mirror of
https://github.com/wassname/pandas-ta.git
synced 2026-06-27 16:10:07 +08:00
added overlap indicators and tests
This commit is contained in:
Kevin Johnson
@@ -0,0 +1,5 @@
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init:
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pip install -r requirements.txt
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test:
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python3 -m unittest discover -v
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@@ -68,6 +68,34 @@ help(pd.DataFrame().ta.log_return)
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# Technical Analysis Indicators (by Category)
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## _Overlap_ (19)
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* _Double Exponential Moving Average_: **dema**
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* _Exponential Moving Average_: **ema**
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* _Fibonacci's Weighted Moving Average_: **fwma**
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* _High-Low Average_: **hl2**
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* _High-Low-Close Average_: **hlc3**
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* Commonly known as 'Typical Price' in Technical Analysis literature
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* _Hull Exponential Moving Average_: **hma**
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* _Ichimoku Kinkō Hyō_: **ichimoku**
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* Use: help(ta.ichimoku). Returns two DataFrames.
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* _Midpoint_: **midpoint**
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* _Midprice_: **midprice**
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* _Open-High-Low-Close Average_: **ohlc4**
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* _Pascal's Weighted Moving Average_: **pwma**
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* _William's Moving Average_: **rma**
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* _Simple Moving Average_: **sma**
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* _T3 Moving Average_: **t3**
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* _Triple Exponential Moving Average_: **tema**
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* _Triangular Moving Average_: **trima**
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* _Volume Weighted Average Price_: **vwap**
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* _Volume Weighted Moving Average_: **vwma**
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* _Weighted Moving Average_: **wma**
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| _Simple Moving Averages_ (SMA) and _Bollinger Bands_ (BBANDS) |
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|:--------:|
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|  |
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## _Performance_ (2)
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Use parameter: cumulative=**True** for cumulative results.
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@@ -81,6 +109,7 @@ Use parameter: cumulative=**True** for cumulative results.
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# Inspiration
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Inspired by Bukosabino: https://github.com/bukosabino/ta
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* Original TA-LIB http://ta-lib.org/
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* Bukosabino: https://github.com/bukosabino/ta
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Please leave any comments, feedback, or suggestions.
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@@ -1,4 +1,3 @@
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# -*- coding: utf-8 -*-
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name = "pandas_ta"
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"""
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.. moduleauthor:: Kevin Johnson
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+154
-3
@@ -3,8 +3,9 @@ import time
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import pandas as pd
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from pandas.core.base import PandasObject
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from .utils import *
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from .overlap import *
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from .performance import *
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from .utils import *
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class BasePandasObject(PandasObject):
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@@ -229,9 +230,159 @@ class AnalysisIndicators(BasePandasObject):
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else:
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print(s)
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# Overlap Indicators
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def dema(self, close=None, length=None, offset=None, **kwargs):
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close = self._get_column(close, 'close')
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result = dema(close=close, length=length, offset=offset, **kwargs)
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self._append(result, **kwargs)
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return result
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def ema(self, close=None, length=None, offset=None, adjust=None, **kwargs):
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close = self._get_column(close, 'close')
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result = ema(close=close, length=length, offset=offset, adjust=adjust, **kwargs)
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self._append(result, **kwargs)
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return result
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def fwma(self, close=None, length=None, offset=None, **kwargs):
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close = self._get_column(close, 'close')
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result = fwma(close=close, length=length, offset=offset, **kwargs)
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self._append(result, **kwargs)
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return result
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def hl2(self, high=None, low=None, offset=None, **kwargs):
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high = self._get_column(high, 'high')
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low = self._get_column(low, 'low')
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result = hl2(high=high, low=low, offset=offset, **kwargs)
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self._append(result, **kwargs)
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return result
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def hlc3(self, high=None, low=None, close=None, offset=None, **kwargs):
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high = self._get_column(high, 'high')
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low = self._get_column(low, 'low')
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close = self._get_column(close, 'close')
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result = hlc3(high=high, low=low, close=close, offset=offset, **kwargs)
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self._append(result, **kwargs)
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return result
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def hma(self, close=None, length=None, offset=None, **kwargs):
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close = self._get_column(close, 'close')
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result = hma(close=close, length=length, offset=offset, **kwargs)
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self._append(result, **kwargs)
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return result
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def ichimoku(self, high=None, low=None, close=None, tenkan=None, kijun=None, senkou=None, offset=None, **kwargs):
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high = self._get_column(high, 'high')
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low = self._get_column(low, 'low')
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close = self._get_column(close, 'close')
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# result, span = ichimoku(high=high, low=low, close=close, tenkan=tenkan, kijun=kijun, senkou=senkou, offset=offset, **kwargs)
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result = ichimoku(high=high, low=low, close=close, tenkan=tenkan, kijun=kijun, senkou=senkou, offset=offset, **kwargs)
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self._append(result, **kwargs)
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# return result, span
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return result
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def midpoint(self, close=None, length=None, offset=None, **kwargs):
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close = self._get_column(close, 'close')
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result = midpoint(close=close, length=length, offset=offset, **kwargs)
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self._append(result, **kwargs)
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return result
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def midprice(self, high=None, low=None, length=None, offset=None, **kwargs):
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high = self._get_column(high, 'high')
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low = self._get_column(low, 'low')
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result = midprice(high=high, low=low, length=length, offset=offset, **kwargs)
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self._append(result, **kwargs)
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return result
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def ohlc4(self, open_=None, high=None, low=None, close=None, offset=None, **kwargs):
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open_ = self._get_column(open_, 'open')
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high = self._get_column(high, 'high')
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low = self._get_column(low, 'low')
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close = self._get_column(close, 'close')
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result = ohlc4(open_=open_, high=high, low=low, close=close, offset=offset, **kwargs)
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self._append(result, **kwargs)
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return result
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def pwma(self, close=None, length=None, offset=None, **kwargs):
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close = self._get_column(close, 'close')
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result = pwma(close=close, length=length, offset=offset, **kwargs)
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self._append(result, **kwargs)
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return result
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def rma(self, close=None, length=None, offset=None, **kwargs):
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close = self._get_column(close, 'close')
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result = rma(close=close, length=length, offset=offset, **kwargs)
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self._append(result, **kwargs)
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return result
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def sma(self, close=None, length=None, offset=None, **kwargs):
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close = self._get_column(close, 'close')
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result = sma(close=close, length=length, offset=offset, **kwargs)
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self._append(result, **kwargs)
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return result
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def t3(self, close=None, length=None, a=None, offset=None, **kwargs):
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close = self._get_column(close, 'close')
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result = t3(close=close, length=length, a=a, offset=offset, **kwargs)
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self._append(result, **kwargs)
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return result
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def tema(self, close=None, length=None, offset=None, **kwargs):
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close = self._get_column(close, 'close')
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result = tema(close=close, length=length, offset=offset, **kwargs)
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self._append(result, **kwargs)
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return result
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def trima(self, close=None, length=None, offset=None, **kwargs):
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close = self._get_column(close, 'close')
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result = trima(close=close, length=length, offset=offset, **kwargs)
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self._append(result, **kwargs)
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return result
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def vwap(self, high=None, low=None, close=None, volume=None, offset=None, **kwargs):
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high = self._get_column(high, 'high')
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low = self._get_column(low, 'low')
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close = self._get_column(close, 'close')
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volume = self._get_column(volume, 'volume')
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result = vwap(high=high, low=low, close=close, volume=volume, offset=offset, **kwargs)
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self._append(result, **kwargs)
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return result
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def vwma(self, close=None, volume=None, length=None, offset=None, **kwargs):
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close = self._get_column(close, 'close')
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volume = self._get_column(volume, 'volume')
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result = vwma(close=close, volume=close, length=length, offset=offset, **kwargs)
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self._append(result, **kwargs)
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return result
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def wma(self, close=None, length=None, offset=None, **kwargs):
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close = self._get_column(close, 'close')
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result = wma(close=close, length=length, offset=offset, **kwargs)
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self._append(result, **kwargs)
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return result
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# Performance Indicators
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def log_return(self, close=None, length=None, cumulative=False, percent=False, offset=None, **kwargs):
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close = self._get_column(close, 'close')
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result = log_return(close=close, length=length, cumulative=cumulative, percent=percent, offset=offset, **kwargs)
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@@ -244,4 +395,4 @@ class AnalysisIndicators(BasePandasObject):
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close = self._get_column(close, 'close')
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result = percent_return(close=close, length=length, cumulative=cumulative, percent=percent, offset=offset, **kwargs)
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self._append(result, **kwargs)
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return result
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return result
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File diff suppressed because it is too large
Load Diff
+26
-1
@@ -27,10 +27,29 @@ def combination(**kwargs):
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return numerator // denominator
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def df_error_analysis(dfA, dfB, **kwargs):
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""" """
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corr_method = kwargs.pop('corr_method', 'pearson')
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# Find their differences
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diff = dfA - dfB
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df = pd.DataFrame({'diff': diff.describe()})
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extra = pd.DataFrame([diff.var(), diff.mad(), diff.sem(), dfA.corr(dfB, method=corr_method)], index=['var', 'mad', 'sem', 'corr'])
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# Append the differences to the DataFrame
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df = df['diff'].append(extra, ignore_index=False)[0]
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# For plotting
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# diff.hist()
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# if diff[diff > 0].any():
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# diff.plot(kind='kde')
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return df
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def fibonacci(**kwargs):
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"""Fibonacci Sequence as a numpy array"""
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n = int(math.fabs(kwargs.pop('n', 2)))
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zero = kwargs.pop('zero', True)
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zero = kwargs.pop('zero', False)
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weighted = kwargs.pop('weighted', False)
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if zero:
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@@ -71,6 +90,7 @@ def pascals_triangle(**kwargs):
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"""
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n = int(math.fabs(kwargs.pop('n', 0)))
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weighted = kwargs.pop('weighted', False)
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inverse = kwargs.pop('inverse', False)
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sink = kwargs.pop('all', False)
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# Calculation
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@@ -88,6 +108,11 @@ def pascals_triangle(**kwargs):
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if sink:
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return triangle, triangle_sum, triangle_avg, inverted, weights, inv_weights, triangle_avg
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if weighted:
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# Needs to be fixed: n=3 => [1, 2, 1], t=4
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# weighted: [1/4, 2/4, 1/4]
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#
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# if inverse:
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# return inv_weights
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return weights
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else:
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return triangle
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@@ -6,7 +6,7 @@ long_description = "A Python 3 Pandas Extension of Technical Analysis Indicators
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setup(
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name = "pandas_ta",
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packages = ["pandas_ta"],
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version = "0.0.4a",
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version = "0.0.5a",
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description=long_description,
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long_description=long_description,
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author = "Kevin Johnson",
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@@ -0,0 +1,5 @@
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import os
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import sys
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sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
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import pandas_ta
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@@ -0,0 +1,4 @@
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from pandas import read_csv
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CORRELATION_THRESHOLD = 0.99 # Less than 0.99 is undesirable
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sample_data = read_csv('data/sample.csv', index_col=0, parse_dates=True, infer_datetime_format=False, keep_date_col=True)
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@@ -0,0 +1,227 @@
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from .context import pandas_ta
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from .data import sample_data, CORRELATION_THRESHOLD
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from unittest import TestCase, skip
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import pandas.util.testing as pdt
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from pandas import DataFrame, Series
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import talib as tal
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VERBOSE = False
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class TestOverlap(TestCase):
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@classmethod
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def setUpClass(cls):
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cls.data = sample_data
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cls.open = cls.data['open']
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cls.high = cls.data['high']
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cls.low = cls.data['low']
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cls.close = cls.data['close']
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cls.volume = cls.data['volume']
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cls.correlation_threshold = CORRELATION_THRESHOLD
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@classmethod
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def tearDownClass(cls):
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del cls.data
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del cls.open
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del cls.high
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del cls.low
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del cls.close
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del cls.volume
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def setUp(self):
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self.overlap = pandas_ta.overlap
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def tearDown(self):
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del self.overlap
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def test_dema(self):
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dema = self.overlap.dema(self.close)
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self.assertIsInstance(dema, Series)
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self.assertEqual(dema.name, 'DEMA_10')
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try:
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tal_dema = tal.DEMA(self.close, 10)
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pdt.assert_series_equal(dema, tal_dema, check_names=False)
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except AssertionError as ae:
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analysis = pandas_ta.utils.df_error_analysis(dema, tal_dema)
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print(f"\nanalysis['corr']: {round(analysis['corr'], 3)}") if VERBOSE else None
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if analysis['corr'] < self.correlation_threshold:
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raise AssertionError(f"dema has low correlation: {analysis['corr']}")
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def test_ema(self):
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ema = self.overlap.ema(self.close, presma=False)
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self.assertIsInstance(ema, Series)
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self.assertEqual(ema.name, 'EMA_10')
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try:
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tal_ema = tal.EMA(self.close, 10)
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pdt.assert_series_equal(ema, tal_ema, check_names=False)
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except AssertionError as ae:
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analysis = pandas_ta.utils.df_error_analysis(ema, tal_ema)
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print(f"\nanalysis['corr']: {analysis['corr']}") if VERBOSE else None
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if analysis['corr'] < self.correlation_threshold:
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raise AssertionError(f"ema has low correlation: {analysis['corr']}")
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def test_fwma(self):
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fwma = self.overlap.fwma(self.close)
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self.assertIsInstance(fwma, Series)
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self.assertEqual(fwma.name, 'FWMA_10')
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def test_hl2(self):
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hl2 = self.overlap.hl2(self.high, self.low)
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self.assertIsInstance(hl2, Series)
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self.assertEqual(hl2.name, 'HL2')
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def test_hlc3(self):
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hlc3 = self.overlap.hlc3(self.high, self.low, self.close)
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self.assertIsInstance(hlc3, Series)
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self.assertEqual(hlc3.name, 'HLC3')
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try:
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tal_typicalprice = tal.TYPPRICE(self.high, self.low, self.close)
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pdt.assert_series_equal(hlc3, tal_typicalprice, check_names=False)
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except AssertionError as ae:
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analysis = pandas_ta.utils.df_error_analysis(hlc3, tal_typicalprice)
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print(f"\nanalysis['corr']: {analysis['corr']}") if VERBOSE else None
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if analysis['corr'] < self.correlation_threshold:
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raise AssertionError(f"hlc3/typicalprice has low correlation: {analysis['corr']}")
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def test_hma(self):
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hma = self.overlap.hma(self.close)
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self.assertIsInstance(hma, Series)
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self.assertEqual(hma.name, 'HMA_10')
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# @skip("close index requires Daily dates")
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def test_ichimoku(self):
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ichimoku = self.overlap.ichimoku(self.high, self.low, self.close)
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self.assertIsInstance(ichimoku, DataFrame)
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self.assertEqual(ichimoku.name, 'ICHIMOKU_9_26_52')
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def test_midpoint(self):
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# talib.MIDPOINT(timeperiod >= 2)
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midpoint = self.overlap.midpoint(self.close)
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self.assertIsInstance(midpoint, Series)
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self.assertEqual(midpoint.name, 'MIDPOINT_2')
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try:
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tal_midpoint = tal.MIDPOINT(self.close, 2)
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pdt.assert_series_equal(midpoint, tal_midpoint, check_names=False)
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except AssertionError as ae:
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analysis = pandas_ta.utils.df_error_analysis(midpoint, tal_midpoint)
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print(f"\nanalysis['corr']: {analysis['corr']}") if VERBOSE else None
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if analysis['corr'] < self.correlation_threshold:
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raise AssertionError(f"midpoint has low correlation: {analysis['corr']}")
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def test_midprice(self):
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# talib.MIDPRICE(timeperiod >= 2)
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midprice = self.overlap.midprice(self.high, self.low)
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self.assertIsInstance(midprice, Series)
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self.assertEqual(midprice.name, 'MIDPRICE_2')
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try:
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tal_midprice = tal.MIDPRICE(self.high, self.low, 2)
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pdt.assert_series_equal(midprice, tal_midprice, check_names=False)
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except AssertionError as ae:
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analysis = pandas_ta.utils.df_error_analysis(midprice, tal_midprice)
|
||||
print(f"\nanalysis['corr']: {analysis['corr']}") if VERBOSE else None
|
||||
if analysis['corr'] < self.correlation_threshold:
|
||||
raise AssertionError(f"midprice has low correlation: {analysis['corr']}")
|
||||
|
||||
def test_ohlc4(self):
|
||||
ohlc4 = self.overlap.ohlc4(self.open, self.high, self.low, self.close)
|
||||
self.assertIsInstance(ohlc4, Series)
|
||||
self.assertEqual(ohlc4.name, 'OHLC4')
|
||||
|
||||
def test_pwma(self):
|
||||
pwma = self.overlap.pwma(self.close)
|
||||
self.assertIsInstance(pwma, Series)
|
||||
self.assertEqual(pwma.name, 'PWMA_10')
|
||||
|
||||
def test_rma(self):
|
||||
rma = self.overlap.rma(self.close)
|
||||
self.assertIsInstance(rma, Series)
|
||||
self.assertEqual(rma.name, 'RMA_10')
|
||||
|
||||
def test_sma(self):
|
||||
sma = self.overlap.sma(self.close)
|
||||
self.assertIsInstance(sma, Series)
|
||||
self.assertEqual(sma.name, 'SMA_10')
|
||||
|
||||
try:
|
||||
tal_sma = tal.SMA(self.close, 10)
|
||||
pdt.assert_series_equal(sma, tal_sma, check_names=False)
|
||||
except AssertionError as ae:
|
||||
analysis = pandas_ta.utils.df_error_analysis(sma, tal_sma)
|
||||
print(f"\nanalysis['corr']: {analysis['corr']}") if VERBOSE else None
|
||||
if analysis['corr'] < self.correlation_threshold:
|
||||
raise AssertionError(f"sma has low correlation: {analysis['corr']}")
|
||||
|
||||
def test_t3(self):
|
||||
t3 = self.overlap.t3(self.close)
|
||||
self.assertIsInstance(t3, Series)
|
||||
self.assertEqual(t3.name, 'T3_10_0.7')
|
||||
|
||||
try:
|
||||
tal_t3 = tal.T3(self.close, 10)
|
||||
pdt.assert_series_equal(t3, tal_t3, check_names=False)
|
||||
except AssertionError as ae:
|
||||
analysis = pandas_ta.utils.df_error_analysis(t3, tal_t3)
|
||||
print(f"\nanalysis['corr']: {analysis['corr']}") if VERBOSE else None
|
||||
if analysis['corr'] < self.correlation_threshold:
|
||||
raise AssertionError(f"t3 has low correlation: {analysis['corr']}")
|
||||
|
||||
def test_tema(self):
|
||||
tema = self.overlap.tema(self.close)
|
||||
self.assertIsInstance(tema, Series)
|
||||
self.assertEqual(tema.name, 'TEMA_10')
|
||||
|
||||
try:
|
||||
tal_tema = tal.TEMA(self.close, 10)
|
||||
pdt.assert_series_equal(tema, tal_tema, check_names=False)
|
||||
except AssertionError as ae:
|
||||
analysis = pandas_ta.utils.df_error_analysis(tema, tal_tema)
|
||||
print(f"\nanalysis['corr']: {analysis['corr']}") if VERBOSE else None
|
||||
if analysis['corr'] < self.correlation_threshold:
|
||||
raise AssertionError(f"tema has low correlation: {analysis['corr']}")
|
||||
|
||||
def test_trima(self):
|
||||
trima = self.overlap.trima(self.close)
|
||||
self.assertIsInstance(trima, Series)
|
||||
self.assertEqual(trima.name, 'TRIMA_10')
|
||||
|
||||
try:
|
||||
tal_trima = tal.TRIMA(self.close, 10)
|
||||
pdt.assert_series_equal(trima, tal_trima, check_names=False)
|
||||
except AssertionError as ae:
|
||||
analysis = pandas_ta.utils.df_error_analysis(trima, tal_trima)
|
||||
print(f"\nanalysis['corr']: {analysis['corr']}") if VERBOSE else None
|
||||
if analysis['corr'] < self.correlation_threshold:
|
||||
raise AssertionError(f"trima has low correlation: {analysis['corr']}")
|
||||
|
||||
def test_vwap(self):
|
||||
vwap = self.overlap.vwap(self.high, self.low, self.close, self.volume)
|
||||
self.assertIsInstance(vwap, Series)
|
||||
self.assertEqual(vwap.name, 'VWAP')
|
||||
|
||||
def test_vwma(self):
|
||||
vwma = self.overlap.vwma(self.close, self.volume)
|
||||
self.assertIsInstance(vwma, Series)
|
||||
self.assertEqual(vwma.name, 'VWMA_10')
|
||||
|
||||
def test_wma(self):
|
||||
wma = self.overlap.wma(self.close)
|
||||
self.assertIsInstance(wma, Series)
|
||||
self.assertEqual(wma.name, 'WMA_10')
|
||||
|
||||
try:
|
||||
tal_wma = tal.WMA(self.close, 10)
|
||||
pdt.assert_series_equal(wma, tal_wma, check_names=False)
|
||||
except AssertionError as ae:
|
||||
analysis = pandas_ta.utils.df_error_analysis(wma, tal_wma)
|
||||
print(f"\nanalysis['corr']: {analysis['corr']}") if VERBOSE else None
|
||||
if analysis['corr'] < self.correlation_threshold:
|
||||
raise AssertionError(f"wma has low correlation: {analysis['corr']}")
|
||||
@@ -0,0 +1,123 @@
|
||||
from .context import pandas_ta
|
||||
from .data import sample_data
|
||||
|
||||
from unittest import TestCase
|
||||
from pandas import DataFrame
|
||||
|
||||
|
||||
|
||||
class TestOverlapExtension(TestCase):
|
||||
@classmethod
|
||||
def setUpClass(cls):
|
||||
cls.data = sample_data
|
||||
|
||||
@classmethod
|
||||
def tearDownClass(cls):
|
||||
del cls.data
|
||||
|
||||
|
||||
def setUp(self):
|
||||
pass
|
||||
|
||||
def tearDown(self):
|
||||
pass
|
||||
|
||||
|
||||
def test_dema_ext(self):
|
||||
self.data.ta.dema(append=True)
|
||||
self.assertIsInstance(self.data, DataFrame)
|
||||
self.assertEqual(self.data.columns[-1], 'DEMA_10')
|
||||
|
||||
def test_ema_ext(self):
|
||||
self.data.ta.ema(append=True)
|
||||
self.assertIsInstance(self.data, DataFrame)
|
||||
self.assertEqual(self.data.columns[-1], 'EMA_10')
|
||||
|
||||
def test_fwma_ext(self):
|
||||
self.data.ta.fwma(append=True)
|
||||
self.assertIsInstance(self.data, DataFrame)
|
||||
self.assertEqual(self.data.columns[-1], 'FWMA_10')
|
||||
|
||||
def test_hl2_ext(self):
|
||||
self.data.ta.hl2(append=True)
|
||||
self.assertIsInstance(self.data, DataFrame)
|
||||
self.assertEqual(self.data.columns[-1], 'HL2')
|
||||
|
||||
def test_hlc3_ext(self):
|
||||
self.data.ta.hlc3(append=True)
|
||||
self.assertIsInstance(self.data, DataFrame)
|
||||
self.assertEqual(self.data.columns[-1], 'HLC3')
|
||||
|
||||
def test_hma_ext(self):
|
||||
self.data.ta.hma(append=True)
|
||||
self.assertIsInstance(self.data, DataFrame)
|
||||
self.assertEqual(self.data.columns[-1], 'HMA_10')
|
||||
|
||||
def test_ichimoku_ext(self):
|
||||
self.data.ta.ichimoku(append=True)
|
||||
self.assertIsInstance(self.data, DataFrame)
|
||||
self.assertEqual(self.data.columns[-5], 'ISA_9')
|
||||
self.assertEqual(self.data.columns[-4], 'ISB_26')
|
||||
self.assertEqual(self.data.columns[-3], 'ITS_9')
|
||||
self.assertEqual(self.data.columns[-2], 'IKS_26')
|
||||
self.assertEqual(self.data.columns[-1], 'ICS_26')
|
||||
|
||||
def test_midpoint_ext(self):
|
||||
self.data.ta.midpoint(append=True)
|
||||
self.assertIsInstance(self.data, DataFrame)
|
||||
self.assertEqual(self.data.columns[-1], 'MIDPOINT_2')
|
||||
|
||||
def test_midprice_ext(self):
|
||||
self.data.ta.midprice(append=True)
|
||||
self.assertIsInstance(self.data, DataFrame)
|
||||
self.assertEqual(self.data.columns[-1], 'MIDPRICE_2')
|
||||
|
||||
def test_ohlc4_ext(self):
|
||||
self.data.ta.ohlc4(append=True)
|
||||
self.assertIsInstance(self.data, DataFrame)
|
||||
self.assertEqual(self.data.columns[-1], 'OHLC4')
|
||||
|
||||
def test_pwma_ext(self):
|
||||
self.data.ta.pwma(append=True)
|
||||
self.assertIsInstance(self.data, DataFrame)
|
||||
self.assertEqual(self.data.columns[-1], 'PWMA_10')
|
||||
|
||||
def test_rma_ext(self):
|
||||
self.data.ta.rma(append=True)
|
||||
self.assertIsInstance(self.data, DataFrame)
|
||||
self.assertEqual(self.data.columns[-1], 'RMA_10')
|
||||
|
||||
def test_sma_ext(self):
|
||||
self.data.ta.sma(append=True)
|
||||
self.assertIsInstance(self.data, DataFrame)
|
||||
self.assertEqual(self.data.columns[-1], 'SMA_10')
|
||||
|
||||
def test_t3_ext(self):
|
||||
self.data.ta.t3(append=True)
|
||||
self.assertIsInstance(self.data, DataFrame)
|
||||
self.assertEqual(self.data.columns[-1], 'T3_10_0.7')
|
||||
|
||||
def test_tema_ext(self):
|
||||
self.data.ta.tema(append=True)
|
||||
self.assertIsInstance(self.data, DataFrame)
|
||||
self.assertEqual(self.data.columns[-1], 'TEMA_10')
|
||||
|
||||
def test_trima_ext(self):
|
||||
self.data.ta.trima(append=True)
|
||||
self.assertIsInstance(self.data, DataFrame)
|
||||
self.assertEqual(self.data.columns[-1], 'TRIMA_10')
|
||||
|
||||
def test_vwap_ext(self):
|
||||
self.data.ta.vwap(append=True)
|
||||
self.assertIsInstance(self.data, DataFrame)
|
||||
self.assertEqual(self.data.columns[-1], 'VWAP')
|
||||
|
||||
def test_vwma_ext(self):
|
||||
self.data.ta.vwma(append=True)
|
||||
self.assertIsInstance(self.data, DataFrame)
|
||||
self.assertEqual(self.data.columns[-1], 'VWMA_10')
|
||||
|
||||
def test_wma_ext(self):
|
||||
self.data.ta.wma(append=True)
|
||||
self.assertIsInstance(self.data, DataFrame)
|
||||
self.assertEqual(self.data.columns[-1], 'WMA_10')
|
||||
@@ -1,15 +1,15 @@
|
||||
from unittest import TestCase
|
||||
import numpy.testing as npt
|
||||
import pandas.util.testing as pdt
|
||||
from pandas import DataFrame, read_csv, Series
|
||||
from .context import pandas_ta
|
||||
from .data import sample_data
|
||||
|
||||
from unittest import TestCase
|
||||
from pandas import Series
|
||||
|
||||
from pandas_ta import performance
|
||||
|
||||
|
||||
class TestPerformace(TestCase):
|
||||
@classmethod
|
||||
def setUpClass(cls):
|
||||
cls.data = read_csv('data/sample.csv', index_col=0, parse_dates=True, infer_datetime_format=False, keep_date_col=True)
|
||||
cls.data = sample_data
|
||||
cls.close = cls.data['close']
|
||||
|
||||
@classmethod
|
||||
@@ -19,7 +19,7 @@ class TestPerformace(TestCase):
|
||||
|
||||
|
||||
def setUp(self):
|
||||
self.performance = performance
|
||||
self.performance = pandas_ta.performance
|
||||
|
||||
def tearDown(self):
|
||||
del self.performance
|
||||
@@ -39,5 +39,4 @@ class TestPerformace(TestCase):
|
||||
self.assertEqual(percent_return.name, 'PCTRET_1')
|
||||
|
||||
cumpercent_return = self.performance.percent_return(self.close, cumulative=True)
|
||||
self.assertEqual(cumpercent_return.name, 'CUMPCTRET_1')
|
||||
|
||||
self.assertEqual(cumpercent_return.name, 'CUMPCTRET_1')
|
||||
@@ -1,21 +1,21 @@
|
||||
from unittest import TestCase
|
||||
import numpy.testing as npt
|
||||
import pandas.util.testing as pdt
|
||||
from pandas import DataFrame, read_csv, Series
|
||||
from .context import pandas_ta
|
||||
from .data import sample_data
|
||||
|
||||
from unittest import TestCase
|
||||
# import numpy.testing as npt
|
||||
# import pandas.util.testing as pdt
|
||||
from pandas import DataFrame#, Series
|
||||
|
||||
import pandas_ta as ta
|
||||
|
||||
|
||||
class TestPerformaceExtension(TestCase):
|
||||
@classmethod
|
||||
def setUpClass(cls):
|
||||
cls.data = read_csv('data/sample.csv', index_col=0, parse_dates=True, infer_datetime_format=False, keep_date_col=True)
|
||||
cls.close = cls.data['close']
|
||||
cls.data = sample_data
|
||||
|
||||
@classmethod
|
||||
def tearDownClass(cls):
|
||||
del cls.data
|
||||
del cls.close
|
||||
|
||||
|
||||
def setUp(self):
|
||||
@@ -41,9 +41,4 @@ class TestPerformaceExtension(TestCase):
|
||||
|
||||
self.data.ta.percent_return(append=True, cumulative=True)
|
||||
self.assertIsInstance(self.data, DataFrame)
|
||||
self.assertEqual(self.data.columns[-1], 'CUMPCTRET_1')
|
||||
|
||||
# Example
|
||||
# pta_sma = self.data[self.data.columns[-1]]
|
||||
# tal_sma = tal.SMA(self.close)
|
||||
# pdt.assert_series_equal(pta_sma, tal_sma)
|
||||
self.assertEqual(self.data.columns[-1], 'CUMPCTRET_1')
|
||||
+3
-2
@@ -1,4 +1,4 @@
|
||||
from pandas_ta import utils as utils
|
||||
from .context import pandas_ta
|
||||
|
||||
from unittest import TestCase
|
||||
from unittest.mock import patch
|
||||
@@ -7,9 +7,10 @@ import numpy as np
|
||||
import numpy.testing as npt
|
||||
from pandas import DataFrame
|
||||
|
||||
|
||||
class TestUtilities(TestCase):
|
||||
def setUp(self):
|
||||
self.utils = utils
|
||||
self.utils = pandas_ta.utils
|
||||
|
||||
def tearDown(self):
|
||||
del self.utils
|
||||
|
||||
Reference in New Issue
Block a user