Timeframe
5m
Direction
Long Only
Stoploss
-10.0%
Trailing Stop
No
ROI
0m: 100.0%
Interface Version
2
Startup Candles
30
Indicators
2
freqtrade/freqtrade-strategies
from freqtrade.strategy.interface import IStrategy
from pandas import DataFrame
import talib.abstract as ta
from freqtrade.strategy import DecimalParameter, IntParameter, CategoricalParameter
# SMAOffset
# source: https://github.com/davidzr/freqtrade-strategies/blob/main/strategies/SMAOffset/SMAOffset.py
ma_types = {
"SMA": ta.SMA,
"EMA": ta.EMA,
}
class SMAO(IStrategy):
INTERFACE_VERSION = 2
# hyperopt and paste results here
# Buy hyperspace params:
buy_params = {
"base_nb_candles_buy": 30,
"buy_trigger": "SMA",
"low_offset": 0.958,
}
# Sell hyperspace params:
sell_params = {
"base_nb_candles_sell": 30,
"high_offset": 1.012,
"sell_trigger": "EMA",
}
# Stoploss:
stoploss = -0.1
# ROI table:
minimal_roi = {
"0": 1,
}
base_nb_candles_buy = IntParameter(
5, 80, default=buy_params["base_nb_candles_buy"], space="buy"
)
base_nb_candles_sell = IntParameter(
5, 80, default=sell_params["base_nb_candles_sell"], space="sell"
)
low_offset = DecimalParameter(
0.8, 0.99, default=buy_params["low_offset"], space="buy"
)
high_offset = DecimalParameter(
0.8, 1.1, default=sell_params["high_offset"], space="sell"
)
buy_trigger = CategoricalParameter(
ma_types.keys(), default=buy_params["buy_trigger"], space="buy"
)
sell_trigger = CategoricalParameter(
ma_types.keys(), default=sell_params["sell_trigger"], space="sell"
)
# Trailing stop:
trailing_stop = False
# trailing_stop_positive = 0.0001
# trailing_stop_positive_offset = 0
# trailing_only_offset_is_reached = False
# Optimal timeframe for the strategy
timeframe = "5m"
use_exit_signal = True
exit_profit_only = False
process_only_new_candles = True
startup_candle_count = 30
plot_config = {
"main_plot": {
"ma_offset_buy": {"color": "orange"},
"ma_offset_sell": {"color": "orange"},
},
}
use_custom_stoploss = False
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
if not self.config["runmode"].value == "hyperopt":
dataframe["ma_offset_buy"] = (
ma_types[self.buy_trigger.value](
dataframe, int(self.base_nb_candles_buy.value)
)
* self.low_offset.value
)
dataframe["ma_offset_sell"] = (
ma_types[self.sell_trigger.value](
dataframe, int(self.base_nb_candles_sell.value)
)
* self.high_offset.value
)
return dataframe
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
if self.config["runmode"].value == "hyperopt":
dataframe["ma_offset_buy"] = (
ma_types[self.buy_trigger.value](
dataframe, int(self.base_nb_candles_buy.value)
)
* self.low_offset.value
)
dataframe.loc[
(
(dataframe["close"] < dataframe["ma_offset_buy"])
& (dataframe["volume"] > 0)
),
"buy",
] = 1
return dataframe
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
if self.config["runmode"].value == "hyperopt":
dataframe["ma_offset_sell"] = (
ma_types[self.sell_trigger.value](
dataframe, int(self.base_nb_candles_sell.value)
)
* self.high_offset.value
)
dataframe.loc[
(
(dataframe["close"] > dataframe["ma_offset_sell"])
& (dataframe["volume"] > 0)
),
"sell",
] = 1
return dataframe