Financial Data Science Financial Performance Analysis

Internal

• Financial Data Science

Overview

fred_api_key = '...'
start_date = '2023-07-14'
csv_data_file = './O.csv'
excel_data_file = './O.xlsx'
column_name = 'Fidelity Self'

import math
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.ticker as ptick
from fredapi import Fred

# load the DataFrame from CSV or Excel
df = pd.read_csv(data_file, parse_dates=["Date"])
df = pd.DataFrame(pd.read_excel(excel_data_file, parse_dates=["Date"])) 

# make it a time series DataFrame
df = df.set_index('Date')

# declare the function that converts the dollar amount
def dollar_to_int(s):
    if isinstance(s, str):
        return int(s[1:].replace(',',''))
    elif math.isnan(s):
        return s # we will interpolate later

# extract a specific time series ('Fidelity Self', 'Fidelity Managed', etc.)
ts = df[column_name].apply(dollar_to_int)
# resample and interpolate
ts = ts.resample('D').interpolate()

# get the SP&500
fred = Fred(api_key=fred_api_key)
sp500 = fred.get_series(series_id="SP500")
sp500 = sp500.resample('D').interpolate()

#
# apply a window, normalize and compute the percentage difference
#
ts = ts.loc[start_date:]
sp500 = sp500.loc[start_date:]
sp500_perf = sp500.apply(lambda x: x * ts.loc[start_date] / sp500.loc[start_date])
sp500_perc_diff = ts.sub(sp500_perf).div(sp500_perf).mul(100)

# graph
plt.style.use('seaborn-v0_8-whitegrid')

fig, ax = plt.subplots()
fig.autofmt_xdate()
fig.set_figwidth(20)
ax.set_ylabel("amount")
ax.yaxis.set_major_formatter(ptick.FormatStrFormatter('% 1.0f')) 
ax.plot(sp500_perf, lw=0.5, color='black')
ax.plot(ts, lw=0.5, color='blue')

fig2, ax2 = plt.subplots()
fig2.autofmt_xdate()
fig2.set_figwidth(25)
ax2.set_ylabel("percentage")
ax2.yaxis.set_major_formatter(ptick.PercentFormatter()) 
ax2.plot(sp500_perc_diff, lw=0.5, color='indigo')

plt.show()