def test_hyperopt_parameters():
from skopt.space import Categorical, Integer, Real
with pytest.raises(OperationalException, match=r"Name is determined.*"):
IntParameter(low=0, high=5, default=1, name='hello')
with pytest.raises(OperationalException,
match=r"IntParameter space must be.*"):
IntParameter(low=0, default=5, space='buy')
with pytest.raises(OperationalException,
match=r"RealParameter space must be.*"):
RealParameter(low=0, default=5, space='buy')
with pytest.raises(OperationalException,
match=r"DecimalParameter space must be.*"):
DecimalParameter(low=0, default=5, space='buy')
with pytest.raises(OperationalException,
match=r"IntParameter space invalid\."):
IntParameter([0, 10], high=7, default=5, space='buy')
with pytest.raises(OperationalException,
match=r"RealParameter space invalid\."):
RealParameter([0, 10], high=7, default=5, space='buy')
with pytest.raises(OperationalException,
match=r"DecimalParameter space invalid\."):
DecimalParameter([0, 10], high=7, default=5, space='buy')
with pytest.raises(OperationalException,
match=r"CategoricalParameter space must.*"):
CategoricalParameter(['aa'], default='aa', space='buy')
with pytest.raises(TypeError):
BaseParameter(opt_range=[0, 1], default=1, space='buy')
intpar = IntParameter(low=0, high=5, default=1, space='buy')
assert intpar.value == 1
assert isinstance(intpar.get_space(''), Integer)
fltpar = RealParameter(low=0.0, high=5.5, default=1.0, space='buy')
assert isinstance(fltpar.get_space(''), Real)
assert fltpar.value == 1
fltpar = DecimalParameter(low=0.0,
high=5.5,
default=1.0004,
decimals=3,
space='buy')
assert isinstance(fltpar.get_space(''), Integer)
assert fltpar.value == 1
fltpar._set_value(2222)
assert fltpar.value == 2.222
catpar = CategoricalParameter(['buy_rsi', 'buy_macd', 'buy_none'],
default='buy_macd',
space='buy')
assert isinstance(catpar.get_space(''), Categorical)
assert catpar.value == 'buy_macd'
def test_hyperopt_parameters():
from skopt.space import Categorical, Integer, Real
with pytest.raises(OperationalException, match=r"Name is determined.*"):
IntParameter(low=0, high=5, default=1, name='hello')
with pytest.raises(OperationalException,
match=r"IntParameter space must be.*"):
IntParameter(low=0, default=5, space='buy')
with pytest.raises(OperationalException,
match=r"RealParameter space must be.*"):
RealParameter(low=0, default=5, space='buy')
with pytest.raises(OperationalException,
match=r"DecimalParameter space must be.*"):
DecimalParameter(low=0, default=5, space='buy')
with pytest.raises(OperationalException,
match=r"IntParameter space invalid\."):
IntParameter([0, 10], high=7, default=5, space='buy')
with pytest.raises(OperationalException,
match=r"RealParameter space invalid\."):
RealParameter([0, 10], high=7, default=5, space='buy')
with pytest.raises(OperationalException,
match=r"DecimalParameter space invalid\."):
DecimalParameter([0, 10], high=7, default=5, space='buy')
with pytest.raises(OperationalException,
match=r"CategoricalParameter space must.*"):
CategoricalParameter(['aa'], default='aa', space='buy')
with pytest.raises(TypeError):
BaseParameter(opt_range=[0, 1], default=1, space='buy')
intpar = IntParameter(low=0, high=5, default=1, space='buy')
assert intpar.value == 1
assert isinstance(intpar.get_space(''), Integer)
assert isinstance(intpar.range, range)
assert len(list(intpar.range)) == 1
# Range contains ONLY the default / value.
assert list(intpar.range) == [intpar.value]
intpar.in_space = True
assert len(list(intpar.range)) == 6
assert list(intpar.range) == [0, 1, 2, 3, 4, 5]
fltpar = RealParameter(low=0.0, high=5.5, default=1.0, space='buy')
assert isinstance(fltpar.get_space(''), Real)
assert fltpar.value == 1
fltpar = DecimalParameter(low=0.0,
high=5.5,
default=1.0004,
decimals=3,
space='buy')
assert isinstance(fltpar.get_space(''), Integer)
assert fltpar.value == 1
catpar = CategoricalParameter(['buy_rsi', 'buy_macd', 'buy_none'],
default='buy_macd',
space='buy')
assert isinstance(catpar.get_space(''), Categorical)
assert catpar.value == 'buy_macd'
class mabStra(IStrategy):
# #################### RESULTS PASTE PLACE ####################
# ROI table:
minimal_roi = {"0": 0.598, "644": 0.166, "3269": 0.115, "7289": 0}
# Stoploss:
stoploss = -0.128
# Buy hypers
timeframe = '4h'
# #################### END OF RESULT PLACE ####################
# buy params
buy_mojo_ma_timeframe = IntParameter(2, 100, default=7, space='buy')
buy_fast_ma_timeframe = IntParameter(2, 100, default=14, space='buy')
buy_slow_ma_timeframe = IntParameter(2, 100, default=28, space='buy')
buy_div_max = DecimalParameter(0,
2,
decimals=4,
default=2.25446,
space='buy')
buy_div_min = DecimalParameter(0,
2,
decimals=4,
default=0.29497,
space='buy')
# sell params
sell_mojo_ma_timeframe = IntParameter(2, 100, default=7, space='sell')
sell_fast_ma_timeframe = IntParameter(2, 100, default=14, space='sell')
sell_slow_ma_timeframe = IntParameter(2, 100, default=28, space='sell')
sell_div_max = DecimalParameter(0,
2,
decimals=4,
default=1.54593,
space='sell')
sell_div_min = DecimalParameter(0,
2,
decimals=4,
default=2.81436,
space='sell')
def populate_indicators(self, dataframe: DataFrame,
metadata: dict) -> DataFrame:
# SMA - ex Moving Average
dataframe['buy-mojoMA'] = ta.SMA(
dataframe, timeperiod=self.buy_mojo_ma_timeframe.value)
dataframe['buy-fastMA'] = ta.SMA(
dataframe, timeperiod=self.buy_fast_ma_timeframe.value)
dataframe['buy-slowMA'] = ta.SMA(
dataframe, timeperiod=self.buy_slow_ma_timeframe.value)
dataframe['sell-mojoMA'] = ta.SMA(
dataframe, timeperiod=self.sell_mojo_ma_timeframe.value)
dataframe['sell-fastMA'] = ta.SMA(
dataframe, timeperiod=self.sell_fast_ma_timeframe.value)
dataframe['sell-slowMA'] = ta.SMA(
dataframe, timeperiod=self.sell_slow_ma_timeframe.value)
return dataframe
def populate_buy_trend(self, dataframe: DataFrame,
metadata: dict) -> DataFrame:
dataframe.loc[(
(dataframe['buy-mojoMA'].div(dataframe['buy-fastMA']) > self.
buy_div_min.value) &
(dataframe['buy-mojoMA'].div(dataframe['buy-fastMA']) < self.
buy_div_max.value) &
(dataframe['buy-fastMA'].div(dataframe['buy-slowMA']) > self.
buy_div_min.value) &
(dataframe['buy-fastMA'].div(dataframe['buy-slowMA']) < self.
buy_div_max.value)), 'buy'] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame,
metadata: dict) -> DataFrame:
dataframe.loc[(
(dataframe['sell-fastMA'].div(dataframe['sell-mojoMA']) > self.
sell_div_min.value) &
(dataframe['sell-fastMA'].div(dataframe['sell-mojoMA']) < self.
sell_div_max.value) &
(dataframe['sell-slowMA'].div(dataframe['sell-fastMA']) > self.
sell_div_min.value) &
(dataframe['sell-slowMA'].div(dataframe['sell-fastMA']) < self.
sell_div_max.value)), 'sell'] = 1
return dataframe
class Heracles(IStrategy):
########################################## RESULT PASTE PLACE ##########################################
# 10/100: 25 trades. 18/4/3 Wins/Draws/Losses. Avg profit 5.92%. Median profit 6.33%. Total profit 0.04888306 BTC ( 48.88Σ%). Avg duration 4 days, 6:24:00 min. Objective: -11.42103
# Buy hyperspace params:
buy_params = {
"buy_crossed_indicator_shift": 9,
"buy_div_max": 0.75,
"buy_div_min": 0.16,
"buy_indicator_shift": 15,
}
# Sell hyperspace params:
sell_params = {}
# ROI table:
minimal_roi = {"0": 0.598, "644": 0.166, "3269": 0.115, "7289": 0}
# Stoploss:
stoploss = -0.256
# Optimal timeframe use it in your config
timeframe = '4h'
########################################## END RESULT PASTE PLACE ######################################
# buy params
buy_div_min = DecimalParameter(0, 1, default=0.16, decimals=2, space='buy')
buy_div_max = DecimalParameter(0, 1, default=0.75, decimals=2, space='buy')
buy_indicator_shift = IntParameter(0, 20, default=16, space='buy')
buy_crossed_indicator_shift = IntParameter(0, 20, default=9, space='buy')
def populate_indicators(self, dataframe: DataFrame,
metadata: dict) -> DataFrame:
dataframe = dropna(dataframe)
dataframe['volatility_kcw'] = ta.volatility.keltner_channel_wband(
dataframe['high'],
dataframe['low'],
dataframe['close'],
window=20,
window_atr=10,
fillna=False,
original_version=True)
dataframe['volatility_dcp'] = ta.volatility.donchian_channel_pband(
dataframe['high'],
dataframe['low'],
dataframe['close'],
window=10,
offset=0,
fillna=False)
return dataframe
def populate_buy_trend(self, dataframe: DataFrame,
metadata: dict) -> DataFrame:
"""
Buy strategy Hyperopt will build and use.
"""
conditions = []
IND = 'volatility_dcp'
CRS = 'volatility_kcw'
DFIND = dataframe[IND]
DFCRS = dataframe[CRS]
d = DFIND.shift(self.buy_indicator_shift.value).div(
DFCRS.shift(self.buy_crossed_indicator_shift.value))
# print(d.min(), "\t", d.max())
conditions.append(
d.between(self.buy_div_min.value, self.buy_div_max.value))
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:
"""
Sell strategy Hyperopt will build and use.
"""
dataframe.loc[:, 'sell'] = 0
return dataframe
class Zeus(IStrategy):
# * 1/43: 86 trades. 72/6/8 Wins/Draws/Losses. Avg profit 12.66%. Median profit 11.99%. Total profit 0.10894395 BTC ( 108.94Σ%). Avg duration 3 days, 0:31:00 min. Objective: -48.48793
# "max_open_trades": 10,
# "stake_currency": "BTC",
# "stake_amount": 0.01,
# "tradable_balance_ratio": 0.99,
# "timeframe": "4h",
# "dry_run_wallet": 0.1,
# Buy hyperspace params:
buy_params = {
"buy_cat": "<R",
"buy_real": 0.0128,
}
# Sell hyperspace params:
sell_params = {
"sell_cat": "=R",
"sell_real": 0.9455,
}
# ROI table:
minimal_roi = {"0": 0.564, "567": 0.273, "2814": 0.12, "7675": 0}
# Stoploss:
stoploss = -0.256
buy_real = DecimalParameter(0.001,
0.999,
decimals=4,
default=0.11908,
space='buy')
buy_cat = CategoricalParameter([">R", "=R", "<R"],
default='<R',
space='buy')
sell_real = DecimalParameter(0.001,
0.999,
decimals=4,
default=0.59608,
space='sell')
sell_cat = CategoricalParameter([">R", "=R", "<R"],
default='>R',
space='sell')
# Buy hypers
timeframe = '4h'
def populate_indicators(self, dataframe: DataFrame,
metadata: dict) -> DataFrame:
# Add all ta features
dataframe['trend_ichimoku_base'] = ta.trend.ichimoku_base_line(
dataframe['high'],
dataframe['low'],
window1=9,
window2=26,
visual=False,
fillna=False)
KST = ta.trend.KSTIndicator(close=dataframe['close'],
roc1=10,
roc2=15,
roc3=20,
roc4=30,
window1=10,
window2=10,
window3=10,
window4=15,
nsig=9,
fillna=False)
dataframe['trend_kst_diff'] = KST.kst_diff()
# Normalization
tib = dataframe['trend_ichimoku_base']
dataframe['trend_ichimoku_base'] = (tib - tib.min()) / (tib.max() -
tib.min())
tkd = dataframe['trend_kst_diff']
dataframe['trend_kst_diff'] = (tkd - tkd.min()) / (tkd.max() -
tkd.min())
return dataframe
def populate_buy_trend(self, dataframe: DataFrame,
metadata: dict) -> DataFrame:
conditions = []
IND = 'trend_ichimoku_base'
REAL = self.buy_real.value
OPR = self.buy_cat.value
DFIND = dataframe[IND]
# print(DFIND.mean())
if OPR == ">R":
conditions.append(DFIND > REAL)
elif OPR == "=R":
conditions.append(np.isclose(DFIND, REAL))
elif OPR == "<R":
conditions.append(DFIND < REAL)
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 = []
IND = 'trend_kst_diff'
REAL = self.sell_real.value
OPR = self.sell_cat.value
DFIND = dataframe[IND]
# print(DFIND.mean())
if OPR == ">R":
conditions.append(DFIND > REAL)
elif OPR == "=R":
conditions.append(np.isclose(DFIND, REAL))
elif OPR == "<R":
conditions.append(DFIND < REAL)
if conditions:
dataframe.loc[reduce(lambda x, y: x & y, conditions), 'sell'] = 1
return dataframe
class GodStraNew(IStrategy):
# #################### RESULTS PASTE PLACE ####################
# #################### END OF RESULT PLACE ####################
# TODO: Its not dry code!
# Buy Hyperoptable Parameters/Spaces.
buy_crossed_indicator0 = CategoricalParameter(god_genes_with_timeperiod,
default="ADD-20",
space='buy')
buy_crossed_indicator1 = CategoricalParameter(god_genes_with_timeperiod,
default="ASIN-6",
space='buy')
buy_crossed_indicator2 = CategoricalParameter(god_genes_with_timeperiod,
default="CDLEVENINGSTAR-50",
space='buy')
buy_indicator0 = CategoricalParameter(god_genes_with_timeperiod,
default="SMA-100",
space='buy')
buy_indicator1 = CategoricalParameter(god_genes_with_timeperiod,
default="WILLR-50",
space='buy')
buy_indicator2 = CategoricalParameter(god_genes_with_timeperiod,
default="CDLHANGINGMAN-20",
space='buy')
buy_operator0 = CategoricalParameter(operators, default="/<R", space='buy')
buy_operator1 = CategoricalParameter(operators, default="<R", space='buy')
buy_operator2 = CategoricalParameter(operators, default="CB", space='buy')
buy_real_num0 = DecimalParameter(0,
1,
decimals=DECIMALS,
default=0.89009,
space='buy')
buy_real_num1 = DecimalParameter(0,
1,
decimals=DECIMALS,
default=0.56953,
space='buy')
buy_real_num2 = DecimalParameter(0,
1,
decimals=DECIMALS,
default=0.38365,
space='buy')
# Sell Hyperoptable Parameters/Spaces.
sell_crossed_indicator0 = CategoricalParameter(
god_genes_with_timeperiod, default="CDLSHOOTINGSTAR-150", space='sell')
sell_crossed_indicator1 = CategoricalParameter(god_genes_with_timeperiod,
default="MAMA-1-100",
space='sell')
sell_crossed_indicator2 = CategoricalParameter(god_genes_with_timeperiod,
default="CDLMATHOLD-6",
space='sell')
sell_indicator0 = CategoricalParameter(god_genes_with_timeperiod,
default="CDLUPSIDEGAP2CROWS-5",
space='sell')
sell_indicator1 = CategoricalParameter(god_genes_with_timeperiod,
default="CDLHARAMICROSS-150",
space='sell')
sell_indicator2 = CategoricalParameter(god_genes_with_timeperiod,
default="CDL2CROWS-5",
space='sell')
sell_operator0 = CategoricalParameter(operators,
default="<R",
space='sell')
sell_operator1 = CategoricalParameter(operators, default="D", space='sell')
sell_operator2 = CategoricalParameter(operators,
default="/>R",
space='sell')
sell_real_num0 = DecimalParameter(0,
1,
decimals=DECIMALS,
default=0.09731,
space='sell')
sell_real_num1 = DecimalParameter(0,
1,
decimals=DECIMALS,
default=0.81657,
space='sell')
sell_real_num2 = DecimalParameter(0,
1,
decimals=DECIMALS,
default=0.87267,
space='sell')
# Stoploss:
stoploss = -1
# Buy hypers
timeframe = '4h'
def populate_indicators(self, dataframe: DataFrame,
metadata: dict) -> DataFrame:
'''
It's good to calculate all indicators in all time periods here and so optimize the strategy.
But this strategy can take much time to generate anything that may not use in his optimization.
I just calculate the specific indicators in specific time period inside buy and sell strategy populator methods if needed.
Also, this method (populate_indicators) just calculates default value of hyperoptable params
so using this method have not big benefits instade of calculating useable things inside buy and sell trand populators
'''
return dataframe
def populate_buy_trend(self, dataframe: DataFrame,
metadata: dict) -> DataFrame:
conditions = list()
# TODO: Its not dry code!
buy_indicator = self.buy_indicator0.value
buy_crossed_indicator = self.buy_crossed_indicator0.value
buy_operator = self.buy_operator0.value
buy_real_num = self.buy_real_num0.value
condition, dataframe = condition_generator(dataframe, buy_operator,
buy_indicator,
buy_crossed_indicator,
buy_real_num)
conditions.append(condition)
# backup
buy_indicator = self.buy_indicator1.value
buy_crossed_indicator = self.buy_crossed_indicator1.value
buy_operator = self.buy_operator1.value
buy_real_num = self.buy_real_num1.value
condition, dataframe = condition_generator(dataframe, buy_operator,
buy_indicator,
buy_crossed_indicator,
buy_real_num)
conditions.append(condition)
buy_indicator = self.buy_indicator2.value
buy_crossed_indicator = self.buy_crossed_indicator2.value
buy_operator = self.buy_operator2.value
buy_real_num = self.buy_real_num2.value
condition, dataframe = condition_generator(dataframe, buy_operator,
buy_indicator,
buy_crossed_indicator,
buy_real_num)
conditions.append(condition)
if conditions:
dataframe.loc[reduce(lambda x, y: x & y, conditions), 'buy'] = 1
# print(len(dataframe.keys()))
return dataframe
def populate_sell_trend(self, dataframe: DataFrame,
metadata: dict) -> DataFrame:
conditions = list()
# TODO: Its not dry code!
sell_indicator = self.sell_indicator0.value
sell_crossed_indicator = self.sell_crossed_indicator0.value
sell_operator = self.sell_operator0.value
sell_real_num = self.sell_real_num0.value
condition, dataframe = condition_generator(dataframe, sell_operator,
sell_indicator,
sell_crossed_indicator,
sell_real_num)
conditions.append(condition)
sell_indicator = self.sell_indicator1.value
sell_crossed_indicator = self.sell_crossed_indicator1.value
sell_operator = self.sell_operator1.value
sell_real_num = self.sell_real_num1.value
condition, dataframe = condition_generator(dataframe, sell_operator,
sell_indicator,
sell_crossed_indicator,
sell_real_num)
conditions.append(condition)
sell_indicator = self.sell_indicator2.value
sell_crossed_indicator = self.sell_crossed_indicator2.value
sell_operator = self.sell_operator2.value
sell_real_num = self.sell_real_num2.value
condition, dataframe = condition_generator(dataframe, sell_operator,
sell_indicator,
sell_crossed_indicator,
sell_real_num)
conditions.append(condition)
if conditions:
dataframe.loc[reduce(lambda x, y: x & y, conditions), 'sell'] = 1
return dataframe
class mabStra(IStrategy):
# buy params
buy_mojo_ma_timeframe = IntParameter(2, 100, default=7, space='buy')
buy_fast_ma_timeframe = IntParameter(2, 100, default=14, space='buy')
buy_slow_ma_timeframe = IntParameter(2, 100, default=28, space='buy')
buy_div_max = DecimalParameter(0, 2, decimals=4, default=2.25446, space='buy')
buy_div_min = DecimalParameter(0, 2, decimals=4, default=0.29497, space='buy')
# sell params
sell_mojo_ma_timeframe = IntParameter(2, 100, default=7, space='sell')
sell_fast_ma_timeframe = IntParameter(2, 100, default=14, space='sell')
sell_slow_ma_timeframe = IntParameter(2, 100, default=28, space='sell')
sell_div_max = DecimalParameter(0, 2, decimals=4, default=1.54593, space='sell')
sell_div_min = DecimalParameter(0, 2, decimals=4, default=2.81436, space='sell')
stoploss = -0.1
# Optimal timeframe use it in your config
timeframe = '4h'
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# SMA - ex Moving Average
dataframe['buy-mojoMA'] = ta.SMA(dataframe,
timeperiod=self.buy_mojo_ma_timeframe.value)
dataframe['buy-fastMA'] = ta.SMA(dataframe,
timeperiod=self.buy_fast_ma_timeframe.value)
dataframe['buy-slowMA'] = ta.SMA(dataframe,
timeperiod=self.buy_slow_ma_timeframe.value)
dataframe['sell-mojoMA'] = ta.SMA(dataframe,
timeperiod=self.sell_mojo_ma_timeframe.value)
dataframe['sell-fastMA'] = ta.SMA(dataframe,
timeperiod=self.sell_fast_ma_timeframe.value)
dataframe['sell-slowMA'] = ta.SMA(dataframe,
timeperiod=self.sell_slow_ma_timeframe.value)
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
(dataframe['buy-mojoMA'].div(dataframe['buy-fastMA'])
> self.buy_div_min.value) &
(dataframe['buy-mojoMA'].div(dataframe['buy-fastMA'])
< self.buy_div_max.value) &
(dataframe['buy-fastMA'].div(dataframe['buy-slowMA'])
> self.buy_div_min.value) &
(dataframe['buy-fastMA'].div(dataframe['buy-slowMA'])
< self.buy_div_max.value)
),
'buy'] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
(dataframe['sell-fastMA'].div(dataframe['sell-mojoMA'])
> self.sell_div_min.value) &
(dataframe['sell-fastMA'].div(dataframe['sell-mojoMA'])
< self.sell_div_max.value) &
(dataframe['sell-slowMA'].div(dataframe['sell-fastMA'])
> self.sell_div_min.value) &
(dataframe['sell-slowMA'].div(dataframe['sell-fastMA'])
< self.sell_div_max.value)
),
'sell'] = 1
return dataframe
class Diamond(IStrategy):
# ###################### RESULT PLACE ######################
# Config: 5 x UNLIMITED STOCK costume pair list,
# hyperopt : 5000 x SortinoHyperOptLossDaily,
# 34/5000: 297 trades. 136/156/5 Wins/Draws/Losses. Avg profit 0.49%. Median profit 0.00%. Total profit 45.84477237 USDT ( 33.96Σ%). Avg duration 11:54:00 min. Objective: -46.50379
# Buy hyperspace params:
buy_params = {
"buy_fast_key": "high",
"buy_horizontal_push": 7,
"buy_slow_key": "volume",
"buy_vertical_push": 0.942,
}
# Sell hyperspace params:
sell_params = {
"sell_fast_key": "high",
"sell_horizontal_push": 10,
"sell_slow_key": "low",
"sell_vertical_push": 1.184,
}
# ROI table:
minimal_roi = {"0": 0.242, "13": 0.044, "51": 0.02, "170": 0}
# Stoploss:
stoploss = -0.271
# Trailing stop:
trailing_stop = True
trailing_stop_positive = 0.011
trailing_stop_positive_offset = 0.054
trailing_only_offset_is_reached = False
# timeframe
timeframe = '5m'
# #################### END OF RESULT PLACE ####################
buy_vertical_push = DecimalParameter(0.5,
1.5,
decimals=3,
default=1,
space='buy')
buy_horizontal_push = IntParameter(0, 10, default=0, space='buy')
buy_fast_key = CategoricalParameter(
[
'open',
'high',
'low',
'close',
'volume',
# you can not enable this lines befour you
# populate an indicator for them and set
# the same key name for it
# 'ma_fast', 'ma_slow', {...}
],
default='ma_fast',
space='buy')
buy_slow_key = CategoricalParameter(
[
'open',
'high',
'low',
'close',
'volume',
# 'ma_fast', 'ma_slow', {...}
],
default='ma_slow',
space='buy')
sell_vertical_push = DecimalParameter(0.5,
1.5,
decimals=3,
default=1,
space='sell')
sell_horizontal_push = IntParameter(0, 10, default=0, space='sell')
sell_fast_key = CategoricalParameter(
[
'open',
'high',
'low',
'close',
'volume',
# 'ma_fast', 'ma_slow', {...}
],
default='ma_fast',
space='sell')
sell_slow_key = CategoricalParameter(
[
'open',
'high',
'low',
'close',
'volume',
# 'ma_fast', 'ma_slow', {...}
],
default='ma_slow',
space='sell')
def populate_indicators(self, dataframe: DataFrame,
metadata: dict) -> DataFrame:
# you can add new indicators and enable them inside
# hyperoptable categorical params on the top
# dataframe['ma_fast'] = ta.SMA(dataframe, timeperiod=9)
# dataframe['ma_slow'] = ta.SMA(dataframe, timeperiod=18)
# dataframe['{...}'] = ta.{...}(dataframe, timeperiod={...})
return dataframe
def populate_buy_trend(self, dataframe: DataFrame,
metadata: dict) -> DataFrame:
conditions = []
conditions.append(
qtpylib.crossed_above(
dataframe[self.buy_fast_key.value].shift(
self.buy_horizontal_push.value),
dataframe[self.buy_slow_key.value] *
self.buy_vertical_push.value))
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[self.sell_fast_key.value].shift(
self.sell_horizontal_push.value),
dataframe[self.sell_slow_key.value] *
self.sell_vertical_push.value))
if conditions:
dataframe.loc[reduce(lambda x, y: x & y, conditions), 'sell'] = 1
return dataframe
class Zeus(IStrategy):
# 53/167: 167 trades. 96/66/5 Wins/Draws/Losses. Avg profit 3.00%. Median profit 2.70%. Total profit 0.16479843 BTC ( 164.80Σ%). Avg duration 22:04:00 min. Objective: -63.49577
# Buy hyperspace params:
buy_params = {
"buy_cat": "<R",
"buy_real": 0.0889,
}
# Sell hyperspace params:
sell_params = {
"sell_cat": "=R",
"sell_real": 0.979,
}
# ROI table:
minimal_roi = {"0": 0.336, "134": 0.113, "759": 0.027, "1049": 0}
# Stoploss:
stoploss = -0.245
# Trailing stop:
trailing_stop = True
trailing_stop_positive = 0.35
trailing_stop_positive_offset = 0.375
trailing_only_offset_is_reached = False
buy_real = DecimalParameter(0.001,
0.999,
decimals=4,
default=0.11908,
space='buy')
buy_cat = CategoricalParameter([">R", "=R", "<R"],
default='<R',
space='buy')
sell_real = DecimalParameter(0.001,
0.999,
decimals=4,
default=0.59608,
space='sell')
sell_cat = CategoricalParameter([">R", "=R", "<R"],
default='>R',
space='sell')
# Buy hypers
timeframe = '4h'
def populate_indicators(self, dataframe: DataFrame,
metadata: dict) -> DataFrame:
# Add all ta features
# Clean NaN values
dataframe = dropna(dataframe)
dataframe['trend_ichimoku_base'] = ta.trend.ichimoku_base_line(
dataframe['high'],
dataframe['low'],
window1=9,
window2=26,
visual=False,
fillna=False)
KST = ta.trend.KSTIndicator(close=dataframe['close'],
roc1=10,
roc2=15,
roc3=20,
roc4=30,
window1=10,
window2=10,
window3=10,
window4=15,
nsig=9,
fillna=False)
dataframe['trend_kst_diff'] = KST.kst_diff()
# Normalization
tib = dataframe['trend_ichimoku_base']
dataframe['trend_ichimoku_base'] = (tib - tib.min()) / (tib.max() -
tib.min())
tkd = dataframe['trend_kst_diff']
dataframe['trend_kst_diff'] = (tkd - tkd.min()) / (tkd.max() -
tkd.min())
return dataframe
def populate_buy_trend(self, dataframe: DataFrame,
metadata: dict) -> DataFrame:
conditions = []
IND = 'trend_ichimoku_base'
REAL = self.buy_real.value
OPR = self.buy_cat.value
DFIND = dataframe[IND]
# print(DFIND.mean())
if OPR == ">R":
conditions.append(DFIND > REAL)
elif OPR == "=R":
conditions.append(np.isclose(DFIND, REAL))
elif OPR == "<R":
conditions.append(DFIND < REAL)
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 = []
IND = 'trend_kst_diff'
REAL = self.sell_real.value
OPR = self.sell_cat.value
DFIND = dataframe[IND]
# print(DFIND.mean())
if OPR == ">R":
conditions.append(DFIND > REAL)
elif OPR == "=R":
conditions.append(np.isclose(DFIND, REAL))
elif OPR == "<R":
conditions.append(DFIND < REAL)
if conditions:
dataframe.loc[reduce(lambda x, y: x & y, conditions), 'sell'] = 1
return dataframe