from freqtrade.strategy.interface import IStrategy from pandas import DataFrame import numpy as np from functools import reduce from freqtrade.strategy import stoploss_from_open from freqtrade.strategy.hyper import DecimalParameter from freqtrade.persistence import Trade from datetime import datetime def alligator( dataframe, period_jaw=13, period_teeth=8, period_lips=5, shift_jaw=8, shift_teeth=5, shift_lips=3, ): """Construct Williams Alligator.""" data = dataframe.copy() df_tmp = data[["high", "low"]] col_tmp = "col_tmp" df_tmp = df_tmp.assign( col_tmp=lambda x: (x["high"] + x["low"]) / 2 ) df_j = calculate_smma(df_tmp, period_jaw, 'jaw', col_tmp) df_t = calculate_smma(df_tmp, period_teeth, 'teeth', col_tmp) df_l = calculate_smma(df_tmp, period_lips, 'lips', col_tmp) df_j['jaw'] = df_j['jaw'].shift(shift_jaw) df_t['teeth'] = df_t['teeth'].shift(shift_teeth) df_l['lips'] = df_l['lips'].shift(shift_lips) data = data.merge(df_j, left_index=True, right_index=True) data = data.merge(df_t, left_index=True, right_index=True) data = data.merge(df_l, left_index=True, right_index=True) return data def calculate_smma(df, period, column_name, apply_to): """Calculate Smoothed Moving Average.""" df_tmp = df[[apply_to]] first_val = df_tmp[apply_to].iloc[:period].mean() df_tmp = df_tmp.assign(column_name=None) df_tmp.at[period, column_name] = first_val for index, row in df_tmp.iterrows(): if index > period: smma_val = (df_tmp.at[index - 1, column_name] * (period - 1) + row[apply_to]) / period df_tmp.at[index, column_name] = smma_val df_tmp = df_tmp[[column_name]] return df_tmp class SeeYouLaterTSL(IStrategy): """ My first humble strategy using Williams Alligator Indicator and Fractals Changelog: 0.9 Inital version, some improvements needed 1.0 Code optimizations 1.1 Added custom stoplosse https://github.com/cyberjunky/freqtrade-strategies """ stoploss = -0.99 trailing_stop = False trailing_only_offset_is_reached = True trailing_stop_positive = 0.01 trailing_stop_positive_offset = 0.015 use_sell_signal = True sell_profit_only = True ignore_roi_if_buy_signal = False use_custom_stoploss = True startup_candle_count: int = 30 timeframe = '5m' minimal_roi = { "0": 0.08, "36": 0.031, "50": 0.021, "60": 0.01, "70": 0 } @property def plot_config(self): return { 'main_plot': { 'lips': {'color': 'green'}, 'teeth': {'color': 'red'}, 'jaw': {'color': 'blue'}, }, 'subplots': { "sell": { 'bullish': {'color': 'orange'}, }, "buy": { 'bearish': {'color': 'lightgreen'}, } } } pHSL = DecimalParameter(-0.500, -0.040, default=-0.99, decimals=3, space='sell', load=True) pPF_1 = DecimalParameter(0.008, 0.020, default=0.011, decimals=3, space='sell', load=True) pSL_1 = DecimalParameter(0.008, 0.020, default=0.009, decimals=3, space='sell', load=True) pPF_2 = DecimalParameter(0.040, 0.100, default=0.040, decimals=3, space='sell', load=True) pSL_2 = DecimalParameter(0.020, 0.070, default=0.020, decimals=3, space='sell', load=True) def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float: HSL = self.pHSL.value PF_1 = self.pPF_1.value SL_1 = self.pSL_1.value PF_2 = self.pPF_2.value SL_2 = self.pSL_2.value if current_profit > PF_2: sl_profit = SL_2 + (current_profit - PF_2) elif current_profit > PF_1: sl_profit = SL_1 + ((current_profit - PF_1) * (SL_2 - SL_1) / (PF_2 - PF_1)) else: sl_profit = HSL if sl_profit >= current_profit: return -0.99 return stoploss_from_open(sl_profit, current_profit) def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Adds several different TA indicators to the given DataFrame Performance Note: For the best performance be frugal on the number of indicators you are using. Let uncomment only the indicator you are using in your strategies or your hyperopt configuration, otherwise you will waste your memory and CPU usage. :param dataframe: Dataframe with data from the exchange :param metadata: Additional information, like the currently traded pair :return: a Dataframe with all mandatory indicators for the strategies """ gator_df = alligator(dataframe) dataframe['lips'] = gator_df['lips'] dataframe['jaw'] = gator_df['jaw'] dataframe['teeth'] = gator_df['teeth'] dataframe['bearish'] = ( dataframe['high'].shift(4).lt(dataframe['high'].shift(2)) & dataframe['high'].shift(3).lt(dataframe['high'].shift(2)) & dataframe['high'].shift(1).lt(dataframe['high'].shift(2)) & dataframe['high'].lt(dataframe['high'].shift(2)) ) dataframe['bullish'] = ( dataframe['low'].shift(4).gt(dataframe['low'].shift(2)) & dataframe['low'].shift(3).gt(dataframe['low'].shift(2)) & dataframe['low'].shift(1).gt(dataframe['low'].shift(2)) & dataframe['low'].gt(dataframe['high'].shift(2)) ) return dataframe def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Based on TA indicators, populates the buy signal for the given dataframe If the bullish fractal is active and below the teeth of the gator -> buy :param dataframe: DataFrame :return: DataFrame with buy column """ conditions = [] conditions.append( ( (dataframe['bearish']) & (dataframe['close'] < dataframe['teeth'] ) & (dataframe['volume'] > 0) ) ) if conditions: dataframe.loc[ reduce(lambda x, y: x & y, conditions), 'buy']=1 return dataframe def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Based on TA indicators, populates the sell signal for the given dataframe If the bearish fractal is active and above the teeth of the gator -> sell :param dataframe: DataFrame :return: DataFrame with buy column """ conditions = [] conditions.append( ( (dataframe['bullish']) & (dataframe['close'] > dataframe['teeth'] ) & (dataframe['volume'] > 0) ) ) if conditions: dataframe.loc[ reduce(lambda x, y: x & y, conditions), 'sell']=1 return dataframe