diff --git a/vnpy/talib/__init__.py b/vnpy/talib/__init__.py deleted file mode 100644 index ea38012f..00000000 --- a/vnpy/talib/__init__.py +++ /dev/null @@ -1,265 +0,0 @@ - -import atexit -from itertools import chain -from functools import wraps - -# If pandas is available, wrap talib functions so that they support -# pandas.Series input -try: - from pandas import Series as _pd_Series -except ImportError: - # pandas not available, nothing to wrap - _pandas_wrapper = lambda x: x -else: - def _pandas_wrapper(func): - @wraps(func) - def wrapper(*args, **kwargs): - try: - # Get the index of the first Series object, if any - index = next(arg.index - for arg in chain(args, kwargs.values()) - if isinstance(arg, _pd_Series)) - except StopIteration: - # No pandas.Series passed in; short-circuit - return func(*args, **kwargs) - - # Use Series' float64 values if pandas, else use values as passed - args = [arg.values.astype(float) if isinstance(arg, _pd_Series) else arg - for arg in args] - kwargs = {k: v.values.astype(float) if isinstance(v, _pd_Series) else v - for k, v in kwargs.items()} - - result = func(*args, **kwargs) - - # Series was passed in, Series gets out; re-apply index - if isinstance(result, tuple): - # Handle multi-array results such as BBANDS - return tuple(_pd_Series(arr, index=index) - for arr in result) - return _pd_Series(result, index=index) - - return wrapper - -from ._ta_lib import ( - _ta_initialize, _ta_shutdown, MA_Type, __ta_version__, - _ta_set_unstable_period as set_unstable_period, - _ta_get_unstable_period as get_unstable_period, - __TA_FUNCTION_NAMES__ -) - -func = __import__("_ta_lib", globals(), locals(), __TA_FUNCTION_NAMES__, level=1) -for func_name in __TA_FUNCTION_NAMES__: - wrapped_func = _pandas_wrapper(getattr(func, func_name)) - setattr(func, func_name, wrapped_func) - globals()[func_name] = wrapped_func - -__version__ = '0.4.17' - -# 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 -# 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. - -_ta_initialize() -atexit.register(_ta_shutdown) - -__function_groups__ = { - 'Cycle Indicators': [ - 'HT_DCPERIOD', - 'HT_DCPHASE', - 'HT_PHASOR', - 'HT_SINE', - 'HT_TRENDMODE', - ], - 'Math Operators': [ - 'ADD', - 'DIV', - 'MAX', - 'MAXINDEX', - 'MIN', - 'MININDEX', - 'MINMAX', - 'MINMAXINDEX', - 'MULT', - 'SUB', - 'SUM', - ], - 'Math Transform': [ - 'ACOS', - 'ASIN', - 'ATAN', - 'CEIL', - 'COS', - 'COSH', - 'EXP', - 'FLOOR', - 'LN', - 'LOG10', - 'SIN', - 'SINH', - 'SQRT', - 'TAN', - 'TANH', - ], - 'Momentum Indicators': [ - 'ADX', - 'ADXR', - 'APO', - 'AROON', - 'AROONOSC', - 'BOP', - 'CCI', - 'CMO', - 'DX', - 'MACD', - 'MACDEXT', - 'MACDFIX', - 'MFI', - 'MINUS_DI', - 'MINUS_DM', - 'MOM', - 'PLUS_DI', - 'PLUS_DM', - 'PPO', - 'ROC', - 'ROCP', - 'ROCR', - 'ROCR100', - 'RSI', - 'STOCH', - 'STOCHF', - 'STOCHRSI', - 'TRIX', - 'ULTOSC', - 'WILLR', - ], - 'Overlap Studies': [ - 'BBANDS', - 'DEMA', - 'EMA', - 'HT_TRENDLINE', - 'KAMA', - 'MA', - 'MAMA', - 'MAVP', - 'MIDPOINT', - 'MIDPRICE', - 'SAR', - 'SAREXT', - 'SMA', - 'T3', - 'TEMA', - 'TRIMA', - 'WMA', - ], - 'Pattern Recognition': [ - 'CDL2CROWS', - 'CDL3BLACKCROWS', - 'CDL3INSIDE', - 'CDL3LINESTRIKE', - 'CDL3OUTSIDE', - 'CDL3STARSINSOUTH', - 'CDL3WHITESOLDIERS', - 'CDLABANDONEDBABY', - 'CDLADVANCEBLOCK', - 'CDLBELTHOLD', - 'CDLBREAKAWAY', - 'CDLCLOSINGMARUBOZU', - 'CDLCONCEALBABYSWALL', - 'CDLCOUNTERATTACK', - 'CDLDARKCLOUDCOVER', - 'CDLDOJI', - 'CDLDOJISTAR', - 'CDLDRAGONFLYDOJI', - 'CDLENGULFING', - 'CDLEVENINGDOJISTAR', - 'CDLEVENINGSTAR', - 'CDLGAPSIDESIDEWHITE', - 'CDLGRAVESTONEDOJI', - 'CDLHAMMER', - 'CDLHANGINGMAN', - 'CDLHARAMI', - 'CDLHARAMICROSS', - 'CDLHIGHWAVE', - 'CDLHIKKAKE', - 'CDLHIKKAKEMOD', - 'CDLHOMINGPIGEON', - 'CDLIDENTICAL3CROWS', - 'CDLINNECK', - 'CDLINVERTEDHAMMER', - 'CDLKICKING', - 'CDLKICKINGBYLENGTH', - 'CDLLADDERBOTTOM', - 'CDLLONGLEGGEDDOJI', - 'CDLLONGLINE', - 'CDLMARUBOZU', - 'CDLMATCHINGLOW', - 'CDLMATHOLD', - 'CDLMORNINGDOJISTAR', - 'CDLMORNINGSTAR', - 'CDLONNECK', - 'CDLPIERCING', - 'CDLRICKSHAWMAN', - 'CDLRISEFALL3METHODS', - 'CDLSEPARATINGLINES', - 'CDLSHOOTINGSTAR', - 'CDLSHORTLINE', - 'CDLSPINNINGTOP', - 'CDLSTALLEDPATTERN', - 'CDLSTICKSANDWICH', - 'CDLTAKURI', - 'CDLTASUKIGAP', - 'CDLTHRUSTING', - 'CDLTRISTAR', - 'CDLUNIQUE3RIVER', - 'CDLUPSIDEGAP2CROWS', - 'CDLXSIDEGAP3METHODS', - ], - 'Price Transform': [ - 'AVGPRICE', - 'MEDPRICE', - 'TYPPRICE', - 'WCLPRICE', - ], - 'Statistic Functions': [ - 'BETA', - 'CORREL', - 'LINEARREG', - 'LINEARREG_ANGLE', - 'LINEARREG_INTERCEPT', - 'LINEARREG_SLOPE', - 'STDDEV', - 'TSF', - 'VAR', - ], - 'Volatility Indicators': [ - 'ATR', - 'NATR', - 'TRANGE', - ], - 'Volume Indicators': [ - 'AD', - 'ADOSC', - 'OBV' - ], - } - -def get_functions(): - """ - Returns a list of all the functions supported by TALIB - """ - ret = [] - for group in __function_groups__: - ret.extend(__function_groups__[group]) - return ret - -def get_function_groups(): - """ - Returns a dict with keys of function-group names and values of lists - of function names ie {'group_names': ['function_names']} - """ - return __function_groups__.copy() - -__all__ = ['get_functions', 'get_function_groups'] diff --git a/vnpy/talib/_ta_lib.cp37-win_amd64.pyd b/vnpy/talib/_ta_lib.cp37-win_amd64.pyd deleted file mode 100644 index 677e9b9e..00000000 Binary files a/vnpy/talib/_ta_lib.cp37-win_amd64.pyd and /dev/null differ diff --git a/vnpy/talib/abstract.py b/vnpy/talib/abstract.py deleted file mode 100644 index 2ee738ca..00000000 --- a/vnpy/talib/abstract.py +++ /dev/null @@ -1,27 +0,0 @@ -import talib._ta_lib as _ta_lib -from ._ta_lib import Function as _Function, __TA_FUNCTION_NAMES__, _get_defaults_and_docs - -# add some backwards compat for backtrader -from ._ta_lib import TA_FUNC_FLAGS, TA_INPUT_FLAGS, TA_OUTPUT_FLAGS - -_func_obj_mapping = { - func_name: getattr(_ta_lib, func_name) - for func_name in __TA_FUNCTION_NAMES__ -} - - -def Function(function_name, *args, **kwargs): - func_name = function_name.upper() - if func_name not in _func_obj_mapping: - raise Exception('%s not supported by TA-LIB.' % func_name) - - return _Function( - func_name, _func_obj_mapping[func_name], *args, **kwargs - ) - - -for func_name in __TA_FUNCTION_NAMES__: - globals()[func_name] = Function(func_name) - - -__all__ = ["Function", "_get_defaults_and_docs"] + __TA_FUNCTION_NAMES__ diff --git a/vnpy/talib/deprecated.py b/vnpy/talib/deprecated.py deleted file mode 100644 index 264fc46c..00000000 --- a/vnpy/talib/deprecated.py +++ /dev/null @@ -1,2 +0,0 @@ -# TA_MAType enums -MA_SMA, MA_EMA, MA_WMA, MA_DEMA, MA_TEMA, MA_TRIMA, MA_KAMA, MA_MAMA, MA_T3 = range(9) diff --git a/vnpy/talib/stream.py b/vnpy/talib/stream.py deleted file mode 100644 index ee2cc85e..00000000 --- a/vnpy/talib/stream.py +++ /dev/null @@ -1,6 +0,0 @@ -import talib._ta_lib as _ta_lib -from ._ta_lib import __TA_FUNCTION_NAMES__ - - -for func_name in __TA_FUNCTION_NAMES__: - globals()[func_name] = getattr(_ta_lib, "stream_%s" % func_name) diff --git a/vnpy/talib/test_abstract.py b/vnpy/talib/test_abstract.py deleted file mode 100644 index 8b7e3e6a..00000000 --- a/vnpy/talib/test_abstract.py +++ /dev/null @@ -1,257 +0,0 @@ -import numpy as np -from nose.tools import ( - assert_equals, - assert_true, - assert_false, - assert_raises, - ) - -try: - from collections import OrderedDict -except ImportError: # handle python 2.6 and earlier - from ordereddict import OrderedDict - -import talib -from talib import func -from talib import abstract -from talib.test_data import ford_2012, assert_np_arrays_equal, assert_np_arrays_not_equal - - -def test_pandas(): - import pandas - input_df = pandas.DataFrame(ford_2012) - input_dict = dict((k, pandas.Series(v)) for k, v in ford_2012.items()) - - expected_k, expected_d = func.STOCH(ford_2012['high'], ford_2012['low'], ford_2012['close']) # 5, 3, 0, 3, 0 - output = abstract.Function('stoch', input_df).outputs - assert_true(isinstance(output, pandas.DataFrame)) - assert_np_arrays_equal(expected_k, output['slowk']) - assert_np_arrays_equal(expected_d, output['slowd']) - output = abstract.Function('stoch', input_dict).outputs - assert_true(isinstance(output, list)) - assert_np_arrays_equal(expected_k, output[0]) - assert_np_arrays_equal(expected_d, output[1]) - - expected = func.SMA(ford_2012['close'], 10) - output = abstract.Function('sma', input_df, 10).outputs - assert_true(isinstance(output, pandas.Series)) - assert_np_arrays_equal(expected, output) - output = abstract.Function('sma', input_dict, 10).outputs - assert_true(isinstance(output, np.ndarray)) - assert_np_arrays_equal(expected, output) - - -def test_pandas_series(): - import pandas - input_df = pandas.DataFrame(ford_2012) - output = talib.SMA(input_df['close'], 10) - expected = pandas.Series(func.SMA(ford_2012['close'], 10), - index=input_df.index) - pandas.util.testing.assert_series_equal(output, expected) - - # Test kwargs - output = talib.SMA(real=input_df['close'], timeperiod=10) - pandas.util.testing.assert_series_equal(output, expected) - - # Test talib.func API - output = func.SMA(input_df['close'], timeperiod=10) - pandas.util.testing.assert_series_equal(output, expected) - - # Test multiple outputs such as from BBANDS - _, output, _ = talib.BBANDS(input_df['close'], 10) - expected = pandas.Series(func.BBANDS(ford_2012['close'], 10)[1], - index=input_df.index) - pandas.util.testing.assert_series_equal(output, expected) - - -def test_SMA(): - expected = func.SMA(ford_2012['close'], 10) - assert_np_arrays_equal(expected, abstract.Function('sma', ford_2012, 10).outputs) - assert_np_arrays_equal(expected, abstract.Function('sma')(ford_2012, 10, price='close')) - assert_np_arrays_equal(expected, abstract.Function('sma')(ford_2012, timeperiod=10)) - expected = func.SMA(ford_2012['open'], 10) - assert_np_arrays_equal(expected, abstract.Function('sma', ford_2012, 10, price='open').outputs) - assert_np_arrays_equal(expected, abstract.Function('sma', price='low')(ford_2012, 10, price='open')) - assert_np_arrays_not_equal(expected, abstract.Function('sma', ford_2012, 10, price='open')(timeperiod=20)) - assert_np_arrays_not_equal(expected, abstract.Function('sma', ford_2012)(10, price='close')) - assert_np_arrays_not_equal(expected, abstract.Function('sma', 10)(ford_2012, price='high')) - assert_np_arrays_not_equal(expected, abstract.Function('sma', price='low')(ford_2012, 10)) - input_arrays = {'foobarbaz': ford_2012['open']} - assert_np_arrays_equal(expected, abstract.SMA(input_arrays, 10, price='foobarbaz')) - - -def test_STOCH(): - # check defaults match - expected_k, expected_d = func.STOCH(ford_2012['high'], ford_2012['low'], ford_2012['close']) # 5, 3, 0, 3, 0 - got_k, got_d = abstract.Function('stoch', ford_2012).outputs - assert_np_arrays_equal(expected_k, got_k) - assert_np_arrays_equal(expected_d, got_d) - - expected_k, expected_d = func.STOCH(ford_2012['high'], ford_2012['low'], ford_2012['close']) - got_k, got_d = abstract.Function('stoch', ford_2012)(5, 3, 0, 3, 0) - assert_np_arrays_equal(expected_k, got_k) - assert_np_arrays_equal(expected_d, got_d) - - expected_k, expected_d = func.STOCH(ford_2012['high'], ford_2012['low'], ford_2012['close'], 15) - got_k, got_d = abstract.Function('stoch', ford_2012)(15, 5, 0, 5, 0) - assert_np_arrays_not_equal(expected_k, got_k) - assert_np_arrays_not_equal(expected_d, got_d) - - expected_k, expected_d = func.STOCH(ford_2012['high'], ford_2012['low'], ford_2012['close'], 15, 5, 1, 5, 1) - got_k, got_d = abstract.Function('stoch', ford_2012)(15, 5, 1, 5, 1) - assert_np_arrays_equal(expected_k, got_k) - assert_np_arrays_equal(expected_d, got_d) - -def test_doji_candle(): - expected = func.CDLDOJI(ford_2012['open'], ford_2012['high'], ford_2012['low'], ford_2012['close']) - got = abstract.Function('CDLDOJI').run(ford_2012) - assert_np_arrays_equal(got, expected) - -def test_MAVP(): - mavp = abstract.MAVP - assert_raises(Exception, mavp.set_input_arrays, ford_2012) - input_d = {} - input_d['close'] = ford_2012['close'] - input_d['periods'] = np.arange(30) - assert_true(mavp.set_input_arrays(input_d)) - assert_equals(mavp.input_arrays, input_d) - -def test_info(): - stochrsi = abstract.Function('STOCHRSI') - stochrsi.input_names = {'price': 'open'} - stochrsi.parameters = {'fastd_matype': talib.MA_Type.EMA} - expected = { - 'display_name': 'Stochastic Relative Strength Index', - 'function_flags': ['Function has an unstable period'], - 'group': 'Momentum Indicators', - 'input_names': OrderedDict([('price', 'open')]), - 'name': 'STOCHRSI', - 'output_flags': OrderedDict([ - ('fastk', ['Line']), - ('fastd', ['Line']), - ]), - 'output_names': ['fastk', 'fastd'], - 'parameters': OrderedDict([ - ('timeperiod', 14), - ('fastk_period', 5), - ('fastd_period', 3), - ('fastd_matype', 1), - ]), - } - assert_equals(expected, stochrsi.info) - - expected = { - 'display_name': 'Bollinger Bands', - 'function_flags': ['Output scale same as input'], - 'group': 'Overlap Studies', - 'input_names': OrderedDict([('price', 'close')]), - 'name': 'BBANDS', - 'output_flags': OrderedDict([ - ('upperband', ['Values represent an upper limit']), - ('middleband', ['Line']), - ('lowerband', ['Values represent a lower limit']), - ]), - 'output_names': ['upperband', 'middleband', 'lowerband'], - 'parameters': OrderedDict([ - ('timeperiod', 5), - ('nbdevup', 2), - ('nbdevdn', 2), - ('matype', 0), - ]), - } - assert_equals(expected, abstract.Function('BBANDS').info) - -def test_input_names(): - expected = OrderedDict([('price', 'close')]) - assert_equals(expected, abstract.Function('MAMA').input_names) - - # test setting input_names - obv = abstract.Function('OBV') - expected = OrderedDict([ - ('price', 'open'), - ('prices', ['volume']), - ]) - obv.input_names = expected - assert_equals(obv.input_names, expected) - - obv.input_names = { - 'price': 'open', - 'prices': ['volume'], - } - assert_equals(obv.input_names, expected) - -def test_input_arrays(): - mama = abstract.Function('MAMA') - # test default setting - expected = { - 'open': None, - 'high': None, - 'low': None, - 'close': None, - 'volume': None, - } - assert_equals(expected, mama.get_input_arrays()) - # test setting/getting input_arrays - assert_true(mama.set_input_arrays(ford_2012)) - assert_equals(mama.get_input_arrays(), ford_2012) - assert_raises(Exception, - mama.set_input_arrays, {'hello': 'fail', 'world': 'bye'}) - - # test only required keys are needed - willr = abstract.Function('WILLR') - reqd = willr.input_names['prices'] - input_d = dict([(key, ford_2012[key]) for key in reqd]) - assert_true(willr.set_input_arrays(input_d)) - assert_equals(willr.input_arrays, input_d) - - # test extraneous keys are ignored - input_d['extra_stuffs'] = 'you should never see me' - input_d['date'] = np.random.rand(100) - assert_true(willr.set_input_arrays(input_d)) - - # test missing keys get detected - input_d['open'] = ford_2012['open'] - input_d.pop('close') - assert_raises(Exception, willr.set_input_arrays, input_d) - - # test changing input_names on the Function - willr.input_names = {'prices': ['high', 'low', 'open']} - assert_true(willr.set_input_arrays(input_d)) - -def test_parameters(): - stoch = abstract.Function('STOCH') - expected = OrderedDict([ - ('fastk_period', 5), - ('slowk_period', 3), - ('slowk_matype', 0), - ('slowd_period', 3), - ('slowd_matype', 0), - ]) - assert_equals(expected, stoch.parameters) - - stoch.parameters = {'fastk_period': 10} - expected['fastk_period'] = 10 - assert_equals(expected, stoch.parameters) - - stoch.parameters = {'slowk_period': 8, 'slowd_period': 5} - expected['slowk_period'] = 8 - expected['slowd_period'] = 5 - assert_equals(expected, stoch.parameters) - - stoch.parameters = {'slowd_matype': talib.MA_Type.T3} - expected['slowd_matype'] = 8 - assert_equals(expected, stoch.parameters) - - stoch.parameters = { - 'slowk_matype': talib.MA_Type.WMA, - 'slowd_matype': talib.MA_Type.EMA, - } - expected['slowk_matype'] = 2 - expected['slowd_matype'] = 1 - assert_equals(expected, stoch.parameters) - -def test_lookback(): - assert_equals(abstract.Function('SMA', 10).lookback, 9) - - stochrsi = abstract.Function('stochrsi', 20, 5, 3) - assert_equals(stochrsi.lookback, 26) diff --git a/vnpy/talib/test_data.py b/vnpy/talib/test_data.py deleted file mode 100644 index 3fbaf37f..00000000 --- a/vnpy/talib/test_data.py +++ /dev/null @@ -1,242 +0,0 @@ - -from __future__ import print_function - -import numpy as np - -from nose.tools import assert_equal, assert_not_equal, assert_true - -ford_2012_dates = np.asarray([ 20120103, 20120104, 20120105, 20120106, 20120109, - 20120110, 20120111, 20120112, 20120113, 20120117, 20120118, 20120119, - 20120120, 20120123, 20120124, 20120125, 20120126, 20120127, 20120130, - 20120131, 20120201, 20120202, 20120203, 20120206, 20120207, 20120208, - 20120209, 20120210, 20120213, 20120214, 20120215, 20120216, 20120217, - 20120221, 20120222, 20120223, 20120224, 20120227, 20120228, 20120229, - 20120301, 20120302, 20120305, 20120306, 20120307, 20120308, 20120309, - 20120312, 20120313, 20120314, 20120315, 20120316, 20120319, 20120320, - 20120321, 20120322, 20120323, 20120326, 20120327, 20120328, 20120329, - 20120330, 20120402, 20120403, 20120404, 20120405, 20120409, 20120410, - 20120411, 20120412, 20120413, 20120416, 20120417, 20120418, 20120419, - 20120420, 20120423, 20120424, 20120425, 20120426, 20120427, 20120430, - 20120501, 20120502, 20120503, 20120504, 20120507, 20120508, 20120509, - 20120510, 20120511, 20120514, 20120515, 20120516, 20120517, 20120518, - 20120521, 20120522, 20120523, 20120524, 20120525, 20120529, 20120530, - 20120531, 20120601, 20120604, 20120605, 20120606, 20120607, 20120608, - 20120611, 20120612, 20120613, 20120614, 20120615, 20120618, 20120619, - 20120620, 20120621, 20120622, 20120625, 20120626, 20120627, 20120628, - 20120629, 20120702, 20120703, 20120705, 20120706, 20120709, 20120710, - 20120711, 20120712, 20120713, 20120716, 20120717, 20120718, 20120719, - 20120720, 20120723, 20120724, 20120725, 20120726, 20120727, 20120730, - 20120731, 20120801, 20120802, 20120803, 20120806, 20120807, 20120808, - 20120809, 20120810, 20120813, 20120814, 20120815, 20120816, 20120817, - 20120820, 20120821, 20120822, 20120823, 20120824, 20120827, 20120828, - 20120829, 20120830, 20120831, 20120904, 20120905, 20120906, 20120907, - 20120910, 20120911, 20120912, 20120913, 20120914, 20120917, 20120918, - 20120919, 20120920, 20120921, 20120924, 20120925, 20120926, 20120927, - 20120928, 20121001, 20121002, 20121003, 20121004, 20121005, 20121008, - 20121009, 20121010, 20121011, 20121012, 20121015, 20121016, 20121017, - 20121018, 20121019, 20121022, 20121023, 20121024, 20121025, 20121026, - 20121031, 20121101, 20121102, 20121105, 20121106, 20121107, 20121108, - 20121109, 20121112, 20121113, 20121114, 20121115, 20121116, 20121119, - 20121120, 20121121, 20121123, 20121126, 20121127, 20121128, 20121129, - 20121130, 20121203, 20121204, 20121205, 20121206, 20121207, 20121210, - 20121211, 20121212, 20121213, 20121214, 20121217, 20121218, 20121219, - 20121220, 20121221, 20121224, 20121226, 20121227, 20121228, 20121231 ]) - -ford_2012 = { - 'open': np.asarray([ 11.00, 11.15, 11.33, 11.74, 11.83, 12.00, 11.74, 12.16, - 12.01, 12.20, 12.03, 12.48, 12.55, 12.69, 12.56, 12.80, 13.03, 11.96, - 12.06, 12.47, 12.73, 12.40, 12.47, 12.85, 12.93, 12.91, 12.89, 12.52, - 12.74, 12.46, 12.47, 12.38, 12.84, 12.74, 12.49, 12.27, 12.43, 12.11, - 12.34, 12.28, 12.48, 12.74, 12.67, 12.23, 12.21, 12.41, 12.53, 12.57, - 12.48, 12.64, 12.90, 12.86, 12.52, 12.48, 12.59, 12.48, 12.31, 12.45, - 12.51, 12.35, 12.33, 12.55, 12.50, 12.71, 12.46, 12.38, 12.26, 12.19, - 11.99, 11.94, 11.98, 12.01, 11.98, 11.81, 11.81, 11.71, 11.15, 11.61, - 11.51, 11.71, 12.03, 11.42, 11.25, 11.16, 11.13, 10.84, 10.53, 10.60, - 10.48, 10.83, 10.61, 10.41, 10.34, 10.23, 10.16, 10.08, 10.02, 10.25, - 10.32, 10.50, 10.61, 10.69, 10.73, 10.62, 10.33, 10.15, 10.01, 10.29, - 10.73, 10.48, 10.77, 10.47, 10.39, 10.27, 10.40, 10.35, 10.37, 10.58, - 10.65, 10.35, 10.13, 10.06, 10.05, 9.93, 9.95, 9.50, 9.53, 9.67, 9.47, - 9.46, 9.50, 9.33, 9.26, 9.16, 9.22, 9.28, 9.38, 9.45, 9.28, 9.08, 9.17, - 9.17, 9.05, 8.99, 9.04, 9.13, 9.29, 8.99, 9.02, 9.13, 9.18, 9.25, 9.31, - 9.30, 9.35, 9.45, 9.44, 9.50, 9.65, 9.58, 9.65, 9.50, 9.45, 9.42, 9.51, - 9.37, 9.33, 9.30, 9.39, 9.37, 9.45, 9.66, 9.95, 10.08, 10.18, 10.25, - 10.20, 10.41, 10.27, 10.30, 10.49, 10.48, 10.53, 10.30, 10.35, 9.98, - 10.13, 9.99, 9.89, 10.01, 9.82, 10.06, 10.17, 10.06, 10.21, 10.12, - 10.06, 10.14, 10.11, 10.26, 10.31, 10.36, 10.42, 10.14, 10.02, 10.08, - 10.42, 10.35, 10.70, 11.19, 11.31, 11.15, 11.33, 11.25, 11.07, 10.76, - 11.03, 10.89, 11.02, 10.57, 10.58, 10.65, 10.85, 10.84, 10.98, 11.05, - 11.10, 11.05, 11.32, 11.52, 11.56, 11.40, 11.32, 11.26, 11.27, 11.41, - 11.51, 11.52, 11.46, 11.27, 11.16, 11.48, 11.79, 11.74, 11.55, 11.67, - 12.31, 12.79, 12.55, 12.88, ]), - - 'high': np.asarray([ 11.25, 11.53, 11.63, 11.80, 11.95, 12.05, 12.18, 12.18, - 12.08, 12.26, 12.37, 12.72, 12.64, 12.84, 12.86, 12.98, 13.05, 12.53, - 12.44, 12.51, 12.75, 12.43, 12.84, 13.00, 12.97, 12.96, 12.90, 12.66, - 12.74, 12.58, 12.57, 12.77, 12.88, 12.76, 12.51, 12.44, 12.46, 12.36, - 12.35, 12.55, 12.77, 12.94, 12.68, 12.25, 12.30, 12.55, 12.73, 12.59, - 12.72, 12.90, 13.04, 12.90, 12.68, 12.61, 12.67, 12.54, 12.37, 12.50, - 12.61, 12.36, 12.52, 12.58, 12.65, 12.95, 12.52, 12.58, 12.29, 12.28, - 12.02, 12.13, 12.03, 12.05, 12.00, 11.85, 11.88, 11.72, 11.40, 11.61, - 11.75, 11.93, 12.04, 11.47, 11.34, 11.17, 11.15, 10.87, 10.79, 10.64, - 10.81, 10.86, 10.83, 10.53, 10.34, 10.43, 10.25, 10.18, 10.23, 10.40, - 10.45, 10.62, 10.68, 10.88, 10.75, 10.68, 10.37, 10.18, 10.24, 10.58, - 10.78, 10.68, 10.80, 10.55, 10.49, 10.45, 10.42, 10.40, 10.64, 10.74, - 10.68, 10.40, 10.18, 10.08, 10.10, 10.09, 9.98, 9.60, 9.79, 9.74, 9.52, - 9.47, 9.55, 9.38, 9.28, 9.32, 9.32, 9.35, 9.52, 9.50, 9.35, 9.21, 9.24, - 9.20, 9.11, 9.10, 9.18, 9.28, 9.42, 9.03, 9.15, 9.21, 9.39, 9.38, 9.46, - 9.36, 9.42, 9.66, 9.54, 9.67, 9.66, 9.64, 9.70, 9.56, 9.54, 9.52, 9.52, - 9.44, 9.40, 9.34, 9.43, 9.47, 9.62, 9.96, 10.23, 10.28, 10.25, 10.30, - 10.38, 10.57, 10.42, 10.45, 10.66, 10.52, 10.54, 10.40, 10.37, 10.12, - 10.18, 10.00, 10.08, 10.05, 10.02, 10.15, 10.28, 10.12, 10.25, 10.12, - 10.26, 10.25, 10.25, 10.32, 10.41, 10.57, 10.43, 10.24, 10.11, 10.29, - 10.49, 10.42, 11.17, 11.30, 11.38, 11.35, 11.59, 11.34, 11.23, 11.10, - 11.16, 11.10, 11.05, 10.80, 10.64, 10.90, 11.02, 11.00, 11.10, 11.14, - 11.27, 11.26, 11.53, 11.60, 11.70, 11.44, 11.40, 11.31, 11.50, 11.53, - 11.58, 11.56, 11.50, 11.27, 11.41, 11.68, 11.85, 11.80, 11.86, 12.40, - 12.79, 12.81, 12.88, 13.08, ]), - - 'low': np.asarray([ 10.99, 11.07, 11.24, 11.52, 11.70, 11.63, 11.65, 11.89, - 11.84, 11.96, 12.00, 12.43, 12.45, 12.55, 12.46, 12.70, 12.66, 11.79, - 12.00, 12.20, 12.29, 12.20, 12.39, 12.71, 12.83, 12.80, 12.67, 12.37, - 12.51, 12.34, 12.33, 12.38, 12.71, 12.46, 12.22, 12.16, 12.19, 11.99, - 12.20, 12.25, 12.45, 12.68, 12.41, 12.00, 12.15, 12.32, 12.48, 12.37, - 12.40, 12.63, 12.83, 12.51, 12.48, 12.39, 12.55, 12.24, 12.18, 12.39, - 12.30, 12.18, 12.24, 12.40, 12.44, 12.46, 12.32, 12.38, 12.11, 11.65, - 11.88, 11.86, 11.84, 11.83, 11.88, 11.72, 11.58, 11.39, 11.15, 11.36, - 11.43, 11.67, 11.52, 11.15, 11.11, 11.00, 10.85, 10.63, 10.52, 10.40, - 10.41, 10.66, 10.56, 10.30, 10.10, 10.15, 10.01, 9.96, 10.00, 10.15, - 10.22, 10.38, 10.51, 10.68, 10.52, 10.40, 10.06, 9.91, 9.97, 10.27, - 10.52, 10.38, 10.45, 10.31, 10.22, 10.21, 10.26, 10.26, 10.35, 10.52, - 10.25, 10.18, 9.95, 9.96, 9.97, 9.93, 9.46, 9.30, 9.49, 9.53, 9.40, - 9.31, 9.28, 9.26, 9.12, 9.14, 9.15, 9.12, 9.34, 9.33, 9.18, 9.05, 8.95, - 8.91, 8.83, 8.88, 9.01, 9.12, 8.99, 8.82, 8.96, 9.09, 9.18, 9.24, 9.30, - 9.23, 9.25, 9.42, 9.41, 9.49, 9.60, 9.51, 9.52, 9.40, 9.42, 9.41, 9.38, - 9.31, 9.29, 9.25, 9.31, 9.35, 9.39, 9.66, 9.93, 10.06, 10.13, 10.17, - 10.12, 10.39, 10.26, 10.28, 10.45, 10.35, 10.36, 10.26, 10.06, 9.86, - 10.02, 9.81, 9.88, 9.71, 9.76, 9.96, 10.13, 9.99, 10.02, 9.95, 10.05, - 10.09, 10.09, 10.22, 10.26, 10.33, 10.13, 10.03, 9.97, 10.01, 10.28, - 10.22, 10.60, 10.88, 11.15, 11.13, 11.26, 11.04, 10.89, 10.71, 10.96, - 10.86, 10.62, 10.46, 10.38, 10.65, 10.76, 10.80, 10.96, 10.97, 11.10, - 10.98, 11.32, 11.33, 11.40, 11.23, 11.18, 11.19, 11.26, 11.41, 11.40, - 11.43, 11.21, 11.03, 11.14, 11.40, 11.62, 11.58, 11.47, 11.67, 12.31, - 12.36, 12.52, 12.76, ]), - - 'close': np.asarray([ 11.13, 11.30, 11.59, 11.71, 11.80, 11.80, 12.07, 12.14, - 12.04, 12.02, 12.34, 12.61, 12.59, 12.66, 12.82, 12.93, 12.79, 12.21, - 12.29, 12.42, 12.33, 12.26, 12.79, 12.96, 12.88, 12.84, 12.69, 12.44, - 12.54, 12.48, 12.38, 12.74, 12.75, 12.53, 12.28, 12.40, 12.23, 12.30, - 12.25, 12.38, 12.66, 12.72, 12.46, 12.09, 12.24, 12.46, 12.58, 12.43, - 12.70, 12.88, 12.90, 12.51, 12.63, 12.54, 12.57, 12.32, 12.32, 12.48, - 12.32, 12.32, 12.50, 12.48, 12.62, 12.64, 12.51, 12.47, 12.22, 11.79, - 11.91, 12.07, 11.92, 11.88, 11.91, 11.79, 11.66, 11.41, 11.35, 11.39, - 11.73, 11.87, 11.60, 11.28, 11.23, 11.10, 10.92, 10.67, 10.66, 10.61, - 10.69, 10.71, 10.58, 10.32, 10.15, 10.16, 10.01, 10.01, 10.20, 10.19, - 10.41, 10.59, 10.60, 10.84, 10.66, 10.56, 10.12, 10.04, 10.19, 10.57, - 10.55, 10.66, 10.45, 10.50, 10.30, 10.41, 10.35, 10.34, 10.56, 10.65, - 10.27, 10.19, 10.01, 10.01, 10.02, 10.09, 9.59, 9.39, 9.60, 9.57, 9.50, - 9.45, 9.35, 9.33, 9.13, 9.27, 9.26, 9.34, 9.38, 9.35, 9.21, 9.17, 9.06, - 8.97, 8.96, 9.00, 9.10, 9.24, 9.04, 8.92, 9.09, 9.15, 9.31, 9.35, 9.34, - 9.35, 9.40, 9.44, 9.49, 9.59, 9.63, 9.63, 9.53, 9.49, 9.45, 9.49, 9.39, - 9.34, 9.32, 9.31, 9.34, 9.41, 9.57, 9.92, 10.14, 10.11, 10.15, 10.21, - 10.34, 10.53, 10.39, 10.42, 10.59, 10.44, 10.40, 10.32, 10.09, 10.01, - 10.02, 9.86, 9.93, 9.79, 9.94, 10.11, 10.16, 10.05, 10.10, 9.98, 10.14, - 10.12, 10.22, 10.30, 10.41, 10.43, 10.18, 10.17, 10.00, 10.17, 10.39, - 10.36, 11.16, 11.25, 11.17, 11.25, 11.42, 11.06, 10.90, 10.93, 10.97, - 11.00, 10.67, 10.57, 10.50, 10.83, 10.85, 10.92, 11.10, 11.11, 11.10, - 11.25, 11.53, 11.45, 11.41, 11.31, 11.31, 11.24, 11.48, 11.47, 11.49, - 11.47, 11.27, 11.10, 11.39, 11.67, 11.73, 11.77, 11.86, 12.40, 12.79, - 12.76, 12.87, 12.95, ]), - - 'volume': np.asarray([ 45709900, 79725200, 67877500, 59840700, 53981500, - 121750600, 63806000, 48687700, 46366700, 44398400, 47102700, 70894200, - 43705700, 49379700, 45768400, 54021600, 75470700, 142155300, 57752600, - 46412100, 71669000, 48347600, 78851200, 46363300, 39413500, 35352500, - 52290500, 52505500, 34474400, 39627900, 38174800, 49164400, 30778000, - 38409800, 43326000, 36747600, 31399300, 38703400, 30789000, 62093700, - 68262000, 49063500, 28433700, 57374500, 28440900, 37099100, 36159300, - 30275700, 42783600, 47578500, 55286600, 77119600, 52445700, 40214400, - 27521400, 50117100, 44755000, 26692200, 35070700, 41051700, 51039700, - 36381000, 43966900, 97034200, 51505000, 37939500, 42515300, 77370300, - 34724400, 26988800, 39675000, 31903500, 35981200, 32314000, 48169200, - 52631000, 31269200, 38615200, 45185400, 40889300, 83070300, 46156300, - 43959200, 48572900, 40238400, 53268400, 33235200, 46174500, 54501200, - 42526100, 36561300, 50225200, 41886500, 44321300, 49648900, 50572000, - 38134900, 44295700, 75647800, 45334100, 30430800, 43760600, 44592100, - 54297000, 68237000, 57305600, 38326200, 50458000, 33846100, 30811600, - 35811400, 35130800, 53471900, 37531800, 39442000, 27361000, 37155900, - 40810100, 40062800, 56427300, 44297600, 31871900, 33278900, 38648400, - 138138600, 63388600, 49629300, 31783900, 30355400, 37441600, 33516600, - 32028700, 55111000, 30248300, 28838200, 29510000, 31010000, 33615000, - 27968300, 33773800, 53519200, 44338200, 51798900, 67986800, 40958300, - 41360900, 65973000, 45326500, 38631400, 23819100, 43574500, 22630300, - 30909800, 19618800, 21122000, 21129500, 21308300, 34323700, 34533900, - 38923800, 26281100, 26965500, 23537700, 19574600, 22754200, 23084400, - 26115700, 16459400, 28029200, 37965000, 40608800, 67996400, 60617000, - 43381300, 28165300, 28046500, 50920200, 55934300, 31922200, 34937000, - 42403000, 28755100, 35459800, 28557900, 36866300, 44362600, 25740900, - 44586300, 33445600, 63630000, 51023800, 46855500, 40693900, 25473900, - 38235700, 33951600, 39328700, 24108500, 26466500, 32788400, 29346300, - 44041700, 40493000, 39149700, 32476500, 49339800, 59290900, 43485500, - 137960900, 88770100, 53399000, 37995000, 51232200, 56674900, 45948800, - 40703600, 25723100, 33342900, 45664700, 48879800, 45346200, 39359100, - 34739800, 21181700, 16032200, 26831700, 37610000, 38496900, 57289300, - 41329600, 47746300, 37760200, 33152400, 31065800, 38404500, 26025200, - 36326900, 31099900, 35443200, 36933500, 46983300, 61810400, 54884700, - 47750100, 94489300, 91734900, 140331900, 108315100, 95668600, 106908900 ]), - } - -series = np.array([ 91.50, 94.81, 94.38, 95.09, 93.78, 94.62, 92.53, 92.75, - 90.31, 92.47, 96.12, 97.25, 98.50, 89.88, 91.00, 92.81, 89.16, 89.34, - 91.62, 89.88, 88.38, 87.62, 84.78, 83.00, 83.50, 81.38, 84.44, 89.25, - 86.38, 86.25, 85.25, 87.12, 85.81, 88.97, 88.47, 86.88, 86.81, 84.88, - 84.19, 83.88, 83.38, 85.50, 89.19, 89.44, 91.09, 90.75, 91.44, 89.00, - 91.00, 90.50, 89.03, 88.81, 84.28, 83.50, 82.69, 84.75, 85.66, 86.19, - 88.94, 89.28, 88.62, 88.50, 91.97, 91.50, 93.25, 93.50, 93.16, 91.72, - 90.00, 89.69, 88.88, 85.19, 83.38, 84.88, 85.94, 97.25, 99.88, 104.94, - 106.00, 102.50, 102.41, 104.59, 106.12, 106.00, 106.06, 104.62, 108.62, - 109.31, 110.50, 112.75, 123.00, 119.62, 118.75, 119.25, 117.94, 116.44, - 115.19, 111.88, 110.59, 118.12, 116.00, 116.00, 112.00, 113.75, 112.94, - 116.00, 120.50, 116.62, 117.00, 115.25, 114.31, 115.50, 115.87, 120.69, - 120.19, 120.75, 124.75, 123.37, 122.94, 122.56, 123.12, 122.56, 124.62, - 129.25, 131.00, 132.25, 131.00, 132.81, 134.00, 137.38, 137.81, 137.88, - 137.25, 136.31, 136.25, 134.63, 128.25, 129.00, 123.87, 124.81, 123.00, - 126.25, 128.38, 125.37, 125.69, 122.25, 119.37, 118.50, 123.19, 123.50, - 122.19, 119.31, 123.31, 121.12, 123.37, 127.37, 128.50, 123.87, 122.94, - 121.75, 124.44, 122.00, 122.37, 122.94, 124.00, 123.19, 124.56, 127.25, - 125.87, 128.86, 132.00, 130.75, 134.75, 135.00, 132.38, 133.31, 131.94, - 130.00, 125.37, 130.13, 127.12, 125.19, 122.00, 125.00, 123.00, 123.50, - 120.06, 121.00, 117.75, 119.87, 122.00, 119.19, 116.37, 113.50, 114.25, - 110.00, 105.06, 107.00, 107.87, 107.00, 107.12, 107.00, 91.00, 93.94, - 93.87, 95.50, 93.00, 94.94, 98.25, 96.75, 94.81, 94.37, 91.56, 90.25, - 93.94, 93.62, 97.00, 95.00, 95.87, 94.06, 94.62, 93.75, 98.00, 103.94, - 107.87, 106.06, 104.50, 105.00, 104.19, 103.06, 103.42, 105.27, 111.87, - 116.00, 116.62, 118.28, 113.37, 109.00, 109.70, 109.25, 107.00, 109.19, - 110.00, 109.20, 110.12, 108.00, 108.62, 109.75, 109.81, 109.00, 108.75, - 107.87 ]) - -def assert_np_arrays_equal(expected, got): - for i, value in enumerate(expected): - if np.isnan(value): - assert_true(np.isnan(got[i])) - else: - assert_equal(value, got[i]) - -def assert_np_arrays_not_equal(expected, got): - ''' Verifies expected and got have the same number of leading nan fields, - followed by different floats. - ''' - nans = [] - equals = [] - for i, value in enumerate(expected): - if np.isnan(value): - assert_true(np.isnan(got[i])) - nans.append(value) - else: - try: - assert_not_equal(value, got[i]) - except AssertionError: - equals.append(got[i]) - if len(equals) == len(expected[len(nans):]): - raise AssertionError('Arrays were equal.') - elif equals: - print('Arrays had %i/%i equivalent values.' % (len(equals), len(expected[len(nans):]))) diff --git a/vnpy/talib/test_func.py b/vnpy/talib/test_func.py deleted file mode 100644 index 1b78f5b8..00000000 --- a/vnpy/talib/test_func.py +++ /dev/null @@ -1,143 +0,0 @@ -import numpy as np -from nose.tools import assert_equals, assert_true, assert_raises - -import talib -from talib import func -from talib.test_data import series, assert_np_arrays_equal, assert_np_arrays_not_equal - -def test_talib_version(): - assert_equals(talib.__ta_version__[:5], b'0.4.0') - -def test_num_functions(): - assert_equals(len(talib.get_functions()), 158) - -def test_input_lengths(): - a1 = np.arange(10, dtype=float) - a2 = np.arange(11, dtype=float) - with assert_raises(Exception): - func.BOP(a2, a1, a1, a1) - with assert_raises(Exception): - func.BOP(a1, a2, a1, a1) - with assert_raises(Exception): - func.BOP(a1, a1, a2, a1) - with assert_raises(Exception): - func.BOP(a1, a1, a1, a2) - -def test_input_nans(): - a1 = np.arange(10, dtype=float) - a2 = np.arange(10, dtype=float) - a2[0] = np.nan - a2[1] = np.nan - r1, r2 = func.AROON(a1, a2, 2) - assert_np_arrays_equal(r1, [np.nan, np.nan, np.nan, np.nan, 0, 0, 0, 0, 0, 0]) - assert_np_arrays_equal(r2, [np.nan, np.nan, np.nan, np.nan, 100, 100, 100, 100, 100, 100]) - r1, r2 = func.AROON(a2, a1, 2) - assert_np_arrays_equal(r1, [np.nan, np.nan, np.nan, np.nan, 0, 0, 0, 0, 0, 0]) - assert_np_arrays_equal(r2, [np.nan, np.nan, np.nan, np.nan, 100, 100, 100, 100, 100, 100]) - -def test_unstable_period(): - a = np.arange(10, dtype=float) - r = func.EMA(a, 3) - assert_np_arrays_equal(r, [np.nan, np.nan, 1, 2, 3, 4, 5, 6, 7, 8]) - talib.set_unstable_period('EMA', 5) - r = func.EMA(a, 3) - assert_np_arrays_equal(r, [np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, 6, 7, 8]) - talib.set_unstable_period('EMA', 0) - -def test_MIN(): - result = func.MIN(series, timeperiod=4) - i = np.where(~np.isnan(result))[0][0] - assert_equals(len(series), len(result)) - assert_equals(result[i + 1], 93.780) - assert_equals(result[i + 2], 93.780) - assert_equals(result[i + 3], 92.530) - assert_equals(result[i + 4], 92.530) - values = np.array([np.nan, 5., 4., 3., 5., 7.]) - result = func.MIN(values, timeperiod=2) - assert_np_arrays_equal(result, [np.nan, np.nan, 4, 3, 3, 5]) - -def test_MAX(): - result = func.MAX(series, timeperiod=4) - i = np.where(~np.isnan(result))[0][0] - assert_equals(len(series), len(result)) - assert_equals(result[i + 2], 95.090) - assert_equals(result[i + 3], 95.090) - assert_equals(result[i + 4], 94.620) - assert_equals(result[i + 5], 94.620) - -def test_MOM(): - values = np.array([90.0,88.0,89.0]) - result = func.MOM(values, timeperiod=1) - assert_np_arrays_equal(result, [np.nan, -2, 1]) - result = func.MOM(values, timeperiod=2) - assert_np_arrays_equal(result, [np.nan, np.nan, -1]) - result = func.MOM(values, timeperiod=3) - assert_np_arrays_equal(result, [np.nan, np.nan, np.nan]) - result = func.MOM(values, timeperiod=4) - assert_np_arrays_equal(result, [np.nan, np.nan, np.nan]) - -def test_BBANDS(): - upper, middle, lower = func.BBANDS(series, timeperiod=20, - nbdevup=2.0, nbdevdn=2.0, - matype=talib.MA_Type.EMA) - i = np.where(~np.isnan(upper))[0][0] - assert_true(len(upper) == len(middle) == len(lower) == len(series)) - #assert_true(abs(upper[i + 0] - 98.0734) < 1e-3) - assert_true(abs(middle[i + 0] - 92.8910) < 1e-3) - assert_true(abs(lower[i + 0] - 87.7086) < 1e-3) - #assert_true(abs(upper[i + 13] - 93.674) < 1e-3) - assert_true(abs(middle[i + 13] - 87.679) < 1e-3) - assert_true(abs(lower[i + 13] - 81.685) < 1e-3) - -def test_DEMA(): - result = func.DEMA(series) - i = np.where(~np.isnan(result))[0][0] - assert_true(len(series) == len(result)) - assert_true(abs(result[i + 1] - 86.765) < 1e-3) - assert_true(abs(result[i + 2] - 86.942) < 1e-3) - assert_true(abs(result[i + 3] - 87.089) < 1e-3) - assert_true(abs(result[i + 4] - 87.656) < 1e-3) - -def test_EMAEMA(): - result = func.EMA(series, timeperiod=2) - result = func.EMA(result, timeperiod=2) - i = np.where(~np.isnan(result))[0][0] - assert_true(len(series) == len(result)) - assert_equals(i, 2) - -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, 27.66, 30.80]) - 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, 31.74, 32.51]) - l = np.array([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, 27.03, 28.31]) - c = np.array([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, 31.46, 28.31]) - - result = func.CDL3BLACKCROWS(o, h, l, c) - assert_np_arrays_equal(result, [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -100, 0, 0]) - -def test_RSI(): - a = np.array([0.00000024, 0.00000024, 0.00000024, - 0.00000024, 0.00000024, 0.00000023, - 0.00000024, 0.00000024, 0.00000024, - 0.00000024, 0.00000023, 0.00000024, - 0.00000023, 0.00000024, 0.00000023, - 0.00000024, 0.00000024, 0.00000023, - 0.00000023, 0.00000023], dtype='float64') - result = func.RSI(a, 10) - assert_np_arrays_equal(result, [np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,0,0,0,0,0,0,0,0,0,0]) - result = func.RSI(a * 100000, 10) - assert_np_arrays_equal(result, [np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,33.333333333333329,51.351351351351347,39.491916859122398,51.84807024709005,42.25953803191981,52.101824405061215,52.101824405061215,43.043664867691085,43.043664867691085,43.043664867691085]) - -def test_MAVP(): - a = np.array([1,5,3,4,7,3,8,1,4,6], dtype=float) - b = np.array([2,4,2,4,2,4,2,4,2,4], dtype=float) - result = func.MAVP(a, b, minperiod=2, maxperiod=4) - assert_np_arrays_equal(result, [np.nan,np.nan,np.nan,3.25,5.5,4.25,5.5,4.75,2.5,4.75]) - sma2 = func.SMA(a, 2) - assert_np_arrays_equal(result[4::2], sma2[4::2]) - sma4 = func.SMA(a, 4) - assert_np_arrays_equal(result[3::2], sma4[3::2]) - result = func.MAVP(a, b, minperiod=2, maxperiod=3) - assert_np_arrays_equal(result, [np.nan,np.nan,4,4,5.5,4.666666666666667,5.5,4,2.5,3.6666666666666665]) - sma3 = func.SMA(a, 3) - assert_np_arrays_equal(result[2::2], sma2[2::2]) - assert_np_arrays_equal(result[3::2], sma3[3::2]) diff --git a/vnpy/talib/test_pandas.py b/vnpy/talib/test_pandas.py deleted file mode 100644 index 7c9e8d3b..00000000 --- a/vnpy/talib/test_pandas.py +++ /dev/null @@ -1,50 +0,0 @@ -import numpy as np -import pandas as pd -from nose.tools import assert_equals, assert_is_instance, assert_true - -import talib -from talib.test_data import series, assert_np_arrays_equal - -def test_MOM(): - values = pd.Series([90.0,88.0,89.0], index=[10, 20, 30]) - result = talib.MOM(values, timeperiod=1) - assert_is_instance(result, pd.Series) - assert_np_arrays_equal(result.values, [np.nan, -2, 1]) - assert_np_arrays_equal(result.index, [10, 20, 30]) - result = talib.MOM(values, timeperiod=2) - assert_is_instance(result, pd.Series) - assert_np_arrays_equal(result.values, [np.nan, np.nan, -1]) - assert_np_arrays_equal(result.index, [10, 20, 30]) - result = talib.MOM(values, timeperiod=3) - assert_is_instance(result, pd.Series) - assert_np_arrays_equal(result.values, [np.nan, np.nan, np.nan]) - assert_np_arrays_equal(result.index, [10, 20, 30]) - result = talib.MOM(values, timeperiod=4) - assert_is_instance(result, pd.Series) - assert_np_arrays_equal(result.values, [np.nan, np.nan, np.nan]) - assert_np_arrays_equal(result.index, [10, 20, 30]) - -def test_MAVP(): - a = pd.Series([1,5,3,4,7,3,8,1,4,6], index=range(10, 20), dtype=float) - b = pd.Series([2,4,2,4,2,4,2,4,2,4], index=range(20, 30), dtype=float) - result = talib.MAVP(a, b, minperiod=2, maxperiod=4) - assert_is_instance(result, pd.Series) - assert_np_arrays_equal(result.values, [np.nan,np.nan,np.nan,3.25,5.5,4.25,5.5,4.75,2.5,4.75]) - assert_np_arrays_equal(result.index, range(10, 20)) - sma2 = talib.SMA(a, 2) - assert_is_instance(sma2, pd.Series) - assert_np_arrays_equal(sma2.index, range(10, 20)) - assert_np_arrays_equal(result.values[4::2], sma2.values[4::2]) - sma4 = talib.SMA(a, 4) - assert_is_instance(sma4, pd.Series) - assert_np_arrays_equal(sma4.index, range(10, 20)) - assert_np_arrays_equal(result.values[3::2], sma4.values[3::2]) - result = talib.MAVP(a, b, minperiod=2, maxperiod=3) - assert_is_instance(result, pd.Series) - assert_np_arrays_equal(result.values, [np.nan,np.nan,4,4,5.5,4.666666666666667,5.5,4,2.5,3.6666666666666665]) - assert_np_arrays_equal(result.index, range(10, 20)) - sma3 = talib.SMA(a, 3) - assert_is_instance(sma3, pd.Series) - assert_np_arrays_equal(sma3.index, range(10, 20)) - assert_np_arrays_equal(result.values[2::2], sma2.values[2::2]) - assert_np_arrays_equal(result.values[3::2], sma3.values[3::2]) diff --git a/vnpy/talib/test_stream.py b/vnpy/talib/test_stream.py deleted file mode 100644 index 0abd8dc7..00000000 --- a/vnpy/talib/test_stream.py +++ /dev/null @@ -1,31 +0,0 @@ -import numpy as np -from nose.tools import assert_equals, assert_true, assert_raises - -import talib -from talib import stream - -def test_streaming(): - a = np.array([1,1,2,3,5,8,13], dtype=float) - 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]) - l = np.array([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 = np.array([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) diff --git a/vnpy/trader/utility.py b/vnpy/trader/utility.py index 0b363b6c..ca788227 100644 --- a/vnpy/trader/utility.py +++ b/vnpy/trader/utility.py @@ -7,11 +7,7 @@ from pathlib import Path from typing import Callable import numpy as np - -try: - from vnpy import talib # For windows -except ModuleNotFoundError: - import talib # For linux (needs extra install) +import talib from .object import BarData, TickData