# source: https://raw.githubusercontent.com/Willemstijn/user_data/758a23f80ce7a5dca176e939c4f3f8562d11a304/stratscorer/all_strategies/ATEEZ_s_h.py # ============================================================================================== # ATEEZ trading strategy for SPOT # # Made by: # ______ _ _ _____ _ ______ _ # | _ \ | | | | / __ \ | | | _ \ | | # | | | | _ _ | |_ ___ | |__ | / \/ _ __ _ _ _ __ | |_ ___ | | | | __ _ __| | # | | | || | | || __|/ __|| '_ \ | | | '__|| | | || '_ \ | __|/ _ \ | | | |/ _` | / _` | # | |/ / | |_| || |_| (__ | | | || \__/\| | | |_| || |_) || |_| (_) || |/ /| (_| || (_| | # |___/ \__,_| \__|\___||_| |_| \____/|_| \__, || .__/ \__|\___/ |___/ \__,_| \__,_| # __/ || | # |___/ |_| # Version : 1.0 # Date : 2023-05 # Remarks : # As published, explained and tested in my Youtube video # # Visit my site for more information: https://www.dutchalgotrading.com/ # Become my Patron: https://www.patreon.com/dutchalgotrading # # freqtrade backtesting -s ATEEZ_s -c user_data/spot_config.json --timeframe=1d # freqtrade backtesting -s ATEEZ_s -p BTC/USDT -c user_data/spot_config.json --timeframe=1d # freqtrade plot-dataframe -s ATEEZ_s -p BTC/USDT -c user_data/spot_config.json --timeframe=1d # freqtrade hyperopt -c user_data/spot_config.json -s github_Willemstijn_user_data__ATEEZ_s_h__20230822_152729 --epochs 50 --spaces buy --hyperopt-loss SharpeHyperOptLoss # ============================================================================================== # pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement # flake8: noqa: F401 # isort: skip_file # --- Do not remove these libs --- from functools import reduce import numpy as np import pandas as pd from pandas import DataFrame from datetime import datetime from typing import Optional, Union from freqtrade.strategy import ( BooleanParameter, CategoricalParameter, DecimalParameter, IntParameter, IStrategy, merge_informative_pair, ) # -------------------------------- # Add your lib to import here import talib.abstract as ta import pandas_ta as pta from technical import qtpylib class github_Willemstijn_user_data__ATEEZ_s_h__20230822_152729(IStrategy): # Strategy interface version - allow new iterations of the strategy interface. # Check the documentation or the Sample strategy to get the latest version. INTERFACE_VERSION = 3 # Proposed timeframe for the strategy. Can be altered to your own preferred timeframe. timeframe = "1d" # Can this strategy go short? can_short: bool = False # Minimal ROI designed for the strategy. # Set to 100% minimal_roi = {"0": 1.0} # Optimal stoploss designed for the strategy. # Set to 10% stoploss = -0.15 # Trailing stoploss trailing_stop = False # 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 # Set to the default of 30. startup_candle_count: int = 30 # Optional order type mapping. order_types = { "entry": "limit", "exit": "limit", "stoploss": "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 indicators (Moving averages, ...) 'main_plot': { 'ema21': {'color': 'red'}, 'ema100': {'color': 'blue'}, }, 'subplots': { # Subplots - each dict defines one additional plot "TSI": { 'tsi': {'color': 'blue'}, 'tsi_signal': {'color': 'orange'}, }, "APO": { 'apo': {'color': 'purple'}, }, "ZSCORE": { 'zscore': {'color': 'orange'}, } } } # Optimization spaces quick_ema = IntParameter(13, 56, default=21, space="buy") slow_ema = IntParameter(13, 56, default=100, space="buy") zscore_val= IntParameter(20, 51, default=50, space="buy") def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # Below are the ATEEZ indicators for the strategy used. # Absolute Price Oscillator # True Strength Index # Ema 21 # Ema 100 # Z-score for val in self.quick_ema.range: dataframe[f'quick_ema_{val}'] = pta.ema(close = dataframe['close'], length = val) for val in self.slow_ema.range: dataframe[f'slow_ema_{val}'] = pta.ema(close = dataframe['close'], length = val) for val in self.zscore_val.range: dataframe[f'zscore_{val}'] = pta.zscore(close = dataframe['close'], length = val) dataframe['apo']=pta.apo(close=dataframe['close'], mamode='ema') fa = 13 sl = 25 si = 13 dataframe['tsi'] = pta.tsi(close = dataframe['close'], fast = fa, slow = sl, signal = si)[f'TSI_{fa}_{sl}_{si}'] dataframe['tsi_signal'] = pta.tsi(close = dataframe['close'], fast = fa, slow = sl, signal = si)[f'TSIs_{fa}_{sl}_{si}'] # first check if dataprovider is available if self.dp: if self.dp.runmode.value in ("live", "dry_run"): ob = self.dp.orderbook(metadata["pair"], 1) dataframe["best_bid"] = ob["bids"][0][0] dataframe["best_ask"] = ob["asks"][0][0] # #print(self) # #print(metadata) # #print(dataframe) # #print(dataframe[['date', 'close', 'apo', 'tsi','tsi_signal', 'ema21', 'ema100', 'zscore','signal','advice_changed']][dataframe['advice_changed']==True].tail(15)) return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # Long trades here: conditions = [] conditions.append( (dataframe['apo'] > 0) & ( dataframe['close'] > dataframe[f'quick_ema_{self.quick_ema.value}']) & (dataframe['close'] > dataframe[f'slow_ema_{self.slow_ema.value}']) & (dataframe[f'quick_ema_{self.quick_ema.value}'] > dataframe[f'slow_ema_{self.slow_ema.value}']) & (dataframe['tsi'] > dataframe['tsi_signal']) & (dataframe['tsi'] > 0) & (dataframe[f'zscore_{self.zscore_val.value}'] > 0.0) & (dataframe["volume"] > 0) # Guard ) if conditions: dataframe.loc[ reduce(lambda x, y: x & y, conditions), 'enter_long'] = 1 # # For short trades, use the section below # dataframe.loc[ # ( # # If sell short signal is True, then enter a short trade # (dataframe["signal"] == 'short') # # & (dataframe["advice_changed"] == True) # & (dataframe["volume"] > 0) # Guard # ), # ["enter_short", "enter_tag"], # ] = (1, "short_signal") return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # Exit long here: conditions = [] conditions.append( (dataframe['tsi'] < dataframe['tsi_signal']) & (dataframe["volume"] > 0) # Guard ) if conditions: dataframe.loc[ reduce(lambda x, y: x & y, conditions), 'exit_long'] = 1 # # For short trades, use the section below # dataframe.loc[ # ( # # The exit signal for shorts trades is pretty straightforward. # # Sell when the close price is below the kijun sen # (dataframe["short_exit"] == True) # & (dataframe["volume"] > 0) # Guard # ), # ["exit_short", "exit_tag"], # ] = (1, "exit_short_signal") return dataframe