# source: https://raw.githubusercontent.com/remiotore/freqtrade/44beaeb6a420cd8e9f2e4ea93e11d6cfa192ee03/strategies/Miku_PP_v3.py from freqtrade.strategy import IStrategy, merge_informative_pair from pandas import DataFrame import talib.abstract as ta import logging import freqtrade.vendor.qtpylib.indicators as qtpylib import pandas as pd import numpy as np import technical.indicators as ftt from freqtrade.exchange import timeframe_to_minutes from technical.util import resample_to_interval, resampled_merge logger = logging.getLogger(__name__) def pivots_points(dataframe: pd.DataFrame, timeperiod=1, levels=4) -> pd.DataFrame: """ Pivots Points https://www.tradingview.com/support/solutions/43000521824-pivot-points-standard/ Formula: Pivot = (Previous High + Previous Low + Previous Close)/3 Resistance #1 = (2 x Pivot) - Previous Low Support #1 = (2 x Pivot) - Previous High Resistance #2 = (Pivot - Support #1) + Resistance #1 Support #2 = Pivot - (Resistance #1 - Support #1) Resistance #3 = (Pivot - Support #2) + Resistance #2 Support #3 = Pivot - (Resistance #2 - Support #2) ... :param dataframe: :param timeperiod: Period to compare (in ticker) :param levels: Num of support/resistance desired :return: dataframe """ data = {} low = qtpylib.rolling_mean( series=pd.Series(index=dataframe.index, data=dataframe["low"]), window=timeperiod ) high = qtpylib.rolling_mean( series=pd.Series(index=dataframe.index, data=dataframe["high"]), window=timeperiod ) data["pivot"] = qtpylib.rolling_mean(series=qtpylib.typical_price(dataframe), window=timeperiod) data["r1"] = data['pivot'] + 0.382 * (high - low) data["rS1"] = data['pivot'] + 0.0955 * (high - low) data["s1"] = data["pivot"] - 0.382 * (high - low) for i in range(2, levels + 1): prev_support = data["s" + str(i - 1)] prev_resistance = data["r" + str(i - 1)] data["r" + str(i)] = (data["pivot"] - prev_support) + prev_resistance data["s" + str(i)] = data["pivot"] - (prev_resistance - prev_support) return pd.DataFrame(index=dataframe.index, data=data) def create_ichimoku(dataframe, conversion_line_period, displacement, base_line_periods, laggin_span): ichimoku = ftt.ichimoku(dataframe, conversion_line_period=conversion_line_period, base_line_periods=base_line_periods, laggin_span=laggin_span, displacement=displacement ) dataframe[f'tenkan_sen_{conversion_line_period}'] = ichimoku['tenkan_sen'] dataframe[f'kijun_sen_{conversion_line_period}'] = ichimoku['kijun_sen'] dataframe[f'senkou_a_{conversion_line_period}'] = ichimoku['senkou_span_a'] dataframe[f'senkou_b_{conversion_line_period}'] = ichimoku['senkou_span_b'] class Github_remiotore_freqtrade__Miku_PP_v3__20260111_210550(IStrategy): """ Github_remiotore_freqtrade__Miku_PP_v3__20260111_210550 La base de la Estrategia es: Miku_PP_v2 y Miku_1m_5m_CSen44_1_5m Provando en: Miku_1m_5m_CSen444v2_N_1_5 SymphonIK """ timeframe = '5m' informative_timeframe = '1d' startup_candle_count = 444 # MAXIMUM ICHIMOKU process_only_new_candles = True minimal_roi = { "0": 10, } plot_config = { 'main_plot': { 'pivot_1d': {}, 'rS1_1d': {}, 'r1_1d': {}, 's1_1d': {}, 'senkou_b_88': {}, }, 'subplots': { 'MACD': { 'macd_1h': {'color': 'blue'}, 'macdsignal_1h': {'color': 'orange'}, }, } } stoploss = -0.10 def informative_pairs(self): pairs = self.dp.current_whitelist() informative_pairs = [(pair, self.informative_timeframe) for pair in pairs] if self.dp: for pair in pairs: informative_pairs += [(pair, "1d")] return informative_pairs def slow_tf_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ """ dataframe1d = self.dp.get_pair_dataframe( pair=metadata['pair'], timeframe="1d") pp = pivots_points(dataframe1d) dataframe1d['pivot'] = pp['pivot'] dataframe1d['r1'] = pp['r1'] dataframe1d['s1'] = pp['s1'] dataframe1d['rS1'] = pp['rS1'] dataframe = merge_informative_pair( dataframe, dataframe1d, self.timeframe, "1d", ffill=True) """ """ """ create_ichimoku(dataframe, conversion_line_period=9, displacement=26, base_line_periods=26, laggin_span=52) """ create_ichimoku(dataframe, conversion_line_period=20, displacement=88, base_line_periods=88, laggin_span=88) create_ichimoku(dataframe, conversion_line_period=88, displacement=444, base_line_periods=88, laggin_span=88) create_ichimoku(dataframe, conversion_line_period=355, displacement=880, base_line_periods=175, laggin_span=175) dataframe['ema20'] = ta.EMA(dataframe, timeperiod=20) """ Notes: Start Trading * En 1m dataframe['ichimoku_ok'] = ( (dataframe['kijun_sen_355_5m'] >= dataframe['tenkan_sen_355_5m']) & (dataframe['senkou_a_100'] > dataframe['senkou_b_100']) & (dataframe['senkou_a_20'] > dataframe['senkou_b_20']) & (dataframe['kijun_sen_20'] > dataframe['tenkan_sen_444']) & (dataframe['senkou_a_9'] > dataframe['senkou_a_20']) & (dataframe['tenkan_sen_20'] >= dataframe['kijun_sen_20']) & (dataframe['tenkan_sen_9'] >= dataframe['tenkan_sen_20']) & (dataframe['tenkan_sen_9'] >= dataframe['kijun_sen_9']) ).astype('int') * En 5m dataframe['ichimoku_ok'] = ( (dataframe['close'] > dataframe['pivot_1d']) & (dataframe['r1_1d'] > dataframe['close']) & (dataframe['kijun_sen_355'] >= dataframe['tenkan_sen_355']) & (dataframe['senkou_a_20'] > dataframe['senkou_b_20']) & (dataframe['kijun_sen_20'] > dataframe['tenkan_sen_88']) & (dataframe['senkou_a_9'] > dataframe['senkou_a_20']) & (dataframe['tenkan_sen_20'] >= dataframe['kijun_sen_20']) & (dataframe['tenkan_sen_9'] >= dataframe['tenkan_sen_20']) & (dataframe['tenkan_sen_9'] >= dataframe['kijun_sen_9']) ).astype('int') (dataframe['pivot_1d'] > dataframe['ema20_5m']) anulo ema20_5m para ver si hace entradas en Dry Run dataframe['trending_over'] = ( ( (dataframe['senkou_b_444'] > dataframe['close']) ) | ( (dataframe['pivot_1d'] > dataframe['close']) ) ).astype('int') return dataframe """ dataframe['pivots_ok'] = ( (dataframe['close'] > dataframe['pivot_1d']) & (dataframe['rS1_1d'] > dataframe['close']) & (dataframe['kijun_sen_355'] >= dataframe['tenkan_sen_355']) & (dataframe['senkou_a_20'] > dataframe['senkou_b_20']) ).astype('int') dataframe['trending_over'] = ( (dataframe['senkou_b_88'] > dataframe['close']) ).astype('int') return dataframe def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe = self.slow_tf_indicators(dataframe, metadata) return dataframe def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ ( (dataframe['pivots_ok'] > 0) ), 'buy'] = 1 return dataframe def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ ( (dataframe['trending_over'] > 0) ), 'sell'] = 1 return dataframe