Lernen Handling Missing Data in Financial Series | Financial Data Analysis with Python

Missing data is a frequent challenge in financial datasets. You might encounter missing values due to market holidays, data collection errors, trading suspensions, or delayed price feeds. For traders, missing data can distort calculations like returns, volatility, and correlations, potentially leading to poor trading decisions or unreliable backtests. Ensuring your data is complete—or appropriately handling gaps—is critical for maintaining the accuracy and integrity of your analysis.


              1234567891011121314151617
            
import pandas as pd
import numpy as np

# Simulate a DataFrame with missing closing prices
dates = pd.date_range("2023-01-01", periods=7)
prices = [100, 101, np.nan, 104, np.nan, 107, 108]
df = pd.DataFrame({"Close": prices}, index=dates)

# Detect missing values
missing_mask = df["Close"].isna()
num_missing = missing_mask.sum()

print("DataFrame with missing values:")
print(df)
print("\nMissing value mask:")
print(missing_mask)
print(f"\nNumber of missing values: {num_missing}")

There are several approaches to dealing with missing data in financial series. You can remove any rows with missing values, which is simple but may discard valuable information if gaps are frequent. More often, you will fill missing values using methods like forward fill—where each gap is filled with the last available value—or interpolation, which estimates missing points based on surrounding data.

Forward fill is commonly used for time series where it makes sense to carry the last known price forward, such as in price charts. Interpolation is useful when you expect values to change smoothly between observed points, as it estimates the missing values by fitting a line or curve between known data.


              123456789101112
            
# Forward fill (propagate last valid observation forward)
df_ffill = df.copy()
df_ffill["Close"] = df_ffill["Close"].ffill()

# Linear interpolation (estimate missing values)
df_interp = df.copy()
df_interp["Close"] = df_interp["Close"].interpolate(method="linear")

print("After forward fill:")
print(df_ffill)
print("\nAfter linear interpolation:")
print(df_interp)

1. What is the difference between forward fill and interpolation when handling missing data?

2. Why is it important to handle missing data before performing trading analysis?

War alles klar?

Danke für Ihr Feedback!

Abschnitt 1. Kapitel 6

Fragen Sie AI

Fragen Sie alles oder probieren Sie eine der vorgeschlagenen Fragen, um unser Gespräch zu beginnen

Suggested prompts:

What are the pros and cons of forward fill versus interpolation?

Can you explain when to use each method for handling missing data?

Are there other techniques for filling missing values in financial data?

Swipe um das Menü anzuzeigen


              1234567891011121314151617
            
import pandas as pd
import numpy as np

# Simulate a DataFrame with missing closing prices
dates = pd.date_range("2023-01-01", periods=7)
prices = [100, 101, np.nan, 104, np.nan, 107, 108]
df = pd.DataFrame({"Close": prices}, index=dates)

# Detect missing values
missing_mask = df["Close"].isna()
num_missing = missing_mask.sum()

print("DataFrame with missing values:")
print(df)
print("\nMissing value mask:")
print(missing_mask)
print(f"\nNumber of missing values: {num_missing}")


              123456789101112
            
# Forward fill (propagate last valid observation forward)
df_ffill = df.copy()
df_ffill["Close"] = df_ffill["Close"].ffill()

# Linear interpolation (estimate missing values)
df_interp = df.copy()
df_interp["Close"] = df_interp["Close"].interpolate(method="linear")

print("After forward fill:")
print(df_ffill)
print("\nAfter linear interpolation:")
print(df_interp)

1. What is the difference between forward fill and interpolation when handling missing data?

2. Why is it important to handle missing data before performing trading analysis?

War alles klar?

Danke für Ihr Feedback!

Abschnitt 1. Kapitel 6