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Ajout dist max / affichage en cours toutes les 30 lignes

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This commit is contained in:
Jérôme Delacotte
2025-07-16 21:22:18 +02:00
parent 340ada3221
commit f10344fff2
1 changed files with 18 additions and 12 deletions
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30
Zeus_8_3_2_B_4_2.py
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@@ -254,8 +254,7 @@ class Zeus_8_3_2_B_4_2(IStrategy):
# val = self.getProbaHausse144(last_candle) # val = self.getProbaHausse144(last_candle)
# allow_to_buy = True #(not self.stop_all) #& (not self.all_down) # allow_to_buy = True #(not self.stop_all) #& (not self.all_down)
allow_to_buy = not self.pairs[pair][ 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')
'stop'] # and val > self.buy_val.value #not last_candle['tendency'] in ('B-', 'B--') # (rate <= float(limit)) | (entry_tag == 'force_entry')
# if allow_to_buy: # if allow_to_buy:
# poly_func, x_future, y_future, count = self.polynomial_forecast( # poly_func, x_future, y_future, count = self.polynomial_forecast(
@@ -455,9 +454,16 @@ class Zeus_8_3_2_B_4_2(IStrategy):
if self.columns_logged % 30 == 0: if self.columns_logged % 30 == 0:
self.printLog( self.printLog(
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':>4}| {'Stake':>5} |" 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':>4}| {'Stake':>5} |"
f"Tdc|{'val':>6}| sma5 |smooth |" f"Tdc|{'val':>6}| sma5 |smooth |Distmax"
) )
self.printLineLog() self.printLineLog()
df = pd.DataFrame.from_dict(self.pairs, orient='index')
colonnes_a_exclure = ['last_candle', 'last_trade', 'last_palier_index', 'stop',
'trade_info', 'last_date', 'expected_profit', 'last_count_of_buys', 'base_stake_amount', 'stop_buy']
df_filtered = df[df['count_of_buys'] > 0].drop(columns=colonnes_a_exclure)
# df_filtered = df_filtered["first_buy", "last_max", "max_touch", "last_sell","last_buy", 'count_of_buys', 'current_profit']
print(df_filtered)
self.columns_logged += 1 self.columns_logged += 1
date = str(date)[:16] if date else "-" date = str(date)[:16] if date else "-"
@@ -492,6 +498,8 @@ class Zeus_8_3_2_B_4_2(IStrategy):
total_counts = str(buys) + '/' + str(sum(pair_data['count_of_buys'] for pair_data in self.pairs.values())) total_counts = str(buys) + '/' + str(sum(pair_data['count_of_buys'] for pair_data in self.pairs.values()))
dist_max = round(100 * (last_candle['max12_1d'] - last_candle['min12_1d']) / last_candle['min12_1d'], 0)
# if trade_type is not None: # if trade_type is not None:
# if np.isnan(last_candle['rsi_1d']): # if np.isnan(last_candle['rsi_1d']):
# string = ' ' # string = ' '
@@ -514,7 +522,7 @@ class Zeus_8_3_2_B_4_2(IStrategy):
# 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}|" # 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}|"
# 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}|" # 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}|"
f"{round(val, 1) or '-' :>6}|" f"{round(val, 1) or '-' :>6}|"
f"{round(last_candle['sma5_deriv1_1h'], 4) or '-' :>7}|{round(last_candle['mid_smooth_1h_deriv1'], 4) or '-' :>7}|" f"{round(last_candle['sma5_deriv1_1h'], 4) or '-' :>7}|{round(last_candle['mid_smooth_1h_deriv1'], 4) or '-' :>7}|{dist_max:>7}"
) )
def printLineLog(self): def printLineLog(self):
@@ -523,7 +531,7 @@ class Zeus_8_3_2_B_4_2(IStrategy):
f"+{'-' * 18}+{'-' * 12}+{'-' * 5}+{'-' * 20}+{'-' * 9}+{'-' * 8}+{'-' * 10}+{'-' * 8}+{'-' * 13}+{'-' * 14}+{'-' * 9}+{'-' * 4}+{'-' * 7}+" f"+{'-' * 18}+{'-' * 12}+{'-' * 5}+{'-' * 20}+{'-' * 9}+{'-' * 8}+{'-' * 10}+{'-' * 8}+{'-' * 13}+{'-' * 14}+{'-' * 9}+{'-' * 4}+{'-' * 7}+"
f"{'-' * 3}" f"{'-' * 3}"
# "+{'-' * 3}+{'-' * 3} # "+{'-' * 3}+{'-' * 3}
f"+{'-' * 6}+{'-' * 7}+{'-' * 7}+" f"+{'-' * 6}+{'-' * 7}+{'-' * 7}+{'-' * 7}+"
) )
def printLog(self, str): def printLog(self, str):
@@ -595,9 +603,7 @@ class Zeus_8_3_2_B_4_2(IStrategy):
(dataframe["close"] - dataframe["bb_lowerband"]) / (dataframe["close"] - dataframe["bb_lowerband"]) /
(dataframe["bb_upperband"] - dataframe["bb_lowerband"]) (dataframe["bb_upperband"] - dataframe["bb_lowerband"])
) )
dataframe["bb_width"] = ( dataframe["bb_width"] = ((dataframe["bb_upperband"] - dataframe["bb_lowerband"]) / dataframe["bb_upperband"])
(dataframe["bb_upperband"] - dataframe["bb_lowerband"]) / dataframe["bb_upperband"]
)
# Normalization # Normalization
# dataframe = self.calculateRegression(dataframe, column='mid_smooth', window=24, degree=4, future_offset=12) # dataframe = self.calculateRegression(dataframe, column='mid_smooth', window=24, degree=4, future_offset=12)
@@ -859,10 +865,10 @@ class Zeus_8_3_2_B_4_2(IStrategy):
# self.paliers = self.get_dca_stakes() # self.paliers = self.get_dca_stakes()
if self.dp.runmode.value in ('backtest'): # if self.dp.runmode.value in ('backtest'):
today = datetime.now().strftime("%Y-%m-%d-%H:%M:%S") # today = datetime.now().strftime("%Y-%m-%d-%H:%M:%S")
dataframe.to_feather(f"user_data/data/binance/{today}-{metadata['pair'].replace('/', '_')}_df.feather") # dataframe.to_feather(f"user_data/data/binance/{today}-{metadata['pair'].replace('/', '_')}_df.feather")
dataframe.to_csv(f"user_data/data/binance/{today}-{metadata['pair'].replace('/', '_')}_df.csv") # dataframe.to_csv(f"user_data/data/binance/{today}-{metadata['pair'].replace('/', '_')}_df.csv")
# #
# df = dataframe # df = dataframe
# #