifit1__20260111_210550(IStrategy): INTERFACE_VERSION = 3 can_short: bool = False class HyperOpt: def stoploss_space(): return [SKDecimal(-0.5, -0.15, decimals=2, name='stoploss')] def trailing_space() -> List[Dimension]: return [ Categorical([True], name='trailing_stop'), SKDecimal(0.01, 0.35, decimals=3, name='trailing_stop_positive'), SKDecimal(0.05, 0.3, decimals=3, name='trailing_stop_positive_offset_p1'), Categorical([True, False], name='trailing_only_offset_is_reached'), ] def max_open_trades_space() -> List[Dimension]: return [ Integer(-1, 8, name='max_open_trades'), ] minimal_roi = { "0": 10, } stoploss = -0.46 trailing_stop = True trailing_stop_positive = 0.01 trailing_stop_positive_offset = 0.152 trailing_only_offset_is_reached = True timeframe = "5m" process_only_new_candles = True use_exit_signal = True exit_profit_only = False ignore_roi_if_entry_signal = False buy_dfma = DecimalParameter(1.01, 1.06, default=1.059, space="buy", optimize=True, load=True) upwneed = BooleanParameter(default=True, space="buy", optimize=True) buy_upw = DecimalParameter(1.001, 1.08, default=1.044, space="buy", optimize=True, load=True) buy_cs = DecimalParameter(1.02, 1.05, default=1.03, space="buy", optimize=True, load=True) fibmaneeed = BooleanParameter(default=True, space="buy", optimize=True) buy_adx = IntParameter(15, 60, default=20, space="buy", optimize=True, load=True) emaneed = BooleanParameter(default=True, space="buy", optimize=True) vwmaneed = BooleanParameter(default=True, space="buy", optimize=True) badtime = BooleanParameter(default=True, space="buy", optimize=True) houra = IntParameter(0, 8, default=4, space="buy", optimize=True, load=True) hourb = IntParameter(9, 16, default=11, space="buy", optimize=True, load=True) hourc = IntParameter(17, 24, default=18, space="buy", optimize=True, load=True) lastvlneed = BooleanParameter(default=True, space="buy", optimize=True) buy_lastvl = IntParameter(2, 10, default=2, space="buy", optimize=True, load=True) last_lowest = IntParameter(10, 100, default=24, space="sell", optimize=True, load=True) cooldown_lookback = IntParameter(2, 48, default=15, space="protection", optimize=True) stop_duration = IntParameter(12, 200, default=135, space="protection", optimize=True) use_stop_protection = BooleanParameter(default=True, space="protection", optimize=True) stop_max_allowed_dd = DecimalParameter(0.05, 0.5, default=0.108, space="protection", optimize=True) use_MaxDrawdown_protection = BooleanParameter(default=True, space="protection", optimize=True) @property def protections(self): prot = [] prot.append({ "method": "CooldownPeriod", "stop_duration_candles": self.cooldown_lookback.value }) if self.use_stop_protection.value: prot.append({ "method": "StoplossGuard", "lookback_period_candles": 24 * 3, "trade_limit": 4, "stop_duration_candles": self.stop_duration.value, "only_per_pair": False }) if self.use_MaxDrawdown_protection.value: prot.append({ "method": "MaxDrawdown", "lookback_period_candles": 24 * 3, "trade_limit": 20, "stop_duration_candles": self.stop_duration.value, "max_allowed_drawdown": self.stop_max_allowed_dd.value }) return prot startup_candle_count: int = 200 order_types = { "entry": "limit", "exit": "limit", "stoploss": "market", "stoploss_on_exchange": False, } order_time_in_force = {"entry": "GTC", "exit": "GTC"} plot_config = { "main_plot": { "fibma": {"color": "white"}, "ema200": {"color": "purple"}, "vwma200": {"color": "green"}, }, "subplots": { "ADX": { "adx": {"color": "yellow"}, }, "RSI": { "rsi": {"color": "red"}, }, }, } def informative_pairs(self): """ Define additional, informative pair/interval combinations to be cached from the exchange. These pair/interval combinations are non-tradeable, unless they are part of the whitelist as well. For more information, please consult the documentation :return: List of tuples in the format (pair, interval) Sample: return [("ETH/USDT", "5m"), ("BTC/USDT", "15m"), ] """ return [] def leverage(self, pair: str, current_rate: float, proposed_leverage: float, max_leverage: float, side: str, **kwargs) -> float: return 10.0 def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe['hour'] = dataframe['date'].dt.hour dataframe['minute'] = dataframe['date'].dt.minute dataframe["whale_volume_threshold"] = get_whale_volume_threshold(dataframe,volmean=200,mean_multiplier=5) dataframe['volinc'] = 0 dataframe.loc[dataframe['volume'] > dataframe["whale_volume_threshold"], 'volinc'] = 1 dataframe["volcheck"] = dataframe['volume']> dataframe["whale_volume_threshold"] dataframe['obv'] = ta.OBV(dataframe) dataframe['fibma'] = fibma(dataframe, MAtype=1, src=1) dataframe['obv_slope'] = dataframe['obv'] - dataframe['obv'].shift(1) dataframe['adx'] = ta.ADX(dataframe) dataframe['rsi'] = ta.RSI(dataframe["close"],14) dataframe['ema200'] = ta.EMA(dataframe["close"],200) dataframe["vwma200"] = vwma(dataframe,"close",200) return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: longconditions = [] longconditions.append(dataframe["close"] < dataframe["open"]*self.buy_cs.value) longconditions.append(dataframe["close"] < dataframe["fibma"]*self.buy_dfma.value) if self.upwneed.value: longconditions.append(dataframe["close"] * self.buy_upw.value > dataframe["high"]) if self.fibmaneeed.value: longconditions.append(dataframe["fibma"] > dataframe["fibma"].shift(1)) longconditions.append(dataframe["close"] > dataframe["open"]) longconditions.append(dataframe['adx'] > self.buy_adx.value) longconditions.append(dataframe['obv_slope'] > 0) if self.emaneed.value: longconditions.append(dataframe['close'] > dataframe['ema200'] ) if self.vwmaneed.value: longconditions.append(dataframe['close'] > dataframe['vwma200']) if self.badtime.value: longconditions.append(~((dataframe['hour'] >= self.houra.value) & (dataframe['hour'] < self.houra.value+2))) longconditions.append(~((dataframe['hour'] >= self.hourb.value) & (dataframe['hour'] < self.hourb.value+2))) longconditions.append(~((dataframe['hour'] >= self.hourc.value) & (dataframe['hour'] < (self.hourc.value+2)%24))) longconditions.append(dataframe['volcheck'] == True ) if self.lastvlneed.value: longconditions.append(dataframe['volinc'].rolling(window=24 ).sum() < self.buy_lastvl.value) longconditions.append(dataframe['volume'] > 0) if longconditions: dataframe.loc[ reduce(lambda x, y: x & y, longconditions), 'enter_long'] = 1 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ ( ( (dataframe['close'] < dataframe['low'].rolling(self.last_lowest.value).min().shift(1)) ) ), 'exit_long'] =1 return dataframe