Financial Data Science Financial Performance Analysis: Difference between revisions
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| (13 intermediate revisions by the same user not shown) | |||
| Line 5: | Line 5: | ||
<syntaxhighlight lang='py'> | <syntaxhighlight lang='py'> | ||
fred_api_key = '...' | |||
start_date = '2023-07-14' | |||
data_file = './finance-data/Test.xlsx' | |||
column_name = 'Balance' | |||
import math | import math | ||
import pandas as pd | import pandas as pd | ||
| Line 11: | Line 17: | ||
from fredapi import Fred | from fredapi import Fred | ||
# load the DataFrame | # load the DataFrame from Excel | ||
df = pd. | df = pd.DataFrame(pd.read_excel(data_file, parse_dates=["Date"])) | ||
# make it a time series DataFrame | # make it a time series DataFrame | ||
df = df.set_index('Date') | df = df.set_index('Date') | ||
# extract a specific time series ('Fidelity Self', 'Fidelity Managed', etc.) | # extract a specific time series ('Fidelity Self', 'Fidelity Managed', etc.) | ||
ts = df[column_name] | |||
# resample and interpolate | # resample and interpolate | ||
ts = ts.resample('D').interpolate() | |||
# get the SP&500 | # get the SP&500 | ||
fred = Fred(api_key= | fred = Fred(api_key=fred_api_key) | ||
sp500 = fred.get_series(series_id="SP500") | sp500 = fred.get_series(series_id="SP500") | ||
sp500 = sp500.resample('D').interpolate() | sp500 = sp500.resample('D').interpolate() | ||
| Line 38: | Line 36: | ||
# apply a window, normalize and compute the percentage difference | # apply a window, normalize and compute the percentage difference | ||
# | # | ||
ts = ts.loc[start_date:] | |||
sp500 = sp500.loc[start_date:] | sp500 = sp500.loc[start_date:] | ||
sp500_perf = sp500.apply(lambda x: x * | sp500_perf = sp500.apply(lambda x: x * ts.loc[start_date] / sp500.loc[start_date]) | ||
sp500_perc_diff = | sp500_perc_diff = ts.sub(sp500_perf).div(sp500_perf).mul(100) | ||
# graph | # graph | ||
plt.style.use('seaborn-v0_8-whitegrid') | |||
fig, ax = plt.subplots() | fig, ax = plt.subplots() | ||
fig.autofmt_xdate() | fig.autofmt_xdate() | ||
| Line 51: | Line 50: | ||
ax.yaxis.set_major_formatter(ptick.FormatStrFormatter('% 1.0f')) | ax.yaxis.set_major_formatter(ptick.FormatStrFormatter('% 1.0f')) | ||
ax.plot(sp500_perf, lw=0.5, color='black') | ax.plot(sp500_perf, lw=0.5, color='black') | ||
ax.plot( | ax.plot(ts, lw=0.5, color='blue') | ||
fig2, ax2 = plt.subplots() | fig2, ax2 = plt.subplots() | ||
Latest revision as of 03:56, 31 October 2023
Internal
Overview
fred_api_key = '...'
start_date = '2023-07-14'
data_file = './finance-data/Test.xlsx'
column_name = 'Balance'
import math
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.ticker as ptick
from fredapi import Fred
# load the DataFrame from Excel
df = pd.DataFrame(pd.read_excel(data_file, parse_dates=["Date"]))
# make it a time series DataFrame
df = df.set_index('Date')
# extract a specific time series ('Fidelity Self', 'Fidelity Managed', etc.)
ts = df[column_name]
# 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()