This is a strategy template to get you started. More information in https://www.freqtrade.io/en/latest/strategy-customization/
Timeframe
1m
Direction
Long Only
Stoploss
-3.6%
Trailing Stop
Yes
ROI
0m: 2.1%, 74m: 1.5%, 123m: 1.0%, 137m: 0.0%
Interface Version
2
Startup Candles
N/A
Indicators
14
freqtrade/freqtrade-strategies
Strategy 003 author@: Gerald Lonlas github@: https://github.com/freqtrade/freqtrade-strategies
# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
# flake8: noqa: F401
# --- Do not remove these libs ---
from logging import NullHandler, fatal
import numpy as np # noqa
import pandas as pd # noqa
from pandas import DataFrame, Series
from freqtrade.strategy import IStrategy
from freqtrade.strategy import (merge_informative_pair, CategoricalParameter, DecimalParameter, IntParameter)
from freqtrade.exchange import timeframe_to_prev_date
from datetime import datetime
from freqtrade.persistence import Trade
from freqtrade.strategy import stoploss_from_open
# --------------------------------
# Add your lib to import here
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
from technical.indicators import indicators
from freqtrade.persistence import Trade
from datetime import datetime
class volume1m(IStrategy):
"""
This is a strategy template to get you started.
More information in https://www.freqtrade.io/en/latest/strategy-customization/
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 methods: populate_indicators, populate_buy_trend, populate_sell_trend
You should keep:
- timeframe, minimal_roi, stoploss, trailing_*
"""
# 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".
# ROI table:
# minimal_roi = {
# "0": 0.02106,
# "74": 0.01537,
# "123": 0.01027,
# "137": 0
# }
# # Stoploss:
# stoploss = -0.03574
minimal_roi = {"25": 0.011}
stoploss = -0.015
# Trailing stop:
trailing_stop = True
trailing_stop_positive = 0.005
trailing_stop_positive_offset = 0.006
trailing_only_offset_is_reached = True
# trailing_stop = True
# trailing_stop_positive = 0.004
# Optimal timeframe for the strategy.
timeframe = '1m'
inf_15m = '15m' # informative tf
# 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 = 240
# 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'
}
# Hyperoptable parameters
# buy_rsi = IntParameter(low=1, high=30, default=30, space='buy', optimize=True, load=True)
# sell_rsi = IntParameter(low=35, high=100, default=40, space='sell', optimize=True, load=True)
# use_custom_sell = True
# use_custom_stoploss = True
def informative_pairs(self):
pairs = self.dp.current_whitelist()
informative_pairs = [(pair, self.inf_15m) for pair in pairs]
return informative_pairs
def informative_15m_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
assert self.dp, "DataProvider is required for multiple timeframes."
# Get the informative pair
informative_15m = self.dp.get_pair_dataframe(
pair=metadata['pair'], timeframe=self.inf_15m)
stoch_rfast = ta.STOCHRSI(informative_15m, timeperiod=14)
informative_15m['rfastd'] = stoch_rfast['fastd']
informative_15m['rfastk'] = stoch_rfast['fastk']
informative_15m['angle'] = ta.LINEARREG_ANGLE(informative_15m['close'], timeperiod=21)
informative_15m['lr_middle'] = ta.LINEARREG(informative_15m['close'], timeperiod=25)
informative_15m['atr'] = ta.ATR(informative_15m,timeperiod=7)
informative_15m['lr_lower1.0'] = informative_15m['lr_middle'] - informative_15m['atr']
informative_15m['angle']=ta.LINEARREG_ANGLE(informative_15m['close'], timeperiod=5)
informative_15m['volumeM'] = informative_15m['volume'].rolling(40).mean()
return informative_15m
def normal_tf_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
This method can also be loaded from the strategy, if it doesn't exist in the hyperopt class.
"""
dataframe['adx'] = ta.ADX(dataframe)
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
dataframe['macdsignal'] = macd['macdsignal']
dataframe['rsi'] = ta.RSI(dataframe, timeperiod=21)
dataframe['mfi'] = ta.MFI(dataframe)
stoch_fast = ta.STOCHF(dataframe, timeperiod=31)
dataframe['fastd'] = stoch_fast['fastd']
dataframe['fastk'] = stoch_fast['fastk']
stoch_rfast = ta.STOCHRSI(dataframe, timeperiod=30)
dataframe['rfastd'] = stoch_rfast['fastd']
dataframe['rfastk'] = stoch_rfast['fastk']
dataframe['minus_di'] = ta.MINUS_DI(dataframe, timeperiod=5)
dataframe['plus_di'] = ta.PLUS_DI(dataframe, timeperiod=5)
# Bollinger bands
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
dataframe['bb_lowerband'] = bollinger['lower']
dataframe['bb_upperband'] = bollinger['upper']
dataframe['sar'] = ta.SAR(dataframe)
dataframe['ohlc4']=(dataframe['open'] + dataframe['high'] + dataframe['low'] + dataframe['close']) / 4
dataframe['hlc3']=(dataframe['high'] + dataframe['low'] + dataframe['close']) / 3
dataframe['hl2']=(dataframe['high'] + dataframe['low'] ) / 2
dataframe['ol']=(dataframe['open'] / dataframe['low']) - 1
dataframe['cl']=dataframe['close'] / dataframe['low']
dataframe['ho']=(dataframe['high'] / dataframe['open']) - 1
dataframe['hma19'] = qtpylib.hma(dataframe['close'], 19)
dataframe['hma8'] = qtpylib.hma(dataframe['hl2'], 8)
dataframe['cci'] = (ta.CCI(dataframe, timeperiod=21)/2)
dataframe['sma60']=ta.SMA(dataframe['close'], timeperiod=60)
dataframe['vema']=ta.EMA(dataframe['volume'], timeperiod=34)
dataframe['vwma'] = (ta.SMA(dataframe['close']*dataframe['volume'], timeperiod=9)/ta.SMA(dataframe['volume'], timeperiod=9))
dataframe['evwma'] = (ta.EMA(dataframe['close']*dataframe['volume'], timeperiod=9)/ta.EMA(dataframe['volume'], timeperiod=9))
dataframe['angle'] = ta.LINEARREG_ANGLE(dataframe['close'], timeperiod=9)
dataframe['lr_middle'] = ta.LINEARREG(dataframe['close'], timeperiod=25)
dataframe['atr'] = ta.ATR(dataframe,timeperiod=7)
dataframe['lr_lower1.0'] = dataframe['lr_middle'] - dataframe['atr']
dataframe['var'] = ta.VAR(dataframe['ho'], timeperiod=7)
dataframe['tsf'] = ta.TSF(dataframe['ho'], timeperiod=7)
dataframe['stddev'] = ta.STDDEV(dataframe['ho'], timeperiod=7)
dataframe['shom']= ta.SMA(dataframe['ho'], timeperiod=14)
dataframe['vshom']=(ta.SMA(dataframe['ho']*dataframe['volume']*dataframe['lr_middle'], timeperiod=14)/ta.SMA(dataframe['volume']*dataframe['lr_middle'], timeperiod=14))
dataframe['solm']= ta.SMA(dataframe['ol'], timeperiod=14)
dataframe['vsolm']= (ta.SMA(dataframe['ol']*dataframe['volume'], timeperiod=14)/ta.SMA(dataframe['volume'], timeperiod=14))
dataframe['eshom']= ta.EMA(dataframe['ho'], timeperiod=14)
dataframe['evshom']=(ta.EMA(dataframe['ho']*(dataframe['tsf'])*dataframe['volume'], timeperiod=14)/ta.SMA(dataframe['volume']*(dataframe['tsf']), timeperiod=14))
dataframe['esolm']= ta.EMA(dataframe['ol'], timeperiod=14)
dataframe['evsolm']= (ta.EMA(dataframe['ol']*dataframe['volume'], timeperiod=14)/ta.EMA(dataframe['volume'], timeperiod=14))
dataframe['volumeM'] = dataframe['volume'].rolling(20).mean()
Percent = 0.4
# changeLONGSHORT = 1
dataframe['upsignal']=(ta.EMA(dataframe['close'],timeperiod=7))+((ta.EMA(dataframe['close'],timeperiod=7))*Percent/100)
dataframe['downsignal']=(ta.EMA(dataframe['close'],timeperiod=7))-(ta.EMA(dataframe['close'],timeperiod=7)*Percent/100)
return dataframe
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# The indicators for the 1h informative timeframe
informative_15m = self.informative_15m_indicators(dataframe, metadata)
dataframe = merge_informative_pair(
dataframe, informative_15m, self.timeframe, self.inf_15m, ffill=True)
# The indicators for the normal (5m) timeframe
dataframe = self.normal_tf_indicators(dataframe, metadata)
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# print(" {} -- {}".format(dataframe['volume'], dataframe['volume'].rolling(21).mean()))
dataframe.loc[
(
# (dataframe['upsignal'].shift(3) < dataframe['hma19'])&
# (qtpylib.crossed_above(dataframe['close'] , dataframe['vwma'])) &
# (qtpylib.crossed_below(dataframe['sar'] , dataframe['hma19']))&
# (dataframe['ol'] > 1.015)
# (dataframe['ol'] > 1.0312) &
# ((dataframe['ol'] - dataframe['cl']) > 0.0512) &
# (dataframe['mfi'] < 20)
# (dataframe['rfastk'] < 1)&
# (dataframe['plus_di'] < 2.5)
# (dataframe['adx'] < 23)&
# (dataframe['downsignal'] < dataframe['hma19'])
# (dataframe['minus_di'] > dataframe['mfi'])&
# (dataframe['minus_di'] >40)&
# (dataframe['minus_di'] <49)
#((dataframe['adx'] - dataframe['mfi']) < 3)
# (qtpylib.crossed_above(dataframe['minus_di'], dataframe['plus_di']))
# (dataframe['angle'] < -50)&
# (qtpylib.crossed_above(dataframe['vshom'], dataframe['vsolm']))
# ((dataframe['angle'] < -50) &
# (qtpylib.crossed_above(dataframe['angle'], -70)))
# |
# ((dataframe['angle'] > 0 ) &
# (qtpylib.crossed_above(dataframe['angle'], 15)))
# (qtpylib.crossed_above(dataframe['close'], dataframe['lr_lower1.0']))
# (dataframe['rfastd'] < 10)&
# (dataframe['lr_lower1.0'] < dataframe['downsignal'])&
# (dataframe['angle'] < -80)&
# (qtpylib.crossed_above(dataframe['rfastd'], dataframe['atr']))
# (dataframe['angle_1h'] < -80)&
# (dataframe['angle'] < -80)&
# (dataframe['macd'] <dataframe['angle'])&
# (dataframe['rfastd'].shift(1) < 1)&
# (qtpylib.crossed_above(dataframe['rfastd'], 1))
# (dataframe['rfastd_1h'] < 10)&
# (qtpylib.crossed_above(dataframe['rfastd'], dataframe['rfastd_1h']))
# (dataframe['close'] < dataframe['bb_lowerband'])&
# (dataframe['angle_15m'] > 0) &
(dataframe['volume'] > 0) &
(dataframe['close'] > dataframe['close'].shift(1)) &
(dataframe['volumeM_15m'] <= dataframe['volume'])
),
'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['downsignal'] > dataframe['hma19'])&
# (qtpylib.crossed_above(dataframe['close'], dataframe['hma19']))
# (qtpylib.crossed_above(dataframe['rfastd'], 89))
# (
# (dataframe['mfi'] > 70)
# |
# (dataframe['angle'] > 87)&
# (qtpylib.crossed_below(dataframe['fastd'], 95))
# |
# (dataframe['cci'] > 130)
# |
# ((dataframe['rfastk'] > 98) & (dataframe['minus_di'] < 25))
# )
# (((qtpylib.crossed_above(dataframe['close'], dataframe['bb_upperband']))&
# (dataframe['rfastd'] > 97))
# |
# ((qtpylib.crossed_above(dataframe['rfastk'], dataframe['rfastd']))&
# (dataframe['rfastd'] > 97))
# )
# (dataframe['angle_15m'] > 75)&
# (dataframe['angle'] > 85)&
(dataframe['bb_upperband'] < dataframe['close'])&
(dataframe['rfastd_15m'].shift(1) > 99)&
(qtpylib.crossed_below(dataframe['rfastd'], dataframe['rfastd_15m']))
),
'sell'] =1
return dataframe