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.
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(
…
