Timeframe
1m
Direction
Long Only
Stoploss
-5.0%
Trailing Stop
No
ROI
0m: 1.3%
Interface Version
N/A
Startup Candles
N/A
Indicators
0
freqtrade/freqtrade-strategies
freqtrade/freqtrade-strategies
this strategy is based around the idea of generating a lot of potentatils buys and make tiny profits on each trade
freqtrade/freqtrade-strategies
this strategy is based around the idea of generating a lot of potentatils buys and make tiny profits on each trade
# --- Do not remove these libs ---
from freqtrade.strategy.interface import IStrategy
from typing import Dict, List
from functools import reduce
from pandas import DataFrame
from freqtrade.strategy import DecimalParameter
import numpy as np
# --------------------------------
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
def bollinger_bands(stock_price, window_size, num_of_std):
rolling_mean = stock_price.rolling(window=window_size).mean()
rolling_std = stock_price.rolling(window=window_size).std()
lower_band = rolling_mean - (rolling_std * num_of_std)
return rolling_mean, lower_band
class BinHV45_stash(IStrategy):
minimal_roi = {
"0": 0.0125
}
stoploss = -0.05
timeframe = '1m'
df_close_bbdelta = DecimalParameter(0.005, 0.06, default=0.008, space='buy', optimize=True, load=True)
df_close_closedelta = DecimalParameter(0.01, 0.03, default=0.0175, space='buy', optimize=True, load=True)
df_tail_bbdelta = DecimalParameter(0.15, 0.45, default=0.25, space='buy', optimize=True, load=True)
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
mid, lower = bollinger_bands(dataframe['close'], window_size=40, num_of_std=2)
dataframe['mid'] = np.nan_to_num(mid)
dataframe['lower'] = np.nan_to_num(lower)
dataframe['bbdelta'] = (dataframe['mid'] - dataframe['lower']).abs()
dataframe['pricedelta'] = (dataframe['open'] - dataframe['close']).abs()
dataframe['closedelta'] = (dataframe['close'] - dataframe['close'].shift()).abs()
dataframe['tail'] = (dataframe['close'] - dataframe['low']).abs()
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
dataframe['lower'].shift().gt(0) &
dataframe['bbdelta'].gt(dataframe['close'] * self.df_close_bbdelta.value) &
dataframe['closedelta'].gt(dataframe['close'] * self.df_close_closedelta.value) &
dataframe['tail'].lt(dataframe['bbdelta'] * self.df_tail_bbdelta.value) &
dataframe['close'].lt(dataframe['lower'].shift()) &
dataframe['close'].le(dataframe['close'].shift())
),
'buy'] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
no sell signal
"""
dataframe.loc[:, 'sell'] = 0
return dataframe