Resampling and Cleaning a DataFrame for Customized Calendar and Timetable

Resampling and cleaning a pandas DataFrame are essential steps when working with time-series data in Python. In this article, we will explore how to resample and clean a DataFrame for use with Zipline’s customized trading calendar.

Understanding the Problem

The problem presented in the Stack Overflow question is related to preparing a DataFrame for use with Zipline. The user wants to resample a timeseries dataset from 2:15am till 21:58pm only on business days, and then clean the resulting DataFrame by removing rows outside of trading hours (21:59pm - 2:15am) and weekends.

Resampling a DataFrame

Resampling involves aggregating data over a specified period. In this case, we want to resample the timeseries data from the original minute-level frequency to a daily frequency using the min rule.

import pandas as pd
import numpy as np

# Create a sample DataFrame with time-series data
data = {'time': pd.date_range('2020-03-01', periods=100, freq='m'),
        'open': np.random.randint(8000, 10000, 100),
        'high': np.random.randint(9000, 11000, 100),
        'low': np.random.randint(7000, 9000, 100),
        'close': np.random.randint(7500, 9500, 100),
        'volume': np.random.randint(5000, 15000, 100)}

df = pd.DataFrame(data)

# Convert the time column to datetime type
df['time'] = pd.to_datetime(df['time'])

# Resample the data from minute frequency to daily frequency using the min rule
df_resampled = df.resample('D')['open'].mean()

print(df_resampled)

Cleaning Rows with Undesired Times, Weekends, and Holidays

Once we have resampled the data, we need to clean the rows that fall outside of our desired trading hours (21:59pm - 2:15am) and weekends.

We can achieve this by using the between method to filter out the unwanted rows. We will also use the dt accessor to access date-related methods.

# Clean the resampled DataFrame by removing rows outside of trading hours
trading_hours = df_resampled.between('21:59', '02:15')
df_cleaned = df_resampled[trading_hours]

print(df_cleaned)

Handling Missing Minutes in Specific Timeframes

Another option is to check if there are missing minutes in a specific timeframe without resampling. We can use the isnull method to detect missing values.

# Check for missing minutes in a specific timeframe
missing_minutes = df_resampled.isnull().any(axis=1)
print(missing_minutes)

# Filter out rows with missing minutes
df_filtered = df_resampled[~missing_minutes]

Additional Considerations

When working with time-series data, it’s essential to consider the following:

• Time Zone: Make sure to account for time zones when dealing with datetime data.
• Date Range: Ensure that your date range accurately represents the start and end dates of your trading hours.
• Business Days: Use a library like pandas to handle business days correctly.

Conclusion

Resampling and cleaning a pandas DataFrame are critical steps when preparing time-series data for use with Zipline’s customized trading calendar. By understanding how to resample, clean, and filter the data, you can create a more accurate and reliable dataset for your trading strategies.

Last modified on 2024-06-09