add support for pandas input data to the streaming functions

Author: briancappello

requirements_test.txt

@@ -1,2 +1,3 @@
 -r requirements.txt
 pandas==0.24.2
+nose==1.3.7

talib/__init__.py

@@ -34,6 +34,12 @@ def wrapper(*args, **kwargs):
 
             result = func(*args, **kwargs)
 
+            # check to see if we got a streaming result
+            first_result = result[0] if isinstance(result, tuple) else result
+            is_streaming_fn_result = not hasattr(first_result, '__len__')
+            if is_streaming_fn_result:
+                return result
+
             # Series was passed in, Series gets out; re-apply index
             if isinstance(result, tuple):
                 # Handle multi-array results such as BBANDS
@@ -58,11 +64,18 @@ def wrapper(*args, **kwargs):
     setattr(func, func_name, wrapped_func)
     globals()[func_name] = wrapped_func
 
+stream_func_names = ['stream_%s' % fname for fname in __TA_FUNCTION_NAMES__]
+stream = __import__("stream", globals(), locals(), stream_func_names, level=1)
+for func_name, stream_func_name in zip(__TA_FUNCTION_NAMES__, stream_func_names):
+    wrapped_func = _pandas_wrapper(getattr(stream, func_name))
+    setattr(stream, func_name, wrapped_func)
+    globals()[stream_func_name] = wrapped_func
+
 __version__ = '0.4.18'
 
 # In order to use this python library, talib (i.e. this __file__) will be
 # imported at some point, either explicitly or indirectly via talib.func
-# or talib.abstract. Here, we handle initalizing and shutting down the
+# or talib.abstract. Here, we handle initializing and shutting down the
 # underlying TA-Lib. Initialization happens on import, before any other TA-Lib
 # functions are called. Finally, when the python process exits, we shutdown
 # the underlying TA-Lib.

talib/test_stream.py

@@ -1,4 +1,6 @@
 import numpy as np
+import pandas as pd
+
 from nose.tools import assert_equals, assert_true, assert_raises
 
 import talib
@@ -21,6 +23,23 @@ def test_streaming():
     r = stream.MOM(a, timeperiod=7)
     assert_true(np.isnan(r))
 
+def test_streaming_pandas():
+    a = pd.Series([1,1,2,3,5,8,13])
+    r = stream.MOM(a, timeperiod=1)
+    assert_equals(r, 5)
+    r = stream.MOM(a, timeperiod=2)
+    assert_equals(r, 8)
+    r = stream.MOM(a, timeperiod=3)
+    assert_equals(r, 10)
+    r = stream.MOM(a, timeperiod=4)
+    assert_equals(r, 11)
+    r = stream.MOM(a, timeperiod=5)
+    assert_equals(r, 12)
+    r = stream.MOM(a, timeperiod=6)
+    assert_equals(r, 12)
+    r = stream.MOM(a, timeperiod=7)
+    assert_true(np.isnan(r))
+
 def test_CDL3BLACKCROWS():
     o = np.array([39.00, 39.00, 39.00, 39.00, 39.00, 39.00, 39.00, 39.00, 39.00, 39.00, 39.00, 39.00, 39.00, 39.00, 40.32, 40.51, 38.09, 35.00])
     h = np.array([40.84, 40.84, 40.84, 40.84, 40.84, 40.84, 40.84, 40.84, 40.84, 40.84, 40.84, 40.84, 40.84, 40.84, 41.69, 40.84, 38.12, 35.50])
@@ -29,3 +48,12 @@ def test_CDL3BLACKCROWS():
 
     r = stream.CDL3BLACKCROWS(o, h, l, c)
     assert_equals(r, -100)
+
+def test_CDL3BLACKCROWS_pandas():
+    o = pd.Series([39.00, 39.00, 39.00, 39.00, 39.00, 39.00, 39.00, 39.00, 39.00, 39.00, 39.00, 39.00, 39.00, 39.00, 40.32, 40.51, 38.09, 35.00])
+    h = pd.Series([40.84, 40.84, 40.84, 40.84, 40.84, 40.84, 40.84, 40.84, 40.84, 40.84, 40.84, 40.84, 40.84, 40.84, 41.69, 40.84, 38.12, 35.50])
+    l = pd.Series([35.80, 35.80, 35.80, 35.80, 35.80, 35.80, 35.80, 35.80, 35.80, 35.80, 35.80, 35.80, 35.80, 35.80, 39.26, 36.73, 33.37, 30.03])
+    c = pd.Series([40.29, 40.29, 40.29, 40.29, 40.29, 40.29, 40.29, 40.29, 40.29, 40.29, 40.29, 40.29, 40.29, 40.29, 40.46, 37.08, 33.37, 30.03])
+
+    r = stream.CDL3BLACKCROWS(o, h, l, c)
+    assert_equals(r, -100)