Financial Data Science Financial Performance Analysis: Difference between revisions
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Line 9: | Line 9: | ||
import matplotlib.pyplot as plt | import matplotlib.pyplot as plt | ||
import matplotlib.ticker as ptick | import matplotlib.ticker as ptick | ||
from fredapi import Fred | |||
# load the DataFrame | # load the DataFrame | ||
Line 30: | Line 31: | ||
fidelity_managed = df['Fidelity Managed'].apply(dollar_to_int) | fidelity_managed = df['Fidelity Managed'].apply(dollar_to_int) | ||
fidelity_managed = fidelity_managed[fidelity_managed != 0] | fidelity_managed = fidelity_managed[fidelity_managed != 0] | ||
# get the SP&500 | |||
fred = Fred(api_key='...') | |||
sp500 = fred.get_series(series_id="SP500") | |||
# graph | # graph |
Revision as of 00:46, 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 0
else:
return -1
# extract a specific time series and plot it
fidelity_self = df['Fidelity Self'].apply(dollar_to_int)
fidelity_self = fidelity_self[fidelity_self != 0]
fidelity_managed = df['Fidelity Managed'].apply(dollar_to_int)
fidelity_managed = fidelity_managed[fidelity_managed != 0]
# 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)
ax.plot(fidelity_self)
plt.show()