Stratégie toujours foireuse
This commit is contained in:
Jérôme Delacotte
@@ -149,13 +149,13 @@ class Zeus_8_3_2_B_4_2(IStrategy):
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'mid_smooth_deriv1_1d': {
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"color": "blue"
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},
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'mid_smooth_deriv1_1h': {
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'mid_smooth_1h_deriv1': {
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"color": "red"
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},
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'mid_smooth_deriv2_1d': {
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"color": "pink"
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},
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'mid_smooth_deriv2_1h': {
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'mid_smooth_1h_deriv2': {
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"color": "#da59a6"
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}
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}
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@@ -205,7 +205,7 @@ class Zeus_8_3_2_B_4_2(IStrategy):
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sell_allow_decrease = DecimalParameter(0.005, 0.02, default=0.2, decimals=2, space='sell', optimize=True, load=True)
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# Probabilité de hausse pour futur_percent_3h (en %):
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# mid_smooth_deriv1_1h_bin B5 B4 B3 B2 B1 N0 H1 H2 H3 H4 H5
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# mid_smooth_1h_deriv1_bin B5 B4 B3 B2 B1 N0 H1 H2 H3 H4 H5
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# sma24_deriv1_1h_bin
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# B5 41.0 47.2 48.1 45.6 74.0 65.9 66.5 83.8 77.8 72.1 81.0
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# B4 41.2 35.8 48.4 46.5 59.9 60.2 75.8 79.4 84.6 83.0 78.5
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@@ -226,7 +226,7 @@ class Zeus_8_3_2_B_4_2(IStrategy):
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# Données sous forme de dictionnaire
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# Bornes des quantiles pour
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mid_smooth_deriv1_24_bins = [-37.4852, -0.7541, -0.4233, -0.2510, -0.1338, -0.0389, 0.0496, 0.1464, 0.2660, 0.4384, 0.7697, 48.2985]
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mid_smooth_24_deriv1_bins = [-37.4852, -0.7541, -0.4233, -0.2510, -0.1338, -0.0389, 0.0496, 0.1464, 0.2660, 0.4384, 0.7697, 48.2985]
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sma144_deriv1_bins = [-0.2592, -0.0166, -0.0091, -0.0051, -0.0025, -0.0005, 0.0012, 0.0034, 0.0062, 0.0105, 0.0183, 0.2436]
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smooth24_sma144_deriv1_matrice = {
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@@ -315,14 +315,14 @@ class Zeus_8_3_2_B_4_2(IStrategy):
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smooth_sma_24_diff_numeric_matrice = smooth_sma_24_diff_matrice_df.reindex(index=ordered_labels, columns=ordered_labels).values
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# Bornes des quantiles pour
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mid_smooth_deriv1_1h = [-11.5091, -0.4887, -0.1902, -0.0823, -0.0281, -0.0008, 0.0110, 0.0439, 0.1066, 0.2349, 0.5440, 14.7943]
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mid_smooth_1h_deriv1 = [-11.5091, -0.4887, -0.1902, -0.0823, -0.0281, -0.0008, 0.0110, 0.0439, 0.1066, 0.2349, 0.5440, 14.7943]
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# Bornes des quantiles pour
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mid_smooth_deriv2_1h = [-6.2109, -0.2093, -0.0900, -0.0416, -0.0171, -0.0035, 0.0033, 0.0168, 0.0413, 0.0904, 0.2099, 6.2109]
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mid_smooth_1h_deriv2 = [-6.2109, -0.2093, -0.0900, -0.0416, -0.0171, -0.0035, 0.0033, 0.0168, 0.0413, 0.0904, 0.2099, 6.2109]
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# =========================================================================
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# variables pour probabilité jour
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# Bornes des quantiles pour
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mid_smooth_deriv1_1h_1d_bins = [-11.5091, -0.4887, -0.1902, -0.0823, -0.0281, -0.0008, 0.0110, 0.0439, 0.1066, 0.2349, 0.5440, 14.7943]
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mid_smooth_1h_deriv1_1d_bins = [-11.5091, -0.4887, -0.1902, -0.0823, -0.0281, -0.0008, 0.0110, 0.0439, 0.1066, 0.2349, 0.5440, 14.7943]
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# Bornes des quantiles pour
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sma24_deriv1_1h_1d_bins = [-2.1101, -0.1413, -0.0768, -0.0433, -0.0196, -0.0028, 0.0120, 0.0304, 0.0560, 0.0933, 0.1568, 0.7793]
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@@ -360,10 +360,10 @@ class Zeus_8_3_2_B_4_2(IStrategy):
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last_candle = dataframe.iloc[-1].squeeze()
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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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val = self.getProbaHausse144(last_candle)
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# val = self.getProbaHausse144(last_candle)
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# allow_to_buy = True #(not self.stop_all) #& (not self.all_down)
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allow_to_buy = not self.pairs[pair]['stop'] and val > self.buy_val.value #not last_candle['tendency'] in ('B-', 'B--') # (rate <= float(limit)) | (entry_tag == 'force_entry')
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allow_to_buy = not self.pairs[pair]['stop'] #and val > self.buy_val.value #not last_candle['tendency'] in ('B-', 'B--') # (rate <= float(limit)) | (entry_tag == 'force_entry')
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if allow_to_buy:
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self.trades = list()
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@@ -461,9 +461,6 @@ class Zeus_8_3_2_B_4_2(IStrategy):
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baisse = self.pairs[pair]['max_profit'] - current_profit
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mx = self.pairs[pair]['max_profit'] / 5
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if (last_candle['deriv1_1h'] < 0 and last_candle['deriv2_1h'] < 0) and ((baisse > mx) & (current_profit > expected_profit)):
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return 'Drv_' + str(count_of_buys)
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self.pairs[pair]['count_of_buys'] = count_of_buys
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self.pairs[pair]['current_profit'] = current_profit
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self.pairs[pair]['max_profit'] = max(self.pairs[pair]['max_profit'], current_profit)
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@@ -473,6 +470,9 @@ class Zeus_8_3_2_B_4_2(IStrategy):
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# if (last_candle['tendency'] in ('H++', 'H+')) and (last_candle['rsi'] < 80):
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# return None
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#
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if (last_candle['mid_smooth_1h_deriv1'] < 0 and before_last_candle['mid_smooth_1h_deriv1'] >= 0) and (current_profit > expected_profit):
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return 'Drv_' + str(count_of_buys)
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# if (baisse > mx) & (current_profit > expected_profit):
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# self.trades = list()
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# return 'mx_' + str(count_of_buys)
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@@ -556,34 +556,36 @@ class Zeus_8_3_2_B_4_2(IStrategy):
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max_touch = '' #round(last_candle['max12_1d'], 1) #round(self.pairs[pair]['max_touch'], 1)
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pct_max = round((last_candle['close'] - self.pairs[pair]['first_buy']) / self.pairs[pair]['first_buy'], 3) # round(100 * self.pairs[pair]['current_profit'], 1)
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if trade_type is not None:
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if np.isnan(last_candle['rsi_1d']):
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string = ' '
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else:
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string = (str(int(last_candle['rsi_1d']))) + " " + str(int(last_candle['rsi_deriv1_1d']))
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trade_type = trade_type \
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+ " " + string \
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+ " " + str(int(last_candle['rsi_1h'])) \
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+ " " + str(int(last_candle['rsi_deriv1_1h']))
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# if trade_type is not None:
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# if np.isnan(last_candle['rsi_1d']):
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# string = ' '
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# else:
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# string = (str(int(last_candle['rsi_1d']))) + " " + str(int(last_candle['rsi_deriv1_1d']))
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# trade_type = trade_type \
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# + " " + string \
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# + " " + str(int(last_candle['rsi_1h'])) \
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# + " " + str(int(last_candle['rsi_deriv1_1h']))
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val144 = self.getProbaHausse144(last_candle)
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val1h = self.getProbaHausse1h(last_candle)
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# val144 = self.getProbaHausse144(last_candle)
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# val1h = self.getProbaHausse1h(last_candle)
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self.printLog(
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f"| {date:<16} | {action:<10} | {pair[0:3]:<3} | {trade_type or '-':<18} |{rate or '-':>9}| {dispo or '-':>6} "
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f"| {profit or '-':>8} | {pct_max or '-':>6} | {round(self.pairs[pair]['max_touch'], 2) or '-':>11} | {last_lost or '-':>12} "
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f"| {int(self.pairs[pair]['last_max']) or '-':>7} |{buys or '-':>4}|{stake or '-':>7}"
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f"|{last_candle['tendency'] or '-':>3}|{last_candle['tendency_1h'] or '-':>3}|{last_candle['tendency_1d'] or '-':>3}"
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f"|{round(last_candle['mid_smooth_deriv1_24'],3) or '-':>6}|{round(last_candle['mid_smooth_deriv1_1h'],3) or '-':>6}|{round(last_candle['mid_smooth_deriv1_1d'],3) or '-' :>6}|"
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f"{round(last_candle['mid_smooth_deriv2_24'],3) or '-' :>6}|{round(last_candle['mid_smooth_deriv2_1h'],3) or '-':>6}|{round(last_candle['mid_smooth_deriv2_1d'],3) or '-':>6}|"
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f"{round(val144, 1) or '-' :>6}|{round(val1h, 1) or '-':>6}|"
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f"|{last_candle['tendency'] or '-':>3}|" #{last_candle['tendency_1h'] or '-':>3}|{last_candle['tendency_1d'] or '-':>3}"
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# f"|{round(last_candle['mid_smooth_24_deriv1'],3) or '-':>6}|{round(last_candle['mid_smooth_1h_deriv1'],3) or '-':>6}|{round(last_candle['mid_smooth_deriv1_1d'],3) or '-' :>6}|"
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# f"{round(last_candle['mid_smooth_24_deriv2'],3) or '-' :>6}|{round(last_candle['mid_smooth_1h_deriv2'],3) or '-':>6}|{round(last_candle['mid_smooth_deriv2_1d'],3) or '-':>6}|"
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# f"{round(val144, 1) or '-' :>6}|{round(val1h, 1) or '-':>6}|"
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)
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def printLineLog(self):
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# f"sum1h|sum1d|Tdc|Tdh|Tdd| drv1 |drv_1h|drv_1d|"
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self.printLog(
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f"+{'-' * 18}+{'-' * 12}+{'-' * 5}+{'-' * 20}+{'-' * 9}+{'-' * 8}+{'-' * 10}+{'-' * 8}+{'-' * 13}+{'-' * 14}+{'-' * 9}+{'-' * 4}+{'-' * 7}+"
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f"{'-' * 3}+{'-' * 3}+{'-' * 3}+{'-' * 6}+{'-' * 6}+{'-' * 6}+"
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f"{'-' * 3}"
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#"+{'-' * 3}+{'-' * 3}
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# f"+{'-' * 6}+{'-' * 6}+{'-' * 6}+{'-' * 6}+{'-' * 6}+{'-' * 6}+"
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)
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def printLog(self, str):
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@@ -594,8 +596,8 @@ class Zeus_8_3_2_B_4_2(IStrategy):
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def add_tendency_column(self, dataframe: pd.DataFrame, suffixe='') -> pd.DataFrame:
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def tag_by_derivatives(row):
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d1 = row[f"mid_smooth_deriv1{suffixe}"]
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d2 = row[f"mid_smooth_deriv2{suffixe}"]
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d1 = row[f"mid_smooth{suffixe}_deriv1"]
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d2 = row[f"mid_smooth{suffixe}_deriv2"]
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d1_lim_inf = -0.01
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d1_lim_sup = 0.01
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if d1 >= d1_lim_inf and d1 <= d1_lim_sup: # and d2 >= d2_lim_inf and d2 <= d2_lim_sup:
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@@ -664,18 +666,11 @@ class Zeus_8_3_2_B_4_2(IStrategy):
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)
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# Normalization
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dataframe['average_line'] = dataframe['close'].mean()
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dataframe['average_line_50'] = talib.MIDPOINT(dataframe['close'], timeperiod=50)
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dataframe['average_line_288'] = talib.MIDPOINT(dataframe['close'], timeperiod=288)
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dataframe['average_line_288_098'] = dataframe['average_line_288'] * 0.98
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dataframe['average_line_288_099'] = dataframe['average_line_288'] * 0.99
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# Compter les baisses consécutives
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self.calculateDownAndUp(dataframe, limit=0.0001)
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dataframe = self.calculateRegression(dataframe, column='mid_smooth', window=24, degree=4, future_offset=12)
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dataframe = self.calculateRegression(dataframe, column='mid_smooth_24', window=24, degree=4, future_offset=12)
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# dataframe = self.calculateRegression(dataframe, column='mid_smooth', window=24, degree=4, future_offset=12)
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# dataframe = self.calculateRegression(dataframe, column='mid_smooth_24', window=24, degree=4, future_offset=12)
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################### INFORMATIVE 1h
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informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe="1h")
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@@ -684,32 +679,24 @@ class Zeus_8_3_2_B_4_2(IStrategy):
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informative['haclose'] = heikinashi['close']
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informative['hapercent'] = (informative['haclose'] - informative['haopen']) / informative['haclose']
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informative = self.calculateDerivation(informative, window=12)
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# informative = self.calculateDerivation(informative, window=12)
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# informative = self.apply_regression_derivatives(informative, column='mid', window=5, degree=4)
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# informative['volatility'] = talib.STDDEV(informative['close'], timeperiod=14) / informative['close']
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# informative['atr'] = (talib.ATR(informative['high'], informative['low'], informative['close'], timeperiod=14)) / informative['close']
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informative['rsi'] = talib.RSI(informative['close']) #, timeperiod=7)
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self.calculeDerivees(informative, 'rsi')
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informative['max12'] = talib.MAX(informative['close'], timeperiod=12)
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informative['min12'] = talib.MIN(informative['close'], timeperiod=12)
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informative['sma5'] = talib.SMA(informative, timeperiod=5)
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self.calculeDerivees(informative, 'sma5')
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informative['sma24'] = talib.SMA(informative, timeperiod=24)
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self.calculeDerivees(informative, 'sma24')
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self.calculateDownAndUp(informative, limit=0.0012)
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informative['close_smooth'] = self.conditional_smoothing(informative['mid'], threshold=0.0015)
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informative['smooth'], informative['deriv1'], informative['deriv2'] = self.smooth_and_derivatives(informative['close_smooth'])
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informative['deriv1'] = 100 * informative['deriv1'] / informative['mid']
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informative['deriv2'] = 1000 * informative['deriv2'] / informative['mid']
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# self.calculateDownAndUp(informative, limit=0.0012)
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dataframe = merge_informative_pair(dataframe, informative, self.timeframe, "1h", ffill=True)
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################### INFORMATIVE 1d
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informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe="1d")
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informative = self.calculateDerivation(informative, window=5, factor_1=10000, factor_2=1000)
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# informative = self.calculateDerivation(informative, window=5, factor_1=10000, factor_2=1000)
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# informative['volatility'] = talib.STDDEV(informative['close'], timeperiod=14) / informative['close']
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# informative['atr'] = (talib.ATR(informative['high'], informative['low'], informative['close'], timeperiod=14)) / informative['close']
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@@ -719,11 +706,16 @@ class Zeus_8_3_2_B_4_2(IStrategy):
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informative['max3'] = talib.MAX(informative['close'], timeperiod=3)
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informative['min3'] = talib.MIN(informative['close'], timeperiod=3)
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informative['rsi'] = talib.RSI(informative['close']) #, timeperiod=7)
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self.calculeDerivees(informative, 'rsi')
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# informative['rsi'] = talib.RSI(informative['close']) #, timeperiod=7)
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# self.calculeDerivees(informative, 'rsi')
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#
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# informative['sma5'] = talib.SMA(informative, timeperiod=5)
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# self.calculeDerivees(informative, 'sma5')
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informative['sma5'] = talib.SMA(informative, timeperiod=5)
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self.calculeDerivees(informative, 'sma5')
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# informative['close_smooth'] = self.conditional_smoothing(informative['mid'].dropna(), threshold=0.0015).dropna()
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# informative['smooth'], informative['deriv1'], informative['deriv2'] = self.smooth_and_derivatives(informative['close_smooth'])
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# informative['deriv1'] = 100 * informative['deriv1'] / informative['mid']
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# informative['deriv2'] = 1000 * informative['deriv2'] / informative['mid']
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dataframe = merge_informative_pair(dataframe, informative, self.timeframe, "1d", ffill=True)
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@@ -761,59 +753,64 @@ class Zeus_8_3_2_B_4_2(IStrategy):
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# dataframe['amount'] = amount
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print(f"amount= {amount}")
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# dataframe['mid_smooth_tag'] = qtpylib.crossed_below(dataframe['mid_smooth_deriv1_24'], dataframe['mid_smooth_deriv2_24'])
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# dataframe['mid_smooth_tag'] = qtpylib.crossed_below(dataframe['mid_smooth_24_deriv1'], dataframe['mid_smooth_deriv2_24'])
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# ===============================
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# lissage des valeurs horaires
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horizon_h = 12 * 5
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horizon_d = 24 * 5
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dataframe['mid_smooth_1h'] = dataframe['mid_smooth_1h'].rolling(window=horizon_h).mean()
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dataframe["mid_smooth_deriv1_1h"] = dataframe["mid_smooth_1h"].rolling(horizon_h).mean().diff() / horizon_h
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dataframe["mid_smooth_deriv2_1h"] = horizon_h * dataframe["mid_smooth_deriv1_1h"].rolling(horizon_h).mean().diff()
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dataframe['mid_smooth_1h'] = dataframe['mid'].rolling(window=6).mean()
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dataframe["mid_smooth_1h_deriv1"] = 100 * dataframe["mid_smooth_1h"].diff() / dataframe['mid_smooth_1h']
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dataframe["mid_smooth_1h_deriv2"] = 10 * dataframe["mid_smooth_1h_deriv1"].diff()
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dataframe['sma5_1h'] = dataframe['sma5_1h'].rolling(window=horizon_h).mean()
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dataframe['sma5_deriv1_1h'] = dataframe['sma5_deriv1_1h'].rolling(window=horizon_h).mean()
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dataframe['sma24_1h'] = dataframe['sma24_1h'].rolling(window=horizon_h).mean()
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dataframe['sma24_deriv1_1h'] = dataframe['sma24_deriv1_1h'].rolling(window=horizon_h).mean()
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# dataframe['close_smooth_1h'] = self.conditional_smoothing(dataframe['mid'].rolling(window=3).mean().dropna(), threshold=0.0005)
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# dataframe['smooth_1h'], dataframe['deriv1_1h'], dataframe['deriv2_1h'] = self.smooth_and_derivatives(dataframe['close_smooth_1h'])
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# dataframe['deriv1_1h'] = 100 * dataframe['deriv1_1h'] / dataframe['mid_smooth_1h']
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# dataframe['deriv2_1h'] = 1000 * dataframe['deriv2_1h'] / dataframe['mid_smooth_1h']
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dataframe = self.calculateRegression(dataframe, column='mid_smooth_1h', window=horizon_h * 12, degree=4, future_offset=24)
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# dataframe['sma5_1h'] = dataframe['sma5_1h'].rolling(window=horizon_h).mean()
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# dataframe['sma5_deriv1_1h'] = dataframe['sma5_deriv1_1h'].rolling(window=horizon_h).mean()
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# dataframe['sma24_1h'] = dataframe['sma24_1h'].rolling(window=horizon_h).mean()
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# dataframe['sma24_deriv1_1h'] = dataframe['sma24_deriv1_1h'].rolling(window=horizon_h).mean()
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# dataframe = self.calculateRegression(dataframe, column='mid_smooth_1h', window=horizon_h * 12, degree=4, future_offset=24)
|
||||
|
||||
# Suppose que df['close'] est ton prix de clôture
|
||||
|
||||
dataframe['close_smooth'] = self.conditional_smoothing(dataframe['mid'], threshold=0.0015)
|
||||
# dataframe['close_smooth_24'] = self.conditional_smoothing(dataframe['mid'].rolling(24).mean().dropna(), threshold=0.0015)
|
||||
# dataframe['smooth_24'], dataframe['smooth_24_deriv1'], dataframe['smooth_24_deriv2'] = self.smooth_and_derivatives(dataframe['close_smooth_24'])
|
||||
# dataframe['smooth_24_deriv1'] = 100 * dataframe['smooth_24_deriv1'] / dataframe['mid_smooth_24']
|
||||
# dataframe['smooth_24_deriv2'] = 100 * dataframe['smooth_24_deriv2'] / dataframe['mid_smooth_24']
|
||||
|
||||
dataframe['close_smooth'] = self.conditional_smoothing(dataframe['mid'].rolling(3).mean().dropna(), threshold=0.001)
|
||||
dataframe['smooth'], dataframe['deriv1'], dataframe['deriv2'] = self.smooth_and_derivatives(dataframe['close_smooth'])
|
||||
dataframe['deriv1'] = 100 * dataframe['deriv1'] / dataframe['mid']
|
||||
dataframe['deriv2'] = 1000 * dataframe['deriv2'] / dataframe['mid']
|
||||
dataframe['deriv2'] = 100 * dataframe['deriv2'] / dataframe['mid']
|
||||
|
||||
# ===============================
|
||||
# Lissage des valeurs Journalières
|
||||
dataframe['mid_smooth_1d'] = dataframe['mid_smooth_1d'].rolling(window=horizon_d * 5).mean()
|
||||
dataframe["mid_smooth_deriv1_1d"] = dataframe["mid_smooth_1d"].rolling(horizon_d).mean().diff() / horizon_d
|
||||
dataframe["mid_smooth_deriv2_1d"] = horizon_d * dataframe["mid_smooth_deriv1_1d"].rolling(horizon_d).mean().diff()
|
||||
|
||||
dataframe['close_smooth_1d'] = self.conditional_smoothing(dataframe['mid_smooth_1d'], threshold=0.0015)
|
||||
dataframe['smooth_1d'], dataframe['deriv1_1d'], dataframe['deriv2_1d'] = self.smooth_and_derivatives(dataframe['close_smooth_1d'])
|
||||
dataframe['deriv1_1d'] = 10 * dataframe['deriv1_1d'] / dataframe['mid_smooth_1d']
|
||||
dataframe['deriv2_1d'] = 100 * dataframe['deriv2_1d'] / dataframe['mid_smooth_1d']
|
||||
|
||||
dataframe['sma5_1d'] = dataframe['sma5_1d'].rolling(window=horizon_d * 5).mean()
|
||||
dataframe['sma5_deriv1_1d'] = dataframe['sma5_deriv1_1d'].rolling(window=horizon_d).mean()
|
||||
# horizon_d = 24 * 5
|
||||
# dataframe['mid_smooth_1d'] = dataframe['mid_smooth_1d'].rolling(window=horizon_d * 5).mean()
|
||||
# dataframe["mid_smooth_deriv1_1d"] = dataframe["mid_smooth_1d"].rolling(horizon_d).mean().diff() / horizon_d
|
||||
# dataframe["mid_smooth_deriv2_1d"] = horizon_d * dataframe["mid_smooth_deriv1_1d"].rolling(horizon_d).mean().diff()
|
||||
#
|
||||
# dataframe['sma5_1d'] = dataframe['sma5_1d'].rolling(window=horizon_d * 5).mean()
|
||||
# dataframe['sma5_deriv1_1d'] = dataframe['sma5_deriv1_1d'].rolling(window=horizon_d).mean()
|
||||
# dataframe['sma24_1d'] = dataframe['sma24_1d'].rolling(window=horizon_d).mean()
|
||||
# dataframe['sma24_deriv1_1d'] = dataframe['sma24_deriv1_1d'].rolling(window=horizon_d).mean()
|
||||
# dataframe = self.calculateRegression(dataframe, column='mid_smooth_1d', window=24, degree=4, future_offset=12)
|
||||
|
||||
dataframe['percent_with_previous_day'] = 100 * (dataframe['close'] - dataframe['close_1d']) / dataframe['close']
|
||||
dataframe['percent_with_max_hour'] = 100 * (dataframe['close'] - dataframe['max12_1h']) / dataframe['close']
|
||||
|
||||
dataframe['futur_percent_1h'] = 100 * ((dataframe['mid_smooth_1h'].shift(-12) - dataframe['mid_smooth_1h']) / dataframe['mid_smooth_1h']).rolling(horizon_h).mean()
|
||||
dataframe['futur_percent_3h'] = 100 * ((dataframe['mid_smooth_1h'].shift(-36) - dataframe['mid_smooth_1h']) / dataframe['mid_smooth_1h']).rolling(horizon_h).mean()
|
||||
dataframe['futur_percent_5h'] = 100 * ((dataframe['mid_smooth_1h'].shift(-60) - dataframe['mid_smooth_1h']) / dataframe['mid_smooth_1h']).rolling(horizon_h).mean()
|
||||
dataframe['futur_percent_12h'] = 100 * ((dataframe['mid_smooth_1h'].shift(-144) - dataframe['mid_smooth_1h']) / dataframe['mid_smooth_1h']).rolling(horizon_h).mean()
|
||||
# dataframe['percent_with_previous_day'] = 100 * (dataframe['close'] - dataframe['close_1d']) / dataframe['close']
|
||||
# dataframe['percent_with_max_hour'] = 100 * (dataframe['close'] - dataframe['max12_1h']) / dataframe['close']
|
||||
#
|
||||
# dataframe['futur_percent_1h'] = 100 * ((dataframe['mid_smooth_1h'].shift(-12) - dataframe['mid_smooth_1h']) / dataframe['mid_smooth_1h']).rolling(horizon_h).mean()
|
||||
# dataframe['futur_percent_3h'] = 100 * ((dataframe['mid_smooth_1h'].shift(-36) - dataframe['mid_smooth_1h']) / dataframe['mid_smooth_1h']).rolling(horizon_h).mean()
|
||||
# dataframe['futur_percent_5h'] = 100 * ((dataframe['mid_smooth_1h'].shift(-60) - dataframe['mid_smooth_1h']) / dataframe['mid_smooth_1h']).rolling(horizon_h).mean()
|
||||
# dataframe['futur_percent_12h'] = 100 * ((dataframe['mid_smooth_1h'].shift(-144) - dataframe['mid_smooth_1h']) / dataframe['mid_smooth_1h']).rolling(horizon_h).mean()
|
||||
|
||||
# dataframe['futur_percent_1d'] = 100 * (dataframe['close'].shift(-1) - dataframe['close']) / dataframe['close']
|
||||
# dataframe['futur_percent_3d'] = 100 * (dataframe['close'].shift(-3) - dataframe['close']) / dataframe['close']
|
||||
|
||||
self.calculateProbabilite2Index(dataframe, ['futur_percent_12h'], 'mid_smooth_deriv1_1d', 'sma24_deriv1_1h')
|
||||
# self.calculateProbabilite2Index(dataframe, ['futur_percent_12h'], 'mid_smooth_deriv1_1d', 'sma24_deriv1_1h')
|
||||
|
||||
return dataframe
|
||||
|
||||
@@ -837,10 +834,10 @@ class Zeus_8_3_2_B_4_2(IStrategy):
|
||||
# 1. Calcul du lissage par moyenne mobile médiane
|
||||
dataframe[f"mid_smooth{suffixe}"] = dataframe['close'].rolling(window=window).mean()
|
||||
# 2. Dérivée première = différence entre deux bougies successives
|
||||
dataframe[f"mid_smooth_deriv1{suffixe}"] = round(factor_1 * dataframe[f"mid_smooth{suffixe}"].rolling(window=3).mean().diff() / dataframe[f"mid_smooth{suffixe}"], 4)
|
||||
dataframe[f"mid_smooth{suffixe}_deriv1"] = round(factor_1 * dataframe[f"mid_smooth{suffixe}"].rolling(window=3).mean().diff() / dataframe[f"mid_smooth{suffixe}"], 4)
|
||||
|
||||
# 3. Dérivée seconde = différence de la dérivée première
|
||||
dataframe[f"mid_smooth_deriv2{suffixe}"] = round(factor_2 * dataframe[f"mid_smooth_deriv1{suffixe}"].rolling(window=3).mean().diff(), 4)
|
||||
dataframe[f"mid_smooth{suffixe}_deriv2"] = round(factor_2 * dataframe[f"mid_smooth{suffixe}_deriv1"].rolling(window=3).mean().diff(), 4)
|
||||
dataframe = self.add_tendency_column(dataframe, suffixe)
|
||||
return dataframe
|
||||
|
||||
@@ -863,19 +860,6 @@ class Zeus_8_3_2_B_4_2(IStrategy):
|
||||
# print('adjust exit price ' + str(self.adjust_exit_price(dataframe.iloc[-1])))
|
||||
print('calcul expected_profit ' + str(expected_profit))
|
||||
|
||||
buy_level = dataframe['average_line_50'] #dataframe['buy_level'] # self.get_buy_level(pair, dataframe)
|
||||
|
||||
# dataframe.loc[
|
||||
# (
|
||||
# (dataframe['max200_diff'] >= 0.01)
|
||||
# & (dataframe['percent12'] < -0.002)
|
||||
# & (dataframe['open'] < dataframe['average_line_288_099'])
|
||||
# & (dataframe['open'] < dataframe['average_line_50'])
|
||||
# & (dataframe['min12'].shift(2) == dataframe['min12'])
|
||||
# & (dataframe['up_count'] > 0)
|
||||
# & (dataframe["bb_width"] > 0.01)
|
||||
# ), ['enter_long', 'enter_tag']] = (1, 'mx200')
|
||||
|
||||
# dataframe.loc[
|
||||
# (
|
||||
# (dataframe['percent'] > 0)
|
||||
@@ -884,102 +868,103 @@ class Zeus_8_3_2_B_4_2(IStrategy):
|
||||
|
||||
dataframe.loc[
|
||||
(
|
||||
(dataframe['deriv1_1h'] >= 0)
|
||||
& (dataframe['deriv1'] >= 0)
|
||||
& (dataframe['deriv1'].shift(1) <= 0)
|
||||
& (dataframe['deriv1'] >= dataframe['deriv1'].shift(1))
|
||||
), ['enter_long', 'enter_tag']] = (1, 'smooth')
|
||||
|
||||
# dataframe.loc[
|
||||
# (
|
||||
# (dataframe['low'] < dataframe['min200'])
|
||||
# & (dataframe['min50'] == dataframe['min50'].shift(3))
|
||||
# & (dataframe['tendency'] != "B-")
|
||||
# ), ['enter_long', 'enter_tag']] = (1, 'low')
|
||||
# (dataframe['deriv2_1h'].shift(2) >= dataframe['deriv2_1h'].shift(1))
|
||||
# & (dataframe['deriv2_1h'].shift(1) <= dataframe['deriv2_1h'])
|
||||
# (dataframe['deriv1_1h'] >= -0.01)
|
||||
# & (dataframe['deriv2_1h'] >= -0.00)
|
||||
(dataframe['mid_smooth_1h_deriv1'].shift(1) <= 0)
|
||||
& (dataframe['mid_smooth_1h_deriv1'] >= 0)
|
||||
#
|
||||
#
|
||||
# (dataframe['mid_smooth_1h_deriv1'] >= 0)
|
||||
# & (dataframe['mid_smooth_1h_deriv1'] >= 0)
|
||||
# & (dataframe['mid_smooth_1h_deriv1'].shift(1) <= 0)
|
||||
# & (dataframe['mid_smooth_1h_deriv1'] >= dataframe['mid_smooth_1h_deriv1'].shift(1))
|
||||
), ['enter_long', 'enter_tag']] = (1, 'smth')
|
||||
|
||||
dataframe['test'] = np.where(dataframe['enter_long'] == 1, dataframe['close'] * 1.01, np.nan)
|
||||
|
||||
if self.dp.runmode.value in ('backtest'):
|
||||
today = datetime.now().strftime("%Y-%m-%d-%H:%M:%S")
|
||||
dataframe.to_feather(f"user_data/data/binance/{today}-{metadata['pair'].replace('/', '_')}_df.feather")
|
||||
|
||||
df = dataframe
|
||||
|
||||
# Colonnes à traiter
|
||||
# futur_cols = ['futur_percent_1h', 'futur_percent_3h', 'futur_percent_5h', 'futur_percent_12h']
|
||||
futur_cols = ['futur_percent_1h']
|
||||
|
||||
# Tranches équitables par quantiles
|
||||
|
||||
indic_1 = 'mid_smooth_deriv1_24'
|
||||
indic_2 = 'sma144_deriv1'
|
||||
#indic_2 = 'percent_with_max_hour'
|
||||
# indic_1 = 'mid_smooth_deriv1_1h'
|
||||
# indic_2 = 'sma5_deriv1_1d'
|
||||
|
||||
self.calculateProbabilite2Index(df, futur_cols, indic_1, indic_2)
|
||||
#
|
||||
# df = dataframe
|
||||
#
|
||||
# # Colonnes à traiter
|
||||
# # futur_cols = ['futur_percent_1h', 'futur_percent_3h', 'futur_percent_5h', 'futur_percent_12h']
|
||||
# futur_cols = ['futur_percent_1h']
|
||||
#
|
||||
# # Tranches équitables par quantiles
|
||||
#
|
||||
# indic_1 = 'mid_smooth_24_deriv1'
|
||||
# indic_2 = 'sma144_deriv1'
|
||||
# #indic_2 = 'percent_with_max_hour'
|
||||
# # indic_1 = 'mid_smooth_1h_deriv1'
|
||||
# # indic_2 = 'sma5_deriv1_1d'
|
||||
#
|
||||
# self.calculateProbabilite2Index(df, futur_cols, indic_1, indic_2)
|
||||
|
||||
return dataframe
|
||||
|
||||
def calculateProbabilite2Index(self, df, futur_cols, indic_1, indic_2):
|
||||
# # Définition des tranches pour les dérivées
|
||||
# bins_deriv = [-np.inf, -0.05, -0.01, 0.01, 0.05, np.inf]
|
||||
# labels = ['forte baisse', 'légère baisse', 'neutre', 'légère hausse', 'forte hausse']
|
||||
#
|
||||
# # Ajout des colonnes bin (catégorisation)
|
||||
# df[f"{indic_1}_bin"] = pd.cut(df['mid_smooth_deriv1_1h'], bins=bins_deriv, labels=labels)
|
||||
# df[f"{indic_2}_bin"] = pd.cut(df['mid_smooth_deriv1_1d'], bins=bins_deriv, labels=labels)
|
||||
#
|
||||
# # Colonnes de prix futur à analyser
|
||||
# futur_cols = ['futur_percent_1h', 'futur_percent_2h', 'futur_percent_3h', 'futur_percent_4h', 'futur_percent_5h']
|
||||
#
|
||||
# # Calcul des moyennes et des effectifs
|
||||
# grouped = df.groupby([f"{indic_2}_bin", f"{indic_1}_bin"])[futur_cols].agg(['mean', 'count'])
|
||||
#
|
||||
# pd.set_option('display.width', 200) # largeur max affichage
|
||||
# def calculateProbabilite2Index(self, df, futur_cols, indic_1, indic_2):
|
||||
# # # Définition des tranches pour les dérivées
|
||||
# # bins_deriv = [-np.inf, -0.05, -0.01, 0.01, 0.05, np.inf]
|
||||
# # labels = ['forte baisse', 'légère baisse', 'neutre', 'légère hausse', 'forte hausse']
|
||||
# #
|
||||
# # # Ajout des colonnes bin (catégorisation)
|
||||
# # df[f"{indic_1}_bin"] = pd.cut(df['mid_smooth_1h_deriv1'], bins=bins_deriv, labels=labels)
|
||||
# # df[f"{indic_2}_bin"] = pd.cut(df['mid_smooth_deriv1_1d'], bins=bins_deriv, labels=labels)
|
||||
# #
|
||||
# # # Colonnes de prix futur à analyser
|
||||
# # futur_cols = ['futur_percent_1h', 'futur_percent_2h', 'futur_percent_3h', 'futur_percent_4h', 'futur_percent_5h']
|
||||
# #
|
||||
# # # Calcul des moyennes et des effectifs
|
||||
# # grouped = df.groupby([f"{indic_2}_bin", f"{indic_1}_bin"])[futur_cols].agg(['mean', 'count'])
|
||||
# #
|
||||
# # pd.set_option('display.width', 200) # largeur max affichage
|
||||
# # pd.set_option('display.max_columns', None)
|
||||
# pd.set_option('display.max_columns', None)
|
||||
pd.set_option('display.max_columns', None)
|
||||
pd.set_option('display.width', 300) # largeur max affichage
|
||||
|
||||
# nettoyage
|
||||
# series = df[f"{indic_2}"].dropna()
|
||||
# unique_vals = df[f"{indic_2}"].nunique()
|
||||
# print(unique_vals)
|
||||
# print(df[f"{indic_2}"])
|
||||
n = len(self.labels)
|
||||
|
||||
df[f"{indic_1}_bin"], bins_1h = pd.qcut(df[f"{indic_1}"], q=n, labels=self.labels, retbins=True,
|
||||
duplicates='drop')
|
||||
df[f"{indic_2}_bin"], bins_1d = pd.qcut(df[f"{indic_2}"], q=n, labels=self.labels, retbins=True,
|
||||
duplicates='drop')
|
||||
# Affichage formaté pour code Python
|
||||
print(f"Bornes des quantiles pour {indic_1} : [{', '.join([f'{b:.4f}' for b in bins_1h])}]")
|
||||
print(f"Bornes des quantiles pour {indic_2} : [{', '.join([f'{b:.4f}' for b in bins_1d])}]")
|
||||
# Agrégation
|
||||
grouped = df.groupby([f"{indic_2}_bin", f"{indic_1}_bin"], observed=True)[futur_cols].agg(['mean', 'count'])
|
||||
# Affichage
|
||||
with pd.option_context('display.max_rows', None, 'display.max_columns', None):
|
||||
print(grouped.round(4))
|
||||
# Ajout des probabilités de hausse
|
||||
for col in futur_cols:
|
||||
df[f"{col}_is_up"] = df[col] > 0
|
||||
|
||||
# Calcul de la proba de hausse
|
||||
proba_up = df.groupby([f"{indic_2}_bin", f"{indic_1}_bin"], observed=True)[f"{col}_is_up"].mean().unstack()
|
||||
|
||||
print(f"\nProbabilité de hausse pour {col} (en %):")
|
||||
with pd.option_context('display.max_rows', None, 'display.max_columns', None):
|
||||
print((proba_up * 100).round(1))
|
||||
|
||||
# Affichage formaté des valeurs comme tableau Python
|
||||
with pd.option_context('display.max_rows', None, 'display.max_columns', None):
|
||||
df_formatted = (proba_up * 100).round(1)
|
||||
|
||||
print("data = {")
|
||||
for index, row in df_formatted.iterrows():
|
||||
row_values = ", ".join([f"{val:.1f}" for val in row])
|
||||
print(f"'{index}': [{row_values}], ")
|
||||
print("}")
|
||||
# pd.set_option('display.width', 300) # largeur max affichage
|
||||
#
|
||||
# # nettoyage
|
||||
# # series = df[f"{indic_2}"].dropna()
|
||||
# # unique_vals = df[f"{indic_2}"].nunique()
|
||||
# # print(unique_vals)
|
||||
# # print(df[f"{indic_2}"])
|
||||
# n = len(self.labels)
|
||||
#
|
||||
# df[f"{indic_1}_bin"], bins_1h = pd.qcut(df[f"{indic_1}"], q=n, labels=self.labels, retbins=True,
|
||||
# duplicates='drop')
|
||||
# df[f"{indic_2}_bin"], bins_1d = pd.qcut(df[f"{indic_2}"], q=n, labels=self.labels, retbins=True,
|
||||
# duplicates='drop')
|
||||
# # Affichage formaté pour code Python
|
||||
# print(f"Bornes des quantiles pour {indic_1} : [{', '.join([f'{b:.4f}' for b in bins_1h])}]")
|
||||
# print(f"Bornes des quantiles pour {indic_2} : [{', '.join([f'{b:.4f}' for b in bins_1d])}]")
|
||||
# # Agrégation
|
||||
# grouped = df.groupby([f"{indic_2}_bin", f"{indic_1}_bin"], observed=True)[futur_cols].agg(['mean', 'count'])
|
||||
# # Affichage
|
||||
# with pd.option_context('display.max_rows', None, 'display.max_columns', None):
|
||||
# print(grouped.round(4))
|
||||
# # Ajout des probabilités de hausse
|
||||
# for col in futur_cols:
|
||||
# df[f"{col}_is_up"] = df[col] > 0
|
||||
#
|
||||
# # Calcul de la proba de hausse
|
||||
# proba_up = df.groupby([f"{indic_2}_bin", f"{indic_1}_bin"], observed=True)[f"{col}_is_up"].mean().unstack()
|
||||
#
|
||||
# print(f"\nProbabilité de hausse pour {col} (en %):")
|
||||
# with pd.option_context('display.max_rows', None, 'display.max_columns', None):
|
||||
# print((proba_up * 100).round(1))
|
||||
#
|
||||
# # Affichage formaté des valeurs comme tableau Python
|
||||
# with pd.option_context('display.max_rows', None, 'display.max_columns', None):
|
||||
# df_formatted = (proba_up * 100).round(1)
|
||||
#
|
||||
# print("data = {")
|
||||
# for index, row in df_formatted.iterrows():
|
||||
# row_values = ", ".join([f"{val:.1f}" for val in row])
|
||||
# print(f"'{index}': [{row_values}], ")
|
||||
# print("}")
|
||||
|
||||
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
# dataframe.loc[
|
||||
@@ -1111,7 +1096,7 @@ class Zeus_8_3_2_B_4_2(IStrategy):
|
||||
return None
|
||||
|
||||
def getProbaHausse144(self, last_candle):
|
||||
value_1 = self.getValuesFromTable(self.mid_smooth_deriv1_24_bins, last_candle['mid_smooth_deriv1_24'])
|
||||
value_1 = self.getValuesFromTable(self.mid_smooth_24_deriv1_bins, last_candle['mid_smooth_24_deriv1'])
|
||||
value_2 = self.getValuesFromTable(self.sma144_deriv1_bins, last_candle['sma144_deriv1'])
|
||||
|
||||
val = self.approx_val_from_bins(
|
||||
@@ -1122,7 +1107,7 @@ class Zeus_8_3_2_B_4_2(IStrategy):
|
||||
return val
|
||||
|
||||
def getProbaHausse1h(self, last_candle):
|
||||
value_1 = self.getValuesFromTable(self.mid_smooth_1h_bins, last_candle['mid_smooth_deriv1_1h'])
|
||||
value_1 = self.getValuesFromTable(self.mid_smooth_1h_bins, last_candle['mid_smooth_1h_deriv1'])
|
||||
value_2 = self.getValuesFromTable(self.sma24_deriv1_1h_bins, last_candle['sma24_deriv1_1h'])
|
||||
|
||||
val = self.approx_val_from_bins(matrice=self.smooth_smadiff_matrice_df, numeric_matrice=self.smooth_smadiff_numeric_matrice,
|
||||
@@ -1199,69 +1184,69 @@ class Zeus_8_3_2_B_4_2(IStrategy):
|
||||
#
|
||||
# Filtrer les signaux: ne prendre un signal haussier que si dérivée1 > 0 et dérivée2 > 0.
|
||||
# Détecter les zones de retournement: quand dérivée1 ≈ 0 et que dérivée2 change de signe.
|
||||
def calculateRegression(self,
|
||||
dataframe: DataFrame,
|
||||
column= 'close',
|
||||
window= 50,
|
||||
degree=3,
|
||||
future_offset: int = 10 # projection à n bougies après
|
||||
) -> DataFrame:
|
||||
df = dataframe.copy()
|
||||
|
||||
regression_fit = []
|
||||
regression_future_fit = []
|
||||
|
||||
regression_fit = []
|
||||
regression_future_fit = []
|
||||
|
||||
for i in range(len(df)):
|
||||
if i < window:
|
||||
regression_fit.append(np.nan)
|
||||
regression_future_fit.append(np.nan)
|
||||
continue
|
||||
|
||||
# Fin de la fenêtre d’apprentissage
|
||||
end_index = i
|
||||
start_index = i - window
|
||||
y = df[column].iloc[start_index:end_index].values
|
||||
|
||||
# Si les données sont insuffisantes (juste par précaution)
|
||||
if len(y) < window:
|
||||
regression_fit.append(np.nan)
|
||||
regression_future_fit.append(np.nan)
|
||||
continue
|
||||
|
||||
# x centré pour meilleure stabilité numérique
|
||||
x = np.linspace(-1, 1, window)
|
||||
coeffs = np.polyfit(x, y, degree)
|
||||
poly = np.poly1d(coeffs)
|
||||
|
||||
# Calcul point présent (dernier de la fenêtre)
|
||||
x_now = x[-1]
|
||||
regression_fit.append(poly(x_now))
|
||||
|
||||
# Calcul point futur, en ajustant si on dépasse la fin
|
||||
remaining = len(df) - i - 1
|
||||
effective_offset = min(future_offset, remaining)
|
||||
x_future = x_now + (effective_offset / window) * 2 # respect du même pas
|
||||
regression_future_fit.append(poly(x_future))
|
||||
|
||||
df[f"{column}_regression"] = regression_fit
|
||||
# 2. Dérivée première = différence entre deux bougies successives
|
||||
df[f"{column}_regression_deriv1"] = round(100 * df[f"{column}_regression"].diff() / df[f"{column}_regression"], 4)
|
||||
|
||||
# 3. Dérivée seconde = différence de la dérivée première
|
||||
df[f"{column}_regression_deriv2"] = round(10 * df[f"{column}_regression_deriv1"].rolling(int(window / 4)).mean().diff(), 4)
|
||||
|
||||
df[f"{column}_future_{future_offset}"] = regression_future_fit
|
||||
|
||||
# def calculateRegression(self,
|
||||
# dataframe: DataFrame,
|
||||
# column= 'close',
|
||||
# window= 50,
|
||||
# degree=3,
|
||||
# future_offset: int = 10 # projection à n bougies après
|
||||
# ) -> DataFrame:
|
||||
# df = dataframe.copy()
|
||||
#
|
||||
# regression_fit = []
|
||||
# regression_future_fit = []
|
||||
#
|
||||
# regression_fit = []
|
||||
# regression_future_fit = []
|
||||
#
|
||||
# for i in range(len(df)):
|
||||
# if i < window:
|
||||
# regression_fit.append(np.nan)
|
||||
# regression_future_fit.append(np.nan)
|
||||
# continue
|
||||
#
|
||||
# # Fin de la fenêtre d’apprentissage
|
||||
# end_index = i
|
||||
# start_index = i - window
|
||||
# y = df[column].iloc[start_index:end_index].values
|
||||
#
|
||||
# # Si les données sont insuffisantes (juste par précaution)
|
||||
# if len(y) < window:
|
||||
# regression_fit.append(np.nan)
|
||||
# regression_future_fit.append(np.nan)
|
||||
# continue
|
||||
#
|
||||
# # x centré pour meilleure stabilité numérique
|
||||
# x = np.linspace(-1, 1, window)
|
||||
# coeffs = np.polyfit(x, y, degree)
|
||||
# poly = np.poly1d(coeffs)
|
||||
#
|
||||
# # Calcul point présent (dernier de la fenêtre)
|
||||
# x_now = x[-1]
|
||||
# regression_fit.append(poly(x_now))
|
||||
#
|
||||
# # Calcul point futur, en ajustant si on dépasse la fin
|
||||
# remaining = len(df) - i - 1
|
||||
# effective_offset = min(future_offset, remaining)
|
||||
# x_future = x_now + (effective_offset / window) * 2 # respect du même pas
|
||||
# regression_future_fit.append(poly(x_future))
|
||||
#
|
||||
# df[f"{column}_regression"] = regression_fit
|
||||
# # 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}_regression_deriv1"] = round(100 * df[f"{column}_regression"].diff() / df[f"{column}_regression"], 4)
|
||||
#
|
||||
# # 3. Dérivée seconde = différence de la dérivée première
|
||||
# df[f"{column}_future_{future_offset}_deriv2"] = round(10 * df[f"{column}_future_{future_offset}_deriv1"].rolling(int(window / 4)).mean().diff(), 4)
|
||||
|
||||
return df
|
||||
# df[f"{column}_regression_deriv2"] = round(10 * df[f"{column}_regression_deriv1"].rolling(int(window / 4)).mean().diff(), 4)
|
||||
#
|
||||
# df[f"{column}_future_{future_offset}"] = regression_future_fit
|
||||
#
|
||||
# # # 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)
|
||||
# #
|
||||
# # # 3. Dérivée seconde = différence de la dérivée première
|
||||
# # df[f"{column}_future_{future_offset}_deriv2"] = round(10 * df[f"{column}_future_{future_offset}_deriv1"].rolling(int(window / 4)).mean().diff(), 4)
|
||||
#
|
||||
# return df
|
||||
|
||||
def getValuesFromTable(self, values, value):
|
||||
for i in range(len(values) - 1):
|
||||
@@ -1401,16 +1386,30 @@ class Zeus_8_3_2_B_4_2(IStrategy):
|
||||
smoothed.append(last)
|
||||
return pd.Series(smoothed, index=series.index)
|
||||
|
||||
|
||||
# Lissage + dérivées
|
||||
def smooth_and_derivatives(self, series, window=25, polyorder=3):
|
||||
y = series.values
|
||||
smoothed = savgol_filter(y, window_length=window, polyorder=polyorder)
|
||||
deriv1 = savgol_filter(y, window_length=window, polyorder=polyorder, deriv=1)
|
||||
deriv2 = savgol_filter(y, window_length=window, polyorder=polyorder, deriv=2)
|
||||
series = series.copy()
|
||||
if series.isna().sum() > 0:
|
||||
series = series.fillna(method='ffill').fillna(method='bfill') # Si tu veux éviter toute NaN
|
||||
|
||||
smooth = self.causal_savgol(series, window=window, polyorder=polyorder)
|
||||
deriv1 = np.diff(smooth, prepend=smooth[0])
|
||||
deriv2 = np.diff(deriv1, prepend=deriv1[0])
|
||||
|
||||
return pd.Series(smooth, index=series.index), pd.Series(deriv1, index=series.index), pd.Series(deriv2, index=series.index)
|
||||
|
||||
def causal_savgol(self, series, window=25, polyorder=3):
|
||||
result = []
|
||||
half_window = window # Fenêtre complète dans le passé
|
||||
for i in range(len(series)):
|
||||
if i < half_window:
|
||||
result.append(np.nan)
|
||||
continue
|
||||
window_series = series[i - half_window:i]
|
||||
if window_series.isna().any():
|
||||
result.append(np.nan)
|
||||
continue
|
||||
coeffs = np.polyfit(range(window), window_series, polyorder)
|
||||
poly = np.poly1d(coeffs)
|
||||
result.append(poly(window - 1))
|
||||
return pd.Series(result, index=series.index)
|
||||
|
||||
return (
|
||||
pd.Series(smoothed, index=series.index),
|
||||
pd.Series(deriv1, index=series.index),
|
||||
pd.Series(deriv2, index=series.index),
|
||||
)
|
||||
|
||||
Reference in New Issue
Block a user