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원자재가격과 주가의 관계 분석

경희대학교 산업경영공학과 김태호 Github

import pandas as pd
from pandas import Series,DataFrame
import numpy as np
from dateutil.parser import parse
import dateutil

# For Visualization
import matplotlib.pyplot as plt
from matplotlib import pyplot
import seaborn as sns
sns.set_style('whitegrid')
%matplotlib inline

# For time stamps
from datetime import datetime

import warnings
warnings.filterwarnings("ignore")
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
from datetime import datetime

path = "c:/Windows/Fonts/malgun.ttf"
import platform
from matplotlib import font_manager, rc
if platform.system() == 'Darwin':
    rc('font', family='AppleGothic')
elif platform.system() == 'Windows':
    font_name = font_manager.FontProperties(fname=path).get_name()
    rc('font', family=font_name)
else:
    print('Unknown system... sorry~~~~')    

plt.rcParams['axes.unicode_minus'] = False

데이터 전처리

KOSPI만 선택

item_Charge = pd.read_csv("st_data_itemChargeFull.tsv", sep = '\t')
item_Charge['date'] = item_Charge['date'].apply(str)
item_Charge['date'] = item_Charge['date'].apply(dateutil.parser.parse)
KOSPI = item_Charge.loc[item_Charge[list(item_Charge.columns)[2]]=="KOSPI",:]
KOSPI.head()
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itemcode itemname market date close_val change_val change_rate acc_quant debt_total face_val ... operating_profit operating_profit_increasing_rate prev_quant property_total reserve_ratio roa roe sales sales_increasing_rate trade_stop_yn
0 000020 동화약품 KOSPI 2015-01-07 5540.0 70.0 1.26 42517.0 732.0 1000 ... 112.0 NaN 62744.0 3706.0 967.43 NaN NaN 3066.0 NaN N
1 000020 동화약품 KOSPI 2015-01-13 5600.0 40.0 0.71 39527.0 732.0 1000 ... 112.0 NaN 62744.0 3706.0 967.43 NaN NaN 3066.0 NaN N
2 000020 동화약품 KOSPI 2015-01-16 5640.0 -110.0 -1.95 57341.0 732.0 1000 ... 112.0 NaN 62744.0 3706.0 967.43 NaN NaN 3066.0 NaN N
3 000020 동화약품 KOSPI 2015-01-19 5680.0 40.0 0.70 49574.0 732.0 1000 ... 112.0 NaN 62744.0 3706.0 967.43 NaN NaN 3066.0 NaN N
4 000020 동화약품 KOSPI 2015-01-22 5550.0 -60.0 -1.08 77386.0 732.0 1000 ... 112.0 NaN 62744.0 3706.0 967.43 NaN NaN 3066.0 NaN N

5 rows × 27 columns

KOSPI_=KOSPI.pivot_table('close_val',index='date',columns='itemname')
KOSPI_clo = KOSPI_.dropna(axis = 1 ).fillna(method='ffill').pct_change().fillna(0)
KOSPI_val = KOSPI_.dropna(axis = 1 ).fillna(method='ffill')
KOSPI_val.head()
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itemname AJ렌터카 AK홀딩스 BGF BNK금융지주 BYC BYC우 CJ CJ CGV CJ대한통운 CJ씨푸드 ... 후성 휠라코리아 휴니드 휴비스 휴스틸 휴켐스 흥국화재 흥국화재2우B 흥국화재우 흥아해운
date
2015-01-07 17750.0 86600.0 74500.0 13500.0 252000.0 168500.0 158500.0 59800.0 183500.0 2595.0 ... 3080.0 119000.0 4510.0 11600.0 17350.0 25550.0 3730.0 6140.0 2460.0 2805.0
2015-01-13 17650.0 88000.0 73700.0 14000.0 254000.0 170000.0 161500.0 60500.0 192000.0 2590.0 ... 3245.0 118000.0 4820.0 11750.0 17600.0 25200.0 3900.0 6200.0 2465.0 2655.0
2015-01-16 17300.0 92300.0 75100.0 14000.0 252500.0 169000.0 164500.0 59300.0 182000.0 2560.0 ... 3215.0 115000.0 4970.0 11800.0 17150.0 26800.0 4230.0 6160.0 2410.0 2710.0
2015-01-19 17250.0 88200.0 75100.0 14250.0 252500.0 170500.0 164500.0 59300.0 182000.0 2540.0 ... 3160.0 115500.0 5050.0 11800.0 16850.0 27050.0 4190.0 6300.0 2450.0 2575.0
2015-01-22 17000.0 90300.0 75000.0 13950.0 251000.0 175000.0 168500.0 59600.0 172000.0 2520.0 ... 3205.0 116000.0 5090.0 11650.0 16800.0 26800.0 4175.0 6180.0 2495.0 2570.0

5 rows × 802 columns

st_data_goodsForeign = pd.read_csv("st_data_goodsForeign.tsv", sep = '\t')
st_data_goodsForeign['date'] = st_data_goodsForeign['date'].apply(str)
st_data_goodsForeign['date'] = st_data_goodsForeign['date'].apply(dateutil.parser.parse)
st_data_goodsForeign.head()
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itemcode itemname date close_val change_val open_val high_val low_val
0 LME@AAY 알루미늄합금 2015-01-07 1790.0 -15.0 1790.0 1790.0 1780.0
1 LME@AAY 알루미늄합금 2015-01-13 1835.0 NaN 1835.0 1835.0 1825.0
2 LME@AAY 알루미늄합금 2015-01-16 1820.0 NaN 1820.0 1820.0 1810.0
3 LME@AAY 알루미늄합금 2015-01-19 1820.0 NaN 1820.0 1820.0 1810.0
4 LME@AAY 알루미늄합금 2015-01-22 1870.0 10.0 1870.0 1870.0 1860.0 
goodsForeign=st_data_goodsForeign.pivot_table('close_val',index='date',columns='itemname')
goodsForeign_clo = goodsForeign.fillna(method='ffill').pct_change().fillna(0)
goodsForeign_val = goodsForeign.fillna(method='ffill')
goodsForeign_val.head()
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itemname 구리 난방유 납 니켈 대두 대두박 대두유 설탕 소맥 쌀 아연 알루미늄합금 옥수수 주석 천연가스
date
2015-01-07 6170.0 1.6999 1820.0 15455.0 1065.6 368.5 32.98 14.78 579.4 11.410 2145.5 1790.0 411.4 19680.0 2.871
2015-01-13 5915.0 1.6330 1800.5 14780.0 1015.6 349.7 32.39 14.87 548.0 11.240 2083.0 1835.0 400.2 19555.0 2.943
2015-01-16 5660.0 1.6656 1766.5 14360.0 1003.0 326.2 33.39 15.33 532.6 11.325 2056.5 1820.0 401.0 19325.0 3.127
2015-01-19 5671.0 1.6411 1827.5 14400.0 1003.0 326.2 33.39 15.33 532.6 11.325 2068.5 1820.0 401.0 19280.0 2.995
2015-01-22 5742.0 1.6379 1881.5 14720.0 988.6 330.1 31.97 15.91 533.6 11.160 2144.0 1870.0 399.2 19580.0 2.835 
TD = pd.concat([KOSPI_val,goodsForeign_val],axis=1)
TD = TD.reset_index().fillna(method='ffill')
for i in range(0,336):
    if i%3 != 0:
        TD.drop( i,inplace = True)
FTD = TD.set_index('date')
FTD.head()
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itemname AJ렌터카 AK홀딩스 BGF BNK금융지주 BYC BYC우 CJ CJ CGV CJ대한통운 CJ씨푸드 ... 대두박 대두유 설탕 소맥 쌀 아연 알루미늄합금 옥수수 주석 천연가스
date
2015-01-07 17750.0 86600.0 74500.0 13500.0 252000.0 168500.0 158500.0 59800.0 183500.0 2595.0 ... 368.5 32.98 14.78 579.4 11.410 2145.5 1790.0 411.4 19680.0 2.871
2015-01-19 17250.0 88200.0 75100.0 14250.0 252500.0 170500.0 164500.0 59300.0 182000.0 2540.0 ... 326.2 33.39 15.33 532.6 11.325 2068.5 1820.0 401.0 19280.0 2.995
2015-02-03 17350.0 98000.0 85000.0 14450.0 282000.0 171500.0 173000.0 60200.0 200000.0 2525.0 ... 340.6 30.80 14.47 513.6 10.385 2146.5 1840.0 401.0 18950.0 2.754
2015-02-12 16500.0 91500.0 88000.0 14450.0 295000.0 170500.0 177500.0 64700.0 197000.0 2600.0 ... 330.5 32.01 15.04 521.2 10.330 2129.5 1785.0 398.2 17900.0 2.713
2015-02-27 16800.0 101000.0 89800.0 14900.0 304500.0 186500.0 173500.0 64300.0 181000.0 2610.0 ... 353.7 32.80 13.77 517.4 10.470 2060.5 1800.0 401.2 17920.0 2.734

5 rows × 817 columns

del TD['date']
TD.head()
corr = TD.corr(method = 'pearson')
corr_TD = corr.iloc[:-16,-15:]
corr_TD.head()
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itemname 구리 난방유 납 니켈 대두 대두박 대두유 설탕 소맥 쌀 아연 알루미늄합금 옥수수 주석 천연가스
itemname
AJ렌터카 0.253434 0.357430 -0.180675 0.491654 -0.115987 0.033026 -0.198628 -0.551920 0.578182 0.134875 -0.117926 0.354760 0.391342 -0.238737 0.049452
AK홀딩스 0.284143 0.319556 -0.028906 0.572011 0.177118 0.423597 -0.119239 -0.449531 0.480114 0.331932 -0.006332 0.668936 0.434461 -0.125864 -0.080484
BGF -0.840062 -0.796421 -0.665365 -0.663681 0.135000 -0.171780 0.112896 0.434287 -0.213475 -0.261622 -0.742201 -0.237837 0.009903 -0.649587 -0.506187
BNK금융지주 0.085814 0.101259 -0.238705 0.352453 0.075016 0.130878 -0.052747 -0.365024 0.446003 0.125338 -0.264136 0.536734 0.446926 -0.389719 -0.135423
BYC -0.787079 -0.844172 -0.445330 -0.739622 0.107104 -0.327591 0.282540 0.573646 -0.448948 -0.358966 -0.559299 -0.355283 -0.213558 -0.434541 -0.579782

데이터 시각화

원자재 가격과 양의 상관관계를 갖는 주식

corr_TD.iloc[:,0].sort_values(ascending=False).head()
itemname
POSCO     0.890243
SK하이닉스    0.885247
하나금융지주    0.865733
NH투자증권    0.858859
KB금융      0.854524
Name: 구리, dtype: float64

from sklearn import preprocessing

col = ['POSCO', 'SK하이닉스', '하나금융지주', 'NH투자증권', 'KB금융', '구리']

x = FTD[col].values
min_max_scaler = preprocessing.MinMaxScaler()

x_scaled = min_max_scaler.fit_transform(x.astype(float))
cu = pd.DataFrame(x_scaled, columns = col, index = FTD.index)

cu.head()
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POSCO SK하이닉스 하나금융지주 NH투자증권 KB금융 구리
date
2015-01-07 0.546296 0.330261 0.309278 0.155107 0.154161 0.644206
2015-01-19 0.513889 0.324117 0.371134 0.274905 0.195089 0.471332
2015-02-03 0.428241 0.319508 0.344624 0.274905 0.234652 0.479647
2015-02-12 0.462963 0.318740 0.243004 0.306431 0.203274 0.479647
2015-02-27 0.490741 0.317972 0.284242 0.438840 0.263302 0.543911 
plt.plot(cu['POSCO'],ls = ":", label = 'POSCO')
plt.plot(cu['SK하이닉스'],ls = ":", label = 'SK하이닉스')
plt.plot(cu['하나금융지주'],ls = ":", label = '하나금융지주')
plt.plot(cu['NH투자증권'],ls = ":", label = 'NH투자증권')
plt.plot(cu['KB금융'],ls = ":", label = 'KB금융')
plt.plot(cu['구리'], label = '구리')

plt.legend(loc = 'best')
plt.grid()
plt.show()
plt.savefig('구리.jpg')

png

<Figure size 432x288 with 0 Axes>

corr_TD.iloc[:,1].sort_values(ascending=False).head()
itemname
금호석유      0.849365
신대양제지     0.792479
SK하이닉스    0.788649
LS산전      0.786274
KG케미칼     0.779705
Name: 난방유, dtype: float64

from sklearn import preprocessing

col = ['금호석유', '신대양제지', 'SK하이닉스', 'LS산전', 'KG케미칼', '난방유']

x = FTD[col].values
min_max_scaler = preprocessing.MinMaxScaler()

x_scaled = min_max_scaler.fit_transform(x.astype(float))
cu = pd.DataFrame(x_scaled, columns = col, index = FTD.index)

cu.head()
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금호석유 신대양제지 SK하이닉스 LS산전 KG케미칼 난방유
date
2015-01-07 0.508148 0.141916 0.330261 0.486553 0.241379 0.524168
2015-01-19 0.555556 0.112575 0.324117 0.545232 0.295977 0.483911
2015-02-03 0.629630 0.143713 0.319508 0.545232 0.218391 0.624538
2015-02-12 0.540741 0.159880 0.318740 0.559902 0.178161 0.670546
2015-02-27 0.551111 0.171257 0.317972 0.579462 0.221264 0.711625 
plt.plot(cu['금호석유'],ls = ":", label = '금호석유')
plt.plot(cu['신대양제지'],ls = ":", label = '신대양제지')
plt.plot(cu['SK하이닉스'],ls = ":", label = 'SK하이닉스')
plt.plot(cu['LS산전'],ls = ":", label = 'LS산전')
plt.plot(cu['KG케미칼'],ls = ":", label = 'KG케미칼')
plt.plot(cu['난방유'], label = '난방유')

plt.legend(loc = 'best')
plt.grid()
plt.show()

png

corr_TD.iloc[:,13].sort_values(ascending=False).head()
itemname
카프로      0.838551
LS       0.837574
이구산업     0.816536
POSCO    0.803074
황금에스티    0.779738
Name: 주석, dtype: float64

from sklearn import preprocessing

col = ['카프로', 'LS', '이구산업', 'POSCO', '황금에스티', '주석']

x = FTD[col].values
min_max_scaler = preprocessing.MinMaxScaler()

x_scaled = min_max_scaler.fit_transform(x.astype(float))
cu = pd.DataFrame(x_scaled, columns = col, index = FTD.index)

cu.head()
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카프로 LS 이구산업 POSCO 황금에스티 주석
date
2015-01-07 0.061184 0.410669 0.062035 0.546296 0.024415 0.741765
2015-01-19 0.218421 0.347163 0.064516 0.513889 0.000000 0.694706
2015-02-03 0.179605 0.359018 0.096774 0.428241 0.018820 0.655882
2015-02-12 0.187500 0.332769 0.114144 0.462963 0.034588 0.532353
2015-02-27 0.228947 0.402202 0.168734 0.490741 0.072228 0.534706 
plt.plot(cu['카프로'],ls = ":", label = '카프로')
plt.plot(cu['LS'],ls = ":", label = 'LS')
plt.plot(cu['이구산업'],ls = ":", label = '이구산업')
plt.plot(cu['POSCO'],ls = ":", label = 'POSCO')
plt.plot(cu['황금에스티'],ls = ":", label = '황금에스티')
plt.plot(cu['주석'], label = '주석')

plt.legend(loc = 'best')
plt.grid()
plt.show()

png

원자재 가격과 음의 상관관계를 갖는 주식

corr_TD.iloc[:,0].sort_values(ascending=True).head()
itemname
미래아이앤지   -0.871926
BGF      -0.840062
CJ대한통운   -0.820994
한국전력     -0.798898
하이트론     -0.793636
Name: 구리, dtype: float64

col = ['미래아이앤지', 'BGF', 'CJ대한통운', '한국전력', '하이트론', '구리']

x = FTD[col].values
min_max_scaler = preprocessing.MinMaxScaler()

x_scaled = min_max_scaler.fit_transform(x.astype(float))
cu = pd.DataFrame(x_scaled, columns = col, index = FTD.index)

cu.head()
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미래아이앤지 BGF CJ대한통운 한국전력 하이트론 구리
date
2015-01-07 0.401904 0.306223 0.547739 0.483740 0.279188 0.644206
2015-01-19 0.381209 0.308971 0.532663 0.379404 0.231810 0.471332
2015-02-03 0.401904 0.354307 0.713568 0.460705 0.200508 0.479647
2015-02-12 0.354305 0.368045 0.683417 0.531165 0.370558 0.479647
2015-02-27 0.377070 0.376288 0.522613 0.525745 0.494078 0.543911 
plt.plot(cu['미래아이앤지'],ls = ":", label = '미래아이앤지')
plt.plot(cu['BGF'],ls = ":", label = 'BGF')
plt.plot(cu['CJ대한통운'],ls = ":", label = 'CJ대한통운')
plt.plot(cu['한국전력'],ls = ":", label = '한국전력')
plt.plot(cu['하이트론'],ls = ":", label = '하이트론')
plt.plot(cu['구리'], label = '구리')

plt.legend(loc = 'best')
plt.grid()
plt.show()

png

corr_TD.iloc[:,1].sort_values(ascending=True).head()
itemname
BYC      -0.844172
NPC      -0.827650
동성화학     -0.823397
조일알미늄    -0.821647
미래아이앤지   -0.814216
Name: 난방유, dtype: float64

col = ['BYC', 'NPC', '동성화학', '조일알미늄', '미래아이앤지', '난방유']

x = FTD[col].values
min_max_scaler = preprocessing.MinMaxScaler()

x_scaled = min_max_scaler.fit_transform(x.astype(float))
cu = pd.DataFrame(x_scaled, columns = col, index = FTD.index)

plt.plot(cu['BYC'],ls = ":", label = 'BYC')
plt.plot(cu['NPC'],ls = ":", label = 'NPC')
plt.plot(cu['동성화학'],ls = ":", label = '동성화학')
plt.plot(cu['조일알미늄'],ls = ":", label = '조일알미늄')
plt.plot(cu['미래아이앤지'],ls = ":", label = '미래아이앤지')
plt.plot(cu['난방유'], label = '난방유')

plt.legend(loc = 'best')
plt.grid()
plt.show()

png

corr_TD.iloc[:,13].sort_values(ascending=True).head()
itemname
한세예스24홀딩스   -0.901462
쿠쿠홀딩스       -0.885435
S&T홀딩스      -0.884960
전방          -0.870637
사조해표        -0.868335
Name: 주석, dtype: float64

col = ['한세예스24홀딩스', '쿠쿠홀딩스', 'S&T홀딩스', '전방', '사조해표', '주석']

x = FTD[col].values
min_max_scaler = preprocessing.MinMaxScaler()

x_scaled = min_max_scaler.fit_transform(x.astype(float))
cu = pd.DataFrame(x_scaled, columns = col, index = FTD.index)

plt.plot(cu['한세예스24홀딩스'],ls = ":", label = '한세예스24홀딩스')
plt.plot(cu['쿠쿠홀딩스'],ls = ":", label = '쿠쿠홀딩스')
plt.plot(cu['S&T홀딩스'],ls = ":", label = 'S&T홀딩스')
plt.plot(cu['전방'],ls = ":", label = '전방')
plt.plot(cu['사조해표'],ls = ":", label = '사조해표')
plt.plot(cu['주석'], label = '주석')

plt.legend(loc = 'best')
plt.grid()
plt.show()

png

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A Study on the Relationship between Raw Material Price and Stock Price

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