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Strategy005.py

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+
+# --- Do not remove these libs ---
+from freqtrade.strategy import IStrategy
+from freqtrade.strategy import CategoricalParameter, IntParameter
+from functools import reduce
+from pandas import DataFrame
+# --------------------------------
+
+import talib.abstract as ta
+import freqtrade.vendor.qtpylib.indicators as qtpylib
+import numpy # noqa
+
+
+class Strategy005(IStrategy):
+    """
+    Strategy 005
+    author@: Gerald Lonlas
+    github@: https://github.com/freqtrade/freqtrade-strategies
+
+    How to use it?
+    > python3 ./freqtrade/main.py -s Strategy005
+    """
+    INTERFACE_VERSION = 2
+
+    # Minimal ROI designed for the strategy.
+    # This attribute will be overridden if the config file contains "minimal_roi"
+    minimal_roi = {
+        "1440": 0.01,
+        "80": 0.02,
+        "40": 0.03,
+        "20": 0.04,
+        "0":  0.05
+    }
+
+    # Optimal stoploss designed for the strategy
+    # This attribute will be overridden if the config file contains "stoploss"
+    stoploss = -1
+
+    # Optimal timeframe for the strategy
+    timeframe = '5m'
+
+    # trailing stoploss
+    trailing_stop = False
+    trailing_stop_positive = 0.01
+    trailing_stop_positive_offset = 0.02
+
+    # run "populate_indicators" only for new candle
+    process_only_new_candles = False
+
+    # Experimental settings (configuration will overide these if set)
+    use_sell_signal = True
+    sell_profit_only = True
+    ignore_roi_if_buy_signal = False
+
+    # Optional order type mapping
+    order_types = {
+        'buy': 'limit',
+        'sell': 'limit',
+        'stoploss': 'market',
+        'stoploss_on_exchange': False
+    }
+
+    buy_volumeAVG = IntParameter(low=50, high=300, default=70, space='buy', optimize=True)
+    buy_rsi = IntParameter(low=1, high=100, default=30, space='buy', optimize=True)
+    buy_fastd = IntParameter(low=1, high=100, default=30, space='buy', optimize=True)
+    buy_fishRsiNorma = IntParameter(low=1, high=100, default=30, space='buy', optimize=True)
+
+    sell_rsi = IntParameter(low=1, high=100, default=70, space='sell', optimize=True)
+    sell_minusDI = IntParameter(low=1, high=100, default=50, space='sell', optimize=True)
+    sell_fishRsiNorma = IntParameter(low=1, high=100, default=50, space='sell', optimize=True)
+    sell_trigger = CategoricalParameter(["rsi-macd-minusdi", "sar-fisherRsi"],
+                                        default=30, space='sell', optimize=True)
+
+    # Buy hyperspace params:
+    buy_params = {
+        "buy_fastd": 1,
+        "buy_fishRsiNorma": 5,
+        "buy_rsi": 26,
+        "buy_volumeAVG": 150,
+    }
+
+    # Sell hyperspace params:
+    sell_params = {
+        "sell_fishRsiNorma": 30,
+        "sell_minusDI": 4,
+        "sell_rsi": 74,
+        "sell_trigger": "rsi-macd-minusdi",
+    }
+
+    def informative_pairs(self):
+        """
+        Define additional, informative pair/interval combinations to be cached from the exchange.
+        These pair/interval combinations are non-tradeable, unless they are part
+        of the whitelist as well.
+        For more information, please consult the documentation
+        :return: List of tuples in the format (pair, interval)
+            Sample: return [("ETH/USDT", "5m"),
+                            ("BTC/USDT", "15m"),
+                            ]
+        """
+        return []
+
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """
+        Adds several different TA indicators to the given DataFrame
+
+        Performance Note: For the best performance be frugal on the number of indicators
+        you are using. Let uncomment only the indicator you are using in your strategies
+        or your hyperopt configuration, otherwise you will waste your memory and CPU usage.
+        """
+
+        # MACD
+        macd = ta.MACD(dataframe)
+        dataframe['macd'] = macd['macd']
+        dataframe['macdsignal'] = macd['macdsignal']
+
+        # Minus Directional Indicator / Movement
+        dataframe['minus_di'] = ta.MINUS_DI(dataframe)
+
+        # RSI
+        dataframe['rsi'] = ta.RSI(dataframe)
+
+        # Inverse Fisher transform on RSI, values [-1.0, 1.0] (https://goo.gl/2JGGoy)
+        rsi = 0.1 * (dataframe['rsi'] - 50)
+        dataframe['fisher_rsi'] = (numpy.exp(2 * rsi) - 1) / (numpy.exp(2 * rsi) + 1)
+        # Inverse Fisher transform on RSI normalized, value [0.0, 100.0] (https://goo.gl/2JGGoy)
+        dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1)
+
+        # Stoch fast
+        stoch_fast = ta.STOCHF(dataframe)
+        dataframe['fastd'] = stoch_fast['fastd']
+        dataframe['fastk'] = stoch_fast['fastk']
+
+        # Overlap Studies
+        # ------------------------------------
+
+        # SAR Parabol
+        dataframe['sar'] = ta.SAR(dataframe)
+
+        # SMA - Simple Moving Average
+        dataframe['sma'] = ta.SMA(dataframe, timeperiod=40)
+
+        return dataframe
+
+    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """
+        Based on TA indicators, populates the buy signal for the given dataframe
+        :param dataframe: DataFrame
+        :return: DataFrame with buy column
+        """
+        dataframe.loc[
+            # Prod
+            (
+                (dataframe['close'] > 0.00000200) &
+                (dataframe['volume'] > dataframe['volume'].rolling(self.buy_volumeAVG.value).mean() * 4) &
+                (dataframe['close'] < dataframe['sma']) &
+                (dataframe['fastd'] > dataframe['fastk']) &
+                (dataframe['rsi'] > self.buy_rsi.value) &
+                (dataframe['fastd'] > self.buy_fastd.value) &
+                (dataframe['fisher_rsi_norma'] < self.buy_fishRsiNorma.value)
+            ),
+            'buy'] = 1
+
+        return dataframe
+
+    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """
+        Based on TA indicators, populates the sell signal for the given dataframe
+        :param dataframe: DataFrame
+        :return: DataFrame with buy column
+        """
+
+        conditions = []
+        if self.sell_trigger.value == 'rsi-macd-minusdi':
+            conditions.append(qtpylib.crossed_above(dataframe['rsi'], self.sell_rsi.value))
+            conditions.append(dataframe['macd'] < 0)
+            conditions.append(dataframe['minus_di'] > self.sell_minusDI.value)
+        if self.sell_trigger.value == 'sar-fisherRsi':
+            conditions.append(dataframe['sar'] > dataframe['close'])
+            conditions.append(dataframe['fisher_rsi'] > self.sell_fishRsiNorma.value)
+
+        if conditions:
+            dataframe.loc[reduce(lambda x, y: x & y, conditions), 'sell'] = 1
+
+        return dataframe