Frictrade

FrictradeLearning.py

@@ -456,7 +456,7 @@ class FrictradeLearning(IStrategy):
         # Add all ta features
         pair = metadata['pair']
         short_pair = self.getShortName(pair)
-        self.path = f"user_data/plots/{short_pair}/" + ("valide/" if not self.dp.runmode.value in ('backtest') else '')
+        self.path = f"user_data/strategies/plots/{short_pair}/" + ("valide/" if not self.dp.runmode.value in ('backtest') else '')
 
         heikinashi = qtpylib.heikinashi(dataframe)
         dataframe['haopen'] = heikinashi['open']
@@ -490,6 +490,16 @@ class FrictradeLearning(IStrategy):
                 & (dataframe["sma5_sqrt"] > 5)
         )
 
+        dataframe["sma12_sqrt"] = (
+                np.sqrt(np.abs(dataframe["sma12"] - dataframe["sma12"].shift(1)))
+                + np.sqrt(np.abs(dataframe["sma12"].shift(3) - dataframe["sma12"].shift(1)))
+        )
+        dataframe["sma12_inv"] = (
+                (dataframe["sma12"].shift(2) >= dataframe["sma12"].shift(1))
+                & (dataframe["sma12"].shift(1) <= dataframe["sma12"])
+                & (dataframe["sma12_sqrt"] > 5)
+        )
+
         dataframe["percent"] = dataframe['mid'].pct_change()
         dataframe["percent3"] = dataframe['mid'].pct_change(3).rolling(3).mean()
         dataframe["percent12"] = dataframe['mid'].pct_change(12).rolling(12).mean()
@@ -723,9 +733,9 @@ class FrictradeLearning(IStrategy):
             self.trainModel(dataframe, metadata)
 
         short_pair = self.getShortName(pair)
-        path=f"user_data/plots/{short_pair}/"
+        # path=f"user_data/strategies/plots/{short_pair}/"
 
-        self.model = joblib.load(f"{path}/{short_pair}_rf_model.pkl")
+        self.model = joblib.load(f"{self.path}/{short_pair}_rf_model.pkl")
 
         # Préparer les features pour la prédiction
         features = dataframe[self.model_indicators].fillna(0)
@@ -824,7 +834,12 @@ class FrictradeLearning(IStrategy):
 
         # Buy = prediction > threshold
         dataframe["buy"] = 0
-        dataframe.loc[dataframe["ml_prob"] > 0.5, ['enter_long', 'enter_tag']] = (1, f"future")
+        dataframe.loc[
+            (dataframe["ml_prob"].shift(3) < 0.2)
+            & (dataframe['low'].shift(3) < dataframe['min180'].shift(3))
+            & (dataframe['min180'].shift(3) == dataframe['min180']),
+            ['enter_long', 'enter_tag']
+        ] = (1, f"future")
         dataframe['test'] = np.where(dataframe['enter_long'] == 1, dataframe['close'] * 1.003, np.nan)
 
         return dataframe
@@ -1060,9 +1075,10 @@ class FrictradeLearning(IStrategy):
 
         # FIN ########################## ALGO ATH
 
-        condition = last_candle['hapercent'] > 0 and last_candle['sma24_deriv1'] > 0 \
-                    and last_candle['close'] < self.pairs[pair]['first_buy'] \
-                    and last_candle['ml_prob'] > 0.65
+        condition = last_candle['percent'] > 0 and last_candle['sma24_deriv1'] > 0 \
+                    and last_candle['close'] < self.pairs[pair]['first_buy']
+                    # and last_candle['ml_prob'] > 0.65
+
         limit_buy = 40
         # or (last_candle['close'] <= last_candle['min180'] and hours > 3)
         if (decline >= dca_threshold) and condition:
@@ -1206,12 +1222,12 @@ class FrictradeLearning(IStrategy):
         position = (mid - min_) / (max_ - min_)
         zone = int(position * 3)  # 0 à 2
 
-        if zone == 0:
-            current_trailing_stop_positive = self.trailing_stop_positive
-            current_trailing_stop_positive_offset = self.trailing_stop_positive_offset * 2
-        if minutes > 1440:
-            current_trailing_only_offset_is_reached = False
-            current_trailing_stop_positive_offset = self.trailing_stop_positive_offset
+        # if zone == 0:
+        #     current_trailing_stop_positive = self.trailing_stop_positive
+        #     current_trailing_stop_positive_offset = self.trailing_stop_positive_offset * 2
+        # if minutes > 1440:
+        #     current_trailing_only_offset_is_reached = False
+        #     current_trailing_stop_positive_offset = self.trailing_stop_positive_offset
         # if zone == 1:
 
         # ----- 5) Calcul du trailing stop dynamique -----
@@ -1236,7 +1252,7 @@ class FrictradeLearning(IStrategy):
                 stake=0
             )
 
-        if last_candle['ml_prob'] > 0.65:
+        if last_candle['sma12'] > last_candle['sma24']:
             return None
         # if last_candle['sma24_deriv1'] > 0 : #and minutes < 180 and baisse < 30: # and last_candle['sma5_deriv1'] > -0.15:
         #     if (minutes < 180):
@@ -1371,7 +1387,7 @@ class FrictradeLearning(IStrategy):
         pd.set_option('display.max_rows', None)
         pd.set_option('display.max_columns', None)
         pd.set_option("display.width", 200)
-        path=f"user_data/plots/{pair}/"
+        path=self.path #f"user_data/plots/{pair}/"
         os.makedirs(path, exist_ok=True)
 
         # # Étape 1 : sélectionner numériques
@@ -1452,7 +1468,7 @@ class FrictradeLearning(IStrategy):
         plt.yticks(rotation=0)
 
         # --- Sauvegarde ---
-        output_path = f"{path}/Matrice_de_correlation_temperature.png"
+        output_path = f"{self.path}/Matrice_de_correlation_temperature.png"
         plt.savefig(output_path, bbox_inches="tight", dpi=150)
         plt.close(fig)
 
@@ -1594,7 +1610,7 @@ class FrictradeLearning(IStrategy):
         )
 
         # Sauvegarde du graphique sur disque
-        output_path = f"{path}/shap_waterfall.png"
+        output_path = f"{self.path}/shap_waterfall.png"
         plt.savefig(output_path, dpi=200, bbox_inches='tight')
         plt.close()  # ferme la figure proprement
 
@@ -1625,13 +1641,13 @@ class FrictradeLearning(IStrategy):
 
         # Graphs
         fig = plot_optimization_history(study)
-        fig.write_html(f"{path}/optimization_history.html")
+        fig.write_html(f"{self.path}/optimization_history.html")
         fig = plot_param_importances(study)
-        fig.write_html(f"{path}/param_importances.html")
+        fig.write_html(f"{self.path}/param_importances.html")
         fig = plot_slice(study)
-        fig.write_html(f"{path}/slice.html")
+        fig.write_html(f"{self.path}/slice.html")
         fig = plot_parallel_coordinate(study)
-        fig.write_html(f"{path}/parallel_coordinates.html")
+        fig.write_html(f"{self.path}/parallel_coordinates.html")
 
 
         # 2️⃣ Sélection des features AVANT calibration
@@ -1677,14 +1693,14 @@ class FrictradeLearning(IStrategy):
         feat_imp.plot(kind='bar', figsize=(12, 6))
         plt.title("Feature importances")
         # plt.show()
-        plt.savefig(f"{path}/Feature importances.png", bbox_inches='tight')
+        plt.savefig(f"{self.path}/Feature importances.png", bbox_inches='tight')
 
         result = permutation_importance(self.train_model, X_valid, y_valid, scoring='f1', n_repeats=10, random_state=42)
         perm_imp = pd.Series(result.importances_mean, index=X_valid.columns).sort_values(ascending=False)
         perm_imp.plot(kind='bar', figsize=(12, 6))
         plt.title("Permutation feature importance")
         # plt.show()
-        plt.savefig(f"{path}/Permutation feature importance.png", bbox_inches='tight')
+        plt.savefig(f"{self.path}/Permutation feature importance.png", bbox_inches='tight')
 
         # Shap
         explainer = shap.TreeExplainer(self.train_model)
@@ -1695,7 +1711,7 @@ class FrictradeLearning(IStrategy):
 
         # Force plot pour une observation
         force_plot = shap.force_plot(explainer.expected_value, shap_values[0, :], X_valid.iloc[0, :])
-        shap.save_html(f"{path}/shap_force_plot.html", force_plot)
+        shap.save_html(f"{self.path}/shap_force_plot.html", force_plot)
 
         fig, ax = plt.subplots(figsize=(24, 48))
         PartialDependenceDisplay.from_estimator(
@@ -1705,7 +1721,7 @@ class FrictradeLearning(IStrategy):
             kind="average",
             ax=ax
         )
-        fig.savefig(f"{path}/PartialDependenceDisplay.png", bbox_inches="tight")
+        fig.savefig(f"{self.path}/PartialDependenceDisplay.png", bbox_inches="tight")
         plt.close(fig)
 
         best_f1 = 0
@@ -1726,7 +1742,7 @@ class FrictradeLearning(IStrategy):
         print(f"Accuracy: {acc:.3f}")
 
         # 7️⃣ Sauvegarde du modèle
-        joblib.dump(self.train_model, f"{path}/{pair}_rf_model.pkl")
+        joblib.dump(self.train_model, f"{self.path}/{pair}_rf_model.pkl")
         print(f"✅ Modèle sauvegardé sous {pair}_rf_model.pkl")
 
 

Genetic.json

@@ -0,0 +1,25 @@
+{
+  "strategy_name": "Genetic",
+  "params": {
+    "roi": {
+      "0": 0.21029,
+      "11": 0.05876,
+      "57": 0.02191,
+      "281": 0
+    },
+    "stoploss": {
+      "stoploss": -1.0
+    },
+    "max_open_trades": {
+      "max_open_trades": 80
+    },
+    "trailing": {
+      "trailing_stop": true,
+      "trailing_stop_positive": 0.028,
+      "trailing_stop_positive_offset": 0.105,
+      "trailing_only_offset_is_reached": true
+    }
+  },
+  "ft_stratparam_v": 1,
+  "export_time": "2025-12-04 23:58:24.045587+00:00"
+}

Genetic.py

@@ -160,7 +160,7 @@ class Genetic(IStrategy):
     }
 
     # Stoploss:
-    stoploss = -0.07693
+    stoploss = -1
 
     # Optimal ticker interval for the strategy
     ticker_interval = '2h'
@@ -359,4 +359,5 @@ class Genetic(IStrategy):
 
     def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
 
-        return dataframe
+        return dataframe
+

GodStra.py

@@ -11,8 +11,13 @@
 
 # --- Do not remove these libs ---
 from freqtrade.strategy.interface import IStrategy
+from datetime import timedelta, datetime
+from freqtrade.persistence import Trade
+from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter, stoploss_from_open,
+                                IntParameter, IStrategy, merge_informative_pair, informative, stoploss_from_absolute)
 from pandas import DataFrame
 # --------------------------------
+from datetime import timezone, timedelta
 
 # Add your lib to import here
 # import talib.abstract as ta
@@ -24,6 +29,38 @@ import numpy as np
 
 
 class GodStra(IStrategy):
+    position_adjustment_enable = True
+
+    columns_logged = False
+    pairs = {
+        pair: {
+            "first_buy": 0,
+            "last_buy": 0.0,
+            "last_min": 999999999999999.5,
+            "last_max": 0,
+            "trade_info": {},
+            "max_touch": 0.0,
+            "last_sell": 0.0,
+            'count_of_buys': 0,
+            'current_profit': 0,
+            'expected_profit': 0,
+            'previous_profit': 0,
+            "last_candle": {},
+            "last_count_of_buys": 0,
+            'base_stake_amount': 0,
+            'stop_buy': False,
+            'last_date': 0,
+            'stop': False,
+            'max_profit': 0,
+            'first_amount': 0,
+            'total_amount': 0,
+            'has_gain': 0,
+            'force_sell': False,
+            'force_buy': False
+        }
+        for pair in ["BTC/USDC", "ETH/USDC", "DOGE/USDC", "XRP/USDC", "SOL/USDC",
+                     "BTC/USDT", "ETH/USDT", "DOGE/USDT", "XRP/USDT", "SOL/USDT"]
+    }
     # 5/66:      9 trades. 8/0/1 Wins/Draws/Losses. Avg profit  21.83%. Median profit  35.52%. Total profit  1060.11476586 USDT ( 196.50Σ%). Avg duration 3440.0 min. Objective: -7.06960
     # +--------+---------+----------+------------------+--------------+-------------------------------+----------------+-------------+
     # |   Best |   Epoch |   Trades |    Win Draw Loss |   Avg profit |                        Profit |   Avg duration |   Objective |
@@ -67,7 +104,7 @@ class GodStra(IStrategy):
     trailing_stop_positive_offset = 0.2684
     trailing_only_offset_is_reached = True
     # Buy hypers
-    timeframe = '12h'
+    timeframe = '1m'
     print('Add {\n\t"method": "AgeFilter",\n\t"min_days_listed": 30\n},\n to your pairlists in config (Under StaticPairList)')
 
     def dna_size(self, dct: dict):
@@ -169,3 +206,150 @@ class GodStra(IStrategy):
             'sell'] = 1
 
         return dataframe
+
+    def adjust_trade_position(self, trade: Trade, current_time: datetime,
+                              current_rate: float, current_profit: float, min_stake: float,
+                              max_stake: float, **kwargs):
+        # ne rien faire si ordre deja en cours
+        print('ici')
+        if trade.has_open_orders:
+            # self.printLog("skip open orders")
+            return None
+        if (self.wallets.get_available_stake_amount() < 10):  # or trade.stake_amount >= max_stake:
+            return 0
+
+        dataframe, _ = self.dp.get_analyzed_dataframe(trade.pair, self.timeframe)
+        last_candle = dataframe.iloc[-1].squeeze()
+        before_last_candle = dataframe.iloc[-2].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())
+        hours_since_first_buy = (current_time - trade.open_date_utc).seconds / 3600.0
+        days_since_first_buy = (current_time - trade.open_date_utc).days
+        hours = (current_time - trade.date_last_filled_utc).total_seconds() / 3600.0
+        count_of_buys = trade.nr_of_successful_entries
+        current_time_utc = current_time.astimezone(timezone.utc)
+        open_date = trade.open_date.astimezone(timezone.utc)
+        days_since_open = (current_time_utc - open_date).days
+        pair = trade.pair
+        profit = trade.calc_profit(current_rate) #round(current_profit * trade.stake_amount, 1)
+        last_lost = self.getLastLost(last_candle, pair)
+        pct_first = 0
+
+        total_counts = sum(
+            pair_data['count_of_buys'] for pair_data in self.pairs.values() if not self.getShortName(pair) == 'BTC')
+
+        if self.pairs[pair]['first_buy']:
+            pct_first = self.getPctFirstBuy(pair, last_candle)
+
+        lim = 0.3
+        if (len(dataframe) < 1):
+            # self.printLog("skip dataframe")
+            return None
+
+        # Dernier prix d'achat réel (pas le prix moyen)
+        last_fill_price = self.pairs[trade.pair]['last_buy'] #trade.open_rate  # remplacé juste après ↓
+
+        # if len(trade.orders) > 0:
+        #     # On cherche le dernier BUY exécuté
+        #     buy_orders = [o for o in trade.orders if o.is_buy and o.status == "closed"]
+        #     if buy_orders:
+        #         last_fill_price = buy_orders[-1].price
+
+        # baisse relative
+        dca_threshold = 0.0025 * count_of_buys
+        decline = (last_fill_price - current_rate) / last_fill_price
+        increase = - decline
+
+        # if decline >= self.dca_threshold:
+        #     # Exemple : on achète 50% du montant du dernier trade
+        #     last_amount = buy_orders[-1].amount if buy_orders else 0
+        #     stake_amount = last_amount * current_rate * 0.5
+        #     return stake_amount
+
+        condition = last_candle['percent'] > 0 #and last_candle['sma24_deriv1'] > 0
+        limit_buy = 40
+        # or (last_candle['close'] <= last_candle['min180'] and hours > 3)
+        if (decline >= dca_threshold) and condition:
+            print('decline')
+            try:
+                if self.pairs[pair]['has_gain'] and profit > 0:
+                    self.pairs[pair]['force_sell'] = True
+                    self.pairs[pair]['previous_profit'] = profit
+                    return None
+
+                max_amount = self.config.get('stake_amount') * 2.5
+                stake_amount = min(min(max_amount, self.wallets.get_available_stake_amount()),
+                                   self.adjust_stake_amount(pair, last_candle))
+                # print(f"profit={profit} previous={self.pairs[pair]['previous_profit']} count_of_buys={trade.nr_of_successful_entries}")
+                if stake_amount > 0:
+                    self.pairs[pair]['previous_profit'] = profit
+                    trade_type = "Loss " + (last_candle['enter_tag'] if last_candle['enter_long'] == 1 else '')
+                    self.pairs[trade.pair]['count_of_buys'] += 1
+                    self.pairs[pair]['total_amount'] += stake_amount
+                    # self.log_trade(
+                    #     last_candle=last_candle,
+                    #     date=current_time,
+                    #     action="🟧 Loss -",
+                    #     dispo=dispo,
+                    #     pair=trade.pair,
+                    #     rate=current_rate,
+                    #     trade_type=trade_type,
+                    #     profit=round(profit, 1),
+                    #     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
+
+                    # df = pd.DataFrame.from_dict(self.pairs, orient='index')
+                    # colonnes_a_exclure = ['last_candle', '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']
+                    #
+                    # self.printLog(df_filtered)
+
+                    return stake_amount
+                return None
+            except Exception as exception:
+                self.printLog(exception)
+                return None
+
+        if current_profit > dca_threshold and (increase >= dca_threshold and self.wallets.get_available_stake_amount() > 0):
+            print('increase')
+
+            try:
+                self.pairs[pair]['previous_profit'] = profit
+                stake_amount = max(20, min(self.wallets.get_available_stake_amount(), self.adjust_stake_amount(pair, last_candle)))
+                if stake_amount > 0:
+                    self.pairs[pair]['has_gain'] += 1
+
+                    trade_type = 'Gain +' + (last_candle['enter_tag'] if last_candle['enter_long'] == 1 else '')
+                    self.pairs[trade.pair]['count_of_buys'] += 1
+                    self.pairs[pair]['total_amount'] += stake_amount
+                    # self.log_trade(
+                    #     last_candle=last_candle,
+                    #     date=current_time,
+                    #     action="🟡 Gain +",
+                    #     dispo=dispo,
+                    #     pair=trade.pair,
+                    #     rate=current_rate,
+                    #     trade_type='Gain',
+                    #     profit=round(profit, 1),
+                    #     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
+                return None
+            except Exception as exception:
+                self.printLog(exception)
+                return None
+
+        return None

GodStraHo.py

@@ -14,7 +14,10 @@ import pandas as pd  # noqa
 from pandas import DataFrame
 from skopt.space import Categorical, Dimension, Integer, Real  # noqa
 
-from freqtrade.optimize.hyperopt_interface import IHyperOpt
+from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter, stoploss_from_open,
+                                IntParameter, IStrategy, merge_informative_pair, informative, stoploss_from_absolute)
+
+# from freqtrade.optimize.hyperopt_interface import IHyperOpt
 
 # --------------------------------
 # Add your lib to import here
@@ -52,7 +55,7 @@ GodGenes = ["open", "high", "low", "close", "volume", "volume_adi", "volume_obv"
             "others_dr", "others_dlr", "others_cr"]
 
 
-class GodStraHo(IHyperOpt):
+class GodStraHo(IStrategy):
 
     @staticmethod
     def indicator_space() -> List[Dimension]:

GodStraJD.json

@@ -0,0 +1,53 @@
+{
+  "strategy_name": "GodStraJD",
+  "params": {
+    "roi": {
+      "0": 0.598,
+      "644": 0.166,
+      "3269": 0.115,
+      "7289": 0
+    },
+    "max_open_trades": {
+      "max_open_trades": 80
+    },
+    "buy": {
+      "buy_crossed_indicator0": "WMA-100",
+      "buy_crossed_indicator1": "KAMA-110",
+      "buy_crossed_indicator2": "CDLKICKING-55",
+      "buy_indicator0": "MACD-0-50",
+      "buy_indicator1": "SAREXT-15",
+      "buy_indicator2": "DEMA-12",
+      "buy_operator0": "<R",
+      "buy_operator1": "C",
+      "buy_operator2": "D",
+      "buy_real_num0": 0.7,
+      "buy_real_num1": 0.7,
+      "buy_real_num2": 0.7
+    },
+    "sell": {
+      "sell_crossed_indicator0": "CDLDARKCLOUDCOVER-15",
+      "sell_crossed_indicator1": "CDLHANGINGMAN-6",
+      "sell_crossed_indicator2": "CDLMATHOLD-55",
+      "sell_indicator0": "MEDPRICE-15",
+      "sell_indicator1": "T3-100",
+      "sell_indicator2": "CDLDOJISTAR-12",
+      "sell_operator0": "=R",
+      "sell_operator1": "DT",
+      "sell_operator2": "CA",
+      "sell_real_num0": 0.7,
+      "sell_real_num1": 0.7,
+      "sell_real_num2": 0.6
+    },
+    "stoploss": {
+      "stoploss": -0.122
+    },
+    "trailing": {
+      "trailing_stop": true,
+      "trailing_stop_positive": 0.058,
+      "trailing_stop_positive_offset": 0.097,
+      "trailing_only_offset_is_reached": true
+    }
+  },
+  "ft_stratparam_v": 1,
+  "export_time": "2025-12-04 23:29:46.366351+00:00"
+}

GodStraJD.py

@@ -470,7 +470,7 @@ class GodStraJD(IStrategy):
     # Stoploss:
     stoploss = -0.128
     # Buy hypers
-    timeframe = '4h'
+    timeframe = '1m'
 
     # Trailing stoploss
     trailing_stop = True

GodStraJD3_1.py

@@ -474,7 +474,7 @@ class GodStraJD3_1(IStrategy):
     # Stoploss:
     stoploss = -1
     # Buy hypers
-    timeframe = '4h'
+    timeframe = '1m'
 
     # Trailing stoploss
     trailing_stop = True

GodStraJD3_4.py

@@ -668,8 +668,7 @@ class GodStraJD3_4(IStrategy):
     #     # Use default stake amount.
     #     return proposed_stake
 
-    def custom_sell(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float,
-                    current_profit: float, **kwargs):
+    def custom_exit(self, pair, trade, current_time, current_rate, current_profit, **kwargs):
         dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
         last_candle = dataframe.iloc[-1].squeeze()
         previous_last_candle = dataframe.iloc[-2].squeeze()
@@ -971,8 +970,6 @@ class GodStraJD3_4(IStrategy):
         if conditions:
             dataframe.loc[
                 (reduce(lambda x, y: x & y, conditions)
-                 # & (dataframe['percent_4h'] > 0)
-                 & (dataframe['percent3_4h'] <= 0)
                  ),
                 'buy']=1
         # print(len(dataframe.keys()))

HammerReversalStrategy.py

@@ -86,7 +86,7 @@ class HammerReversalStrategy(IStrategy):
     }
 
     stoploss = -1
-    timeframe = '1h'
+    timeframe = '1m'
     position_adjustment_enable = True
     columns_logged = False
     max_entry_position_adjustment = 20