[Python] 2 Create a risk-return map for your asset portfolio

Twitter introduced the risk-return map of the portfolio that combines SPXL and TMF.

Save the code to create the graph here.

Obtain the TMF, SPXL, VTI csv files from the following site in advance (the period is MAX). (Actually, I should just apply the code to get ...)

TMF: https://www.nasdaq.com/market-activity/funds-and-etfs/tmf/historical SPXL: https://www.nasdaq.com/market-activity/funds-and-etfs/spxl/historical VTI: https://www.nasdaq.com/market-activity/funds-and-etfs/vti/historical

Save the files with the file names "TMF.csv", "SPXL.csv", and "VTI.csv", respectively.

1. Load the library.

#Invite frends to the party...
import numpy as np
import pandas as pd
from datetime import datetime
import seaborn as sns
sns.set()
import matplotlib.pyplot as plt
from pandas.plotting import register_matplotlib_converters
register_matplotlib_converters()

2. A function that combines csv files into one DataFrame

def make_df(etfs):
    df = pd.DataFrame()
    for etf in etfs:
        csvfile = etf +'.csv'
        csv_data = pd.read_csv(csvfile)
        csv_data.Date = pd.to_datetime(csv_data.Date)
        csv_data = csv_data.set_index('Date')
        csv_data= csv_data.rename(columns={' Close/Last': etf})
        df[etf] = csv_data[etf]
        df = df.sort_index().dropna()
    return df

3. A function that adds a diversified portfolio index to a DataFrame

def make_port_df(df):
    #Return calculation
    rtn = (df /  df.shift(1) -1).fillna(0)

    #Create portfolio data for each ratio
    for w in [9, 8, 7, 6, 5, 4, 3, 2, 1]:
        rtn['SPXL'+str(w)+' : TMF'+str(10-w)] =rtn['SPXL']*(w/10) + rtn['TMF']*(1-w/10)
    
    rtn += 1
    df = rtn.cumprod()
    
    return df 

4. A function that creates a risk-return map

def rr_map(df):
    #Monthly return calculation
    dfm = df.resample('M').ffill()
    m_rtn = (dfm /  dfm.shift(1) -1).dropna()

    #Risk calculation
    std = (m_rtn.std())*(12**0.5)
    
    #Annual return calculation
    cum = m_rtn+1
    a_rtn = (cum.product())**(12/len(m_rtn))-1
    
    #Risk-return map creation
    plt.figure(figsize=(6, 6))
    for asset in list(a_rtn.index):
        plt.scatter(x = std[asset], y = a_rtn[asset],label = asset)
    plt.xlabel('Risk')
    plt.ylabel('Return')
    plt.xlim(0,std.max()*1.1)
    plt.ylim(0,a_rtn.max()*1.1)
    plt.legend(loc = 'best')
    plt.title('Risk Return Map')
    
    plt.savefig('rr_map.png',bbox_inches="tight")

4. Execute the above function

etfs = ['SPXL','TMF','VTI']
df = make_df(etfs)
df = make_port_df(df)
rr_map(df)

↓ This graph is displayed.

5. Summary

Having about half of the SPXL and half of the TMF is likely to result in a portfolio with better risk-return characteristics than VTI (investment at your own risk).

6. Next issue

-[] Diversified portfolio of multiple assets