Add 29 optional indicators populate_indicators()

freqtrade/analyze.py

@@ -11,7 +11,7 @@
 from pandas import DataFrame, to_datetime
 
 from freqtrade.exchange import get_ticker_history
-from freqtrade.vendor.qtpylib.indicators import awesome_oscillator, crossed_above
+from freqtrade.vendor.qtpylib.indicators import awesome_oscillator, PandasObject as qtpylib
 
 logger = logging.getLogger(__name__)
 
@@ -40,34 +40,181 @@ def parse_ticker_dataframe(ticker: list) -> DataFrame:
 def populate_indicators(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.
     """
-    dataframe['sar'] = ta.SAR(dataframe)
+
+    # Momentum Indicator
+    # ------------------------------------
+
+    # ADX
     dataframe['adx'] = ta.ADX(dataframe)
-    stoch = ta.STOCHF(dataframe)
-    dataframe['fastd'] = stoch['fastd']
-    dataframe['fastk'] = stoch['fastk']
-    dataframe['blower'] = ta.BBANDS(dataframe, nbdevup=2, nbdevdn=2)['lowerband']
-    dataframe['sma'] = ta.SMA(dataframe, timeperiod=40)
-    dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
+
+    # Awesome oscillator
+    dataframe['ao'] = awesome_oscillator(dataframe)
+    """
+    # Commodity Channel Index: values Oversold:<-100, Overbought:>100
+    dataframe['cci'] = ta.CCI(dataframe)
+    """
+    # MACD
+    macd = ta.MACD(dataframe)
+    dataframe['macd'] = macd['macd']
+    dataframe['macdsignal'] = macd['macdsignal']
+    dataframe['macdhist'] = macd['macdhist']
+
+    # MFI
     dataframe['mfi'] = ta.MFI(dataframe)
+
+    # Minus Directional Indicator / Movement
+    dataframe['minus_dm'] = ta.MINUS_DM(dataframe)
+    dataframe['minus_di'] = ta.MINUS_DI(dataframe)
+
+    # Plus Directional Indicator / Movement
+    dataframe['plus_dm'] = ta.PLUS_DM(dataframe)
+    dataframe['plus_di'] = ta.PLUS_DI(dataframe)
+    """
+    # ROC
+    dataframe['roc'] = ta.ROC(dataframe)
+    """
+    # RSI
     dataframe['rsi'] = ta.RSI(dataframe)
-    dataframe['cci'] = ta.CCI(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
+    stoch = ta.STOCH(dataframe)
+    dataframe['slowd'] = stoch['slowd']
+    dataframe['slowk'] = stoch['slowk']
+    """
+    # Stoch fast
+    stoch_fast = ta.STOCHF(dataframe)
+    dataframe['fastd'] = stoch_fast['fastd']
+    dataframe['fastk'] = stoch_fast['fastk']
+    """
+    # Stoch RSI
+    stoch_rsi = ta.STOCHRSI(dataframe)
+    dataframe['fastd_rsi'] = stoch_rsi['fastd']
+    dataframe['fastk_rsi'] = stoch_rsi['fastk']
+    """
+
+    # Overlap Studies
+    # ------------------------------------
+
+    # Bollinger bands
+    bollinger = ta.BBANDS(dataframe, nbdevup=2, nbdevdn=2)
+    dataframe['bb_lowerband'] = bollinger['lowerband']
+    """
+    dataframe['bb_middleband'] = bollinger['middleband']
+    dataframe['bb_upperband'] = bollinger['upperband']
+    """
+    # EMA - Exponential Moving Average
     dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5)
     dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)
     dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
     dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)
-    dataframe['ao'] = awesome_oscillator(dataframe)
-    macd = ta.MACD(dataframe)
-    dataframe['macd'] = macd['macd']
-    dataframe['macdsignal'] = macd['macdsignal']
-    dataframe['macdhist'] = macd['macdhist']
+    """
+    dataframe['ema200'] = ta.EMA(dataframe, timeperiod=200)
+    """
+    # SAR Parabol
+    dataframe['sar'] = ta.SAR(dataframe)
+
+    # SMA - Simple Moving Average
+    dataframe['sma'] = ta.SMA(dataframe, timeperiod=40)
+
+    # TEMA - Triple Exponential Moving Average
+    dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
+
+    # Cycle Indicator
+    # ------------------------------------
+    # Hilbert Transform Indicator - SineWave
     hilbert = ta.HT_SINE(dataframe)
     dataframe['htsine'] = hilbert['sine']
     dataframe['htleadsine'] = hilbert['leadsine']
-    dataframe['plus_dm'] = ta.PLUS_DM(dataframe)
-    dataframe['plus_di'] = ta.PLUS_DI(dataframe)
-    dataframe['minus_dm'] = ta.MINUS_DM(dataframe)
-    dataframe['minus_di'] = ta.MINUS_DI(dataframe)
+
+    # Pattern Recognition - Bullish candlestick patterns
+    # ------------------------------------
+    """
+    # Hammer: values [0, 100]
+    dataframe['CDLHAMMER'] = ta.CDLHAMMER(dataframe)
+
+    # Inverted Hammer: values [0, 100]
+    dataframe['CDLINVERTEDHAMMER'] = ta.CDLINVERTEDHAMMER(dataframe)
+
+    # Dragonfly Doji: values [0, 100]
+    dataframe['CDLDRAGONFLYDOJI'] = ta.CDLDRAGONFLYDOJI(dataframe)
+
+    # Piercing Line: values [0, 100]
+    dataframe['CDLPIERCING'] = ta.CDLPIERCING(dataframe) # values [0, 100]
+
+    # Morningstar: values [0, 100]
+    dataframe['CDLMORNINGSTAR'] = ta.CDLMORNINGSTAR(dataframe) # values [0, 100]
+
+    # Three White Soldiers: values [0, 100]
+    dataframe['CDL3WHITESOLDIERS'] = ta.CDL3WHITESOLDIERS(dataframe) # values [0, 100]
+    """
+
+    # Pattern Recognition - Bearish candlestick patterns
+    # ------------------------------------
+    """
+    # Hanging Man: values [0, 100]
+    dataframe['CDLHANGINGMAN'] = ta.CDLHANGINGMAN(dataframe)
+
+    # Shooting Star: values [0, 100]
+    dataframe['CDLSHOOTINGSTAR'] = ta.CDLSHOOTINGSTAR(dataframe)
+
+    # Gravestone Doji: values [0, 100]
+    dataframe['CDLGRAVESTONEDOJI'] = ta.CDLGRAVESTONEDOJI(dataframe)
+
+    # Dark Cloud Cover: values [0, 100]
+    dataframe['CDLDARKCLOUDCOVER'] = ta.CDLDARKCLOUDCOVER(dataframe)
+
+    # Evening Doji Star: values [0, 100]
+    dataframe['CDLEVENINGDOJISTAR'] = ta.CDLEVENINGDOJISTAR(dataframe)
+
+    # Evening Star: values [0, 100]
+    dataframe['CDLEVENINGSTAR'] = ta.CDLEVENINGSTAR(dataframe)
+    """
+
+    # Pattern Recognition - Bullish/Bearish candlestick patterns
+    # ------------------------------------
+    """
+    # Three Line Strike: values [0, -100, 100]
+    dataframe['CDL3LINESTRIKE'] = ta.CDL3LINESTRIKE(dataframe)
+
+    # Spinning Top: values [0, -100, 100]
+    dataframe['CDLSPINNINGTOP'] = ta.CDLSPINNINGTOP(dataframe) # values [0, -100, 100]
+
+    # Engulfing: values [0, -100, 100]
+    dataframe['CDLENGULFING'] = ta.CDLENGULFING(dataframe) # values [0, -100, 100]
+
+    # Harami: values [0, -100, 100]
+    dataframe['CDLHARAMI'] = ta.CDLHARAMI(dataframe) # values [0, -100, 100]
+
+    # Three Outside Up/Down: values [0, -100, 100]
+    dataframe['CDL3OUTSIDE'] = ta.CDL3OUTSIDE(dataframe) # values [0, -100, 100]
+
+    # Three Inside Up/Down: values [0, -100, 100]
+    dataframe['CDL3INSIDE'] = ta.CDL3INSIDE(dataframe) # values [0, -100, 100]
+    """
+
+    # Chart type
+    # ------------------------------------
+    """
+    # Heikinashi stategy
+    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
 
 
@@ -102,8 +249,8 @@ def populate_sell_trend(dataframe: DataFrame) -> DataFrame:
     dataframe.loc[
         (
             (
-                (crossed_above(dataframe['rsi'], 70)) |
-                (crossed_above(dataframe['fastd'], 70))
+                (qtpylib.crossed_above(dataframe['rsi'], 70)) |
+                (qtpylib.crossed_above(dataframe['fastd'], 70))
             ) &
             (dataframe['adx'] > 10) &
             (dataframe['minus_di'] > 0)

freqtrade/optimize/hyperopt.py

@@ -189,7 +189,7 @@ def populate_buy_trend(dataframe: DataFrame) -> DataFrame:
 
         # TRIGGERS
         triggers = {
-            'lower_bb': dataframe['tema'] <= dataframe['blower'],
+            'lower_bb': dataframe['tema'] <= dataframe['bb_lowerband'],
             'faststoch10': (crossed_above(dataframe['fastd'], 10.0)),
             'ao_cross_zero': (crossed_above(dataframe['ao'], 0.0)),
             'ema5_cross_ema10': (crossed_above(dataframe['ema5'], dataframe['ema10'])),