Merge pull request #6 from kylejusticemagnuson/python-3-upgrade

Python 3 upgrade

.gitignore

@@ -6,7 +6,9 @@ dist/
 __pycache__/
 *.so
 *~
+.vscode
 
 # due to using tox and pytest
 .tox
 .cache
+.bak

README.md

@@ -2,6 +2,8 @@
 
 This library contains various financial technical indicators that can be used to analyze data.
 
+Now compatible with both Python 2.7 and Python 3.6
+
 The complete list of indicators in this library:
 ```
 Accumulation/Distribution
@@ -81,7 +83,6 @@ Volume Oscillator
 Weighted Moving Average
 Williams %R
 ```
-pyti is currently only compatible with Python 2.7
 
 Install using pip:
 ```
@@ -99,5 +100,24 @@ res = ema(data, period)
 # res = [np.nan, 6.75, 4.0, 5.25, 3.75, 7.5, 6.0]
 ```
 
+Running the whole test suite using tox:
+```
+tox
+```
+
+To run tests for just Python 2.7 use
+```
+tox -e py27 tests
+```
+or just for Python 3.6
+```
+tox -e py36 tests
+```
+
+To specifically run one test in a specific Python version:
+```
+tox -e py36 -- tests/test_file_you_want_to_test:TestClassName.test_method_name
+```
+
 If there is an indicator that you would like to see added or believe there is an error in one of the existing ones, feel free to submit it to Issues. 
 If you would like to add your own indicator, fork the project and submit a pull request. Contributions are always welcome.

pyti/accumulation_distribution.py

@@ -1,5 +1,7 @@
+from __future__ import absolute_import
 import numpy as np
 from pyti import catch_errors
+from six.moves import range
 
 
 def accumulation_distribution(close_data, high_data, low_data, volume):

pyti/aroon.py

@@ -1,6 +1,8 @@
+from __future__ import absolute_import
 import numpy as np
 from pyti import catch_errors
 from pyti.function_helper import fill_for_noncomputable_vals
+from six.moves import range
 
 
 def aroon_up(data, period):
@@ -13,15 +15,11 @@ def aroon_up(data, period):
     catch_errors.check_for_period_error(data, period)
     period = int(period)
 
-    aroon_up = map(
-        lambda idx:
-        ((period -
+    a_up = [((period -
             data[idx+1-period:idx+1].index(np.max(data[idx+1-period:idx+1]))) /
-            period) * 100,
-        range(period-1, len(data))
-        )
-    aroon_up = fill_for_noncomputable_vals(data, aroon_up)
-    return aroon_up
+            float(period)) * 100 for idx in range(period-1, len(data))]
+    a_up = fill_for_noncomputable_vals(data, a_up)
+    return a_up
 
 
 def aroon_down(data, period):
@@ -34,11 +32,8 @@ def aroon_down(data, period):
     catch_errors.check_for_period_error(data, period)
     period = int(period)
 
-    aroon_down = map(
-        lambda idx:
-        ((period -
+    a_down = [((period -
             data[idx+1-period:idx+1].index(np.min(data[idx+1-period:idx+1]))) /
-            period) * 100,
-        range(period-1, len(data)))
-    aroon_down = fill_for_noncomputable_vals(data, aroon_down)
-    return aroon_down
+            float(period)) * 100 for idx in range(period-1, len(data))]
+    a_down = fill_for_noncomputable_vals(data, a_down)
+    return a_down

pyti/average_true_range.py

@@ -1,5 +1,7 @@
+from __future__ import absolute_import
 import numpy as np
 from pyti.true_range import true_range
+from six.moves import range
 
 
 def average_true_range(close_data, period):

pyti/average_true_range_percent.py

@@ -1,3 +1,4 @@
+from __future__ import absolute_import
 import numpy as np
 from pyti import catch_errors
 from pyti.average_true_range import (

pyti/bollinger_bands.py

@@ -1,9 +1,11 @@
+from __future__ import absolute_import
 import numpy as np
 from pyti import catch_errors
 from pyti.function_helper import fill_for_noncomputable_vals
 from pyti.simple_moving_average import (
     simple_moving_average as sma
     )
+from six.moves import range
 
 
 def upper_bollinger_band(data, period, std_mult=2.0):

pyti/catch_errors.py

@@ -23,7 +23,7 @@ def check_for_input_len_diff(*args):
     message that informs the developer that the data set's lengths are not the
     same.
     """
-    arrays_len = map(lambda arr: len(arr), args)
+    arrays_len = [len(arr) for arr in args]
     if not all(a == arrays_len[0] for a in arrays_len):
         err_msg = ("Error: mismatched data lengths, check to ensure that all "
                    "input data is the same length and valid")

pyti/chaikin_money_flow.py

@@ -1,6 +1,8 @@
+from __future__ import absolute_import
 import numpy as np
 from pyti import catch_errors
 from pyti.function_helper import fill_for_noncomputable_vals
+from six.moves import range
 
 
 def chaikin_money_flow(close_data, high_data, low_data, volume, period):
@@ -18,13 +20,9 @@ def chaikin_money_flow(close_data, high_data, low_data, volume, period):
     high_data = np.array(high_data)
     low_data = np.array(low_data)
     volume = np.array(volume)
-    cmf = map(
-        lambda idx:
-        sum((((close_data[idx+1-period:idx+1] - low_data[idx+1-period:idx+1]) -
+    cmf = [sum((((close_data[idx+1-period:idx+1] - low_data[idx+1-period:idx+1]) -
             (high_data[idx+1-period:idx+1] - close_data[idx+1-period:idx+1])) /
             (high_data[idx+1-period:idx+1] - low_data[idx+1-period:idx+1])) *
-            volume[idx+1-period:idx+1]) / sum(volume[idx+1-period:idx+1]),
-        range(period-1, len(close_data))
-        )
+            volume[idx+1-period:idx+1]) / sum(volume[idx+1-period:idx+1]) for idx in range(period-1, len(close_data))]
     cmf = fill_for_noncomputable_vals(close_data, cmf)
     return cmf

pyti/chande_momentum_oscillator.py

@@ -1,6 +1,8 @@
+from __future__ import absolute_import
 import numpy as np
 import warnings
 from pyti import catch_errors
+from six.moves import range
 
 
 def chande_momentum_oscillator(close_data, period):
@@ -14,21 +16,15 @@ def chande_momentum_oscillator(close_data, period):
 
     close_data = np.array(close_data)
 
-    moving_period_diffs = map(
-        lambda idx:
-            map(lambda i:
-                (close_data[idx+1-period:idx+1][i] -
-                 close_data[idx+1-period:idx+1][i-1]),
-                range(1, len(close_data[idx+1-period:idx+1]))),
-            range(0, len(close_data))
-        )
+    moving_period_diffs = [[(close_data[idx+1-period:idx+1][i] -
+                 close_data[idx+1-period:idx+1][i-1]) for i in range(1, len(close_data[idx+1-period:idx+1]))] for idx in range(0, len(close_data))]
 
     sum_up = []
     sum_down = []
     for period_diffs in moving_period_diffs:
-        ups = map(lambda val: val if val > 0 else 0, period_diffs)
+        ups = [val if val > 0 else 0 for val in period_diffs]
         sum_up.append(sum(ups))
-        downs = map(lambda val: abs(val) if val < 0 else 0, period_diffs)
+        downs = [abs(val) if val < 0 else 0 for val in period_diffs]
         sum_down.append(sum(downs))
 
     sum_up = np.array(sum_up)

pyti/commodity_channel_index.py

@@ -1,3 +1,4 @@
+from __future__ import absolute_import
 import numpy as np
 from pyti import catch_errors
 from pyti.typical_price import typical_price

pyti/detrended_price_oscillator.py

@@ -1,6 +1,8 @@
+from __future__ import absolute_import
 import numpy as np
 from pyti import catch_errors
 from pyti.function_helper import fill_for_noncomputable_vals
+from six.moves import range
 
 
 def detrended_price_oscillator(data, period):
@@ -12,10 +14,6 @@ def detrended_price_oscillator(data, period):
     """
     catch_errors.check_for_period_error(data, period)
     period = int(period)
-    dop = map(
-        lambda idx:
-        data[idx] - np.mean(data[idx+1-((period/2)+1):idx+1]),
-        range(period-1, len(data))
-        )
+    dop = [data[idx] - np.mean(data[idx+1-(int(period/2)+1):idx+1]) for idx in range(period-1, len(data))]
     dop = fill_for_noncomputable_vals(data, dop)
     return dop

pyti/directional_indicators.py

@@ -1,3 +1,4 @@
+from __future__ import absolute_import
 import numpy as np
 from pyti import catch_errors
 from pyti.smoothed_moving_average import (
@@ -6,6 +7,7 @@
 from pyti.average_true_range import (
     average_true_range as atr
     )
+from six.moves import range
 
 
 def calculate_up_moves(high_data):
@@ -15,10 +17,7 @@ def calculate_up_moves(high_data):
     Formula:
     UPMOVE = Ht - Ht-1
     """
-    up_moves = map(
-        lambda idx: high_data[idx] - high_data[idx-1],
-        range(1, len(high_data))
-        )
+    up_moves = [high_data[idx] - high_data[idx-1] for idx in range(1, len(high_data))]
     return [np.nan] + up_moves
 
 
@@ -29,10 +28,7 @@ def calculate_down_moves(low_data):
     Formula:
     DWNMOVE = Lt-1 - Lt
     """
-    down_moves = map(
-        lambda idx: low_data[idx-1] - low_data[idx],
-        range(1, len(low_data))
-        )
+    down_moves = [low_data[idx-1] - low_data[idx] for idx in range(1, len(low_data))]
     return [np.nan] + down_moves
 
 

pyti/double_exponential_moving_average.py

@@ -1,3 +1,4 @@
+from __future__ import absolute_import
 from pyti import catch_errors
 from pyti.exponential_moving_average import (
     exponential_moving_average as ema

pyti/double_smoothed_stochastic.py

@@ -1,9 +1,11 @@
+from __future__ import absolute_import
 import numpy as np
 from pyti import catch_errors
 from pyti.function_helper import fill_for_noncomputable_vals
 from pyti.exponential_moving_average import (
     exponential_moving_average as ema
     )
+from six.moves import range
 
 
 def double_smoothed_stochastic(data, period):
@@ -14,17 +16,9 @@ def double_smoothed_stochastic(data, period):
     dss = 100 *  EMA(Close - Lowest Low) / EMA(Highest High - Lowest Low)
     """
     catch_errors.check_for_period_error(data, period)
-    lows = map(
-        lambda idx:
-        data[idx] - np.min(data[idx+1-period:idx+1]),
-        range(period-1, len(data))
-        )
+    lows = [data[idx] - np.min(data[idx+1-period:idx+1]) for idx in range(period-1, len(data))]
     sm_lows = ema(ema(lows, period), period)
-    highs = map(
-        lambda idx:
-        np.max(data[idx+1-period:idx+1]) - np.min(data[idx+1-period:idx+1]),
-        range(period-1, len(data))
-        )
+    highs = [np.max(data[idx+1-period:idx+1]) - np.min(data[idx+1-period:idx+1]) for idx in range(period-1, len(data))]
     sm_highs = ema(ema(highs, period), period)
     dss = (sm_lows / sm_highs) * 100
 

pyti/exponential_moving_average.py

@@ -1,5 +1,7 @@
+from __future__ import absolute_import
 from pyti import catch_errors
 from pyti.function_helper import fill_for_noncomputable_vals
+from six.moves import range
 
 
 def exponential_moving_average(data, period):
@@ -13,12 +15,8 @@ def exponential_moving_average(data, period):
     where: w = 2 / (N + 1)
     """
     catch_errors.check_for_period_error(data, period)
-    emas = map(
-        lambda idx:
-        exponential_moving_average_helper(
-            data[idx - period + 1:idx + 1], period),
-        range(period - 1, len(data))
-        )
+    emas = [exponential_moving_average_helper(
+            data[idx - period + 1:idx + 1], period) for idx in range(period - 1, len(data))]
     emas = fill_for_noncomputable_vals(data, emas)
     return emas
 

pyti/function_helper.py

@@ -1,3 +1,4 @@
+from __future__ import absolute_import
 import numpy as np
 
 

pyti/hull_moving_average.py

@@ -1,3 +1,4 @@
+from __future__ import absolute_import
 import numpy as np
 from pyti import catch_errors
 from pyti.weighted_moving_average import (

pyti/ichimoku_cloud.py

@@ -1,19 +1,17 @@
+from __future__ import absolute_import
 import numpy as np
 from pyti import catch_errors
 from pyti.function_helper import fill_for_noncomputable_vals
+from six.moves import range
 
 
 def conversion_base_line_helper(data, period):
     """
     The only real difference between TenkanSen and KijunSen is the period value
     """
     catch_errors.check_for_period_error(data, period)
-    cblh = map(
-        lambda idx:
-        (np.max(data[idx+1-period:idx+1]) +
-            np.min(data[idx+1-period:idx+1])) / 2,
-        range(period-1, len(data))
-        )
+    cblh = [(np.max(data[idx+1-period:idx+1]) +
+            np.min(data[idx+1-period:idx+1])) / 2 for idx in range(period-1, len(data))]
 
     cblh = fill_for_noncomputable_vals(data, cblh)
     return cblh

pyti/keltner_bands.py

@@ -1,3 +1,4 @@
+from __future__ import absolute_import
 import numpy as np
 from pyti import catch_errors
 from pyti.simple_moving_average import (

pyti/linear_weighted_moving_average.py

@@ -1,5 +1,7 @@
+from __future__ import absolute_import
 from pyti import catch_errors
 from pyti.function_helper import fill_for_noncomputable_vals
+from six.moves import range
 
 
 def linear_weighted_moving_average(data, period):
@@ -11,13 +13,9 @@ def linear_weighted_moving_average(data, period):
     """
     catch_errors.check_for_period_error(data, period)
 
-    idx_period = range(1, period+1)
-    lwma = map(
-        lambda idx:
-        (sum([i * idx_period[data[idx-(period-1):idx+1].index(i)]
+    idx_period = list(range(1, period+1))
+    lwma = [(sum([i * idx_period[data[idx-(period-1):idx+1].index(i)]
               for i in data[idx-(period-1):idx+1]])) /
-            sum(range(1, len(data[idx+1-period:idx+1])+1)),
-        range(period-1, len(data))
-        )
+            sum(range(1, len(data[idx+1-period:idx+1])+1)) for idx in range(period-1, len(data))]
     lwma = fill_for_noncomputable_vals(data, lwma)
     return lwma