Calcul 20250101-20250714 482 269 max 11
This commit is contained in:
Jérôme Delacotte
@@ -62,7 +62,7 @@ class Zeus_8_3_2_B_4_2(IStrategy):
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"2814": 0.12,
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"7675": 0
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}
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stakes = 50 # days
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stakes = 40
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# Stoploss:
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stoploss = -1 # 0.256
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@@ -165,12 +165,12 @@ class Zeus_8_3_2_B_4_2(IStrategy):
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pairs = {
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pair: {
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"first_buy": 0,
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"last_buy": 0.0,
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"last_min": 999999999999999.5,
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"last_max": 0,
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"trade_info": {},
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"max_touch": 0.0,
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"last_min": 999999999999999.5,
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"last_sell": 0.0,
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"last_buy": 0.0,
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'count_of_buys': 0,
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'current_profit': 0,
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'expected_profit': 0,
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@@ -212,37 +212,60 @@ class Zeus_8_3_2_B_4_2(IStrategy):
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protection_fibo = IntParameter(1, 10, default=2, space='protection')
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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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labels = ['B5', 'B4', 'B3', 'B2', 'B1', 'N0', 'H1', 'H2', 'H3', 'H4', 'H5']
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index_labels = ['B5', 'B4', 'B3', 'B2', 'B1', 'N0', 'H1', 'H2', 'H3', 'H4', 'H5']
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# Récupération des labels ordonnés
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ordered_labels = ['B5', 'B4', 'B3', 'B2', 'B1', 'N0', 'H1', 'H2', 'H3', 'H4', 'H5']
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# labels = ['B5', 'B4', 'B3', 'B2', 'B1', 'N0', 'H1', 'H2', 'H3', 'H4', 'H5']
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# index_labels = ['B5', 'B4', 'B3', 'B2', 'B1', 'N0', 'H1', 'H2', 'H3', 'H4', 'H5']
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# ordered_labels = ['B5', 'B4', 'B3', 'B2', 'B1', 'N0', 'H1', 'H2', 'H3', 'H4', 'H5']
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labels = ['B3', 'B2', 'B1', 'N0', 'H1', 'H2', 'H3']
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index_labels = ['B3', 'B2', 'B1', 'N0', 'H1', 'H2', 'H3']
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ordered_labels = ['B3', 'B2', 'B1', 'N0', 'H1', 'H2', 'H3']
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label_to_index = {label: i for i, label in enumerate(ordered_labels)}
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# # =========================================================================
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# # variables pour probabilité
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# # Bornes des quantiles pour
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# ema_volume = [-8.9178, -0.0196, -0.0096, -0.0053, -0.0026, -0.0007, 0.0009, 0.0029, 0.0056, 0.0101, 0.0200, 3.8009]
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# # Bornes des quantiles pour
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# mid_smooth_1h_deriv1 = [-1.0482, -0.0571, -0.0336, -0.0206, -0.0113, -0.0033, 0.0044, 0.0127, 0.0225, 0.0356, 0.0591, 0.8335]
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#
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# ema_volume_mid_smooth_1h_deriv1_matrice = {
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# 'B5': [28.0, 32.8, 33.6, 36.4, 35.5, 35.6, 40.1, 40.9, 45.9, 49.7, 52.2],
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# 'B4': [33.9, 37.2, 38.6, 40.7, 39.7, 43.0, 46.2, 47.1, 51.9, 55.9, 61.1],
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# 'B3': [36.4, 41.3, 39.1, 41.8, 44.6, 46.1, 50.3, 47.9, 47.6, 57.0, 58.5],
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# 'B2': [40.7, 40.6, 40.9, 44.6, 48.0, 48.4, 48.5, 53.5, 53.0, 54.8, 53.3],
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# 'B1': [37.5, 41.4, 48.0, 46.3, 48.5, 49.1, 53.7, 53.4, 56.4, 56.7, 62.8],
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# 'N0': [47.0, 44.3, 45.6, 47.0, 52.9, 52.2, 55.7, 53.0, 57.6, 58.1, 63.4],
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# 'H1': [44.1, 46.2, 49.4, 49.3, 52.2, 53.7, 58.2, 57.1, 59.0, 61.6, 61.3],
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# 'H2': [51.0, 44.7, 49.4, 51.3, 54.9, 57.9, 56.7, 58.1, 60.3, 60.6, 65.6],
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# 'H3': [50.5, 48.3, 49.9, 60.4, 57.8, 56.3, 60.2, 61.9, 62.2, 65.3, 68.3],
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# 'H4': [43.1, 53.6, 58.1, 61.4, 58.7, 62.6, 61.3, 65.4, 67.5, 68.2, 71.4],
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# 'H5': [56.6, 56.2, 57.7, 63.8, 64.8, 64.7, 66.5, 68.8, 70.9, 72.8, 76.6],
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#
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# }
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#
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# ema_volume_mid_smooth_1h_deriv1_matrice_df = pd.DataFrame(ema_volume_mid_smooth_1h_deriv1_matrice, index=index_labels)
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# # Extraction de la matrice numérique
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# ema_volume_mid_smooth_1h_deriv1_numeric_matrice = ema_volume_mid_smooth_1h_deriv1_matrice_df.reindex(index=ordered_labels, columns=ordered_labels).values
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# =========================================================================
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# variables pour probabilité
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# Bornes des quantiles pour
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ema_volume = [-8.9178, -0.0196, -0.0096, -0.0053, -0.0026, -0.0007, 0.0009, 0.0029, 0.0056, 0.0101, 0.0200, 3.8009]
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# Bornes des quantiles pour
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mid_smooth_1h_deriv1 = [-1.0482, -0.0571, -0.0336, -0.0206, -0.0113, -0.0033, 0.0044, 0.0127, 0.0225, 0.0356, 0.0591, 0.8335]
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ema_volume_mid_smooth_1h_deriv1_matrice = {
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'B5': [28.0, 32.8, 33.6, 36.4, 35.5, 35.6, 40.1, 40.9, 45.9, 49.7, 52.2],
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'B4': [33.9, 37.2, 38.6, 40.7, 39.7, 43.0, 46.2, 47.1, 51.9, 55.9, 61.1],
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'B3': [36.4, 41.3, 39.1, 41.8, 44.6, 46.1, 50.3, 47.9, 47.6, 57.0, 58.5],
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'B2': [40.7, 40.6, 40.9, 44.6, 48.0, 48.4, 48.5, 53.5, 53.0, 54.8, 53.3],
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'B1': [37.5, 41.4, 48.0, 46.3, 48.5, 49.1, 53.7, 53.4, 56.4, 56.7, 62.8],
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'N0': [47.0, 44.3, 45.6, 47.0, 52.9, 52.2, 55.7, 53.0, 57.6, 58.1, 63.4],
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'H1': [44.1, 46.2, 49.4, 49.3, 52.2, 53.7, 58.2, 57.1, 59.0, 61.6, 61.3],
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'H2': [51.0, 44.7, 49.4, 51.3, 54.9, 57.9, 56.7, 58.1, 60.3, 60.6, 65.6],
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'H3': [50.5, 48.3, 49.9, 60.4, 57.8, 56.3, 60.2, 61.9, 62.2, 65.3, 68.3],
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'H4': [43.1, 53.6, 58.1, 61.4, 58.7, 62.6, 61.3, 65.4, 67.5, 68.2, 71.4],
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'H5': [56.6, 56.2, 57.7, 63.8, 64.8, 64.7, 66.5, 68.8, 70.9, 72.8, 76.6],
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# paliers dérivées jour sma5
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sma5_deriv1 = [-1.1726, -0.2131, -0.1012, -0.0330, 0.0169, 0.0815, 0.2000, 4.0335]
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sma5_deriv2 = [-1.9190, -0.1388, -0.0644, -0.0202, 0.0209, 0.0646, 0.1377, 4.2987]
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sma5_derive1_2_matrice = {
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'B3': [8.6, 10.8, 34.6, 35.0, 58.8, 61.9, 91.2],
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'B2': [0.0, 12.5, 9.1, 57.1, 63.3, 79.3, 89.5],
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'B1': [6.1, 12.5, 22.0, 46.8, 61.5, 70.0, 100.0],
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'N0': [0.0, 10.7, 37.0, 43.5, 75.0, 75.9, 100.0],
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'H1': [0.0, 18.5, 32.4, 35.9, 76.8, 82.9, 92.0],
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'H2': [0.0, 21.9, 16.0, 39.5, 69.7, 83.3, 100.0],
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'H3': [9.5, 29.2, 41.2, 57.9, 53.8, 86.8, 92.3],
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}
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ema_volume_mid_smooth_1h_deriv1_matrice_df = pd.DataFrame(ema_volume_mid_smooth_1h_deriv1_matrice, index=index_labels)
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sma5_derive1_2_matrice_df = pd.DataFrame(sma5_derive1_2_matrice, index=index_labels)
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# Extraction de la matrice numérique
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ema_volume_mid_smooth_1h_deriv1_numeric_matrice = ema_volume_mid_smooth_1h_deriv1_matrice_df.reindex(index=ordered_labels, columns=ordered_labels).values
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sma5_derive1_2_numeric_matrice = sma5_derive1_2_matrice_df.reindex(index=ordered_labels, columns=ordered_labels).values
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# paliers = {}
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@@ -295,6 +318,9 @@ class Zeus_8_3_2_B_4_2(IStrategy):
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self.pairs[pair]['count_of_buys'] = 1
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self.pairs[pair]['current_profit'] = 0
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self.pairs[pair]['last_palier_index'] = -1
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self.pairs[pair]['last_max'] = max(last_candle['haclose'], self.pairs[pair]['last_max'])
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self.pairs[pair]['last_min'] = min(last_candle['haclose'], self.pairs[pair]['last_min'])
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dispo = round(self.wallets.get_available_stake_amount())
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self.printLineLog()
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@@ -477,8 +503,8 @@ class Zeus_8_3_2_B_4_2(IStrategy):
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return
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if self.columns_logged % 30 == 0:
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self.printLog(
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f"| {'Date':<16} | {'Action':<10} |{'Pair':<5}| {'Trade Type':<18} |{'Rate':>8} | {'Dispo':>6} | {'Profit':>8} | {'Pct':>6} | {'max_touch':>11} | {'last_lost':>12} | {'last_max':>7}|{'Buys':>5}| {'Stake':>5} |"
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f"Tdc|{'val':>6}|Distmax|s201d|s5_1d|smt1h|smt2h|"
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f"| {'Date':<16} | {'Action':<10} |{'Pair':<5}| {'Trade Type':<18} |{'Rate':>8} | {'Dispo':>6} | {'Profit':>8} | {'Pct':>6} | {'max_touch':>11} | {'last_lost':>12} | {'last_max':>7}| {'last_max':>7}|{'Buys':>5}| {'Stake':>5} |"
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f"Tdc|{'val':>6}|Distmax|s201d|s5_1d|s5_2d|smt1h|smt2h|"
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)
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self.printLineLog()
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df = pd.DataFrame.from_dict(self.pairs, orient='index')
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@@ -536,32 +562,38 @@ class Zeus_8_3_2_B_4_2(IStrategy):
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# val144 = self.getProbaHausse144(last_candle)
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# val1h = self.getProbaHausse1h(last_candle)
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val = self.getProbaHausse(last_candle)
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val = self.getProbaHausseSma5d(last_candle)
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pct60 = round(100 * self.getPct60D(pair, last_candle), 2)
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color = GREEN if profit > 0 else RED
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color_sma20 = GREEN if last_candle['sma20_deriv1_1d'] > 0 else RED
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color_sma5 = GREEN if last_candle['sma5_deriv1_1d'] > 0 else RED
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color_sma5_2 = GREEN if last_candle['sma5_deriv2_1d'] > 0 else RED
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color_smooth_1h = GREEN if last_candle['mid_smooth_1h_deriv1'] > 0 else RED
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color_smooth2_1h = GREEN if last_candle['mid_smooth_1h_deriv2'] > 0 else RED
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last_max = int(self.pairs[pair]['last_max']) if self.pairs[pair]['last_max'] > 1 else round(self.pairs[pair]['last_max'],3)
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last_min = int(self.pairs[pair]['last_min']) if self.pairs[pair]['last_min'] > 1 else round(self.pairs[pair]['last_min'], 3)
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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"| {color}{profit or '-':>8}{RESET} | {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} |{total_counts or '-':>5}|{stake or '-':>7}"
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f"| {last_max or '-':>7} | {last_min or '-':>7} |{total_counts or '-':>5}|{stake or '-':>7}"
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f"|{last_candle['tendency_12'] 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(val, 1) or '-' :>6}|"
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f"{dist_max:>7}|{color_sma20}{round(last_candle['sma20_deriv1_1d'],2):>5}{RESET}|{color_sma5}{round(last_candle['sma5_deriv1_1d'],2):>5}{RESET}"
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f"{dist_max:>7}|{color_sma20}{round(last_candle['sma20_deriv1_1d'],2):>5}{RESET}"
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f"|{color_sma5}{round(last_candle['sma5_deriv1_1d'],2):>5}{RESET}|{color_sma5_2}{round(last_candle['sma5_deriv2_1d'],2):>5}{RESET}"
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f"|{color_smooth_1h}{round(last_candle['mid_smooth_1h_deriv1'],2):>5}{RESET}|{color_smooth2_1h}{round(last_candle['mid_smooth_1h_deriv2'],2):>5}{RESET}"
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f"|{last_candle['min60_1d']}|{last_candle['max60_1d']}"
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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}+{'-' * 5}+{'-' * 7}+"
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f"+{'-' * 18}+{'-' * 12}+{'-' * 5}+{'-' * 20}+{'-' * 9}+{'-' * 8}+{'-' * 10}+{'-' * 8}+{'-' * 13}+{'-' * 14}+{'-' * 9}{'-' * 9}+{'-' * 5}+{'-' * 7}+"
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f"{'-' * 3}"
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# "+{'-' * 3}+{'-' * 3}
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f"+{'-' * 6}+{'-' * 7}+{'-' * 5}+{'-' * 5}+{'-' * 5}+{'-' * 5}+"
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@@ -713,11 +745,11 @@ class Zeus_8_3_2_B_4_2(IStrategy):
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# poly_func, x_future, y_future, count = self.polynomial_forecast(informative['sma5_deriv1_1d'], window=24, degree=4)
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# informative['futur_percent_3'] = 100 * ((informative['sma5'].shift(-3) - informative['sma5']) / informative['sma5'])
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# informative['futur_percent_3'] = 100 * ((informative['sma5'].shift(-1) - informative['sma5']) / informative['sma5'])
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# futur_cols = ['futur_percent_3']
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# indic_1 = 'sma5_deriv1'
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# indic_2 = 'sma20_deriv1'
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#
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# indic_2 = 'sma5_deriv2'
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# self.calculateProbabilite2Index(informative, futur_cols, indic_1, indic_2)
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dataframe = merge_informative_pair(dataframe, informative, self.timeframe, "1d", ffill=True)
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@@ -834,7 +866,13 @@ class Zeus_8_3_2_B_4_2(IStrategy):
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# self.calculateProbabilite2Index(dataframe, futur_cols=['futur_percent_3h'], indic_1='ema_volume',
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# indic_2='mid_smooth_1h_deriv1')
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dataframe['proba_hausse'] = dataframe.apply(lambda row: self.getProbaHausse(row), axis=1)
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# dataframe['proba_hausse'] = dataframe.apply(lambda row: self.getProbaHausseEmaVolume(row), axis=1)
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# dataframe['futur_percent_3'] = 100 * ((dataframe['sma5'].shift(-1) - dataframe['sma5']) / dataframe['sma5'])
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# futur_cols = ['futur_percent_3']
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# indic_1 = 'mid_smooth_1h_deriv1'
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# indic_2 = 'mid_smooth_1h_deriv2'
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# self.calculateProbabilite2Index(dataframe, futur_cols, indic_1, indic_2)
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return dataframe
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@@ -1065,6 +1103,7 @@ class Zeus_8_3_2_B_4_2(IStrategy):
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# lim = - (0.012 + 0.001 * (count_of_buys - 1) + (0.002 * count_of_buys if count_of_buys > 10 else 0.001 * count_of_buys if count_of_buys > 5 else 0))
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else:
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# lim = self.calculatePctSliding(pair, last_candle)
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pct = 0.025
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# lim = - pct - (count_of_buys * 0.001)
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lim = self.getLimitBuy(pair, last_candle, pct)
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@@ -1315,13 +1354,27 @@ class Zeus_8_3_2_B_4_2(IStrategy):
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return lim
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def getProbaHausse(self, last_candle):
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value_1 = self.getValuesFromTable(self.ema_volume, last_candle['ema_volume'])
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value_2 = self.getValuesFromTable(self.mid_smooth_1h_deriv1, last_candle['mid_smooth_1h_deriv1'])
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# def getProbaHausseEmaVolume(self, last_candle):
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# value_1 = self.getValuesFromTable(self.ema_volume, last_candle['ema_volume'])
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# value_2 = self.getValuesFromTable(self.mid_smooth_1h_deriv1, last_candle['mid_smooth_1h_deriv1'])
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#
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# val = self.approx_val_from_bins(
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# matrice=self.ema_volume_mid_smooth_1h_deriv1_matrice_df,
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# numeric_matrice=self.ema_volume_mid_smooth_1h_deriv1_numeric_matrice,
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# row_label=value_2,
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# col_label=value_1
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# )
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# return val
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def getProbaHausseSma5d(self, last_candle):
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value_1 = self.getValuesFromTable(self.sma5_deriv1, last_candle['sma5_deriv1_1d'])
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value_2 = self.getValuesFromTable(self.sma5_deriv2, last_candle['sma5_deriv2_1d'])
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# print(f"{last_candle['sma5_deriv1_1d']} => {value_1} / {last_candle['sma5_deriv2_1d']} => {value_2}")
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val = self.approx_val_from_bins(
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matrice=self.ema_volume_mid_smooth_1h_deriv1_matrice_df,
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numeric_matrice=self.ema_volume_mid_smooth_1h_deriv1_numeric_matrice,
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matrice=self.sma5_derive1_2_matrice_df,
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numeric_matrice=self.sma5_derive1_2_numeric_matrice,
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row_label=value_2,
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col_label=value_1
|
||||
)
|
||||
@@ -1341,31 +1394,51 @@ class Zeus_8_3_2_B_4_2(IStrategy):
|
||||
|
||||
adjusted_stake_amount = base_stake_amount * ecart
|
||||
|
||||
# if not pair in ('BTC/USDT', 'BTC/USDC'):
|
||||
# # factors = [1, 1.2, 1.3, 1.4]
|
||||
# adjusted_stake_amount = base_stake_amount
|
||||
# else :
|
||||
# # full = self.wallets.get_total_stake_amount()
|
||||
# # base_stake_amount = min(100, full / self.stakes)
|
||||
#
|
||||
# first_price = self.pairs[pair]['first_buy']
|
||||
# if (first_price == 0):
|
||||
# first_price = last_candle['close']
|
||||
#
|
||||
# last_max = last_candle['max12_1d']
|
||||
# pct = 5
|
||||
# if last_max > 0:
|
||||
# pct = 100 * (last_max - first_price) / last_max
|
||||
#
|
||||
# factor = self.multi_step_interpolate(pct, self.thresholds, self.factors)
|
||||
# adjusted_stake_amount = base_stake_amount * factor # max(base_stake_amount, min(100, base_stake_amount * percent_4))
|
||||
|
||||
# pct = 100 * abs(self.getPctFirstBuy(pair, last_candle))
|
||||
#
|
||||
# factor = self.multi_step_interpolate(pct, self.thresholds, self.factors)
|
||||
|
||||
return adjusted_stake_amount
|
||||
|
||||
def calculateAmountSliding(self, pair, last_candle):
|
||||
val = last_candle['close']
|
||||
min_sliding = min(last_candle['min60_1d'], val)
|
||||
max_sliding = max(last_candle['max60_1d'], val)
|
||||
min_abs = self.pairs[pair]['last_min']
|
||||
max_abs = self.pairs[pair]['last_max']
|
||||
full = self.wallets.get_total_stake_amount()
|
||||
stake = full / self.stakes
|
||||
|
||||
out_min = stake / 2
|
||||
out_max = stake * 2
|
||||
# Clamp sliding range within absolute bounds
|
||||
min_sliding = max(min_sliding, min_abs)
|
||||
max_sliding = min(max_sliding, max_abs)
|
||||
|
||||
# Avoid division by zero
|
||||
if max_sliding == min_sliding:
|
||||
return out_max # Or midpoint, or default value
|
||||
|
||||
# Inverse linear interpolation
|
||||
position = (val - min_sliding) / (max_sliding - min_sliding)
|
||||
return out_max - position * (out_max - out_min)
|
||||
|
||||
def calculatePctSliding(self, pair, last_candle):
|
||||
val = last_candle['close']
|
||||
min_sliding = last_candle['min60_1d']
|
||||
max_sliding = last_candle['max60_1d']
|
||||
min_abs = self.pairs[pair]['last_min']
|
||||
max_abs = self.pairs[pair]['last_max']
|
||||
out_min = 0.025
|
||||
out_max = 0.08
|
||||
# Clamp sliding range within absolute bounds
|
||||
min_sliding = max(min_sliding, min_abs)
|
||||
max_sliding = min(max_sliding, max_abs)
|
||||
|
||||
# Avoid division by zero
|
||||
if max_sliding == min_sliding:
|
||||
return out_max # Or midpoint, or default value
|
||||
|
||||
# Inverse linear interpolation
|
||||
position = (val - min_sliding) / (max_sliding - min_sliding)
|
||||
return out_max - position * (out_max - out_min)
|
||||
|
||||
def expectedProfit(self, pair: str, last_candle: DataFrame):
|
||||
pct_to_max = 0.004
|
||||
max_60 = last_candle['max60_1d']
|
||||
@@ -1894,7 +1967,7 @@ class Zeus_8_3_2_B_4_2(IStrategy):
|
||||
return True # BTC toujours autorisé
|
||||
|
||||
# Filtrer les paires non-BTC
|
||||
non_btc_pairs = [p for p in self.pairs] # if not p.startswith('BTC')]
|
||||
non_btc_pairs = [p for p in self.pairs if not p.startswith('BTC')]
|
||||
|
||||
# Compter les positions actives sur les paires non-BTC
|
||||
max_nb_trades = 0
|
||||
@@ -1920,7 +1993,21 @@ class Zeus_8_3_2_B_4_2(IStrategy):
|
||||
|
||||
self.should_enter_trade_count = 0
|
||||
|
||||
# if last_candle['mid_smooth_1h_deriv1'] < -0.15 and last_candle['mid_smooth_1h_deriv2'] <= -0.15:
|
||||
# return False
|
||||
#
|
||||
# if last_candle['sma20_deriv1_1d'] < -0.08 and last_candle['sma5_deriv1_1d'] <= -0.1:
|
||||
# return False
|
||||
#
|
||||
# if (self.pairs[pair]['count_of_buys'] >= 5):
|
||||
# val = self.getProbaHausseSma5d(last_candle)
|
||||
# if (val < 30):
|
||||
# return False
|
||||
|
||||
if last_candle['sma5_deriv1_1d'] < -0.1:
|
||||
return False
|
||||
|
||||
if max_pair != '' :
|
||||
return (max_pair == pair and self.pairs[pair]['count_of_buys'] <= 5) or pct_max < - 0.1
|
||||
return max_pair == pair
|
||||
else:
|
||||
return True
|
||||
|
||||
Reference in New Issue
Block a user