first commit

StrategyPierrick.py

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+# pr#agma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
+# isort: skip_file
+# --- Do not remove these libs ---
+from datetime import datetime
+
+import numpy as np  # noqa
+import pandas as pd  # noqa
+from pandas import DataFrame
+
+from freqtrade.strategy.interface import IStrategy
+
+# --------------------------------
+# Add your lib to import here
+import talib.abstract as ta
+import freqtrade.vendor.qtpylib.indicators as qtpylib
+
+# This class is a sample. Feel free to customize it.
+class StrategyPierrick(IStrategy):
+
+    # Strategy interface version - allow new iterations of the strategy interface.
+    # Check the documentation or the Sample strategy to get the latest version.
+    INTERFACE_VERSION = 2
+
+
+    # ROI table:
+    minimal_roi = {
+        #"0": 0.015
+        "0": 0.5
+    }
+
+    # Stoploss:
+    stoploss = -1
+    trailing_stop = True
+    trailing_stop_positive = 0.001
+    trailing_stop_positive_offset = 0.0175 #0.015
+    trailing_only_offset_is_reached = True
+
+    #max_open_trades = 3
+
+    # Optimal ticker interval for the strategy.
+    timeframe = '5m'
+
+    # Run "populate_indicators()" only for new candle.
+    process_only_new_candles = False
+
+    # These values can be overridden in the "ask_strategy" section in the config.
+    use_sell_signal = True
+    sell_profit_only = False
+    ignore_roi_if_buy_signal = False
+
+    # Number of candles the strategy requires before producing valid signals
+    startup_candle_count: int = 30
+
+    # Optional order type mapping.
+    order_types = {
+        'buy': 'limit',
+        'sell': 'limit',
+        'stoploss': 'market',
+        'stoploss_on_exchange': False
+    }
+
+    # Optional order time in force.
+    order_time_in_force = {
+        'buy': 'gtc',
+        'sell': 'gtc'
+    }
+
+    plot_config = {
+        # Main plot indicators (Moving averages, ...)
+        'main_plot': {
+            'bb_lowerband': {'color': 'white'},
+            'bb_upperband': {'color': 'white'},
+        },
+        'subplots': {
+            # Subplots - each dict defines one additional plot
+            "BB": {
+                'bb_width': {'color': 'white'},
+            },
+            "Aaron": {
+                'aroonup': {'color': 'blue'},
+                'aroondown': {'color': 'red'}
+            }
+        }
+    }
+
+    def informative_pairs(self):
+
+        return []
+
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+
+        # MACD
+        #macd = ta.MACD(dataframe)
+        #dataframe['macd'] = macd['macd']
+        #dataframe['macdsignal'] = macd['macdsignal']
+        #dataframe['macdhist'] = macd['macdhist']
+
+        # RSI
+        #dataframe['rsi'] = ta.RSI(dataframe)
+
+        # 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']
+        dataframe["bb_percent"] = (
+            (dataframe["close"] - dataframe["bb_lowerband"]) /
+            (dataframe["bb_upperband"] - dataframe["bb_lowerband"])
+        )
+        dataframe["bb_width"] = (
+            (dataframe["bb_upperband"] - dataframe["bb_lowerband"]) / dataframe["bb_middleband"]
+        )
+
+        return dataframe
+
+    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+
+        dataframe.loc[
+            (
+                (
+                    (dataframe['close'] < dataframe['bb_lowerband'])
+                    & (dataframe['bb_width'] >= 0.065)
+                    #& (dataframe['rsi'] < 45) 
+                    & (dataframe['volume'] > 0)
+                )
+            ),
+            'buy'] = 1
+        return dataframe
+
+    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+
+        dataframe.loc[
+            (
+
+            ),
+
+            'sell'] = 1
+        return dataframe
+