Simplifying Financial Data Wrangling with Python: Tips and Techniques#

Common Challenges in Financial Data Wrangling#

• Incomplete or missing data: Financial data might contain missing values due to lapses in reporting or data-provider errors.
• Inconsistent formats: Data can appear in CSV, Excel, JSON, or specialized file formats.
• Time-series peculiarities: Public markets close on weekends and holidays, leading to gaps in regular time intervals.
• Large dataset sizes: Tick-by-tick data, for instance, can comprise millions of rows.

By mastering Pythons ecosystem, you can address these challenges methodically, automate repetitive steps, and spend more time interpreting insights rather than dealing with tedious data issues.

Installing Python and Essential Libraries#

If youre new to Python, a straightforward way to get started is through the Anaconda distribution, which bundles Python with a suite of scientific libraries. This setup facilitates a quick start for data wrangling in finance.

Alternatively, you can install Python from the official website (python.org) and then use pip (Pythons default package manager) to install libraries individually.

Example terminal commands using pip:

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pip install pandas numpy matplotlib seaborn requests

A Quick Python Refresher#

Before diving into data wrangling, familiarize yourself with basic Python concepts:

• Variables and Data Types: Integers, floats, strings, lists, dictionaries, etc.
• Control Flow: If-else statements, loops (for, while).
• Functions: Reusable code blocks defined with def.
• Modules and Imports: Organizing your code into separate modules and importing libraries.

For example, a simple Python function to calculate daily returns might look like this:

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def calculate_daily_returns(prices_list):
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    """
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    Calculate daily percentage returns from a list of prices.
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    Returns a list of daily returns.
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    """
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    returns = []
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    for i in range(1, len(prices_list)):
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        daily_return = (prices_list[i] - prices_list[i-1]) / prices_list[i-1]
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        returns.append(daily_return)
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    return returns

Pandas#

Pandas is the cornerstone of Python data analysis. It offers the powerful DataFrame data structureakin to a spreadsheetfeaturing rich functions for cleaning, transforming, and analyzing data.

Key features for financial data:

• Time-Series Capabilities: Indexing, slicing, and resampling by dates.
• Integration with Other Libraries: Pandas DataFrames integrate well with NumPy, matplotlib, and statsmodels.
• Flexible Data I/O: Reading and writing data from/to CSV, Excel, SQL databases, APIs.

Example usage:

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import pandas as pd
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# Reading a CSV file into a DataFrame
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df = pd.read_csv('financial_data.csv')
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# Getting first few rows
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print(df.head())
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# Checking information about columns
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print(df.info())

NumPy#

NumPy provides high-performance arrays and mathematical functions. While Pandas is often enough for data manipulation, NumPy arrays are useful for optimized computations, correlation calculations, or linear algebra tasks. In finance, you may rely on NumPy for:

• Vectorized calculations for large datasets.
• Efficient memory usage.

Matplotlib#

Matplotlib is the foundational plotting library in Python. Many other libraries build on Matplotlibs functionality. It lets you create static, animated, and interactive visualizations. For finance, you might use Matplotlib to generate:

• Line charts for stock prices or currency exchange rates.
• Histograms for return distributions.
• Candlestick charts for daily open-high-low-close (OHLC) data (using specialized libraries built on Matplotlib, like mplfinance).

Seaborn#

Seaborn simplifies creating attractive statistical plots. When exploring relationships within financial datasets, Seaborns default styles and color palettes help convey insights more effectively. Popular Seaborn features:

• pairplot for visualizing pairwise relationships.
• heatmap for correlation matrices.
• distplot for examining return or price distributions.

Reading CSV and Excel Files#

CSV files remain a dominant file format for exchanging tabular data. Reading them in Pandas is as simple as:

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import pandas as pd
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# CSV
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stocks_df = pd.read_csv('stocks.csv')
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# Excel (requires 'openpyxl' or 'xlrd' depending on the file format)
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bonds_df = pd.read_excel('bonds.xlsx', sheet_name='Data')

Reading from APIs#

Many data providers (Yahoo Finance, Alpha Vantage, Quandl, etc.) allow fetching financial data through RESTful APIs. You can use Pythons requests library to make GET/POST requests, parse the JSON response, and store it in a DataFrame.

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import requests
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import pandas as pd
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api_key = 'YOUR_API_KEY'
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symbol = 'AAPL'
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url = f'https://www.alphavantage.co/query?function=TIME_SERIES_DAILY&symbol={symbol}&apikey={api_key}&outputsize=full'
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response = requests.get(url)
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data_json = response.json()
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time_series = data_json['Time Series (Daily)']
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records = []
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for date, stats in time_series.items():
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    records.append({
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        'Date': date,
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        'Open': float(stats['1. open']),
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        'High': float(stats['2. high']),
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        'Low': float(stats['3. low']),
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        'Close': float(stats['4. close']),
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        'Volume': int(stats['5. volume'])
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    })
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df_api = pd.DataFrame(records)
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df_api['Date'] = pd.to_datetime(df_api['Date'])
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df_api.set_index('Date', inplace=True)
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df_api.sort_index(inplace=True)
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print(df_api.head())

Combining Multiple Data Sources#

In practice, youll unify data from several files or APIs. Pandas?merge function (similar to SQLs JOIN) and concat can combine DataFrames efficiently.

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stocks_and_bonds = pd.concat([stocks_df, bonds_df], ignore_index=True)

Or you might merge two datasets on a common key:

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merged_df = pd.merge(stocks_df, bonds_df, on='Date', suffixes=('_stock', '_bond'))

Handling Missing Data#

Financial datasets may have missing prices due to market holidays or data feed interruptions. Pandas provides multiple strategies for dealing with NaN (Not a Number) cases:

• Drop Missing Values:

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  df.dropna(inplace=True)  # Removes rows with any missing values
  

  Use this method cautiously, as it can discard valuable data.

• Fill Missing Values:

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  df.fillna(method='ffill', inplace=True)  # Forward fill
  

  Forward filling sets missing dates to the latest known (previous rows) value, commonly used for stock or index data to smooth time-series gaps.

Detecting Outliers#

Outliers can skew your analysis. Although not every outlier is incorrect, its useful to identify them:

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import numpy as np
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# Calculate Z-score
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df['price_zscore'] = (df['Price'] - df['Price'].mean()) / df['Price'].std()
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outliers = df[np.abs(df['price_zscore']) > 3]

This snippet calculates a Z-score and flags any points that deviate by more than three standard deviations from the mean. With financial data, a large outlier could signify:

• A fat-finger error (erroneous trade).
• A sudden stock split (legitimate drastic price drop).
• A data feed glitch.

Renaming Columns and Correcting Data Types#

Inconsistent column names can complicate merges. Pandas lets you rename columns easily:

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df.rename(columns={'Adj Close': 'Adjusted_Close'}, inplace=True)

Additionally, ensure that numeric columns are in integer or float format, and date columns are in datetime format:

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df['Date'] = pd.to_datetime(df['Date'])
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df['Volume'] = df['Volume'].astype(int)

Grouping and Aggregation#

In finance, grouping by sectors or industries to calculate summary statistics (mean return, total volume) is common:

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grouped = df.groupby('Industry')['Daily_Return'].mean()
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print(grouped)

Pivot Tables#

Pivot tables help you capture multidimensional summaries. Suppose you want average monthly returns by sector:

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df['Month'] = df['Date'].dt.to_period('M')
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pivot_table = df.pivot_table(
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    index='Month',
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    columns='Sector',
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    values='Daily_Return',
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    aggfunc='mean'
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)
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print(pivot_table)

Merging and Joining DataFrames#

For instance, you might combine fundamental data (like earnings, revenue) with market data (like prices) to form a more comprehensive analysis. Pandas supports various join methods:

• Left join: how='left'
• Right join: how='right'
• Inner join: how='inner'
• Outer join: how='outer'

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fundamentals = pd.read_csv('fundamentals.csv')
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market_data = pd.read_csv('market_data.csv')
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combined_df = pd.merge(
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    fundamentals,
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    market_data,
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    on=['Date', 'Symbol'],
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    how='inner'
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)

Descriptive Statistics#

Pandas makes generating summary statistics straightforward:

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print(df.describe())

This reveals distribution aspects like mean, standard deviation, minimum, and maximum values for numeric columns. In finance, you might specifically check average daily returns or annualized volatility.

Visualizing Distributions#

Analyzing return distributions can help you assess risk profiles. A histogram with Seaborn is simple:

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import seaborn as sns
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import matplotlib.pyplot as plt
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sns.histplot(df['Daily_Return'], kde=True)
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plt.title('Distribution of Daily Returns')
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plt.show()

If the distribution deviates significantly from normality, advanced risk models may be necessary.

Time-Series Plots#

For financial markets, time-series plots are a backbone:

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df.set_index('Date', inplace=True)
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df['Close'].plot(figsize=(10, 6))
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plt.title('Closing Price Over Time')
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plt.xlabel('Date')
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plt.ylabel('Price')
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plt.show()

This plot quickly reveals whether a stock trend is upward or downward and how volatile it has been.

Correlation Analysis#

Financial instruments often move together. Quantify these relationships with a correlation matrix:

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corr_matrix = df.corr()
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sns.heatmap(corr_matrix, cmap='RdBu_r', annot=True)
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plt.title('Correlation Matrix')
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plt.show()

Resampling#

Financial data may arrive in daily, weekly, or monthly granularityor even in irregular intervals. Use resampling to reconcile different frequencies:

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# Convert index to datetime (if not already)
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df.index = pd.to_datetime(df.index)
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# Resample daily data to monthly average
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monthly_df = df['Close'].resample('M').mean()

Rolling Windows#

To analyze moving averages, rolling correlations, or historical volatility:

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df['30d_MA'] = df['Close'].rolling(window=30).mean()
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df['30d_volatility'] = df['Daily_Return'].rolling(window=30).std() * (252**0.5)  # Annualized volatility

Handling Market Holidays and Missing Dates#

Stock markets dont operate every calendar day. If your analysis demands consistent daily intervals, fill or remove non-trading days:

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

This forces each day (including weekends) into the dataset and forward-fills missing values.

Statistical Analysis#

Leverage libraries like statsmodels for:

• ARIMA/GARCH: Forecasting volatility or returns.
• Regression Analysis: Factor loadings on assets, e.g., the Fama-French model.

Example of running a simple linear regression with statsmodels:

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import statsmodels.api as sm
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X = combined_df[['Market_Return', 'Size_Factor', 'Value_Factor']]
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y = combined_df['Asset_Return']
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# Add a constant for intercept
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X = sm.add_constant(X)
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model = sm.OLS(y, X).fit()
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print(model.summary())

Machine Learning Approaches#

Predicting asset prices remains an area of active research. While no model is perfect, you can apply machine learning methods from scikit-learn or deep learning frameworks (TensorFlow, PyTorch) for tasks like:

• Predicting returns: Using regression or classification models.
• Identifying market regimes: Clustering to segment bull vs. bear market conditions.
• Algorithmic trading: Reinforcement learning to optimize trading strategies.

Example: Using a Random Forest to predict whether the stock return will beat a market index:

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from sklearn.ensemble import RandomForestClassifier
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from sklearn.model_selection import train_test_split
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# Features and labels
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features = df[['Market_Return', 'Volume_Change', 'Daily_Volatility']]
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labels = (df['Stock_Return'] > df['Market_Return']).astype(int)
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X_train, X_test, y_train, y_test = train_test_split(features, labels, test_size=0.2, random_state=42)
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model = RandomForestClassifier(n_estimators=100, random_state=42)
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model.fit(X_train, y_train)
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print("Training Accuracy:", model.score(X_train, y_train))
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print("Test Accuracy:", model.score(X_test, y_test))

Automated Data Pipelines#

If you require near real-time data processingcommon in algorithmic trading systemsconsider solutions like Spark Streaming or Kafka. For Python-based orchestration:

• Apache Airflow: Manages complex data pipelines.
• Luigi: Simplifies building pipelines with modular tasks.

1. Performance Optimization with Vectorization#

Always look for vectorized operations in Pandas before resorting to Python loops. NumPy-based calculations can improve performance significantly. For instance, calculating daily returns across an entire DataFrame:

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df['Daily_Return'] = df['Close'].pct_change()

This vectorized operation is faster and cleaner than a manual loop.

2. Memory Management#

Large data can strain memory. Use chunk-based loading:

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chunks = pd.read_csv('large_file.csv', chunksize=100000)
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for chunk in chunks:
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    # Process each chunk here
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    ...

Aggregate results or save partial computations to disk, then combine them later.

3. Database Solutions#

For extremely large datasets, consider storing data in a SQL or NoSQL database and querying/filtering subsets as needed. Libraries like SQLAlchemy simplify integrated workflows:

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from sqlalchemy import create_engine
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engine = create_engine('sqlite:///financial_data.db')
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df.to_sql('stocks', engine, if_exists='replace')

4. Parallel Processing#

Leverage multi-processing or libraries like Dask to distribute computations across multiple cores or machines. This is especially valuable for tasks like:

• Multiple symbol analysis in parallel.
• Rolling correlation calculations over large time windows.

Example Table of Pros/Cons of Scalable Solutions#