# source: https://raw.githubusercontent.com/Roshan7869/algotrading/db07ceb71ac165a38fe200a86ae48586ed040f60/user_data/strategies/generated/GenStrategy_C018.py # directory_url: https://github.com/Roshan7869/algotrading/blob/main/user_data/strategies/generated/ # User: Roshan7869 # Repository: algotrading # --------------------"""Auto-generated strategy: Higher Time Frame Bias Entry During Asia ID: C018 | Tier: tier2 | Source: composed Components: entry: 1, exit: 1, filter: 1, risk_management: 1, market_structure: 1 | Chunks: 5 """ import numpy as np import pandas as pd from pandas import DataFrame from datetime import timezone from typing import Optional, Union from freqtrade.strategy import IStrategy, DecimalParameter, IntParameter import talib.abstract as ta import freqtrade.vendor.qtpylib.indicators as qtpylib class Github_Roshan7869_algotrading__GenStrategy_C018__20260817_183107(IStrategy): INTERFACE_VERSION = 3 timeframe = "1h" can_short = False minimal_roi = {"0": 0.10, "60": 0.05, "120": 0.02, "240": 0.01} stoploss = -0.06 trailing_stop = True trailing_stop_positive = 0.025 trailing_stop_positive_offset = 0.05 trailing_only_offset_is_reached = True process_only_new_candles = True startup_candle_count = 200 order_types = {"entry": "limit", "exit": "market", "stoploss": "market", "stoploss_on_exchange": False} order_time_in_force = {"entry": "GTC", "exit": "GTC"} atr_multiplier = DecimalParameter(1.5, 3.5, default=2.0, decimals=1, space="sell", optimize=True, load=True) risk_reward = DecimalParameter(1.5, 3.0, default=2.0, decimals=1, space="sell", optimize=True, load=True) def leverage(self, pair, current_time, current_rate, proposed_leverage, max_leverage, entry_tag, side, **kwargs) -> float: return min(3.0, max_leverage) def informative_pairs(self): pairs = self.dp.current_whitelist() informative_pairs = [(pair, "4h") for pair in pairs] informative_pairs.append(("BTC/USDT:USDT", self.timeframe)) return informative_pairs def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: bb = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) dataframe["bb_lower"] = bb["lower"] dataframe["bb_middle"] = bb["mid"] dataframe["bb_upper"] = bb["upper"] dataframe["bb_width"] = (dataframe["bb_upper"] - dataframe["bb_lower"]) / dataframe["bb_middle"] dataframe["bb_pctb"] = (dataframe["close"] - dataframe["bb_lower"]) / (dataframe["bb_upper"] - dataframe["bb_lower"]) dataframe["ema_slow"] = ta.EMA(dataframe, timeperiod=50) dataframe["ema_fast"] = ta.EMA(dataframe, timeperiod=9) dataframe["ema_medium"] = ta.EMA(dataframe, timeperiod=21) dataframe["ema_trend"] = ta.EMA(dataframe, timeperiod=200) dataframe["adx"] = ta.ADX(dataframe, timeperiod=14) dataframe["atr"] = ta.ATR(dataframe, timeperiod=14) dataframe["volume_ma"] = dataframe["volume"].rolling(window=20).mean() dataframe["volume_ratio"] = dataframe["volume"] / dataframe["volume_ma"] # BTC macro filter if self.dp and metadata.get('pair') != 'BTC/USDT:USDT': try: btc = self.dp.get_pair_dataframe("BTC/USDT:USDT", self.timeframe) if len(btc) > 0: btc['ema_50'] = ta.EMA(btc, timeperiod=50) btc['ema_200'] = ta.EMA(btc, timeperiod=200) btc = btc[['date', 'ema_50', 'ema_200']].copy() btc.columns = ['date', 'btc_ema_50', 'btc_ema_200'] dataframe = pd.merge(dataframe, btc, on='date', how='left') dataframe['btc_ema_50'] = dataframe['btc_ema_50'].ffill() dataframe['btc_ema_200'] = dataframe['btc_ema_200'].ffill() dataframe['btc_bullish'] = dataframe['btc_ema_50'] > dataframe['btc_ema_200'] else: dataframe['btc_bullish'] = True except Exception: dataframe['btc_bullish'] = True else: dataframe['btc_bullish'] = True return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe["enter_long"] = 0 dataframe["enter_short"] = 0 dataframe["enter_tag"] = "" volume_ok = dataframe["volume"] > dataframe["volume_ma"] trend_ok = dataframe["adx"] > 20 above_trend = dataframe["close"] > dataframe["ema_trend"] below_trend = dataframe["close"] < dataframe["ema_trend"] # Entry: breakout long_conditions = (dataframe["close"] > dataframe["bb_upper"]) & (dataframe["volume"] > dataframe["volume_ma"]) & (dataframe["adx"] > 20) # Short: short_conditions = (dataframe["close"] < dataframe["bb_lower"]) & (dataframe["volume"] > dataframe["volume_ma"]) & (dataframe["adx"] > 20) long_conditions = long_conditions & volume_ok & trend_ok & above_trend & dataframe["btc_bullish"] short_conditions = short_conditions & volume_ok & trend_ok & below_trend dataframe.loc[long_conditions, ["enter_long", "enter_tag"]] = [1, "C018_long"] dataframe.loc[short_conditions, ["enter_short", "enter_tag"]] = [1, "C018_short"] return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe["exit_long"] = 0 dataframe["exit_short"] = 0 exit_long = qtpylib.crossed_below(dataframe["close"], dataframe["ema_medium"]) exit_short = qtpylib.crossed_above(dataframe["close"], dataframe["ema_medium"]) dataframe.loc[exit_long, ["exit_long", "exit_tag"]] = [1, "ema_exit_long"] dataframe.loc[exit_short, ["exit_short", "exit_tag"]] = [1, "ema_exit_short"] return dataframe def custom_exit(self, pair, trade, current_time, current_rate, current_profit, **kwargs) -> Optional[Union[str, bool]]: dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) if len(dataframe) < 1: return None trade_date = trade.open_date_utc.replace(tzinfo=timezone.utc) try: entry_candle = dataframe[dataframe["date"] <= trade_date].iloc[-1] atr_value = entry_candle["atr"] except (IndexError, KeyError): return None if pd.isna(atr_value) or atr_value <= 0: return None atr_move = atr_value * self.atr_multiplier.value if trade.is_short: target_profit_pct = (atr_move * self.risk_reward.value) / current_rate if current_profit >= target_profit_pct: return f"short_tp_{self.risk_reward.value}r" else: tp_price = trade.open_rate + (atr_move * self.risk_reward.value) if current_rate >= tp_price: return f"long_tp_{self.risk_reward.value}r" return None