Timeframe
15m
Direction
Long Only
Stoploss
-75.0%
Trailing Stop
No
ROI
0m: 50000.0%
Interface Version
3
Startup Candles
N/A
Indicators
2
freqtrade/freqtrade-strategies
freqtrade/freqtrade-strategies
Sample strategy implementing Informative Pairs - compares stake_currency with USDT. Not performing very well - but should serve as an example how to use a referential pair against USDT. author@: xmatthias github@: https://github.com/freqtrade/freqtrade-strategies
freqtrade/freqtrade-strategies
# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
# flake8: noqa: F401
# isort: skip_file
# --- Do not remove these libs ---
from warnings import simplefilter
import numpy as np
from numpy import NaN # noqa
import pandas as pd # noqa
from pandas import DataFrame
import copy
from datetime import datetime
from freqtrade.persistence import Trade
from technical.util import (resample_to_interval, resampled_merge)
from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter,
IStrategy, IntParameter, stoploss_from_open, informative)
# --------------------------------
# Add your lib to import here
import talib.abstract as ta
import pandas_ta as pta
import freqtrade.vendor.qtpylib.indicators as qtpylib
import warnings
warnings.filterwarnings(
'ignore', message='The objective has been evaluated at this point before.')
simplefilter(action="ignore", category=pd.errors.PerformanceWarning)
pd.set_option('display.max_rows', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.width', None)
pd.set_option('display.max_colwidth', None)
pd.options.mode.chained_assignment = None
# --------------------------------
class SuperReversal_mtf(IStrategy):
# Strategy interface version - allow new iterations of the strategy interface.
# Check the documentation or the Sample strategy to get the latest version.
INTERFACE_VERSION = 3
# Can this strategy go short?
can_short: bool = True
process_only_new_candles: bool = True
# Minimal ROI designed for the strategy.
# This attribute will be overridden if the config file contains "minimal_roi".
minimal_roi = {
"0": 500.00
}
st_short_atr_window = IntParameter(8, 20, default=10, space="buy", optimize=True)
st_short_atr_multiplier = DecimalParameter(1.0, 6.0, decimals=1, default=5.5, space="buy", optimize=True)
short_ema_window = IntParameter(3, 600, default=30, space="buy", optimize=True)
long_ema_window = IntParameter(10, 600, default=545, space="buy", optimize=True)
# Optimal stoploss designed for the strategy.
# This attribute will be overridden if the config file contains "stoploss".
stoploss = -0.75
# Trailing stoploss
trailing_stop: bool = False
# trailing_only_offset_is_reached = False
# trailing_stop_positive = 0.01
# trailing_stop_positive_offset = 0.0 # Disabled / not configured
# Optimal timeframe for the strategy.
timeframe = '15m'
# Number of candles the strategy requires before producing valid signals
startup_candle_count: int = 20
# Optional order type mapping.
order_types = {
'entry': 'limit',
'exit': 'limit',
'stoploss': 'market',
'stoploss_on_exchange': False
}
# Optional order time in force.
order_time_in_force = {
'entry': 'gtc',
'exit': 'gtc'
}
@informative('1h')
def populate_indicators_1h(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
st = pta.supertrend(dataframe['high'], dataframe['low'], dataframe['close'],
length=int(self.st_short_atr_window.value), multiplier=self.st_short_atr_multiplier.value)
dataframe['super_trend_direction'] = st['SUPERTd_' + str(self.st_short_atr_window.value) + "_" + str(self.st_short_atr_multiplier.value)]
dataframe["ema_short"] = ta.EMA(dataframe, timeperiod=int(self.short_ema_window.value))
dataframe["ema_long"] = ta.EMA(dataframe, timeperiod=int(self.long_ema_window.value))
#print(dataframe.tail(2))
return dataframe
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe["ema_short_val"] = int(self.short_ema_window.value)
dataframe["ema_long_val"] = int(self.long_ema_window.value)
return dataframe
def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float,
proposed_stake: float, min_stake: float, max_stake: float,
leverage: float, entry_tag: str, side: str,
**kwargs) -> float:
if min_stake>proposed_stake:
proposed_stake = min_stake
return proposed_stake
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
(dataframe["super_trend_direction_1h"] == 1)
&
(dataframe["ema_short_1h"] > dataframe["ema_long_1h"])
&
(dataframe["high"] > dataframe[f"ema_short_1h"])
&
(dataframe["low"] < dataframe[f"ema_short_1h"])
),
'enter_long'] = 1
dataframe.loc[
(
(dataframe["super_trend_direction_1h"] == -1)
&
(dataframe["ema_short_1h"] < dataframe["ema_long_1h"])
&
(dataframe["high"] > dataframe[f"ema_short_1h"])
&
(dataframe["low"] < dataframe[f"ema_short_1h"])
),
'enter_short'] = 1
return dataframe
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
((dataframe["super_trend_direction_1h"] == -1) | (dataframe["ema_short_1h"] < dataframe["ema_long_1h"]))
&
(dataframe["high"] > dataframe["ema_short_1h"])
&
(dataframe["low"] < dataframe["ema_short_1h"])
),
'exit_long'] = 1
dataframe.loc[
(
((dataframe["super_trend_direction_1h"] == 1) | (dataframe["ema_short_1h"] > dataframe["ema_long_1h"]))
&
(dataframe["high"] > dataframe["ema_short_1h"])
&
(dataframe["low"] < dataframe["ema_short_1h"])
),
'exit_short'] = 1
return dataframe
################################################################################################
class SuperReversal_mtf_5min(SuperReversal_mtf):
timeframe = '5m'