Add a strategy

README.md

@@ -34,16 +34,17 @@ bot.
 - [Offer me a coffee](#offer-me-a-coffee)
 
 ## Free trading strategies
-Value below are result from backtesting from 2017-12-19 to 2017-01-20 and  
+Value below are result from backtesting from 2018-01-10 to 2018-01-30 and  
 `experimental.sell_profit_only` enabled. More detail on each strategy 
 page.
 
 |  Strategy | Buy count | AVG profit % | Total profit | AVG duration |
 |-----------|-----------|--------------|--------------|--------------|
-| [Strategy 001](https://github.com/glonlas/freqtrade-strategies/issues/1) | 287 | 2.39 | 0.02763202 |  1306.3 |
-| [Strategy 002](https://github.com/glonlas/freqtrade-strategies/issues/2) | 158 | 2.67 | 0.01686667 |  387.9 |
-| [Strategy 003](https://github.com/glonlas/freqtrade-strategies/issues/3) | 147 | 2.21 | 0.01277113 |  694.9 | 
-| [Strategy 004](https://github.com/glonlas/freqtrade-strategies/issues/4) | 232 | 2.11 | 0.01977185 |  455.3 | 
+| [Strategy 001](https://github.com/glonlas/freqtrade-strategies/issues/1) | 55 | 0.05 | 0.00012102 |  476.1 |
+| [Strategy 002](https://github.com/glonlas/freqtrade-strategies/issues/2) | 9 | 3.21 | 0.00114807 |  189.4 |
+| [Strategy 003](https://github.com/glonlas/freqtrade-strategies/issues/3) | 14 | 1.47 | 0.00081740 |  227.5 | 
+| [Strategy 004](https://github.com/glonlas/freqtrade-strategies/issues/4) | 37 | 0.69 | 0.00102128 |  367.3 | 
+| [Strategy 005](https://github.com/glonlas/freqtrade-strategies/issues/11) | 180 | 1.16 | 0.00827589 |  156.2 | 
 
 
 Strategies from this repo are free to use and feel free to update them. 

user_data/strategies/strategy005.py

@@ -0,0 +1,125 @@
+
+# --- Do not remove these libs ---
+from freqtrade.strategy.interface import IStrategy
+from typing import Dict, List
+from hyperopt import hp
+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/glonlas/freqtrade-strategies
+
+    How to use it?
+    > python3 ./freqtrade/main.py -s Strategy005
+    """
+
+    # 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 = -0.5
+
+    # Optimal ticker interval for the strategy
+    ticker_interval = '5m'
+
+    def populate_indicators(self, dataframe: DataFrame) -> 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) -> 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'].mean() * 4) &
+                (dataframe['close'] < dataframe['sma']) &
+                (dataframe['fastd'] > dataframe['fastk']) &
+                (dataframe['rsi'] > 0) &
+                (dataframe['fastd'] > 0) &
+                # (dataframe['fisher_rsi'] < -0.94)
+                (dataframe['fisher_rsi_norma'] < 38.900000000000006)
+            ),
+            'buy'] = 1
+
+        return dataframe
+
+    def populate_sell_trend(self, dataframe: DataFrame) -> DataFrame:
+        """
+        Based on TA indicators, populates the sell signal for the given dataframe
+        :param dataframe: DataFrame
+        :return: DataFrame with buy column
+        """
+        dataframe.loc[
+            # Prod
+            (
+                (qtpylib.crossed_above(dataframe['rsi'], 50)) &
+                (dataframe['macd'] < 0) &
+                (dataframe['minus_di'] > 0)
+            ) |
+            (
+                (dataframe['sar'] > dataframe['close']) &
+                (dataframe['fisher_rsi'] > 0.3)
+            ),
+
+            'sell'] = 1
+        return dataframe