first commit

Strategy001.py

@@ -0,0 +1,121 @@
+
+# --- Do not remove these libs ---
+from freqtrade.strategy import IStrategy
+from typing import Dict, List
+from functools import reduce
+from pandas import DataFrame
+# --------------------------------
+
+import talib.abstract as ta
+import freqtrade.vendor.qtpylib.indicators as qtpylib
+
+
+class Strategy001(IStrategy):
+    """
+    Strategy 001
+    author@: Gerald Lonlas
+    github@: https://github.com/freqtrade/freqtrade-strategies
+
+    How to use it?
+    > python3 ./freqtrade/main.py -s Strategy001
+    """
+
+    # Minimal ROI designed for the strategy.
+    # This attribute will be overridden if the config file contains "minimal_roi"
+    minimal_roi = {
+        "60":  0.01,
+        "30":  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 = -0.10
+
+    # 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
+    }
+
+    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.
+        """
+
+        dataframe['ema20'] = ta.EMA(dataframe, timeperiod=20)
+        dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
+        dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)
+
+        heikinashi = qtpylib.heikinashi(dataframe)
+        dataframe['ha_open'] = heikinashi['open']
+        dataframe['ha_close'] = heikinashi['close']
+
+        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[
+            (
+                qtpylib.crossed_above(dataframe['ema20'], dataframe['ema50']) &
+                (dataframe['ha_close'] > dataframe['ema20']) &
+                (dataframe['ha_open'] < dataframe['ha_close'])  # green bar
+            ),
+            '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
+        """
+        dataframe.loc[
+            (
+                qtpylib.crossed_above(dataframe['ema50'], dataframe['ema100']) &
+                (dataframe['ha_close'] < dataframe['ema20']) &
+                (dataframe['ha_open'] > dataframe['ha_close'])  # red bar
+            ),
+            'sell'] = 1
+        return dataframe