Nixtla Correlation Mapper

Nixtla Correlation Mapper

Identifies correlations between multiple contracts and generates hedging strategies for portfolio risk management.

Overview

Analyzes relationships between assets in a portfolio to suggest hedging strategies. Takes CSV data with multiple time series, calculates correlation matrix, identifies significant relationships, and outputs hedge recommendations with visualizations. Generates correlation heatmap, rolling correlation plots, and hedge effectiveness charts.

Prerequisites

Tools: Read, Write, Bash, Glob, Grep

Environment: None required (optional: NIXTLA_TIMEGPT_API_KEY for forecasted correlations)

Packages:

pip install pandas numpy scipy matplotlib seaborn

Input Format: CSV with columns: unique_id (contract identifier), ds (date), y (price/value)

Instructions

Step 1: Prepare Data

Load multi-series contract data and calculate returns. Uses {baseDir}/scripts/prepare_data.py.

python scripts/prepare_data.py contracts.csv --method log --output-dir results/

Output: prices_wide.csv, returns.csv

Step 2: Calculate Correlations

Calculate correlation matrix and identify significant pairs. Uses {baseDir}/scripts/correlation_analysis.py.

python scripts/correlation_analysis.py \
  --returns results/returns.csv \
  --method pearson \
  --threshold 0.5 \
  --rolling-window 30 \
  --output-dir results/

Output: correlation_matrix.csv, correlation_pvalues.csv, high_correlations.json, rolling_correlations.csv

Step 3: Generate Hedge Recommendations

Calculate optimal hedge ratios using regression or minimum variance methods. Uses {baseDir}/scripts/hedge_recommendations.py.

python scripts/hedge_recommendations.py \
  --returns results/returns.csv \
  --correlation results/correlation_matrix.csv \
  --method ols \
  --top-n 10 \
  --portfolio-value 100000 \
  --output-dir results/

Output: hedge_recommendations.csv, hedge_recommendations.json, hedged_portfolio.csv

Step 4: Create Visualizations

Generate correlation heatmap, rolling correlation plot, and hedge effectiveness chart. Uses {baseDir}/scripts/visualize.py.

python scripts/visualize.py \
  --correlation results/correlation_matrix.csv \
  --rolling results/rolling_correlations.csv \
  --recommendations results/hedge_recommendations.json \
  --output-dir results/ \
  --top-n 5

Output: correlation_heatmap.png, rolling_correlation.png, hedge_effectiveness.png

Step 5: Generate Report

Create comprehensive markdown report with all analysis results. Uses {baseDir}/scripts/generate_report.py.

python scripts/generate_report.py \
  --correlation results/correlation_matrix.csv \
  --high-correlations results/high_correlations.json \
  --recommendations results/hedge_recommendations.json \
  --output results/correlation_report.md

Output: correlation_report.md

Output

• correlation_matrix.csv: Full pairwise correlation matrix
• correlation_heatmap.png: Visual correlation heatmap
• correlation_pvalues.csv: Statistical significance p-values
• high_correlations.json: Pairs exceeding correlation threshold
• hedge_recommendations.csv: Detailed hedging strategies with ratios
• hedged_portfolio.csv: Sample portfolio allocation with long/short positions
• rolling_correlations.csv: Time-series correlation stability
• rolling_correlation.png: Rolling correlation visualization
• hedge_effectiveness.png: Variance reduction by contract pair
• correlation_report.md: Comprehensive analysis report

Error Handling

Error: Input file not found

• Verify file path with ls -la
• Check current directory and use absolute paths

Error: Missing required columns

• Ensure CSV has unique_id, ds, y columns
• Verify column names match exactly (case-sensitive)

Error: Insufficient data points

• Need at least 30 data points per contract for reliable correlations
• Verify data has sufficient time-series history

Error: Invalid data format

• Check that y values are numeric (not strings)
• Ensure dates are parseable (ISO format recommended)
• Remove or handle missing values

Error: Insufficient contracts

• Need at least 2 contracts for correlation analysis
• Verify unique_id column has multiple distinct values

Examples

Example 1: Crypto Portfolio

Input (portfolio.csv):

unique_id,ds,y
BTC,2024-01-01,42000
ETH,2024-01-01,2200
BTC,2024-01-02,42500
ETH,2024-01-02,2250

Workflow:

python scripts/prepare_data.py portfolio.csv
python scripts/correlation_analysis.py
python scripts/hedge_recommendations.py
python scripts/visualize.py
python scripts/generate_report.py

Result: Correlation 0.85 between BTC-ETH, hedge ratio -0.95, variance reduction 72%

Example 2: Prediction Market Contracts

Input: 5 election-related prediction market contracts

Command:

python scripts/prepare_data.py elections.csv --output-dir election_analysis/
python scripts/correlation_analysis.py --threshold 0.7 --output-dir election_analysis/
python scripts/hedge_recommendations.py --top-n 5 --output-dir election_analysis/
python scripts/visualize.py --output-dir election_analysis/
python scripts/generate_report.py --output election_analysis/report.md

Result: Identified 3 pairs with correlation > 0.7, top hedge reduces variance by 62%

Resources

Scripts: All analysis scripts located in {baseDir}/scripts/

• prepare_data.py: Data loading, pivoting, returns calculation
• correlation_analysis.py: Correlation matrix, p-values, rolling correlations
• hedge_recommendations.py: Hedge ratios, portfolio allocation
• visualize.py: Heatmaps, rolling plots, effectiveness charts
• generate_report.py: Comprehensive markdown report

Correlation Methods: Pearson (linear), Spearman (rank-based), Kendall (concordance)

Hedge Methods: OLS regression (standard), Minimum variance (risk-minimizing)

Interpretation:

• Strong correlation: |r| > 0.7 (high co-movement)
• Moderate: 0.3 < |r| < 0.7 (partial relationship)
• Weak: |r| < 0.3 (minimal relationship)
• Negative correlation: r < -0.5 (good hedge potential)