TAQ Average Responses Physical

Computes the average response in physical time scale of a group of stocks depending on the average spread value for a year.

To run this part of the code is necessary to have the results from the module TAQ Responses Physical and TAQ Average Spread.

Modules

The code is divided in four parts:
  • Tools: some functions for repetitive actions.
  • Analysis: code to analyze the data.
  • Main: code to run the implementation.

Tools

TAQ data tools module.

The functions in the module do small repetitive tasks, that are used along the whole implementation. These tools improve the way the tasks are standardized in the modules that use them.

This script requires the following modules:
  • matplotlib
  • os
  • pandas
  • pickle
The module contains the following functions:
  • taq_save_data - saves computed data.
  • taq_save_plot - saves figures.
  • taq_function_header_print_data - prints info about the function running.
  • taq_function_header_print_plot - prints info about the plot.
  • taq_start_folders - creates folders to save data and plots.
  • taq_initial_message - prints the initial message with basic information.
  • taq_business_days - creates a list of week days for a year.
  • main - the main function of the script.
taq_data_tools_avg_responses_physical.main()[source]

The main function of the script.

The main function is used to test the functions in the script.

Returns:None.
taq_data_tools_avg_responses_physical.taq_bussiness_days(year)[source]

Generates a list with the dates of the bussiness days in a year

Parameters:year – string of the year to be analyzed (i.e ‘2008’).
Returns:list.
taq_data_tools_avg_responses_physical.taq_function_header_print_data(function_name, ticker_i, ticker_j, year, month, day)[source]

Prints a header of a function that generates data when it is running.

Parameters:
  • function_name – name of the function that generates the data.
  • ticker_i – string of the abbreviation of the stock to be analyzed (i.e. ‘AAPL’).
  • ticker_j – string of the abbreviation of the stock to be analyzed (i.e. ‘AAPL’).
  • year – string of the year to be analyzed (i.e ‘2016’).
  • month – string of the month to be analyzed (i.e ‘07’).
  • day – string of the day to be analyzed (i.e ‘07’).
Returns:

None – The function prints a message and does not return a value.
taq_data_tools_avg_responses_physical.taq_function_header_print_plot(function_name, ticker_i, ticker_j, year, month, day)[source]

Prints a header of a function that generates a plot when it is running.

Parameters:
  • function_name – name of the function that generates the plot.
  • ticker_i – string of the abbreviation of the stock to be analyzed (i.e. ‘AAPL’).
  • ticker_j – string of the abbreviation of the stock to be analyzed (i.e. ‘AAPL’).
  • year – string of the year to be analyzed (i.e ‘2016’).
  • month – string of the month to be analyzed (i.e ‘07’).
  • day – string of the day to be analyzed (i.e ‘07’).
Returns:

None – The function prints a message and does not return a value.
taq_data_tools_avg_responses_physical.taq_initial_message()[source]

Prints the initial message with basic information.

Returns:None – The function prints a message and does not return a value.
taq_data_tools_avg_responses_physical.taq_save_data(function_name, data, ticker_i, ticker_j, year, month, day)[source]

Saves computed data in pickle files.

Saves the data generated in the functions of the taq_data_analysis_avg_responses_physical module in pickle files.

Parameters:
  • function_name – name of the function that generates the data.
  • data – data to be saved. The data can be of different types.
  • ticker_i – string of the abbreviation of the stock to be analyzed (i.e. ‘AAPL’).
  • ticker_j – string of the abbreviation of the stock to be analyzed (i.e. ‘AAPL’).
  • year – string of the year to be analyzed (i.e ‘2016’).
  • month – string of the month to be analyzed (i.e ‘07’).
  • day – string of the day to be analyzed (i.e ‘07’).
Returns:

None – The function saves the data in a file and does not return a value.
taq_data_tools_avg_responses_physical.taq_save_plot(function_name, figure, ticker_i, ticker_j, year, month)[source]

Saves plot in png files.

Saves the plot generated in the functions of the taq_data_plot_avg_responses_physical module in png files.

Parameters:
  • function_name – name of the function that generates the plot.
  • figure – figure object that is going to be save.
  • ticker_i – string of the abbreviation of the stock to be analyzed (i.e. ‘AAPL’).
  • ticker_j – string of the abbreviation of the stock to be analyzed (i.e. ‘AAPL’).
  • year – string of the year to be analyzed (i.e ‘2016’).
  • month – string of the month to be analyzed (i.e ‘07’).
Returns:

None – The function save the plot in a file and does not return a value.
taq_data_tools_avg_responses_physical.taq_start_folders(year)[source]

Creates the initial folders to save the data and plots.

Parameters:year – string of the year to be analyzed (i.e ‘2016’).
Returns:None – The function creates folders and does not return a value.

Analysis

TAQ data analysis module.

The functions in the module analyze the data from the NASDAQ stock market, computing the average self-response functions for a year grouping the stocks depending on the spread.

This script requires the following modules:
  • numpy
  • pandas
  • pickle
  • taq_data_tools_avg_responses_physical
The module contains the following functions:
  • taq_tickers_spread_data - obtains the tickers and the spread for the classification.
  • taq_self_response_year_avg_responses_physical_data - computes the average self response for groups of tickers in a year.
  • main - the main function of the script.
taq_data_analysis_avg_responses_physical.main()[source]

The main function of the script.

The main function is used to test the functions in the script.

Returns:None.
taq_data_analysis_avg_responses_physical.taq_self_response_year_avg_responses_physical_data(tickers, year)[source]

Computes the avg self-response for groups of tickers in a year.

Using the taq_self_response_day_avg_responses_physical_data function computes the average of self-response functions for different tickers for a year.

Parameters:
  • tickers – list of the string abbreviation of the stocks to be analyzed (i.e. [‘AAPL’, ‘MSFT’]).
  • year – string of the year to be analyzed (i.e ‘2016’).
Returns:

tuple – The function returns a tuple with numpy arrays.
taq_data_analysis_avg_responses_physical.taq_tickers_spread_data(year)[source]

Obtains the tickers and the spread range for the classification.

Parameters:year – string of the year to be analyzed (i.e. ‘2016’).
Returns:tuple – The function returns a tuple with a numpy array and a list.

Main

TAQ data main module.

The functions in the module run the complete extraction, analysis and plot of the TAQ data.

This script requires the following modules:
  • itertools
  • multiprocessing
  • pandas
  • pickle
  • taq_data_analysis_avg_responses_physical
  • taq_data_plot_avg_responses_physical
  • taq_data_tools_avg_responses_physical
The module contains the following functions:
  • taq_data_plot_generator - generates all the analysis and plots from the TAQ data.
  • main - the main function of the script.
taq_data_main_avg_responses_physical.main()[source]

The main function of the script.

The main function extract, analyze and plot the data.

Returns:None.
taq_data_main_avg_responses_physical.taq_data_plot_generator(year)[source]

Generates all the analysis and plots from the TAQ data.

Parameters:
  • div – integer of the number of divisions in the tickers (i.e. 5).
  • year – string of the year to be analyzed (i.e ‘2016’).
Returns:

None – The function saves the data in a file and does not return a value.