add(SAP) : added reordered code files · pcwpower1/StockAnalysisInPython@f8170df · GitHub
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add(SAP) : added reordered code files
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‎02 Python Programming/ch02_01_ImageProcessing.py‎

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‎03 NumPy and Pandas/ch03_01_KOSPI_MDD.py‎

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from pandas_datareader import data as pdr
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import yfinance as yf
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yf.pdr_override()
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import matplotlib.pyplot as plt
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kospi = pdr.get_data_yahoo('^KS11', '2004-01-04')
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window = 252
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peak = kospi['Adj Close'].rolling(window, min_periods=1).max()
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drawdown = kospi['Adj Close']/peak - 1.0
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max_dd = drawdown.rolling(window, min_periods=1).min()
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plt.figure(figsize=(9, 7))
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plt.subplot(211)
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kospi['Close'].plot(label='KOSPI', title='KOSPI MDD', grid=True, legend=True)
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plt.subplot(212)
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drawdown.plot(c='blue', label='KOSPI DD', grid=True, legend=True)
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max_dd.plot(c='red', label='KOSPI MDD', grid=True, legend=True)
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plt.show()
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‎03 NumPy and Pandas/ch03_02_DowKospi_Scatter.py‎

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import pandas as pd
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from pandas_datareader import data as pdr
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import yfinance as yf
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yf.pdr_override()
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dow = pdr.get_data_yahoo('^DJI', '2000-01-04')
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kospi = pdr.get_data_yahoo('^KS11', '2000-01-04')
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df = pd.DataFrame({'DOW' dow['Close'], 'KOSPI' kospi['Close']})
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df = df.fillna(method='bfill')
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df = df.fillna(method='ffill')
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import matplotlib.pyplot as plt
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plt.figure(figsize=(7, 7))
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plt.scatter(df['DOW'], df['KOSPI'], marker='.')
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plt.xlabel('Dow Jones Industrial Average')
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plt.ylabel('KOSPI')
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plt.show()

‎03 NumPy and Pandas/ch03_03_DowKospi_Regression.py‎

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import pandas as pd
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from pandas_datareader import data as pdr
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import yfinance as yf
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yf.pdr_override()
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from scipy import stats
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import matplotlib.pylab as plt
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dow = pdr.get_data_yahoo('^DJI', '2000-01-04')
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kospi = pdr.get_data_yahoo('^KS11', '2000-01-04')
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df = pd.DataFrame({'X' dow['Close'], 'Y' kospi['Close']})
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df = df.fillna(method='bfill')
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df = df.fillna(method='ffill')
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regr = stats.linregress(df.X, df.Y)
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regr_line = f'Y = {regr.slope.2f} X + {regr.intercept.2f}'
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plt.figure(figsize=(7, 7))
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plt.plot(df.X, df.Y, '.')
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plt.plot(df.X, regr.slope df.X + regr.intercept, 'r')
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plt.legend(['DOW x KOSPI', regr_line])
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plt.title(f'DOW x KOSPI (R = {regr.rvalue.2f})')
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plt.xlabel('Dow Jones Industrial Average')
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plt.ylabel('KOSPI')
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plt.show()

‎04 Web Scraping/ch04_01_Celltrion_PlotChart.py‎

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import pandas as pd
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from urllib.request import urlopen
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from bs4 import BeautifulSoup
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from matplotlib import pyplot as plt
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url = 'https://finance.naver.com/item/sise_day.nhn?code=068270&page=1'
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with urlopen(url) as doc
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html = BeautifulSoup(doc, 'lxml')
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pgrr = html.find('td', class_='pgRR')
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s = str(pgrr.a['href']).split('=')
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last_page = s[-1]
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df = pd.DataFrame()
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sise_url = 'https://finance.naver.com/item/sise_day.nhn?code=068270'
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for page in range(1, int(last_page)+1)
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page_url = '{}&page={}'.format(sise_url, page)
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df = df.append(pd.read_html(page_url, header=0)[0])
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df = df.dropna()
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df = df.iloc[030]
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df = df.sort_values(by='날짜')
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plt.title('Celltrion (close)')
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plt.xticks(rotation=90)
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plt.plot(df['날짜'], df['종가'], 'co-')
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plt.show()

‎04 Web Scraping/ch04_02_Celltrion_CandleChart.py‎

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import pandas as pd
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from urllib.request import urlopen
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from bs4 import BeautifulSoup
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from matplotlib import pyplot as plt
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from matplotlib import dates as mdates
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from mpl_finance import candlestick_ohlc
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from datetime import datetime
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url = 'https://finance.naver.com/item/sise_day.nhn?code=068270&page=1'
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with urlopen(url) as doc:
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html = BeautifulSoup(doc, 'lxml')
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pgrr = html.find('td', class_='pgRR')
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s = str(pgrr.a['href']).split('=')
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last_page = s[-1]
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df = pd.DataFrame()
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sise_url = 'https://finance.naver.com/item/sise_day.nhn?code=068270'
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for page in range(1, int(last_page)+1):
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page_url = '{}&page={}'.format(sise_url, page)
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df = df.append(pd.read_html(page_url, header=0)[0])
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df = df.dropna()
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df = df.iloc[0:30]
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df = df.sort_values(by='날짜')
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for idx in range(0, len(df)):
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dt = datetime.strptime(df['날짜'].values[idx], '%Y.%m.%d').date()
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df['날짜'].values[idx] = mdates.date2num(dt)
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ohlc = df[['날짜','시가','고가','저가','종가']]
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plt.figure(figsize=(9, 6))
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ax = plt.subplot(1, 1, 1)
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plt.title('Celltrion (candle stick)')
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candlestick_ohlc(ax, ohlc.values, width=0.7, colorup='red', colordown='blue')
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ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))
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plt.grid(color='gray', linestyle='--')
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plt.show()

‎05 Stock Price API/ch05_01_NaverDatabase_SEC.py‎

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import matplotlib.pyplot as plt
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from Investar import Analyzer # ①
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mk = Analyzer.MarketDB() # ②
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df = mk.get_daily_price('005930', '2017-07-10', '2018-06-30') # ③
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plt.figure(figsize=(9, 6))
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plt.subplot(2, 1, 1)
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plt.title('Samsung Electronics (Investar Data)')
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plt.plot(df.index, df['close'], 'c', label='Close') # ④
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plt.legend(loc='best')
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plt.subplot(2, 1, 2)
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plt.bar(df.index, df['volume'], color='g', label='Volume')
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plt.legend(loc='best')
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plt.show()

‎05 Stock Price API/ch05_02_YahooFinance_SEC.py‎

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from pandas_datareader import data as pdr
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import yfinance as yf
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yf.pdr_override()
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import matplotlib.pyplot as plt
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df = pdr.get_data_yahoo('005930.KS', '2017-01-01') # ①
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plt.figure(figsize=(9, 6))
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plt.subplot(2, 1, 1) # ②
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plt.title('Samsung Electronics (Yahoo Finance)')
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plt.plot(df.index, df['Close'], 'c', label='Close') # ③
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plt.plot(df.index, df['Adj Close'], 'b--', label='Adj Close') # ④
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plt.legend(loc='best')
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plt.subplot(2, 1, 2) # ⑤
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plt.bar(df.index, df['Volume'], color='g', label='Volume') # ⑥
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plt.legend(loc='best')
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plt.show()

‎06 Trading Strategy/ch06_01_EfficientFrontier.py‎

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import numpy as np
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import pandas as pd
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import matplotlib.pyplot as plt
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from Investar import Analyzer
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mk = Analyzer.MarketDB()
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stocks = ['삼성전자', 'SK하이닉스', '현대자동차', 'NAVER']
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df = pd.DataFrame()
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for s in stocks:
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df[s] = mk.get_daily_price(s, '2016-01-04', '2018-04-27')['close']
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daily_ret = df.pct_change()
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annual_ret = daily_ret.mean() * 252
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daily_cov = daily_ret.cov()
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annual_cov = daily_cov * 252
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port_ret = []
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port_risk = []
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port_weights = []
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for _ in range(20000):
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weights = np.random.random(len(stocks))
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weights /= np.sum(weights)
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returns = np.dot(weights, annual_ret)
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risk = np.sqrt(np.dot(weights.T, np.dot(annual_cov, weights)))
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port_ret.append(returns)
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port_risk.append(risk)
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port_weights.append(weights)
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portfolio = {'Returns': port_ret, 'Risk': port_risk}
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for i, s in enumerate(stocks):
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portfolio[s] = [weight[i] for weight in port_weights]
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df = pd.DataFrame(portfolio)
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df = df[['Returns', 'Risk'] + [s for s in stocks]]
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df.plot.scatter(x='Risk', y='Returns', figsize=(8, 6), grid=True)
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plt.title('Efficient Frontier')
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plt.xlabel('Risk')
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plt.ylabel('Expected Returns')
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plt.show()

‎06 Trading Strategy/ch06_02_PortfolioOptimization.py‎

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