{"id":61491,"date":"2020-10-02T09:40:19","date_gmt":"2020-10-02T13:40:19","guid":{"rendered":"https:\/\/ibkrcampus.com\/?p=61491"},"modified":"2022-11-21T09:46:24","modified_gmt":"2022-11-21T14:46:24","slug":"trading-index-trin-formula-calculation-strategy-in-python-part-iii","status":"publish","type":"post","link":"https:\/\/www.interactivebrokers.com\/campus\/ibkr-quant-news\/trading-index-trin-formula-calculation-strategy-in-python-part-iii\/","title":{"rendered":"Trading Index (TRIN): Formula, Calculation & Strategy in Python \u2013 Part III"},"content":{"rendered":"\n

See Part I<\/a> for a definition of TRIN <\/em>and Part II<\/a><\/em> for instructions on how to calculate TRIN in Python.<\/em><\/p>\n\n\n\n

Next, we will find out the number of advancing stocks (ones which ended in green) and the number of declining stocks (ones which ended in red) from the previous day.<\/p>\n\n\n\n

\n# Add the number of stocks that ended in green from previous day
\n# and number of stocks that ended in red from previous day
\ndirection_cols = [col + ‘_direction’ for col in stocks]
\nprices[‘advancing_stocks’] = prices[direction_cols].sum(axis=1)
\nprices[‘declining_stocks’] = len(direction_cols) – prices[‘advancing_stocks’]

\n\n# Print the “pos_direction” and “neg_direction” columns from dataframe
\nprices[[‘advancing_stocks’, ‘declining_stocks’]].head()\n<\/p>\n\n\n\n

Output:<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

Now, we will generate the sum of the volume of the stocks ended in positive and negative from the previous day since this is required to calculate AD volume.<\/p>\n\n\n\n

\n# Rename volume dataframe columns by adding ‘_volume’ , to help merge prices and volume dataframes
\nvolume_cols = [col + ‘_volume’ for col in stocks]
\nvolume.rename(columns=dict(zip(stocks, volume_cols)), inplace=True)

\n\n# Print the first two rows of the volume dataframe
\nvolume.head(2)\n<\/p>\n\n\n\n

Output:<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

Further, we will find out advancing and declining stocks and advancing and declining volume of stocks. Then, finally, we will calculate the TRIN value.<\/p>\n\n\n\n

\n# Merge both prices, volume data-frames
\nmergedDf = prices.merge(volume, left_index=True, right_on=’Date’)

\n\n# Create vol_direction column for every stock, to sum positive ended and negative ended stocks volume
\nfor col in stocks:
\nvolume_col = col + ‘_volume’
\ndirection_col = col + ‘_direction’
\nvol_direction = col + ‘_vol_direction’
\nmergedDf[vol_direction] = mergedDf[volume_col] * mergedDf[direction_col]

\n\n# Add the volume of all positive ended stocks to ‘total_pos_volume’
\n# And sum of the volume of negative ended stocks to ‘total_neg_volume’
\nvol_direction_cols = [col + ‘_vol_direction’ for col in stocks]
\nmergedDf[‘total_volume’] = mergedDf[volume_cols].sum(axis=1)
\nmergedDf[‘advancing_volume’] = mergedDf[vol_direction_cols].sum(axis=1)
\nmergedDf[‘declining_volume’] = mergedDf[‘total_volume’] – \\
\nmergedDf[‘advancing_volume’]

\n\n# Print ‘pos_direction’,’neg_direction’, ‘total_pos_volume’ and ‘total_neg_volume’ columns from dataframe
\nmergedDf[[‘advancing_stocks’, ‘declining_stocks’,
\n‘advancing_volume’, ‘declining_volume’]].head()\n<\/p>\n\n\n\n

Output:<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

\n# Calculate TRIN ratio
\nmergedDf[‘AD_ratio’] = mergedDf[‘advancing_stocks’]\/ mergedDf[‘declining_stocks’]
\nmergedDf[‘AD_volume’] = mergedDf[‘advancing_volume’]\/ mergedDf[‘declining_volume’]
\nmergedDf[‘TRIN’] = mergedDf[‘AD_ratio’]\/ mergedDf[‘AD_volume’]
\nmergedDf[‘TRIN’].head()\n<\/p>\n\n\n\n

Output:<\/p>\n\n\n\n

Date<\/p>\n\n\n\n

01-04-2020         NaN<\/p>\n\n\n\n

02-04-2020    1.289376<\/p>\n\n\n\n

03-04-2020    0.686072<\/p>\n\n\n\n

06-04-2020    3.285942<\/p>\n\n\n\n

07-04-2020    0.693863<\/p>\n\n\n\n

Name: TRIN, dtype: float64<\/p>\n\n\n\n

Stay tuned for the next installment in which the author will plot a graph to find out the TRIN value for S&P500.<\/em><\/p>\n\n\n\n

Visit QuantInsti for additional ready-to-use code: https:\/\/blog.quantinsti.com\/trin\/<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

Join QuantInsti for a Python tutorial on how to find out the number of advancing stocks and declining stocks.<\/p>\n","protected":false},"author":368,"featured_media":25709,"comment_status":"closed","ping_status":"closed","sticky":true,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":"","jetpack_post_was_ever_published":false},"categories":[339,343,349,338,350,341,344],"tags":[851,1383,1225,1224,595,494,1291,8328,8327],"contributors-categories":[13654],"class_list":["post-61491","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-market-sentiment","tag-numpy","tag-pandas","tag-python","tag-quant","tag-technical-analysis","tag-trading-signal","tag-trin-indicator","contributors-categories-quantinsti"],"pp_statuses_selecting_workflow":false,"pp_workflow_action":"current","pp_status_selection":"publish","acf":[],"yoast_head":"\nTrading 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