Financial Data Science Financial Performance Analysis: Difference between revisions
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Line 22: | Line 22: | ||
return int(s[1:].replace(',','')) | return int(s[1:].replace(',','')) | ||
elif math.isnan(s): | elif math.isnan(s): | ||
return | return s # we will interpolate later | ||
# extract a specific time series ('Fidelity Self', 'Fidelity Managed', etc.) | |||
fid_slf = df['Fidelity Self'].apply(dollar_to_int) | |||
# resample and interpolate | |||
fid_slf = fid_slf.resample('D').interpolate() | |||
# get the SP&500 | # get the SP&500 |
Revision as of 03:21, 21 October 2023
Internal
Overview
import math
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.ticker as ptick
from fredapi import Fred
# load the DataFrame
df = pd.read_csv("./finances.csv", 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.)
fid_slf = df['Fidelity Self'].apply(dollar_to_int)
# resample and interpolate
fid_slf = fid_slf.resample('D').interpolate()
# get the SP&500
fred = Fred(api_key='...')
sp500 = fred.get_series(series_id="SP500")
# graph
fig, ax = plt.subplots()
fig.autofmt_xdate()
ax.set_ylabel("amount")
ax.yaxis.set_major_formatter(mt.FormatStrFormatter('% 1.2f'))
ax.plot(fidelity_self, lw=0.5)
ax.plot(fidelity_managed, lw=0.5)
plt.show()