# source: https://raw.githubusercontent.com/QuantVela/CryptoQuantX/cdfe09a85578af31284d8ac7d81d3abd295615f1/BreakoutCatcher/Break1h.py # pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement # flake8: noqa: F401 # isort: skip_file # --- Do not remove these libs --- import numpy as np import pandas as pd from pandas import DataFrame from datetime import datetime, timedelta, timezone from typing import Optional, Union, List from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter, IntParameter, IStrategy, merge_informative_pair, informative) from freqtrade.persistence import Trade # -------------------------------- # Add your lib to import here import talib.abstract as ta import pandas_ta as pta from technical import qtpylib import logging logger = logging.getLogger(__name__) class Github_QuantVela_CryptoQuantX__Break1h__20250121_092131(IStrategy): # Strategy interface version - allow new iterations of the strategy interface. INTERFACE_VERSION = 3 # Optimal timeframe for the strategy. timeframe = '1h' # Can this strategy go short? can_short: bool = False # Minimal ROI designed for the strategy. # This attribute will be overridden if the config file contains "minimal_roi". minimal_roi = {} # Optimal stoploss designed for the strategy. # This attribute will be overridden if the config file contains "stoploss". stoploss = -0.50 # Trailing stoploss trailing_stop = False # trailing_only_offset_is_reached = False # trailing_stop_positive = 0.01 # trailing_stop_positive_offset = 0.0 # Disabled / not configured # Run "populate_indicators()" only for new candle. process_only_new_candles = True # These values can be overridden in the config. use_exit_signal = True exit_profit_only = False ignore_roi_if_entry_signal = False # Number of candles the strategy requires before producing valid signals startup_candle_count: int = 800 # Strategy parameters # buy_rsi = IntParameter(10, 40, default=30, space="buy") # sell_rsi = IntParameter(60, 90, default=70, space="sell") # Optional order type mapping. order_types = { 'entry': 'limit', 'exit': 'limit', 'stoploss': 'market', "emergency_exit": "market", "force_entry": "market", "force_exit": "market", 'stoploss_on_exchange': False } # Optional order time in force. order_time_in_force = { 'entry': 'GTC', 'exit': 'GTC' } @property def plot_config(self): return { 'main_plot': { 'high_30': {'color': '#E8E5A9'}, 'low_10': {'color': '#CA9C6E'} }, 'subplots': { "ATR": { 'atr': {'color': '#BA9FF1'}, # 平均真实范围 }, } } def informative_pairs(self): pairs = self.dp.current_whitelist() informative_pairs = [(pair, '1d') for pair in pairs] informative_pairs += [("BTC/USDT", "1d")] return informative_pairs def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: if not self.dp: # Don't do anything if DataProvider is not available. return dataframe # dataframe['tr'] = ta.TRANGE(dataframe) # 计算真实波动幅度（TR） # dataframe['atr'] = ta.ATR(dataframe, timeperiod=10*24) # 计算 ATR dataframe['high_30'] = dataframe['high'].rolling(window=30*24).max() dataframe['low_10'] = dataframe['low'].rolling(window=10*24).min() # 为BTC计算MA50 btc_inf_tf = '1d' btc_informative = self.dp.get_pair_dataframe(pair="BTC/USDT", timeframe=btc_inf_tf) #close_1d_x btc_informative['btc_ma50'] = ta.SMA(btc_informative, timeperiod=50) #btc_ma50_1d dataframe = merge_informative_pair(dataframe, btc_informative, self.timeframe, btc_inf_tf, ffill=True) pair_inf_tf = '1d' pair_informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=pair_inf_tf) #close_1d_y pair_informative[f'{metadata["pair"].replace("/", "_")}_atr'] = ta.ATR(pair_informative, timeperiod=10) #SOL_USDT_atr_1d # Merge the specified pair informative dataframe = merge_informative_pair(dataframe, pair_informative, self.timeframe, pair_inf_tf, ffill=True) # pd.set_option('display.max_columns', None) # print(dataframe.iloc[-30:]) return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ ( (dataframe['close_1d_x'] > dataframe['btc_ma50_1d']) & #BTC>MA50 (dataframe['high'] >= dataframe['high_30']) ), 'enter_long'] = 1 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ ( (qtpylib.crossed_below(dataframe['close_1d_x'], dataframe['btc_ma50_1d']))#BTC 1: if current_time.hour == 0: logger.info(f"pair{pair} exit because days {(current_time - trade.open_date_utc).days} hour: {current_time.hour}") return 'exit_after_3_days' # if current_rate <= low_10: # logger.info(f'current_rate{current_rate}<=low_10{low_10}') # return 'exit_low_10' # position_adjustment_enable = True # max_entry_position_adjustment = 2 # def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float, # proposed_stake: float, min_stake: Optional[float], max_stake: float, # leverage: float, entry_tag: Optional[str], side: str, # **kwargs) -> float: # dataframe, _ = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe) # current_candle = dataframe.iloc[-1].squeeze() # atr = current_candle[f'{pair.replace("/", "_")}_atr_1d'] # risk_factor = 0.01 # total_wallet_balance = self.wallets.get_total_stake_amount() # # 每次交易使用总资金的 33% # position_size = (total_wallet_balance * risk_factor * current_rate) / (atr * (self.max_entry_position_adjustment + 1)) # logger.info(f'{pair} position_size = total amount{total_wallet_balance}*{risk_factor}*current rate{current_rate}/(atr{atr}*3) = {position_size}') # return position_size # def adjust_trade_position(self, trade: Trade, current_time: datetime, # current_rate: float, current_profit: float, # min_stake: Optional[float], max_stake: float, # current_entry_rate: float, current_exit_rate: float, # current_entry_profit: float, current_exit_profit: float, # **kwargs) -> Optional[float]: # dataframe, _ = self.dp.get_analyzed_dataframe(pair=trade.pair, timeframe=self.timeframe) # current_candle = dataframe.iloc[-1].squeeze() # atr = current_candle[f'{trade.pair.replace("/", "_")}_atr_1d'] # if trade.trade_direction == "long": # # 获取已成交的入场订单 # filled_entries = trade.select_filled_orders(trade.entry_side) # count_of_entries = trade.nr_of_successful_entries # try: # stake_amount = filled_entries[0].stake_amount # last_order_price = filled_entries[-1].average # # logger.info(f'{trade.pair} current_rate={current_rate}, price={last_order_price + 0.5 * atr}') # # 如果当前价格大于或等于最后一次订单价格加上 1/2 ATR，买入相同数量的stake_amount # if current_rate >= last_order_price + 0.5 * atr: # logger.info(f'{trade.pair} add position size={stake_amount}, atr={atr}, price={last_order_price + 0.5 * atr}') # return stake_amount # except Exception as exception: # return None # if trade.trade_direction == "short": # # 获取已成交的入场订单 # filled_entries = trade.select_filled_orders(trade.entry_side) # count_of_entries = trade.nr_of_successful_entries # try: # stake_amount = filled_entries[0].stake_amount # last_order_price = filled_entries[-1].average # # 如果当前价格小于或等于最后一次订单价格减去 1/2 ATR，加仓相同数量的stake_amount # if current_rate <= last_order_price - 0.5 * atr: # return stake_amount # except Exception as exception: # return None # return None # use_custom_stoploss = True # def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, # current_rate: float, current_profit: float, after_fill: bool, # **kwargs) -> Optional[float]: # dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) # current_candle = dataframe.iloc[-1].squeeze() # atr = current_candle[f'{pair.replace("/", "_")}_atr_1d'] # if trade.trade_direction == "long": # filled_entries = trade.select_filled_orders(trade.entry_side) # # count_of_entries = trade.nr_of_successful_entries # try: # last_order_price = filled_entries[-1].average # stoploss_price = last_order_price - atr * 2 #To do:回测看下atr1和 2 哪个更好，海龟是 2 # average_entry_price = trade.open_rate # stoploss_percentage = -round((average_entry_price - stoploss_price) / average_entry_price, 4) # logger.info(f'{pair} stoploss_price{stoploss_price} = last_order_price{last_order_price} - atr{atr}*2, stoploss_percentage{stoploss_percentage}') # return stoploss_percentage # except Exception as exception: # return None # return None