BNKU,TQQQ,SOXL 예상수익률 비교

import numpy as np

import matplotlib.pyplot as plt

import pandas as pd

import pandas_datareader.data as web

from datetime import datetime

import seaborn as sns

t1 = []

def stock_anaysis(tick_list, yr_len, end = datetime.now()):

start = datetime(end.year - yr_len,end.month,end.day)

tick_rate_df = web.DataReader(tick_list,'yahoo',start,end)['Adj Close'].pct_change()

indexes = web.DataReader(['^GSPC','^DJI','^IXIC'], 'yahoo', start, end)['Adj Close'].pct_change()

SNP = rate_difference_df(index = indexes['^GSPC'], tickers = tick_rate_df)*100

SNP.name = 'S&P 500'

DOW = rate_difference_df(index= indexes['^DJI'], tickers = tick_rate_df)*100

DOW.name = 'Dow Jones Industrial Average'

Nasdaq = rate_difference_df(index = indexes['^IXIC'], tickers = tick_rate_df)*100

Nasdaq.name = 'Nasdaq'

f = plt.figure(figsize=(20,20))

f.add_subplot(311)

draw_a_plot(SNP)

f.add_subplot(312)

draw_a_plot(DOW)

f.add_subplot(313)

draw_a_plot(Nasdaq)

plt.show()

def draw_a_plot (tick):

area = np.pi*10

plt.scatter(tick.mean(), tick.std(),alpha = 0.5,s =area)

plt.ylim([0,5])

plt.xlim([-0.5,0.5])

plt.title(f'Expected Daily Return Ratio minus {tick.name}')

plt.xlabel(f'Average Daily Return minus {tick.name}/%')

plt.ylabel('Risk Level (Standard Deviation)')

plt.axvline(x=0, linewidth=4, color='g')

for label, x, y in zip(tick.columns, tick.mean(), tick.std()):

plt.annotate(

f'{label}',

xy = (x, y), xytext = (20, 20),

textcoords = 'offset points', ha = 'left', va = 'bottom',

arrowprops = dict(arrowstyle = '-', connectionstyle = 'angle,angleA=-90,angleB=180,rad=5'))

plt.grid()

def rate_difference_df (index, tickers):

rate_diff_df = tickers.copy(deep = True)

for j in tickers.columns:

rate_diff_df[j] = tickers[j] - index

rate_diff_df = rate_diff_df.dropna()

return rate_diff_df

def annual_analysis(ticker, yearlen = 12, end = datetime.now(), with_number = False):

sns.set_style('whitegrid')

start = datetime(end.year - yearlen,1,1)

tick_rate = web.DataReader(ticker,'yahoo',start,end)['Adj Close'].pct_change(periods = 1)

snp = web.DataReader('^GSPC', 'yahoo', start, end)['Adj Close'].pct_change(periods = 1)

tick_diff = (tick_rate-snp)*100

tick_diff = tick_rate.dropna()

tick_diff_df = pd.DataFrame(tick_diff)

tick_diff_df['date'] = tick_diff_df.index

tick_diff_df['year'] = tick_diff_df.date.dt.year

tick_diff_df['month'] = tick_diff_df.date.dt.month

sns.set_theme()

piv_tick = tick_diff_df.pivot_table(index='month', columns='year', values='Adj Close',aggfunc=np.sum)

piv_tick = piv_tick.round(decimals=2)

f, ax = plt.subplots(figsize=(20, 20))

global t1

t1 = piv_tick.sum(axis=0)

plt.title(f'{ticker} - S&P 500 Monthly Change Rate for {yearlen} Years')

sns.heatmap(piv_tick, annot=with_number, linewidths=.5, ax=ax, cmap='RdYlGn')

plt.show()

 

annual_analysis('TQQQ', with_number=True)

stock_anaysis(['BNKU','NRGU','SOXL','TQQQ'], yr_len = 1)

print(t1)