#environment #!pip install yfinance --upgrade --no-cache-dir #!pip install PyPortfolioOpt==1.2.1

# Import modules import requests import io import pandas as pd import requests import json from datetime import datetime import numpy as np import yfinance as yf import seaborn as sns import plotly.express as px import plotly.graph_objs as go import matplotlib.pyplot as plt import scipy.stats as st from pypfopt.efficient_frontier import EfficientFrontier from pypfopt import risk_models from pypfopt import expected_returns from pypfopt import cla from pypfopt.plotting import Plotting from pypfopt.discrete_allocation import DiscreteAllocation, get_latest_prices from matplotlib.ticker import FuncFormatter ## JHU Vaccination Rates (Taken From: https://github.com/owid/covid-19-data/tree/master/public/data) url = 'https://raw.githubusercontent.com/owid/covid-19-data/master/public/data/owid-covid-data.csv' download = requests.get(url).content covid = pd.read_csv(io.StringIO(download.decode('utf-8')), parse_dates=['date']) covid.tail()

sns.scatterplot('date', 'new_cases', data=covid, hue='continent')

us = covid[covid['location']=='United States'] us_covid = us.set_index('date') us_covid.info() us_covid.head()

df1 = pd.DataFrame() df1['new cases'] = us_covid['new_cases_smoothed'] df1['new deaths'] = us_covid['new_deaths_smoothed'] df1.dropna() df1.plot(secondary_y = ["new deaths"], title = 'New Cases and Deaths')

df2 = pd.DataFrame() df2['positive rate'] = us_covid['positive_rate'] df2['fatality rate'] = us_covid['total_deaths']/us_covid['total_cases'] df2.dropna() df2.plot(secondary_y = ["fatality rate"], title = 'Positive Rate and Fatality Rate')

df3 = pd.DataFrame() df3['icu rate'] = us_covid['icu_patients']/us_covid['total_cases'] df3['hosp rate'] = us_covid['hosp_patients']/us_covid['total_cases'] df3['fatality rate'] = df2['fatality rate'] df3.dropna() df3.plot(secondary_y = ["fatality rate"], title = 'Hospital Rate and Fatality Rate')

df4 = pd.DataFrame() df4['vaccination rate'] = us_covid['people_fully_vaccinated']/us_covid['population'] df4['fatality rate'] = df2['fatality rate'] df4.dropna() df4.plot(secondary_y = ["fatality rate"], title = 'Vaccination Rate and Fatality Rate')

assets_list = ['VV','VB', 'GOVT', 'LQD','HYG','USRT','GLD', 'BTC-USD'] start = datetime(2020,1,23) end = datetime(2021,9,15) assets_df = yf.download(assets_list, start=start, end=end) assets = assets_df['Adj Close'] assets.ffill() benchmark_df = yf.download(['SPY'], start=start, end=end) benchmark = benchmark_df['Adj Close'] benchmark.ffill() assets.tail()

# Check NaN values in the data nullin_df = pd.DataFrame(assets,columns=assets_list) print(nullin_df.isnull().sum()) # Calculate portfolio mean return mu = expected_returns.mean_historical_return(assets) print(mu) # Calculate portfolio return variance sigma = risk_models.sample_cov(assets) print(sigma)

# weight bounds in negative allows shorting of stocks ef = EfficientFrontier(mu, sigma, weight_bounds=(-1,1)) # optional constraints possible, read pypfopt documentation. sharpe_portfolio=ef.max_sharpe(risk_free_rate=0.005) sharpe_portfolio_wt=ef.clean_weights() print(sharpe_portfolio_wt) Plotting.plot_weights(sharpe_portfolio_wt)

latest_prices = get_latest_prices(assets) # Allocate Portfolio Value in $ as required to show number of shares/stocks to buy, # also bounds for shorting will affect allocation # Maximum Sharpe Portfolio Allocation $1000000 da = DiscreteAllocation(sharpe_portfolio_wt, latest_prices, total_portfolio_value=1000000) allocation, leftover = da.greedy_portfolio() print(allocation) print("Leftover Fund value for the maximum Sharpe portfolio is ${:.2f}".format(leftover))

max_sharpe_cla = cla.CLA(mu, sigma) max_sharpe_cla.max_sharpe() Plotting.plot_efficient_frontier(max_sharpe_cla, show_assets="True")

sharpe_portfolio_wt_list = list(sharpe_portfolio_wt.values()) ret_data1 = assets.pct_change()[1:] weighted_returns1 = (sharpe_portfolio_wt_list * ret_data1) portfolio_ret1 = pd.DataFrame(weighted_returns1.sum(axis=1)) ret_data1 = ret_data1.merge(portfolio_ret1, on="Date", how="left") ret_data1 = ret_data1.rename(columns={0: "portfolio_ret"}) ret_data1.head() ret_benchmark = benchmark.pct_change()[1:]

ret_data1['cumulative_portfolio_ret'] = (ret_data1['portfolio_ret'] + 1).cumprod() ret_benchmark['cumulative_spy_ret'] = (ret_benchmark + 1).cumprod() ret_data1.tail()

sns.scatterplot('Date', 'cumulative_portfolio_ret', data=ret_data1) sns.scatterplot('Date', 'cumulative_spy_ret', data=ret_benchmark)

plt.hist(ret_data1['portfolio_ret'], bins=10)

st.norm.interval(alpha=0.95, loc=np.mean(ret_data1['portfolio_ret']), scale=st.sem(ret_data1['portfolio_ret']))

ef = EfficientFrontier(mu, sigma, weight_bounds=(-1,1)) min_vol_portfolio=ef.min_volatility() min_vol_portfolio_wt=ef.clean_weights() print(min_vol_portfolio_wt) Plotting.plot_weights(min_vol_portfolio_wt)

da = DiscreteAllocation(min_vol_portfolio_wt, latest_prices, total_portfolio_value=1000000) allocation, leftover = da.greedy_portfolio() print(allocation) print("Leftover Fund value for the maximum Sharpe portfolio is ${:.2f}".format(leftover))

min_vol_cla = cla.CLA(mu, sigma) min_vol_cla.min_volatility() Plotting.plot_efficient_frontier(min_vol_cla, show_assets="True")

min_vol_portfolio_wt_list = list(min_vol_portfolio_wt.values()) ret_data2 = assets.pct_change()[1:] weighted_returns2 = (min_vol_portfolio_wt_list * ret_data2) portfolio_ret2 = pd.DataFrame(weighted_returns2.sum(axis=1)) ret_data2 = ret_data2.merge(portfolio_ret2, on="Date", how="left") ret_data2 = ret_data2.rename(columns={0: "portfolio_ret"}) ret_data2.head()

ret_data2['cumulative_portfolio_ret'] = (ret_data2['portfolio_ret'] + 1).cumprod() ret_data2.tail()

sns.scatterplot('Date', 'cumulative_portfolio_ret', data=ret_data2) sns.scatterplot('Date', 'cumulative_spy_ret', data=ret_benchmark)

plt.hist(ret_data2['portfolio_ret'], bins=10)

st.norm.interval(alpha=0.95, loc=np.mean(ret_data2['portfolio_ret']), scale=st.sem(ret_data2['portfolio_ret']))