TAQ Statistics¶

Computes some statistics of the data as average quotes, average trades, average spread and change in the midpoint price.

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

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_function_header_print_data - prints info about the function running.
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_statistics.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_statistics.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_statistics.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_statistics.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_statistics.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_statistics 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_statistics.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 statistics from the NASDAQ stock market.

This script requires the following modules:

itertools
multiprocessing
numpy
pandas
taq_data_tools_statistics

The module contains the following functions:

taq_quotes_trades_day_statistics_data - statistics of quotes and trades for a day.
taq_quotes_trades_year_statistics_data - statistics of quotes and trades for a year.
taq_midpoint_day_statistics_data - statistics midpoint price day
taq_midpoint_year_statistics_data - statistics midpoint price year
main - the main function of the script.

taq_data_analysis_statistics.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_statistics.taq_midpoint_day_statistics_data(ticker, date)[source]¶

Obtain the midpoint price statistics for a day.

Using the quotes files, obtain the midpoint price and the percentage of change between the last midpoint price in a second and the average midpoint price of the second.

Parameters:	ticker – string of the abbreviation of the stock to be analyzed (i.e. ‘AAPL’). date – string with the date of the data to be extracted (i.e. ‘2008-01-02’).
Returns:	tuple – The function returns a tuple with float values.

taq_data_analysis_statistics.taq_midpoint_year_statistics_data(tickers, year)[source]¶

Obtain the midpoint price statistics for a year.

Using the taq_midpoint_day_statistics_data function computes the statistics of the last midpoint price in a second and the average midpoint of the second 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:	None – The function saves the data in a file and does not return a value.

taq_data_analysis_statistics.taq_quotes_trades_day_statistics_data(ticker, date)[source]¶

Obtain the quotes and trades statistics for a day.

Using the quotes files, obtain the statistics of the average spread, number of quotes and number of trades for a day.

Parameters:	ticker – string of the abbreviation of the stock to be analyzed (i.e. ‘AAPL’). date – string with the date of the data to be extracted (i.e. ‘2008-01-02’).
Returns:	tuple – The function returns a tuple with float values.

taq_data_analysis_statistics.taq_quotes_trades_year_statistics_data(tickers, year)[source]¶

Obtain the quotes and trades statistics for a year.

Using the taq_quotes_trades_day_statistics_data function computes the statistics of the average spread, number of quotes and number of trades 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:	None – The function saves the data in a file and does not return a value.

Main¶

TAQ data main module.

The functions in the module run the complete analysis of the TAQ data.

This script requires the following modules:

itertools
multiprocessing
pandas
taq_data_analysis_statistics
taq_data_tools_statistics

The module contains the following functions:

taq_data_generator - generates all the analysis of the TAQ data.
main - the main function of the script.

taq_data_main_statistics.main()[source]¶

The main function of the script.

The main function extract, analyze and plot the data.

Returns:	None.

taq_data_main_statistics.taq_data_generator(tickers, year)[source]¶

Generates all the analysis of the TAQ data.

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