참고

This page was generated from tutorials/finance/11_time_series.ipynb.

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Loading and Processing Stock-Market Time-Series Data

소개

Across many problems in finance, one starts with time series. Here, we showcase how to generate pseudo-random time-series, download actual stock-market time series from a number of common providers, and how to compute time-series similarity measures.

%matplotlib inline
from qiskit.finance import QiskitFinanceError
from qiskit.finance.data_providers import *
import warnings
warnings.filterwarnings("ignore", category=DeprecationWarning)
import datetime
import matplotlib.pyplot as plt
from pandas.plotting import register_matplotlib_converters
register_matplotlib_converters()

data = RandomDataProvider(tickers=["TICKER1", "TICKER2"],
                 start = datetime.datetime(2016, 1, 1),
                 end = datetime.datetime(2016, 1, 30),
                 seed = 1)
data.run()

Once the data are loaded, you can run a variety of algorithms on those to aggregate the data. Notably, you can compute the covariance matrix or a variant, which would consider alternative time-series similarity measures based on dynamic time warping (DTW). In DTW, changes that vary in speed, e.g., one stock’s price following another stock’s price with a small delay, can be accommodated.

means = data.get_mean_vector()
print("Means:")
print(means)

rho = data.get_similarity_matrix()
print("A time-series similarity measure:")
print(rho)
plt.imshow(rho)
plt.show()

cov = data.get_covariance_matrix()
print("A covariance matrix:")
print(cov)
plt.imshow(cov)
plt.show()

Means:
[33.97683271 97.61130683]
A time-series similarity measure:
[[1.00000000e+00 5.41888011e-04]
 [5.41888011e-04 1.00000000e+00]]

A covariance matrix:
[[2.08413157 0.20842107]
 [0.20842107 1.99542187]]

If you wish, you can look into the underlying pseudo-random time-series using. Please note that the private class members (starting with underscore) may change in future releases of Qiskit.

print("The underlying evolution of stock prices:")
for (cnt, s) in enumerate(data._tickers):
    plt.plot(data._data[cnt], label=s)
plt.legend()
plt.xticks(rotation=90)
plt.show()

for (cnt, s) in enumerate(data._tickers):
    print(s)
    print(data._data[cnt])

The underlying evolution of stock prices:

TICKER1
[33.345584192064784, 34.167202335565946, 34.49763941174933, 33.19448218014497, 34.099838046818086, 34.5462126191821, 34.009259383821814, 34.59037748801817, 34.95494988420424, 35.24908238085977, 35.27750462217556, 35.82421760878801, 35.08776352178634, 34.92485357379329, 34.442734261113316, 35.04158047374794, 35.0813025812296, 34.78884583026451, 34.00693736790767, 33.7497451272888, 33.757887307807145, 33.48228440250777, 34.77634821690598, 35.783072532211776, 33.07191005324581, 31.182896807278134, 31.008124715222973, 30.585934303646617, 30.799577301145227]
TICKER2
[96.8774156647853, 98.99525441983634, 97.88323365714406, 97.50562865001707, 99.5484002575094, 100.19510325371124, 100.85816662608751, 100.34416025440004, 98.69608508354439, 98.86354982776713, 98.97256391558868, 97.7452118613441, 97.06198519956354, 96.98994151983632, 96.04518989677554, 95.94691992892332, 96.04240295639278, 96.07798919344826, 95.57169753513395, 96.16544560691977, 97.0566125612021, 97.37746086576867, 96.55923063837835, 97.29088292216379, 96.78944290369674, 97.66860352198472, 96.59681610510728, 97.51128330823606, 97.49121985362058]

명확히 티커의 수와 이름 및 날짜 범위를 조정할 수 있습니다.

data = RandomDataProvider(tickers=["CompanyA", "CompanyB", "CompanyC"],
                 start = datetime.datetime(2015, 1, 1),
                 end = datetime.datetime(2016, 1, 30),
                 seed = 1)
data.run()
for (cnt, s) in enumerate(data._tickers):
    plt.plot(data._data[cnt], label=s)
plt.legend()
plt.xticks(rotation=90)
plt.show()

닫는 가격에 대한 액세스-시리즈

실시간 데이터에 대한 액세스는 일반적으로 지불을 필요로 하지만, Wikipedia와 Quand1 무료로 마감 가격에 액세스할 수 있다: https://www.quandl.com/?modal=register에 등록한 후에는 실제 나스닥 문제의 실제적인 스티커와 Quand1 에서 얻는 액세스 토큰을 지정해야 한다. 공분산 및 시계열 행렬의 계산에는 최소한 두 개의 티커가 필요하지만, 수백 개의 티커는 Quand1의 공정한 사용 한계를 넘어설 수 있다.

stocks = ["GOOG", "AAPL"]

token = "REPLACE-ME"
if token != "REPLACE-ME":
    try:
        wiki = WikipediaDataProvider(
                         token = token,
                         tickers = stocks,
                         start = datetime.datetime(2016,1,1),
                         end = datetime.datetime(2016,1,30))
        wiki.run()
    except QiskitFinanceError as ex:
        print(ex)
        print("Error retrieving data.")

데이터가 로드되면 공분산 행렬 또는 DTW 변형을 다시 계산할 수 있습니다.

if token != "REPLACE-ME":
    if wiki._data:
        if wiki._n <= 1:
            print("Not enough wiki data to plot covariance or time-series similarity. Please use at least two tickers.")
        else:
            rho = wiki.get_similarity_matrix()
            print("A time-series similarity measure:")
            print(rho)
            plt.imshow(rho)
            plt.show()

            cov = wiki.get_covariance_matrix()
            print("A covariance matrix:")
            print(cov)
            plt.imshow(cov)
            plt.show()
    else:
        print('No wiki data loaded.')

원하는 경우 다음을 사용하여 기본 시간 시리즈를 조사할 수 있습니다.

if token != "REPLACE-ME":
    if wiki._data:
        print("The underlying evolution of stock prices:")
        for (cnt, s) in enumerate(stocks):
            plt.plot(wiki._data[cnt], label=s)
        plt.legend()
        plt.xticks(rotation=90)
        plt.show()

        for (cnt, s) in enumerate(stocks):
            print(s)
            print(wiki._data[cnt])
    else:
        print('No wiki data loaded.')

[Optional] 설정 토큰을 설정하여 최신의 세분화된 시간-시리즈

전문 데이터를 다운로드하려면 주요 제공자 중 하나를 사용하여 토큰을 설정해야 합니다. 이제 나스닥 및 NYSE 문제에 대한 데이터를 조정하고, 가격을 조정하고 매일 조정하는 가격, 나스닥 및 NYSE 문제에 대한 조정과 같은 집계를 제공할 수 있는 나스닥 데이터의 데이터를 설명합니다.

If you don’t have NASDAQ Data on Demand license, you can contact NASDAQ (cf. https://business.nasdaq.com/intel/GIS/Nasdaq-Data-on-Demand.html) to obtain a trial or paid license.

If and when you have access to NASDAQ Data on Demand using your own token, you should replace REPLACE-ME below with the token. To assure the security of the connection, you should also have your own means of validating NASDAQ’s certificates. The DataOnDemandProvider constructor has an optional argument verify, which can be None or a string or a boolean. If it is None, certify certificates will be used (default). If verify is a string, it should be pointing to a certificate for the HTTPS connection to NASDAQ (dataondemand.nasdaq.com), either in the form of a CA_BUNDLE file or a directory wherein to look.

token = "REPLACE-ME"
if token != "REPLACE-ME":
    try:
        nasdaq = DataOnDemandProvider(token = token,
                     tickers = ["GOOG", "AAPL"],
                     start = datetime.datetime(2016,1,1),
                     end = datetime.datetime(2016,1,2))
        nasdaq.run()
        for (cnt, s) in enumerate(nasdaq._tickers):
            plt.plot(nasdaq._data[cnt], label=s)
        plt.legend()
        plt.xticks(rotation=90)
        plt.show()
    except QiskitFinanceError as ex:
        print(ex)
        print("Error retrieving data.")

주식 시장 데이터의 또 다른 주요 공급업체인 EDI (Exchange Data International) 는 아프리카, 아시아, 극동, 라틴 아메리카, 중동, 그리고 더 설정된 것들 뿐만 아니라 아프리카, 아시아, 극동, 라틴 아메리카, 중동과 같은 100개 이상의 신흥 시장을 조회하는 데 사용될 수 있는 API를 사용할 수 있습니다. 예를 들어#exchangehttps://www.exchange-data.com/pricing-data/adjusted-prices.php 전체에 대한 개요를 볼 수 있다.

액세스를 다시 요청하려면 아래에 있는 REPLACE를 대체할 유효한 액세스 토큰이 필요합니다. 이 토큰은 다음과 같이 시험 또는 지불 방식으로 얻을 수 있다: https://www.quandl.com/

token = "REPLACE-ME"
if token != "REPLACE-ME":
    try:
        lse = ExchangeDataProvider(token = token,
                     tickers = ["AEO", "ABBY", "ADIG", "ABF",
                                "AEP", "AAL", "AGK", "AFN", "AAS", "AEFS"],
                     stockmarket = StockMarket.LONDON,
                     start=datetime.datetime(2018, 1, 1),
                     end=datetime.datetime(2018, 12, 31))
        lse.run()
        for (cnt, s) in enumerate(lse._tickers):
            plt.plot(lse._data[cnt], label=s)
        plt.legend()
        plt.xticks(rotation=90)
        plt.show()
    except QiskitFinanceError as ex:
        print(ex)
        print("Error retrieving data.")

또한 야후의 금융 데이터에 액세스할 수 있는 서비스를 제공하지 않아도 된다. 금융.

try:
    data = YahooDataProvider(
                 tickers = ["AEO", "ABBY", "AEP", "AAL", "AFN"],
                 start=datetime.datetime(2018, 1, 1),
                 end=datetime.datetime(2018, 12, 31))
    data.run()
    for (cnt, s) in enumerate(data._tickers):
        plt.plot(data._data[cnt], label=s)
    plt.legend()
    plt.xticks(rotation=90)
    plt.show()
except QiskitFinanceError as ex:
    data = None
    print(ex)

For the actual use of the data, please see the portfolio_optimization or portfolio_diversification notebooks.

import qiskit.tools.jupyter
%qiskit_version_table
%qiskit_copyright

Version Information

Qiskit Software	Version
Qiskit	None
Terra	0.15.1
Aer	0.6.1
Ignis	0.4.0
Aqua	0.7.5
IBM Q Provider	0.8.0
System information
Python	3.7.4 (default, Aug 13 2019, 15:17:50) [Clang 4.0.1 (tags/RELEASE_401/final)]
OS	Darwin
CPUs	2
Memory (Gb)	12.0
Wed Aug 12 13:20:52 2020 EDT

This code is a part of Qiskit

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This code is licensed under the Apache License, Version 2.0. You may
obtain a copy of this license in the LICENSE.txt file in the root directory
of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.

Any modifications or derivative works of this code must retain this
copyright notice, and modified files need to carry a notice indicating
that they have been altered from the originals.