name changed and many other changes(Now Its the pure Dimond)
name changed and many other changes(Now Its the pure Dimond)
This commit is contained in:
Masoud Azizi
@@ -1,133 +0,0 @@
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# It is AfghanWoman Strategy.
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# That takes her own rights like Afghanstan women
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# Those who still proud and hopeful.
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# Those who the most beautiful creatures in the depths of the darkest.
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# Those who shine like diamonds buried in the heart of the desert ...
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# Why not help when we can?
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# If we believe there is no man left with them
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# (Which is probably the product of the thought of painless corpses)
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# Where has our humanity gone?
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# Where has humanity gone?
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# Why not help when we can?
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# Author: @Mablue (Masoud Azizi)
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# github: https://github.com/mablue/
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# (First Hyperopt it.A hyperopt file is available)
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# freqtrade hyperopt --hyperopt-loss SharpeHyperOptLoss --spaces buy sell --strategy AfghanWoman -j 2
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# freqtrade backtesting --strategy AfghanWoman
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# --- Do not remove these libs ---
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from freqtrade.strategy.hyper import IntParameter
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from freqtrade.strategy.interface import IStrategy
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from pandas import DataFrame
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# --------------------------------
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# Add your lib to import here
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import talib.abstract as ta
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from functools import reduce
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##### SETINGS #####
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# It hyperopt just one set of params for all buy and sell strategies if true.
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DUALFIT = False
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# how much Candles to check trand
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TCC = 1
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### END SETINGS ###
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class AfghanWoman(IStrategy):
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# ###################### RESULT PLACE ######################
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# * 4/100: 80 trades. 28/0/52 Wins/Draws/Losses. Avg profit 2.64%. Median profit -0.19%. Total profit 3366.29721754 USDT ( 336.63Σ%). Avg duration 9:09:00 min. Objective: -8.35746
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# Buy hyperspace params:
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buy_params = {
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"buy_count": 2,
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"buy_gap": 15,
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"buy_shift": 14,
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}
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# Sell hyperspace params:
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sell_params = {
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"sell_count": 5,
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"sell_gap": 13,
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"sell_shift": 4,
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}
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# ROI table:
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minimal_roi = {
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"0": 1,
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"13": 1,
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"64": 1,
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"178": 1
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}
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# Stoploss:
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stoploss = -0.256
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# Buy hypers
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timeframe = '5m'
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# #################### END OF RESULT PLACE ####################
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buy_count = IntParameter(2, 25, default=6, space='buy')
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buy_gap = IntParameter(2, 25, default=13, space='buy')
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buy_shift = IntParameter(0, 25, default=14, space='buy')
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if not DUALFIT:
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sell_count = IntParameter(2, 25, default=20, space='sell')
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sell_gap = IntParameter(2, 25, default=3, space='sell')
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sell_shift = IntParameter(0, 25, default=0, space='sell')
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def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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dataframe['PLUS_DM'] = ta.PLUS_DM(dataframe, timeperiod=14)
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dataframe['MINUS_DM'] = ta.MINUS_DM(dataframe, timeperiod=14)
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return dataframe
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def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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conditions = []
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conditions.append(dataframe['PLUS_DM'] > dataframe['MINUS_DM'])
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count = gap = shift = None
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count = self.buy_count.value
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gap = self.buy_gap.value
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shift = self.buy_shift.value
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for i in range(1, count+1):
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dataframe[f'buy-ma-{i}'] = ta.EMA(dataframe,
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timeperiod=int(i * gap))
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for s in range(1, shift+1):
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if i > 1:
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conditions.append(
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dataframe[f'buy-ma-{i}'].shift(s) >
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dataframe[f'buy-ma-{i-1}'].shift(s)
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)
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if conditions:
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dataframe.loc[
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reduce(lambda x, y: x & y, conditions),
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'buy']=1
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return dataframe
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def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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conditions = []
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conditions.append(dataframe['PLUS_DM'] <= dataframe['MINUS_DM'])
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count = gap = shift = None
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if DUALFIT:
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count = self.buy_count.value
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gap = self.buy_gap.value
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shift = self.buy_shift.value
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else:
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count = self.sell_count.value
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gap = self.sell_gap.value
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shift = self.sell_shift.value
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for i in range(1, count+1):
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dataframe[f'buy-ma-{i}'] = ta.EMA(dataframe,
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timeperiod=int(i * gap))
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for s in range(1, shift+1):
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if i > 1:
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conditions.append(
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dataframe[f'buy-ma-{i}'].shift(s) <
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dataframe[f'buy-ma-{i-1}'].shift(s)
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)
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if conditions:
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dataframe.loc[
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reduce(lambda x, y: x & y, conditions),
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'sell']=1
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return dataframe
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@@ -0,0 +1,147 @@
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# 𝐼𝓉 𝒾𝓈 𝒟𝒾𝓂𝑜𝓃𝒹 𝒮𝓉𝓇𝒶𝓉𝑒𝑔𝓎.
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# 𝒯𝒽𝒶𝓉 𝓉𝒶𝓀𝑒𝓈 𝒽𝑒𝓇 𝑜𝓌𝓃 𝓇𝒾𝑔𝒽𝓉𝓈 𝓁𝒾𝓀𝑒 𝒜𝒻𝑔𝒽𝒶𝓃𝒾𝓈𝓉𝒶𝓃 𝓌𝑜𝓂𝑒𝓃
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# 𝒯𝒽𝑜𝓈𝑒 𝓌𝒽𝑜 𝓈𝓉𝒾𝓁𝓁 𝓅𝓇𝑜𝓊𝒹 𝒶𝓃𝒹 𝒽𝑜𝓅𝑒𝒻𝓊𝓁.
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# 𝒯𝒽𝑜𝓈𝑒 𝓌𝒽𝑜 𝓉𝒽𝑒 𝓂𝑜𝓈𝓉 𝒷𝑒𝒶𝓊𝓉𝒾𝒻𝓊𝓁 𝒸𝓇𝑒𝒶𝓉𝓊𝓇𝑒𝓈 𝒾𝓃 𝓉𝒽𝑒 𝒹𝑒𝓅𝓉𝒽𝓈 𝑜𝒻 𝓉𝒽𝑒 𝒹𝒶𝓇𝓀𝑒𝓈𝓉.
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# 𝒯𝒽𝑜𝓈𝑒 𝓌𝒽𝑜 𝓈𝒽𝒾𝓃𝑒 𝓁𝒾𝓀𝑒 𝒹𝒾𝒶𝓂𝑜𝓃𝒹𝓈 𝒷𝓊𝓇𝒾𝑒𝒹 𝒾𝓃 𝓉𝒽𝑒 𝒽𝑒𝒶𝓇𝓉 𝑜𝒻 𝓉𝒽𝑒 𝒹𝑒𝓈𝑒𝓇𝓉 ...
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# 𝒲𝒽𝓎 𝓃𝑜𝓉 𝒽𝑒𝓁𝓅 𝓌𝒽𝑒𝓃 𝓌𝑒 𝒸𝒶𝓃?
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# 𝐼𝒻 𝓌𝑒 𝒷𝑒𝓁𝒾𝑒𝓋𝑒 𝓉𝒽𝑒𝓇𝑒 𝒾𝓈 𝓃𝑜 𝓂𝒶𝓃 𝓁𝑒𝒻𝓉 𝓌𝒾𝓉𝒽 𝓉𝒽𝑒𝓂
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# (𝒲𝒽𝒾𝒸𝒽 𝒾𝓈 𝓅𝓇𝑜𝒷𝒶𝒷𝓁𝓎 𝓉𝒽𝑒 𝓅𝓇𝑜𝒹𝓊𝒸𝓉 𝑜𝒻 𝓉𝒽𝑒 𝓉𝒽𝑜𝓊𝑔𝒽𝓉 𝑜𝒻 𝓅𝒶𝒾𝓃𝓁𝑒𝓈𝓈 𝒸𝑜𝓇𝓅𝓈𝑒𝓈)
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# 𝒲𝒽𝑒𝓇𝑒 𝒽𝒶𝓈 𝑜𝓊𝓇 𝒽𝓊𝓂𝒶𝓃𝒾𝓉𝓎 𝑔𝑜𝓃𝑒?
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# 𝒲𝒽𝑒𝓇𝑒 𝒽𝒶𝓈 𝒽𝓊𝓂𝒶𝓃𝒾𝓉𝓎 𝑔𝑜𝓃𝑒?
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# 𝒲𝒽𝓎 𝓃𝑜𝓉 𝒽𝑒𝓁𝓅 𝓌𝒽𝑒𝓃 𝓌𝑒 𝒸𝒶𝓃?
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# IMPORTANT: This strategy
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# designed for "ZERO" loss and "UNDER"
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# 15 minuts avg duration.So if you have more
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# loss and more avg, Its "NOT" normal result, and
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# you will change config.json variables and hyperoption commands
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# Thanks To @xmatthias if he was approve the last version of This strategy
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# That just a lazy code. I never can reach to this strategy(Now its really a dimond.)
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# * freqtrade hyperopt --hyperopt-loss ShortTradeDurHyperOptLoss --spaces all --strategy 𝒟𝒾𝓂𝑜𝓃𝒹 -e 700 -j 2 --timerange 20210810-20210813
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# * freqtrade backtesting --strategy 𝒟𝒾𝓂𝑜𝓃𝒹
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# Author: @Mablue (Masoud Azizi)
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# github: https://github.com/mablue/
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# (First Hyperopt it.A hyperopt file is available)
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# --- Do not remove these libs ---
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from freqtrade.strategy.hyper import CategoricalParameter, DecimalParameter, IntParameter
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from freqtrade.strategy.interface import IStrategy
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from pandas import DataFrame
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# --------------------------------
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# Add your lib to import here
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import talib.abstract as ta
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from functools import reduce
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import freqtrade.vendor.qtpylib.indicators as qtpylib
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##### SETINGS #####
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# It hyperopt just one set of params for all buy and sell strategies if true.
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DUALFIT = False
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COUNT = 10
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GAP = 3
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### END SETINGS ###
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class Dimond(IStrategy):
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# ###################### RESULT PLACE ######################
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# * 6/700: 1 trades. 1/0/0 Wins/Draws/Losses. Avg profit 17.68%. Median profit 17.68%. Total profit 58.94100000 USDT ( 5.89Σ%). Avg duration 0:00:00 min. Objective: 1.79949
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# Buy hyperspace params:
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buy_params = {
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"buy_fast": 31,
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"buy_push": 0.72,
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"buy_shift": -7,
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"buy_slow": 2,
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}
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# Sell hyperspace params:
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sell_params = {
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"sell_fast": 17,
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"sell_push": 1.493,
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"sell_shift": -7,
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"sell_slow": 28,
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}
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# ROI table:
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minimal_roi = {
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"0": 0.177,
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"31": 0.059,
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"61": 0.021,
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"170": 0
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}
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# Stoploss:
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stoploss = -0.241
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# Trailing stop:
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trailing_stop = True
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trailing_stop_positive = 0.13
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trailing_stop_positive_offset = 0.189
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trailing_only_offset_is_reached = True
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# Buy hypers
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timeframe = '5m'
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# #################### END OF RESULT PLACE ####################
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buy_push = DecimalParameter(0, 2, decimals=3, default=1, space='buy')
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buy_shift = IntParameter(-10, 0, default=-6, space='buy')
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buy_fast = IntParameter(2, 50, default=9, space='buy')
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buy_slow = IntParameter(2, 50, default=18, space='buy')
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if not DUALFIT:
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sell_push = DecimalParameter(
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0, 2, decimals=3, default=1, space='sell')
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sell_shift = IntParameter(-10, 0, default=-6, space='sell')
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sell_fast = IntParameter(2, 50, default=9, space='sell')
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sell_slow = IntParameter(2, 50, default=18, space='sell')
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def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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return dataframe
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def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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dataframe['buy_ema_fast'] = ta.SMA(
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dataframe, timeperiod=int(self.buy_fast.value))
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dataframe['buy_ema_slow'] = ta.SMA(
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dataframe, timeperiod=int(self.buy_slow.value))
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conditions = []
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conditions.append(
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qtpylib.crossed_above(
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dataframe['buy_ema_fast'].shift(self.buy_shift.value),
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dataframe['buy_ema_slow'].shift(
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self.buy_shift.value)*self.buy_push.value
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)
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)
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if conditions:
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dataframe.loc[
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reduce(lambda x, y: x & y, conditions),
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'buy']=1
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return dataframe
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def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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push = self.buy_push.value
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shift = self.buy_shift.value
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ema_fast = dataframe['buy_ema_fast']
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ema_slow = dataframe['buy_ema_slow']
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if not DUALFIT:
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push = self.sell_push.value
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shift = self.sell_shift.value
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ema_fast = dataframe['sell_ema_fast'] = ta.SMA(
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dataframe, timeperiod=int(self.buy_fast.value))
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ema_slow = dataframe['sell_ema_slow'] = ta.SMA(
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dataframe, timeperiod=int(self.buy_slow.value))
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conditions = []
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conditions.append(
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qtpylib.crossed_below(
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ema_fast.shift(shift),
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ema_slow.shift(shift)*push
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)
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)
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if conditions:
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dataframe.loc[
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reduce(lambda x, y: x & y, conditions),
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'sell']=1
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return dataframe
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Block a user