Timeframe
1d
Direction
Long Only
Stoploss
-23.9%
Trailing Stop
No
ROI
0m: 55.3%, 8580m: 37.7%, 15227m: 15.3%, 45742m: 0.0%
Interface Version
2
Startup Candles
N/A
Indicators
1
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
Strategy 003 author@: Gerald Lonlas github@: https://github.com/freqtrade/freqtrade-strategies
freqtrade/freqtrade-strategies
# Start hyperopt with the following command:
# freqtrade backtesting --config config.json --strategy SmaCrossStrategy
# --- Do not remove these libs ---
import numpy as np # noqa
import pandas as pd # noqa
from functools import reduce
from pandas import DataFrame
from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter,IStrategy, IntParameter)
# --- Add your lib to import here ---
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
# --- Generic strategy settings ---
class SmaCrossStrategy(IStrategy):
INTERFACE_VERSION = 2
# Determine timeframe and # of candles before strategysignals becomes valid
timeframe = '1d'
startup_candle_count: int = 25
# Determine roi take profit and stop loss points
minimal_roi = {
"0": 0.553,
"8580": 0.377,
"15227": 0.153,
"45742": 0
}
stoploss = -0.239
trailing_stop = False
use_sell_signal = True
sell_profit_only = False
sell_profit_offset = 0.0
ignore_roi_if_buy_signal = False
# --- Used indicators of strategy code ----
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe['buy_quick_sma'] = ta.SMA(dataframe, timeperiod=15)
dataframe['buy_slow_sma'] = ta.SMA(dataframe, timeperiod=40)
dataframe['sell_quick_sma'] = ta.SMA(dataframe, timeperiod=11)
dataframe['sell_slow_sma'] = ta.SMA(dataframe, timeperiod=48)
return dataframe
# --- Buy settings ---
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
(qtpylib.crossed_above(dataframe['buy_quick_sma'], dataframe['buy_slow_sma']))
),
'buy'] = 1
return dataframe
# --- long settings ---
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
(qtpylib.crossed_below(dataframe['sell_quick_sma'], dataframe['sell_slow_sma']))
),
'sell'] = 1
return dataframe