first commit

2025-03-06 11:01:43 +01:00
commit 7c239227d8
558 changed files with 108235 additions and 0 deletions
--- a/SMAOG.py
+++ b/SMAOG.py
@@ -0,0 +1,109 @@
+from datetime import datetime, timedelta
+import talib.abstract as ta
+from freqtrade.persistence import Trade
+from freqtrade.strategy import CategoricalParameter
+from freqtrade.strategy import DecimalParameter, IntParameter
+from freqtrade.strategy.interface import IStrategy
+from pandas import DataFrame
+
+# og        @tirail
+# author    @Jooopieeert#0239
+
+ma_types = {
+    'SMA': ta.SMA,
+    'EMA': ta.EMA,
+}
+class SMAOG(IStrategy):
+    INTERFACE_VERSION = 2
+    buy_params = {
+        "base_nb_candles_buy": 26,
+        "buy_trigger": "SMA",
+        "low_offset": 0.968,
+        "pair_is_bad_0_threshold": 0.555,
+        "pair_is_bad_1_threshold": 0.172,
+        "pair_is_bad_2_threshold": 0.198,
+    }
+    sell_params = {
+        "base_nb_candles_sell": 28,
+        "high_offset": 0.985,
+        "sell_trigger": "EMA",
+    }
+    base_nb_candles_buy = IntParameter(16, 45, default=buy_params['base_nb_candles_buy'], space='buy', optimize=False, load=True)
+    base_nb_candles_sell = IntParameter(16, 45, default=sell_params['base_nb_candles_sell'], space='sell', optimize=False, load=True)
+    low_offset = DecimalParameter(0.8, 0.99, default=buy_params['low_offset'], space='buy', optimize=False, load=True)
+    high_offset = DecimalParameter(0.8, 1.1, default=sell_params['high_offset'], space='sell', optimize=False, load=True)
+    buy_trigger = CategoricalParameter(ma_types.keys(), default=buy_params['buy_trigger'], space='buy', optimize=False, load=True)
+    sell_trigger = CategoricalParameter(ma_types.keys(), default=sell_params['sell_trigger'], space='sell', optimize=False, load=True)
+    pair_is_bad_0_threshold = DecimalParameter(0.0, 0.600, default=0.220, space='buy', optimize=True, load=True)
+    pair_is_bad_1_threshold = DecimalParameter(0.0, 0.350, default=0.090, space='buy', optimize=True, load=True)
+    pair_is_bad_2_threshold = DecimalParameter(0.0, 0.200, default=0.060, space='buy', optimize=True, load=True)
+
+    timeframe = '5m'
+    stoploss = -0.23
+    minimal_roi = {"0": 10,}
+    trailing_stop = True
+    trailing_only_offset_is_reached = True
+    trailing_stop_positive = 0.01
+    trailing_stop_positive_offset = 0.02
+    use_sell_signal = True
+    sell_profit_only = False
+    ignore_roi_if_buy_signal = False
+    process_only_new_candles = True
+    startup_candle_count = 400
+
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        if not self.config['runmode'].value == 'hyperopt':
+            dataframe['ma_offset_buy'] = ma_types[self.buy_trigger.value](dataframe, int(self.base_nb_candles_buy.value)) * self.low_offset.value
+            dataframe['ma_offset_sell'] = ma_types[self.sell_trigger.value](dataframe, int(self.base_nb_candles_sell.value)) * self.high_offset.value
+            dataframe['pair_is_bad'] = (
+                    (((dataframe['open'].rolling(144).min() - dataframe['close']) / dataframe[
+                        'close']) >= self.pair_is_bad_0_threshold.value) |
+                    (((dataframe['open'].rolling(12).min() - dataframe['close']) / dataframe[
+                        'close']) >= self.pair_is_bad_1_threshold.value) |
+                    (((dataframe['open'].rolling(2).min() - dataframe['close']) / dataframe[
+                        'close']) >= self.pair_is_bad_2_threshold.value)).astype('int')
+            dataframe['ema_50'] = ta.EMA(dataframe, timeperiod=50)
+            dataframe['ema_200'] = ta.EMA(dataframe, timeperiod=200)
+            dataframe['rsi_exit'] = ta.RSI(dataframe, timeperiod=2)
+        return dataframe
+
+    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        if self.config['runmode'].value == 'hyperopt':
+            dataframe['ma_offset_buy'] = ma_types[self.buy_trigger.value](dataframe, int(self.base_nb_candles_buy.value)) * self.low_offset.value
+            dataframe['pair_is_bad'] = (
+                    (((dataframe['open'].rolling(144).min() - dataframe['close']) / dataframe[
+                        'close']) >= self.pair_is_bad_0_threshold.value) |
+                    (((dataframe['open'].rolling(12).min() - dataframe['close']) / dataframe[
+                        'close']) >= self.pair_is_bad_1_threshold.value) |
+                    (((dataframe['open'].rolling(2).min() - dataframe['close']) / dataframe[
+                        'close']) >= self.pair_is_bad_2_threshold.value)).astype('int')
+            dataframe['ema_50'] = ta.EMA(dataframe, timeperiod=50)
+            dataframe['ema_200'] = ta.EMA(dataframe, timeperiod=200)
+            dataframe['rsi_exit'] = ta.RSI(dataframe, timeperiod=2)
+
+        dataframe.loc[
+            (
+                    (dataframe['ema_50'] > dataframe['ema_200']) &
+                    (dataframe['close'] > dataframe['ema_200']) &
+                    (dataframe['pair_is_bad'] < 1) &
+                    (dataframe['close'] < dataframe['ma_offset_buy']) &
+                    (dataframe['volume'] > 0)
+            ),
+            'buy'] = 1
+        return dataframe
+
+    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        if self.config['runmode'].value == 'hyperopt':
+            dataframe['ma_offset_sell'] = ta.EMA(dataframe, int(self.base_nb_candles_sell.value)) * self.high_offset.value
+        dataframe.loc[
+            (
+                    (dataframe['close'] > dataframe['ma_offset_sell']) &
+                    (
+                        (dataframe['open'] < dataframe['open'].shift(1)) |
+                        (dataframe['rsi_exit'] < 50) |
+                        (dataframe['rsi_exit'] < dataframe['rsi_exit'].shift(1))
+                    ) &
+                    (dataframe['volume'] > 0)
+            ),
+            'sell'] = 1
+        return dataframe