글

[Python/파이썬] LSTM pyupbit tensorflow keras sklearn - 2. LSTM 모델을 활용한 BITCOIN 예측

이미지
  1. BITCOIN Dataset 불러오기 - pyupbit 사용 # 패키지선언 import os import pyupbit as py import pandas as pd import numpy as np import seaborn as sns import matplotlib import matplotlib.pyplot as plt ㅤ py . get_tickers ( fiat = 'USD' ) Output ['USDT-BTC', 'USDT-ETH', 'USDT-XRP', 'USDT-ETC', 'USDT-OMG', 'USDT-ADA', 'USDT-TUSD', 'USDT-SC', 'USDT-TRX', 'USDT-BCH', 'USDT-DGB', 'USDT-DOGE', 'USDT-ZRX', 'USDT-RVN', 'USDT-BAT'] ㅤ py.get_current_price(['USDT-BTC','USDT-ETH']) Output {'USDT-BTC': 20878.37330684, 'USDT-ETH': 1225.49151905} ㅤ tickers = ['USDT-BTC','USDT-ETH','USDT-XRP','USDT-ADA','USDT-LTC'] interval = 'minute60' ㅤ from tqdm import tqdm coin_set = [] for ticker in tqdm(tickers): coin = py.get_ohlcv(ticker=ticker,count=20000,interval=interval,to='2022-01-01'...
댓글 쓰기
자세한 내용 보기

[Python/파이썬] LSTM FinanceDataReader tensorflow keras sklearn - 1. LSTM 모델을 활용한 S&P500 예측

이미지
1. S&P500 Dataset 불러오기 - FinanceDataReader 사용 # 패키지 선언 import pandas as pd import numpy as np import FinanceDataReader as fdr import seaborn as sns import matplotlib import matplotlib.pyplot as plt ㅤ # S&P500 지수 (NYSE) sp = fdr.DataReader('US500', '2020-01-01', '2022-01-01') sp Output Close Open High Low Volume Change Date 2020-01-02 3257.85 3244.67 3258.14 3235.53 0.0 0.0084 2020-01-03 3234.85 3226.36 3246.15 3222.34 0.0 -0.0071 2020-01-06 3246.28 3217.55 3246.84 3214.64 0.0 0.0035 2020-01-07 3237.18 3241.86 3244.91 3232.43 0.0 -0.0028 2020-01-08 3253.05 3238.59 3267.07 3236.67 0.0 0.0049 ... ... ... ... ... ... ... 2021-12-27 4791.19 4733.99 4791.49 4733.99 0.0 0.0138 2021-12-28 4786.36 4795.49 4807.02 4780.04 0.0 -0.0010 2021-12-29 4793.06 4788.64 4804.06 4778.08 0.0 0.0014 2021-12-30 4778.73 4794.23 4808.93 4775.33 0.0 -0.0030 2021-12-31 4766.18 4775.21 4786.83 4765.75 0.0 -0.0026 2. Data Scaling from sklearn.preprocessing import MinMaxScaler sca...
댓글 쓰기
자세한 내용 보기

[Python/파이썬] Numpy Pandas Matplotlib Seaborn Sklearn - 3. 신용등급 MinMaxScaler plot

이미지
1. 엑셀 파일 불러오기 # 패키지 선언 import numpy as npimport matplotlib import pandas as pd import matplotlib import matplotlib.pyplot as plt from matplotlib import font_manager, rc import seaborn as sns from sklearn.preprocessing import MinMaxScaler # TC_EN_AREA_CRISIS_INFO.csv 분기별 데이터 추출 및 변환 코드 df1=pd.read_excel('C:/fintech6/_project1/_src/sample_01.xlsx', index_col=0) # 2020-1Q df2=pd.read_excel('C:/fintech6/_project1/_src/sample_02.xlsx', index_col=0) # 2020-2Q df3=pd.read_excel('C:/fintech6/_project1/_src/sample_03.xlsx', index_col=0) # 2020-3Q *TC_EN_AREA_CRISIS_INFO_sample(신용등급).csv ( 출처: 경기지역경제포털,  KED신용등급, https://bigdata-region.kr/#/dataset/6f393bec-a1e1-4e09-8075-8c53e19d51f5) 2.  업종코드 선택 # I : 숙박 및 음식점업 code1 = df1['INDUTY_LCLAS_CODE'] == 'I' df_code1=df1[code1] code2 = df2['INDUTY_LCLAS_CODE'] == 'I' df_code2=df2[code2] code3 = df3['INDUTY_LCLAS_CODE'] == 'I' df_code3=df3[code3] 3. 신용등급 구간별(A등급/B등급/C등급/D등급) 합계 li...
댓글 쓰기
자세한 내용 보기