Calcul 20240101-20250514 1050.151 247.414$ => 4,25 max 11mises
This commit is contained in:
Jérôme Delacotte
@@ -224,7 +224,7 @@ class Zeus_8_3_2_B_4_2(IStrategy):
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# Extraction de la matrice numérique
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# Extraction de la matrice numérique
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sma5_deriv1_1h_mid_smooth_3_deriv1__numeric_matrice = sma5_deriv1_1h_mid_smooth_3_deriv1_matrice_df.reindex(index=ordered_labels, columns=ordered_labels).values
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sma5_deriv1_1h_mid_smooth_3_deriv1__numeric_matrice = sma5_deriv1_1h_mid_smooth_3_deriv1_matrice_df.reindex(index=ordered_labels, columns=ordered_labels).values
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paliers = {}
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# paliers = {}
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# =========================================================================
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# =========================================================================
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# Parameters hyperopt
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# Parameters hyperopt
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@@ -234,7 +234,6 @@ class Zeus_8_3_2_B_4_2(IStrategy):
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buy_horizon_predict_1h = IntParameter(1, 6, default=2, space='buy')
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buy_horizon_predict_1h = IntParameter(1, 6, default=2, space='buy')
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# buy_level_predict_1h = IntParameter(2, 5, default=4, space='buy')
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# buy_level_predict_1h = IntParameter(2, 5, default=4, space='buy')
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def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float, time_in_force: str,
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def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float, time_in_force: str,
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current_time: datetime, entry_tag: Optional[str], **kwargs) -> bool:
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current_time: datetime, entry_tag: Optional[str], **kwargs) -> bool:
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@@ -411,14 +410,14 @@ class Zeus_8_3_2_B_4_2(IStrategy):
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# Juste au cas où (devrait jamais arriver)
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# Juste au cas où (devrait jamais arriver)
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return factors[-1]
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return factors[-1]
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def interpolate_factor(self, pct: float, start_pct: float = 5, end_pct: float = 30,
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# def interpolate_factor(self, pct: float, start_pct: float = 5, end_pct: float = 30,
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start_factor: float = 1.0, end_factor: float = 2.0) -> float:
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# start_factor: float = 1.0, end_factor: float = 2.0) -> float:
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if pct <= start_pct:
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# if pct <= start_pct:
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return start_factor
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# return start_factor
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if pct >= end_pct:
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# if pct >= end_pct:
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return end_factor
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# return end_factor
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# interpolation linéaire
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# # interpolation linéaire
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return start_factor + (pct - start_pct) * (end_factor - start_factor) / (end_pct - start_pct)
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# return start_factor + (pct - start_pct) * (end_factor - start_factor) / (end_pct - start_pct)
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def log_trade(self, action, pair, date, trade_type=None, rate=None, dispo=None, profit=None, buys=None, stake=None,
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def log_trade(self, action, pair, date, trade_type=None, rate=None, dispo=None, profit=None, buys=None, stake=None,
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last_candle=None):
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last_candle=None):
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@@ -916,22 +915,50 @@ class Zeus_8_3_2_B_4_2(IStrategy):
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hours_since_first_buy = (current_time - trade.open_date_utc).seconds / 3600.0
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hours_since_first_buy = (current_time - trade.open_date_utc).seconds / 3600.0
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days_since_first_buy = (current_time - trade.open_date_utc).days
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days_since_first_buy = (current_time - trade.open_date_utc).days
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hours = (current_time - trade.date_last_filled_utc).total_seconds() / 3600.0
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hours = (current_time - trade.date_last_filled_utc).total_seconds() / 3600.0
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count_of_buys = trade.nr_of_successful_entries
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current_time_utc = current_time.astimezone(timezone.utc)
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open_date = trade.open_date.astimezone(timezone.utc)
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days_since_open = (current_time_utc - open_date).days
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pair = trade.pair
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pct_first = 0
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if self.pairs[pair]['first_buy']:
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pct_first = round((last_candle['close'] - self.pairs[pair]['first_buy']) / self.pairs[pair]['first_buy'], 3)
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pct = 0.012
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if count_of_buys == 1:
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pct_max = current_profit
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else:
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if self.pairs[trade.pair]['last_buy']:
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pct_max = round((last_candle['close'] - self.pairs[trade.pair]['last_buy']) / self.pairs[trade.pair]['last_buy'], 4)
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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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lim = - pct - (count_of_buys * 0.001)
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# lim = - (0.012 * (1 + round(count_of_buys / 5)) + 0.001 * (count_of_buys - 1))
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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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pct = 0.05
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lim = - pct - (count_of_buys * 0.001)
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if (len(dataframe) < 1):
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if (len(dataframe) < 1):
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print("skip dataframe")
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print("skip dataframe")
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return None
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return None
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pair = trade.pair
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if self.dp.runmode.value in ('dry_run'):
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if self.dp.runmode.value in ('dry_run'):
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if pair not in ('BTC/USDT', 'BTC/USDC', 'XRP/USDT', 'XRP/USDC', 'ETH/USDT', 'ETH/USDC'):
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if pair not in ('BTC/USDT', 'BTC/USDC', 'XRP/USDT', 'XRP/USDC', 'ETH/USDT', 'ETH/USDC'):
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# print(f"skip pair {pair}")
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# print(f"skip pair {pair}")
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return None
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return None
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else:
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# else:
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if pair not in ('BTC/USDT', 'BTC/USDC'):
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# if pair not in ('BTC/USDT', 'BTC/USDC'):
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# print(f"skip pair {pair}")
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# btc_count = self.pairs['BTC/USDT']['count_of_buys'] + self.pairs['BTC/USDC']['count_of_buys']
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return None
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# # print(f"skip pair {pair}")
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# if (btc_count > 4 or count_of_buys + 1 > btc_count) and pct_max < 0.20:
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# return None
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# # déclenche un achat si bougie rouge importante
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# # déclenche un achat si bougie rouge importante
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# stake_amount = self.config.get('stake_amount', 100)
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# stake_amount = self.config.get('stake_amount')
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# stake_amount = min(stake_amount, self.wallets.get_available_stake_amount())
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# stake_amount = min(stake_amount, self.wallets.get_available_stake_amount())
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# current_time = current_time.astimezone(timezone.utc)
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# current_time = current_time.astimezone(timezone.utc)
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# seconds_since_filled = (current_time - trade.date_last_filled_utc).total_seconds()
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# seconds_since_filled = (current_time - trade.date_last_filled_utc).total_seconds()
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@@ -995,24 +1022,6 @@ class Zeus_8_3_2_B_4_2(IStrategy):
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#
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#
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# return None
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# return None
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count_of_buys = trade.nr_of_successful_entries
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current_time_utc = current_time.astimezone(timezone.utc)
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open_date = trade.open_date.astimezone(timezone.utc)
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days_since_open = (current_time_utc - open_date).days
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pct_first = 0
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if self.pairs[pair]['first_buy']:
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pct_first = round((last_candle['close'] - self.pairs[pair]['first_buy']) / self.pairs[pair]['first_buy'], 3)
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pct = 0.012
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if count_of_buys == 1:
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pct_max = current_profit
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else:
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if self.pairs[trade.pair]['last_buy']:
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pct_max = round((last_candle['close'] - self.pairs[trade.pair]['last_buy']) / self.pairs[trade.pair]['last_buy'], 4)
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else:
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pct_max = - pct
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lim = - pct - (count_of_buys * 0.001)
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# index = self.get_palier_index(pct_first)
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# index = self.get_palier_index(pct_first)
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# if index is None:
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# if index is None:
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# return None
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# return None
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@@ -1044,7 +1053,8 @@ class Zeus_8_3_2_B_4_2(IStrategy):
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# > - 0.03 ==>Avg. stake amount 253.535 USDT │ Total trade volume 145312.936 USDT 284 │ 1.19 │ 1014.898 │ 50.74| 1 day, 17:54:00 │ 283 0 1 99.6 │ 0.684 USDT 0.02% │
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# > - 0.03 ==>Avg. stake amount 253.535 USDT │ Total trade volume 145312.936 USDT 284 │ 1.19 │ 1014.898 │ 50.74| 1 day, 17:54:00 │ 283 0 1 99.6 │ 0.684 USDT 0.02% │
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# > - 0.015 ==>Avg. stake amount 249.107 USDT │ Total trade volume 138186.861 USDT 275 │ 1.20 │ 901.976 │ 45.1 │ 1 day, 19:17:00 │ 274 0 1 99.6 │ 0.684 USDT 0.02%
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# > - 0.015 ==>Avg. stake amount 249.107 USDT │ Total trade volume 138186.861 USDT 275 │ 1.20 │ 901.976 │ 45.1 │ 1 day, 19:17:00 │ 274 0 1 99.6 │ 0.684 USDT 0.02%
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condition = True #last_candle['mid_smooth_1h_deriv1'] > - 0.05 #(last_candle['mid_smooth_3_deriv1'] > self.buy_mid_smooth_3_deriv1.value) and (last_candle['mid_smooth_24_deriv1'] > self.buy_mid_smooth_24_deriv1.value)
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condition = (last_candle['sma5_deriv1_1h'] > 0 or count_of_buys <= 5)
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#last_candle['mid_smooth_1h_deriv1'] > - 0.05 #(last_candle['mid_smooth_3_deriv1'] > self.buy_mid_smooth_3_deriv1.value) and (last_candle['mid_smooth_24_deriv1'] > self.buy_mid_smooth_24_deriv1.value)
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# (last_candle['enter_long'] == 1 & (count_of_buys < 3)) \
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# (last_candle['enter_long'] == 1 & (count_of_buys < 3)) \
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# or ((before_last_candle['mid_re_smooth_3_deriv1'] <= 0) & (last_candle['mid_re_smooth_3_deriv1'] >= 0) & (3 <= count_of_buys < 6)) \
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# or ((before_last_candle['mid_re_smooth_3_deriv1'] <= 0) & (last_candle['mid_re_smooth_3_deriv1'] >= 0) & (3 <= count_of_buys < 6)) \
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# or ((before_last_candle['mid_smooth_1h_deriv1'] <= 0) & (last_candle['mid_smooth_1h_deriv1'] >= 0) & (6 <= count_of_buys))
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# or ((before_last_candle['mid_smooth_1h_deriv1'] <= 0) & (last_candle['mid_smooth_1h_deriv1'] >= 0) & (6 <= count_of_buys))
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@@ -1064,7 +1074,7 @@ class Zeus_8_3_2_B_4_2(IStrategy):
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if count < 3:
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if count < 3:
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return None
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return None
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max_amount = self.config.get('stake_amount', 100) * 2.5
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max_amount = self.config.get('stake_amount') * 2.5
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# stake_amount = min(stake_amount, self.wallets.get_available_stake_amount())
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# stake_amount = min(stake_amount, self.wallets.get_available_stake_amount())
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stake_amount = min(min(max_amount, self.wallets.get_available_stake_amount()),
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stake_amount = min(min(max_amount, self.wallets.get_available_stake_amount()),
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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])
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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])
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@@ -1151,7 +1161,7 @@ class Zeus_8_3_2_B_4_2(IStrategy):
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def adjust_stake_amount(self, pair: str, last_candle: DataFrame):
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def adjust_stake_amount(self, pair: str, last_candle: DataFrame):
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# Calculer le minimum des 14 derniers jours
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# Calculer le minimum des 14 derniers jours
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base_stake_amount = self.config.get('stake_amount', 100) # Montant de base configuré
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base_stake_amount = self.config.get('stake_amount') # Montant de base configuré
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first_price = self.pairs[pair]['first_buy']
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first_price = self.pairs[pair]['first_buy']
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if (first_price == 0):
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if (first_price == 0):
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@@ -1280,56 +1290,56 @@ class Zeus_8_3_2_B_4_2(IStrategy):
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return self.labels[i]
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return self.labels[i]
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return self.labels[-1] # cas limite pour la borne max
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return self.labels[-1] # cas limite pour la borne max
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def interpolated_val_from_bins(self, row_pos, col_pos):
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# def interpolated_val_from_bins(self, row_pos, col_pos):
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"""
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# """
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Renvoie une approximation interpolée (bilinéaire) d'une valeur dans la matrice
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# Renvoie une approximation interpolée (bilinéaire) d'une valeur dans la matrice
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à partir de positions flottantes dans l'index (ligne) et les colonnes.
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# à partir de positions flottantes dans l'index (ligne) et les colonnes.
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#
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Parameters:
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# Parameters:
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matrix_df (pd.DataFrame): Matrice des probabilités (index/colonnes = labels).
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# matrix_df (pd.DataFrame): Matrice des probabilités (index/colonnes = labels).
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row_pos (float): Position réelle de la ligne (0 = B5, 10 = H5).
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# row_pos (float): Position réelle de la ligne (0 = B5, 10 = H5).
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col_pos (float): Position réelle de la colonne (0 = B5, 10 = H5).
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# col_pos (float): Position réelle de la colonne (0 = B5, 10 = H5).
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#
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Returns:
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# Returns:
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float: Valeur interpolée, ou NaN si en dehors des bornes.
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# float: Valeur interpolée, ou NaN si en dehors des bornes.
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"""
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# """
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#
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# Labels ordonnés
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# # Labels ordonnés
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n = len(self.labels)
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# n = len(self.labels)
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#
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# Vérification des limites
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# # Vérification des limites
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if not (0 <= row_pos <= n - 1) or not (0 <= col_pos <= n - 1):
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# if not (0 <= row_pos <= n - 1) or not (0 <= col_pos <= n - 1):
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return np.nan
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# return np.nan
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#
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# Conversion des labels -> matrice
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# # Conversion des labels -> matrice
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matrix = self.smooth_smadiff_matrice_df.reindex(index=self.labels, columns=self.labels).values
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# matrix = self.smooth_smadiff_matrice_df.reindex(index=self.labels, columns=self.labels).values
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#
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# Coordonnées entières (inférieures)
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# # Coordonnées entières (inférieures)
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i = int(np.floor(row_pos))
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# i = int(np.floor(row_pos))
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j = int(np.floor(col_pos))
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# j = int(np.floor(col_pos))
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#
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# Coefficients pour interpolation
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# # Coefficients pour interpolation
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dx = row_pos - i
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# dx = row_pos - i
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dy = col_pos - j
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# dy = col_pos - j
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#
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# Précautions sur les bords
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# # Précautions sur les bords
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if i >= n - 1: i = n - 2; dx = 1.0
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# if i >= n - 1: i = n - 2; dx = 1.0
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if j >= n - 1: j = n - 2; dy = 1.0
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# if j >= n - 1: j = n - 2; dy = 1.0
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#
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# Récupération des 4 valeurs voisines
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# # Récupération des 4 valeurs voisines
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v00 = matrix[i][j]
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# v00 = matrix[i][j]
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v10 = matrix[i + 1][j]
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# v10 = matrix[i + 1][j]
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v01 = matrix[i][j + 1]
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# v01 = matrix[i][j + 1]
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v11 = matrix[i + 1][j + 1]
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# v11 = matrix[i + 1][j + 1]
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#
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# Interpolation bilinéaire
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# # Interpolation bilinéaire
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interpolated = (
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# interpolated = (
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(1 - dx) * (1 - dy) * v00 +
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# (1 - dx) * (1 - dy) * v00 +
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dx * (1 - dy) * v10 +
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# dx * (1 - dy) * v10 +
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(1 - dx) * dy * v01 +
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# (1 - dx) * dy * v01 +
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dx * dy * v11
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# dx * dy * v11
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)
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# )
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return interpolated
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# return interpolated
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def approx_val_from_bins(self, matrice, numeric_matrice, row_label, col_label):
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def approx_val_from_bins(self, matrice, numeric_matrice, row_label, col_label):
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"""
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"""
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@@ -1415,7 +1425,7 @@ class Zeus_8_3_2_B_4_2(IStrategy):
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def smooth_and_derivatives(self, series, window=25, polyorder=3):
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def smooth_and_derivatives(self, series, window=25, polyorder=3):
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series = series.copy()
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series = series.copy()
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if series.isna().sum() > 0:
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if series.isna().sum() > 0:
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series = series.fillna(method='ffill').fillna(method='bfill') # Si tu veux éviter toute NaN
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series = series.ffill().bfill() # Si tu veux éviter toute NaN
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smooth = self.causal_savgol(series, window=window, polyorder=polyorder)
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smooth = self.causal_savgol(series, window=window, polyorder=polyorder)
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deriv1 = np.diff(smooth, prepend=smooth[0])
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deriv1 = np.diff(smooth, prepend=smooth[0])
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@@ -1487,85 +1497,85 @@ class Zeus_8_3_2_B_4_2(IStrategy):
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return paliers
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return paliers
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def get_dca_stakes(self,
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# def get_dca_stakes(self,
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max_drawdown: float = 0.65,
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# max_drawdown: float = 0.65,
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base_stake: float = 100.0,
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# base_stake: float = 100.0,
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first_steps: list[float] = [0.01, 0.01, 0.015, 0.015],
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# first_steps: list[float] = [0.01, 0.01, 0.015, 0.015],
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growth: float = 1.2,
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# growth: float = 1.2,
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stake_growth: float = 1.15
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# stake_growth: float = 1.15
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) -> list[tuple[float, float]]:
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# ) -> list[tuple[float, float]]:
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"""
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# """
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Génère les paliers de drawdown et leurs stakes associés.
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# Génère les paliers de drawdown et leurs stakes associés.
|
||||||
|
|
||||||
:param max_drawdown: Maximum drawdown (ex: 0.65 pour -65%)
|
|
||||||
:param base_stake: Mise initiale
|
|
||||||
:param first_steps: Paliers de départ (plus resserrés)
|
|
||||||
:param growth: Multiplicateur d'espacement des paliers
|
|
||||||
:param stake_growth: Croissance multiplicative des mises
|
|
||||||
:return: Liste de tuples (palier_pct, stake)
|
|
||||||
[(-0.01, 100.0), (-0.02, 115.0), (-0.035, 132.25), (-0.05, 152.09), (-0.068, 174.9),
|
|
||||||
(-0.0896, 201.14), (-0.1155, 231.31), (-0.1466, 266.0), (-0.1839, 305.9), (-0.2287, 351.79),
|
|
||||||
(-0.2825, 404.56), (-0.347, 465.24), (-0.4244, 535.03), (-0.5173, 615.28), (-0.6287, 707.57)]
|
|
||||||
"""
|
|
||||||
paliers = [
|
|
||||||
(-0.01, 100.0), (-0.02, 115.0), (-0.035, 130), (-0.05, 150), (-0.07, 150),
|
|
||||||
(-0.10, 150), (-0.15, 150), (-0.20, 150), (-0.25, 150),
|
|
||||||
(-0.30, 200), (-0.40, 200),
|
|
||||||
(-0.50, 300), (-0.60, 400), (-0.70, 500), (-0.80, 1000)
|
|
||||||
]
|
|
||||||
|
|
||||||
# cumulated = 0.0
|
|
||||||
# stake = base_stake
|
|
||||||
#
|
#
|
||||||
# # Étapes initiales
|
# :param max_drawdown: Maximum drawdown (ex: 0.65 pour -65%)
|
||||||
# for step in first_steps:
|
# :param base_stake: Mise initiale
|
||||||
# cumulated += step
|
# :param first_steps: Paliers de départ (plus resserrés)
|
||||||
# paliers.append((round(-cumulated, 4), round(stake, 2)))
|
# :param growth: Multiplicateur d'espacement des paliers
|
||||||
# stake *= stake_growth
|
# :param stake_growth: Croissance multiplicative des mises
|
||||||
|
# :return: Liste de tuples (palier_pct, stake)
|
||||||
|
# [(-0.01, 100.0), (-0.02, 115.0), (-0.035, 132.25), (-0.05, 152.09), (-0.068, 174.9),
|
||||||
|
# (-0.0896, 201.14), (-0.1155, 231.31), (-0.1466, 266.0), (-0.1839, 305.9), (-0.2287, 351.79),
|
||||||
|
# (-0.2825, 404.56), (-0.347, 465.24), (-0.4244, 535.03), (-0.5173, 615.28), (-0.6287, 707.57)]
|
||||||
|
# """
|
||||||
|
# paliers = [
|
||||||
|
# (-0.01, 100.0), (-0.02, 115.0), (-0.035, 130), (-0.05, 150), (-0.07, 150),
|
||||||
|
# (-0.10, 150), (-0.15, 150), (-0.20, 150), (-0.25, 150),
|
||||||
|
# (-0.30, 200), (-0.40, 200),
|
||||||
|
# (-0.50, 300), (-0.60, 400), (-0.70, 500), (-0.80, 1000)
|
||||||
|
# ]
|
||||||
#
|
#
|
||||||
# # Étapes suivantes
|
# # cumulated = 0.0
|
||||||
# step = first_steps[-1]
|
# # stake = base_stake
|
||||||
# while cumulated < max_drawdown:
|
# #
|
||||||
# step *= growth
|
# # # Étapes initiales
|
||||||
# cumulated += step
|
# # for step in first_steps:
|
||||||
# if cumulated >= max_drawdown:
|
# # cumulated += step
|
||||||
|
# # paliers.append((round(-cumulated, 4), round(stake, 2)))
|
||||||
|
# # stake *= stake_growth
|
||||||
|
# #
|
||||||
|
# # # Étapes suivantes
|
||||||
|
# # step = first_steps[-1]
|
||||||
|
# # while cumulated < max_drawdown:
|
||||||
|
# # step *= growth
|
||||||
|
# # cumulated += step
|
||||||
|
# # if cumulated >= max_drawdown:
|
||||||
|
# # break
|
||||||
|
# # paliers.append((round(-cumulated, 4), round(stake, 2)))
|
||||||
|
# # stake *= stake_growth
|
||||||
|
#
|
||||||
|
# return paliers
|
||||||
|
|
||||||
|
# def get_active_stake(self, pct: float) -> float:
|
||||||
|
# """
|
||||||
|
# Renvoie la mise correspondant au drawdown `pct`.
|
||||||
|
#
|
||||||
|
# :param pct: drawdown courant (négatif, ex: -0.043)
|
||||||
|
# :param paliers: liste de tuples (drawdown, stake)
|
||||||
|
# :return: stake correspondant
|
||||||
|
# """
|
||||||
|
# abs_pct = abs(pct)
|
||||||
|
# stake = self.paliers[0][1] # stake par défaut
|
||||||
|
#
|
||||||
|
# for palier, s in self.paliers:
|
||||||
|
# if abs_pct >= abs(palier):
|
||||||
|
# stake = s
|
||||||
|
# else:
|
||||||
# break
|
# break
|
||||||
# paliers.append((round(-cumulated, 4), round(stake, 2)))
|
#
|
||||||
# stake *= stake_growth
|
# return stake
|
||||||
|
|
||||||
return paliers
|
# def get_palier_index(self, pct):
|
||||||
|
# """
|
||||||
def get_active_stake(self, pct: float) -> float:
|
# Retourne l'index du palier franchi pour un pourcentage de baisse donné (pct).
|
||||||
"""
|
# On cherche le palier le plus profond atteint (dernier franchi).
|
||||||
Renvoie la mise correspondant au drawdown `pct`.
|
# """
|
||||||
|
# for i in reversed(range(len(self.paliers))):
|
||||||
:param pct: drawdown courant (négatif, ex: -0.043)
|
# seuil, _ = self.paliers[i]
|
||||||
:param paliers: liste de tuples (drawdown, stake)
|
# #print(f"pct={pct} seuil={seuil}")
|
||||||
:return: stake correspondant
|
# if pct <= seuil:
|
||||||
"""
|
# # print(pct)
|
||||||
abs_pct = abs(pct)
|
# return i
|
||||||
stake = self.paliers[0][1] # stake par défaut
|
# return None # Aucun palier atteint
|
||||||
|
|
||||||
for palier, s in self.paliers:
|
|
||||||
if abs_pct >= abs(palier):
|
|
||||||
stake = s
|
|
||||||
else:
|
|
||||||
break
|
|
||||||
|
|
||||||
return stake
|
|
||||||
|
|
||||||
def get_palier_index(self, pct):
|
|
||||||
"""
|
|
||||||
Retourne l'index du palier franchi pour un pourcentage de baisse donné (pct).
|
|
||||||
On cherche le palier le plus profond atteint (dernier franchi).
|
|
||||||
"""
|
|
||||||
for i in reversed(range(len(self.paliers))):
|
|
||||||
seuil, _ = self.paliers[i]
|
|
||||||
#print(f"pct={pct} seuil={seuil}")
|
|
||||||
if pct <= seuil:
|
|
||||||
# print(pct)
|
|
||||||
return i
|
|
||||||
return None # Aucun palier atteint
|
|
||||||
|
|
||||||
# def poly_regression_predictions(self, series: pd.Series, window: int = 20, degree: int = 2, n_future: int = 3) -> pd.DataFrame:
|
# def poly_regression_predictions(self, series: pd.Series, window: int = 20, degree: int = 2, n_future: int = 3) -> pd.DataFrame:
|
||||||
# """
|
# """
|
||||||
@@ -1634,34 +1644,34 @@ class Zeus_8_3_2_B_4_2(IStrategy):
|
|||||||
|
|
||||||
return poly, x_future, y_future, count
|
return poly, x_future, y_future, count
|
||||||
|
|
||||||
def calculateStats2(self, df, index, target):
|
# def calculateStats2(self, df, index, target):
|
||||||
# Nombre de tranches (modifiable)
|
# # Nombre de tranches (modifiable)
|
||||||
n_bins_indice = 11
|
# n_bins_indice = 11
|
||||||
n_bins_valeur = 11
|
# n_bins_valeur = 11
|
||||||
|
#
|
||||||
# Tranches dynamiques
|
# # Tranches dynamiques
|
||||||
# df['indice_tranche'] = pd.qcut(df[f"{index}"], q=n_bins_indice, duplicates='drop')
|
# # df['indice_tranche'] = pd.qcut(df[f"{index}"], q=n_bins_indice, duplicates='drop')
|
||||||
# df['valeur_tranche'] = pd.qcut(df[f"{target}"], q=n_bins_valeur, duplicates='drop')
|
# # df['valeur_tranche'] = pd.qcut(df[f"{target}"], q=n_bins_valeur, duplicates='drop')
|
||||||
|
#
|
||||||
df[f"{index}_bin"], bins_1h = pd.qcut(df[f"{index}"], q=n_bins_indice, labels=self.labels, retbins=True,
|
# df[f"{index}_bin"], bins_1h = pd.qcut(df[f"{index}"], q=n_bins_indice, labels=self.labels, retbins=True,
|
||||||
duplicates='drop')
|
# duplicates='drop')
|
||||||
df[f"{target}_bin"], bins_1d = pd.qcut(df[f"{target}"], q=n_bins_valeur, labels=self.labels, retbins=True,
|
# df[f"{target}_bin"], bins_1d = pd.qcut(df[f"{target}"], q=n_bins_valeur, labels=self.labels, retbins=True,
|
||||||
duplicates='drop')
|
# duplicates='drop')
|
||||||
# Affichage formaté pour code Python
|
# # Affichage formaté pour code Python
|
||||||
print(f"Bornes des quantiles pour {index} : [{', '.join([f'{b:.4f}' for b in bins_1h])}]")
|
# print(f"Bornes des quantiles pour {index} : [{', '.join([f'{b:.4f}' for b in bins_1h])}]")
|
||||||
print(f"Bornes des quantiles pour {target} : [{', '.join([f'{b:.4f}' for b in bins_1d])}]")
|
# print(f"Bornes des quantiles pour {target} : [{', '.join([f'{b:.4f}' for b in bins_1d])}]")
|
||||||
|
#
|
||||||
# Tableau croisé (compte)
|
# # Tableau croisé (compte)
|
||||||
tableau = pd.crosstab(df[f"{index}_bin"], df[f"{target}_bin"])
|
# tableau = pd.crosstab(df[f"{index}_bin"], df[f"{target}_bin"])
|
||||||
|
#
|
||||||
# Facultatif : en pourcentages
|
# # Facultatif : en pourcentages
|
||||||
tableau_pct = tableau.div(tableau.sum(axis=1), axis=0) * 100
|
# tableau_pct = tableau.div(tableau.sum(axis=1), axis=0) * 100
|
||||||
|
#
|
||||||
# Affichage
|
# # Affichage
|
||||||
print("Répartition brute :")
|
# print("Répartition brute :")
|
||||||
print(tableau)
|
# print(tableau)
|
||||||
print("\nRépartition en % par ligne :")
|
# print("\nRépartition en % par ligne :")
|
||||||
print(tableau_pct.round(2))
|
# print(tableau_pct.round(2))
|
||||||
|
|
||||||
def calculateStats(self, df, index, target):
|
def calculateStats(self, df, index, target):
|
||||||
# Nombre de tranches (modifiable)
|
# Nombre de tranches (modifiable)
|
||||||
|
|||||||
Reference in New Issue
Block a user