Calcul 20250101-20250714 464.183 202.763 56539
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Jérôme Delacotte
@@ -103,31 +103,12 @@ class Zeus_8_3_2_B_4_2(IStrategy):
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}
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},
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"subplots": {
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"Pct": {
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"sma20_deriv1": {
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'color': "green"
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},
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"down_pct": {
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"color": "blue"
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},
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"down_pct_1h": {
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"color": "red"
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},
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"down_pct_1d": {
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"color": "red"
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}
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},
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"Rsi": {
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"rsi": {
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"color": "pink"
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},
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"rsi_1h": {
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"color": "red"
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},
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"rsi_1d": {
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"color": "blue"
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}
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},
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},
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"Rsi_deriv1": {
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"rsi_deriv1_1h": {
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@@ -136,33 +117,48 @@ class Zeus_8_3_2_B_4_2(IStrategy):
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"rsi_deriv1_1d": {
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"color": "blue"
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},
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},
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"Down": {
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"down_count_1h": {
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"sma60_deriv1": {
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"color": "green"
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},
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"up_count_1h": {
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"color": "blue"
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}
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},
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"Rsi_deriv2": {
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"rsi_deriv2_1h": {
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"color": "red"
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},
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"rsi_deriv2_1d": {
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"color": "blue"
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},
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"sma60_deriv2": {
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"color": "green"
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}
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},
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# "Down": {
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# "down_count_1h": {
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# "color": "green"
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# },
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# "up_count_1h": {
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# "color": "blue"
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# }
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# },
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# "Diff": {
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# "sma10_deriv1": {
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# "color": "#74effc"
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# }
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# },
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"smooth": {
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'mid_smooth_deriv1_1d': {
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"color": "blue"
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},
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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_1h_deriv2': {
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"color": "#da59a6"
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}
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# "smooth": {
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# 'sma60_deriv1': {
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# "color": "blue"
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# },
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# # 'mid_smooth_1h_deriv1': {
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# # "color": "red"
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# # },
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# 'sma5_deriv2_1h': {
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# "color": "pink"
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# },
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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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}
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}
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}
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@@ -461,7 +457,7 @@ class Zeus_8_3_2_B_4_2(IStrategy):
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max_profit = self.pairs[pair]['max_profit']
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baisse = 0
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if profit > 0:
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baisse = 100 * abs(max_profit - profit) / max_profit
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baisse = max_profit - profit
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mx = max_profit / 5
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self.pairs[pair]['count_of_buys'] = count_of_buys
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self.pairs[pair]['current_profit'] = profit
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@@ -510,9 +506,12 @@ class Zeus_8_3_2_B_4_2(IStrategy):
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# self.pairs[pair]['force_sell'] = False
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# return 'stoploss_' + pair_name + '_' + str(count_of_buys) + '_' + str(self.pairs[pair]['has_gain'])
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if baisse > 15 and profit > expected_profit:
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factor = 1
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if (self.getShortName(pair) == 'BTC'):
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factor = 0.5
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if baisse > 2 and baisse > factor * self.pairs[pair]['total_amount'] / 100:
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self.pairs[pair]['force_sell'] = False
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self.pairs[pair]['force_buy'] = (self.pairs[pair]['count_of_buys'] - self.pairs[pair]['has_gain'] > 5)
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self.pairs[pair]['force_buy'] = (self.pairs[pair]['count_of_buys'] - self.pairs[pair]['has_gain'] > 3)
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return 'Baisse_' + pair_name + '_' + str(count_of_buys) + '_' + str(self.pairs[pair]['has_gain'])
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#
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# # if 1 <= count_of_buys <= 3:
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@@ -809,12 +808,9 @@ class Zeus_8_3_2_B_4_2(IStrategy):
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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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self.calculeDerivees(informative, 'volatility')
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informative['atr'] = (talib.ATR(informative['high'], informative['low'], informative['close'], timeperiod=14)) / \
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informative['close']
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self.calculeDerivees(informative, 'atr')
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informative['rsi'] = talib.RSI(informative['close']) # , timeperiod=7)
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informative['rsi'] = informative['rsi'].rolling(5).mean()
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self.calculeDerivees(informative, 'rsi', horizon=5)
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informative['sma5'] = talib.SMA(informative, timeperiod=5)
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informative['sma24'] = talib.SMA(informative, timeperiod=24)
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@@ -1156,7 +1152,7 @@ class Zeus_8_3_2_B_4_2(IStrategy):
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# & (dataframe['mid_smooth_deriv1'] >= dataframe['mid_smooth_deriv1'].shift(1))
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# ), ['enter_long', 'enter_tag']] = (1, 'down')
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factor = 1.01
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if pair == "BTC/USDT" or pair == "BTC/USDC":
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if (self.getShortName(pair) == 'BTC'):
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factor = factor / 2
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dataframe.loc[
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(
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@@ -1315,7 +1311,7 @@ class Zeus_8_3_2_B_4_2(IStrategy):
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pct_first = 0
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total_counts = sum(
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pair_data['count_of_buys'] for pair_data in self.pairs.values() if not pair in ('BTC/USDT', 'BTC/USDC'))
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pair_data['count_of_buys'] for pair_data in self.pairs.values() if not self.getShortName(pair) == 'BTC')
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if self.pairs[pair]['first_buy']:
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pct_first = self.getPctFirstBuy(pair, last_candle)
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@@ -1329,7 +1325,7 @@ class Zeus_8_3_2_B_4_2(IStrategy):
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else:
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pct_max = - pct
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if pair in ('BTC/USDT', 'BTC/USDC') or count_of_buys <= 2:
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if (self.getShortName(pair) == 'BTC') or count_of_buys <= 2:
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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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# lim = - (0.012 * (1 + round(count_of_buys / 5)) + 0.001 * (count_of_buys - 1))
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@@ -1545,12 +1541,12 @@ class Zeus_8_3_2_B_4_2(IStrategy):
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last_lost = self.getLastLost(last_candle, pair)
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if (False and hours > 6 and pct_first < -0.05
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and last_candle['sma5_deriv1_1h'] > 0
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and last_candle['rsi'] < 65 and last_candle['rsi_1h'] < 65
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and last_candle['rsi_deriv1'] > 0 and last_candle['rsi_deriv2'] > 0
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and last_candle['mid_smooth_12_deriv1'] > 0
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and last_candle['mid_smooth_12_deriv2'] > 0
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if (hours > 6
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# and last_candle['sma60_deriv1'] > 0
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and last_candle['rsi_1h'] < 65
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and last_candle['rsi_deriv1_1h'] > 0
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and last_candle['mid_smooth_5_deriv1_1d'] > 0
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and last_candle['sma60_deriv1'] > 0
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):
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try:
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stake_amount = self.pairs[pair]['first_amount'] / 2
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@@ -1719,10 +1715,10 @@ class Zeus_8_3_2_B_4_2(IStrategy):
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return out_max - position * (out_max - out_min)
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def expectedProfit(self, pair: str, last_candle: DataFrame):
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lim = 0.008
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lim = 0.01
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pct = 0.002
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if pair == "BTC/USDT" or pair == "BTC/USDC":
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lim = 0.004
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if (self.getShortName(pair) == 'BTC'):
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lim = 0.005
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pct = 0.001
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pct_to_max = lim + pct * self.pairs[pair]['count_of_buys']
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# if self.pairs[pair]['count_of_buys'] > 6:
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@@ -1733,7 +1729,7 @@ class Zeus_8_3_2_B_4_2(IStrategy):
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# if last_candle['close'] < max_60:
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# pct_to_max = 0.25 * (max_60 - last_candle['close']) / max_60
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# pct_to_max = pct_to_max * (2 - pctClose60)
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expected_profit = min(3 * lim, max(lim, pct_to_max)) # 0.004 + 0.002 * self.pairs[pair]['count_of_buys'] #min(0.01, first_max)
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expected_profit = lim * self.pairs[pair]['total_amount'] #min(3 * lim, max(lim, pct_to_max)) # 0.004 + 0.002 * self.pairs[pair]['count_of_buys'] #min(0.01, first_max)
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# print(
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# f"Expected profit price={current_price:.4f} min_max={min_max:.4f} min_14={min_14_days:.4f} max_14={max_14_days:.4f} percent={percent:.4f} expected_profit={expected_profit:.4f}")
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