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TEST SHORT

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This commit is contained in:
Jérôme Delacotte
2026-03-28 20:47:50 +01:00
parent 812aa01135
commit 5b1e8bede4
1 changed files with 169 additions and 87 deletions
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EmptyShort.py
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@@ -151,7 +151,8 @@ class EmptyShort(IStrategy):
'current_trade': None, 'current_trade': None,
'last_trade': None, 'last_trade': None,
'force_stop': False, 'force_stop': False,
'count_of_lost': 0 'count_of_lost': 0,
'take_profit': 0.01
} }
for pair in ["BTC/USDC", "BTC/USDT", 'BTC/USDT:USDT'] for pair in ["BTC/USDC", "BTC/USDT", 'BTC/USDT:USDT']
} }
@@ -261,7 +262,7 @@ class EmptyShort(IStrategy):
# if last_candle['enter_tag'] in ['fall', 'bear', 'Force', 'Range-']: # if last_candle['enter_tag'] in ['fall', 'bear', 'Force', 'Range-']:
# amount = self.wallets.get_available_stake_amount() / self.mises_bear.value # amount = self.wallets.get_available_stake_amount() / self.mises_bear.value
# else: # else:
amount = self.wallets.get_available_stake_amount() / self.mises_bull.value # / (2 * self.pairs[pair]['count_of_lost'] + 1) amount = self.wallets.get_available_stake_amount() #/ self.mises_bull.value # / (2 * self.pairs[pair]['count_of_lost'] + 1)
# factor = 1 # factor = 1
# #
@@ -439,6 +440,21 @@ class EmptyShort(IStrategy):
self.pairs[pair]['total_amount'] = stake_amount self.pairs[pair]['total_amount'] = stake_amount
# print(f"Buy {pair} {current_time} {entry_tag} dispo={dispo} amount={stake_amount} rate={rate} rate={rate}") # print(f"Buy {pair} {current_time} {entry_tag} dispo={dispo} amount={stake_amount} rate={rate} rate={rate}")
# sl_price = rate * (1 - last_candle['up_pct_1h'] / 2)
#
# self.custom_trade_info[pair] = {
# "custom_exit": last_candle['up_pct_1h'] / 200
# }
# trade = kwargs.get("trade", None)
# if trade:
# trade.set_custom_data({
# # "stoploss": rate * 0.95,
# "takeprofit": last_candle['up_pct_1h'] / 200
# })
self.pairs[pair]['take_profit'] = last_candle['up_pct_1h'] / 200
self.log_trade( self.log_trade(
last_candle=last_candle, last_candle=last_candle,
date=current_time, date=current_time,
@@ -477,7 +493,7 @@ class EmptyShort(IStrategy):
profit = trade.calc_profit(rate) profit = trade.calc_profit(rate)
force = self.pairs[pair]['force_sell'] force = self.pairs[pair]['force_sell']
allow_to_sell = (last_candle['hapercent'] < 0 and profit > 0) or force \ allow_to_sell = (last_candle['hapercent'] < 0 and profit > 0) or force \
or (trade.enter_tag == 'short' and profit > 0 and last_candle['hapercent'] > 0) or (exit_reason == 'force_exit') or (exit_reason == 'stop_loss') or (exit_reason == 'trailing_stop_loss') or (trade.enter_tag == 'short' ) or (exit_reason == 'force_exit') or (exit_reason == 'stop_loss') or (exit_reason == 'trailing_stop_loss')
minutes = int(round((current_time - trade.date_last_filled_utc).total_seconds() / 60, 0)) minutes = int(round((current_time - trade.date_last_filled_utc).total_seconds() / 60, 0))
@@ -547,12 +563,13 @@ class EmptyShort(IStrategy):
is_short = (trade.enter_tag == 'short') is_short = (trade.enter_tag == 'short')
if trade.enter_tag == 'short': if trade.enter_tag == 'short':
if current_profit < self.sell_force_sell.value \ tp_price = self.pairs[pair]['take_profit']
and last_candle[f"close"] > last_candle[self.sell_indicator.value]: # if current_profit < self.sell_force_sell.value \
self.pairs[pair]['force_sell'] = True # and last_candle[f"close"] > last_candle[self.sell_indicator.value]:
return 'sma' # self.pairs[pair]['force_sell'] = True
# return 'sma'
if current_profit > 0.01 and \ #
if current_profit > tp_price and \
(baisse > self.baisse.value and last_candle[f"close"] > last_candle[self.b30_indicateur.value]) \ (baisse > self.baisse.value and last_candle[f"close"] > last_candle[self.b30_indicateur.value]) \
and last_candle['hapercent'] > 0: and last_candle['hapercent'] > 0:
self.pairs[pair]['force_sell'] = True self.pairs[pair]['force_sell'] = True
@@ -572,30 +589,23 @@ class EmptyShort(IStrategy):
# stake=0 # stake=0
# ) # )
else: else:
if self.pairs[pair]['current_trade'].enter_tag in ['bear', 'Force', 'Range-']: # if self.pairs[pair]['current_trade'].enter_tag in ['bear', 'Force', 'Range-']:
if current_profit < - 0.02 and last_candle[f"close"] <= last_candle['sma60'] and self.wallets.get_available_stake_amount() < 50: # if current_profit < - 0.02 and last_candle[f"close"] <= last_candle['sma60'] and self.wallets.get_available_stake_amount() < 50:
self.pairs[pair]['force_sell'] = True # self.pairs[pair]['force_sell'] = True
return 'smaBF' # return 'smaBF'
else: # else:
if current_profit < self.sell_force_sell.value \ # if current_profit < self.sell_force_sell.value \
and last_candle[f"close"] <= last_candle[self.sell_indicator.value]: # and last_candle[f"close"] <= last_candle[self.sell_indicator.value]:
self.pairs[pair]['force_sell'] = True # self.pairs[pair]['force_sell'] = True
return 'sma' # return 'sma'
if is_short: if current_profit > 0.00 and \
if current_profit > 0.005 and \ (baisse > self.baisse.value and last_candle[f"close"] <= last_candle[self.b30_indicateur.value]) \
(baisse > self.baisse.value and last_candle[f"close"] > last_candle[self.b30_indicateur.value]) \ and last_candle['hapercent'] <0 :
and last_candle['hapercent'] > 0: if current_profit > 0.01:
self.pairs[pair]['force_sell'] = True self.pairs[pair]['force_buy'] = True
return 'B30Sht' self.pairs[pair]['force_sell'] = True
else: return 'B30'
if current_profit > 0.00 and \
(baisse > self.baisse.value and last_candle[f"close"] <= last_candle[self.b30_indicateur.value]) \
and last_candle['hapercent'] <0 :
if current_profit > 0.01:
self.pairs[pair]['force_buy'] = True
self.pairs[pair]['force_sell'] = True
return 'B30'
# if profit > 0 and baisse > 0.5 and last_candle['hapercent'] <0 and last_candle[f"close"] <= last_candle['sma12']: # if profit > 0 and baisse > 0.5 and last_candle['hapercent'] <0 and last_candle[f"close"] <= last_candle['sma12']:
# self.pairs[pair]['force_sell'] = True # self.pairs[pair]['force_sell'] = True
@@ -699,12 +709,15 @@ class EmptyShort(IStrategy):
dataframe['zero'] = 0 dataframe['zero'] = 0
dataframe[f"percent"] = dataframe['close'].pct_change() dataframe[f"percent"] = dataframe['close'].pct_change()
for timeperiod in timeperiods: for timeperiod in timeperiods + long_timeperiods:
dataframe[f"mid_smooth{timeperiod}"] = dataframe['mid'].rolling(timeperiod).mean() dataframe[f"mid_smooth{timeperiod}"] = dataframe['mid'].rolling(timeperiod).mean()
dataframe[f'max{timeperiod}'] = talib.MAX(dataframe['close'], timeperiod=timeperiod) dataframe[f'max{timeperiod}'] = talib.MAX(dataframe['close'], timeperiod=timeperiod)
dataframe[f'min{timeperiod}'] = talib.MIN(dataframe['close'], timeperiod=timeperiod) dataframe[f'min{timeperiod}'] = talib.MIN(dataframe['close'], timeperiod=timeperiod)
dataframe[f"percent{timeperiod}"] = dataframe['close'].pct_change(timeperiod) dataframe[f"percent{timeperiod}"] = dataframe['close'].pct_change(timeperiod)
dataframe[f"sma{timeperiod}"] = dataframe['mid'].ewm(span=timeperiod, adjust=False).mean() # dataframe[f"sma{timeperiod}"] = dataframe['mid'].ewm(span=timeperiod, adjust=False).mean()
ema = dataframe['mid'].ewm(span=timeperiod, adjust=False).mean()
dataframe[f"sma{timeperiod}"] = ema.ewm(span=int(timeperiod / 2), adjust=False).mean()
# dataframe[f"high{timeperiod}"] = dataframe['high'].ewm(span=timeperiod, adjust=False).mean() # dataframe[f"high{timeperiod}"] = dataframe['high'].ewm(span=timeperiod, adjust=False).mean()
# dataframe[f"low{timeperiod}"] = dataframe['low'].ewm(span=timeperiod, adjust=False).mean() # dataframe[f"low{timeperiod}"] = dataframe['low'].ewm(span=timeperiod, adjust=False).mean()
# dataframe = self.calculateRegression(dataframe, column=f"high{timeperiod}", window=10, degree=1, future_offset=12) # dataframe = self.calculateRegression(dataframe, column=f"high{timeperiod}", window=10, degree=1, future_offset=12)
@@ -712,9 +725,9 @@ class EmptyShort(IStrategy):
self.calculeDerivees(dataframe, f"sma{timeperiod}", timeframe=self.timeframe, ema_period=timeperiod) self.calculeDerivees(dataframe, f"sma{timeperiod}", timeframe=self.timeframe, ema_period=timeperiod)
dataframe = self.calculateRegression(dataframe, column='mid', window=30, degree=1, future_offset=12) dataframe = self.calculateRegression(dataframe, column='mid', window=30, degree=1, future_offset=6)
dataframe = self.calculateRegression(dataframe, column='sma24', window=30, degree=1, future_offset=12) dataframe = self.calculateRegression(dataframe, column='sma120', window=60, degree=2, future_offset=30)
self.calculeDerivees(dataframe, "sma24_regression", timeframe=self.timeframe, ema_period=12) # self.calculeDerivees(dataframe, "sma24_regression", timeframe=self.timeframe, ema_period=12)
dataframe["percent"] = dataframe["mid"].pct_change(1) dataframe["percent"] = dataframe["mid"].pct_change(1)
dataframe["percent3"] = dataframe["mid"].pct_change(3) dataframe["percent3"] = dataframe["mid"].pct_change(3)
@@ -738,7 +751,7 @@ class EmptyShort(IStrategy):
informative['haclose'] = heikinashi['close'] informative['haclose'] = heikinashi['close']
informative['hapercent'] = (informative['haclose'] - informative['haopen']) / informative['haclose'] informative['hapercent'] = (informative['haclose'] - informative['haopen']) / informative['haclose']
informative['mid'] = informative['open'] + (informative['close'] - informative['open']) / 2 informative['mid'] = informative['open'] + (informative['close'] - informative['open']) / 2
for timeperiod in timeperiods: for timeperiod in [3, 5, 12]:
informative[f'max{timeperiod}'] = talib.MAX(informative['close'], timeperiod=timeperiod) informative[f'max{timeperiod}'] = talib.MAX(informative['close'], timeperiod=timeperiod)
informative[f'min{timeperiod}'] = talib.MIN(informative['close'], timeperiod=timeperiod) informative[f'min{timeperiod}'] = talib.MIN(informative['close'], timeperiod=timeperiod)
# informative[f"range{timeperiod}"] = ((informative["close"] - informative[f'min{timeperiod}']) / (informative[f'max{timeperiod}'] - informative[f'min{timeperiod}'])) # informative[f"range{timeperiod}"] = ((informative["close"] - informative[f'min{timeperiod}']) / (informative[f'max{timeperiod}'] - informative[f'min{timeperiod}']))
@@ -746,9 +759,9 @@ class EmptyShort(IStrategy):
informative[f"sma{timeperiod}"] = informative['mid'].ewm(span=timeperiod, adjust=False).mean() informative[f"sma{timeperiod}"] = informative['mid'].ewm(span=timeperiod, adjust=False).mean()
self.calculeDerivees(informative, f"sma{timeperiod}", timeframe=self.timeframe, ema_period=timeperiod) self.calculeDerivees(informative, f"sma{timeperiod}", timeframe=self.timeframe, ema_period=timeperiod)
informative = self.calculateRegression(informative, column='mid', window=10, degree=1, future_offset=12) informative = self.calculateRegression(informative, column='mid', window=10, degree=1, future_offset=2)
informative = self.calculateRegression(informative, column='sma3', window=10, degree=1, future_offset=12) informative = self.calculateRegression(informative, column='sma3', window=10, degree=1, future_offset=2)
informative = self.calculateRegression(informative, column='low', window=10, degree=1, future_offset=12) informative = self.calculateRegression(informative, column='low', window=10, degree=1, future_offset=2)
for timeperiod in long_timeperiods: for timeperiod in long_timeperiods:
informative[f"sma{timeperiod}"] = informative['mid'].ewm(span=timeperiod, adjust=False).mean() informative[f"sma{timeperiod}"] = informative['mid'].ewm(span=timeperiod, adjust=False).mean()
@@ -758,20 +771,22 @@ class EmptyShort(IStrategy):
self.calculeDerivees(informative, f"rsi", timeframe=self.timeframe, ema_period=14) self.calculeDerivees(informative, f"rsi", timeframe=self.timeframe, ema_period=14)
informative['max_rsi_12'] = talib.MAX(informative['rsi'], timeperiod=12) informative['max_rsi_12'] = talib.MAX(informative['rsi'], timeperiod=12)
informative['max_rsi_24'] = talib.MAX(informative['rsi'], timeperiod=24) informative['max_rsi_24'] = talib.MAX(informative['rsi'], timeperiod=24)
informative['min_rsi_12'] = talib.MIN(informative['rsi'], timeperiod=12)
informative['min_rsi_24'] = talib.MIN(informative['rsi'], timeperiod=24)
informative[f'stop_buying_deb'] = qtpylib.crossed_below(informative[f"sma12"], informative['sma36']) & (informative['close'] < informative['sma100']) # informative[f'stop_buying_deb'] = qtpylib.crossed_below(informative[f"sma12"], informative['sma36']) & (informative['close'] < informative['sma100'])
informative[f'stop_buying_end'] = qtpylib.crossed_above(informative[f"sma12"], informative['sma36']) & (informative['close'] > informative['sma100']) # informative[f'stop_buying_end'] = qtpylib.crossed_above(informative[f"sma12"], informative['sma36']) & (informative['close'] > informative['sma100'])
latched = np.zeros(len(informative), dtype=bool) # latched = np.zeros(len(informative), dtype=bool)
#
for i in range(1, len(informative)): # for i in range(1, len(informative)):
if informative['stop_buying_deb'].iloc[i]: # if informative['stop_buying_deb'].iloc[i]:
latched[i] = True # latched[i] = True
elif informative['stop_buying_end'].iloc[i]: # elif informative['stop_buying_end'].iloc[i]:
latched[i] = False # latched[i] = False
else: # else:
latched[i] = latched[i - 1] # latched[i] = latched[i - 1]
informative['stop_buying'] = latched # informative['stop_buying'] = latched
informative = self.calculateDownAndUp(informative, limit=0.0001) informative = self.calculateDownAndUp(informative, limit=0.0001)
dataframe = merge_informative_pair(dataframe, informative, self.timeframe, "1h", ffill=True) dataframe = merge_informative_pair(dataframe, informative, self.timeframe, "1h", ffill=True)
@@ -787,7 +802,7 @@ class EmptyShort(IStrategy):
# informative['haclose'] = heikinashi['close'] # informative['haclose'] = heikinashi['close']
# informative['hapercent'] = (informative['haclose'] - informative['haopen']) / informative['haclose'] # informative['hapercent'] = (informative['haclose'] - informative['haopen']) / informative['haclose']
informative['mid'] = informative['open'] + (informative['close'] - informative['open']) / 2 informative['mid'] = informative['open'] + (informative['close'] - informative['open']) / 2
for timeperiod in timeperiods: for timeperiod in [3, 5, 12]:
informative[f'max{timeperiod}'] = talib.MAX(informative['close'], timeperiod=timeperiod) informative[f'max{timeperiod}'] = talib.MAX(informative['close'], timeperiod=timeperiod)
informative[f'min{timeperiod}'] = talib.MIN(informative['close'], timeperiod=timeperiod) informative[f'min{timeperiod}'] = talib.MIN(informative['close'], timeperiod=timeperiod)
# informative[f"range{timeperiod}"] = ((informative["close"] - informative[f'min{timeperiod}']) / (informative[f'max{timeperiod}'] - informative[f'min{timeperiod}'])) # informative[f"range{timeperiod}"] = ((informative["close"] - informative[f'min{timeperiod}']) / (informative[f'max{timeperiod}'] - informative[f'min{timeperiod}']))
@@ -804,19 +819,23 @@ class EmptyShort(IStrategy):
informative['max_rsi_12'] = talib.MAX(informative['rsi'], timeperiod=12) informative['max_rsi_12'] = talib.MAX(informative['rsi'], timeperiod=12)
informative['max_rsi_24'] = talib.MAX(informative['rsi'], timeperiod=24) informative['max_rsi_24'] = talib.MAX(informative['rsi'], timeperiod=24)
informative[f'stop_buying_deb'] = qtpylib.crossed_below(informative[f"sma12"], informative['sma36']) & (informative['close'] < informative['sma100']) # informative = self.calculateRegression(informative, column='mid', window=10, degree=1, future_offset=2)
informative[f'stop_buying_end'] = qtpylib.crossed_above(informative[f"sma12"], informative['sma36']) & (informative['close'] > informative['sma100']) informative = self.calculateRegression(informative, column='sma3', window=10, degree=1, future_offset=2)
# informative = self.calculateRegression(informative, column='low', window=10, degree=1, future_offset=2)
latched = np.zeros(len(informative), dtype=bool) # informative[f'stop_buying_deb'] = qtpylib.crossed_below(informative[f"sma12"], informative['sma36']) & (informative['close'] < informative['sma100'])
# informative[f'stop_buying_end'] = qtpylib.crossed_above(informative[f"sma12"], informative['sma36']) & (informative['close'] > informative['sma100'])
for i in range(1, len(informative)): #
if informative['stop_buying_deb'].iloc[i]: # latched = np.zeros(len(informative), dtype=bool)
latched[i] = True #
elif informative['stop_buying_end'].iloc[i]: # for i in range(1, len(informative)):
latched[i] = False # if informative['stop_buying_deb'].iloc[i]:
else: # latched[i] = True
latched[i] = latched[i - 1] # elif informative['stop_buying_end'].iloc[i]:
informative['stop_buying'] = latched # latched[i] = False
# else:
# latched[i] = latched[i - 1]
# informative['stop_buying'] = latched
dataframe = merge_informative_pair(dataframe, informative, self.timeframe, "1d", ffill=True) dataframe = merge_informative_pair(dataframe, informative, self.timeframe, "1d", ffill=True)
# ###################################################################################################### # ######################################################################################################
@@ -979,14 +998,14 @@ class EmptyShort(IStrategy):
# ['enter_long', 'enter_tag'] # ['enter_long', 'enter_tag']
# ] = (1, 'cross_min') # ] = (1, 'cross_min')
conditions = list() # conditions = list()
conditions.append(dataframe['sma12_deriv1'] > 0.00) # conditions.append(dataframe['sma12_deriv1'] > 0.00)
conditions.append(dataframe['sma60_deriv1'] > 0.0) # conditions.append(dataframe['sma60_deriv1'] > 0.0)
conditions.append(dataframe['sma5_deriv1_1h'] > 0.0) # conditions.append(dataframe['sma5_deriv1_1h'] > 0.0)
conditions.append(dataframe['sma12_deriv1_1h'] > 0.0) # conditions.append(dataframe['sma12_deriv1_1h'] > 0.0)
conditions.append(dataframe['sma24_deriv1_1h'] > 0.0) # conditions.append(dataframe['sma24_deriv1_1h'] > 0.0)
conditions.append(dataframe['sma100_deriv1_1h'] > 0.0) # conditions.append(dataframe['sma100_deriv1_1h'] > 0.0)
conditions.append(dataframe[f"range_pos"] < 0.025) # conditions.append(dataframe[f"range_pos"] < 0.025)
# conditions.append(dataframe['sma12_deriv1_1h'] > 0.0) # conditions.append(dataframe['sma12_deriv1_1h'] > 0.0)
# # conditions.append(dataframe['close_1h'] < dataframe[f'sma{self.buy_longue.value}_1h']) # # conditions.append(dataframe['close_1h'] < dataframe[f'sma{self.buy_longue.value}_1h'])
# # conditions.append(dataframe['has_cross_min'].rolling(6).max() == 1) # # conditions.append(dataframe['has_cross_min'].rolling(6).max() == 1)
@@ -1022,10 +1041,9 @@ class EmptyShort(IStrategy):
# dataframe.loc[ # dataframe.loc[
# ( # (
# (qtpylib.crossed_above( # # (qtpylib.crossed_above(dataframe['sma60'], dataframe['mid_regression_1h']))
# dataframe['sma60'], # (dataframe['rsi_1h'] <= 30)
# dataframe['mid_regression_1h']) # & (dataframe['min_rsi_12_1h'] == dataframe['rsi_1h'])
# )
# ), # ),
# ['enter_long', 'enter_tag'] # ['enter_long', 'enter_tag']
# ] = (1, 'long') # ] = (1, 'long')
@@ -1037,13 +1055,34 @@ class EmptyShort(IStrategy):
# dataframe['mid_regression_1h']) # dataframe['mid_regression_1h'])
# ) & # ) &
( (
(dataframe['close'] > dataframe['max5_1h']) # (dataframe['close'] > dataframe['max5_1h'])
& (dataframe['sma12'] < dataframe['sma12'].shift(1)) # & (dataframe['sma12'] < dataframe['sma12'].shift(1))
# # & (dataframe['sma120_inv'] == 1)
# & (dataframe['close'] > dataframe['close_1h'])
# & (dataframe['close'] > dataframe['close_1d'])
# (dataframe['rsi_1h'] >= 65)
# & (dataframe['max_rsi_12_1h'] == dataframe['rsi_1h'])
# & (dataframe['hapercent'] < 0)
# & (dataframe['sma60'] < dataframe['sma60'].shift(1))
(dataframe['sma12_1h'] < dataframe['sma12_1h'].shift(61))
& (dataframe['close'] > dataframe['close_1d'])
) )
), ),
['enter_short', 'enter_tag'] ['enter_short', 'enter_tag']
] = (1, 'short') ] = (1, 'short')
# dataframe.loc[
# (
# (dataframe['close'] >= dataframe['max5_1h'])
# & (dataframe['close'] >= dataframe['max12_1d'])
# & (dataframe['sma12_1h'] <= dataframe['max12_1d'])
# & (dataframe['mid_1h'] <= dataframe['mid_1h'].shift(61))
# & (dataframe['hapercent'] < 0)
# # & (dataframe['sma60'] < dataframe['sma60'].shift(1))
# ),
# ['enter_short', 'enter_tag']
# ] = (1, 'short')
return dataframe return dataframe
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
@@ -1102,15 +1141,31 @@ class EmptyShort(IStrategy):
# if conditions: # if conditions:
# dataframe.loc[reduce(lambda x, y: x & y, conditions), ['exit_long', 'exit_tag']] = (1, 'god') # dataframe.loc[reduce(lambda x, y: x & y, conditions), ['exit_long', 'exit_tag']] = (1, 'god')
# dataframe.loc[
# (
# # (qtpylib.crossed_above(
# # dataframe['sma'],
# # dataframe['mid_regression_1h'])
# # )
# (dataframe['rsi_1h'] >= 70)
# & (dataframe['max_rsi_12_1h'] == dataframe['rsi_1h'])
# ),
# ['exit_long', 'exit_tag']
# ] = (1, 'long')
dataframe.loc[ dataframe.loc[
( (
(qtpylib.crossed_above( # (qtpylib.crossed_above(
dataframe['sma60'], # dataframe['sma'],
dataframe['mid_regression_1h']) # dataframe['mid_regression_1h'])
) # )
(dataframe['rsi_1h'] <= 30)
& (dataframe['min_rsi_12_1h'] == dataframe['rsi_1h'])
& (dataframe['hapercent'] > 0)
& (dataframe['sma60'] >= dataframe['sma60'].shift(1))
), ),
['exit_short', 'exit_tag'] ['exit_short', 'exit_tag']
] = (1, 'long') ] = (1, 'short')
return dataframe return dataframe
@@ -1143,7 +1198,34 @@ class EmptyShort(IStrategy):
dataframe[d1_col] = (dataframe[name] - dataframe[name].shift(3)) / dataframe[name].shift(3) dataframe[d1_col] = (dataframe[name] - dataframe[name].shift(3)) / dataframe[name].shift(3)
dataframe[d2_col] = (dataframe[d1_col] - dataframe[d1_col].shift(1)) dataframe[d2_col] = (dataframe[d1_col] - dataframe[d1_col].shift(1))
dataframe[f"{name}{suffixe}_inv"] = np.where(cond_bas, -1, np.where(cond_haut, 1, 0)) # dataframe[f"{name}{suffixe}_inv"] = np.where(cond_bas, -1, np.where(cond_haut, 1, 0))
# col = f"{name}{suffixe}"
# slope = dataframe[col].diff()
# threshold = 0.02 #slope.rolling(20).std() * 0.1
# sign = np.sign(slope.where(slope.abs() > threshold, 0))
# turn = sign.diff()
# dataframe[f"{name}{suffixe}_inv"] = np.where(turn == -2, 1, np.where(turn == 2, -1, 0))
# # dataframe[f"{name}{suffixe}_inv"] = (
# # (dataframe[f"{name}{suffixe}_inv"] != 0) &
# # (dataframe['slope'].abs() > dataframe['slope'].rolling(20).mean())
# # )
col = f"{name}{suffixe}"
# pente (variation)
dataframe['slope'] = dataframe[col].diff()
# signe de la pente
dataframe['sign'] = dataframe['slope'].apply(lambda x: 1 if x > 0 else (-1 if x < 0 else 0))
# changement de signe = inversion
dataframe[f"{name}{suffixe}_turn"] = dataframe['sign'].diff()
# mapping :
# +2 -> passage -1 → +1 => creux => inversion haussière
# -2 -> passage +1 → -1 => sommet => inversion baissière
col_inv = f"{name}{suffixe}_inv"
dataframe[col_inv] = 0
dataframe.loc[dataframe[f"{name}{suffixe}_turn"] == -2, col_inv] = 1 # sommet (max)
dataframe.loc[dataframe[f"{name}{suffixe}_turn"] == 2, col_inv] = -1 # creux (min)
short = d1.rolling(pmin).mean() short = d1.rolling(pmin).mean()
long = d1.rolling(ema_period).mean() long = d1.rolling(ema_period).mean()
@@ -1446,7 +1528,7 @@ class EmptyShort(IStrategy):
# df[f"{column}_regression_deriv2"] = round( # df[f"{column}_regression_deriv2"] = round(
# 10 * df[f"{column}_regression_deriv1"].rolling(int(window / 4)).mean().diff(), 4) # 10 * df[f"{column}_regression_deriv1"].rolling(int(window / 4)).mean().diff(), 4)
# #
# df[f"{column}_future_{future_offset}"] = regression_future_fit df[f"{column}_future_{future_offset}"] = regression_future_fit
# # 2. Dérivée première = différence entre deux bougies successives # # 2. Dérivée première = différence entre deux bougies successives
# df[f"{column}_future_{future_offset}_deriv1"] = round(100 * df[f"{column}_future_{future_offset}"].diff() / df[f"{column}_future_{future_offset}"], 4) # df[f"{column}_future_{future_offset}_deriv1"] = round(100 * df[f"{column}_future_{future_offset}"].diff() / df[f"{column}_future_{future_offset}"], 4)