achat sur mid_smooth_deriv1 > 0

Zeus_8_3_2_B_4_2.py

@@ -325,6 +325,8 @@ class Zeus_8_3_2_B_4_2(IStrategy):
         self.pairs[pair]['current_profit'] = current_profit
         self.pairs[pair]['max_profit'] = max(self.pairs[pair]['max_profit'], current_profit)
 
+        if (last_candle['mid_smooth_deriv1'] >= 0):
+            return None
         if (last_candle['tendency'] in ('H++', 'H+')) and (last_candle['rsi'] < 80):
             return None
 
@@ -432,7 +434,7 @@ class Zeus_8_3_2_B_4_2(IStrategy):
             f"| {round(self.pairs[pair]['last_max'], 0) or '-':>7} |{buys or '-':>4}|{stake or '-':>7}"
             f"|{round(last_candle['sma5_diff_sum_1h'], 2) or '-':>6}|{round(last_candle['sma5_diff_sum_1d'], 2) or '-':>6}"
             f"|{last_candle['tendency'] or '-':>3}|{last_candle['tendency_1h'] or '-':>3}|{last_candle['tendency_1d'] or '-':>3}"
-            f"|{round(last_candle['mid_smooth_deriv1']) or '-':>3}|{round(last_candle['mid_smooth_deriv1_1h']) or '-':>5}|{round(last_candle['mid_smooth_deriv1_1d']) or '-' :>5}"
+            f"|{round(last_candle['mid_smooth_deriv1'],3) or '-':>6}|{round(last_candle['mid_smooth_deriv1_1h'],3) or '-':>6}|{round(last_candle['mid_smooth_deriv1_1d'],3) or '-' :>6}"
             # f"|{round(last_candle['mid_smooth_deriv2']) or '-' :>3 }|{round(last_candle['mid_smooth_deriv2_1h']) or '-':>5}|{round(last_candle['mid_smooth_deriv2_1d']) or '-':>5}"
         )
 
@@ -512,10 +514,6 @@ class Zeus_8_3_2_B_4_2(IStrategy):
         dataframe['rsi_diff'] = dataframe['rsi'].diff()
         dataframe['rsi_diff_2'] = dataframe['rsi_diff'].diff()
 
-        dataframe['rsi60'] = talib.RSI(dataframe['close'], timeperiod=60)
-        dataframe['rsi60_diff'] = dataframe['rsi60'].rolling(60).mean().diff()
-        dataframe['rsi60_diff2'] = dataframe['rsi60_diff'].rolling(60).mean().diff()
-
         # Bollinger Bands
         bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
         dataframe['bb_lowerband'] = bollinger['lower']
@@ -563,6 +561,8 @@ class Zeus_8_3_2_B_4_2(IStrategy):
 
         informative['sma5'] = talib.SMA(informative, timeperiod=5)
         informative['sma5_diff'] = 100 * informative['sma5'].diff() / informative['sma5']
+        informative['sma24'] = talib.SMA(informative, timeperiod=24)
+        informative['sma24_diff'] = 100 * informative['sma24'].diff() / informative['sma24']
         informative['sma5_pct'] = 100 * (informative['sma5'] - informative['sma5'].shift(1)) / informative['sma5']
         informative['sma5_diff_sum'] = (informative['sma5_pct'].rolling(5).sum()) / 5
         informative['sma5_diff2_sum'] = informative['sma5_diff_sum'].diff()
@@ -694,22 +694,15 @@ class Zeus_8_3_2_B_4_2(IStrategy):
 
         dataframe.loc[
             (
-                # (dataframe['rsi_1h'] < 70)
+                # (dataframe['down_count'].shift(1) < - 1)
-                # & (dataframe['rsi_diff_1h'] > -5)
+                # & (dataframe['down_count'] == 0)
-                # (dataframe["bb_width"] > 0.01)
+                (dataframe['mid_smooth_deriv1'] > 0)
-                (dataframe['down_count'].shift(1) < - 1)
-                & (dataframe['down_count'] == 0)
-                # & (dataframe['mid_smooth_deriv1'] <= 0.01)
-                & (dataframe['mid_smooth_deriv1'] >= -0.01)
-                # & (dataframe['tendency'] != "B--")
-                # & (dataframe['tendency'] != "B-")
             ), ['enter_long', 'enter_tag']] = (1, 'down')
 
         dataframe.loc[
             (
                 (dataframe['low'] < dataframe['min200'])
                 & (dataframe['min50'] == dataframe['min50'].shift(3))
-                #
                 & (dataframe['tendency'] != "B-")
             ), ['enter_long', 'enter_tag']] = (1, 'low')
 
@@ -740,23 +733,36 @@ class Zeus_8_3_2_B_4_2(IStrategy):
 
         dataframe, _ = self.dp.get_analyzed_dataframe(trade.pair, self.timeframe)
         last_candle = dataframe.iloc[-1].squeeze()
+        last_candle_1 = dataframe.iloc[-2].squeeze()
+        last_candle_2 = dataframe.iloc[-3].squeeze()
         last_candle_3 = dataframe.iloc[-4].squeeze()
+        last_candle_previous_1h = dataframe.iloc[-13].squeeze()
         # prépare les données
         current_time = current_time.astimezone(timezone.utc)
         open_date = trade.open_date.astimezone(timezone.utc)
         dispo = round(self.wallets.get_available_stake_amount())
+        days_since_first_buy = (current_time - trade.open_date_utc).days
         hours = (current_time - trade.date_last_filled_utc).total_seconds() / 3600.0
 
         if (len(dataframe) < 1):
             print("skip dataframe")
-
             return None
         pair = trade.pair
+        if self.dp.runmode.value in ('dry_run'):
             if pair not in ('BTC/USDT', 'BTC/USDC', 'XRP/USDT', 'XRP/USDC', 'ETH/USDT', 'ETH/USDC'):
                 print(f"skip pair {pair}")
                 return None
+        else:
+            if pair not in ('BTC/USDT', 'BTC/USDC'):
+                print(f"skip pair {pair}")
+                return None
+
         count_of_buys = trade.nr_of_successful_entries
 
+        # if (days_since_first_buy >= 5 and count_of_buys >= 4 and last_candle['sma5_pct_1d'] < 0):
+        #     # print(f"waiting day increase pair {pair}")
+        #     return None
+
         # if 'buy' in last_candle:
         #     condition = (last_candle['buy'] == 1)
         # else:
@@ -794,7 +800,7 @@ class Zeus_8_3_2_B_4_2(IStrategy):
             and (last_candle['sma5_diff_sum_1d'] > -1 or count_of_buys <= 9) \
             and (pct_max < lim):
             try:
-
+                #  and (last_candle['mid_smooth_deriv1_1h'] > 0 and last_candle['mid_smooth_deriv1_1h'] >= last_candle_previous_1h['mid_smooth_deriv1_1h'])
                 # and (last_candle['mid_smooth_deriv1_1d'] > -1000 or last_candle['mid_smooth_deriv1_1h'] > 200) \
                 #     and (last_candle['mid_smooth_deriv1_1d'] > -1500) \
                 #     and not (last_candle['mid_smooth_deriv1_1d'] < - 500 and last_candle['mid_smooth_deriv1_1h'] < 0) \
@@ -823,6 +829,42 @@ class Zeus_8_3_2_B_4_2(IStrategy):
             except Exception as exception:
                 print(exception)
                 return None
+
+        # if (count_of_buys < limit_buy and pct_max > pct and current_profit > 0.004) \
+        #     and (last_candle['rsi_diff_1h'] >= -5) \
+        #     and (last_candle['tendency'] in ('P', 'H++', 'DH', 'H+')) \
+        #     and (last_candle['mid_smooth_deriv1'] > 0.015):
+        #     try:
+        #
+        #         # and (last_candle['mid_smooth_deriv1_1d'] > -1000 or last_candle['mid_smooth_deriv1_1h'] > 200) \
+        #         #     and (last_candle['mid_smooth_deriv1_1d'] > -1500) \
+        #         #     and not (last_candle['mid_smooth_deriv1_1d'] < - 500 and last_candle['mid_smooth_deriv1_1h'] < 0) \
+        #
+        #         max_amount = self.config.get('stake_amount', 100) * 2.5
+        #         stake_amount = min(min(max_amount, self.wallets.get_available_stake_amount()),
+        #                            self.adjust_stake_amount(pair, last_candle) - 10 * pct_first / pct)  # min(200, self.adjust_stake_amount(pair, last_candle) * self.fibo[count_of_buys])
+        #
+        #         trade_type = last_candle['enter_tag'] if last_candle['enter_long'] == 1 else 'pct48'
+        #         self.log_trade(
+        #             last_candle=last_candle,
+        #             date=current_time,
+        #             action="Gain +",
+        #             dispo=dispo,
+        #             pair=trade.pair,
+        #             rate=current_rate,
+        #             trade_type=trade_type,
+        #             profit=round(current_profit, 4),  # round(current_profit * trade.stake_amount, 2),
+        #             buys=trade.nr_of_successful_entries + 1,
+        #             stake=round(stake_amount, 2)
+        #         )
+        #         self.pairs[trade.pair]['last_buy'] = current_rate
+        #         self.pairs[trade.pair]['max_touch'] = last_candle['close']
+        #         self.pairs[trade.pair]['last_candle'] = last_candle
+        #         return stake_amount
+        #     except Exception as exception:
+        #         print(exception)
+        #         return None
+
         return None
 
     def adjust_stake_amount(self, pair: str, last_candle: DataFrame):