TAQ Responses Trade¶

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

All the results obtained with the TAQ Responses Trade modules are the base to the other implementations (TAQ Trade Shift and TAQ Responses Trade Shift)

Modules¶

The code is divided in four parts:

Tools: some functions for repetitive actions.
Analysis: code to analyze the data.
Plot: code to plot 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_responses_trade.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_responses_trade.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_responses_trade.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_responses_trade.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_responses_trade.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_responses_trade.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_responses_trade 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_responses_trade.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_responses_trade 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_responses_trade.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 self- and cross-response functions.

This script requires the following modules:

itertools
multiprocessing
numpy
os
pandas
pickle
taq_data_tools_responses_trade

The module contains the following functions:

taq_midpoint_trade_data - obtains the midpoint price in trade time scale.
taq_trade_signs_trade_data - computes the trade signs of every trade.
taq_self_response_day_responses_trade_data - computes the self response of a day.
taq_self_response_year_responses_trade_data - computes the self response of a year.
taq_cross_response_day_responses_trade_data - computes the cross response of a day.
taq_cross_response_year_responses_trade_data - computes the cross response of a year.
main - the main function of the script.

taq_data_analysis_responses_trade.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_responses_trade.taq_cross_response_day_responses_trade_data(ticker_i, ticker_j, date)[source]¶

Computes the cross-response of a day.

Using the midpoint price of ticker i and trade signs of ticker j computes the cross-response during different time lags (\(\tau\)) for a day.

Parameters:	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’). date – string with the date of the data to be extracted (i.e. ‘2008-01-02’).
Returns:	tuple – The function returns a tuple with numpy arrays.

taq_data_analysis_responses_trade.taq_cross_response_year_responses_trade_data(ticker_i, ticker_j, year)[source]¶

Computes the cross-response of a year.

Using the taq_cross_response_day_responses_trade_data function computes the cross-response function for a year.

Parameters:	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’).
Returns:	tuple – The function returns a tuple with numpy arrays.

taq_data_analysis_responses_trade.taq_self_response_day_responses_trade_data(ticker, date)[source]¶

Computes the self-response of a day.

Using the midpoint price and trade signs of a ticker computes the self- response during different time lags (\(\tau\)) 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 numpy arrays.

taq_data_analysis_responses_trade.taq_self_response_year_responses_trade_data(ticker, year)[source]¶

Computes the self-response of a year.

Using the taq_self_response_day_responses_trade_data function computes the self-response function for a year.

Parameters:	ticker – string of the abbreviation of stock to be analyzed (i.e. ‘AAPL’). year – string of the year to be analyzed (i.e ‘2016’).
Returns:	tuple – The function returns a tuple with numpy arrays.

taq_data_analysis_responses_trade.taq_trade_signs_trade_data(ticker, date)[source]¶

Computes the trade signs of every trade.

Using the dayly TAQ data computes the trade signs of every trade in a day. The trade signs are computed using Eq. 1 of the paper. As the trades signs are not directly given by the TAQ data, they must be infered by the trades prices. For further calculations, the function returns the values for the time range from 9h40 to 15h50.

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 numpy arrays.

Plot¶

TAQ data plot module.

The functions in the module plot the data obtained in the taq_data_analysis_responses_trade module.

This script requires the following modules:

matplotlib
pickle
taq_data_tools_responses_trade

The module contains the following functions:

taq_self_response_year_avg_responses_trade_plot - plots the self-response average for a year.
taq_cross_response_year_avg_responses_trade_plot - plots the cross- response average for a year.
main - the main function of the script.

taq_data_plot_responses_trade.main()[source]¶

The main function of the script.

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

Returns:	None.

taq_data_plot_responses_trade.taq_cross_response_year_avg_responses_trade_plot(ticker_i, ticker_j, year)[source]¶

Plots the cross-response average for a year.

Parameters:	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 ‘2008’)
Returns:	None – The function saves the plot in a file and does not return a value.

taq_data_plot_responses_trade.taq_self_response_year_avg_responses_trade_plot(ticker, year)[source]¶

Plots the self-response average for a year.

Parameters:	ticker – string of the abbreviation of the stock to be analyzed (i.e. ‘AAPL’). year – string of the year to be analyzed (i.e ‘2008’).
Returns:	None – The function saves the plot in a file and does not return a value.

Main¶

TAQ data main module.

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

This script requires the following modules:

itertools.product
multiprocessing
pandas
taq_data_analysis_responses_trade
taq_data_plot_responses_trade
taq_data_tools_responses_trade

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_responses_trade.main()[source]¶

The main function of the script.

The main function extract, analyze and plot the data.

Returns:	None.

taq_data_main_responses_trade.taq_data_plot_generator(tickers, year)[source]¶

Generates all the analysis and plots from 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.