'''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
* pandas
* pickle
* taq_data_tools_responses_activity
The module contains the following functions:
* taq_self_response_day_responses_activity_data - computes the self
response of a day.
* taq_self_response_year_responses_activity_data_data - computes the
self-response of a year.
* taq_cross_response_day_responses_activity_data - computes the cross
response of a day.
* taq_cross_response_year_responses_activity_data_data - computes the
cross-response of a year.
* main - the main function of the script.
.. moduleauthor:: Juan Camilo Henao Londono <www.github.com/juanhenao21>
'''
# ----------------------------------------------------------------------------
# Modules
from itertools import product as iprod
import multiprocessing as mp
import numpy as np
import pandas as pd
import pickle
import taq_data_tools_responses_activity
__tau__ = 1000
# ----------------------------------------------------------------------------
[docs]def taq_trades_count_responses_activity_data(ticker, date):
"""Counts the number of trades per second.
Using the data obtained with the taq_trade_signs_trade_data function,
implemented in the taq_data_analysis responses_trade, counts the number
of trades per second.
:param ticker: string of the abbreviation of the stock to be analyzed
(i.e. 'AAPL').
:param date: string with the date of the data to be extracted
(i.e. '2008-01-02').
:return: tuple -- The function returns a tuple with numpy arrays.
"""
date_sep = date.split('-')
year = date_sep[0]
month = date_sep[1]
day = date_sep[2]
function_name = taq_trades_count_responses_activity_data.__name__
try:
# Load data
t, _, trade_sign_i = pickle.load(open(
f'../../taq_data/responses_trade_data_{year}/taq_trade'
+ f'_signs_trade_data/taq_trade_signs_trade_data'
+ f'_{year}{month}{day}_{ticker}.pickle', 'rb'))
# Open market time [34801, 57000]
full_time = np.array(range(34801, 57001))
trades_count = np.zeros(len(full_time))
# Count the number of trades in every second
for t_idx, t_val in enumerate(full_time):
condition = t_val == t
trades_count[t_idx] = len(trade_sign_i[condition])
# Save data
taq_data_tools_responses_activity \
.taq_save_data(function_name, (full_time, trades_count), ticker,
ticker, year, month, day)
return (full_time, trades_count)
except FileNotFoundError as e:
print('No data')
print(e)
print()
return None
# ----------------------------------------------------------------------------
[docs]def taq_self_response_day_responses_activity_data(ticker, date):
"""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 (:math:`\tau`) for a day.
:param ticker: string of the abbreviation of the stock to be analyzed
(i.e. 'AAPL').
:param date: string with the date of the data to be extracted
(i.e. '2008-01-02').
:return: tuple -- The function returns a tuple with numpy arrays.
"""
date_sep = date.split('-')
year = date_sep[0]
month = date_sep[1]
day = date_sep[2]
try:
# Load data
midpoint = pickle.load(open(
f'../../taq_data/responses_physical_data_{year}/taq_midpoint'
+ f'_physical_data/taq_midpoint_physical_data_midpoint'
+ f'_{year}{month}{day}_{ticker}.pickle', 'rb'))
_, _, trade_sign = pickle.load(open(
f'../../taq_data/responses_physical_data_{year}/taq_trade'
+ f'_signs_physical_data/taq_trade_signs_physical_data'
+ f'_{year}{month}{day}_{ticker}.pickle', 'rb'))
_, trade_count = pickle.load(open(
f'../../taq_data/responses_activity_data_{year}/taq_trades'
+ f'_count_responses_activity_data/taq_trades_count_responses'
+ f'_activity_data_{year}{month}{day}_{ticker}.pickle', 'rb'))
assert len(midpoint) == len(trade_sign)
assert len(midpoint) == len(trade_count)
# Array of the average of each tau. 10^3 s is used in the paper
self_response_tau = np.zeros(__tau__)
num = np.zeros(__tau__)
# Calculating the midpoint price return and the self response function
# Depending on the tau value
for tau_idx in range(__tau__):
trade_sign_tau = trade_sign[:-tau_idx - 1]
trade_count_tau = trade_count[:-tau_idx - 1]
trade_count_len = np.sum(trade_count_tau)
num[tau_idx] = trade_count_len
# Obtain the midpoint price return. Displace the numerator tau
# values to the right and compute the return
# midpoint price returns
log_return_sec = (midpoint[tau_idx + 1:]
- midpoint[:-tau_idx - 1]) \
/ midpoint[:-tau_idx - 1]
# Obtain the self response value
if (trade_count_len != 0):
product = log_return_sec * trade_sign_tau * trade_count_tau
self_response_tau[tau_idx] = np.sum(product)
return (self_response_tau, num)
except FileNotFoundError as e:
print('No data')
print(e)
print()
zeros = np.zeros(__tau__)
return (zeros, zeros)
# ----------------------------------------------------------------------------
[docs]def taq_self_response_year_responses_activity_data(ticker, year):
"""Computes the self-response of a year.
Using the taq_self_response_day_responses_activity_data function
computes the self-response function for a year.
:param ticker: string of the abbreviation of stock to be analyzed
(i.e. 'AAPL').
:param year: string of the year to be analyzed (i.e '2016').
:return: tuple -- The function returns a tuple with numpy arrays.
"""
function_name = taq_self_response_year_responses_activity_data \
.__name__
taq_data_tools_responses_activity \
.taq_function_header_print_data(function_name, ticker, ticker, year,
'', '')
dates = taq_data_tools_responses_activity.taq_bussiness_days(year)
self_values = []
args_prod = iprod([ticker], dates)
# Parallel computation of the self-responses. Every result is appended to
# a list
with mp.Pool(processes=mp.cpu_count()) as pool:
self_values.append(pool.starmap(
taq_self_response_day_responses_activity_data, args_prod))
# To obtain the total self-response, I sum over all the self-response
# values and all the amount of trades (averaging values)
self_v_final = np.sum(self_values[0], axis=0)
self_response_val = self_v_final[0] / self_v_final[1]
self_response_avg = self_v_final[1]
# Saving data
taq_data_tools_responses_activity \
.taq_save_data(function_name, self_response_val, ticker, ticker, year,
'', '')
return (self_response_val, self_response_avg)
# ----------------------------------------------------------------------------
[docs]def taq_cross_response_day_responses_activity_data(ticker_i, ticker_j, date):
"""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 (:math:`\tau`) for a day.
:param ticker_i: string of the abbreviation of the stock to be analyzed
(i.e. 'AAPL').
:param ticker_j: string of the abbreviation of the stock to be analyzed
(i.e. 'AAPL').
:param date: string with the date of the data to be extracted
(i.e. '2008-01-02').
:return: tuple -- The function returns a tuple with numpy arrays.
"""
date_sep = date.split('-')
year = date_sep[0]
month = date_sep[1]
day = date_sep[2]
if (ticker_i == ticker_j):
# Self-response
return None
else:
try:
# Load data
midpoint_i = pickle.load(open(
f'../../taq_data/responses_physical_data_{year}/taq'
+ f'_midpoint_physical_data/taq_midpoint_physical_data'
+ f'_midpoint_{year}{month}{day}_{ticker_i}.pickle', 'rb'))
_, _, trade_sign_j = pickle.load(open(
f'../../taq_data/responses_physical_data_{year}/taq_trade_'
+ f'signs_physical_data/taq_trade_signs_physical_data'
+ f'_{year}{month}{day}_{ticker_j}.pickle', 'rb'))
_, trade_count_j = pickle.load(open(
f'../../taq_data/responses_activity_data_{year}/taq'
+ f'_trades_count_responses_activity_data/taq_trades'
+ f'_count_responses_activity_data_{year}{month}{day}'
+ f'_{ticker_j}.pickle', 'rb'))
assert len(midpoint_i) == len(trade_sign_j)
assert len(midpoint_i) == len(trade_count_j)
# Array of the average of each tau. 10^3 s is used in the paper
cross_response_tau = np.zeros(__tau__)
num = np.zeros(__tau__)
# Calculating the midpoint return and the cross response function
# Depending on the tau value
for tau_idx in range(__tau__):
trade_sign_tau = trade_sign_j[:-tau_idx - 1]
trade_count_tau = trade_count_j[:-tau_idx - 1]
trade_count_len = np.sum(trade_count_tau)
num[tau_idx] = trade_count_len
# Obtain the midpoint price return. Displace the numerator tau
# values to the right and compute the return
log_return_i_sec = (midpoint_i[tau_idx + 1:]
- midpoint_i[:-tau_idx - 1]) \
/ midpoint_i[:-tau_idx - 1]
# Obtain the cross response value
if (trade_count_len != 0):
product = log_return_i_sec * trade_sign_tau \
* trade_count_tau
cross_response_tau[tau_idx] = np.sum(product)
return (cross_response_tau, num)
except FileNotFoundError as e:
print('No data')
print(e)
print()
zeros = np.zeros(__tau__)
return (zeros, zeros)
# ----------------------------------------------------------------------------
[docs]def taq_cross_response_year_responses_activity_data(ticker_i, ticker_j, year):
"""Computes the cross-response of a year.
Using the taq_cross_response_day_responses_activity_data function computes
the cross-response function for a year.
:param ticker_i: string of the abbreviation of the stock to be analyzed
(i.e. 'AAPL').
:param ticker_j: string of the abbreviation of the stock to be analyzed
(i.e. 'AAPL').
:param year: string of the year to be analyzed (i.e '2016').
:return: tuple -- The function returns a tuple with numpy arrays.
"""
if (ticker_i == ticker_j):
# Self-response
return None
else:
function_name = taq_cross_response_year_responses_activity_data \
.__name__
taq_data_tools_responses_activity \
.taq_function_header_print_data(function_name, ticker_i, ticker_j,
year, '', '')
dates = taq_data_tools_responses_activity.taq_bussiness_days(year)
cross_values = []
args_prod = iprod([ticker_i], [ticker_j], dates)
# Parallel computation of the cross-responses. Every result is appended
# to a list
with mp.Pool(processes=mp.cpu_count()) as pool:
cross_values.append(pool.starmap(
taq_cross_response_day_responses_activity_data, args_prod))
# To obtain the total cross-response, I sum over all the cross-response
# values and all the amount of trades (averaging values)
cross_v_final = np.sum(cross_values[0], axis=0)
cross_response_val = cross_v_final[0] / cross_v_final[1]
cross_response_avg = cross_v_final[1]
# Saving data
taq_data_tools_responses_activity \
.taq_save_data(function_name, cross_response_val, ticker_i,
ticker_j, year, '', '')
return (cross_response_val, cross_response_avg)
# ----------------------------------------------------------------------------
[docs]def main():
"""The main function of the script.
The main function is used to test the functions in the script.
:return: None.
"""
pass
return None
# ----------------------------------------------------------------------------
if __name__ == "__main__":
main()