Correlation and Cointegration Analysis

Overview

Correlation analysis is a foundational tool for pairs trading, portfolio construction, and risk management. This skill covers four analysis modes (co-movement discovery / return-correlation deep dive / sector clustering / realized correlation), a full cointegration-testing framework, cross-market linkage analysis, and the complete workflow from analytics to pair-trading signals.

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Mode 1: Co-Movement Discovery

Use case: Given a target asset, scan a universe for highly correlated assets and build a candidate pool with similar industry or factor exposure, for use in pairs trading or substitute identification.

Workflow

1. Pull daily return series for the target asset and N candidates
2. Compute Pearson / Spearman correlations between the target and each candidate
3. Rank by correlation in descending order and keep Top-K (usually K=10-20)
4. Run cointegration tests on the Top-K set to retain pairs with real long-run equilibrium
5. Output the candidate pool and a correlation summary

import pandas as pd
import numpy as np
from scipy.stats import pearsonr, spearmanr

def scan_correlated_assets(
    target_returns: pd.Series,
    universe_returns: pd.DataFrame,
    top_k: int = 20,
    min_corr: float = 0.5,
    method: str = "pearson",
) -> pd.DataFrame:
    """Scan for assets that are highly correlated with the target asset.

    Args:
        target_returns: Daily return series for the target asset
        universe_returns: Candidate-universe return matrix, columns are symbols
        top_k: Number of top candidates to return
        min_corr: Minimum absolute-correlation threshold
        method: "pearson" or "spearman"

    Returns:
        A DataFrame containing symbol / corr / p_value / rank
    """
    aligned = universe_returns.dropna(axis=1, how="any")
    aligned, target_aligned = aligned.align(target_returns, join="inner", axis=0)