{"id":84143,"date":"2021-04-16T09:47:48","date_gmt":"2021-04-16T13:47:48","guid":{"rendered":"https:\/\/ibkrcampus.com\/?p=84143"},"modified":"2022-11-21T09:47:20","modified_gmt":"2022-11-21T14:47:20","slug":"how-to-create-kalman-filter-in-python-part-vii","status":"publish","type":"post","link":"https:\/\/www.interactivebrokers.com\/campus\/ibkr-quant-news\/how-to-create-kalman-filter-in-python-part-vii\/","title":{"rendered":"How to Create Kalman Filter in Python \u2013 Part VII"},"content":{"rendered":"\n

In the final installment of this series, Rekhit Pachanekar demonstrates how to code in Python to create a sample pairs trading script.<\/em> See Part I<\/a>, Part II<\/a> , Part III<\/a>,  Part IV<\/a>, Part V<\/a> and Part VI<\/a> of this series for details on the statistical terms and concepts used for creating Kalman Filter.<\/em><\/p>\n\n\n\n

Pairs trading strategy<\/h3>\n\n\n\n

In pairs trading<\/a> strategy we buy one stock and sell the other stock choosing the quantity as hedge ratio. <\/p>\n\n\n\n

\n# Use the observed values of the price to get a rolling mean and z_score
\nmean, cov = kf.filter(ratio.values)
\ndata[‘mean’] = mean.squeeze()
\ndata[‘cov’] = cov.squeeze()
\ndata[‘std’] = np.sqrt(data[‘cov’])
\ndata = data.dropna()

\n\ndata[‘ma’] = data[‘ratio’].rolling(5).mean()
\ndata[‘z_score’] = (data[‘ma’] – data[‘mean’])\/data[‘std’]

\n\n# Initialise positions as zero
\ndata[‘position_1’] = np.nan
\ndata[‘position_2’] = np.nan

\n\n# Generate buy, sell and square off signals as: z<-1 buy, z>1 sell and -1\nfor i in range (data.shape[0]):
\n if data[‘z_score’].iloc[i] < -1:
\n data[‘position_1’].iloc[i] = 1
\n data[‘position_2’].iloc[i] = -round(data[‘ratio’].iloc[i],0)
\n if data[‘z_score’].iloc[i] > 1:
\n data[‘position_1’].iloc[i] = -1
\n data[‘position_2’].iloc[i] = round(data[‘ratio’].iloc[i],0)
\n if (abs(data[‘z_score’].iloc[i]) < 1) & (abs(data['z_score'].iloc[i]) > 0):
\n data[‘position_1’].iloc[i] = 0
\n data[‘position_2’].iloc[i] = 0

\n \n# Calculate returns
\ndata[‘returns’] = ((data[‘BAJAJ’]-data[‘BAJAJ’].shift(1))\/data[‘BAJAJ’].shift(1))*data[‘position_1’].shift(1)+ ((data[‘HERO’]-data[‘HERO’].shift(1))\/data
[‘HERO’].shift(1))*data[‘position_2’].shift(1)
\ndata[‘returns’].sum()\n<\/p>\n\n\n\n

The output is: 0.12282433836398741<\/p>\n\n\n\n

Download the full code: https:\/\/blog.quantinsti.com\/kalman-filter\/<\/a>. You can learn more about pairs trading strategies in the statistical arbitrage<\/a> course on Quantra.<\/em><\/p>\n\n\n\n

All data and information provided in this article are for informational purposes only. QuantInsti\u00ae makes no representations as to accuracy, completeness, currentness, suitability, or validity of any information in this article and will not be liable for any errors, omissions, or delays in this information or any losses, injuries, or damages arising from its display or use. All information is provided on an as-is basis.<\/em><\/small><\/p>\n","protected":false},"excerpt":{"rendered":"

In the final installment of this series, Rekhit Pachanekar demonstrates how to code in Python to create a sample pairs trading script.<\/p>\n","protected":false},"author":91,"featured_media":25709,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":"","jetpack_post_was_ever_published":false},"categories":[339,343,349,338,350,341,344],"tags":[851,9484,8778,9487,8920,4272,9242,8710,4659,8777,9564,9486,9482,595,9483,4580,1048,4271,9152,9565,8155],"contributors-categories":[13654],"class_list":["post-84143","post","type-post","status-publish","format-standard","has-post-thumbnail","category-data-science","category-programing-languages","category-python-development","category-ibkr-quant-news","category-quant-asia-pacific","category-quant-development","category-quant-regions","tag-algo-trading","tag-auquan_toolbox","tag-bessels-correction","tag-datetime","tag-equations-in-kalman-filter","tag-kalman-filter","tag-kalman-gain-equation","tag-mathematical-finance","tag-matplotlib","tag-normal-distribution","tag-numpy-pairs","tag-poly1d","tag-pykalman","tag-python","tag-qq-training-wheels","tag-seaborn","tag-standard-deviation","tag-statistical-arbitrage","tag-status-update-equation","tag-trading-pandas","tag-variance","contributors-categories-quantinsti"],"pp_statuses_selecting_workflow":false,"pp_workflow_action":"current","pp_status_selection":"publish","acf":[],"yoast_head":"\nHow 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