# --- Do not remove these libs ---
from freqtrade.strategy.interface import IStrategy
from pandas import DataFrame
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
import pandas as pd # noqa
pd.options.mode.chained_assignment = None # default='warn'
import technical.indicators as ftt
from functools import reduce
from datetime import datetime, timedelta
from freqtrade.strategy import merge_informative_pair
import numpy as np
from freqtrade.strategy import stoploss_from_open
class ichiV1(IStrategy):
INTERFACE_VERSION = 2
# NOTE: settings as of the 25th july 21
# Buy hyperspace params:
buy_params = {
"buy_trend_above_senkou_level": 1,
"buy_trend_bullish_level": 4,
"buy_fan_magnitude_shift_value": 3,
"buy_min_fan_magnitude_gain": 1.0013 # NOTE: Good value (Win% ~70%), alot of trades
#"buy_min_fan_magnitude_gain": 1.008 # NOTE: Very save value (Win% ~90%), only the biggest moves 1.008,
}
# Sell hyperspace params:
# NOTE: was 15m but kept bailing out in dryrun
sell_params = {
"sell_trend_indicator": "trend_close_1h",
}
# ROI table:
minimal_roi = {
"0": 0.3
}
# Stoploss:
stoploss = -0.1
# Optimal timeframe for the strategy
timeframe = '15m'
startup_candle_count = 96
process_only_new_candles = False
trailing_stop = True
trailing_stop_positive = 0.06
trailing_stop_positive_offset = 0.08
trailing_only_offset_is_reached = True
use_sell_signal = True
sell_profit_only = False
ignore_roi_if_buy_signal = True
plot_config = {
'main_plot': {
# fill area between senkou_a and senkou_b
'senkou_a': {
'color': 'green', #optional
'fill_to': 'senkou_b',
'fill_label': 'Ichimoku Cloud', #optional
'fill_color': 'rgba(255,76,46,0.2)', #optional
},
# plot senkou_b, too. Not only the area to it.
'senkou_b': {},
'trend_close_5m': {'color': '#FF5733'},
'trend_close_15m': {'color': '#FF8333'},
'trend_close_30m': {'color': '#FFB533'},
'trend_close_1h': {'color': '#FFE633'},
'trend_close_2h': {'color': '#E3FF33'},
'trend_close_4h': {'color': '#C4FF33'},
'trend_close_6h': {'color': '#61FF33'},
'trend_close_8h': {'color': '#33FF7D'}
},
'subplots': {
'fan_magnitude': {
'fan_magnitude': {}
},
'fan_magnitude_gain': {
'fan_magnitude_gain': {}
}
}
}
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# Populate Heikin Ashi candles
heikinashi = qtpylib.heikinashi(dataframe)
dataframe['open'] = heikinashi['open']
# dataframe['close'] = heikinashi['close']
dataframe['high'] = heikinashi['high']
dataframe['low'] = heikinashi['low']
# Shift close price by 1 candle to avoid lookahead bias, so the close is
# the previous period's close
dataframe['trend_close_5m'] = dataframe['close'].shift(1)
dataframe['trend_close_15m'] = ta.EMA(dataframe['trend_close_5m'], timeperiod=3)
dataframe['trend_close_30m'] = ta.EMA(dataframe['trend_close_5m'], timeperiod=6)
dataframe['trend_close_1h'] = ta.EMA(dataframe['trend_close_5m'], timeperiod=12)
dataframe['trend_close_2h'] = ta.EMA(dataframe['trend_close_5m'], timeperiod=24)
dataframe['trend_close_4h'] = ta.EMA(dataframe['trend_close_5m'], timeperiod=48)
dataframe['trend_close_6h'] = ta.EMA(dataframe['trend_close_5m'], timeperiod=72)
dataframe['trend_close_8h'] = ta.EMA(dataframe['trend_close_5m'], timeperiod=96)
# Shift open price by 1 candle to avoid lookahead bias, so the open is
# the previous period's open
dataframe['trend_open_5m'] = dataframe['open'].shift(1)
dataframe['trend_open_15m'] = ta.EMA(dataframe['trend_open_5m'], timeperiod=3)
dataframe['trend_open_30m'] = ta.EMA(dataframe['trend_open_5m'], timeperiod=6)
dataframe['trend_open_1h'] = ta.EMA(dataframe['trend_open_5m'], timeperiod=12)
dataframe['trend_open_2h'] = ta.EMA(dataframe['trend_open_5m'], timeperiod=24)
dataframe['trend_open_4h'] = ta.EMA(dataframe['trend_open_5m'], timeperiod=48)
dataframe['trend_open_6h'] = ta.EMA(dataframe['trend_open_5m'], timeperiod=72)
dataframe['trend_open_8h'] = ta.EMA(dataframe['trend_open_5m'], timeperiod=96)
dataframe['fan_magnitude'] = (dataframe['trend_close_1h'] / dataframe['trend_close_8h'])
dataframe['fan_magnitude_gain'] = dataframe['fan_magnitude'] / dataframe['fan_magnitude'].shift(1)
ichimoku = ftt.ichimoku(dataframe.shift(1), conversion_line_period=20, base_line_periods=60, laggin_span=120, displacement=30)
dataframe['chikou_span'] = ichimoku['chikou_span'] # do not use this in live, it has lookahead bias
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_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
conditions = []
# Trending market
if self.buy_params['buy_trend_above_senkou_level'] >= 1:
conditions.append(dataframe['trend_close_5m'] > dataframe['senkou_a'])
conditions.append(dataframe['trend_close_5m'] > dataframe['senkou_b'])
if self.buy_params['buy_trend_above_senkou_level'] >= 2:
conditions.append(dataframe['trend_close_15m'] > dataframe['senkou_a'])
conditions.append(dataframe['trend_close_15m'] > dataframe['senkou_b'])
if self.buy_params['buy_trend_above_senkou_level'] >= 3:
conditions.append(dataframe['trend_close_30m'] > dataframe['senkou_a'])
conditions.append(dataframe['trend_close_30m'] > dataframe['senkou_b'])
if self.buy_params['buy_trend_above_senkou_level'] >= 4:
conditions.append(dataframe['trend_close_1h'] > dataframe['senkou_a'])
conditions.append(dataframe['trend_close_1h'] > dataframe['senkou_b'])
if self.buy_params['buy_trend_above_senkou_level'] >= 5:
conditions.append(dataframe['trend_close_2h'] > dataframe['senkou_a'])
conditions.append(dataframe['trend_close_2h'] > dataframe['senkou_b'])
if self.buy_params['buy_trend_above_senkou_level'] >= 6:
conditions.append(dataframe['trend_close_4h'] > dataframe['senkou_a'])
conditions.append(dataframe['trend_close_4h'] > dataframe['senkou_b'])
if self.buy_params['buy_trend_above_senkou_level'] >= 7:
conditions.append(dataframe['trend_close_6h'] > dataframe['senkou_a'])
conditions.append(dataframe['trend_close_6h'] > dataframe['senkou_b'])
if self.buy_params['buy_trend_above_senkou_level'] >= 8:
conditions.append(dataframe['trend_close_8h'] > dataframe['senkou_a'])
conditions.append(dataframe['trend_close_8h'] > dataframe['senkou_b'])
# Trends bullish
if self.buy_params['buy_trend_bullish_level'] >= 1:
conditions.append(dataframe['trend_close_5m'] > dataframe['trend_open_5m'])
if self.buy_params['buy_trend_bullish_level'] >= 2:
conditions.append(dataframe['trend_close_15m'] > dataframe['trend_open_15m'])
if self.buy_params['buy_trend_bullish_level'] >= 3:
conditions.append(dataframe['trend_close_30m'] > dataframe['trend_open_30m'])
if self.buy_params['buy_trend_bullish_level'] >= 4:
conditions.append(dataframe['trend_close_1h'] > dataframe['trend_open_1h'])
if self.buy_params['buy_trend_bullish_level'] >= 5:
conditions.append(dataframe['trend_close_2h'] > dataframe['trend_open_2h'])
if self.buy_params['buy_trend_bullish_level'] >= 6:
conditions.append(dataframe['trend_close_4h'] > dataframe['trend_open_4h'])
if self.buy_params['buy_trend_bullish_level'] >= 7:
conditions.append(dataframe['trend_close_6h'] > dataframe['trend_open_6h'])
if self.buy_params['buy_trend_bullish_level'] >= 8:
conditions.append(dataframe['trend_close_8h'] > dataframe['trend_open_8h'])
# Trends magnitude
conditions.append(dataframe['fan_magnitude_gain'] >= self.buy_params['buy_min_fan_magnitude_gain'])
conditions.append(dataframe['fan_magnitude'] > 1)
for x in range(self.buy_params['buy_fan_magnitude_shift_value']):
conditions.append(dataframe['fan_magnitude'].shift(x+1) < dataframe['fan_magnitude'])
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:
conditions = []
conditions.append(qtpylib.crossed_below(dataframe['trend_close_5m'], dataframe[self.sell_params['sell_trend_indicator']]))
if conditions:
dataframe.loc[
reduce(lambda x, y: x & y, conditions),
'sell'] = 1
return dataframe