How to handle currency exchange rate gaps during holidays and weekends?

Gaps in exchange rate data over weekends and holidays are common, as banks and exchanges do not publish data on these days.

Several approaches can be used to handle such gaps:

• Fill with the last known value (forward fill) — use the last business day's rate for weekends.
• Interpolation — if high accuracy is needed, interpolate values between neighboring business days.
• Ignore weekends — analyze data only on business days, without creating entries for weekends.

Example in Python with pandas:

import pandas as pd

# Example data with gaps
dates = pd.to_datetime(['2024-04-25', '2024-04-26', '2024-04-29'])
courses = [75.0, 75.2, 75.5]
df = pd.DataFrame({'date': dates, 'rate': courses}).set_index('date')

# Create a complete date range
full_range = pd.date_range(start=df.index.min(), end=df.index.max())
df = df.reindex(full_range)

# Fill gaps with the previous day's value
df['rate'] = df['rate'].fillna(method='ffill')

print(df)

This approach helps avoid data gaps and ensures continuity of the time series.