Merge pull request #154 from mablue/master

mabStra Strategy + Hyperopt

user_data/hyperopts/mabStraHo.py

@@ -0,0 +1,95 @@
+# by: Mablue (Masoud Azizi)
+
+# --- Do not remove these libs ---
+from functools import reduce
+from typing import Any, Callable, Dict, List
+
+import numpy as np  # noqa
+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
+
+# --------------------------------
+# Add your lib to import here
+import talib.abstract as ta  # noqa
+import freqtrade.vendor.qtpylib.indicators as qtpylib
+
+
+class mabStraHo(IHyperOpt):
+
+    @staticmethod
+    def indicator_space() -> List[Dimension]:
+        """
+        Define your Hyperopt space for searching buy strategy parameters.
+        """
+        return [
+            Real(0, 1, name='buy-div-min'),
+            Real(0, 1, name='buy-div-max'),
+        ]
+
+    @staticmethod
+    def buy_strategy_generator(params: Dict[str, Any]) -> Callable:
+        """
+        Define the buy strategy parameters to be used by Hyperopt.
+        """
+        def populate_buy_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
+            """
+            Buy strategy Hyperopt will build and use.
+            """
+            conditions = []
+            # GUARDS AND TRENDS
+
+            # div result limited to 0~1 so allways in buy position slowMa is lower than fastMa
+            # optimum number will calculate by this two lines
+            conditions.append(dataframe['buy-fastMA'].div(dataframe['buy-slowMA'])
+                              > params['buy-div-min'])
+            conditions.append(dataframe['buy-fastMA'].div(dataframe['buy-slowMA'])
+                              < params['buy-div-max'])
+
+            if conditions:
+                dataframe.loc[
+                    reduce(lambda x, y: x & y, conditions),
+                    'buy'] = 1
+
+            return dataframe
+
+        return populate_buy_trend
+
+    @staticmethod
+    def sell_indicator_space() -> List[Dimension]:
+        """
+        Define your Hyperopt space for searching sell strategy parameters.
+        """
+        return [
+            Real(0, 1, name='sell-div-min'),
+            Real(0, 1, name='sell-div-max'),
+        ]
+
+    @staticmethod
+    def sell_strategy_generator(params: Dict[str, Any]) -> Callable:
+        """
+        Define the sell strategy parameters to be used by Hyperopt.
+        """
+        def populate_sell_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
+            """
+            Sell strategy Hyperopt will build and use.
+            """
+            conditions = []
+
+            # GUARDS AND TRENDS
+
+            conditions.append(dataframe['sell-slowMA'].div(dataframe['sell-fastMA'])
+                              > params['sell-div-min'])
+            conditions.append(dataframe['sell-slowMA'].div(dataframe['sell-fastMA'])
+                              < params['sell-div-max'])
+
+            if conditions:
+                dataframe.loc[
+                    reduce(lambda x, y: x & y, conditions),
+                    'sell'] = 1
+
+            return dataframe
+
+        return populate_sell_trend

user_data/strategies/mabStra.py

@@ -0,0 +1,78 @@
+# author: Masoud Azizi @mablue
+
+# --- Do not remove these libs ---
+from freqtrade.strategy.interface import IStrategy
+from pandas import DataFrame
+# --------------------------------
+
+# Add your lib to import here
+import talib.abstract as ta
+import freqtrade.vendor.qtpylib.indicators as qtpylib
+
+FTF, STF = 5, 10
+
+
+class mabStra(IStrategy):
+
+    # 100/100:    727 trades. 486/191/50 Wins/Draws/Losses. Avg profit   3.53 % . Median profit   5.97 % . Total profit  1502.52014358 USDT (2566.80Σ %). Avg duration 1396.1 min. Objective: -15.62092
+
+    # Buy hyperspace params:
+    buy_params = {
+        'buy-div-max': 0.96451, 'buy-div-min': 0.22313
+    }
+
+    # Sell hyperspace params:
+    sell_params = {
+        'sell-div-max': 0.75476, 'sell-div-min': 0.16599
+    }
+
+    # ROI table:
+    minimal_roi = {
+        "0": 0.45574,
+        "307": 0.21971,
+        "428": 0.06762,
+        "1387": 0
+    }
+
+    # Stoploss:
+    stoploss = -0.34773
+
+    # Trailing stop:
+    trailing_stop = True
+    trailing_stop_positive = 0.01573
+    trailing_stop_positive_offset = 0.06651
+    trailing_only_offset_is_reached = True
+    # Optimal timeframe use it in your config
+    timeframe = '1h'
+
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        # SMA - ex Moving Average
+        dataframe['buy-fastMA'] = ta.SMA(dataframe, timeperiod=FTF)
+        dataframe['buy-slowMA'] = ta.SMA(dataframe, timeperiod=STF)
+        dataframe['sell-fastMA'] = ta.SMA(dataframe, timeperiod=FTF)
+        dataframe['sell-slowMA'] = ta.SMA(dataframe, timeperiod=STF)
+        return dataframe
+
+    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+
+        dataframe.loc[
+            (
+                (dataframe['buy-fastMA'].div(dataframe['buy-slowMA'])
+                    > self.buy_params['buy-div-min']) &
+                (dataframe['buy-fastMA'].div(dataframe['buy-slowMA'])
+                    < self.buy_params['buy-div-max'])
+            ),
+            'buy'] = 1
+
+        return dataframe
+
+    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        dataframe.loc[
+            (
+                (dataframe['sell-slowMA'].div(dataframe['sell-fastMA'])
+                    > self.sell_params['sell-div-min']) &
+                (dataframe['sell-slowMA'].div(dataframe['sell-fastMA'])
+                    < self.sell_params['sell-div-max'])
+            ),
+            'sell'] = 1
+        return dataframe