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NotebookShelf
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ForexVolume.ipynb
Notebook
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Out[15]:
<matplotlib.axes._subplots.AxesSubplot at 0xe105208>
Out[16]:
<matplotlib.axes._subplots.AxesSubplot at 0xf27b1d0>
Out[17]:
<matplotlib.axes._subplots.AxesSubplot at 0xf46d588>
OLS Regression Results
==============================================================================
Dep. Variable: EURUSD_CLSH R-squared: 0.767
Model: OLS Adj. R-squared: 0.767
Method: Least Squares F-statistic: 1775.
Date: Fri, 11 Aug 2017 Prob (F-statistic): 1.16e-172
Time: 11:47:47 Log-Likelihood: -373.03
No. Observations: 541 AIC: 750.1
Df Residuals: 539 BIC: 758.7
Df Model: 1
Covariance Type: nonrobust
===============================================================================
coef std err t P>|t| [0.025 0.975]
-------------------------------------------------------------------------------
EURUSD_FXCM 0.8758 0.021 42.130 0.000 0.835 0.917
const 9.975e-17 0.021 4.8e-15 1.000 -0.041 0.041
==============================================================================
Omnibus: 158.138 Durbin-Watson: 1.146
Prob(Omnibus): 0.000 Jarque-Bera (JB): 1159.931
Skew: 1.074 Prob(JB): 1.33e-252
Kurtosis: 9.844 Cond. No. 1.00
==============================================================================
Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
Out[37]:
|
Coefficient |
R Squared |
Significance at 99% |
EURUSD |
0.875820 |
0.767061 |
True |
USDJPY |
0.906553 |
0.821839 |
True |
GBPUSD |
0.873854 |
0.763621 |
True |
USDCHF |
0.780793 |
0.609638 |
True |
EURCHF |
0.685604 |
0.470052 |
True |
AUDUSD |
0.832147 |
0.692469 |
True |
USDCAD |
0.659832 |
0.435379 |
True |
NZDUSD |
0.588424 |
0.346243 |
True |
EURGBP |
0.409120 |
0.167379 |
True |
EURJPY |
0.791885 |
0.627083 |
True |
GBPJPY |
0.724331 |
0.524656 |
True |
EURAUD |
0.359925 |
0.129546 |
True |
EURCAD |
0.278091 |
0.077335 |
True |
AUDJPY |
0.757590 |
0.573943 |
True |
Out[41]:
<matplotlib.axes._subplots.AxesSubplot at 0xcaf1128>