first commit

Reco_1.py

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+# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
+# isort: skip_file
+import numpy as np  # noqa
+import pandas as pd  # noqa
+
+from freqtrade.strategy import IStrategy, merge_informative_pair
+from pandas import DataFrame
+
+# Add your lib to import here
+import talib.abstract as ta
+import freqtrade.vendor.qtpylib.indicators as qtpylib
+
+class Reco_1(IStrategy):
+
+    minimal_roi = {
+        "0": 1
+    }
+
+    # Stoploss
+    stoploss = -0.05
+
+    # Trailing stoploss
+    trailing_stop = True
+    trailing_only_offset_is_reached = True
+    trailing_stop_positive = 0.015
+    trailing_stop_positive_offset = 0.02
+
+    # Optimal ticker interval for the strategy.
+    timeframe = '5m'
+
+    # These values can be overridden in the "ask_strategy" section in the config.
+    use_sell_signal = False
+    sell_profit_only = False
+    ignore_roi_if_buy_signal = False
+
+    # Optional order type mapping.
+    order_types = {
+        'buy': 'market',
+        'sell': 'market',
+        'stoploss': 'market',
+        'stoploss_on_exchange': True
+    }
+
+    # Optional order time in force.
+    order_time_in_force = {
+        'buy': 'gtc',
+        'sell': 'gtc'
+    }
+
+    def informative_pairs(self):
+
+        # get access to all pairs available in whitelist.
+        pairs = self.dp.current_whitelist()
+        # Assign tf to each pair so they can be downloaded and cached for strategy.
+        informative_pairs = [(pair, '6h') for pair in pairs]
+        # Optionally Add additional "static" pairs
+        informative_pairs += [("ETH/USDT", "5m"),
+                              ("BTC/TUSD", "5m"),
+							  ("ALGO/BTC", "5m"),
+					          ("ATOM/BTC", "5m"),
+					          ("BAT/BTC", "5m"),
+					          ("BCH/BTC", "5m"),
+					          ("BRD/BTC", "5m"),
+					          ("EOS/BTC", "5m"),
+					          ("ETH/BTC", "5m"),
+					          ("IOTA/BTC", "5m"),
+					          ("LINK/BTC", "5m"),
+					          ("LTC/BTC", "5m"),
+					          ("NEO/BTC", "5m"),
+					          ("NXS/BTC", "5m"),
+					          ("XMR/BTC", "5m"),
+					          ("XRP/BTC", "5m"),
+					          ("XTZ/BTC", "5m"),
+                            ]
+        return informative_pairs
+
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        if not self.dp:
+            # Don't do anything if DataProvider is not available.
+            return dataframe
+
+        inf_tf = '6h'
+        # Get the informative pair
+        informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe = inf_tf)
+        # Get the 18 hours 3WHITESOLDIERS
+        informative['CDL3WHITESOLDIERS'] = ta.CDL3WHITESOLDIERS(informative, timeperiod=3)
+
+        # Use the helper function merge_informative_pair to safely merge the pair
+        # Automatically renames the columns and merges a shorter timeframe dataframe and a longer timeframe informative pair
+        dataframe = merge_informative_pair(dataframe, informative, self.timeframe, inf_tf, ffill = True)
+
+        # Calculate 3WHITESOLDIERS of the original dataframe (5m timeframe)
+        dataframe['CDL3WHITESOLDIERS'] = ta.CDL3WHITESOLDIERS(dataframe, timeperiod=3)
+
+
+        # Momentum Indicators
+        # MFI
+        dataframe['mfi'] = ta.MFI(dataframe)
+
+        # # Chart type
+        # # Heikin Ashi Strategy
+        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
+
+    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+
+         dataframe.loc[
+            (
+                (qtpylib.crossed_above(dataframe['mfi'] > 18)) &
+	        	(dataframe['CDL3WHITESOLDIERS_6h'] == True) &
+				(dataframe['volume'] > 0)
+	    	),
+            'buy'] = 1
+
+         return dataframe
+
+    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+
+        dataframe.loc[
+            (
+            ),
+            'sell'] = 1 
+
+        return dataframe