Updated

user_data/strategies/test_strategy.py

@@ -1,6 +1,9 @@
 
 # --- Do not remove these libs ---
 from freqtrade.strategy.interface import IStrategy
+from typing import Dict, List
+from hyperopt import hp
+from functools import reduce
 from pandas import DataFrame
 # --------------------------------
 
@@ -10,12 +13,40 @@ import freqtrade.vendor.qtpylib.indicators as qtpylib
 import numpy # noqa
 
 
-# Update this variable if you change the class name
+# Make a backup of default_strategy then move this file over your default strategy in freqtrade/strategy and to run:
-class_name = 'TestStrategy'
+# 'python3.6 freqtrade/main.py -c config.json hyperopt -s all --realistic-simulation -i 5'
+# Make sure to take note of the if statements at the bottom of the file, and the pattern of > < and >= <= and the triggers configuration.
+# Then, take note of the results: There is one trigger in the results, map the trigger as an example:
+# 'Trigger: 7'
+# The 7th trigger in this file's if statement such as:
+# 'sar_reversal': (qtpylib.crossed_above(
+#     dataframe['close'], dataframe['sar']
+#
+# Drop the trigger into 'def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame:' into your default_strategy backup as:
+# dataframe['close'], dataframe['sar'] &
+# Then for your other values such as: 'Mfi-Value: 15.00'
+# The if statement looks like: conditions.append(dataframe['mfi'] < params['mfi']['value']
+# Drop this in as:
+# (dataframe['mfi'] < 15.00) &
+# The last condition in 'def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame:' will have no &
 
 
-# This class is a sample. Feel free to customize it.
+### As an example the sell/buy trend looks like this:
-class TestStrategy(IStrategy):
+#        dataframe.loc[
+#            (
+#                (dataframe['adx'] > 70) &
+#                (dataframe['tema'] < dataframe['tema'].shift(1))
+#            ),
+#            'sell'] = 1
+#
+
+# Once done, move your backup strategy back over this one and backtest.
+
+
+class_name = 'DefaultStrategy'
+
+
+class DefaultStrategy(IStrategy):
     """
     This is a test strategy to inspire you.
     More information in https://github.com/gcarq/freqtrade/blob/develop/docs/bot-optimization.md
@@ -62,7 +93,7 @@ class TestStrategy(IStrategy):
         # ADX
         dataframe['adx'] = ta.ADX(dataframe)
 
-        """
+
         # Awesome oscillator
         dataframe['ao'] = qtpylib.awesome_oscillator(dataframe)
 
@@ -114,18 +145,28 @@ class TestStrategy(IStrategy):
         stoch_rsi = ta.STOCHRSI(dataframe)
         dataframe['fastd_rsi'] = stoch_rsi['fastd']
         dataframe['fastk_rsi'] = stoch_rsi['fastk']
-        """
+
 
         # Overlap Studies
         # ------------------------------------
 
+
+        # Previous Bollinger bands
+        # Because ta.BBANDS implementation is broken with small numbers, it actually
+        # returns middle band for all the three bands. Switch to qtpylib.bollinger_bands
+        # and use middle band instead.
+        # Is broken
+        """
+        dataframe['blower'] = ta.BBANDS(dataframe, nbdevup=2, nbdevdn=2)['lowerband']
+        """
+
         # Bollinger bands
         bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
         dataframe['bb_lowerband'] = bollinger['lower']
         dataframe['bb_middleband'] = bollinger['mid']
         dataframe['bb_upperband'] = bollinger['upper']
 
-        """
+
         # EMA - Exponential Moving Average
         dataframe['ema3'] = ta.EMA(dataframe, timeperiod=3)
         dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5)
@@ -138,7 +179,6 @@ class TestStrategy(IStrategy):
 
         # SMA - Simple Moving Average
         dataframe['sma'] = ta.SMA(dataframe, timeperiod=40)
-        """
 
         # TEMA - Triple Exponential Moving Average
         dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
@@ -152,7 +192,7 @@ class TestStrategy(IStrategy):
 
         # Pattern Recognition - Bullish candlestick patterns
         # ------------------------------------
-        """
+
         # Hammer: values [0, 100]
         dataframe['CDLHAMMER'] = ta.CDLHAMMER(dataframe)
         # Inverted Hammer: values [0, 100]
@@ -165,11 +205,11 @@ class TestStrategy(IStrategy):
         dataframe['CDLMORNINGSTAR'] = ta.CDLMORNINGSTAR(dataframe) # values [0, 100]
         # Three White Soldiers: values [0, 100]
         dataframe['CDL3WHITESOLDIERS'] = ta.CDL3WHITESOLDIERS(dataframe) # values [0, 100]
-        """
+
 
         # Pattern Recognition - Bearish candlestick patterns
         # ------------------------------------
-        """
+
         # Hanging Man: values [0, 100]
         dataframe['CDLHANGINGMAN'] = ta.CDLHANGINGMAN(dataframe)
         # Shooting Star: values [0, 100]
@@ -182,11 +222,10 @@ class TestStrategy(IStrategy):
         dataframe['CDLEVENINGDOJISTAR'] = ta.CDLEVENINGDOJISTAR(dataframe)
         # Evening Star: values [0, 100]
         dataframe['CDLEVENINGSTAR'] = ta.CDLEVENINGSTAR(dataframe)
-        """
+ 
-
         # Pattern Recognition - Bullish/Bearish candlestick patterns
         # ------------------------------------
-        """
+
         # Three Line Strike: values [0, -100, 100]
         dataframe['CDL3LINESTRIKE'] = ta.CDL3LINESTRIKE(dataframe)
         # Spinning Top: values [0, -100, 100]
@@ -199,18 +238,18 @@ class TestStrategy(IStrategy):
         dataframe['CDL3OUTSIDE'] = ta.CDL3OUTSIDE(dataframe) # values [0, -100, 100]
         # Three Inside Up/Down: values [0, -100, 100]
         dataframe['CDL3INSIDE'] = ta.CDL3INSIDE(dataframe) # values [0, -100, 100]
-        """
+
 
         # Chart type
         # ------------------------------------
-        """
+
         # Heikinashi stategy
         heikinashi = qtpylib.heikinashi(dataframe)
         dataframe['ha_open'] = heikinashi['open']
         dataframe['ha_close'] = heikinashi['close']
         dataframe['ha_high'] = heikinashi['high']
         dataframe['ha_low'] = heikinashi['low']
-        """
+
 
         return dataframe
 
@@ -220,12 +259,65 @@ class TestStrategy(IStrategy):
         :param dataframe: DataFrame
         :return: DataFrame with buy column
         """
-        dataframe.loc[
+        if 'uptrend_long_ema' in params and params['uptrend_long_ema']['enabled']:
-            (
+            conditions.append(dataframe['ema50'] > dataframe['ema100'])
-                (dataframe['adx'] > 30) &
+        if 'macd_below_zero' in params and params['macd_below_zero']['enabled']:
-                (dataframe['tema'] <= dataframe['bb_middleband']) &
+            conditions.append(dataframe['macd'] < 0)
-                (dataframe['tema'] > dataframe['tema'].shift(1))
+        if 'uptrend_short_ema' in params and params['uptrend_short_ema']['enabled']:
+            conditions.append(dataframe['ema5'] > dataframe['ema10'])
+        if 'mfi' in params and params['mfi']['enabled']:
+            conditions.append(dataframe['mfi'] < params['mfi']['value'])
+        if 'fastd' in params and params['fastd']['enabled']:
+            conditions.append(dataframe['fastd'] < params['fastd']['value'])
+        if 'adx' in params and params['adx']['enabled']:
+            conditions.append(dataframe['adx'] > params['adx']['value'])
+        if 'rsi' in params and params['rsi']['enabled']:
+            conditions.append(dataframe['rsi'] < params['rsi']['value'])
+        if 'over_sar' in params and params['over_sar']['enabled']:
+            conditions.append(dataframe['close'] > dataframe['sar'])
+        if 'green_candle' in params and params['green_candle']['enabled']:
+            conditions.append(dataframe['close'] > dataframe['open'])
+        if 'uptrend_sma' in params and params['uptrend_sma']['enabled']:
+            prevsma = dataframe['sma'].shift(1)
+            conditions.append(dataframe['sma'] > prevsma)
+         # TRIGGERS
+        triggers = {
+            'lower_bb': (
+                dataframe['close'] < dataframe['bb_lowerband']
             ),
+            'lower_bb_tema': (
+                dataframe['tema'] < dataframe['bb_lowerband']
+            ),
+            'faststoch10': (qtpylib.crossed_above(
+                dataframe['fastd'], 10.0
+            )),
+            'ao_cross_zero': (qtpylib.crossed_above(
+                dataframe['ao'], 0.0
+            )),
+            'ema3_cross_ema10': (qtpylib.crossed_above(
+                dataframe['ema3'], dataframe['ema10']
+            )),
+            'macd_cross_signal': (qtpylib.crossed_above(
+                dataframe['macd'], dataframe['macdsignal']
+            )),
+            'sar_reversal': (qtpylib.crossed_above(
+                dataframe['close'], dataframe['sar']
+            )),
+            'ht_sine': (qtpylib.crossed_above(
+                dataframe['htleadsine'], dataframe['htsine']
+            )),
+            'heiken_reversal_bull': (
+                (qtpylib.crossed_above(dataframe['ha_close'], dataframe['ha_open'])) &
+                (dataframe['ha_low'] == dataframe['ha_open'])
+            ),
+            'di_cross': (qtpylib.crossed_above(
+                dataframe['plus_di'], dataframe['minus_di']
+            )),
+        }
+        conditions.append(triggers.get(params['trigger']['type']))
+
+        dataframe.loc[
+            reduce(lambda x, y: x & y, conditions),
             'buy'] = 1
 
         return dataframe
@@ -239,8 +331,142 @@ class TestStrategy(IStrategy):
         dataframe.loc[
             (
                 (dataframe['adx'] > 70) &
-                (dataframe['tema'] > dataframe['bb_middleband']) &
                 (dataframe['tema'] < dataframe['tema'].shift(1))
             ),
             'sell'] = 1
         return dataframe
+
+    def hyperopt_space(self) -> List[Dict]:
+        """
+        Define your Hyperopt space for the strategy
+        :return: Dict
+        """
+        space = {
+            'macd_below_zero': hp.choice('macd_below_zero', [
+                {'enabled': False},
+                {'enabled': True}
+            ]),
+            'mfi': hp.choice('mfi', [
+                {'enabled': False},
+                {'enabled': True, 'value': hp.quniform('mfi-value', 5, 25, 1)}
+            ]),
+            'fastd': hp.choice('fastd', [
+                {'enabled': False},
+                {'enabled': True, 'value': hp.quniform('fastd-value', 10, 50, 1)}
+            ]),
+            'adx': hp.choice('adx', [
+                {'enabled': False},
+                {'enabled': True, 'value': hp.quniform('adx-value', 15, 50, 1)}
+            ]),
+            'rsi': hp.choice('rsi', [
+                {'enabled': False},
+                {'enabled': True, 'value': hp.quniform('rsi-value', 20, 40, 1)}
+            ]),
+            'uptrend_long_ema': hp.choice('uptrend_long_ema', [
+                {'enabled': False},
+                {'enabled': True}
+            ]),
+            'uptrend_short_ema': hp.choice('uptrend_short_ema', [
+                {'enabled': False},
+                {'enabled': True}
+            ]),
+            'over_sar': hp.choice('over_sar', [
+                {'enabled': False},
+                {'enabled': True}
+            ]),
+            'green_candle': hp.choice('green_candle', [
+                {'enabled': False},
+                {'enabled': True}
+            ]),
+            'uptrend_sma': hp.choice('uptrend_sma', [
+                {'enabled': False},
+                {'enabled': True}
+            ]),
+            'trigger': hp.choice('trigger', [
+                {'type': 'lower_bb'},
+                {'type': 'lower_bb_tema'},
+                {'type': 'faststoch10'},
+                {'type': 'ao_cross_zero'},
+                {'type': 'ema3_cross_ema10'},
+                {'type': 'macd_cross_signal'},
+                {'type': 'sar_reversal'},
+                {'type': 'ht_sine'},
+                {'type': 'heiken_reversal_bull'},
+                {'type': 'di_cross'},
+            ]),
+            'stoploss': hp.uniform('stoploss', -0.5, -0.02),
+        }
+        return space
+
+    def buy_strategy_generator(self, params) -> None:
+        """
+        Define the buy strategy parameters to be used by hyperopt
+        """
+        def populate_buy_trend(dataframe: DataFrame) -> DataFrame:
+            conditions = []
+            # GUARDS AND TRENDS
+            if 'uptrend_long_ema' in params and params['uptrend_long_ema']['enabled']:
+                conditions.append(dataframe['ema50'] > dataframe['ema100'])
+            if 'macd_below_zero' in params and params['macd_below_zero']['enabled']:
+                conditions.append(dataframe['macd'] < 0)
+            if 'uptrend_short_ema' in params and params['uptrend_short_ema']['enabled']:
+                conditions.append(dataframe['ema5'] > dataframe['ema10'])
+            if 'mfi' in params and params['mfi']['enabled']:
+                conditions.append(dataframe['mfi'] < params['mfi']['value'])
+            if 'fastd' in params and params['fastd']['enabled']:
+                conditions.append(dataframe['fastd'] < params['fastd']['value'])
+            if 'adx' in params and params['adx']['enabled']:
+                conditions.append(dataframe['adx'] > params['adx']['value'])
+            if 'rsi' in params and params['rsi']['enabled']:
+                conditions.append(dataframe['rsi'] < params['rsi']['value'])
+            if 'over_sar' in params and params['over_sar']['enabled']:
+                conditions.append(dataframe['close'] > dataframe['sar'])
+            if 'green_candle' in params and params['green_candle']['enabled']:
+                conditions.append(dataframe['close'] > dataframe['open'])
+            if 'uptrend_sma' in params and params['uptrend_sma']['enabled']:
+                prevsma = dataframe['sma'].shift(1)
+                conditions.append(dataframe['sma'] > prevsma)
+
+            # TRIGGERS
+            triggers = {
+                'lower_bb': (
+                    dataframe['close'] < dataframe['bb_lowerband']
+                ),
+                'lower_bb_tema': (
+                    dataframe['tema'] < dataframe['bb_lowerband']
+                ),
+                'faststoch10': (qtpylib.crossed_above(
+                    dataframe['fastd'], 10.0
+                )),
+                'ao_cross_zero': (qtpylib.crossed_above(
+                    dataframe['ao'], 0.0
+                )),
+                'ema3_cross_ema10': (qtpylib.crossed_above(
+                    dataframe['ema3'], dataframe['ema10']
+                )),
+                'macd_cross_signal': (qtpylib.crossed_above(
+                    dataframe['macd'], dataframe['macdsignal']
+                )),
+                'sar_reversal': (qtpylib.crossed_above(
+                    dataframe['close'], dataframe['sar']
+                )),
+                'ht_sine': (qtpylib.crossed_above(
+                    dataframe['htleadsine'], dataframe['htsine']
+                )),
+                'heiken_reversal_bull': (
+                    (qtpylib.crossed_above(dataframe['ha_close'], dataframe['ha_open'])) &
+                    (dataframe['ha_low'] == dataframe['ha_open'])
+                ),
+                'di_cross': (qtpylib.crossed_above(
+                    dataframe['plus_di'], dataframe['minus_di']
+                )),
+            }
+            conditions.append(triggers.get(params['trigger']['type']))
+
+            dataframe.loc[
+                reduce(lambda x, y: x & y, conditions),
+                'buy'] = 1
+
+            return dataframe
+
+        return populate_buy_trend