TEST HYPEROPTS
This commit is contained in:
Jérôme Delacotte
31
Empty.json
31
Empty.json
@@ -14,12 +14,7 @@
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"trailing_only_offset_is_reached": false
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},
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"max_open_trades": {
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"max_open_trades": 20
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},
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"protection": {
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"b30_indicateur": "sma36",
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"baisse": 0.3,
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"drop_from_last_entry": -0.03
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"max_open_trades": 1
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},
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"buy": {
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"buy_deriv1_sma12d": -0.04,
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@@ -30,17 +25,25 @@
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"buy_deriv2_sma60": -0.001,
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"buy_longue": 160,
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"buy_longue_derive": "sma80_deriv1_1d",
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"start_bear_deriv1": -0.004,
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"start_bear_deriv2": 0.002,
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"start_bear_indicator": "sma80",
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"start_bull_deriv1": 0.001,
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"start_bull_deriv2": -0.002,
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"start_bull_indicator": "sma120"
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"start_bear_deriv1": 0.003,
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"start_bear_deriv2": 0.003,
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"start_bear_indicator": "sma24",
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"start_bull_deriv1": -0.005,
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"start_bull_deriv2": 0.0,
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"start_bull_indicator": "sma3"
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},
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"protection": {
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"drop_from_last_entry": -0.03,
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"mises": 1
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},
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"sell": {
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"sell_score_indicator": "sma24_score"
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"b30_indicateur": "sma36",
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"baisse": 0.25,
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"sell_force_sell": -0.169,
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"sell_indicator": "sma3",
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"sell_score_indicator": "sma3_score"
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}
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},
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"ft_stratparam_v": 1,
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"export_time": "2026-02-28 17:28:20.867470+00:00"
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"export_time": "2026-03-07 11:58:51.712499+00:00"
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}
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61
Empty.py
61
Empty.py
@@ -216,8 +216,12 @@ class Empty(IStrategy):
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# baisses = list()
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# for i in range(0, 0.5, 0.05):
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# baisses.append(i)
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baisse = DecimalParameter(0, 0.5, decimals=2, default=0.3, space='protection', optimize=True, load=True)
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b30_indicateur = CategoricalParameter(sma_indicators_h, default="sma36", space='protection', optimize=True, load=True)
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mises = IntParameter(1, 10, default=1, space='protection')
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sell_force_sell = DecimalParameter(-0.2, 0, decimals=3, default=-0.02, space='sell')
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sell_indicator = CategoricalParameter(sma_indicators, default="sma36", space='sell', optimize=True, load=True)
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baisse = DecimalParameter(0.1, 0.5, decimals=2, default=0.3, space='sell', optimize=True, load=True)
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b30_indicateur = CategoricalParameter(sma_indicators_h, default="sma36", space='sell', optimize=True, load=True)
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# lost_indicator = CategoricalParameter(sma_deriv1_indicators, default="sma5_deriv1", space='protection')
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@@ -250,10 +254,10 @@ class Empty(IStrategy):
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# sl_max = self.wallets.get_available_stake_amount() / 2
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#
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# amount = sl_min + (1 - range_pos) * (sl_max - sl_min)
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if last_candle['enter_tag'] == 'fall':
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if last_candle['enter_tag'] in ['fall', 'bear', 'Force', 'Range-']:
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amount = self.wallets.get_available_stake_amount() / 5
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else:
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amount = self.wallets.get_available_stake_amount() # / (2 * self.pairs[pair]['count_of_lost'] + 1)
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amount = self.wallets.get_available_stake_amount() / self.mises.value # / (2 * self.pairs[pair]['count_of_lost'] + 1)
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# factor = 1
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#
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@@ -364,6 +368,8 @@ class Empty(IStrategy):
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last_candle_2 = dataframe.iloc[-2].squeeze()
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last_candle_3 = dataframe.iloc[-3].squeeze()
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if entry_tag == 'Range-':
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self.pairs[pair]['count_of_lost'] = 0
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if entry_tag == 'Force':
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if self.pairs[pair]['count_of_lost'] >= 1:
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self.pairs[pair]['count_of_lost'] = 0
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@@ -546,15 +552,15 @@ class Empty(IStrategy):
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# buys=count_of_buys,
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# stake=0
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# )
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# if self.pairs[pair]['current_trade'].enter_tag == 'fall':
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# if current_profit < - 0.02 and last_candle[f"close"] <= last_candle['sma60'] and self.wallets.get_available_stake_amount() < 50:
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# self.pairs[pair]['force_sell'] = True
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# return 'sma60'
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# else:
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if current_profit < - 0.02 and last_candle[f"close"] <= last_candle['sma60']:
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if self.pairs[pair]['current_trade'].enter_tag in ['bear', 'Force', 'Range-']:
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if current_profit < - 0.02 and last_candle[f"close"] <= last_candle['sma60'] and self.wallets.get_available_stake_amount() < 50:
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self.pairs[pair]['force_sell'] = True
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return 'sma60'
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return 'smaBF'
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else:
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if current_profit < self.sell_force_sell.value \
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and last_candle[f"close"] <= last_candle[self.sell_indicator.value]:
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self.pairs[pair]['force_sell'] = True
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return 'sma'
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if profit > max(5, expected_profit) and \
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(baisse > self.baisse.value and last_candle[f"close"] <= last_candle[self.b30_indicateur.value]) \
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@@ -743,8 +749,8 @@ class Empty(IStrategy):
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# #####################################################################################
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# CA MONTE !!
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# #####################################################################################
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# conditions.append(dataframe[f"{self.start_bull_indicator.value}_deriv1_1d" ] > self.start_bull_deriv1.value)
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# conditions.append(dataframe[f"{self.start_bull_indicator.value}_deriv2_1d"] > self.start_bull_deriv2.value)
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conditions.append(dataframe[f"{self.start_bull_indicator.value}_deriv1_1d" ] > self.start_bull_deriv1.value)
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conditions.append(dataframe[f"{self.start_bull_indicator.value}_deriv2_1d"] > self.start_bull_deriv2.value)
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conditions.append(dataframe['sma12_deriv1'] > self.buy_deriv1_sma60.value)
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conditions.append(dataframe['sma5_deriv1_1d'] > self.buy_deriv1_sma5d.value)
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conditions.append(dataframe['sma12_deriv1_1d'] > self.buy_deriv1_sma12d.value)
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@@ -791,11 +797,11 @@ class Empty(IStrategy):
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conditions.append(dataframe['mid_smooth12'] > dataframe['mid_smooth12'].shift(1))
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conditions.append(dataframe['sma100_deriv1_1d'] > 0)
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conditions.append(dataframe[f"range_pos"] < 0.01)
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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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['enter_long', 'enter_tag']
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] = (1, 'dist')
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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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# ['enter_long', 'enter_tag']
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# ] = (1, 'dist')
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# #####################################################################################
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# CA BAISSE !!
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@@ -866,16 +872,17 @@ class Empty(IStrategy):
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conditions.append(dataframe['sma12_deriv1_1d'] > 0.0)
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conditions.append(dataframe['sma24_deriv1_1d'] > 0.0)
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conditions.append(dataframe['sma100_deriv1_1d'] > 0.0)
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conditions.append(dataframe[f"range_pos"] < 0.025)
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# conditions.append(dataframe['sma12_deriv1_1d'] > 0.0)
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# # conditions.append(dataframe['close_1d'] < dataframe[f'sma{self.buy_longue.value}_1d'])
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# # conditions.append(dataframe['has_cross_min'].rolling(6).max() == 1)
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# # conditions.append(dataframe['mid_smooth5'] > dataframe['mid_smooth5'].shift(1))
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# conditions.append(dataframe['min12'] == dataframe['min12'].shift(3))
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# conditions.append((dataframe['percent24'] < -0.025) | (dataframe['percent12'] < -0.025))
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dataframe.loc[
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reduce(lambda x, y: x & y, conditions),
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['enter_long', 'enter_tag']
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] = (1, 'Rise')
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# dataframe.loc[
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# reduce(lambda x, y: x & y, conditions),
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# ['enter_long', 'enter_tag']
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# ] = (1, 'Rise')
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# conditions = list()
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# conditions.append(dataframe['has_cross_min_6'] == True)
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@@ -885,6 +892,14 @@ class Empty(IStrategy):
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# ['enter_long', 'enter_tag']
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# ] = (1, 'Force')
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# conditions = list()
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# conditions.append(dataframe['range_pos'] < -0.03)
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# conditions.append(dataframe['min36'] == dataframe['min36'].shift(3))
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# dataframe.loc[
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# reduce(lambda x, y: x & y, conditions),
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# ['enter_long', 'enter_tag']
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# ] = (1, 'Range-')
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return dataframe
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def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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46
Empty5m.json
Normal file
46
Empty5m.json
Normal file
@@ -0,0 +1,46 @@
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{
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"strategy_name": "Empty5m",
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"params": {
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"roi": {
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"0": 5
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},
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"stoploss": {
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"stoploss": -1.0
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},
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"trailing": {
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"trailing_stop": false,
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"trailing_stop_positive": null,
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"trailing_stop_positive_offset": 0.0,
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"trailing_only_offset_is_reached": false
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},
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"max_open_trades": {
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"max_open_trades": 1
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},
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"buy": {
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"buy_longue_derive": "sma60_deriv1_1h",
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"buy_deriv1_sma12d": 0.0002,
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"buy_deriv1_sma5d": -0.0006,
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"buy_deriv1_sma60": -0.0003,
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"buy_longue": 180,
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"start_bear_deriv1": 0.005,
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"start_bear_deriv2": -0.003,
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"start_bear_indicator": "sma60",
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"start_bull_deriv1": -0.001,
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"start_bull_deriv2": 0.0,
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"start_bull_indicator": "sma12"
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},
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"protection": {
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"drop_from_last_entry": -0.03,
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"mises_bear": 5,
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"mises_bull": 1
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},
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"sell": {
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"b30_indicateur": "sma24",
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"baisse": 0.14,
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"sell_force_sell": -0.135,
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"sell_indicator": "sma12"
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}
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},
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"ft_stratparam_v": 1,
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"export_time": "2026-03-07 14:31:15.874478+00:00"
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}
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1317
Empty5m.py
Normal file
1317
Empty5m.py
Normal file
File diff suppressed because it is too large
Load Diff
@@ -852,28 +852,28 @@ class FrictradeLearning(IStrategy):
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short_pair = self.getShortName(pair)
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# path=f"user_data/strategies/plots/{short_pair}/"
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self.model = joblib.load(f"{self.path}/{short_pair}_rf_model.pkl")
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# Préparer les features pour la prédiction
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features = dataframe[self.model_indicators].fillna(0)
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# Prédiction : probabilité que le prix monte
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# Affichage des colonnes intérressantes dans le model
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features_pruned, kept_features = self.prune_features(
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model=self.model,
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dataframe=dataframe,
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feature_columns=self.model_indicators,
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importance_threshold=0.005 # enlever features < % importance
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)
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probs = self.model.predict_proba(features)[:, 1]
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# Sauvegarder la probabilité pour l’analyse
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dataframe['ml_prob'] = probs
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if False and self.dp.runmode.value in ('backtest'):
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self.inspect_model(self.model)
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# self.model = joblib.load(f"{self.path}/{short_pair}_rf_model.pkl")
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#
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# # Préparer les features pour la prédiction
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# features = dataframe[self.model_indicators].fillna(0)
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#
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# # Prédiction : probabilité que le prix monte
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#
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# # Affichage des colonnes intérressantes dans le model
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# features_pruned, kept_features = self.prune_features(
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# model=self.model,
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# dataframe=dataframe,
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# feature_columns=self.model_indicators,
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# importance_threshold=0.005 # enlever features < % importance
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# )
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#
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# probs = self.model.predict_proba(features)[:, 1]
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#
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# # Sauvegarder la probabilité pour l’analyse
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# dataframe['ml_prob'] = probs
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#
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# if False and self.dp.runmode.value in ('backtest'):
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# self.inspect_model(self.model)
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#
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# absolute_min = dataframe['absolute_min'].min()
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@@ -910,6 +910,9 @@ class FrictradeLearning(IStrategy):
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horizon = 180
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self.calculateScores(dataframe, horizon)
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dataframe['cross_sma60'] = qtpylib.crossed_below(dataframe["sma12"], dataframe['sma60'])
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# val = 90000
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# steps = 12
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# [0.018, 0.022, 0.025, 0.028, 0.032, 0.035, 0.038, 0.042, 0.045, 0.048, 0.052, 0.055]
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@@ -1066,22 +1069,34 @@ class FrictradeLearning(IStrategy):
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# threshold = 0.4 #self.buy_threshold # ex: 0.80 or 1.10 depending on your model
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# 20% des signaux les plus forts
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threshold = np.percentile(dataframe["ml_prob"], 80)
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# threshold = np.percentile(dataframe["ml_prob"], 80)
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# Buy = prediction > threshold
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dataframe["buy"] = 0
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# dataframe.loc[
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# # (dataframe["ml_prob"].shift(1) < dataframe["ml_prob"])
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# (dataframe['sma60_deriv1'] > -0.0000)
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# & (dataframe['sma12_deriv1'] > 0)
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# & (dataframe['sma12'] < dataframe['sma60'])
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# # & (dataframe['rsi'] < 77)
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# # & (dataframe['heat_score_1h'] < 0.5)
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# # & (dataframe['sma180_deriv1'] > 0)
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# # & (dataframe['open'] < dataframe['max180'] * 0.997)
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# # & (dataframe['min180'].shift(3) == dataframe['min180'])
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# , ['enter_long', 'enter_tag']
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# ] = (1, f"future")
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dataframe.loc[
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(dataframe["ml_prob"].shift(1) < dataframe["ml_prob"])
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& (dataframe['sma24_deriv1'] > 0)
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& (dataframe['sma5_deriv1'] > 0)
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& (dataframe['sma5_deriv2'] > 0)
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& (dataframe['rsi'] < 77)
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& (dataframe['heat_score_1h'] < 0.5)
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# & (dataframe['sma180_deriv1'] > 0)
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# & (dataframe['open'] < dataframe['max180'] * 0.997)
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# & (dataframe['min180'].shift(3) == dataframe['min180'])
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# (dataframe["ml_prob"].shift(1) < dataframe["ml_prob"])
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(
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(dataframe['close'].shift(3) < dataframe['min180'].shift(3)) |
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(dataframe['close'].shift(4) < dataframe['min180'].shift(4)) |
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(dataframe['close'].shift(5) < dataframe['min180'].shift(5))
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)
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& (dataframe['hapercent'] > 0)
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, ['enter_long', 'enter_tag']
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] = (1, f"future")
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] = (1, f"min180")
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dataframe['test'] = np.where(dataframe['enter_long'] == 1, dataframe['close'] * 1.01, np.nan)
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return dataframe
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@@ -1210,18 +1225,14 @@ class FrictradeLearning(IStrategy):
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return max(round(y), 1) # évite les valeurs négatives
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def adjust_stake_amount(self, pair: str, last_candle: DataFrame):
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if self.pairs[pair]['first_amount'] > 0:
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mises = self.pairs[pair]['mises']
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count = self.pairs[pair]['count_of_buys'] - self.pairs[pair]['has_gain']
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return mises[count] if count < len(mises) else self.pairs[pair]['first_amount']
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ath = max(self.pairs[pair]['last_max'], self.get_last_ath_before_candle(last_candle))
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full, mises, steps = self.calculateMises(pair, ath, last_candle['mid'])
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base_stake = mises[self.pairs[pair]['count_of_buys']] if self.pairs[pair]['count_of_buys'] < len(
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mises) else full / (steps * 2)
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return base_stake
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if (self.pairs[pair]['first_amount'] > 0):
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amount = min(self.wallets.get_available_stake_amount(), self.pairs[pair]['first_amount'])
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else:
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if last_candle['enter_tag'] in ['fall', 'bear', 'Force', 'Range-']:
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amount = self.wallets.get_available_stake_amount() / 5
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else:
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amount = self.wallets.get_available_stake_amount() / 3# / (2 * self.pairs[pair]['count_of_lost'] + 1)
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return min(amount, self.wallets.get_available_stake_amount())
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def calculateMises(self, pair, ath, val):
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# ath = max(self.pairs[pair]['last_max'], self.get_last_ath_before_candle(last_candle))
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@@ -1515,8 +1526,19 @@ class FrictradeLearning(IStrategy):
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# buys=count_of_buys,
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# stake=0
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# )
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if profit < 0.65: #5 or last_candle['rsi_1d'] < 30:
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return None
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if current_profit < - 0.02 and last_candle[f"close"] <= last_candle['sma60']:
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self.pairs[pair]['force_sell'] = True
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return 'sma60'
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if profit > 5 and \
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(baisse > 0.25 and last_candle[f"close"] <= last_candle['sma24']) \
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and last_candle['hapercent'] <0 :
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self.pairs[pair]['force_sell'] = True
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return 'B30'
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|
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if profit > 0 and last_candle['cross_sma60']: #5 or last_candle['rsi_1d'] < 30:
|
||||
return 'Cross'
|
||||
|
||||
if last_candle['max_rsi_24'] > 88 and last_candle['hapercent'] < 0\
|
||||
and last_candle['sma5_deriv2'] < -0.1:
|
||||
@@ -1535,44 +1557,48 @@ class FrictradeLearning(IStrategy):
|
||||
# if (minutes > 1440 and last_candle['sma60_deriv1'] > 0) :
|
||||
# return None
|
||||
|
||||
# ----- 4) OFFSET : faut-il attendre de dépasser trailing_stop_positive_offset ? -----
|
||||
if current_trailing_only_offset_is_reached and max_profit > current_trailing_stop_positive_offset:
|
||||
# Max profit pas atteint ET perte < 2 * current_trailing_stop_positive
|
||||
if profit > limit: # 2 * current_trailing_stop_positive:
|
||||
print(
|
||||
f"{current_time} trailing non atteint trailing_stop={round(trailing_stop, 4)} profit={round(profit, 4)} "
|
||||
f"max={round(max_profit, 4)} offset={round(current_trailing_stop_positive_offset, 4)} baisse={round(baisse,2)}")
|
||||
return None # ne pas activer le trailing encore
|
||||
else:
|
||||
print(
|
||||
f"{current_time} trailing atteint trailing_stop={round(trailing_stop, 4)} profit={round(profit, 4)} "
|
||||
f"max={round(max_profit, 4)} offset={round(current_trailing_stop_positive_offset, 4)} baisse={round(baisse,2)}")
|
||||
else:
|
||||
# # ----- 4) OFFSET : faut-il attendre de dépasser trailing_stop_positive_offset ? -----
|
||||
# if current_trailing_only_offset_is_reached and max_profit > current_trailing_stop_positive_offset:
|
||||
# # Max profit pas atteint ET perte < 2 * current_trailing_stop_positive
|
||||
# if profit > limit: # 2 * current_trailing_stop_positive:
|
||||
# print(
|
||||
# f"1 - {current_time} trailing_stop={round(trailing_stop, 4)} profit={round(profit, 4)} max={round(max_profit, 4)} "
|
||||
# f"limit={round(limit, 4)} offset={round(current_trailing_stop_positive_offset, 4)}"
|
||||
# f"{current_time} trailing non atteint trailing_stop={round(trailing_stop, 4)} profit={round(profit, 4)} "
|
||||
# f"max={round(max_profit, 4)} offset={round(current_trailing_stop_positive_offset, 4)} baisse={round(baisse,2)}")
|
||||
# return None # ne pas activer le trailing encore
|
||||
# else:
|
||||
# print(
|
||||
# f"{current_time} trailing atteint trailing_stop={round(trailing_stop, 4)} profit={round(profit, 4)} "
|
||||
# f"max={round(max_profit, 4)} offset={round(current_trailing_stop_positive_offset, 4)} baisse={round(baisse,2)}")
|
||||
# else:
|
||||
# # print(
|
||||
# # f"1 - {current_time} trailing_stop={round(trailing_stop, 4)} profit={round(profit, 4)} max={round(max_profit, 4)} "
|
||||
# # f"limit={round(limit, 4)} offset={round(current_trailing_stop_positive_offset, 4)}"
|
||||
# # f" baisse={round(baisse,2)} {round(last_candle['sma180_deriv1'], 4)} {round(last_candle['sma60_deriv1'], 4)} {round(last_candle['sma24_deriv1'], 4)}")
|
||||
#
|
||||
# return None
|
||||
# # Sinon : trailing actif dès le début
|
||||
#
|
||||
# # ----- 6) Condition de vente -----
|
||||
# if 0 < profit <= trailing_stop: # and last_candle['mid'] < last_candle['sma5']: # and profit > current_trailing_stop_positive_offset:
|
||||
# self.pairs[pair]['force_buy'] = True
|
||||
# print(
|
||||
# f"{current_time} Condition de vente trailing_stop={round(trailing_stop, 4)} profit={round(profit, 4)} max={round(max_profit, 4)} "
|
||||
# f"{round(limit, 4)} offset={round(current_trailing_stop_positive_offset, 4)} "
|
||||
# f"baisse={round(baisse,2)}")
|
||||
#
|
||||
# return f"stop_{count_of_buys}_{self.pairs[pair]['has_gain']}"
|
||||
|
||||
# print(
|
||||
# f"2 - {current_time} trailing_stop={round(trailing_stop, 4)} profit={round(profit, 4)} max={round(max_profit, 4)} "
|
||||
# f"{round(limit, 4)} offset={round(current_trailing_stop_positive_offset, 4)} "
|
||||
# f"baisse={round(baisse,2)} {round(last_candle['sma180_deriv1'], 4)} {round(last_candle['sma60_deriv1'], 4)} {round(last_candle['sma24_deriv1'], 4)}")
|
||||
|
||||
return None
|
||||
# Sinon : trailing actif dès le début
|
||||
|
||||
# ----- 6) Condition de vente -----
|
||||
if 0 < profit <= trailing_stop: # and last_candle['mid'] < last_candle['sma5']: # and profit > current_trailing_stop_positive_offset:
|
||||
self.pairs[pair]['force_buy'] = True
|
||||
print(
|
||||
f"{current_time} Condition de vente trailing_stop={round(trailing_stop, 4)} profit={round(profit, 4)} max={round(max_profit, 4)} "
|
||||
f"{round(limit, 4)} offset={round(current_trailing_stop_positive_offset, 4)} "
|
||||
f"baisse={round(baisse,2)}")
|
||||
|
||||
return f"stop_{count_of_buys}_{self.pairs[pair]['has_gain']}"
|
||||
|
||||
print(
|
||||
f"2 - {current_time} trailing_stop={round(trailing_stop, 4)} profit={round(profit, 4)} max={round(max_profit, 4)} "
|
||||
f"{round(limit, 4)} offset={round(current_trailing_stop_positive_offset, 4)} "
|
||||
f"baisse={round(baisse,2)} {round(last_candle['sma180_deriv1'], 4)} {round(last_candle['sma60_deriv1'], 4)} {round(last_candle['sma24_deriv1'], 4)}")
|
||||
|
||||
return None
|
||||
|
||||
def informative_pairs(self):
|
||||
# get access to all pairs available in whitelist.
|
||||
pairs = self.dp.current_whitelist()
|
||||
@@ -2440,6 +2466,21 @@ class FrictradeLearning(IStrategy):
|
||||
# d2s_col = f"{name}{suffixe}_deriv2_smooth"
|
||||
tendency_col = f"{name}{suffixe}_state"
|
||||
|
||||
d1_col = f"{name}{suffixe}_deriv1"
|
||||
d2_col = f"{name}{suffixe}_deriv2"
|
||||
tendency_col = f"{name}{suffixe}_state"
|
||||
|
||||
series = dataframe[f"{name}{suffixe}"]
|
||||
d1 = series.diff()
|
||||
d2 = d1.diff()
|
||||
pmin = int(ema_period / 3)
|
||||
cond_bas = (d1.rolling(pmin).mean() > d1.rolling(ema_period).mean())
|
||||
cond_haut = (d1.rolling(pmin).mean() < d1.rolling(ema_period).mean())
|
||||
|
||||
dataframe[d1_col] = (dataframe[name] - dataframe[name].shift(3)) / dataframe[name].shift(3)
|
||||
dataframe[d2_col] = (dataframe[d1_col] - dataframe[d1_col].shift(1))
|
||||
|
||||
|
||||
factor1 = 100 * (ema_period / 5)
|
||||
factor2 = 10 * (ema_period / 5)
|
||||
|
||||
@@ -2450,15 +2491,15 @@ class FrictradeLearning(IStrategy):
|
||||
dataframe[f"{name}{suffixe}_dist"] = (dataframe['close'] - dataframe[f"{name}{suffixe}"]) / dataframe[
|
||||
f"{name}{suffixe}"]
|
||||
|
||||
# dérivée relative simple
|
||||
dataframe[d1_col] = (dataframe[name] - dataframe[name].shift(1)) / dataframe[name].shift(1)
|
||||
# lissage EMA
|
||||
dataframe[d1_col] = factor1 * dataframe[d1_col].ewm(span=ema_period, adjust=False).mean()
|
||||
|
||||
# dataframe[d1_col] = dataframe[d1_col].rolling(window=ema_period, center=True).median()
|
||||
|
||||
dataframe[d2_col] = dataframe[d1_col] - dataframe[d1_col].shift(1)
|
||||
dataframe[d2_col] = factor2 * dataframe[d2_col].ewm(span=ema_period, adjust=False).mean()
|
||||
# # dérivée relative simple
|
||||
# dataframe[d1_col] = (dataframe[name] - dataframe[name].shift(1)) / dataframe[name].shift(1)
|
||||
# # lissage EMA
|
||||
# dataframe[d1_col] = factor1 * dataframe[d1_col].ewm(span=ema_period, adjust=False).mean()
|
||||
#
|
||||
# # dataframe[d1_col] = dataframe[d1_col].rolling(window=ema_period, center=True).median()
|
||||
#
|
||||
# dataframe[d2_col] = dataframe[d1_col] - dataframe[d1_col].shift(1)
|
||||
# dataframe[d2_col] = factor2 * dataframe[d2_col].ewm(span=ema_period, adjust=False).mean()
|
||||
|
||||
# epsilon adaptatif via rolling percentile
|
||||
p_low_d1 = dataframe[d1_col].rolling(window=window, min_periods=1).quantile(0.05)
|
||||
|
||||
Reference in New Issue
Block a user