Timeframe
5m
Direction
Long Only
Stoploss
-25.8%
Trailing Stop
Yes
ROI
0m: 43.8%, 86m: 16.2%, 241m: 6.1%, 435m: 0.0%
Interface Version
2
Startup Candles
N/A
Indicators
8
freqtrade/freqtrade-strategies
Strategy 003 author@: Gerald Lonlas github@: https://github.com/freqtrade/freqtrade-strategies
import numpy as np # noqa
import pandas as pd # noqa
from pandas import DataFrame
from pandas.core.series import Series
from functools import reduce
import pandas_ta as pta
import technical.indicators as technicali
from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter,
IStrategy, IntParameter)
# --------------------------------
# Add your lib to import here
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
rangeUpper = 60
rangeLower = 5
def valuewhen(dataframe, condition, source, occurrence):
copy = dataframe.copy()
copy['colFromIndex'] = copy.index
copy = copy.sort_values(by=[condition, 'colFromIndex'], ascending=False).reset_index(drop=True)
copy['valuewhen'] = np.where(copy[condition] > 0, copy[source].shift(-occurrence), 100)
copy['valuewhen'] = copy['valuewhen'].fillna(100)
copy['barrsince'] = copy['colFromIndex'] - copy['colFromIndex'].shift(-occurrence)
copy.loc[
(
(rangeLower <= copy['barrsince']) &
(copy['barrsince'] <= rangeUpper)
)
, "in_range"] = 1
copy['in_range'] = copy['in_range'].fillna(0)
copy = copy.sort_values(by=['colFromIndex'], ascending=True).reset_index(drop=True)
return copy['valuewhen'], copy['in_range']
def EWO(dataframe, ema_length=5, ema2_length=35):
df = dataframe.copy()
ema1 = ta.EMA(df, timeperiod=5)
ema2 = ta.EMA(df, timeperiod=35)
emadif = (ema1 - ema2) / df['close'] * 100
return emadif
def wavetrend(df:DataFrame) -> Series:
# inputs
n1 = 9 # Channel Length
n2 = 12 # Average Length
ap = (df['high'] + df['low'] + df['close']) / 3 # HLC3
# wavetrend calculation
esa = ta.EMA(ap, timeperiod=3)
d = ta.EMA(abs(ap - esa), timeperiod=9)
ci = (ap - esa) / (0.015 * d)
wt1 = ta.EMA(ci, timeperiod=n2)
wt2 = ta.SMA(wt1, timeperiod=3)
wtVWAP = wt1 - wt2
return pd.Series(data={
"wt1": wt1,
"wt2": wt2,
"wtVWAP": wtVWAP
})
def hlc3(df):
return (df['high'] + df['low'] + df['close']) / 3
def fractalize(osrc):
fcopy = osrc.copy()
top_fractal = fcopy.shift(4) < fcopy.shift (2) & fcopy.shift(3) < fcopy.shift(2) & fcopy.shift(2) > fcopy.shift(1) & fcopy.shift(2) > fcopy
bot_fractal = fcopy.shift(4) > fcopy.shift (2) & fcopy.shift(3) > fcopy.shift(2) & fcopy.shift(2) < fcopy.shift(1) & fcopy.shift(2) < fcopy
class cipher5mgold(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 = 2
# # ROI table:
'''minimal_roi = {
"0": 0.438,
"86": 0.162,
"241": 0.061,
"435": 0
}'''
buy_params = {
"buy_aroondown": 34,
"buy_aroonup": 82,
"buy_arrondown_cat": False,
"buy_arronup_cat": False,
"buy_gold_cat1": False,
"buy_gold_cat2": False,
"buy_mfi": 17,
"buy_mfi_cat": True,
"buy_schaff_cat": False,
"buy_schafff_val": 33,
"buy_vwap_cat": False,
"buy_wt_cat": True,
"buy_wt_oversold": -64,
"ewo_high": 0.453,
"fast_ewo": 48,
"rsi_buy": 37,
"slow_ewo": 191,
"use_bull": False,
"use_hidden_bull": False,
}
# Sell hyperspace params:
sell_params = {
"sell_aroondown": 56,
"sell_aroonup": 35,
"sell_arrondown_cat": True,
"sell_arronup_cat": False,
"sell_mfi": 60,
"sell_mfi_cat": False,
"sell_schaff_cat": False,
"sell_schafff_val": 72,
"sell_vwap_cat": False,
"sell_wt_cat": True,
"sell_wt_overbought": 40,
"use_bear": True,
"use_hidden_bear": False,
}
# ROI table:
minimal_roi = {
"0": 1,
}
stoploss = -0.258
# Trailing stop:
trailing_stop = True
trailing_stop_positive = 0.073
trailing_stop_positive_offset = 0.1
trailing_only_offset_is_reached = False
use_custom_stoploss = False
# Optimal timeframe for the strategy.
timeframe = '5m'
#inf_1h = '1h'
# Run "populate_indicators()" only for new candle.
process_only_new_candles = True
# These values can be overridden in the "ask_strategy" section in the config.
use_sell_signal = True
sell_profit_only = False
ignore_roi_if_buy_signal = True
startup_candle_count: int = 30
oslevel3 = -100
period = 10
#buyval
buy_mfi = IntParameter(10, 90, default = 50, space = 'buy' )
fast_ewo = IntParameter(35, 75, default = 50, space = 'buy' )
slow_ewo = IntParameter(130, 210, default = 200, space = 'buy' )
buy_wt_oversold = IntParameter(-65, -30, default = -53, space = 'buy' )
buy_aroonup = IntParameter(40, 100, default = 77, space = 'buy')
buy_aroondown = IntParameter(0, 40, default = 24, space = 'buy' )
buy_schafff_val = IntParameter(20, 40, default = 25, space = 'buy' )
#sell val
sell_mfi = IntParameter(10, 90, default = 50, space = 'sell' )
sell_wt_overbought = IntParameter(30, 65, default = 53, space = 'sell' )
sell_aroonup = IntParameter(0, 75, default = 24, space = 'sell' )
sell_aroondown = IntParameter(40, 100, default = 68, space = 'sell')
sell_schafff_val = IntParameter(65, 90, default = 75, space = 'sell' )
#buy cat
#buy_mfi_cat = CategoricalParameter([True, False], default = True, space = 'buy' )
buy_vwap_cat = CategoricalParameter([True, False], default = True, space = 'buy' )
buy_arrondown_cat = CategoricalParameter([True, False], default = True, space = 'buy' )
buy_arronup_cat = CategoricalParameter([True, False], default = True, space = 'buy' )
buy_wt_cat = CategoricalParameter([True, False], default = True, space = 'buy' )
buy_schaff_cat = CategoricalParameter([True, False], default = True, space = 'buy' )
#buy_gold_cat1 = CategoricalParameter([True, False], default = True, space = 'buy' )
#buy_gold_cat2 = CategoricalParameter([True, False], default = True, space = 'buy' )
#sell cat
sell_mfi_cat = CategoricalParameter([True, False], default = True, space = 'sell' )
sell_wt_cat = CategoricalParameter([True, False], default = True, space = 'sell' )
sell_vwap_cat = CategoricalParameter([True, False], default = True, space = 'sell' )
sell_arronup_cat = CategoricalParameter([True, False], default = True, space = 'sell' )
sell_arrondown_cat = CategoricalParameter([True, False], default = True, space = 'sell' )
sell_schaff_cat = CategoricalParameter([True, False], default = True, space = 'sell' )
#RSIDIV
use_bull = CategoricalParameter([True, False], default = True, space = 'buy' )
use_hidden_bull = CategoricalParameter([True, False], default = True, space = 'buy' )
use_bear = CategoricalParameter([True, False], default = True, space = 'sell' )
use_hidden_bear = CategoricalParameter([True, False], default = True, space = 'sell' )
ewo_high = DecimalParameter(0, 7.0, default=5.835, space='buy', optimize=True)
rsi_buy = IntParameter(30, 70, default=50, space='buy', optimize=True)
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
#MFI
dataframe['mfi'] = ta.MFI(dataframe, timeperiod = 60)
#VWAP
dataframe['vwap'] = qtpylib.rolling_vwap(dataframe)
#wavtrend
wavetrendi = wavetrend(dataframe)
dataframe['wt1'] = wavetrendi['wt1']
dataframe['wt2'] = wavetrendi['wt2']
dataframe['wtVWAP'] = wavetrendi['wtVWAP']
wbl = 60
dataframe['oscwt'] = dataframe['wt2'].fillna(0)
dataframe['wtmin'] = dataframe['oscwt'].rolling(wbl).min()
dataframe['wtprevmin'] = np.where(dataframe['wtmin'] > dataframe['wtmin'].shift(), dataframe['wtmin'].shift(), dataframe['wtmin'])
dataframe.loc[
(dataframe['oscwt'] == dataframe['wtprevmin']),
'wtplfound'] = 1
dataframe['wtplfound'] = dataframe['wtplfound'].fillna(0)
dataframe['wtmax'] = dataframe['oscwt'].rolling(wbl).max()
dataframe['wtprevmax'] = np.where(dataframe['wtmax'] < dataframe['wtmax'].shift(), dataframe['wtmax'].shift(), dataframe['wtmax'])
dataframe.loc[
(dataframe['oscwt'] == dataframe['wtprevmax']),
'wtphfound'] = 1
dataframe['wtphfound'] = dataframe['wtphfound'].fillna(0)
#--Regular WT bullish
dataframe['wtvaluwhen_plfound_osc'], dataframe['wtinrange_plfound_osc'] = valuewhen(dataframe, 'wtplfound', 'oscwt', 1)
dataframe.loc[
(
(dataframe['oscwt'] > dataframe['wtvaluwhen_plfound_osc'])&
( dataframe['wtinrange_plfound_osc'] == 1)
),
'wtoschl'] = 1
dataframe['wtvaluewhen_plfound_low'], dataframe['wtinrange_plFound_low'] = valuewhen(dataframe, 'wtplfound', 'low', 1)
dataframe.loc[
(dataframe['low'] < dataframe['wtvaluewhen_plfound_low'])
, 'wtpriceLL'] = 1
#bullCond = plotBull and priceLL and oscHL and plFound
dataframe.loc[
(
(dataframe['wtpriceLL'] == 1) &
(dataframe['wtoschl'] == 1) &
(dataframe['wtplfound'] == 1)
)
, 'wtbullCond'] = 1
# // Hidden Bullish
# // Osc: Lower Low
#
# oscLL = osc[lbR] < valuewhen(plFound, osc[lbR], 1) and _inRange(plFound[1])
dataframe['wtvaluewhen_plFound_osc'], dataframe['wtinrange_plFound_osc'] = valuewhen(dataframe, 'wtplfound', 'oscwt', 1)
dataframe.loc[
(
(dataframe['oscwt'] < dataframe['wtvaluewhen_plFound_osc']) &
(dataframe['wtinrange_plFound_osc'] == 1)
)
, 'wtoscLL'] = 1
#
# // Price: Higher Low
#
# priceHL = low[lbR] > valuewhen(plFound, low[lbR], 1)
dataframe['wtvaluewhen_plFound_low'], dataframe['wtinrange_plFound_low'] = valuewhen(dataframe,'wtplfound', 'low', 1)
dataframe.loc[
(dataframe['low'] > dataframe['wtvaluewhen_plFound_low'])
, 'wtpriceHL'] = 1
# hiddenBullCond = plotHiddenBull and priceHL and oscLL and plFound
dataframe.loc[
(
(dataframe['wtpriceHL'] == 1) &
(dataframe['wtoscLL'] == 1) &
(dataframe['wtplfound'] == 1)
)
, 'wthiddenBullCond'] = 1
# // Regular Bearish
# // Osc: Lower High
#
# oscLH = osc[lbR] < valuewhen(phFound, osc[lbR], 1) and _inRange(phFound[1])
dataframe['wtvaluewhen_phFound_osc'], dataframe['wtinrange_phFound_osc'] = valuewhen(dataframe, 'wtphfound', 'oscwt', 1)
dataframe.loc[
(
(dataframe['oscwt'] < dataframe['wtvaluewhen_phFound_osc']) &
(dataframe['wtinrange_phFound_osc'] == 1)
)
, 'wtoscLH'] = 1
#
# // Price: Higher High
#
# priceHH = high[lbR] > valuewhen(phFound, high[lbR], 1)
dataframe['wtvaluewhen_phFound_high'], dataframe['wtinrange_phFound_high'] = valuewhen(dataframe, 'wtphfound', 'high', 1)
dataframe.loc[
(dataframe['high'] > dataframe['wtvaluewhen_phFound_high'])
, 'wtpriceHH'] = 1
#
# bearCond = plotBear and priceHH and oscLH and phFound
dataframe.loc[
(
(dataframe['wtpriceHH'] == 1) &
(dataframe['wtoschl'] == 1) &
(dataframe['wtphfound'] == 1)
)
, 'wtbearCond'] = 1
# // Hidden Bearish
# // Osc: Higher High
#
# oscHH = osc[lbR] > valuewhen(phFound, osc[lbR], 1) and _inRange(phFound[1])
dataframe['wtvaluewhen_phFound_osc'], dataframe['wtinrange_phFound_osc'] = valuewhen(dataframe, 'wtphfound', 'oscwt', 1)
dataframe.loc[
(
(dataframe['oscwt'] > dataframe['wtvaluewhen_phFound_osc']) &
(dataframe['wtinrange_phFound_osc'] == 1)
)
, 'wtoscHH'] = 1
#
# // Price: Lower High
#
# priceLH = high[lbR] < valuewhen(phFound, high[lbR], 1)
dataframe['wtvaluewhen_phFound_high'], dataframe['wtinrange_phFound_high'] = valuewhen(dataframe, 'wtphfound', 'high', 1)
dataframe.loc[
(dataframe['high'] < dataframe['wtvaluewhen_phFound_high'])
, 'wtpriceLH'] = 1
#
# hiddenBearCond = plotHiddenBear and priceLH and oscHH and phFound
dataframe.loc[
(
(dataframe['wtpriceLH'] == 1) &
(dataframe['wtoscHH'] == 1) &
(dataframe['wtphfound'] == 1)
)
, 'wthiddenBearCond'] = 1
#MACD
#RSI+MFI
#stohrsi
dataframe['schaff'] = pta.stc(dataframe)
#aaron
aroon = ta.AROON(dataframe)
dataframe['aroonup'] = aroon['aroonup']
dataframe['aroondown'] = aroon['aroondown']
#Elliot
dataframe['EWO'] = EWO(dataframe, self.fast_ewo, self.slow_ewo)
#RSIDIV
len = 14
src = dataframe['close']
lbL = 10#5
dataframe['osc'] = ta.RSI(src, len)
dataframe['osc'] = dataframe['osc'].fillna(0)
# plFound = na(pivotlow(osc, lbL, lbR)) ? false : true
dataframe['min'] = dataframe['osc'].rolling(lbL).min()
dataframe['prevMin'] = np.where(dataframe['min'] > dataframe['min'].shift(), dataframe['min'].shift(), dataframe['min'])
dataframe.loc[
(dataframe['osc'] == dataframe['prevMin'])
, 'plFound'] = 1
dataframe['plFound'] = dataframe['plFound'].fillna(0)
# phFound = na(pivothigh(osc, lbL, lbR)) ? false : true
dataframe['max'] = dataframe['osc'].rolling(lbL).max()
dataframe['prevMax'] = np.where(dataframe['max'] < dataframe['max'].shift(), dataframe['max'].shift(), dataframe['max'])
dataframe.loc[
(dataframe['osc'] == dataframe['prevMax'])
, 'phFound'] = 1
dataframe['phFound'] = dataframe['phFound'].fillna(0)
#------------------------------------------------------------------------------
# Regular Bullish
# Osc: Higher Low
# oscHL = osc[lbR] > valuewhen(plFound, osc[lbR], 1) and _inRange(plFound[1])
dataframe['valuewhen_plFound_osc'], dataframe['inrange_plFound_osc'] = valuewhen(dataframe, 'plFound', 'osc', 1)
dataframe.loc[
(
(dataframe['osc'] > dataframe['valuewhen_plFound_osc']) &
(dataframe['inrange_plFound_osc'] == 1)
)
, 'oscHL'] = 1
# Price: Lower Low
# priceLL = low[lbR] < valuewhen(plFound, low[lbR], 1)
dataframe['valuewhen_plFound_low'], dataframe['inrange_plFound_low'] = valuewhen(dataframe, 'plFound', 'low', 1)
dataframe.loc[
(dataframe['low'] < dataframe['valuewhen_plFound_low'])
, 'priceLL'] = 1
#bullCond = plotBull and priceLL and oscHL and plFound
dataframe.loc[
(
(dataframe['priceLL'] == 1) &
(dataframe['oscHL'] == 1) &
(dataframe['plFound'] == 1)
)
, 'bullCond'] = 1
# plot(
# plFound ? osc[lbR] : na,
# offset=-lbR,
# title="Regular Bullish",
# linewidth=2,
# color=(bullCond ? bullColor : noneColor)
# )
#
# plotshape(
# bullCond ? osc[lbR] : na,
# offset=-lbR,
# title="Regular Bullish Label",
# text=" Bull ",
# style=shape.labelup,
# location=location.absolute,
# color=bullColor,
# textcolor=textColor
# )
# //------------------------------------------------------------------------------
# // Hidden Bullish
# // Osc: Lower Low
#
# oscLL = osc[lbR] < valuewhen(plFound, osc[lbR], 1) and _inRange(plFound[1])
dataframe['valuewhen_plFound_osc'], dataframe['inrange_plFound_osc'] = valuewhen(dataframe, 'plFound', 'osc', 1)
dataframe.loc[
(
(dataframe['osc'] < dataframe['valuewhen_plFound_osc']) &
(dataframe['inrange_plFound_osc'] == 1)
)
, 'oscLL'] = 1
#
# // Price: Higher Low
#
# priceHL = low[lbR] > valuewhen(plFound, low[lbR], 1)
dataframe['valuewhen_plFound_low'], dataframe['inrange_plFound_low'] = valuewhen(dataframe,'plFound', 'low', 1)
dataframe.loc[
(dataframe['low'] > dataframe['valuewhen_plFound_low'])
, 'priceHL'] = 1
# hiddenBullCond = plotHiddenBull and priceHL and oscLL and plFound
dataframe.loc[
(
(dataframe['priceHL'] == 1) &
(dataframe['oscLL'] == 1) &
(dataframe['plFound'] == 1)
)
, 'hiddenBullCond'] = 1
#
# plot(
# plFound ? osc[lbR] : na,
# offset=-lbR,
# title="Hidden Bullish",
# linewidth=2,
# color=(hiddenBullCond ? hiddenBullColor : noneColor)
# )
#
# plotshape(
# hiddenBullCond ? osc[lbR] : na,
# offset=-lbR,
# title="Hidden Bullish Label",
# text=" H Bull ",
# style=shape.labelup,
# location=location.absolute,
# color=bullColor,
# textcolor=textColor
# )
#
# //------------------------------------------------------------------------------
# // Regular Bearish
# // Osc: Lower High
#
# oscLH = osc[lbR] < valuewhen(phFound, osc[lbR], 1) and _inRange(phFound[1])
dataframe['valuewhen_phFound_osc'], dataframe['inrange_phFound_osc'] = valuewhen(dataframe, 'phFound', 'osc', 1)
dataframe.loc[
(
(dataframe['osc'] < dataframe['valuewhen_phFound_osc']) &
(dataframe['inrange_phFound_osc'] == 1)
)
, 'oscLH'] = 1
#
# // Price: Higher High
#
# priceHH = high[lbR] > valuewhen(phFound, high[lbR], 1)
dataframe['valuewhen_phFound_high'], dataframe['inrange_phFound_high'] = valuewhen(dataframe, 'phFound', 'high', 1)
dataframe.loc[
(dataframe['high'] > dataframe['valuewhen_phFound_high'])
, 'priceHH'] = 1
#
# bearCond = plotBear and priceHH and oscLH and phFound
dataframe.loc[
(
(dataframe['priceHH'] == 1) &
(dataframe['oscLH'] == 1) &
(dataframe['phFound'] == 1)
)
, 'bearCond'] = 1
#
# plot(
# phFound ? osc[lbR] : na,
# offset=-lbR,
# title="Regular Bearish",
# linewidth=2,
# color=(bearCond ? bearColor : noneColor)
# )
#
# plotshape(
# bearCond ? osc[lbR] : na,
# offset=-lbR,
# title="Regular Bearish Label",
# text=" Bear ",
# style=shape.labeldown,
# location=location.absolute,
# color=bearColor,
# textcolor=textColor
# )
#
# //------------------------------------------------------------------------------
# // Hidden Bearish
# // Osc: Higher High
#
# oscHH = osc[lbR] > valuewhen(phFound, osc[lbR], 1) and _inRange(phFound[1])
dataframe['valuewhen_phFound_osc'], dataframe['inrange_phFound_osc'] = valuewhen(dataframe, 'phFound', 'osc', 1)
dataframe.loc[
(
(dataframe['osc'] > dataframe['valuewhen_phFound_osc']) &
(dataframe['inrange_phFound_osc'] == 1)
)
, 'oscHH'] = 1
#
# // Price: Lower High
#
# priceLH = high[lbR] < valuewhen(phFound, high[lbR], 1)
dataframe['valuewhen_phFound_high'], dataframe['inrange_phFound_high'] = valuewhen(dataframe, 'phFound', 'high', 1)
dataframe.loc[
(dataframe['high'] < dataframe['valuewhen_phFound_high'])
, 'priceLH'] = 1
#
# hiddenBearCond = plotHiddenBear and priceLH and oscHH and phFound
dataframe.loc[
(
(dataframe['priceLH'] == 1) &
(dataframe['oscHH'] == 1) &
(dataframe['phFound'] == 1)
)
, 'hiddenBearCond'] = 1
#
# plot(
# phFound ? osc[lbR] : na,
# offset=-lbR,
# title="Hidden Bearish",
# linewidth=2,
# color=(hiddenBearCond ? hiddenBearColor : noneColor)
# )
#
# plotshape(
# hiddenBearCond ? osc[lbR] : na,
# offset=-lbR,
# title="Hidden Bearish Label",
# text=" H Bear ",
# style=shape.labeldown,
# location=location.absolute,
# color=bearColor,
# textcolor=textColor
# )"""
# dataframe['lastrsi'] = valuewhen(dataframe, 'wtplfound', 'osc'[2],0)
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
bconditions = []
#if self.buy_gold_cat1.value:
bconditions.append((dataframe['wtbullCond'] == 1)&
(dataframe['wtprevmin'] <= self.oslevel3)&
(dataframe['wt2'] > self.oslevel3)&
((dataframe['wtprevmin'] - dataframe['wt2']) <= -5)
)
#if self.buy_gold_cat2.value:
bconditions.append((dataframe['bullCond'] == 1)&
(dataframe['wtprevmin'] <= self.oslevel3)&
(dataframe['wt2'] > self.oslevel3)&
((dataframe['wtprevmin'] - dataframe['wt2']) <= -5)
)
#if self.buy_mfi_cat.value == True:
bconditions.append(qtpylib.crossed_above(dataframe['mfi'], self.buy_mfi.value))
if self.use_bull.value:
bconditions.append(
(
(dataframe['bullCond'] > 0) &
(dataframe['EWO'] > self.ewo_high.value) &
#(dataframe['osc'] < self.rsi_buy.value) &
(dataframe['volume'] > 0)
)
)
if self.use_hidden_bull.value:
bconditions.append(
(
(dataframe['hiddenBullCond'] > 0) &
(dataframe['EWO'] > self.ewo_high.value) &
#(dataframe['osc'] < self.rsi_buy.value) &
(dataframe['volume'] > 0)
)
)
#if self.buy_wt_cat.value:
bconditions.append(qtpylib.crossed_above(dataframe['wt1'], dataframe['wt2'])&
((dataframe['wt2']-dataframe['wt1']) <=0)&
(dataframe['wt2']<= self.buy_wt_oversold.value)
)
if self.buy_vwap_cat.value:
bconditions.append(dataframe["close"] <= dataframe['vwap'])
if self.buy_arronup_cat.value == True:
bconditions.append(qtpylib.crossed_above(dataframe['aroonup'], self.buy_aroonup.value))
if self.buy_arrondown_cat.value == True:
bconditions.append(qtpylib.crossed_above(dataframe['aroondown'], self.buy_aroondown.value))
if self.buy_schaff_cat.value:
bconditions.append(qtpylib.crossed_above(dataframe['schaff'], self.buy_schafff_val.value))
bconditions.append(dataframe['volume'] > 0)
if bconditions:
dataframe.loc[
reduce(lambda x, y: x & y, bconditions),
'buy'] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
sconditions = []
if self.sell_mfi_cat.value == True:
sconditions.append(qtpylib.crossed_below(dataframe['mfi'], self.sell_mfi.value))
if self.use_bear.value:
sconditions.append(
(
(dataframe['bearCond'] > 0) &
(dataframe['volume'] > 0)
)
)
if self.use_hidden_bear.value:
sconditions.append(
(
(dataframe['hiddenBearCond'] > 0) &
(dataframe['volume'] > 0)
)
)
#if self.buy_wt_cat.value:
sconditions.append(qtpylib.crossed_below(dataframe['wt1'], dataframe['wt2'])&
((dataframe['wt2']-dataframe['wt1']) >=0)&
(dataframe['wt2']>= self.sell_wt_overbought.value)
)
if self.sell_vwap_cat.value:
sconditions.append(dataframe["close"] >= dataframe['vwap'])
if self.sell_arrondown_cat.value == True:
sconditions.append(qtpylib.crossed_above(dataframe['aroondown'], self.sell_aroondown.value))
if self.sell_arronup_cat.value == True:
sconditions.append(qtpylib.crossed_below(dataframe['aroonup'], self.sell_aroonup.value))
#if self.sell_schaff_cat.value:
sconditions.append(qtpylib.crossed_below(dataframe['schaff'], self.sell_schafff_val.value))
sconditions.append(dataframe['volume'] > 0)
if sconditions:
dataframe.loc[
reduce(lambda x, y: x & y, sconditions),
'sell'] = 1
return dataframe