# source: https://raw.githubusercontent.com/xaemiphor/ft/cdb3143002cd3bf8f1b203d84924bf7a91a391f6/strategies/SimpleIchi.py # pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement # flake8: noqa: F401 # isort: skip_file # --- Do not remove these imports --- import numpy as np import pandas as pd from datetime import datetime, timedelta, timezone from pandas import DataFrame from typing import Optional, Union from freqtrade.strategy import ( IStrategy, Trade, Order, PairLocks, informative, # @informative decorator # Hyperopt Parameters BooleanParameter, CategoricalParameter, DecimalParameter, IntParameter, RealParameter, # timeframe helpers timeframe_to_minutes, timeframe_to_next_date, timeframe_to_prev_date, # Strategy helper functions merge_informative_pair, stoploss_from_absolute, stoploss_from_open, ) # -------------------------------- # Add your lib to import here import talib.abstract as ta from technical import qtpylib from functools import reduce import technical.indicators as ftt timeperiods = [1,3,6,12,24,48,72,96] class Github_xaemiphor_ft__SimpleIchi__20250104_070018(IStrategy): INTERFACE_VERSION = 3 can_short: bool = False minimal_roi = { # "120": 0.0, # exit after 120 minutes at break even # "60": 0.01, # "30": 0.02, # "0": 0.04, } stoploss = -0.275 trailing_stop = False timeframe = "5m" process_only_new_candles = False use_exit_signal = True exit_profit_only = False ignore_roi_if_entry_signal = False # Hyperoptable parameters buy_trend_above_senkou_level = IntParameter(low=1, high=8, default=1, space="buy", optimize=True, load=True) buy_trend_bullish_level = IntParameter(low=1, high=8, default=6, space="buy", optimize=True, load=True) buy_fan_magnitude_shift_value = IntParameter(low=1, high=10, default=3, space="buy", optimize=True, load=True) buy_min_fan_magnitude_gain = DecimalParameter(low=1.000, high=1.010, decimals=3, default=1.002, space="buy", optimize=True, load=True) sell_trend_indicator = CategoricalParameter( timeperiods, default="24", space="sell", optimize=True, load=True, ) # Number of candles the strategy requires before producing valid signals startup_candle_count: int = 200 # 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"} plot_config = { 'main_plot': { 'senkou_a': { 'color': 'green', 'fill_to': 'senkou_b', 'fill_label': 'Ichimoku Cloud', 'fill_color': 'rgba(255,76,46,0.2)', }, 'senkou_b': {}, 'trend_close_1': {'color': '#FF5733'}, 'trend_close_3': {'color': '#FF8333'}, 'trend_close_6': {'color': '#FFB533'}, 'trend_close_12': {'color': '#FFE633'}, 'trend_close_24': {'color': '#E3FF33'}, 'trend_close_48': {'color': '#C4FF33'}, 'trend_close_72': {'color': '#61FF33'}, 'trend_close_96': {'color': '#33FF7D'} }, 'subplots': { 'fan_magnitude': { 'fan_magnitude': {} }, 'fan_magnitude_gain': { 'fan_magnitude_gain': {} } } } def informative_pairs(self): return [] def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: market = self.dp.market(metadata['pair']) dataframe["close_fee"] = (dataframe["close"] * market['maker']) heikinashi = qtpylib.heikinashi(dataframe) dataframe['open'] = heikinashi['open'] #dataframe['close'] = heikinashi['close'] dataframe['high'] = heikinashi['high'] dataframe['low'] = heikinashi['low'] for timeperiod in timeperiods: if timeperiod == 1: dataframe[f'trend_close_{timeperiod}'] = dataframe['close'] dataframe[f'trend_open_{timeperiod}'] = dataframe['open'] else: dataframe[f'trend_close_{timeperiod}'] = ta.EMA(dataframe['close'], timeperiod=timeperiod) dataframe[f'trend_open_{timeperiod}'] = ta.EMA(dataframe['open'], timeperiod=timeperiod) dataframe['fan_magnitude'] = (dataframe['trend_close_12'] / dataframe['trend_close_96']) dataframe['fan_magnitude_gain'] = dataframe['fan_magnitude'] / dataframe['fan_magnitude'].shift(1) ichimoku = ftt.ichimoku(dataframe, conversion_line_period=20, base_line_periods=60, laggin_span=120, displacement=30) dataframe['chikou_span'] = ichimoku['chikou_span'] dataframe['tenkan_sen'] = ichimoku['tenkan_sen'] dataframe['kijun_sen'] = ichimoku['kijun_sen'] dataframe['senkou_a'] = ichimoku['senkou_span_a'] dataframe['senkou_b'] = ichimoku['senkou_span_b'] dataframe['leading_senkou_span_a'] = ichimoku['leading_senkou_span_a'] dataframe['leading_senkou_span_b'] = ichimoku['leading_senkou_span_b'] dataframe['cloud_green'] = ichimoku['cloud_green'] dataframe['cloud_red'] = ichimoku['cloud_red'] dataframe['atr'] = ta.ATR(dataframe) return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] for x in range(10): conditions.append(dataframe["volume"].shift(x) > 0) # Trending market for idx, timeperiod in enumerate(timeperiods): if self.buy_trend_above_senkou_level.value >= idx: conditions.append(dataframe[f'trend_close_{timeperiod}'] > dataframe['senkou_a']) conditions.append(dataframe[f'trend_close_{timeperiod}'] > dataframe['senkou_b']) # Trends bullish for idx, timeperiod in enumerate(timeperiods): if self.buy_trend_bullish_level.value >= idx: conditions.append(dataframe[f'trend_close_{timeperiod}'] > dataframe[f'trend_open_{timeperiod}']) # Trends magnitude conditions.append(dataframe['fan_magnitude_gain'] >= self.buy_min_fan_magnitude_gain.value) conditions.append(dataframe['fan_magnitude'] > 1) for x in range(self.buy_fan_magnitude_shift_value.value): conditions.append(dataframe['fan_magnitude'].shift(x+1) < dataframe['fan_magnitude']) if conditions: dataframe.loc[ reduce(lambda x, y: x & y, conditions), 'enter_long'] = 1 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] for x in range(10): conditions.append(dataframe["volume"].shift(x) > 0) conditions.append(qtpylib.crossed_below(dataframe['trend_close_1'], dataframe[f'trend_close_{self.sell_trend_indicator.value}'])) if conditions: dataframe.loc[ reduce(lambda x, y: x & y, conditions), 'exit_long'] = 1 return dataframe