Merge pull request #3 from krypt0ra/develop

Merging dev into master

app/analyzers/crossover.py

@@ -31,15 +31,24 @@ def analyze(self, key_indicator, key_signal, key_indicator_index,
         new_key_indicator = key_indicator.copy(deep=True)
         for column in new_key_indicator:
             column_indexed_name = '{}_{}'.format(column, key_indicator_index)
-            new_key_indicator.rename(columns={column: column_indexed_name}, inplace=True)
+            new_key_indicator.rename(
+                columns={
+                    column: column_indexed_name},
+                inplace=True)
 
-        crossed_indicator_name = '{}_{}'.format(crossed_signal, crossed_indicator_index)
+        crossed_indicator_name = '{}_{}'.format(
+            crossed_signal, crossed_indicator_index)
         new_crossed_indicator = crossed_indicator.copy(deep=True)
         for column in new_crossed_indicator:
-            column_indexed_name = '{}_{}'.format(column, crossed_indicator_index)
-            new_crossed_indicator.rename(columns={column: column_indexed_name}, inplace=True)
-
-        combined_data = pandas.concat([new_key_indicator, new_crossed_indicator], axis=1)
+            column_indexed_name = '{}_{}'.format(
+                column, crossed_indicator_index)
+            new_crossed_indicator.rename(
+                columns={
+                    column: column_indexed_name},
+                inplace=True)
+
+        combined_data = pandas.concat(
+            [new_key_indicator, new_crossed_indicator], axis=1)
         combined_data.dropna(how='any', inplace=True)
 
         combined_data['is_hot'] = combined_data[key_indicator_name] > combined_data[crossed_indicator_name]

app/analyzers/indicators/adx.py

@@ -10,7 +10,8 @@
 
 
 class Adx(IndicatorUtils):
-    def analyze(self, historical_data, signal=["adx"], period_count=14, hot_thresh=None, cold_thresh=None):
+    def analyze(self, historical_data, signal=[
+                "adx"], period_count=14, hot_thresh=None, cold_thresh=None):
         """
         strength of a trend
         ADX > 25 = strength
@@ -114,8 +115,8 @@ def DM(self, high, low, pdm, ndm):
         """
 
         for index in range(1, high.shape[0]):
-            up_move = high[index] - high[index-1]
-            down_move = low[index-1] - low[index]
+            up_move = high[index] - high[index - 1]
+            down_move = low[index - 1] - low[index]
 
             if up_move > down_move and up_move > 0:
                 pdm[index] = up_move
@@ -139,13 +140,13 @@ def DMsmooth(self, pdm, ndm, pdm_smooth, ndm_smooth, period_count):
         :return: pdm_smooth, ndm_smooth
         """
 
-        pdm_smooth[period_count-1] = pdm[0:period_count].sum() / period_count
+        pdm_smooth[period_count - 1] = pdm[0:period_count].sum() / period_count
         ndm_smooth[period_count - 1] = ndm[0:period_count].sum() / period_count
         for index in range(period_count, pdm.shape[0]):
             pdm_smooth[index] = (
-                pdm[index-1] - (pdm_smooth[index-1]/period_count)) + pdm_smooth[index-1]
+                pdm[index - 1] - (pdm_smooth[index - 1] / period_count)) + pdm_smooth[index - 1]
             ndm_smooth[index] = (
-                ndm[index - 1] - (ndm_smooth[index-1] / period_count)) + ndm_smooth[index-1]
+                ndm[index - 1] - (ndm_smooth[index - 1] / period_count)) + ndm_smooth[index - 1]
 
         return pdm_smooth, ndm_smooth
 
@@ -178,9 +179,9 @@ def ATR(self, tr, atr, period_count):
         :return: atr
         """
 
-        atr[period_count-1] = tr[0:period_count].sum() / period_count
+        atr[period_count - 1] = tr[0:period_count].sum() / period_count
         for index in range(period_count, tr.shape[0]):
-            atr[index] = ((atr[index-1] * (period_count - 1)) +
+            atr[index] = ((atr[index - 1] * (period_count - 1)) +
                           tr[index]) / period_count
 
         return atr
@@ -200,11 +201,11 @@ def ADX(self, pdi, ndi, dx, adx, period_count):
             dx[index] = ((abs(pdi[index] - ndi[index])) /
                          (abs(pdi[index] + ndi[index]))) * 100
 
-        period_count2 = period_count*2
-        adx[period_count2-1] = dx[period_count:period_count2].sum() / \
+        period_count2 = period_count * 2
+        adx[period_count2 - 1] = dx[period_count:period_count2].sum() / \
             period_count
         for index in range(period_count2, dx.shape[0]):
-            adx[index] = ((adx[index-1] * (period_count - 1)) +
+            adx[index] = ((adx[index - 1] * (period_count - 1)) +
                           dx[index]) / period_count
 
         return dx, adx

app/analyzers/indicators/aroon_oscillator.py

@@ -10,7 +10,8 @@
 
 
 class Aroon_oscillator(IndicatorUtils):
-    def analyze(self, historical_data, sma_vol_period, period_count=25, signal=["aroon"], hot_thresh=None, cold_thresh=None):
+    def analyze(self, historical_data, sma_vol_period, period_count=25,
+                signal=["aroon"], hot_thresh=None, cold_thresh=None):
         """Performs an aroon oscillator analysis on the historical data
 
         Args:
@@ -39,18 +40,18 @@ def analyze(self, historical_data, sma_vol_period, period_count=25, signal=["aro
                                         index=dataframe.index
                                         )
 
-        for index in range(0, dataframe.shape[0]-24):
-            id = dataframe.shape[0]-index
+        for index in range(0, dataframe.shape[0] - 24):
+            id = dataframe.shape[0] - index
             id_period = id - period_count
             high_date = dataframe['high'].iloc[id_period:id].idxmax()
             low_date = dataframe['low'].iloc[id_period:id].idxmin()
 
             periods_since_high = id - dataframe.index.get_loc(high_date) - 1
             periods_since_low = id - dataframe.index.get_loc(low_date) - 1
-            aroon_values['aroon_up'][id-1] = 100 * \
+            aroon_values['aroon_up'][id - 1] = 100 * \
                 ((25 - periods_since_high) / 25)
 
-            aroon_values['aroon_down'][id-1] = 100 * \
+            aroon_values['aroon_down'][id - 1] = 100 * \
                 ((25 - periods_since_low) / 25)
 
         aroon_values['aroon'] = aroon_values['aroon_up'] - \

app/analyzers/indicators/bbp.py

@@ -1,5 +1,5 @@
 
-""" 
+"""
 Bollinger Bands indicator
 """
 
@@ -13,7 +13,8 @@
 
 class BBP(IndicatorUtils):
 
-    def analyze(self, historical_data, signal=['bbp'], hot_thresh=0, cold_thresh=0.8, period_count=20, std_dev=2):
+    def analyze(self, historical_data, signal=[
+                'bbp'], hot_thresh=0, cold_thresh=0.8, period_count=20, std_dev=2):
         """Check when close price cross the Upper/Lower bands.
 
         Args:
@@ -23,7 +24,7 @@ def analyze(self, historical_data, signal=['bbp'], hot_thresh=0, cold_thresh=0.8
             hot_thresh (float, optional): Defaults to 0. The threshold at which this might be
                 good to purchase.
             cold_thresh (float, optional): Defaults to 0.8. The threshold at which this might be
-                good to sell.            
+                good to sell.
             std_dev (int, optional): number of std dev to use. Common values are 2 or 1
 
         Returns:

app/analyzers/indicators/bollinger.py

@@ -1,5 +1,5 @@
 
-""" 
+"""
 Bollinger Bands indicator
 """
 
@@ -13,7 +13,8 @@
 
 class Bollinger(IndicatorUtils):
 
-    def analyze(self, historical_data, signal=['close'], hot_thresh=None, cold_thresh=None, period_count=20, std_dev=2):
+    def analyze(self, historical_data, signal=[
+                'close'], hot_thresh=None, cold_thresh=None, period_count=20, std_dev=2):
         """Check when close price cross the Upper/Lower bands.
 
         Args:

app/analyzers/indicators/candle_recognition.py

@@ -7,7 +7,8 @@
 
 
 class Candle_recognition(IndicatorUtils):
-    def analyze(self, historical_data, signal, notification='hot', candle_check=1, hot_thresh=None, cold_thresh=None):
+    def analyze(self, historical_data, signal, notification='hot',
+                candle_check=1, hot_thresh=None, cold_thresh=None):
         """Performs an candle pattern analysis on the historical data
 
         Args:

app/analyzers/indicators/ichimoku.py

@@ -28,8 +28,8 @@ def analyze(self, historical_data, tenkansen_period, kijunsen_period, senkou_spa
             tenkansen_period (int, optional)
             kijunsen_period (int, optional)
             senkou_span_b_period (int, optional)
-            custom_strategy (string, optional): Defaults to None. Name of the custom strategy. The file name and class name 
-                should have the same name as the custom strategy. 
+            custom_strategy (string, optional): Defaults to None. Name of the custom strategy. The file name and class name
+                should have the same name as the custom strategy.
 
         Returns:
             pandas.DataFrame: A dataframe containing the indicators and hot/cold values.
@@ -42,7 +42,7 @@ def analyze(self, historical_data, tenkansen_period, kijunsen_period, senkou_spa
             'kijunsen': [numpy.nan] * dataframe.index.shape[0],
             'leading_span_a': [numpy.nan] * dataframe.index.shape[0],
             'leading_span_b': [numpy.nan] * dataframe.index.shape[0],
-            'chikou_span' : [numpy.nan] * dataframe.index.shape[0]
+            'chikou_span': [numpy.nan] * dataframe.index.shape[0]
         }
 
         ichimoku_values = pandas.DataFrame(ichimoku_columns,
@@ -60,7 +60,8 @@ def analyze(self, historical_data, tenkansen_period, kijunsen_period, senkou_spa
             window=senkou_span_b_period).max()
 
         chikou_span_delay = 26
-        ichimoku_values['chikou_span'] = dataframe['close'].shift(-chikou_span_delay)
+        ichimoku_values['chikou_span'] = dataframe['close'].shift(
+            -chikou_span_delay)
         ichimoku_values['tenkansen'] = (low_tenkansen + high_tenkansen) / 2
         ichimoku_values['kijunsen'] = (low_kijunsen + high_kijunsen) / 2
         ichimoku_values['leading_span_a'] = (
@@ -79,21 +80,23 @@ def analyze(self, historical_data, tenkansen_period, kijunsen_period, senkou_spa
             newindex = pandas.date_range(last_time + timedelta,
                                          freq=timedelta,
                                          periods=cloud_displacement)
-            ichimoku_values = pd.concat([ichimoku_values, pandas.DataFrame(index=newindex)])
+            ichimoku_values = pd.concat(
+                [ichimoku_values, pandas.DataFrame(index=newindex)])
             # cloud offset
             ichimoku_values['leading_span_a'] = ichimoku_values['leading_span_a'].shift(
                 cloud_displacement)
             ichimoku_values['leading_span_b'] = ichimoku_values['leading_span_b'].shift(
                 cloud_displacement)
-    
-            if chart == None:
-                if custom_strategy == None:
+
+            if chart is None:
+                if custom_strategy is None:
                     leading_span_hot = False
                     leading_span_cold = False
                     tk_cross_hot = False
                     tk_cross_cold = False
                     tk_cross_enabled = (('tenkansen' and 'kijunsen') in signal)
-                    leading_span_enabled = (('leading_span_a' and 'leading_span_b') in signal)
+                    leading_span_enabled = (
+                        ('leading_span_a' and 'leading_span_b') in signal)
                     date = dataframe.index[-1]
                     leading_span_date = ichimoku_values.index[-1]
 
@@ -102,19 +105,25 @@ def analyze(self, historical_data, tenkansen_period, kijunsen_period, senkou_spa
                         tk_cross_cold = ichimoku_values['tenkansen'][date] < ichimoku_values['kijunsen'][date]
 
                     if leading_span_enabled:
-                        leading_span_hot = ichimoku_values['leading_span_a'][leading_span_date] > ichimoku_values['leading_span_b'][leading_span_date]
-                        leading_span_cold = ichimoku_values['leading_span_a'][leading_span_date] < ichimoku_values['leading_span_b'][leading_span_date]
+                        leading_span_hot = ichimoku_values['leading_span_a'][
+                            leading_span_date] > ichimoku_values['leading_span_b'][leading_span_date]
+                        leading_span_cold = ichimoku_values['leading_span_a'][
+                            leading_span_date] < ichimoku_values['leading_span_b'][leading_span_date]
 
                     if hot_thresh:
-                        ichimoku_values.at[date, 'is_hot'] = tk_cross_hot or leading_span_hot
+                        ichimoku_values.at[date,
+                                           'is_hot'] = tk_cross_hot or leading_span_hot
 
                     if cold_thresh:
-                        ichimoku_values.at[date, 'is_cold'] = tk_cross_cold or leading_span_cold
+                        ichimoku_values.at[date,
+                                           'is_cold'] = tk_cross_cold or leading_span_cold
                 else:
-                    module = import_module("user_data.strategies." + custom_strategy)
-                    attr = getattr(module, custom_strategy)  
+                    module = import_module(
+                        "user_data.strategies." + custom_strategy)
+                    attr = getattr(module, custom_strategy)
 
-                    custom_hot, custom_cold = attr.analyze(ichimoku_values, dataframe)
+                    custom_hot, custom_cold = attr.analyze(
+                        ichimoku_values, dataframe)
                     date = dataframe.index[-1]
 
                     if hot_thresh:
@@ -123,11 +132,14 @@ def analyze(self, historical_data, tenkansen_period, kijunsen_period, senkou_spa
                     if cold_thresh:
                         ichimoku_values.at[date, 'is_cold'] = custom_cold
 
-                # Undo shifting in order to have the values aligned for displaying
+                # Undo shifting in order to have the values aligned for
+                # displaying
                 ichimoku_values['chikou_span'] = dataframe['close']
-                ichimoku_values['leading_span_a'] = ichimoku_values['leading_span_a'].shift(-cloud_displacement)
-                ichimoku_values['leading_span_b'] = ichimoku_values['leading_span_b'].shift(-cloud_displacement)
-
+                ichimoku_values['leading_span_a'] = ichimoku_values['leading_span_a'].shift(
+                    -cloud_displacement)
+                ichimoku_values['leading_span_b'] = ichimoku_values['leading_span_b'].shift(
+                    -cloud_displacement)
+
                 ichimoku_values.dropna(how='any', inplace=True)
 
         except Exception as e:

app/analyzers/indicators/iiv.py

@@ -8,13 +8,14 @@
 
 
 class IIV(IndicatorUtils):
-    def analyze(self, historical_data, signal=['iiv'], hot_thresh=10, cold_thresh=0):
+    def analyze(self, historical_data, signal=[
+                'iiv'], hot_thresh=10, cold_thresh=0):
         """Performs an analysis about the increase in volumen on the historical data
 
         Args:
             historical_data (list): A matrix of historical OHCLV data.
             signal (list, optional): Defaults to iiv. The indicator line to check hot against.
-            hot_thresh (float, optional): Defaults to 10. 
+            hot_thresh (float, optional): Defaults to 10.
             cold_thresh: Unused