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crypto_rahino
2021-03-19 11:13:07 +02:00
parent 38b96f071f
commit df3c607e57
136 changed files with 13669 additions and 4 deletions
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.env/bin/user_data/strategies/.gitkeep Normal file
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.env/bin/user_data/strategies/__init__.py Normal file
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.env/bin/user_data/strategies/data_eth_usdt_1m.py Normal file
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# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
# isort: skip_file
# --- Do not remove these libs ---
import numpy as np # noqa
import pandas as pd # noqa
from pandas import DataFrame
from talib._ta_lib import ULTOSC, MACD, LINEARREG_ANGLE, TSF, MFI, EMA, MA, BBANDS, CORREL
from freqtrade.strategy.interface import IStrategy
# --------------------------------
# Add your lib to import here
import freqtrade.vendor.qtpylib.indicators as qtpylib
"""
"""
# This class is a sample. Feel free to customize it.
class data_ETHUSDT_1m(IStrategy):
"""
This is a sample strategy to inspire you.
More information in https://github.com/freqtrade/freqtrade/blob/develop/docs/bot-optimization.md
You can:
:return: a Dataframe with all mandatory indicators for the strategies
- Rename the class name (Do not forget to update class_name)
- Add any methods you want to build your strategy
- Add any lib you need to build your strategy
You must keep:
- the lib in the section "Do not remove these libs"
- the prototype for the methods: minimal_roi, stoploss, populate_indicators, populate_buy_trend,
populate_sell_trend, hyperopt_space, buy_strategy_generator
"""
# 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
# Minimal ROI designed for the strategy.
# This attribute will be overridden if the config file contains "minimal_roi".
minimal_roi = {
"0": 0.24724,
"30": 0.1,
"46": 0.05,
"151": 0
}
# Optimal stoploss designed for the strategy.
# This attribute will be overridden if the config file contains "stoploss".
stoploss = -0.1
# Trailing stoploss
trailing_stop = True
trailing_stop_positive = 0.02
trailing_stop_positive_offset = 0.14
trailing_only_offset_is_reached = True
# Optimal ticker interval for the strategy.
timeframe = '1m'
# Run "populate_indicators()" only for new candle.
process_only_new_candles = False
# 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
# Number of candles the strategy requires before producing valid signals
startup_candle_count: int = 30
# Optional order type mapping.
order_types = {
'buy': 'limit',
'sell': 'limit',
'stoploss': 'market',
'stoploss_on_exchange': False
}
# Optional order time in force.
order_time_in_force = {
'buy': 'gtc',
'sell': 'gtc'
}
plot_config = {
'main_plot': {
'tema': {},
'sar': {'color': 'white'},
},
'subplots': {
"MACD": {
'macd': {'color': 'blue'},
'macdsignal': {'color': 'orange'},
},
"RSI": {
'rsi': {'color': 'red'},
}
}
}
def informative_pairs(self):
"""
Define additional, informative pair/interval combinations to be cached from the exchange.
These pair/interval combinations are non-tradeable, unless they are part
of the whitelist as well.
For more information, please consult the documentation
:return: List of tuples in the format (pair, interval)
Sample: return [("ETH/USDT", "5m"),
("BTC/USDT", "15m"),
]
"""
return [("ETH/USDT", "1m")]
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
"""
dataframe['mfi'] = MFI(dataframe['high'], dataframe['low'], dataframe['close'], dataframe['volume'],
timeperiod=14)
dataframe['angle_short'] = LINEARREG_ANGLE(dataframe['close'], timeperiod=10)
dataframe['angle_mid'] = LINEARREG_ANGLE(dataframe['close'], timeperiod=144)
dataframe['angle_long'] = LINEARREG_ANGLE(dataframe['close'], timeperiod=288)
dataframe['tsf_long'] = TSF(dataframe['close'], timeperiod=288)
dataframe['tsf_mid'] = TSF(dataframe['close'], timeperiod=144)
dataframe['tsf_short'] = TSF(dataframe['close'], timeperiod=10)
dataframe['correl_h_l'] = CORREL(dataframe['high'], dataframe['low'], timeperiod=30)
dataframe['correl_close_last_close'] = CORREL(dataframe['close'].shift(1), dataframe['close'], timeperiod=30)
dataframe['correl_tsf_long_close'] = CORREL(dataframe['tsf_long'], dataframe['close'], timeperiod=288)
dataframe['correl_tsf_mid_close'] = CORREL(dataframe['tsf_mid'], dataframe['close'], timeperiod=144)
dataframe['correl_tsf_short_close'] = CORREL(dataframe['tsf_short'], dataframe['close'], timeperiod=30)
dataframe['correl_angle_short_close'] = CORREL(dataframe['angle_short'], dataframe['close'], timeperiod=30)
dataframe['correl_angle_mid_close'] = CORREL(dataframe['angle_mid'], dataframe['close'], timeperiod=144)
dataframe['correl_angle_long_close'] = CORREL(dataframe['angle_long'], dataframe['close'], timeperiod=288)
dataframe['correl_hist_close'] = CORREL(dataframe['macdhist'], dataframe['close'], timeperiod=24)
dataframe['correl_mfi_close'] = CORREL(dataframe['mfi'], dataframe['close'], timeperiod=14)
dataframe['ema'] = EMA(dataframe['close'], timeperiod=8)
dataframe['ma'] = MA(dataframe['close'], timeperiod=8, matype=0)
dataframe['upperband'], dataframe['middleband'], dataframe['lowerband'] = BBANDS(dataframe['close'],
timeperiod=20, nbdevup=2,
nbdevdn=2, matype=0)
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the buy signal for the given dataframe
:param dataframe: DataFrame populated with indicators
:param metadata: Additional information, like the currently traded pair
:return: DataFrame with buy column
"""
dataframe.loc[
(
(qtpylib.crossed_above(dataframe['close'], dataframe['lowerband'])) &
((dataframe['tsf_mid'] - dataframe['close']) > 14) &
(0.5263534368259521 < dataframe['correl_h_l']) &
(0.0728048430667152 < dataframe['correl_tsf_mid_close']) &
(-0.02402596125126566 < dataframe['correl_angle_short_close']) &
(0.2842166347875669 < dataframe['correl_angle_long_close']) &
(-0.6552565064627378 < dataframe['correl_mfi_close']) &
(-0.4853276710303872 < dataframe['correl_hist_close']) &
(0.442188534531633 < dataframe['mfi']) &
(0.6714488827895353 < dataframe['ema']) &
(dataframe['volume'] > 0) # Make sure Volume is not 0
),
'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
:param dataframe: DataFrame populated with indicators
:param metadata: Additional information, like the currently traded pair
:return: DataFrame with buy column
"""
dataframe.loc[
(
(qtpylib.crossed_above(dataframe['close'], dataframe['lowerband'])) &
((dataframe['tsf_mid'] - dataframe['close']) > 68) &
(-0.6225944943311145 < dataframe['correl_h_l']) &
(0.2602396802858502 < dataframe['correl_tsf_mid_close']) &
(0.771988603840956 < dataframe['correl_angle_short_close']) &
(0.33471662411500236 < dataframe['correl_angle_long_close']) &
(-0.9562413964921457 < dataframe['correl_hist_close']) &
(-0.43268559077377733 < dataframe['correl_mfi_close']) &
(-0.25207265197064166 < dataframe['mfi']) &
(-0.00739011415527302 < dataframe['ema']) &
(dataframe['volume'] > 0) # Make sure Volume is not 0
),
'sell'] = 1
return dataframe

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.env/bin/user_data/strategies/ethusdt2_safe_prod1.py Normal file
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# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
# isort: skip_file
# --- Do not remove these libs ---
import numpy as np # noqa
import pandas as pd # noqa
from pandas import DataFrame
from talib._ta_lib import ULTOSC, MACD, LINEARREG_ANGLE, TSF, MFI, EMA, MA, BBANDS, CORREL, MAX, MIN, SAR, CCI, \
HT_TRENDLINE, HT_DCPERIOD, HT_TRENDMODE, HT_SINE, RSI
from freqtrade.strategy import merge_informative_pair
from freqtrade.strategy.interface import IStrategy
# --------------------------------
# Add your lib to import here
import freqtrade.vendor.qtpylib.indicators as qtpylib
"""
"""
# This class is a sample. Feel free to customize it.
class strg2_ETHUSDT_1h_prod1(IStrategy):
"""
This is a sample strategy to inspire you.
More information in https://github.com/freqtrade/freqtrade/blob/develop/docs/bot-optimization.md
You can:
:return: a Dataframe with all mandatory indicators for the strategies
- Rename the class name (Do not forget to update class_name)
- Add any methods you want to build your strategy
- Add any lib you need to build your strategy
You must keep:
- the lib in the section "Do not remove these libs"
- the prototype for the methods: minimal_roi, stoploss, populate_indicators, populate_buy_trend,
populate_sell_trend, hyperopt_space, buy_strategy_generator
"""
# 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
# Minimal ROI designed for the strategy.
# This attribute will be overridden if the config file contains "minimal_roi".
minimal_roi = {
"0": 0.07371,
"9817": 0.0461,
"14487": 0.0254,
"15960": 0
}
order_types = {
'buy': 'market',
'sell': 'market',
'stoploss': 'market',
'stoploss_on_exchange': False
}
# Optimal stoploss designed for the strategy.
# This attribute will be overridden if the config file contains "stoploss".
stoploss = -0.23371
# Trailing stop:
trailing_stop = True
trailing_stop_positive = 0.11193
trailing_stop_positive_offset = 0.20381
trailing_only_offset_is_reached = True
plot_config = {
'main_plot': {
'upperband': {'upperband': 'green'},
'middleband': {'color': 'green'},
'lowerband': {'color': 'green'},
'tsf_mid': {'color': 'white'},
'ema': {'color': 'white'},
},
'subplots': {
"corr": {
'correl_h_l': {'color': 'black'},
},
"correl_tsf_mid_close": {
'correl_tsf_mid_close': {'color': 'grey'},
},
"correl_angle_short_close": {
'correl_angle_short_close': {'color': 'blue'},
},
"correl_angle_long_close": {
'correl_angle_long_close': {'color': 'red'},
},
"correl_mfi_close": {
'correl_mfi_close': {'color': 'black'},
},
"correl_hist_close": {
'correl_tsf_mid_close': {'color': 'red'},
},
"mfi": {
'mfi': {'color': 'yellow'},
},
}
}
def informative_pairs(self):
"""
Define additional, informative pair/interval combinations to be cached from the exchange.
These pair/interval combinations are non-tradeable, unless they are part
of the whitelist as well.
For more information, please consult the documentation
:return: List of tuples in the format (pair, interval)
Sample: return [("ETH/USDT", "5m"),
("BTC/USDT", "15m"),
]
"""
return [("ETH/USDT", "1d")]
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
"""
# dataframe['macd'], dataframe['macdsignal'], dataframe['macdhist'] = MACD(dataframe['close'], fastperiod=12,
# slowperiod=26, signalperiod=7)
# dataframe['cci'] = CCI(dataframe['high'], dataframe['low'], dataframe['close'], timeperiod=30)
dataframe['ao'] = qtpylib.awesome_oscillator(dataframe, weighted=False, fast=5, slow=34)
# dataframe['vwap'] = qtpylib.vwap(dataframe)
# dataframe['vwap'] = qtpylib.rolling_vwap(dataframe)
dataframe['rsi'] = RSI(dataframe['close'], timeperiod=14)
# dataframe['tsf_long'] = TSF(dataframe['close'], timeperiod=200)
# dataframe['tsf_short'] = TSF(dataframe['close'], timeperiod=7)
dataframe['tsf_mid'] = TSF(dataframe['close'], timeperiod=50)
dataframe['angle_tsf_mid'] = LINEARREG_ANGLE(dataframe['tsf_mid'], timeperiod=10)
# dataframe['angle'] = LINEARREG_ANGLE(dataframe['close'], timeperiod=50)
# dataframe['tsf_max'] = MAX(dataframe['tsf_mid'], timeperiod=30)
# dataframe['tsf_min'] = MIN(dataframe['tsf_mid'], timeperiod=30)
# dataframe['sar'] = SAR(dataframe['high'], dataframe['low'])
# dataframe['sine'], dataframe['leadsine'] = HT_SINE(dataframe['close'])
# dataframe['trend'] = HT_TRENDLINE(dataframe['close'])
# dataframe['mfi'] = MFI(dataframe['high'], dataframe['low'], dataframe['close'], dataframe['volume'],
# timeperiod=5)
# dataframe['angle_trend_mid'] = LINEARREG_ANGLE(dataframe['trend'], timeperiod=10)
# dataframe['upperband'], dataframe['middleband'], dataframe['lowerband'] = BBANDS(dataframe['close'],
# timeperiod=30, nbdevup=2,
# nbdevdn=2, matype=0)
# if not self.dp:
# # Don't do anything if DataProvider is not available.
# return dataframe
# # Get the informative pair
# informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe='1d')
# informative['trend'] = HT_TRENDLINE(informative['close'])
# informative['period'] = HT_DCPERIOD(informative['close'])
# informative['mode'] = HT_TRENDMODE(informative['close'])
# # informative['sine'], informative['leadsine'] = HT_SINE(informative['close'])
# informative['angle_trend'] = LINEARREG_ANGLE(informative['trend'], timeperiod=5)
# # informative['sar'] = SAR(informative['high'], informative['low'])
# dataframe = merge_informative_pair(dataframe, informative, self.timeframe, '1d', ffill=True)
# return dataframe.set_index('date')['2021-01-01':].reset_index()
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the buy signal for the given dataframe
:param dataframe: DataFrame populated with indicators
:param metadata: Additional information, like the currently traded pair
:return: DataFrame with buy column
"""
dataframe.loc[
(
(qtpylib.crossed_above(dataframe['angle_tsf_mid'], -50)) &
# (dataframe['sine_1h'] < dataframe['leadsine_1h']) &
# (dataframe['tsf_mid'] > dataframe['close']) &
# (dataframe['ao'] > -5) &
# (dataframe['rsi'] < 36) &
(dataframe['rsi'] > 14) &
(dataframe['volume'] > 0) # Make sure Volume is not 0
),
'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
:param dataframe: DataFrame populated with indicators
:param metadata: Additional information, like the currently traded pair
:return: DataFrame with buy column
"""
dataframe.loc[
(
(qtpylib.crossed_below(dataframe['angle_tsf_mid'], 50)) &
# (dataframe['sine_1h'] > dataframe['leadsine_1h']) &
# (dataframe['sar_1d'] < dataframe['close']) &
# (dataframe['tsf_mid'] < dataframe['close']) &
(dataframe['rsi'] > 50) &
(dataframe['ao'] < -10) &
(dataframe['volume'] > 0) # Make sure Volume is not 0
),
'sell'] = 1
return dataframe

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.env/bin/user_data/strategies/ethusdt_high_risk.py Normal file
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# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
# isort: skip_file
# --- Do not remove these libs ---
import numpy as np # noqa
import pandas as pd # noqa
from pandas import DataFrame
from talib._ta_lib import ULTOSC, MACD, LINEARREG_ANGLE, TSF, MFI, EMA, MA, BBANDS, CORREL, MAX, MIN, SAR, CCI, \
HT_TRENDLINE, HT_DCPERIOD, HT_TRENDMODE, HT_SINE, RSI, NATR, STOCH, STOCHF, STOCHRSI
from freqtrade.strategy import merge_informative_pair
from freqtrade.strategy.interface import IStrategy
# --------------------------------
# Add your lib to import here
import freqtrade.vendor.qtpylib.indicators as qtpylib
"""
"""
# This class is a sample. Feel free to customize it.
class ETHUSDT_1m_high_risk(IStrategy):
"""
This is a sample strategy to inspire you.
More information in https://github.com/freqtrade/freqtrade/blob/develop/docs/bot-optimization.md
You can:
:return: a Dataframe with all mandatory indicators for the strategies
- Rename the class name (Do not forget to update class_name)
- Add any methods you want to build your strategy
- Add any lib you need to build your strategy
You must keep:
- the lib in the section "Do not remove these libs"
- the prototype for the methods: minimal_roi, stoploss, populate_indicators, populate_buy_trend,
populate_sell_trend, hyperopt_space, buy_strategy_generator
"""
# 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
# Minimal ROI designed for the strategy.
# This attribute will be overridden if the config file contains "minimal_roi".
minimal_roi = {
"0": 0.22703036349783817,
"30": 0.09085576426119433,
"82": 0.029443202051755248,
"164": 0
}
order_types = {
'buy': 'market',
'sell': 'market',
'stoploss': 'market',
'stoploss_on_exchange': False
}
# Optimal stoploss designed for the strategy.
# This attribute will be overridden if the config file contains "stoploss".
# Stoploss:
stoploss = -0.22515
# Trailing stop:
trailing_stop = True
trailing_stop_positive = 0.03428
trailing_stop_positive_offset = 0.05094
trailing_only_offset_is_reached = True
plot_config = {
'main_plot': {
'upperband': {'upperband': 'green'},
'middleband': {'color': 'green'},
'lowerband': {'color': 'green'},
'tsf_mid': {'color': 'white'},
'ema': {'color': 'white'},
},
'subplots': {
"corr": {
'correl_h_l': {'color': 'black'},
},
"correl_tsf_mid_close": {
'correl_tsf_mid_close': {'color': 'grey'},
},
"correl_angle_short_close": {
'correl_angle_short_close': {'color': 'blue'},
},
"correl_angle_long_close": {
'correl_angle_long_close': {'color': 'red'},
},
"correl_mfi_close": {
'correl_mfi_close': {'color': 'black'},
},
"correl_hist_close": {
'correl_tsf_mid_close': {'color': 'red'},
},
"mfi": {
'mfi': {'color': 'yellow'},
},
}
}
def informative_pairs(self):
"""
Define additional, informative pair/interval combinations to be cached from the exchange.
These pair/interval combinations are non-tradeable, unless they are part
of the whitelist as well.
For more information, please consult the documentation
:return: List of tuples in the format (pair, interval)
Sample: return [("ETH/USDT", "5m"),
("BTC/USDT", "15m"),
]
"""
return [("ETH/BTC", "1h")]
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
"""
dataframe['trend'] = HT_TRENDLINE(dataframe['close'])
dataframe['tsf_short'] = TSF(dataframe['close'], timeperiod=12)
dataframe['tsf_mid'] = TSF(dataframe['close'], timeperiod=48)
dataframe['vwap_short'] = qtpylib.rolling_vwap(dataframe, window=5)
dataframe['vwap_mid'] = qtpylib.rolling_vwap(dataframe, window=90)
dataframe['vwap_long'] = qtpylib.rolling_vwap(dataframe, window=1440)
dataframe['macd'], dataframe['macdsignal'], dataframe['macdhist'] = MACD(dataframe['close'], fastperiod=12,
slowperiod=26, signalperiod=7)
dataframe['ao_mid'] = qtpylib.awesome_oscillator(dataframe, weighted=False, fast=5, slow=36)
dataframe['ao_short'] = qtpylib.awesome_oscillator(dataframe, weighted=False, fast=5, slow=15)
dataframe['cci'] = CCI(dataframe['high'], dataframe['low'], dataframe['close'], timeperiod=30)
dataframe['rsi'] = RSI(dataframe['close'], timeperiod=14)
dataframe['slowk'], dataframe['slowd'] = STOCH(dataframe['high'], dataframe['low'], dataframe['close'],
fastk_period=5, slowk_period=3,
slowk_matype=0, slowd_period=3,
slowd_matype=0)
dataframe['fastk'], dataframe['fastd'] = STOCHF(dataframe['high'], dataframe['low'], dataframe['close'],
fastk_period=5, fastd_period=3, fastd_matype=0)
dataframe['fastk'], dataframe['fastd'] = STOCHRSI(dataframe['close'], timeperiod=14, fastk_period=5,
fastd_period=3, fastd_matype=0)
dataframe['sar'] = SAR(dataframe['high'], dataframe['low'])
dataframe['min_high'] = MIN(dataframe['high'], timeperiod=5)
dataframe['max_low'] = MAX(dataframe['low'], timeperiod=5)
dataframe['max_low_min_high_ratio'] = dataframe['max_low'] - dataframe['min_high'].shift(15)
dataframe['correl_h_l_30'] = CORREL(dataframe['high'], dataframe['low'], timeperiod=30)
dataframe['angle_tsf_short'] = LINEARREG_ANGLE(dataframe['tsf_short'], timeperiod=5)
dataframe['angle_close'] = LINEARREG_ANGLE(dataframe['close'], timeperiod=5)
dataframe['sine'], dataframe['leadsine'] = HT_SINE(dataframe['close'])
dataframe['mode'] = HT_TRENDMODE(dataframe['close'])
dataframe['corel_mode'] = CORREL(dataframe['mode'], dataframe['close'], timeperiod=30)
dataframe = dataframe.reset_index().dropna()
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the buy signal for the given dataframe
:param dataframe: DataFrame populated with indicators
:param metadata: Additional information, like the currently traded pair
:return: DataFrame with buy column
"""
dataframe.loc[
(
(qtpylib.crossed_below(dataframe['sar'], dataframe['close'])) &
(dataframe['angle_trend'] > 0) &
(dataframe['angle_tsf_mid'] > 0) &
(0.75227 < dataframe['correl_h_l_30']) &
(0.4 < dataframe['correl_h_l_30']) &
# (dataframe['sine_1h'] < dataframe['leadsine_1h']) &
# (dataframe['tsf_mid'] > dataframe['close']) &
# (dataframe['angle'] > 0) &
# (dataframe['rsi'] <50) &
# (dataframe['natr'] > 1.1) &
(dataframe['volume'] > 0) # Make sure Volume is not 0
),
'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
:param dataframe: DataFrame populated with indicators
:param metadata: Additional information, like the currently traded pair
:return: DataFrame with buy column
"""
dataframe.loc[
(
(qtpylib.crossed_above(dataframe['sar'], dataframe['close'])) &
(dataframe['max_low'] - dataframe['min_high'].shift(15) < 0) &
(dataframe['angle_trend'] < 2) &
(dataframe['angle_tsf_mid'] < 2) &
(dataframe['mode'] == 1) &
# (dataframe['close'] < dataframe['vwap']) &
(dataframe['volume'] > 0) # Make sure Volume is not 0
),
'sell'] = 1
return dataframe

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# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
# isort: skip_file
# --- Do not remove these libs ---
import numpy as np # noqa
import pandas as pd # noqa
from pandas import DataFrame
from talib._ta_lib import ULTOSC, MACD, LINEARREG_ANGLE, TSF, MFI, EMA, MA, BBANDS, CORREL, MAX, MIN, SAR, CCI, \
HT_TRENDLINE, HT_DCPERIOD, HT_TRENDMODE, HT_SINE, RSI, NATR, HT_PHASOR
from freqtrade.strategy import merge_informative_pair
from freqtrade.strategy.interface import IStrategy
# --------------------------------
# Add your lib to import here
import freqtrade.vendor.qtpylib.indicators as qtpylib
"""
"""
# This class is a sample. Feel free to customize it.
class ltcusdt_1h(IStrategy):
"""
This is a sample strategy to inspire you.
More information in https://github.com/freqtrade/freqtrade/blob/develop/docs/bot-optimization.md
You can:
:return: a Dataframe with all mandatory indicators for the strategies
- Rename the class name (Do not forget to update class_name)
- Add any methods you want to build your strategy
- Add any lib you need to build your strategy
You must keep:
- the lib in the section "Do not remove these libs"
- the prototype for the methods: minimal_roi, stoploss, populate_indicators, populate_buy_trend,
populate_sell_trend, hyperopt_space, buy_strategy_generator
"""
# 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
# Minimal ROI designed for the strategy.
# This attribute will be overridden if the config file contains "minimal_roi".
minimal_roi = {
"0": 0.04371,
"300": 0.0461,
"14487": 0.0254,
"15960": 0
}
order_types = {
'buy': 'market',
'sell': 'market',
'stoploss': 'market',
'stoploss_on_exchange': False
}
# Optimal stoploss designed for the strategy.
# This attribute will be overridden if the config file contains "stoploss".
stoploss = -0.23
# Trailing stop:
trailing_stop = True
trailing_stop_positive = 0.11193
trailing_stop_positive_offset = 0.20381
trailing_only_offset_is_reached = True
plot_config = {
'main_plot': {
'upperband': {'upperband': 'green'},
'middleband': {'color': 'green'},
'lowerband': {'color': 'green'},
'tsf_mid': {'color': 'white'},
'ema': {'color': 'white'},
},
'subplots': {
"corr": {
'correl_h_l': {'color': 'black'},
},
"correl_tsf_mid_close": {
'correl_tsf_mid_close': {'color': 'grey'},
},
"correl_angle_short_close": {
'correl_angle_short_close': {'color': 'blue'},
},
"correl_angle_long_close": {
'correl_angle_long_close': {'color': 'red'},
},
"correl_mfi_close": {
'correl_mfi_close': {'color': 'black'},
},
"correl_hist_close": {
'correl_tsf_mid_close': {'color': 'red'},
},
"mfi": {
'mfi': {'color': 'yellow'},
},
}
}
def informative_pairs(self):
"""
Define additional, informative pair/interval combinations to be cached from the exchange.
These pair/interval combinations are non-tradeable, unless they are part
of the whitelist as well.
For more information, please consult the documentation
:return: List of tuples in the format (pair, interval)
Sample: return [("ETH/USDT", "5m"),
("BTC/USDT", "15m"),
]
"""
return [("ETH/USDT", "1d")]
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
"""
dataframe['macd'], dataframe['macdsignal'], dataframe['macdhist'] = MACD(dataframe['close'], fastperiod=12,
slowperiod=26, signalperiod=7)
dataframe['mfi'] = MFI(dataframe['high'], dataframe['low'], dataframe['close'], dataframe['volume'],
timeperiod=14)
dataframe['ao'] = qtpylib.awesome_oscillator(dataframe, weighted=False, fast=5, slow=34)
dataframe['tsf_mid'] = TSF(dataframe['close'], timeperiod=48)
dataframe['sar'] = SAR(dataframe['high'], dataframe['low'])
dataframe['natr'] = NATR(dataframe['high'], dataframe['low'], dataframe['close'], timeperiod=14)
dataframe['angle_tsf_mid'] = LINEARREG_ANGLE(dataframe['tsf_mid'], timeperiod=10)
dataframe['sine'], dataframe['leadsine'] = HT_SINE(dataframe['close'])
dataframe['sine'] = dataframe['sine'].multiply(10)
dataframe['leadsine'] = dataframe['leadsine'].multiply(10)
dataframe['trend'] = HT_TRENDLINE(dataframe['close'])
dataframe['mode'] = HT_TRENDMODE(dataframe['close'])
dataframe['inphase'], dataframe['quadrature'] = HT_PHASOR(dataframe['close'])
dataframe['angle_trend_mid'] = LINEARREG_ANGLE(dataframe['trend'], timeperiod=10)
dataframe['angle'] = LINEARREG_ANGLE(dataframe['close'], timeperiod=12)
dataframe['angle_macdsignal'] = LINEARREG_ANGLE(dataframe['macdsignal'], timeperiod=15)
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the buy signal for the given dataframe
:param dataframe: DataFrame populated with indicators
:param metadata: Additional information, like the currently traded pair
:return: DataFrame with buy column
"""
dataframe.loc[
(
(qtpylib.crossed_above(dataframe['leadsine'], dataframe['sine'])) &
# (dataframe['sine_1h'] < dataframe['leadsine_1h']) &
# (dataframe['tsf_mid'] > dataframe['close']) &
(dataframe['ao'] > -5) &
(dataframe['angle_tsf_mid'] > -3) &
(dataframe['volume'] > 0) # Make sure Volume is not 0
),
'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
:param dataframe: DataFrame populated with indicators
:param metadata: Additional information, like the currently traded pair
:return: DataFrame with buy column
"""
dataframe.loc[
(
(qtpylib.crossed_below(dataframe['leadsine'], dataframe['sine'])) &
# (dataframe['sine_1h'] > dataframe['leadsine_1h']) &
# (dataframe['sar_1d'] < dataframe['close']) &
# (dataframe['tsf_mid'] < dataframe['close']) &
# (dataframe['natr'] > 2.5) &
(dataframe['angle_tsf_mid'] < 5) &
(dataframe['volume'] > 0) # Make sure Volume is not 0
),
'sell'] = 1
return dataframe

301
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# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
# isort: skip_file
# --- Do not remove these libs ---
import numpy as np # noqa
import pandas as pd # noqa
from pandas import DataFrame
from talib._ta_lib import ULTOSC, MACD, SAR, LINEARREG_ANGLE, TEMA, STOCHRSI, STOCH, STOCHF, RSI
from freqtrade.strategy.interface import IStrategy
# --------------------------------
# Add your lib to import here
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
# This class is a sample. Feel free to customize it.
class macd_ethbtc_1m(IStrategy):
"""
This is a sample strategy to inspire you.
More information in https://github.com/freqtrade/freqtrade/blob/develop/docs/bot-optimization.md
You can:
:return: a Dataframe with all mandatory indicators for the strategies
- Rename the class name (Do not forget to update class_name)
- Add any methods you want to build your strategy
- Add any lib you need to build your strategy
You must keep:
- the lib in the section "Do not remove these libs"
- the prototype for the methods: minimal_roi, stoploss, populate_indicators, populate_buy_trend,
populate_sell_trend, hyperopt_space, buy_strategy_generator
"""
# Strategy interface version - allow new iterations of the strategy interface.
# Check the documentation or the Sample strategy to get the latest version.
INTERFACE_VERSION = 1
# Minimal ROI designed for the strategy.
# This attribute will be overridden if the config file contains "minimal_roi".
minimal_roi = {
"0": 0.04025819697656752,
"7": 0.015188707936204564,
"18": 0.005472487470606337,
"41": 0
}
# Optimal stoploss designed for the strategy.
# This attribute will be overridden if the config file contains "stoploss".
stoploss = -0.33515742514178193
# Trailing stoploss
trailing_stop = False
trailing_stop_positive = 0.34089
trailing_stop_positive_offset = 0.43254
trailing_only_offset_is_reached = False
# Optimal ticker interval for the strategy.
timeframe = '1m'
# Run "populate_indicators()" only for new candle.
process_only_new_candles = False
# These values can be overridden in the "ask_strategy" section in the config.
use_sell_signal = True
sell_profit_only = True
ignore_roi_if_buy_signal = False
# Number of candles the strategy requires before producing valid signals
startup_candle_count: int = 30
# Optional order type mapping.
order_types = {
'buy': 'market',
'sell': 'market',
'stoploss': 'market',
'stoploss_on_exchange': False
}
# Optional order time in force.
order_time_in_force = {
'buy': 'gtc',
'sell': 'gtc'
}
plot_config = {
'main_plot': {
'close': {},
'sar': {'color': 'white'},
},
'subplots': {
"MACD": {
'macd': {'color': 'blue'},
'macdsignal': {'color': 'orange'},
},
"OU": {
'ou': {'color': 'red'},
}
}
}
def informative_pairs(self):
"""
Define additional, informative pair/interval combinations to be cached from the exchange.
These pair/interval combinations are non-tradeable, unless they are part
of the whitelist as well.
For more information, please consult the documentation
:return: List of tuples in the format (pair, interval)
Sample: return [("ETH/USDT", "5m"),
("BTC/USDT", "15m"),
]
"""
return [("ETH/BTC", "1m")]
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
"""
# MACD
dataframe['macd'], dataframe['macdsignal'], dataframe['macdhist'] = MACD(dataframe['close'], fastperiod=12,
slowperiod=26, signalperiod=9)
dataframe['macd_angle'] = LINEARREG_ANGLE(dataframe['macd'], timeperiod=3)
dataframe['macdhist_angle'] = LINEARREG_ANGLE(dataframe['macd'], timeperiod=3)
# Parabolic SAR
dataframe['sar'] = SAR(dataframe['high'], dataframe['low'], acceleration=0, maximum=0)
dataframe['sar_angle'] = LINEARREG_ANGLE(dataframe['sar'], timeperiod=3)
# Linear angle
dataframe['angle'] = LINEARREG_ANGLE(dataframe['close'], timeperiod=14)
dataframe['tema'] = TEMA(dataframe['close'], timeperiod=30)
dataframe['sr_fastk'], dataframe['sr_fastd'] = STOCHRSI(dataframe['close'], timeperiod=14, fastk_period=5,
fastd_period=3, fastd_matype=0)
dataframe['sr_fastd_angle'] = LINEARREG_ANGLE(dataframe['sr_fastd'], timeperiod=4)
dataframe['slowk'], dataframe['slowd'] = STOCH(dataframe['high'], dataframe['low'], dataframe['close'],
fastk_period=5, slowk_period=3, slowk_matype=0, slowd_period=3,
slowd_matype=0)
dataframe['slowd_angle'] = LINEARREG_ANGLE(dataframe['slowd'], timeperiod=3)
dataframe['sf_fastk'], dataframe['sf_fastd'] = STOCHF(dataframe['high'], dataframe['low'], dataframe['close'], fastk_period=5, fastd_period=3, fastd_matype=0)
dataframe['sf_fastd_angle'] = LINEARREG_ANGLE(dataframe['sf_fastd'], timeperiod=3)
dataframe['rsi'] = RSI(dataframe['close'], timeperiod=14)
dataframe['rsi_angle'] = LINEARREG_ANGLE(dataframe['rsi'], timeperiod=5)
# # first check if dataprovider is available
# if self.dp:
# if self.dp.runmode in ('live', 'dry_run'):
# ob = self.dp.orderbook(metadata['pair'], 1)
# dataframe['best_bid'] = ob['bids'][0][0]
# dataframe['best_ask'] = ob['asks'][0][0]
#
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the buy signal for the given dataframe
:param dataframe: DataFrame populated with indicators
:param metadata: Additional information, like the currently traded pair
:return: DataFrame with buy column
"""
dataframe.loc[
(
(qtpylib.crossed_above(dataframe['close'], dataframe['sar'])) &
(0 > (dataframe['sar'] - dataframe['close'])) &
(1 > (dataframe['sar'] - dataframe['sar'].shift(3))) &
(1 > dataframe['macd']) &
(1 > dataframe['macdhist']) &
(0 < (dataframe['macdhist'] - dataframe['macdhist'].shift(3))) &
(0 < dataframe['angle']) &
(dataframe['volume'] > 0) # Make sure Volume is not 0
),
'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
:param dataframe: DataFrame populated with indicators
:param metadata: Additional information, like the currently traded pair
:return: DataFrame with buy column
"""
dataframe.loc[
(
(qtpylib.crossed_below(dataframe['sar'], dataframe['close'])) &
(dataframe['uo'] < 69) &
(0 > (dataframe['sar'] - dataframe['close'])) &
(1 > (dataframe['sar'] - dataframe['sar'].shift(3))) &
(0 < dataframe['macd']) &
(1 < dataframe['macdhist']) &
(0 > (dataframe['macdhist'] - dataframe['macdhist'].shift(3))) &
(0 > dataframe['angle']) &
(dataframe['volume'] > 0) # Make sure Volume is not 0
),
'sell'] = 1
return dataframe
"""
buy1:
trigger = macd cross above macdsignal
guard = sar < close
guard = 70 > ou > 50
guard = lenear_angle > 0
buy2:
trigger = sar < close
guard = macd > macdsignal
guard = 70 > ou > 50
buy3:
trigger = ou cross below 30 & close > max(close)
sell1:
trigger = macd cross below macdsignal
guard = sar > close
guard = 50 > ou > 20
sell2:
trigger = sar > close
guard = macd < macdsignal
guard = 50 > ou > 20
"""
"""
+--------+---------+----------+------------------+--------------+------------------------------+----------------+-------------+
| Best | Epoch | Trades | Win Draw Loss | Avg profit | Profit | Avg duration | Objective |
|--------+---------+----------+------------------+--------------+------------------------------+----------------+-------------|
| * Best | 3/500 | 1194 | 523 654 17 | -0.12% | -0.06906927 BTC (-138.00%) | 1,559.8 m | 2.71708 |
| * Best | 6/500 | 100 | 20 0 80 | -0.07% | -0.00352831 BTC (-7.05%) | 11.5 m | 1.87067 |
| Best | 37/500 | 12 | 6 0 6 | 0.49% | 0.00294367 BTC (5.88%) | 18.2 m | 1.86009 |
| Best | 67/500 | 10 | 6 0 4 | 0.89% | 0.00443786 BTC (8.87%) | 19.5 m | 1.85245 |
| Best | 91/500 | 73 | 21 5 47 | 0.00% | 0.00007645 BTC (0.15%) | 10.2 m | 1.85215 |
| Best | 92/500 | 48 | 17 2 29 | 0.05% | 0.00116911 BTC (2.34%) | 10.4 m | 1.85189 |
| Best | 94/500 | 12 | 6 0 6 | 0.69% | 0.00416071 BTC (8.31%) | 17.8 m | 1.85143 |
| Best | 110/500 | 18 | 6 1 11 | 0.36% | 0.00327838 BTC (6.55%) | 14.3 m | 1.85113 |
| Best | 257/500 | 48 | 16 0 32 | 0.06% | 0.00154456 BTC (3.09%) | 11.3 m | 1.8505 |
| Best | 388/500 | 74 | 23 14 37 | 0.01% | 0.00045826 BTC (0.92%) | 8.2 m | 1.84665 |
[Epoch 500 of 500 (100%)] || | [Time: 1:04:49, Elapsed Time: 1:04:49]
2021-01-31 01:14:47,851 - freqtrade.optimize.hyperopt - INFO - 500 epochs saved to '/home/yakov/PycharmProjects/freqtrade/.env/bin/user_data/hyperopt_results/hyperopt_results_2021-01-31_00-07-23.pickle'.
Best result:
388/500: 74 trades. 23/14/37 Wins/Draws/Losses. Avg profit 0.01%. Median profit -0.02%. Total profit 0.00045826 BTC ( 0.92Σ%). Avg duration 8.2 min. Objective: 1.84665
# Buy hyperspace params:
buy_params = {
'angle-enabled': False,
'macd-enabled': False,
'macd_value': 0.73579,
'macdhist_shift': 0.87895,
'macdhist_value': 0.29935,
'sar-enabled': False,
'sar_ratio': 0.43819,
'sar_shift': 0.98992,
'trigger': 'sell-macd_cross_signal'
}
# Sell hyperspace params:
sell_params = {
'angle_h_s': 0.07105,
'macd_value_s': 0.08408,
'macdhist_shift_s': -0.72567,
'macdhist_value_s': 0.77324,
'sar_ratio_s': 0.58347,
'sar_shift_s': 0.81212,
'sell-angle-enabled': True,
'sell-macd-enabled': False,
'sell-sar-enabled': False,
'sell-uo-enabled': True,
'trigger': 'sell-macd_cross_signal',
'uo_l_s': 14
}
# ROI table:
minimal_roi = {
"0": 0.07193,
"3": 0.0382,
"5": 0.01183,
"7": 0
}
# Stoploss:
stoploss = -0.25471
# Trailing stop:
trailing_stop = True
trailing_stop_positive = 0.04697
trailing_stop_positive_offset = 0.05329
trailing_only_offset_is_reached = True
"""

196
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# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
# isort: skip_file
# --- Do not remove these libs ---
import numpy as np # noqa
import pandas as pd # noqa
from pandas import DataFrame
from talib._ta_lib import ULTOSC, MACD, SAR, LINEARREG_ANGLE, TEMA, STOCHRSI, STOCH, STOCHF, RSI
from freqtrade.strategy.interface import IStrategy
# --------------------------------
# Add your lib to import here
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
# This class is a sample. Feel free to customize it.
class quick_btcusdt_1m(IStrategy):
"""
This is a sample strategy to inspire you.
More information in https://github.com/freqtrade/freqtrade/blob/develop/docs/bot-optimization.md
You can:
:return: a Dataframe with all mandatory indicators for the strategies
- Rename the class name (Do not forget to update class_name)
- Add any methods you want to build your strategy
- Add any lib you need to build your strategy
You must keep:
- the lib in the section "Do not remove these libs"
- the prototype for the methods: minimal_roi, stoploss, populate_indicators, populate_buy_trend,
populate_sell_trend, hyperopt_space, buy_strategy_generator
"""
# Strategy interface version - allow new iterations of the strategy interface.
# Check the documentation or the Sample strategy to get the latest version.
INTERFACE_VERSION = 1
# Minimal ROI designed for the strategy.
# This attribute will be overridden if the config file contains "minimal_roi".
minimal_roi = {
"0": 0.07186329732926479,
"6": 0.03610260437996321,
"14": 0.014117594921808408,
"23": 0
}
# Optimal stoploss designed for the strategy.
# This attribute will be overridden if the config file contains "stoploss".
stoploss = -0.073946396013718
# Trailing stoploss
trailing_stop = True
trailing_stop_positive = 0.11645094244761
trailing_stop_positive_offset = 0.20201226976340847
trailing_only_offset_is_reached = True
# Optimal ticker interval for the strategy.
timeframe = '1m'
# Run "populate_indicators()" only for new candle.
process_only_new_candles = False
# These values can be overridden in the "ask_strategy" section in the config.
use_sell_signal = True
sell_profit_only = True
ignore_roi_if_buy_signal = False
# Number of candles the strategy requires before producing valid signals
startup_candle_count: int = 30
# Optional order type mapping.
order_types = {
'buy': 'market',
'sell': 'market',
'stoploss': 'market',
'stoploss_on_exchange': False
}
# Optional order time in force.
order_time_in_force = {
'buy': 'gtc',
'sell': 'gtc'
}
plot_config = {
'main_plot': {
'close': {},
'sar': {'color': 'white'},
},
'subplots': {
"MACD": {
'macd': {'color': 'blue'},
'macdsignal': {'color': 'orange'},
},
"OU": {
'ou': {'color': 'red'},
}
}
}
def informative_pairs(self):
"""
Define additional, informative pair/interval combinations to be cached from the exchange.
These pair/interval combinations are non-tradeable, unless they are part
of the whitelist as well.
For more information, please consult the documentation
:return: List of tuples in the format (pair, interval)
Sample: return [("ETH/USDT", "5m"),
("BTC/USDT", "15m"),
]
"""
return [("BTC/USDT", "1m")]
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
"""
# MACD
dataframe['macd'], dataframe['macdsignal'], dataframe['macdhist'] = MACD(dataframe['close'], fastperiod=12,
slowperiod=26, signalperiod=9)
dataframe['macd_angle'] = LINEARREG_ANGLE(dataframe['macd'], timeperiod=3)
dataframe['macdhist_angle'] = LINEARREG_ANGLE(dataframe['macd'], timeperiod=3)
# Linear angle
dataframe['angle'] = LINEARREG_ANGLE(dataframe['close'], timeperiod=14)
dataframe['tema'] = TEMA(dataframe['close'], timeperiod=30)
dataframe['sr_fastk'], dataframe['sr_fastd'] = STOCHRSI(dataframe['close'], timeperiod=14, fastk_period=5,
fastd_period=3, fastd_matype=0)
dataframe['sr_fastd_angle'] = LINEARREG_ANGLE(dataframe['sr_fastd'], timeperiod=4)
dataframe['slowk'], dataframe['slowd'] = STOCH(dataframe['high'], dataframe['low'], dataframe['close'],
fastk_period=5, slowk_period=3, slowk_matype=0, slowd_period=3,
slowd_matype=0)
dataframe['slowd_angle'] = LINEARREG_ANGLE(dataframe['slowd'], timeperiod=3)
dataframe['sf_fastk'], dataframe['sf_fastd'] = STOCHF(dataframe['high'], dataframe['low'], dataframe['close'], fastk_period=5, fastd_period=3, fastd_matype=0)
dataframe['sf_fastd_angle'] = LINEARREG_ANGLE(dataframe['sf_fastd'], timeperiod=3)
dataframe['rsi'] = RSI(dataframe['close'], timeperiod=14)
dataframe['rsi_angle'] = LINEARREG_ANGLE(dataframe['rsi'], timeperiod=5)
# # first check if dataprovider is available
# if self.dp:
# if self.dp.runmode in ('live', 'dry_run'):
# ob = self.dp.orderbook(metadata['pair'], 1)
# dataframe['best_bid'] = ob['bids'][0][0]
# dataframe['best_ask'] = ob['asks'][0][0]
#
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the buy signal for the given dataframe
:param dataframe: DataFrame populated with indicators
:param metadata: Additional information, like the currently traded pair
:return: DataFrame with buy column
"""
dataframe.loc[
(
(qtpylib.crossed_above(dataframe['macd'], dataframe['macdsignal'])) &
(dataframe['sr_fastd_angle'] > -39) &
(dataframe['sf_fastk'] > 33) &
(dataframe['sf_fastd_angle'] > -80) &
(dataframe['rsi_angle'] > 24) &
(dataframe['volume'] > 0) # Make sure Volume is not 0
),
'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
:param dataframe: DataFrame populated with indicators
:param metadata: Additional information, like the currently traded pair
:return: DataFrame with buy column
"""
dataframe.loc[
(
((dataframe['macdhist']) < 0.000241872676925719) &
(dataframe['sr_fastd_angle'] > 71) &
(dataframe['sf_fastd_angle'] < 21) &
(dataframe['rsi_angle'] < 1) &
(dataframe['volume'] > 0) # Make sure Volume is not 0
),
'sell'] = 1
return dataframe

197
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# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
# isort: skip_file
# --- Do not remove these libs ---
import numpy as np # noqa
import pandas as pd # noqa
from pandas import DataFrame
from talib._ta_lib import ULTOSC, MACD, SAR, LINEARREG_ANGLE, TEMA, STOCHRSI, STOCH, STOCHF, RSI
from freqtrade.strategy.interface import IStrategy
# --------------------------------
# Add your lib to import here
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
# This class is a sample. Feel free to customize it.
class quick_ethusdt_1m(IStrategy):
"""
This is a sample strategy to inspire you.
More information in https://github.com/freqtrade/freqtrade/blob/develop/docs/bot-optimization.md
You can:
:return: a Dataframe with all mandatory indicators for the strategies
- Rename the class name (Do not forget to update class_name)
- Add any methods you want to build your strategy
- Add any lib you need to build your strategy
You must keep:
- the lib in the section "Do not remove these libs"
- the prototype for the methods: minimal_roi, stoploss, populate_indicators, populate_buy_trend,
populate_sell_trend, hyperopt_space, buy_strategy_generator
"""
# Strategy interface version - allow new iterations of the strategy interface.
# Check the documentation or the Sample strategy to get the latest version.
INTERFACE_VERSION = 1
# Minimal ROI designed for the strategy.
# This attribute will be overridden if the config file contains "minimal_roi".
minimal_roi = {
"0": 0.07186329732926479,
"6": 0.03610260437996321,
"14": 0.014117594921808408,
"23": 0
}
# Optimal stoploss designed for the strategy.
# This attribute will be overridden if the config file contains "stoploss".
stoploss = -0.073946396013718
# Trailing stoploss
trailing_stop = True
trailing_stop_positive = 0.11645094244761
trailing_stop_positive_offset = 0.20201226976340847
trailing_only_offset_is_reached = True
# Optimal ticker interval for the strategy.
timeframe = '1m'
# Run "populate_indicators()" only for new candle.
process_only_new_candles = False
# These values can be overridden in the "ask_strategy" section in the config.
use_sell_signal = True
sell_profit_only = True
ignore_roi_if_buy_signal = False
# Number of candles the strategy requires before producing valid signals
startup_candle_count: int = 30
# Optional order type mapping.
order_types = {
'buy': 'market',
'sell': 'market',
'stoploss': 'market',
'stoploss_on_exchange': False
}
# Optional order time in force.
order_time_in_force = {
'buy': 'gtc',
'sell': 'gtc'
}
plot_config = {
'main_plot': {
'close': {},
'sar': {'color': 'white'},
},
'subplots': {
"MACD": {
'macd': {'color': 'blue'},
'macdsignal': {'color': 'orange'},
},
"OU": {
'ou': {'color': 'red'},
}
}
}
def informative_pairs(self):
"""
Define additional, informative pair/interval combinations to be cached from the exchange.
These pair/interval combinations are non-tradeable, unless they are part
of the whitelist as well.
For more information, please consult the documentation
:return: List of tuples in the format (pair, interval)
Sample: return [("ETH/USDT", "5m"),
("BTC/USDT", "15m"),
]
"""
return [("ETH/USDT", "1m")]
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
"""
# MACD
dataframe['macd'], dataframe['macdsignal'], dataframe['macdhist'] = MACD(dataframe['close'], fastperiod=12,
slowperiod=26, signalperiod=9)
dataframe['macd_angle'] = LINEARREG_ANGLE(dataframe['macd'], timeperiod=3)
dataframe['macdhist_angle'] = LINEARREG_ANGLE(dataframe['macd'], timeperiod=3)
# Linear angle
dataframe['angle'] = LINEARREG_ANGLE(dataframe['close'], timeperiod=14)
dataframe['tema'] = TEMA(dataframe['close'], timeperiod=30)
dataframe['sr_fastk'], dataframe['sr_fastd'] = STOCHRSI(dataframe['close'], timeperiod=14, fastk_period=5,
fastd_period=3, fastd_matype=0)
dataframe['sr_fastd_angle'] = LINEARREG_ANGLE(dataframe['sr_fastd'], timeperiod=4)
dataframe['slowk'], dataframe['slowd'] = STOCH(dataframe['high'], dataframe['low'], dataframe['close'],
fastk_period=5, slowk_period=3, slowk_matype=0, slowd_period=3,
slowd_matype=0)
dataframe['slowd_angle'] = LINEARREG_ANGLE(dataframe['slowd'], timeperiod=3)
dataframe['sf_fastk'], dataframe['sf_fastd'] = STOCHF(dataframe['high'], dataframe['low'], dataframe['close'], fastk_period=5, fastd_period=3, fastd_matype=0)
dataframe['sf_fastd_angle'] = LINEARREG_ANGLE(dataframe['sf_fastd'], timeperiod=3)
dataframe['rsi'] = RSI(dataframe['close'], timeperiod=14)
dataframe['rsi_angle'] = LINEARREG_ANGLE(dataframe['rsi'], timeperiod=5)
# # first check if dataprovider is available
# if self.dp:
# if self.dp.runmode in ('live', 'dry_run'):
# ob = self.dp.orderbook(metadata['pair'], 1)
# dataframe['best_bid'] = ob['bids'][0][0]
# dataframe['best_ask'] = ob['asks'][0][0]
#
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the buy signal for the given dataframe
:param dataframe: DataFrame populated with indicators
:param metadata: Additional information, like the currently traded pair
:return: DataFrame with buy column
"""
dataframe.loc[
(
(qtpylib.crossed_above(dataframe['macd'], dataframe['macdsignal'])) &
((dataframe['macd_angle']) > -0.007579827857282747) &
(dataframe['tema'] < 0.124417394428206) &
(dataframe['sr_fastd_angle'] > -39) &
(dataframe['sf_fastk'] > 33) &
(dataframe['rsi_angle'] > 24) &
(dataframe['volume'] > 0) # Make sure Volume is not 0
),
'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
:param dataframe: DataFrame populated with indicators
:param metadata: Additional information, like the currently traded pair
:return: DataFrame with buy column
"""
dataframe.loc[
(
((dataframe['macdhist']) < 0.000241872676925719) &
(dataframe['sr_fastd_angle'] > 71) &
(dataframe['sf_fastd_angle'] < 21) &
(dataframe['rsi_angle'] < 1) &
(dataframe['volume'] > 0) # Make sure Volume is not 0
),
'sell'] = 1
return dataframe

205
.env/bin/user_data/strategies/strg1_ada_usdt_1m.py Normal file
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# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
# isort: skip_file
# --- Do not remove these libs ---
import numpy as np # noqa
import pandas as pd # noqa
from pandas import DataFrame
from talib._ta_lib import ULTOSC, MACD, LINEARREG_ANGLE, TSF, MFI, EMA, MA, BBANDS, CORREL, SAR
from freqtrade.strategy.interface import IStrategy
# --------------------------------
# Add your lib to import here
import freqtrade.vendor.qtpylib.indicators as qtpylib
"""
"""
# This class is a sample. Feel free to customize it.
class strg1_ADAUSDT_1m(IStrategy):
"""
This is a sample strategy to inspire you.
More information in https://github.com/freqtrade/freqtrade/blob/develop/docs/bot-optimization.md
You can:
:return: a Dataframe with all mandatory indicators for the strategies
- Rename the class name (Do not forget to update class_name)
- Add any methods you want to build your strategy
- Add any lib you need to build your strategy
You must keep:
- the lib in the section "Do not remove these libs"
- the prototype for the methods: minimal_roi, stoploss, populate_indicators, populate_buy_trend,
populate_sell_trend, hyperopt_space, buy_strategy_generator
"""
# 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
# Minimal ROI designed for the strategy.
# This attribute will be overridden if the config file contains "minimal_roi".
minimal_roi = {
"0": 0.16408611153968805,
"18": 0.07786509535022122,
"76": 0.01087364896379109,
"193": 0
}
# Optimal stoploss designed for the strategy.
# This attribute will be overridden if the config file contains "stoploss".
stoploss = -0.14772112352872754
# Trailing stoploss
trailing_stop = True
trailing_stop_positive = 0.13192197532670782
trailing_stop_positive_offset = 0.19046200322926082
trailing_only_offset_is_reached = False
# Optimal ticker interval for the strategy.
timeframe = '1m'
# Run "populate_indicators()" only for new candle.
process_only_new_candles = False
# 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
# Number of candles the strategy requires before producing valid signals
startup_candle_count: int = 30
# Optional order type mapping.
order_types = {
'buy': 'limit',
'sell': 'limit',
'stoploss': 'market',
'stoploss_on_exchange': False
}
# Optional order time in force.
order_time_in_force = {
'buy': 'gtc',
'sell': 'gtc'
}
plot_config = {
'main_plot': {
'tema': {},
'sar': {'color': 'white'},
},
'subplots': {
"MACD": {
'macd': {'color': 'blue'},
'macdsignal': {'color': 'orange'},
},
"RSI": {
'rsi': {'color': 'red'},
}
}
}
def informative_pairs(self):
"""
Define additional, informative pair/interval combinations to be cached from the exchange.
These pair/interval combinations are non-tradeable, unless they are part
of the whitelist as well.
For more information, please consult the documentation
:return: List of tuples in the format (pair, interval)
Sample: return [("ETH/USDT", "5m"),
("BTC/USDT", "15m"),
]
"""
return [("ADA/USDT", "1m")]
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
"""
dataframe['macd'], dataframe['macdsignal'], dataframe['macdhist'] = MACD(dataframe['close'], fastperiod=10,
slowperiod=24, signalperiod=7)
dataframe['sar'] = SAR(dataframe['high'], dataframe['low'])
dataframe['mfi'] = MFI(dataframe['high'], dataframe['low'], dataframe['close'], dataframe['volume'],
timeperiod=14)
dataframe['angle_short'] = LINEARREG_ANGLE(dataframe['close'], timeperiod=10)
dataframe['angle_mid'] = LINEARREG_ANGLE(dataframe['close'], timeperiod=144)
dataframe['angle_long'] = LINEARREG_ANGLE(dataframe['close'], timeperiod=288)
dataframe['tsf_long'] = TSF(dataframe['close'], timeperiod=288)
dataframe['tsf_mid'] = TSF(dataframe['close'], timeperiod=144)
dataframe['tsf_short'] = TSF(dataframe['close'], timeperiod=10)
dataframe['correl_h_l'] = CORREL(dataframe['high'], dataframe['low'], timeperiod=30)
dataframe['correl_close_last_close'] = CORREL(dataframe['close'].shift(1), dataframe['close'], timeperiod=30)
dataframe['correl_tsf_long_close'] = CORREL(dataframe['tsf_long'], dataframe['close'], timeperiod=288)
dataframe['correl_tsf_mid_close'] = CORREL(dataframe['tsf_mid'], dataframe['close'], timeperiod=144)
dataframe['correl_tsf_short_close'] = CORREL(dataframe['tsf_short'], dataframe['close'], timeperiod=30)
dataframe['correl_angle_short_close'] = CORREL(dataframe['angle_short'], dataframe['close'], timeperiod=30)
dataframe['correl_angle_mid_close'] = CORREL(dataframe['angle_mid'], dataframe['close'], timeperiod=144)
dataframe['correl_angle_long_close'] = CORREL(dataframe['angle_long'], dataframe['close'], timeperiod=288)
dataframe['correl_hist_close'] = CORREL(dataframe['macdhist'], dataframe['close'], timeperiod=24)
dataframe['correl_mfi_close'] = CORREL(dataframe['mfi'], dataframe['close'], timeperiod=14)
dataframe['ema'] = EMA(dataframe['close'], timeperiod=8)
dataframe['ma'] = MA(dataframe['close'], timeperiod=8, matype=0)
dataframe['upperband'], dataframe['middleband'], dataframe['lowerband'] = BBANDS(dataframe['close'],
timeperiod=20, nbdevup=2,
nbdevdn=2, matype=0)
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the buy signal for the given dataframe
:param dataframe: DataFrame populated with indicators
:param metadata: Additional information, like the currently traded pair
:return: DataFrame with buy column
"""
dataframe.loc[
(
(qtpylib.crossed_below(dataframe['sar'], dataframe['close'])) &
((dataframe['close'] - dataframe['tsf_mid']) > 0.6037118990957886) &
((dataframe['close'] - dataframe['tsf_short']) > 0.280886393803766) &
(0.7374370242135467 < dataframe['correl_h_l']) &
(0.598218893737078 < dataframe['correl_tsf_short_close']) &
(-0.5081118861877524 < dataframe['correl_mfi_close']) &
(-0.00808699162233981 < (dataframe['ema'] - dataframe['middleband'])) &
(dataframe['volume'] > 0) # Make sure Volume is not 0
),
'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
:param dataframe: DataFrame populated with indicators
:param metadata: Additional information, like the currently traded pair
:return: DataFrame with buy column
"""
dataframe.loc[
(
(qtpylib.crossed_below(dataframe['sar'], dataframe['close'])) &
((dataframe['tsf_mid'] - dataframe['close']) < 0.44785474790276225) &
((dataframe['tsf_short'] - dataframe['close']) < 0.17576899126417125) &
(0.030726048199457463 < dataframe['correl_h_l']) &
(0.4328953914289484 < dataframe['correl_tsf_short_close']) &
(0.09562688208375203 < dataframe['correl_mfi_close']) &
(-0.00892438828151425 < (dataframe['ema'] - dataframe['middleband'])) &
(dataframe['volume'] > 0) # Make sure Volume is not 0
),
'sell'] = 1
return dataframe

199
.env/bin/user_data/strategies/strg1_btc_usdt_1m.py Normal file
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# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
# isort: skip_file
# --- Do not remove these libs ---
import numpy as np # noqa
import pandas as pd # noqa
from pandas import DataFrame
from talib._ta_lib import ULTOSC, MACD, LINEARREG_ANGLE, TSF, MFI, EMA, MA, BBANDS, CORREL
from freqtrade.strategy.interface import IStrategy
# --------------------------------
# Add your lib to import here
import freqtrade.vendor.qtpylib.indicators as qtpylib
"""
"""
# This class is a sample. Feel free to customize it.
class strg1_BTCUSDT_1m(IStrategy):
"""
This is a sample strategy to inspire you.
More information in https://github.com/freqtrade/freqtrade/blob/develop/docs/bot-optimization.md
You can:
:return: a Dataframe with all mandatory indicators for the strategies
- Rename the class name (Do not forget to update class_name)
- Add any methods you want to build your strategy
- Add any lib you need to build your strategy
You must keep:
- the lib in the section "Do not remove these libs"
- the prototype for the methods: minimal_roi, stoploss, populate_indicators, populate_buy_trend,
populate_sell_trend, hyperopt_space, buy_strategy_generator
"""
# 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
# Minimal ROI designed for the strategy.
# This attribute will be overridden if the config file contains "minimal_roi".
minimal_roi = {
"0": 0.24724,
"14": 0.05512,
"46": 0.01805,
"151": 0
}
# Optimal stoploss designed for the strategy.
# This attribute will be overridden if the config file contains "stoploss".
stoploss = -0.02231
# Trailing stoploss
trailing_stop = True
trailing_stop_positive = 0.34089
trailing_stop_positive_offset = 0.43254
trailing_only_offset_is_reached = False
# Optimal ticker interval for the strategy.
timeframe = '1m'
# Run "populate_indicators()" only for new candle.
process_only_new_candles = False
# 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 = False
# Number of candles the strategy requires before producing valid signals
startup_candle_count: int = 30
# Optional order type mapping.
order_types = {
'buy': 'limit',
'sell': 'limit',
'stoploss': 'market',
'stoploss_on_exchange': False
}
# Optional order time in force.
order_time_in_force = {
'buy': 'gtc',
'sell': 'gtc'
}
plot_config = {
'main_plot': {
'tema': {},
'sar': {'color': 'white'},
},
'subplots': {
"MACD": {
'macd': {'color': 'blue'},
'macdsignal': {'color': 'orange'},
},
"RSI": {
'rsi': {'color': 'red'},
}
}
}
def informative_pairs(self):
"""
Define additional, informative pair/interval combinations to be cached from the exchange.
These pair/interval combinations are non-tradeable, unless they are part
of the whitelist as well.
For more information, please consult the documentation
:return: List of tuples in the format (pair, interval)
Sample: return [("ETH/USDT", "5m"),
("BTC/USDT", "15m"),
]
"""
return [("BTC/USDT", "1m")]
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
"""
dataframe['macd'], dataframe['macdsignal'], dataframe['macdhist'] = MACD(dataframe['close'], fastperiod=10,
slowperiod=24, signalperiod=7)
dataframe['mfi'] = MFI(dataframe['high'], dataframe['low'], dataframe['close'], dataframe['volume'],
timeperiod=14)
dataframe['angle_short'] = LINEARREG_ANGLE(dataframe['close'], timeperiod=10)
dataframe['angle_mid'] = LINEARREG_ANGLE(dataframe['close'], timeperiod=144)
dataframe['angle_long'] = LINEARREG_ANGLE(dataframe['close'], timeperiod=288)
dataframe['tsf_long'] = TSF(dataframe['close'], timeperiod=288)
dataframe['tsf_mid'] = TSF(dataframe['close'], timeperiod=144)
dataframe['tsf_short'] = TSF(dataframe['close'], timeperiod=10)
dataframe['correl_h_l'] = CORREL(dataframe['high'], dataframe['low'], timeperiod=30)
dataframe['correl_close_last_close'] = CORREL(dataframe['close'].shift(1), dataframe['close'], timeperiod=30)
dataframe['correl_tsf_long_close'] = CORREL(dataframe['tsf_long'], dataframe['close'], timeperiod=288)
dataframe['correl_tsf_mid_close'] = CORREL(dataframe['tsf_mid'], dataframe['close'], timeperiod=144)
dataframe['correl_tsf_short_close'] = CORREL(dataframe['tsf_short'], dataframe['close'], timeperiod=30)
dataframe['correl_angle_short_close'] = CORREL(dataframe['angle_short'], dataframe['close'], timeperiod=30)
dataframe['correl_angle_mid_close'] = CORREL(dataframe['angle_mid'], dataframe['close'], timeperiod=144)
dataframe['correl_angle_long_close'] = CORREL(dataframe['angle_long'], dataframe['close'], timeperiod=288)
dataframe['correl_hist_close'] = CORREL(dataframe['macdhist'], dataframe['close'], timeperiod=24)
dataframe['correl_mfi_close'] = CORREL(dataframe['mfi'], dataframe['close'], timeperiod=14)
dataframe['ema'] = EMA(dataframe['close'], timeperiod=8)
dataframe['ma'] = MA(dataframe['close'], timeperiod=8, matype=0)
dataframe['upperband'], dataframe['middleband'], dataframe['lowerband'] = BBANDS(dataframe['close'],
timeperiod=20, nbdevup=2,
nbdevdn=2, matype=0)
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the buy signal for the given dataframe
:param dataframe: DataFrame populated with indicators
:param metadata: Additional information, like the currently traded pair
:return: DataFrame with buy column
"""
dataframe.loc[
(
(qtpylib.crossed_above(dataframe['close'], dataframe['middleband'])) &
(dataframe['mfi'] < 16) &
(dataframe['volume'] > 0) # Make sure Volume is not 0
),
'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
:param dataframe: DataFrame populated with indicators
:param metadata: Additional information, like the currently traded pair
:return: DataFrame with buy column
"""
dataframe.loc[
(
(qtpylib.crossed_above(dataframe['macdsignal'], dataframe['macd'])) &
(dataframe['correl_hist_close'] < 0.9) &
(dataframe['mfi'] > 98) &
(dataframe['middleband'] < 87) &
(dataframe['volume'] > 0) # Make sure Volume is not 0
),
'sell'] = 1
return dataframe

216
.env/bin/user_data/strategies/strg1_doge_usdt_1m.py Normal file
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@@ -0,0 +1,216 @@
# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
# isort: skip_file
# --- Do not remove these libs ---
import numpy as np # noqa
import pandas as pd # noqa
from pandas import DataFrame
from talib._ta_lib import MACD, LINEARREG_ANGLE, TSF, MFI, EMA, MA, BBANDS, CORREL, SAR
from freqtrade.strategy.interface import IStrategy
# --------------------------------
# Add your lib to import here
import freqtrade.vendor.qtpylib.indicators as qtpylib
"""
"""
# This class is a sample. Feel free to customize it.
class strg1_DOGEUSDT_1m(IStrategy):
"""
This is a sample strategy to inspire you.
More information in https://github.com/freqtrade/freqtrade/blob/develop/docs/bot-optimization.md
You can:
:return: a Dataframe with all mandatory indicators for the strategies
- Rename the class name (Do not forget to update class_name)
- Add any methods you want to build your strategy
- Add any lib you need to build your strategy
You must keep:
- the lib in the section "Do not remove these libs"
- the prototype for the methods: minimal_roi, stoploss, populate_indicators, populate_buy_trend,
populate_sell_trend, hyperopt_space, buy_strategy_generator
"""
# 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
# Minimal ROI designed for the strategy.
# This attribute will be overridden if the config file contains "minimal_roi".
minimal_roi = {
"0": 0.24724,
"30": 0.1,
"46": 0.05,
"151": 0
}
# Optimal stoploss designed for the strategy.
# This attribute will be overridden if the config file contains "stoploss".
stoploss = -0.1
# Trailing stoploss
trailing_stop = True
trailing_stop_positive = 0.04
trailing_stop_positive_offset = 0.14
trailing_only_offset_is_reached = True
# Optimal ticker interval for the strategy.
timeframe = '1m'
# Run "populate_indicators()" only for new candle.
process_only_new_candles = False
# 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
# Number of candles the strategy requires before producing valid signals
startup_candle_count: int = 30
# Optional order type mapping.
order_types = {
'buy': 'limit',
'sell': 'limit',
'stoploss': 'market',
'stoploss_on_exchange': False
}
# Optional order time in force.
order_time_in_force = {
'buy': 'gtc',
'sell': 'gtc'
}
plot_config = {
'main_plot': {
'tema': {},
'sar': {'color': 'white'},
},
'subplots': {
"MACD": {
'macd': {'color': 'blue'},
'macdsignal': {'color': 'orange'},
},
"RSI": {
'rsi': {'color': 'red'},
}
}
}
def informative_pairs(self):
"""
Define additional, informative pair/interval combinations to be cached from the exchange.
These pair/interval combinations are non-tradeable, unless they are part
of the whitelist as well.
For more information, please consult the documentation
:return: List of tuples in the format (pair, interval)
Sample: return [("ETH/USDT", "5m"),
("BTC/USDT", "15m"),
]
"""
return [("DOGE/USDT", "1m")]
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
"""
dataframe['macd'], dataframe['macdsignal'], dataframe['macdhist'] = MACD(dataframe['close'], fastperiod=10,
slowperiod=24, signalperiod=7)
dataframe['sar'] = SAR(dataframe['high'], dataframe['low'])
dataframe['mfi'] = MFI(dataframe['high'], dataframe['low'], dataframe['close'], dataframe['volume'],
timeperiod=14)
dataframe['angle_short'] = LINEARREG_ANGLE(dataframe['close'], timeperiod=10)
dataframe['angle_mid'] = LINEARREG_ANGLE(dataframe['close'], timeperiod=144)
dataframe['angle_long'] = LINEARREG_ANGLE(dataframe['close'], timeperiod=288)
dataframe['tsf_long'] = TSF(dataframe['close'], timeperiod=288)
dataframe['tsf_mid'] = TSF(dataframe['close'], timeperiod=144)
dataframe['tsf_short'] = TSF(dataframe['close'], timeperiod=10)
dataframe['correl_h_l'] = CORREL(dataframe['high'], dataframe['low'], timeperiod=30)
dataframe['correl_close_last_close'] = CORREL(dataframe['close'].shift(1), dataframe['close'], timeperiod=30)
dataframe['correl_tsf_long_close'] = CORREL(dataframe['tsf_long'], dataframe['close'], timeperiod=288)
dataframe['correl_tsf_mid_close'] = CORREL(dataframe['tsf_mid'], dataframe['close'], timeperiod=144)
dataframe['correl_tsf_short_close'] = CORREL(dataframe['tsf_short'], dataframe['close'], timeperiod=30)
dataframe['correl_angle_short_close'] = CORREL(dataframe['angle_short'], dataframe['close'], timeperiod=30)
dataframe['correl_angle_mid_close'] = CORREL(dataframe['angle_mid'], dataframe['close'], timeperiod=144)
dataframe['correl_angle_long_close'] = CORREL(dataframe['angle_long'], dataframe['close'], timeperiod=288)
dataframe['correl_hist_close'] = CORREL(dataframe['macdhist'], dataframe['close'], timeperiod=24)
dataframe['correl_mfi_close'] = CORREL(dataframe['mfi'], dataframe['close'], timeperiod=14)
dataframe['ema'] = EMA(dataframe['close'], timeperiod=8)
dataframe['ma'] = MA(dataframe['close'], timeperiod=8, matype=0)
dataframe['upperband'], dataframe['middleband'], dataframe['lowerband'] = BBANDS(dataframe['close'],
timeperiod=20, nbdevup=2,
nbdevdn=2, matype=0)
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the buy signal for the given dataframe
:param dataframe: DataFrame populated with indicators
:param metadata: Additional information, like the currently traded pair
:return: DataFrame with buy column
"""
dataframe.loc[
(
(qtpylib.crossed_above(dataframe['close'], dataframe['lowerband'])) &
((dataframe['tsf_mid'] - dataframe['close']) > 14) &
(0.5263534368259521 < dataframe['correl_h_l']) &
(0.0728048430667152 < dataframe['correl_tsf_mid_close']) &
(-0.02402596125126566 < dataframe['correl_angle_short_close']) &
(0.2842166347875669 < dataframe['correl_angle_long_close']) &
(-0.6552565064627378 < dataframe['correl_mfi_close']) &
(-0.4853276710303872 < dataframe['correl_hist_close']) &
(0.442188534531633 < dataframe['mfi']) &
(0.6714488827895353 < dataframe['ema']) &
(dataframe['volume'] > 0) # Make sure Volume is not 0
),
'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
:param dataframe: DataFrame populated with indicators
:param metadata: Additional information, like the currently traded pair
:return: DataFrame with buy column
"""
dataframe.loc[
(
(qtpylib.crossed_above(dataframe['close'], dataframe['lowerband'])) &
((dataframe['tsf_mid'] - dataframe['close']) > 68) &
(-0.6225944943311145 < dataframe['correl_h_l']) &
(0.2602396802858502 < dataframe['correl_tsf_mid_close']) &
(0.771988603840956 < dataframe['correl_angle_short_close']) &
(0.33471662411500236 < dataframe['correl_angle_long_close']) &
(-0.9562413964921457 < dataframe['correl_hist_close']) &
(-0.43268559077377733 < dataframe['correl_mfi_close']) &
(-0.25207265197064166 < dataframe['mfi']) &
(-0.00739011415527302 < dataframe['ema']) &
(dataframe['volume'] > 0) # Make sure Volume is not 0
),
'sell'] = 1
return dataframe

233
.env/bin/user_data/strategies/strg1_eth_usdt_1m.py Normal file
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# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
# isort: skip_file
# --- Do not remove these libs ---
import numpy as np # noqa
import pandas as pd # noqa
from pandas import DataFrame
from talib._ta_lib import ULTOSC, MACD, LINEARREG_ANGLE, TSF, MFI, EMA, MA, BBANDS, CORREL
from freqtrade.strategy.interface import IStrategy
# --------------------------------
# Add your lib to import here
import freqtrade.vendor.qtpylib.indicators as qtpylib
"""
"""
# This class is a sample. Feel free to customize it.
class strg1_ETHUSDT_1m(IStrategy):
"""
This is a sample strategy to inspire you.
More information in https://github.com/freqtrade/freqtrade/blob/develop/docs/bot-optimization.md
You can:
:return: a Dataframe with all mandatory indicators for the strategies
- Rename the class name (Do not forget to update class_name)
- Add any methods you want to build your strategy
- Add any lib you need to build your strategy
You must keep:
- the lib in the section "Do not remove these libs"
- the prototype for the methods: minimal_roi, stoploss, populate_indicators, populate_buy_trend,
populate_sell_trend, hyperopt_space, buy_strategy_generator
"""
# 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
# Minimal ROI designed for the strategy.
# This attribute will be overridden if the config file contains "minimal_roi".
minimal_roi = {
"0": 0.24724,
"30": 0.1,
"46": 0.05,
"151": 0
}
# Optimal stoploss designed for the strategy.
# This attribute will be overridden if the config file contains "stoploss".
stoploss = -0.2
# Trailing stoploss
trailing_stop = True
trailing_stop_positive = 0.19
trailing_stop_positive_offset = 0.21
trailing_only_offset_is_reached = True
# Optimal ticker interval for the strategy.
timeframe = '1m'
# Run "populate_indicators()" only for new candle.
process_only_new_candles = False
# 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
# Number of candles the strategy requires before producing valid signals
startup_candle_count: int = 30
# Optional order type mapping.
order_types = {
'buy': 'limit',
'sell': 'limit',
'stoploss': 'market',
'stoploss_on_exchange': False
}
# Optional order time in force.
order_time_in_force = {
'buy': 'gtc',
'sell': 'gtc'
}
plot_config = {
'main_plot': {
'upperband': {'upperband': 'green'},
'middleband': {'color': 'green'},
'lowerband': {'color': 'green'},
'tsf_mid': {'color': 'white'},
'ema': {'color': 'white'},
},
'subplots': {
"corr": {
'correl_h_l': {'color': 'black'},
},
"correl_tsf_mid_close": {
'correl_tsf_mid_close': {'color': 'grey'},
},
"correl_angle_short_close": {
'correl_angle_short_close': {'color': 'blue'},
},
"correl_angle_long_close": {
'correl_angle_long_close': {'color': 'red'},
},
"correl_mfi_close": {
'correl_mfi_close': {'color': 'black'},
},
"correl_hist_close": {
'correl_tsf_mid_close': {'color': 'red'},
},
"mfi": {
'mfi': {'color': 'yellow'},
},
}
}
def informative_pairs(self):
"""
Define additional, informative pair/interval combinations to be cached from the exchange.
These pair/interval combinations are non-tradeable, unless they are part
of the whitelist as well.
For more information, please consult the documentation
:return: List of tuples in the format (pair, interval)
Sample: return [("ETH/USDT", "5m"),
("BTC/USDT", "15m"),
]
"""
return [("ETH/USDT", "1m")]
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
"""
dataframe['macd'], dataframe['macdsignal'], dataframe['macdhist'] = MACD(dataframe['close'], fastperiod=10,
slowperiod=24, signalperiod=7)
dataframe['mfi'] = MFI(dataframe['high'], dataframe['low'], dataframe['close'], dataframe['volume'],
timeperiod=14)
dataframe['angle_short'] = LINEARREG_ANGLE(dataframe['close'], timeperiod=10)
dataframe['angle_mid'] = LINEARREG_ANGLE(dataframe['close'], timeperiod=144)
dataframe['angle_long'] = LINEARREG_ANGLE(dataframe['close'], timeperiod=288)
dataframe['tsf_long'] = TSF(dataframe['close'], timeperiod=288)
dataframe['tsf_mid'] = TSF(dataframe['close'], timeperiod=144)
dataframe['tsf_short'] = TSF(dataframe['close'], timeperiod=10)
dataframe['correl_h_l'] = CORREL(dataframe['high'], dataframe['low'], timeperiod=30)
dataframe['correl_close_last_close'] = CORREL(dataframe['close'].shift(1), dataframe['close'], timeperiod=30)
dataframe['correl_tsf_long_close'] = CORREL(dataframe['tsf_long'], dataframe['close'], timeperiod=288)
dataframe['correl_tsf_mid_close'] = CORREL(dataframe['tsf_mid'], dataframe['close'], timeperiod=144)
dataframe['correl_tsf_short_close'] = CORREL(dataframe['tsf_short'], dataframe['close'], timeperiod=30)
dataframe['correl_angle_short_close'] = CORREL(dataframe['angle_short'], dataframe['close'], timeperiod=30)
dataframe['correl_angle_mid_close'] = CORREL(dataframe['angle_mid'], dataframe['close'], timeperiod=144)
dataframe['correl_angle_long_close'] = CORREL(dataframe['angle_long'], dataframe['close'], timeperiod=288)
dataframe['correl_hist_close'] = CORREL(dataframe['macdhist'], dataframe['close'], timeperiod=24)
dataframe['correl_mfi_close'] = CORREL(dataframe['mfi'], dataframe['close'], timeperiod=14)
dataframe['ema'] = EMA(dataframe['close'], timeperiod=8)
dataframe['ma'] = MA(dataframe['close'], timeperiod=8, matype=0)
dataframe['upperband'], dataframe['middleband'], dataframe['lowerband'] = BBANDS(dataframe['close'],
timeperiod=20, nbdevup=2,
nbdevdn=2, matype=0)
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the buy signal for the given dataframe
:param dataframe: DataFrame populated with indicators
:param metadata: Additional information, like the currently traded pair
:return: DataFrame with buy column
"""
dataframe.loc[
(
(qtpylib.crossed_above(dataframe['close'], dataframe['lowerband'])) &
((dataframe['tsf_mid'] - dataframe['close']) > 14) &
(0.5263534368259521 < dataframe['correl_h_l']) &
(0.0728048430667152 < dataframe['correl_tsf_mid_close']) &
(-0.02402596125126566 < dataframe['correl_angle_short_close']) &
(0.2842166347875669 < dataframe['correl_angle_long_close']) &
(-0.6552565064627378 < dataframe['correl_mfi_close']) &
(-0.4853276710303872 < dataframe['correl_hist_close']) &
(0.442188534531633 < dataframe['mfi']) &
(0.6714488827895353 < dataframe['ema']) &
(dataframe['volume'] > 0) # Make sure Volume is not 0
),
'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
:param dataframe: DataFrame populated with indicators
:param metadata: Additional information, like the currently traded pair
:return: DataFrame with buy column
"""
dataframe.loc[
(
(qtpylib.crossed_above(dataframe['close'], dataframe['lowerband'])) &
((dataframe['tsf_mid'] - dataframe['close']) > 68) &
(-0.6225944943311145 < dataframe['correl_h_l']) &
(0.2602396802858502 < dataframe['correl_tsf_mid_close']) &
(0.771988603840956 < dataframe['correl_angle_short_close']) &
(0.33471662411500236 < dataframe['correl_angle_long_close']) &
(-0.9562413964921457 < dataframe['correl_hist_close']) &
(-0.43268559077377733 < dataframe['correl_mfi_close']) &
(-0.25207265197064166 < dataframe['mfi']) &
(-0.00739011415527302 < dataframe['ema']) &
(dataframe['volume'] > 0) # Make sure Volume is not 0
),
'sell'] = 1
return dataframe

216
.env/bin/user_data/strategies/strg1_eth_usdt_1m_prod.py Normal file
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# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
# isort: skip_file
# --- Do not remove these libs ---
import numpy as np # noqa
import pandas as pd # noqa
from pandas import DataFrame
from talib._ta_lib import ULTOSC, MACD, LINEARREG_ANGLE, TSF, MFI, EMA, MA, BBANDS, CORREL
from freqtrade.strategy.interface import IStrategy
# --------------------------------
# Add your lib to import here
import freqtrade.vendor.qtpylib.indicators as qtpylib
"""
"""
# This class is a sample. Feel free to customize it.
class strg1_ETHUSDT_1m_prod(IStrategy):
"""
This is a sample strategy to inspire you.
More information in https://github.com/freqtrade/freqtrade/blob/develop/docs/bot-optimization.md
You can:
:return: a Dataframe with all mandatory indicators for the strategies
- Rename the class name (Do not forget to update class_name)
- Add any methods you want to build your strategy
- Add any lib you need to build your strategy
You must keep:
- the lib in the section "Do not remove these libs"
- the prototype for the methods: minimal_roi, stoploss, populate_indicators, populate_buy_trend,
populate_sell_trend, hyperopt_space, buy_strategy_generator
"""
# 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
# Minimal ROI designed for the strategy.
# This attribute will be overridden if the config file contains "minimal_roi".
minimal_roi = {
"0": 0.24724,
"30": 0.1,
"46": 0.05,
"151": 0
}
# Optimal stoploss designed for the strategy.
# This attribute will be overridden if the config file contains "stoploss".
stoploss = -0.1
# Trailing stoploss
trailing_stop = True
trailing_stop_positive = 0.04
trailing_stop_positive_offset = 0.14
trailing_only_offset_is_reached = True
# Optimal ticker interval for the strategy.
timeframe = '1m'
# Run "populate_indicators()" only for new candle.
process_only_new_candles = False
# 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
# Number of candles the strategy requires before producing valid signals
startup_candle_count: int = 30
# Optional order type mapping.
order_types = {
'buy': 'limit',
'sell': 'limit',
'stoploss': 'market',
'stoploss_on_exchange': False
}
# Optional order time in force.
order_time_in_force = {
'buy': 'gtc',
'sell': 'gtc'
}
plot_config = {
'main_plot': {
'tema': {},
'sar': {'color': 'white'},
},
'subplots': {
"MACD": {
'macd': {'color': 'blue'},
'macdsignal': {'color': 'orange'},
},
"RSI": {
'rsi': {'color': 'red'},
}
}
}
def informative_pairs(self):
"""
Define additional, informative pair/interval combinations to be cached from the exchange.
These pair/interval combinations are non-tradeable, unless they are part
of the whitelist as well.
For more information, please consult the documentation
:return: List of tuples in the format (pair, interval)
Sample: return [("ETH/USDT", "5m"),
("BTC/USDT", "15m"),
]
"""
return [("ETH/USDT", "1m")]
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
"""
dataframe['macd'], dataframe['macdsignal'], dataframe['macdhist'] = MACD(dataframe['close'], fastperiod=10,
slowperiod=24, signalperiod=7)
dataframe['mfi'] = MFI(dataframe['high'], dataframe['low'], dataframe['close'], dataframe['volume'],
timeperiod=14)
dataframe['angle_short'] = LINEARREG_ANGLE(dataframe['close'], timeperiod=10)
dataframe['angle_mid'] = LINEARREG_ANGLE(dataframe['close'], timeperiod=144)
dataframe['angle_long'] = LINEARREG_ANGLE(dataframe['close'], timeperiod=288)
dataframe['tsf_long'] = TSF(dataframe['close'], timeperiod=288)
dataframe['tsf_mid'] = TSF(dataframe['close'], timeperiod=144)
dataframe['tsf_short'] = TSF(dataframe['close'], timeperiod=10)
dataframe['correl_h_l'] = CORREL(dataframe['high'], dataframe['low'], timeperiod=30)
dataframe['correl_close_last_close'] = CORREL(dataframe['close'].shift(1), dataframe['close'], timeperiod=30)
dataframe['correl_tsf_long_close'] = CORREL(dataframe['tsf_long'], dataframe['close'], timeperiod=288)
dataframe['correl_tsf_mid_close'] = CORREL(dataframe['tsf_mid'], dataframe['close'], timeperiod=144)
dataframe['correl_tsf_short_close'] = CORREL(dataframe['tsf_short'], dataframe['close'], timeperiod=30)
dataframe['correl_angle_short_close'] = CORREL(dataframe['angle_short'], dataframe['close'], timeperiod=30)
dataframe['correl_angle_mid_close'] = CORREL(dataframe['angle_mid'], dataframe['close'], timeperiod=144)
dataframe['correl_angle_long_close'] = CORREL(dataframe['angle_long'], dataframe['close'], timeperiod=288)
dataframe['correl_hist_close'] = CORREL(dataframe['macdhist'], dataframe['close'], timeperiod=24)
dataframe['correl_mfi_close'] = CORREL(dataframe['mfi'], dataframe['close'], timeperiod=14)
dataframe['ema'] = EMA(dataframe['close'], timeperiod=8)
dataframe['ma'] = MA(dataframe['close'], timeperiod=8, matype=0)
dataframe['upperband'], dataframe['middleband'], dataframe['lowerband'] = BBANDS(dataframe['close'],
timeperiod=20, nbdevup=2,
nbdevdn=2, matype=0)
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the buy signal for the given dataframe
:param dataframe: DataFrame populated with indicators
:param metadata: Additional information, like the currently traded pair
:return: DataFrame with buy column
"""
dataframe.loc[
(
(qtpylib.crossed_above(dataframe['close'], dataframe['lowerband'])) &
((dataframe['tsf_mid'] - dataframe['close']) > 14) &
(0.5263534368259521 < dataframe['correl_h_l']) &
(0.0728048430667152 < dataframe['correl_tsf_mid_close']) &
(-0.02402596125126566 < dataframe['correl_angle_short_close']) &
(0.2842166347875669 < dataframe['correl_angle_long_close']) &
(-0.6552565064627378 < dataframe['correl_mfi_close']) &
(-0.4853276710303872 < dataframe['correl_hist_close']) &
(0.442188534531633 < dataframe['mfi']) &
(0.6714488827895353 < dataframe['ema']) &
(dataframe['volume'] > 0) # Make sure Volume is not 0
),
'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
:param dataframe: DataFrame populated with indicators
:param metadata: Additional information, like the currently traded pair
:return: DataFrame with buy column
"""
dataframe.loc[
(
(qtpylib.crossed_above(dataframe['close'], dataframe['lowerband'])) &
((dataframe['tsf_mid'] - dataframe['close']) > 68) &
(-0.6225944943311145 < dataframe['correl_h_l']) &
(0.2602396802858502 < dataframe['correl_tsf_mid_close']) &
(0.771988603840956 < dataframe['correl_angle_short_close']) &
(0.33471662411500236 < dataframe['correl_angle_long_close']) &
(-0.9562413964921457 < dataframe['correl_hist_close']) &
(-0.43268559077377733 < dataframe['correl_mfi_close']) &
(-0.25207265197064166 < dataframe['mfi']) &
(-0.00739011415527302 < dataframe['ema']) &
(dataframe['volume'] > 0) # Make sure Volume is not 0
),
'sell'] = 1
return dataframe

189
.env/bin/user_data/strategies/strg2_ada_usdt_1m.py Normal file
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@@ -0,0 +1,189 @@
# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
# isort: skip_file
# --- Do not remove these libs ---
import numpy as np # noqa
import pandas as pd # noqa
from pandas import DataFrame
from talib._ta_lib import ULTOSC, MACD, LINEARREG_ANGLE, TSF, MFI, EMA, MA, BBANDS, CORREL, SAR, HT_TRENDLINE, RSI, NATR
from freqtrade.strategy.interface import IStrategy
# --------------------------------
# Add your lib to import here
import freqtrade.vendor.qtpylib.indicators as qtpylib
"""
"""
# This class is a sample. Feel free to customize it.
class strg2_ADAUSDT_1m(IStrategy):
"""
This is a sample strategy to inspire you.
More information in https://github.com/freqtrade/freqtrade/blob/develop/docs/bot-optimization.md
You can:
:return: a Dataframe with all mandatory indicators for the strategies
- Rename the class name (Do not forget to update class_name)
- Add any methods you want to build your strategy
- Add any lib you need to build your strategy
You must keep:
- the lib in the section "Do not remove these libs"
- the prototype for the methods: minimal_roi, stoploss, populate_indicators, populate_buy_trend,
populate_sell_trend, hyperopt_space, buy_strategy_generator
"""
# 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
# Minimal ROI designed for the strategy.
# This attribute will be overridden if the config file contains "minimal_roi".
minimal_roi = {
"0": 0.0816,
"30": 0.04762,
"60": 0.01142,
"150": 0
}
# Optimal stoploss designed for the strategy.
# This attribute will be overridden if the config file contains "stoploss".
# Stoploss:
stoploss = -0.20102
# Trailing stop:
trailing_stop = True
trailing_stop_positive = 0.10581
trailing_stop_positive_offset = 0.11224
trailing_only_offset_is_reached = False
# Optimal ticker interval for the strategy.
timeframe = '1m'
# Run "populate_indicators()" only for new candle.
process_only_new_candles = False
# These values can be overridden in the "ask_strategy" section in the config.
use_sell_signal = True
sell_profit_only = True
ignore_roi_if_buy_signal = False
# Number of candles the strategy requires before producing valid signals
startup_candle_count: int = 30
# Optional order type mapping.
order_types = {
'buy': 'market',
'sell': 'market',
'stoploss': 'market',
'stoploss_on_exchange': False
}
# Optional order time in force.
order_time_in_force = {
'buy': 'gtc',
'sell': 'gtc'
}
plot_config = {
'main_plot': {
'correl_h_l': {'color': 'black'},
'upperband': {'color': 'green'},
'middleband': {'color': 'green'},
'lowerband': {'color': 'green'},
},
'subplots': {
"MACD": {
'macd': {'color': 'blue'},
'macdsignal': {'color': 'orange'},
},
"mfi": {
'mfi': {'color': 'red'},
}
}
}
def informative_pairs(self):
"""
Define additional, informative pair/interval combinations to be cached from the exchange.
These pair/interval combinations are non-tradeable, unless they are part
of the whitelist as well.
For more information, please consult the documentation
:return: List of tuples in the format (pair, interval)
Sample: return [("ETH/USDT", "5m"),
("BTC/USDT", "15m"),
]
"""
return [("ADA/USDT", "1m")]
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
"""
dataframe['tsf_short'] = TSF(dataframe['close'], timeperiod=5)
dataframe['tsf_mid'] = TSF(dataframe['close'], timeperiod=30)
dataframe['ao'] = qtpylib.awesome_oscillator(dataframe, weighted=False, fast=5, slow=34)
dataframe['rsi'] = RSI(dataframe['close'], timeperiod=14)
dataframe['angle_tsf_mid'] = LINEARREG_ANGLE(dataframe['tsf_mid'], timeperiod=5)
dataframe['angle_tsf_mid'] = LINEARREG_ANGLE(dataframe['tsf_short'], timeperiod=5)
dataframe['macd'], dataframe['macdsignal'], dataframe['macdhist'] = MACD(dataframe['close'], fastperiod=10,
slowperiod=24, signalperiod=7)
dataframe['sar'] = SAR(dataframe['high'], dataframe['low'])
dataframe['mfi'] = MFI(dataframe['high'], dataframe['low'], dataframe['close'], dataframe['volume'],
timeperiod=14)
dataframe['correl_h_l'] = CORREL(dataframe['high'], dataframe['low'], timeperiod=30)
dataframe['upperband'], dataframe['middleband'], dataframe['lowerband'] = BBANDS(dataframe['close'],
timeperiod=30, nbdevup=2,
nbdevdn=2, matype=0)
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the buy signal for the given dataframe
:param dataframe: DataFrame populated with indicators
:param metadata: Additional information, like the currently traded pair
:return: DataFrame with buy column
"""
dataframe.loc[
(
(qtpylib.crossed_below(dataframe['sar'], dataframe['close'])) &
(60 > dataframe['mfi']) &
# (0.15 < dataframe['natr']) &
(dataframe['angle_tsf_mid'] > 0) &
(dataframe['volume'] > 0) # Make sure Volume is not 0
),
'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
:param dataframe: DataFrame populated with indicators
:param metadata: Additional information, like the currently traded pair
:return: DataFrame with buy column
"""
dataframe.loc[
(
(qtpylib.crossed_above(dataframe['sar'], dataframe['close'])) &
# (-0.00354 > dataframe['macdhist']) &
(50.23156 > dataframe['mfi']) &
(dataframe['angle_tsf_mid'] < 0) &
(dataframe['volume'] > 0) # Make sure Volume is not 0
),
'sell'] = 1
return dataframe

189
.env/bin/user_data/strategies/strg2_ada_usdt_1m_prod.py Normal file
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@@ -0,0 +1,189 @@
# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
# isort: skip_file
# --- Do not remove these libs ---
import numpy as np # noqa
import pandas as pd # noqa
from pandas import DataFrame
from talib._ta_lib import ULTOSC, MACD, LINEARREG_ANGLE, TSF, MFI, EMA, MA, BBANDS, CORREL, SAR, HT_TRENDLINE, RSI, NATR
from freqtrade.strategy.interface import IStrategy
# --------------------------------
# Add your lib to import here
import freqtrade.vendor.qtpylib.indicators as qtpylib
"""
"""
# This class is a sample. Feel free to customize it.
class strg2_ADAUSDT_1m(IStrategy):
"""
This is a sample strategy to inspire you.
More information in https://github.com/freqtrade/freqtrade/blob/develop/docs/bot-optimization.md
You can:
:return: a Dataframe with all mandatory indicators for the strategies
- Rename the class name (Do not forget to update class_name)
- Add any methods you want to build your strategy
- Add any lib you need to build your strategy
You must keep:
- the lib in the section "Do not remove these libs"
- the prototype for the methods: minimal_roi, stoploss, populate_indicators, populate_buy_trend,
populate_sell_trend, hyperopt_space, buy_strategy_generator
"""
# 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
# Minimal ROI designed for the strategy.
# This attribute will be overridden if the config file contains "minimal_roi".
minimal_roi = {
"0": 0.0816,
"30": 0.04762,
"60": 0.01142,
"150": 0
}
# Optimal stoploss designed for the strategy.
# This attribute will be overridden if the config file contains "stoploss".
# Stoploss:
stoploss = -0.20102
# Trailing stop:
trailing_stop = True
trailing_stop_positive = 0.10581
trailing_stop_positive_offset = 0.11224
trailing_only_offset_is_reached = False
# Optimal ticker interval for the strategy.
timeframe = '1m'
# Run "populate_indicators()" only for new candle.
process_only_new_candles = False
# These values can be overridden in the "ask_strategy" section in the config.
use_sell_signal = True
sell_profit_only = True
ignore_roi_if_buy_signal = False
# Number of candles the strategy requires before producing valid signals
startup_candle_count: int = 30
# Optional order type mapping.
order_types = {
'buy': 'market',
'sell': 'market',
'stoploss': 'market',
'stoploss_on_exchange': False
}
# Optional order time in force.
order_time_in_force = {
'buy': 'gtc',
'sell': 'gtc'
}
plot_config = {
'main_plot': {
'correl_h_l': {'color': 'black'},
'upperband': {'color': 'green'},
'middleband': {'color': 'green'},
'lowerband': {'color': 'green'},
},
'subplots': {
"MACD": {
'macd': {'color': 'blue'},
'macdsignal': {'color': 'orange'},
},
"mfi": {
'mfi': {'color': 'red'},
}
}
}
def informative_pairs(self):
"""
Define additional, informative pair/interval combinations to be cached from the exchange.
These pair/interval combinations are non-tradeable, unless they are part
of the whitelist as well.
For more information, please consult the documentation
:return: List of tuples in the format (pair, interval)
Sample: return [("ETH/USDT", "5m"),
("BTC/USDT", "15m"),
]
"""
return [("ADA/USDT", "1m")]
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
"""
dataframe['tsf_short'] = TSF(dataframe['close'], timeperiod=5)
dataframe['tsf_mid'] = TSF(dataframe['close'], timeperiod=30)
dataframe['ao'] = qtpylib.awesome_oscillator(dataframe, weighted=False, fast=5, slow=34)
dataframe['rsi'] = RSI(dataframe['close'], timeperiod=14)
dataframe['angle_tsf_mid'] = LINEARREG_ANGLE(dataframe['tsf_mid'], timeperiod=5)
dataframe['angle_tsf_mid'] = LINEARREG_ANGLE(dataframe['tsf_short'], timeperiod=5)
dataframe['macd'], dataframe['macdsignal'], dataframe['macdhist'] = MACD(dataframe['close'], fastperiod=10,
slowperiod=24, signalperiod=7)
dataframe['sar'] = SAR(dataframe['high'], dataframe['low'])
dataframe['mfi'] = MFI(dataframe['high'], dataframe['low'], dataframe['close'], dataframe['volume'],
timeperiod=14)
dataframe['correl_h_l'] = CORREL(dataframe['high'], dataframe['low'], timeperiod=30)
dataframe['upperband'], dataframe['middleband'], dataframe['lowerband'] = BBANDS(dataframe['close'],
timeperiod=30, nbdevup=2,
nbdevdn=2, matype=0)
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the buy signal for the given dataframe
:param dataframe: DataFrame populated with indicators
:param metadata: Additional information, like the currently traded pair
:return: DataFrame with buy column
"""
dataframe.loc[
(
(qtpylib.crossed_below(dataframe['sar'], dataframe['close'])) &
(60 > dataframe['mfi']) &
# (0.15 < dataframe['natr']) &
(dataframe['angle_tsf_mid'] > 0) &
(dataframe['volume'] > 0) # Make sure Volume is not 0
),
'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
:param dataframe: DataFrame populated with indicators
:param metadata: Additional information, like the currently traded pair
:return: DataFrame with buy column
"""
dataframe.loc[
(
(qtpylib.crossed_above(dataframe['sar'], dataframe['close'])) &
# (-0.00354 > dataframe['macdhist']) &
(50.23156 > dataframe['mfi']) &
(dataframe['angle_tsf_mid'] < 0) &
(dataframe['volume'] > 0) # Make sure Volume is not 0
),
'sell'] = 1
return dataframe

178
.env/bin/user_data/strategies/strg2_ada_usdt_1m_sharpe.py Normal file
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@@ -0,0 +1,178 @@
# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
# isort: skip_file
# --- Do not remove these libs ---
import numpy as np # noqa
import pandas as pd # noqa
from pandas import DataFrame
from talib._ta_lib import ULTOSC, MACD, LINEARREG_ANGLE, TSF, MFI, EMA, MA, BBANDS, CORREL, SAR
from freqtrade.strategy.interface import IStrategy
# --------------------------------
# Add your lib to import here
import freqtrade.vendor.qtpylib.indicators as qtpylib
"""
"""
# This class is a sample. Feel free to customize it.
class strg2_ADAUSDT_1m_sharpe(IStrategy):
"""
This is a sample strategy to inspire you.
More information in https://github.com/freqtrade/freqtrade/blob/develop/docs/bot-optimization.md
You can:
:return: a Dataframe with all mandatory indicators for the strategies
- Rename the class name (Do not forget to update class_name)
- Add any methods you want to build your strategy
- Add any lib you need to build your strategy
You must keep:
- the lib in the section "Do not remove these libs"
- the prototype for the methods: minimal_roi, stoploss, populate_indicators, populate_buy_trend,
populate_sell_trend, hyperopt_space, buy_strategy_generator
"""
# 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
# Minimal ROI designed for the strategy.
# This attribute will be overridden if the config file contains "minimal_roi".
minimal_roi = {
"0": 0.17440164714028486,
"38": 0.09394715625863974,
"98": 0.027323845707844324,
"170": 0
}
# Optimal stoploss designed for the strategy.
# This attribute will be overridden if the config file contains "stoploss".
# Stoploss:
stoploss = -0.2399087254823858
# Trailing stop:
trailing_stop = True
trailing_stop_positive = 0.10717125716853501
trailing_stop_positive_offset = 0.13807946709133387
trailing_only_offset_is_reached = True
# Optimal ticker interval for the strategy.
timeframe = '1m'
# Run "populate_indicators()" only for new candle.
process_only_new_candles = False
# 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
# Number of candles the strategy requires before producing valid signals
startup_candle_count: int = 30
# Optional order type mapping.
order_types = {
'buy': 'limit',
'sell': 'limit',
'stoploss': 'market',
'stoploss_on_exchange': False
}
# Optional order time in force.
order_time_in_force = {
'buy': 'gtc',
'sell': 'gtc'
}
plot_config = {
'main_plot': {
'correl_h_l': {'color': 'black'},
'upperband': {'color': 'green'},
'middleband': {'color': 'green'},
'lowerband': {'color': 'green'},
},
'subplots': {
"MACD": {
'macd': {'color': 'blue'},
'macdsignal': {'color': 'orange'},
},
"mfi": {
'mfi': {'color': 'red'},
}
}
}
def informative_pairs(self):
"""
Define additional, informative pair/interval combinations to be cached from the exchange.
These pair/interval combinations are non-tradeable, unless they are part
of the whitelist as well.
For more information, please consult the documentation
:return: List of tuples in the format (pair, interval)
Sample: return [("ETH/USDT", "5m"),
("BTC/USDT", "15m"),
]
"""
return [("ADA/USDT", "1m")]
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
"""
dataframe['macd'], dataframe['macdsignal'], dataframe['macdhist'] = MACD(dataframe['close'], fastperiod=10,
slowperiod=24, signalperiod=7)
dataframe['sar'] = SAR(dataframe['high'], dataframe['low'])
dataframe['mfi'] = MFI(dataframe['high'], dataframe['low'], dataframe['close'], dataframe['volume'],
timeperiod=60)
dataframe['correl_h_l'] = CORREL(dataframe['high'], dataframe['low'], timeperiod=60)
dataframe['upperband'], dataframe['middleband'], dataframe['lowerband'] = BBANDS(dataframe['close'],
timeperiod=35, nbdevup=2,
nbdevdn=2, matype=0)
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the buy signal for the given dataframe
:param dataframe: DataFrame populated with indicators
:param metadata: Additional information, like the currently traded pair
:return: DataFrame with buy column
"""
dataframe.loc[
(
(qtpylib.crossed_above(dataframe['close'], dataframe['middleband'])) &
(0.6452515141186865 < dataframe['correl_h_l']) &
(18 < dataframe['mfi']) &
(dataframe['volume'] > 0) # Make sure Volume is not 0
),
'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
:param dataframe: DataFrame populated with indicators
:param metadata: Additional information, like the currently traded pair
:return: DataFrame with buy column
"""
dataframe.loc[
(
(qtpylib.crossed_below(dataframe['close'], dataframe['middleband'])) &
(0.1 < dataframe['correl_h_l']) &
(17 > dataframe['mfi']) &
(dataframe['volume'] > 0) # Make sure Volume is not 0
),
'sell'] = 1
return dataframe

231
.env/bin/user_data/strategies/strg2_eth_usdt_1m.py Normal file
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@@ -0,0 +1,231 @@
# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
# isort: skip_file
# --- Do not remove these libs ---
import numpy as np # noqa
import pandas as pd # noqa
from pandas import DataFrame
from talib._ta_lib import ULTOSC, MACD, LINEARREG_ANGLE, TSF, MFI, EMA, MA, BBANDS, CORREL
from freqtrade.strategy.interface import IStrategy
# --------------------------------
# Add your lib to import here
import freqtrade.vendor.qtpylib.indicators as qtpylib
"""
"""
# This class is a sample. Feel free to customize it.
class strg2_ETHUSDT_1m(IStrategy):
"""
This is a sample strategy to inspire you.
More information in https://github.com/freqtrade/freqtrade/blob/develop/docs/bot-optimization.md
You can:
:return: a Dataframe with all mandatory indicators for the strategies
- Rename the class name (Do not forget to update class_name)
- Add any methods you want to build your strategy
- Add any lib you need to build your strategy
You must keep:
- the lib in the section "Do not remove these libs"
- the prototype for the methods: minimal_roi, stoploss, populate_indicators, populate_buy_trend,
populate_sell_trend, hyperopt_space, buy_strategy_generator
"""
# 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
# Minimal ROI designed for the strategy.
# This attribute will be overridden if the config file contains "minimal_roi".
minimal_roi = {
"0": 0.14918562374415067,
"34": 0.058385372968253246,
"81": 0.02650295809826818,
"164": 0
}
# Optimal stoploss designed for the strategy.
# This attribute will be overridden if the config file contains "stoploss".
stoploss = -0.22617753002701768
# Trailing stoploss
trailing_stop = True
trailing_stop_positive = 0.32322177120674106
trailing_stop_positive_offset = 0.3600439363018107
trailing_only_offset_is_reached = False
# Optimal ticker interval for the strategy.
timeframe = '1m'
# Run "populate_indicators()" only for new candle.
process_only_new_candles = False
# 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 = False
# Number of candles the strategy requires before producing valid signals
startup_candle_count: int = 30
# Optional order type mapping.
order_types = {
'buy': 'limit',
'sell': 'limit',
'stoploss': 'market',
'stoploss_on_exchange': False
}
# Optional order time in force.
order_time_in_force = {
'buy': 'gtc',
'sell': 'gtc'
}
plot_config = {
'main_plot': {
'upperband': {'upperband': 'green'},
'middleband': {'color': 'green'},
'lowerband': {'color': 'green'},
'tsf_mid': {'color': 'white'},
'ema': {'color': 'white'},
},
'subplots': {
"corr": {
'correl_h_l': {'color': 'black'},
},
"correl_tsf_mid_close": {
'correl_tsf_mid_close': {'color': 'grey'},
},
"correl_angle_short_close": {
'correl_angle_short_close': {'color': 'blue'},
},
"correl_angle_long_close": {
'correl_angle_long_close': {'color': 'red'},
},
"correl_mfi_close": {
'correl_mfi_close': {'color': 'black'},
},
"correl_hist_close": {
'correl_tsf_mid_close': {'color': 'red'},
},
"mfi": {
'mfi': {'color': 'yellow'},
},
}
}
def informative_pairs(self):
"""
Define additional, informative pair/interval combinations to be cached from the exchange.
These pair/interval combinations are non-tradeable, unless they are part
of the whitelist as well.
For more information, please consult the documentation
:return: List of tuples in the format (pair, interval)
Sample: return [("ETH/USDT", "5m"),
("BTC/USDT", "15m"),
]
"""
return [("ETH/USDT", "1m")]
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
"""
dataframe['macd'], dataframe['macdsignal'], dataframe['macdhist'] = MACD(dataframe['close'], fastperiod=12,
slowperiod=26, signalperiod=7)
dataframe['mfi'] = MFI(dataframe['high'], dataframe['low'], dataframe['close'], dataframe['volume'],
timeperiod=30)
dataframe['angle_short'] = LINEARREG_ANGLE(dataframe['close'], timeperiod=30)
dataframe['angle_mid'] = LINEARREG_ANGLE(dataframe['close'], timeperiod=720)
dataframe['angle_long'] = LINEARREG_ANGLE(dataframe['close'], timeperiod=1440)
dataframe['tsf_long'] = TSF(dataframe['close'], timeperiod=1440)
dataframe['tsf_mid'] = TSF(dataframe['close'], timeperiod=720)
dataframe['tsf_short'] = TSF(dataframe['close'], timeperiod=30)
dataframe['correl_h_l'] = CORREL(dataframe['high'], dataframe['low'], timeperiod=30)
dataframe['correl_close_last_close'] = CORREL(dataframe['close'].shift(1), dataframe['close'], timeperiod=30)
dataframe['correl_tsf_long_close'] = CORREL(dataframe['tsf_long'], dataframe['close'], timeperiod=1440)
dataframe['correl_tsf_mid_close'] = CORREL(dataframe['tsf_mid'], dataframe['close'], timeperiod=720)
dataframe['correl_tsf_short_close'] = CORREL(dataframe['tsf_short'], dataframe['close'], timeperiod=30)
dataframe['correl_angle_short_close'] = CORREL(dataframe['angle_short'], dataframe['close'], timeperiod=30)
dataframe['correl_angle_mid_close'] = CORREL(dataframe['angle_mid'], dataframe['close'], timeperiod=720)
dataframe['correl_angle_long_close'] = CORREL(dataframe['angle_long'], dataframe['close'], timeperiod=1440)
dataframe['correl_hist_close'] = CORREL(dataframe['macdhist'], dataframe['close'], timeperiod=30)
dataframe['correl_mfi_close'] = CORREL(dataframe['mfi'], dataframe['close'], timeperiod=30)
dataframe['ema'] = EMA(dataframe['close'], timeperiod=15)
dataframe['ma'] = MA(dataframe['close'], timeperiod=15, matype=0)
dataframe['upperband'], dataframe['middleband'], dataframe['lowerband'] = BBANDS(dataframe['close'],
timeperiod=30, nbdevup=2,
nbdevdn=2, matype=0)
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the buy signal for the given dataframe
:param dataframe: DataFrame populated with indicators
:param metadata: Additional information, like the currently traded pair
:return: DataFrame with buy column
"""
dataframe.loc[
(
(qtpylib.crossed_above(dataframe['close'], dataframe['middleband'])) &
((dataframe['tsf_mid'] - dataframe['close']) > 10) &
((dataframe['tsf_mid'] - dataframe['close']) > -23) &
(dataframe['angle_mid'] > -31) &
(0.6120614546605481 < dataframe['correl_angle_short_close']) &
(0.4128274947005438 < dataframe['correl_angle_mid_close']) &
(18 < dataframe['mfi']) &
(-18 < (dataframe['ema'] - dataframe['middleband'])) &
(dataframe['volume'] > 0) # Make sure Volume is not 0
),
'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
:param dataframe: DataFrame populated with indicators
:param metadata: Additional information, like the currently traded pair
:return: DataFrame with buy column
"""
dataframe.loc[
(
(qtpylib.crossed_below(dataframe['close'], dataframe['middleband'])) &
((dataframe['tsf_mid'] - dataframe['close']) > 29) &
((dataframe['tsf_short'] - dataframe['close']) > -94) &
(dataframe['angle_mid'] > -10) &
(0.2602396802858502 < dataframe['correl_tsf_mid_close']) &
(-0.8240439026300765 < dataframe['correl_angle_short_close']) &
(-0.9736515042784262 < dataframe['correl_angle_mid_close']) &
(80 < dataframe['mfi']) &
(26 < (dataframe['middleband'] - dataframe['ema'])) &
(18 > dataframe['macdhist']) &
(dataframe['volume'] > 0) # Make sure Volume is not 0
),
'sell'] = 1
return dataframe

212
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# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
# isort: skip_file
# --- Do not remove these libs ---
import numpy as np # noqa
import pandas as pd # noqa
from pandas import DataFrame
from talib._ta_lib import MACD, LINEARREG_ANGLE, TSF, MFI, BBANDS, CORREL
from freqtrade.strategy.interface import IStrategy
# --------------------------------
# Add your lib to import here
import freqtrade.vendor.qtpylib.indicators as qtpylib
"""
"""
# This class is a sample. Feel free to customize it.
class strg2_ETHUSDT_1m_360(IStrategy):
"""
This is a sample strategy to inspire you.
More information in https://github.com/freqtrade/freqtrade/blob/develop/docs/bot-optimization.md
You can:
:return: a Dataframe with all mandatory indicators for the strategies
- Rename the class name (Do not forget to update class_name)
- Add any methods you want to build your strategy
- Add any lib you need to build your strategy
You must keep:
- the lib in the section "Do not remove these libs"
- the prototype for the methods: minimal_roi, stoploss, populate_indicators, populate_buy_trend,
populate_sell_trend, hyperopt_space, buy_strategy_generator
"""
# 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
# Minimal ROI designed for the strategy.
# This attribute will be overridden if the config file contains "minimal_roi".
minimal_roi = {
"0": 0.14689,
"23": 0.07987,
"40": 0.03243,
"136": 0
}
# Optimal stoploss designed for the strategy.
# This attribute will be overridden if the config file contains "stoploss".
# Stoploss:
stoploss = -0.18934
# Trailing stop:
trailing_stop = True
trailing_stop_positive = 0.02891
trailing_stop_positive_offset = 0.05001
trailing_only_offset_is_reached = True
# Optimal ticker interval for the strategy.
timeframe = '1m'
# Run "populate_indicators()" only for new candle.
process_only_new_candles = False
# These values can be overridden in the "ask_strategy" section in the config.
use_sell_signal = False
sell_profit_only = True
ignore_roi_if_buy_signal = False
# Number of candles the strategy requires before producing valid signals
startup_candle_count: int = 30
# Optional order type mapping.
order_types = {
'buy': 'limit',
'sell': 'limit',
'stoploss': 'market',
'stoploss_on_exchange': False
}
# Optional order time in force.
order_time_in_force = {
'buy': 'gtc',
'sell': 'gtc'
}
plot_config = {
'main_plot': {
'upperband': {'upperband': 'green'},
'middleband': {'color': 'green'},
'lowerband': {'color': 'green'},
'tsf_mid': {'color': 'white'},
},
'subplots': {
"corr": {
'correl_h_l': {'color': 'black'},
},
"correl_tsf_mid_close": {
'correl_tsf_mid_close': {'color': 'grey'},
},
"correl_angle_short_close": {
'correl_angle_short_close': {'color': 'blue'},
},
"correl_angle_long_close": {
'correl_angle_long_close': {'color': 'red'},
},
"correl_mfi_close": {
'correl_mfi_close': {'color': 'black'},
},
"correl_hist_close": {
'correl_tsf_mid_close': {'color': 'red'},
},
"mfi": {
'mfi': {'color': 'yellow'},
},
}
}
def informative_pairs(self):
"""
Define additional, informative pair/interval combinations to be cached from the exchange.
These pair/interval combinations are non-tradeable, unless they are part
of the whitelist as well.
For more information, please consult the documentation
:return: List of tuples in the format (pair, interval)
Sample: return [("ETH/USDT", "5m"),
("BTC/USDT", "15m"),
]
"""
return [("ETH/USDT", "1m")]
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
"""
dataframe['macd'], dataframe['macdsignal'], dataframe['macdhist'] = MACD(dataframe['close'], fastperiod=12,
slowperiod=26, signalperiod=7)
dataframe['mfi'] = MFI(dataframe['high'], dataframe['low'], dataframe['close'], dataframe['volume'],
timeperiod=30)
dataframe['angle_short'] = LINEARREG_ANGLE(dataframe['close'], timeperiod=15)
dataframe['angle_long'] = LINEARREG_ANGLE(dataframe['close'], timeperiod=360)
dataframe['tsf_mid'] = TSF(dataframe['close'], timeperiod=360)
dataframe['tsf_short'] = TSF(dataframe['close'], timeperiod=15)
dataframe['correl_h_l'] = CORREL(dataframe['high'], dataframe['low'], timeperiod=15)
dataframe['correl_close_last_close'] = CORREL(dataframe['close'].shift(1), dataframe['close'], timeperiod=15)
dataframe['correl_tsf_mid_close'] = CORREL(dataframe['tsf_mid'], dataframe['close'], timeperiod=15)
dataframe['correl_tsf_short_close'] = CORREL(dataframe['tsf_short'], dataframe['close'], timeperiod=15)
dataframe['correl_angle_short_close'] = CORREL(dataframe['angle_short'], dataframe['close'], timeperiod=15)
dataframe['correl_angle_long_close'] = CORREL(dataframe['angle_long'], dataframe['close'], timeperiod=15)
dataframe['correl_hist_close'] = CORREL(dataframe['macdhist'], dataframe['close'], timeperiod=15)
dataframe['correl_mfi_close'] = CORREL(dataframe['mfi'], dataframe['close'], timeperiod=15)
dataframe['upperband'], dataframe['middleband'], dataframe['lowerband'] = BBANDS(dataframe['close'],
timeperiod=30, nbdevup=2,
nbdevdn=2, matype=0)
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the buy signal for the given dataframe
:param dataframe: DataFrame populated with indicators
:param metadata: Additional information, like the currently traded pair
:return: DataFrame with buy column
"""
dataframe.loc[
(
(qtpylib.crossed_above(dataframe['close'], dataframe['middleband'])) &
(0.42804 < dataframe['correl_h_l']) &
(dataframe['angle_short'] > -40) &
(-0.23132 < dataframe['correl_angle_short_close']) &
(0.5 < dataframe['macdhist']) &
(dataframe['volume'] > 0) # Make sure Volume is not 0
),
'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
:param dataframe: DataFrame populated with indicators
:param metadata: Additional information, like the currently traded pair
:return: DataFrame with buy column
"""
dataframe.loc[
(
(qtpylib.crossed_below(dataframe['close'], dataframe['upperband'])) &
(36 < dataframe['angle_short']) &
(0.72362 < dataframe['correl_angle_short_close']) &
(0.40219 < dataframe['correl_h_l']) &
(0 > dataframe['macdhist']) &
(dataframe['volume'] > 0) # Make sure Volume is not 0
),
'sell'] = 1
return dataframe

231
.env/bin/user_data/strategies/strg2_eth_usdt_1m_safe_prod.py Normal file
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# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
# isort: skip_file
# --- Do not remove these libs ---
import numpy as np # noqa
import pandas as pd # noqa
from pandas import DataFrame
from talib._ta_lib import ULTOSC, MACD, LINEARREG_ANGLE, TSF, MFI, EMA, MA, BBANDS, CORREL
from freqtrade.strategy.interface import IStrategy
# --------------------------------
# Add your lib to import here
import freqtrade.vendor.qtpylib.indicators as qtpylib
"""
"""
# This class is a sample. Feel free to customize it.
class strg2_ETHUSDT_1m_prod(IStrategy):
"""
This is a sample strategy to inspire you.
More information in https://github.com/freqtrade/freqtrade/blob/develop/docs/bot-optimization.md
You can:
:return: a Dataframe with all mandatory indicators for the strategies
- Rename the class name (Do not forget to update class_name)
- Add any methods you want to build your strategy
- Add any lib you need to build your strategy
You must keep:
- the lib in the section "Do not remove these libs"
- the prototype for the methods: minimal_roi, stoploss, populate_indicators, populate_buy_trend,
populate_sell_trend, hyperopt_space, buy_strategy_generator
"""
# 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
# Minimal ROI designed for the strategy.
# This attribute will be overridden if the config file contains "minimal_roi".
minimal_roi = {
"0": 0.14918562374415067,
"34": 0.058385372968253246,
"81": 0.02650295809826818,
"164": 0
}
# Optimal stoploss designed for the strategy.
# This attribute will be overridden if the config file contains "stoploss".
stoploss = -0.22617753002701768
# Trailing stoploss
trailing_stop = True
trailing_stop_positive = 0.32322177120674106
trailing_stop_positive_offset = 0.3600439363018107
trailing_only_offset_is_reached = False
# Optimal ticker interval for the strategy.
timeframe = '1m'
# Run "populate_indicators()" only for new candle.
process_only_new_candles = False
# 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 = False
# Number of candles the strategy requires before producing valid signals
startup_candle_count: int = 30
# Optional order type mapping.
order_types = {
'buy': 'limit',
'sell': 'limit',
'stoploss': 'market',
'stoploss_on_exchange': False
}
# Optional order time in force.
order_time_in_force = {
'buy': 'gtc',
'sell': 'gtc'
}
plot_config = {
'main_plot': {
'upperband': {'upperband': 'green'},
'middleband': {'color': 'green'},
'lowerband': {'color': 'green'},
'tsf_mid': {'color': 'white'},
'ema': {'color': 'white'},
},
'subplots': {
"corr": {
'correl_h_l': {'color': 'black'},
},
"correl_tsf_mid_close": {
'correl_tsf_mid_close': {'color': 'grey'},
},
"correl_angle_short_close": {
'correl_angle_short_close': {'color': 'blue'},
},
"correl_angle_long_close": {
'correl_angle_long_close': {'color': 'red'},
},
"correl_mfi_close": {
'correl_mfi_close': {'color': 'black'},
},
"correl_hist_close": {
'correl_tsf_mid_close': {'color': 'red'},
},
"mfi": {
'mfi': {'color': 'yellow'},
},
}
}
def informative_pairs(self):
"""
Define additional, informative pair/interval combinations to be cached from the exchange.
These pair/interval combinations are non-tradeable, unless they are part
of the whitelist as well.
For more information, please consult the documentation
:return: List of tuples in the format (pair, interval)
Sample: return [("ETH/USDT", "5m"),
("BTC/USDT", "15m"),
]
"""
return [("ETH/USDT", "1m")]
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
"""
dataframe['macd'], dataframe['macdsignal'], dataframe['macdhist'] = MACD(dataframe['close'], fastperiod=12,
slowperiod=26, signalperiod=7)
dataframe['mfi'] = MFI(dataframe['high'], dataframe['low'], dataframe['close'], dataframe['volume'],
timeperiod=30)
dataframe['angle_short'] = LINEARREG_ANGLE(dataframe['close'], timeperiod=30)
dataframe['angle_mid'] = LINEARREG_ANGLE(dataframe['close'], timeperiod=720)
dataframe['angle_long'] = LINEARREG_ANGLE(dataframe['close'], timeperiod=1440)
dataframe['tsf_long'] = TSF(dataframe['close'], timeperiod=1440)
dataframe['tsf_mid'] = TSF(dataframe['close'], timeperiod=720)
dataframe['tsf_short'] = TSF(dataframe['close'], timeperiod=30)
dataframe['correl_h_l'] = CORREL(dataframe['high'], dataframe['low'], timeperiod=30)
dataframe['correl_close_last_close'] = CORREL(dataframe['close'].shift(1), dataframe['close'], timeperiod=30)
dataframe['correl_tsf_long_close'] = CORREL(dataframe['tsf_long'], dataframe['close'], timeperiod=1440)
dataframe['correl_tsf_mid_close'] = CORREL(dataframe['tsf_mid'], dataframe['close'], timeperiod=720)
dataframe['correl_tsf_short_close'] = CORREL(dataframe['tsf_short'], dataframe['close'], timeperiod=30)
dataframe['correl_angle_short_close'] = CORREL(dataframe['angle_short'], dataframe['close'], timeperiod=30)
dataframe['correl_angle_mid_close'] = CORREL(dataframe['angle_mid'], dataframe['close'], timeperiod=720)
dataframe['correl_angle_long_close'] = CORREL(dataframe['angle_long'], dataframe['close'], timeperiod=1440)
dataframe['correl_hist_close'] = CORREL(dataframe['macdhist'], dataframe['close'], timeperiod=30)
dataframe['correl_mfi_close'] = CORREL(dataframe['mfi'], dataframe['close'], timeperiod=30)
dataframe['ema'] = EMA(dataframe['close'], timeperiod=15)
dataframe['ma'] = MA(dataframe['close'], timeperiod=15, matype=0)
dataframe['upperband'], dataframe['middleband'], dataframe['lowerband'] = BBANDS(dataframe['close'],
timeperiod=30, nbdevup=2,
nbdevdn=2, matype=0)
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the buy signal for the given dataframe
:param dataframe: DataFrame populated with indicators
:param metadata: Additional information, like the currently traded pair
:return: DataFrame with buy column
"""
dataframe.loc[
(
(qtpylib.crossed_above(dataframe['close'], dataframe['middleband'])) &
((dataframe['tsf_mid'] - dataframe['close']) > 10) &
((dataframe['tsf_mid'] - dataframe['close']) > -23) &
(dataframe['angle_mid'] > -31) &
(0.6120614546605481 < dataframe['correl_angle_short_close']) &
(0.4128274947005438 < dataframe['correl_angle_mid_close']) &
(18 < dataframe['mfi']) &
(-18 < (dataframe['ema'] - dataframe['middleband'])) &
(dataframe['volume'] > 0) # Make sure Volume is not 0
),
'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
:param dataframe: DataFrame populated with indicators
:param metadata: Additional information, like the currently traded pair
:return: DataFrame with buy column
"""
dataframe.loc[
(
(qtpylib.crossed_below(dataframe['close'], dataframe['middleband'])) &
((dataframe['tsf_mid'] - dataframe['close']) > 29) &
((dataframe['tsf_short'] - dataframe['close']) > -94) &
(dataframe['angle_mid'] > -10) &
(0.2602396802858502 < dataframe['correl_tsf_mid_close']) &
(-0.8240439026300765 < dataframe['correl_angle_short_close']) &
(-0.9736515042784262 < dataframe['correl_angle_mid_close']) &
(80 < dataframe['mfi']) &
(26 < (dataframe['middleband'] - dataframe['ema'])) &
(18 > dataframe['macdhist']) &
(dataframe['volume'] > 0) # Make sure Volume is not 0
),
'sell'] = 1
return dataframe

199
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# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
# isort: skip_file
# --- Do not remove these libs ---
import numpy as np # noqa
import pandas as pd # noqa
from pandas import DataFrame
from talib._ta_lib import ULTOSC, MACD, BBANDS, CORREL, MAX, AROON, HT_PHASOR, HT_SINE, HT_DCPHASE, HT_TRENDMODE, \
HT_TRENDLINE, CCI, AROONOSC, \
RSI, MFI, LINEARREG_ANGLE, TSF
from freqtrade.strategy.interface import IStrategy
# --------------------------------
# Add your lib to import here
import freqtrade.vendor.qtpylib.indicators as qtpylib
"""
"""
# This class is a sample. Feel free to customize it.
class strg3_ADAUSDT_1m_test(IStrategy):
"""
This is a sample strategy to inspire you.
More information in https://github.com/freqtrade/freqtrade/blob/develop/docs/bot-optimization.md
You can:
:return: a Dataframe with all mandatory indicators for the strategies
- Rename the class name (Do not forget to update class_name)
- Add any methods you want to build your strategy
- Add any lib you need to build your strategy
You must keep:
- the lib in the section "Do not remove these libs"
- the prototype for the methods: minimal_roi, stoploss, populate_indicators, populate_buy_trend,
populate_sell_trend, hyperopt_space, buy_strategy_generator
"""
# 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
# Minimal ROI designed for the strategy.
# This attribute will be overridden if the config file contains "minimal_roi".
minimal_roi = {
"0": 0.2,
"18": 0.07762,
"65": 0.02142,
"158": 0
}
# Optimal stoploss designed for the strategy.
# This attribute will be overridden if the config file contains "stoploss".
# Stoploss:
stoploss = -0.17
# Trailing stop:
trailing_stop = True
trailing_stop_positive = 0.01
trailing_stop_positive_offset = 0.05
trailing_only_offset_is_reached = True
# Optimal ticker interval for the strategy.
timeframe = '1m'
# Run "populate_indicators()" only for new candle.
process_only_new_candles = False
# 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 = False
# Number of candles the strategy requires before producing valid signals
startup_candle_count: int = 130
# Optional order type mapping.
order_types = {
'buy': 'limit',
'sell': 'limit',
'stoploss': 'market',
'stoploss_on_exchange': False
}
# Optional order time in force.
order_time_in_force = {
'buy': 'gtc',
'sell': 'gtc'
}
plot_config = {
'main_plot': {
},
'subplots': {
"sine": {
'leadsine': {'color': 'green'},
'ht_sine': {'color': 'orange'},
},
"aroon": {
'aroon_down': {'color': 'brown'},
'aroon_up': {'color': 'blue'},
},
"cci": {
'cci': {'color': 'yellow'},
}
}
}
def informative_pairs(self):
"""
Define additional, informative pair/interval combinations to be cached from the exchange.
These pair/interval combinations are non-tradeable, unless they are part
of the whitelist as well.
For more information, please consult the documentation
:return: List of tuples in the format (pair, interval)
Sample: return [("ETH/USDT", "5m"),
("BTC/USDT", "15m"),
]
"""
return [("ADA/USDT", "1m")]
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
"""
dataframe['aroon_down'], dataframe['aroon_up'] = AROON(dataframe['high'], dataframe['low'], timeperiod=120)
dataframe['aroonosc'] = AROONOSC(dataframe['high'], dataframe['low'], timeperiod=120)
dataframe['macd'], dataframe['macdsignal'], dataframe['macdhist'] = MACD(dataframe['close'], fastperiod=30,
slowperiod=60, signalperiod=15)
dataframe['upperband'], dataframe['middleband'], dataframe['lowerband'] = BBANDS(dataframe['close'],
timeperiod=30, nbdevup=2,
nbdevdn=2, matype=0)
dataframe['rsi'] = RSI(dataframe['close'], timeperiod=30)
dataframe['ul'] = ULTOSC(dataframe['high'], dataframe['low'], dataframe['close'], timeperiod1=5, timeperiod2=15,
timeperiod3=30)
# dataframe['slowk'], dataframe['slowd'] = STOCH(dataframe['high'], dataframe['low'], dataframe['close'],
# fastk_period=15, slowk_period=7, slowk_matype=0, slowd_period=7,
# slowd_matype=0)
dataframe['mfi'] = MFI(dataframe['high'], dataframe['low'], dataframe['close'], dataframe['volume'],
timeperiod=30)
dataframe['angle_short'] = LINEARREG_ANGLE(dataframe['close'], timeperiod=30)
dataframe['tsf_short'] = TSF(dataframe['close'], timeperiod=10)
dataframe['correl_h_l'] = CORREL(dataframe['high'], dataframe['low'], timeperiod=30)
dataframe['ht_phase'] = HT_DCPHASE(dataframe['close'])
dataframe['ht_trendmode'] = HT_TRENDMODE(dataframe['close'])
dataframe['ht_trendline'] = HT_TRENDLINE(dataframe['close'])
dataframe['inphase'], dataframe['quadrature'] = HT_PHASOR(dataframe['close'])
dataframe['ht_sine'], dataframe['leadsine'] = HT_SINE(dataframe['close'])
dataframe['correl_sine_trend'] = CORREL(dataframe['leadsine'], dataframe['ht_trendmode'], timeperiod=10)
dataframe['correl_ht_sine_trend'] = CORREL(dataframe['ht_sine'], dataframe['ht_trendmode'], timeperiod=10)
dataframe['correl_ht_sine_close'] = CORREL(dataframe['ht_sine'], dataframe['close'], timeperiod=10)
dataframe['cci'] = CCI(dataframe['high'], dataframe['low'], dataframe['close'], timeperiod=120)
# dataframe['ht_phase_min'] = MIN(dataframe['ht_phase'], timeperiod=30)
# dataframe['ht_phase_max'] = MAX(dataframe['ht_phase'], timeperiod=60)
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the buy signal for the given dataframe
:param dataframe: DataFrame populated with indicators
:param metadata: Additional information, like the currently traded pair
:return: DataFrame with buy column
"""
dataframe.loc[
(
(qtpylib.crossed_above(dataframe['leadsine'], dataframe['ht_sine'])) &
(dataframe['cci'] < 0) &
(dataframe['angle_short'] > 0) &
(dataframe['volume'] > 0) # Make sure Volume is not 0
),
'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
:param dataframe: DataFrame populated with indicators
:param metadata: Additional information, like the currently traded pair
:return: DataFrame with buy column
"""
dataframe.loc[
(
(qtpylib.crossed_below(dataframe['aroon_up'], 83)) &
(dataframe['angle_short'] < 0) &
(dataframe['volume'] > 0) # Make sure Volume is not 0
# Make sure Volume is not 0
),
'sell'] = 1
return dataframe

237
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@@ -0,0 +1,237 @@
# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
# isort: skip_file
# --- Do not remove these libs ---
import numpy as np # noqa
import pandas as pd # noqa
from pandas import DataFrame
from talib._ta_lib import ULTOSC, MACD, LINEARREG_ANGLE, TSF, MFI, EMA, MA, BBANDS, CORREL, CCI, HT_DCPHASE, \
HT_TRENDMODE, HT_TRENDLINE, MIN, MAX, HT_PHASOR, HT_SINE, LINEARREG, ATR, NATR, SAR
from freqtrade.strategy.interface import IStrategy
# --------------------------------
# Add your lib to import here
import freqtrade.vendor.qtpylib.indicators as qtpylib
"""
"""
# This class is a sample. Feel free to customize it.
class strg3_ETHUSDT_1m(IStrategy):
"""
This is a sample strategy to inspire you.
More information in https://github.com/freqtrade/freqtrade/blob/develop/docs/bot-optimization.md
You can:
:return: a Dataframe with all mandatory indicators for the strategies
- Rename the class name (Do not forget to update class_name)
- Add any methods you want to build your strategy
- Add any lib you need to build your strategy
You must keep:
- the lib in the section "Do not remove these libs"
- the prototype for the methods: minimal_roi, stoploss, populate_indicators, populate_buy_trend,
populate_sell_trend, hyperopt_space, buy_strategy_generator
"""
# 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
# Minimal ROI designed for the strategy.
# This attribute will be overridden if the config file contains "minimal_roi".
minimal_roi = {
"0": 0.22703036349783817,
"30": 0.1085576426119433,
"82": 0.029443202051755248,
"164": 0
}
# Optimal stoploss designed for the strategy.
# This attribute will be overridden if the config file contains "stoploss".
stoploss = -0.1963679962572551
# Trailing stop:
trailing_stop = True
trailing_stop_positive = 0.2294395227514193
trailing_stop_positive_offset = 0.3040424465654783
trailing_only_offset_is_reached = False
# Optimal ticker interval for the strategy.
timeframe = '1m'
# Run "populate_indicators()" only for new candle.
process_only_new_candles = False
# These values can be overridden in the "ask_strategy" section in the config.
use_sell_signal = True
sell_profit_only = True
ignore_roi_if_buy_signal = False
# Number of candles the strategy requires before producing valid signals
startup_candle_count: int = 30
# Optional order type mapping.
order_types = {
'buy': 'limit',
'sell': 'limit',
'stoploss': 'market',
'stoploss_on_exchange': False
}
# Optional order time in force.
order_time_in_force = {
'buy': 'gtc',
'sell': 'gtc'
}
plot_config = {
'main_plot': {
'lr': {'color': 'green'},
# 'angle_mid': {'color': 'blue'},
'tsf_mid': {'color': 'orange'},
},
'subplots': {
"correl_h_l": {
'correl_h_l': {'color': 'black'}
},
"cci": {
'cci': {'color': 'red'}
},
"natr": {
'natr': {'color': 'red'}
},
"ht_phase": {
'ht_phase': {'color': 'red'}
},
"ht_trendmode": {
'ht_trendmode': {'color': 'red'}
}
}
}
def informative_pairs(self):
"""
Define additional, informative pair/interval combinations to be cached from the exchange.
These pair/interval combinations are non-tradeable, unless they are part
of the whitelist as well.
For more information, please consult the documentation
:return: List of tuples in the format (pair, interval)
Sample: return [("ETH/USDT", "5m"),
("BTC/USDT", "15m"),
]
"""
return [("ETH/USDT", "1m")]
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
"""
# dataframe['mfi'] = MFI(dataframe['high'], dataframe['low'], dataframe['close'], dataframe['volume'],
# timeperiod=15)
dataframe['cci_30'] = CCI(dataframe['high'], dataframe['low'], dataframe['close'], timeperiod=30)
dataframe['cci_45'] = CCI(dataframe['high'], dataframe['low'], dataframe['close'], timeperiod=45)
dataframe['trend_line'] = HT_TRENDLINE(dataframe['close'])
dataframe['angle_short'] = LINEARREG_ANGLE(dataframe['close'], timeperiod=5)
dataframe['angle_mid'] = LINEARREG_ANGLE(dataframe['close'], timeperiod=10)
dataframe['angle_long'] = LINEARREG_ANGLE(dataframe['close'], timeperiod=30)
dataframe['angle_trend_45'] = LINEARREG_ANGLE(dataframe['trend_line'], timeperiod=45)
dataframe['angle_trend_30'] = LINEARREG_ANGLE(dataframe['trend_line'], timeperiod=30)
# dataframe['atr'] = ATR(dataframe['high'], dataframe['low'], dataframe['close'], timeperiod=30)
dataframe['natr'] = NATR(dataframe['high'], dataframe['low'], dataframe['close'], timeperiod=14)
dataframe['tsf_short'] = TSF(dataframe['close'], timeperiod=30)
dataframe['correl_h_l_30'] = CORREL(dataframe['high'], dataframe['low'], timeperiod=30)
dataframe['correl_h_l_3'] = CORREL(dataframe['high'], dataframe['low'], timeperiod=3)
dataframe['correl_h_l_10'] = CORREL(dataframe['high'], dataframe['low'], timeperiod=10)
# dataframe['correl_close_last_close'] = CORREL(dataframe['close'].shift(1), dataframe['close'], timeperiod=30)
#
# dataframe['correl_tsf_long_close'] = CORREL(dataframe['tsf_long'], dataframe['close'], timeperiod=45)
# dataframe['correl_tsf_mid_close'] = CORREL(dataframe['tsf_mid'], dataframe['close'], timeperiod=45)
# dataframe['correl_tsf_short_close'] = CORREL(dataframe['tsf_short'], dataframe['close'], timeperiod=5)
#
# dataframe['correl_angle_short_close'] = CORREL(dataframe['angle_short'], dataframe['close'], timeperiod=45)
# dataframe['correl_angle_mid_close'] = CORREL(dataframe['angle_mid'], dataframe['close'], timeperiod=45)
# dataframe['correl_angle_long_close'] = CORREL(dataframe['angle_long'], dataframe['close'], timeperiod=45)
#
# dataframe['correl_mfi_close'] = CORREL(dataframe['mfi'], dataframe['close'], timeperiod=45)
#
# dataframe['ema'] = EMA(dataframe['close'], timeperiod=7)
dataframe['upperband'], dataframe['middleband'], dataframe['lowerband'] = BBANDS(dataframe['close'],
timeperiod=28, nbdevup=2,
nbdevdn=2, matype=0)
# dataframe['correl_ht_sine_close'] = CORREL(dataframe['ht_sine'], dataframe['close'], timeperiod=30)
dataframe['sar'] = SAR(dataframe['high'], dataframe['low'], acceleration=0.02, maximum=0.2)
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the buy signal for the given dataframe
:param dataframe: DataFrame populated with indicators
:param metadata: Additional information, like the currently traded pair
:return: DataFrame with buy column
"""
dataframe.loc[
(
(qtpylib.crossed_below(dataframe['sar'], dataframe['close'])) &
(0 > dataframe['cci_30']) &
(0.15 < dataframe['natr']) &
(0 < (dataframe['trend_line'] - dataframe['close'])) &
(0.75227 < dataframe['correl_h_l_30']) &
# (dataframe['angle_long'] > 0) &
# (0.44494 < dataframe['correl_angle_mid_close']) &
# (-0.97597 < dataframe['correl_mfi_close']) & #
# (57 > dataframe['mfi']) &
# ((dataframe['close'] - dataframe['tsf_long']) > -3) &
(dataframe['volume'] > 0) # Make sure Volume is not 0
),
'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
:param dataframe: DataFrame populated with indicators
:param metadata: Additional information, like the currently traded pair
:return: DataFrame with buy column
"""
dataframe.loc[
(
(qtpylib.crossed_above(dataframe['sar'], dataframe['close'])) &
(50 > dataframe['cci_30']) &
# (100 < dataframe['cci_30']) &
(0 < (dataframe['close'] - dataframe['trend_line'])) &
(0.2 < dataframe['natr']) &
(0.4 < dataframe['correl_h_l_30']) &
# (1 == dataframe['ht_trendmode']) &
# ((dataframe['tsf_long'] - dataframe['close']) < -3) &
# (dataframe['angle_mid'] < 50) &
# (dataframe['angle_long'] < 0) &
# (dataframe['angle_short'] > -9) &
# (dataframe['correl_mfi_close'] > -0.4065) & #
# (0.2552 < dataframe['correl_angle_long_close']) &
# (0.46789 < dataframe['correl_angle_mid_close']) &
# (57 < dataframe['mfi']) &
# (0.00585 < (dataframe['quadrature'] - dataframe['inphase'])) &
(dataframe['volume'] > 0) # Make sure Volume is not 0
),
'sell'] = 1
return dataframe

219
.env/bin/user_data/strategies/strg3_eth_usdt_1m_sellonlyprofit.py Normal file
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@@ -0,0 +1,219 @@
# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
# isort: skip_file
# --- Do not remove these libs ---
import numpy as np # noqa
import pandas as pd # noqa
from pandas import DataFrame
from talib._ta_lib import ULTOSC, MACD, LINEARREG_ANGLE, TSF, MFI, EMA, MA, BBANDS, CORREL, CCI, HT_DCPHASE, \
HT_TRENDMODE, HT_TRENDLINE, MIN, MAX, HT_PHASOR, HT_SINE, LINEARREG, ATR, NATR, SAR
from freqtrade.strategy.interface import IStrategy
# --------------------------------
# Add your lib to import here
import freqtrade.vendor.qtpylib.indicators as qtpylib
"""
"""
# This class is a sample. Feel free to customize it.
class strg3_ETHUSDT_1m_sellonlyprofit_prod(IStrategy):
"""
This is a sample strategy to inspire you.
More information in https://github.com/freqtrade/freqtrade/blob/develop/docs/bot-optimization.md
You can:
:return: a Dataframe with all mandatory indicators for the strategies
- Rename the class name (Do not forget to update class_name)
- Add any methods you want to build your strategy
- Add any lib you need to build your strategy
You must keep:
- the lib in the section "Do not remove these libs"
- the prototype for the methods: minimal_roi, stoploss, populate_indicators, populate_buy_trend,
populate_sell_trend, hyperopt_space, buy_strategy_generator
"""
# 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
# Minimal ROI designed for the strategy.
# This attribute will be overridden if the config file contains "minimal_roi".
minimal_roi = {
"0": 0.22703036349783817,
"30": 0.09085576426119433,
"82": 0.029443202051755248,
"164": 0
}
# Optimal stoploss designed for the strategy.
# This attribute will be overridden if the config file contains "stoploss".
stoploss = -0.2263679962572551
# Trailing stop:
trailing_stop = True
trailing_stop_positive = 0.1294395227514193
trailing_stop_positive_offset = 0.13040424465654783
trailing_only_offset_is_reached = False
# Optimal ticker interval for the strategy.
timeframe = '1m'
# Run "populate_indicators()" only for new candle.
process_only_new_candles = False
# These values can be overridden in the "ask_strategy" section in the config.
use_sell_signal = True
sell_profit_only = True
ignore_roi_if_buy_signal = False
# Number of candles the strategy requires before producing valid signals
startup_candle_count: int = 30
# Optional order type mapping.
order_types = {
'buy': 'limit',
'sell': 'limit',
'stoploss': 'market',
'stoploss_on_exchange': False
}
# Optional order time in force.
order_time_in_force = {
'buy': 'gtc',
'sell': 'gtc'
}
plot_config = {
'main_plot': {
'lr': {'color': 'green'},
# 'angle_mid': {'color': 'blue'},
'tsf_mid': {'color': 'orange'},
},
'subplots': {
"correl_h_l": {
'correl_h_l': {'color': 'black'}
},
"cci": {
'cci': {'color': 'red'}
},
"natr": {
'natr': {'color': 'red'}
},
"ht_phase": {
'ht_phase': {'color': 'red'}
},
"ht_trendmode": {
'ht_trendmode': {'color': 'red'}
}
}
}
def informative_pairs(self):
"""
Define additional, informative pair/interval combinations to be cached from the exchange.
These pair/interval combinations are non-tradeable, unless they are part
of the whitelist as well.
For more information, please consult the documentation
:return: List of tuples in the format (pair, interval)
Sample: return [("ETH/USDT", "5m"),
("BTC/USDT", "15m"),
]
"""
return [("ETH/USDT", "1m")]
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
"""
# dataframe['mfi'] = MFI(dataframe['high'], dataframe['low'], dataframe['close'], dataframe['volume'],
# timeperiod=15)
dataframe['cci_30'] = CCI(dataframe['high'], dataframe['low'], dataframe['close'], timeperiod=30)
dataframe['cci_45'] = CCI(dataframe['high'], dataframe['low'], dataframe['close'], timeperiod=45)
dataframe['trend_line'] = HT_TRENDLINE(dataframe['close'])
dataframe['angle_short'] = LINEARREG_ANGLE(dataframe['close'], timeperiod=5)
dataframe['angle_mid'] = LINEARREG_ANGLE(dataframe['close'], timeperiod=10)
dataframe['angle_long'] = LINEARREG_ANGLE(dataframe['close'], timeperiod=30)
dataframe['angle_trend_45'] = LINEARREG_ANGLE(dataframe['trend_line'], timeperiod=45)
dataframe['angle_trend_30'] = LINEARREG_ANGLE(dataframe['trend_line'], timeperiod=30)
# dataframe['atr'] = ATR(dataframe['high'], dataframe['low'], dataframe['close'], timeperiod=30)
dataframe['natr'] = NATR(dataframe['high'], dataframe['low'], dataframe['close'], timeperiod=14)
dataframe['tsf_short'] = TSF(dataframe['close'], timeperiod=30)
dataframe['correl_h_l_30'] = CORREL(dataframe['high'], dataframe['low'], timeperiod=30)
dataframe['correl_h_l_3'] = CORREL(dataframe['high'], dataframe['low'], timeperiod=3)
dataframe['correl_h_l_10'] = CORREL(dataframe['high'], dataframe['low'], timeperiod=10)
dataframe['upperband'], dataframe['middleband'], dataframe['lowerband'] = BBANDS(dataframe['close'],
timeperiod=28, nbdevup=2,
nbdevdn=2, matype=0)
dataframe['sar'] = SAR(dataframe['high'], dataframe['low'], acceleration=0.02, maximum=0.2)
dataframe['angle_trend_fast'] = LINEARREG_ANGLE(dataframe['trend_line'], timeperiod=5)
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the buy signal for the given dataframe
:param dataframe: DataFrame populated with indicators
:param metadata: Additional information, like the currently traded pair
:return: DataFrame with buy column
"""
dataframe.loc[
(
(qtpylib.crossed_below(dataframe['sar'], dataframe['close'])) &
(50 > dataframe['cci_30']) &
(0.15 < dataframe['natr']) &
(dataframe['angle_trend_fast'] > 0) &
(0.75227 < dataframe['correl_h_l_30']) &
(dataframe['volume'] > 0) # Make sure Volume is not 0
),
'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
:param dataframe: DataFrame populated with indicators
:param metadata: Additional information, like the currently traded pair
:return: DataFrame with buy column
"""
dataframe.loc[
(
(qtpylib.crossed_above(dataframe['sar'], dataframe['close'])) &
(50 > dataframe['cci_30']) &
(0.25 < dataframe['natr']) &
(dataframe['angle_trend_fast'] < 0) &
(0.4 < dataframe['correl_h_l_30']) &
# (1 == dataframe['ht_trendmode']) &
# ((dataframe['tsf_long'] - dataframe['close']) < -3) &
# (dataframe['angle_mid'] < 50) &
# (dataframe['angle_long'] < 0) &
# (dataframe['angle_short'] > -9) &
# (dataframe['correl_mfi_close'] > -0.4065) & #
# (0.2552 < dataframe['correl_angle_long_close']) &
# (0.46789 < dataframe['correl_angle_mid_close']) &
# (57 < dataframe['mfi']) &
# (0.00585 < (dataframe['quadrature'] - dataframe['inphase'])) &
(dataframe['volume'] > 0) # Make sure Volume is not 0
),
'sell'] = 1
return dataframe

281
.env/bin/user_data/strategies/strtg2.py Normal file
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@@ -0,0 +1,281 @@
# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
# isort: skip_file
# --- Do not remove these libs ---
import numpy as np # noqa
import pandas as pd # noqa
from pandas import DataFrame
from talib._ta_lib import ULTOSC, MACD, SAR, LINEARREG_ANGLE
from freqtrade.strategy.interface import IStrategy
# --------------------------------
# Add your lib to import here
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
# This class is a sample. Feel free to customize it.
class strg_ETHBTC_1m2(IStrategy):
"""
This is a sample strategy to inspire you.
More information in https://github.com/freqtrade/freqtrade/blob/develop/docs/bot-optimization.md
You can:
:return: a Dataframe with all mandatory indicators for the strategies
- Rename the class name (Do not forget to update class_name)
- Add any methods you want to build your strategy
- Add any lib you need to build your strategy
You must keep:
- the lib in the section "Do not remove these libs"
- the prototype for the methods: minimal_roi, stoploss, populate_indicators, populate_buy_trend,
populate_sell_trend, hyperopt_space, buy_strategy_generator
"""
# 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
# Minimal ROI designed for the strategy.
# This attribute will be overridden if the config file contains "minimal_roi".
minimal_roi = {
"0": 0.04025819697656752,
"7": 0.015188707936204564,
"18": 0.005472487470606337,
"41": 0
}
# Optimal stoploss designed for the strategy.
# This attribute will be overridden if the config file contains "stoploss".
stoploss = -0.33515742514178193
# Trailing stoploss
trailing_stop = False
trailing_stop_positive = 0.34089
trailing_stop_positive_offset = 0.43254
trailing_only_offset_is_reached = False
# Optimal ticker interval for the strategy.
timeframe = '1m'
# Run "populate_indicators()" only for new candle.
process_only_new_candles = False
# 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 = False
# Number of candles the strategy requires before producing valid signals
startup_candle_count: int = 30
# Optional order type mapping.
order_types = {
'buy': 'limit',
'sell': 'limit',
'stoploss': 'market',
'stoploss_on_exchange': False
}
# Optional order time in force.
order_time_in_force = {
'buy': 'gtc',
'sell': 'gtc'
}
plot_config = {
'main_plot': {
'close': {},
'sar': {'color': 'white'},
},
'subplots': {
"MACD": {
'macd': {'color': 'blue'},
'macdsignal': {'color': 'orange'},
},
"OU": {
'ou': {'color': 'red'},
}
}
}
def informative_pairs(self):
"""
Define additional, informative pair/interval combinations to be cached from the exchange.
These pair/interval combinations are non-tradeable, unless they are part
of the whitelist as well.
For more information, please consult the documentation
:return: List of tuples in the format (pair, interval)
Sample: return [("ETH/USDT", "5m"),
("BTC/USDT", "15m"),
]
"""
return [("ETH/BTC", "1m")]
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
"""
# # Ultimate Oscillator
dataframe['uo'] = ULTOSC(dataframe['high'], dataframe['low'], dataframe['close'], timeperiod1=7, timeperiod2=14, timeperiod3=28)
# MACD
dataframe['macd'], dataframe['macdsignal'], dataframe['macdhist'] = MACD(dataframe['close'], fastperiod=12, slowperiod=26, signalperiod=9)
# Parabolic SAR
dataframe['sar'] = SAR(dataframe['high'], dataframe['low'], acceleration=0, maximum=0)
# Linear angle
dataframe['angle'] = LINEARREG_ANGLE(dataframe['close'], timeperiod=14)
# first check if dataprovider is available
if self.dp:
if self.dp.runmode in ('live', 'dry_run'):
ob = self.dp.orderbook(metadata['pair'], 1)
dataframe['best_bid'] = ob['bids'][0][0]
dataframe['best_ask'] = ob['asks'][0][0]
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the buy signal for the given dataframe
:param dataframe: DataFrame populated with indicators
:param metadata: Additional information, like the currently traded pair
:return: DataFrame with buy column
"""
dataframe.loc[
(
(qtpylib.crossed_above(dataframe['close'], dataframe['sar'])) &
(0 > (dataframe['sar'] - dataframe['close'])) &
(1 > (dataframe['sar'] - dataframe['sar'].shift(3))) &
(1 > dataframe['macd']) &
(1 > dataframe['macdhist']) &
(0 < (dataframe['macdhist'] - dataframe['macdhist'].shift(3))) &
(0 < dataframe['angle']) &
(dataframe['volume'] > 0) # Make sure Volume is not 0
),
'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
:param dataframe: DataFrame populated with indicators
:param metadata: Additional information, like the currently traded pair
:return: DataFrame with buy column
"""
dataframe.loc[
(
(qtpylib.crossed_below(dataframe['sar'], dataframe['close'])) &
(dataframe['uo'] < 69) &
(0 > (dataframe['sar'] - dataframe['close'])) &
(1 > (dataframe['sar'] - dataframe['sar'].shift(3))) &
(0 < dataframe['macd']) &
(1 < dataframe['macdhist']) &
(0 > (dataframe['macdhist'] - dataframe['macdhist'].shift(3))) &
(0 > dataframe['angle']) &
(dataframe['volume'] > 0) # Make sure Volume is not 0
),
'sell'] = 1
return dataframe
"""
buy1:
trigger = macd cross above macdsignal
guard = sar < close
guard = 70 > ou > 50
guard = lenear_angle > 0
buy2:
trigger = sar < close
guard = macd > macdsignal
guard = 70 > ou > 50
buy3:
trigger = ou cross below 30 & close > max(close)
sell1:
trigger = macd cross below macdsignal
guard = sar > close
guard = 50 > ou > 20
sell2:
trigger = sar > close
guard = macd < macdsignal
guard = 50 > ou > 20
"""
"""
+--------+---------+----------+------------------+--------------+------------------------------+----------------+-------------+
| Best | Epoch | Trades | Win Draw Loss | Avg profit | Profit | Avg duration | Objective |
|--------+---------+----------+------------------+--------------+------------------------------+----------------+-------------|
| * Best | 3/500 | 1194 | 523 654 17 | -0.12% | -0.06906927 BTC (-138.00%) | 1,559.8 m | 2.71708 |
| * Best | 6/500 | 100 | 20 0 80 | -0.07% | -0.00352831 BTC (-7.05%) | 11.5 m | 1.87067 |
| Best | 37/500 | 12 | 6 0 6 | 0.49% | 0.00294367 BTC (5.88%) | 18.2 m | 1.86009 |
| Best | 67/500 | 10 | 6 0 4 | 0.89% | 0.00443786 BTC (8.87%) | 19.5 m | 1.85245 |
| Best | 91/500 | 73 | 21 5 47 | 0.00% | 0.00007645 BTC (0.15%) | 10.2 m | 1.85215 |
| Best | 92/500 | 48 | 17 2 29 | 0.05% | 0.00116911 BTC (2.34%) | 10.4 m | 1.85189 |
| Best | 94/500 | 12 | 6 0 6 | 0.69% | 0.00416071 BTC (8.31%) | 17.8 m | 1.85143 |
| Best | 110/500 | 18 | 6 1 11 | 0.36% | 0.00327838 BTC (6.55%) | 14.3 m | 1.85113 |
| Best | 257/500 | 48 | 16 0 32 | 0.06% | 0.00154456 BTC (3.09%) | 11.3 m | 1.8505 |
| Best | 388/500 | 74 | 23 14 37 | 0.01% | 0.00045826 BTC (0.92%) | 8.2 m | 1.84665 |
[Epoch 500 of 500 (100%)] || | [Time: 1:04:49, Elapsed Time: 1:04:49]
2021-01-31 01:14:47,851 - freqtrade.optimize.hyperopt - INFO - 500 epochs saved to '/home/yakov/PycharmProjects/freqtrade/.env/bin/user_data/hyperopt_results/hyperopt_results_2021-01-31_00-07-23.pickle'.
Best result:
388/500: 74 trades. 23/14/37 Wins/Draws/Losses. Avg profit 0.01%. Median profit -0.02%. Total profit 0.00045826 BTC ( 0.92Σ%). Avg duration 8.2 min. Objective: 1.84665
# Buy hyperspace params:
buy_params = {
'angle-enabled': False,
'macd-enabled': False,
'macd_value': 0.73579,
'macdhist_shift': 0.87895,
'macdhist_value': 0.29935,
'sar-enabled': False,
'sar_ratio': 0.43819,
'sar_shift': 0.98992,
'trigger': 'sell-macd_cross_signal'
}
# Sell hyperspace params:
sell_params = {
'angle_h_s': 0.07105,
'macd_value_s': 0.08408,
'macdhist_shift_s': -0.72567,
'macdhist_value_s': 0.77324,
'sar_ratio_s': 0.58347,
'sar_shift_s': 0.81212,
'sell-angle-enabled': True,
'sell-macd-enabled': False,
'sell-sar-enabled': False,
'sell-uo-enabled': True,
'trigger': 'sell-macd_cross_signal',
'uo_l_s': 14
}
# ROI table:
minimal_roi = {
"0": 0.07193,
"3": 0.0382,
"5": 0.01183,
"7": 0
}
# Stoploss:
stoploss = -0.25471
# Trailing stop:
trailing_stop = True
trailing_stop_positive = 0.04697
trailing_stop_positive_offset = 0.05329
trailing_only_offset_is_reached = True
"""