Timeframe
1d
Direction
Long Only
Stoploss
-10.0%
Trailing Stop
No
ROI
0m: 5.0%, 20m: 4.0%, 30m: 3.0%, 60m: 1.0%
Interface Version
2
Startup Candles
N/A
Indicators
0
# Start hyperopt with the following command:
# freqtrade hyperopt --config config.json --hyperopt-loss SharpeHyperOptLoss --strategy RsiStrat -e 500 --spaces buy sell --random-state 8711
# --- 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 EmptyHyperopt(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 = {
"60": 0.01,
"30": 0.03,
"20": 0.04,
"0": 0.05
}
stoploss = -0.10
trailing_stop = False
use_sell_signal = True
sell_profit_only = False
sell_profit_offset = 0.0
ignore_roi_if_buy_signal = False
# --- Define spaces for the indicators ---
# --- Used indicators of strategy code ----
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
return dataframe
# --- Buy settings ---
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
conditions = []
conditions.append(( ))
if conditions:
dataframe.loc[
reduce(lambda x, y: x & y, conditions),
'buy'] = 1
return dataframe
# --- Sell settings ---
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
conditions = []
conditions.append(( ))
if conditions:
dataframe.loc[
reduce(lambda x, y: x & y, conditions),
'sell'] = 1
return dataframe