Financial Data Engineering: Sources, Cleaning, and Storage

Module: Fintech AI | Difficulty: Advanced

Data Types

Data Quality

Point-in-Time

import pandas as pd
import numpy as np

class FinancialDataPipeline:
    def __init__(self):
        self.raw_data = None; self.clean_data = None
    def clean(self, df):
        # Remove duplicates
        df = df.drop_duplicates()
        # Handle missing values
        df = df.ffill()
        # Remove outliers
        for col in df.select_dtypes(include=[np.number]).columns:
            q1 = df[col].quantile(0.01)
            q99 = df[col].quantile(0.99)
            df = df[(df[col] >= q1) & (df[col] <= q99)]
        return df
    def align_data(self, data_dict, dates):
        # Align all data to common dates
        aligned = {}
        for name, data in data_dict.items():
            aligned[name] = data.reindex(dates).ffill()
        return aligned

Research Insight: Data quality is the most important factor in quantitative finance. The phrase "garbage in, garbage out" is especially true for financial data. Common issues: survivorship bias, look-ahead bias, and data snooping. Rigorous data engineering is essential for reproducible research.