Crypto Correlation Matrix (Portfolio Diversification Analysis)

What does Crypto Correlation Matrix do?

The Crypto Correlation Matrix actor fetches up to 365 days of daily price history for 15 major crypto assets and computes pairwise Pearson correlation coefficients from their log returns — giving traders and portfolio managers a rigorous, data-driven view of how closely assets move together. Unlike static charts or gut-feel estimates, this actor delivers a full Bitcoin correlation tracker and cross-asset matrix that updates on every run, powered by live CoinGecko price data. No API key is required, and results are structured JSON ready for downstream analysis, visualization, or automated crypto portfolio correlation and rebalancing logic.

Why use Crypto Correlation Matrix?

Business use cases for the leading crypto portfolio correlation actor:

1. Portfolio diversification — Identify truly uncorrelated crypto assets before allocating capital. If BTC and ETH show 0.92 Bitcoin correlation, adding ETH to a BTC portfolio adds almost no diversification benefit. Find the 0.3-correlation outliers instead.
2. Risk management systems — Embed this actor into automated portfolio monitors. Trigger rebalancing alerts when key pair correlations shift significantly from their historical baseline — a leading indicator of regime change.
3. Pair trading and statistical arbitrage — Identify highly correlated pairs (>0.85) as candidates for mean-reversion strategies. When their price ratio diverges, the crypto correlation matrix suggests eventual reversion.
4. Crypto index construction — Build diversified token baskets by selecting assets with low inter-basket correlation. Run the matrix over 90-day windows to ensure sector exposure remains uncorrelated.
5. Research and academic analysis — Export the full matrix to CSV or connect via Apify API for quantitative research into crypto market structure, contagion effects, and sector-level co-movement over custom time windows.

How to use Crypto Correlation Matrix

1. Click Try for free on the Apify Store listing to open the actor.
2. Set lookbackDays (default: 30) to the historical window you want to analyze. Choose 7 for short-term signals, 30 for medium-term, or 90 for long-term structural correlations.
3. Click Start — the actor fetches price history for all 15 default assets and computes the full matrix.
4. Review results in the Dataset tab: each row represents one asset pair with its Pearson correlation value and interpretation label.
5. Export to CSV for use in Excel, Python (pandas), or portfolio analytics tools.
6. Schedule the actor weekly or monthly to track how Bitcoin correlation and cross-asset relationships shift over time.

Input

Parameter	Type	Default	Description
lookbackDays	integer	30	Days of price history for correlation computation (7–365)

Default assets analyzed: BTC, ETH, SOL, BNB, AVAX, LINK, UNI, AAVE, ARB, OP, SUI, INJ, NEAR, TAO, WIF

Output

Each record in the dataset represents one asset pair:

[
  {
    "asset_a": "BTC",
    "asset_b": "ETH",
    "correlation": 0.91,
    "interpretation": "very_high",
    "lookback_days": 30,
    "computed_at": "2026-09-02T10:00:00+00:00"
  },
  {
    "asset_a": "BTC",
    "asset_b": "WIF",
    "correlation": 0.38,
    "interpretation": "low",
    "lookback_days": 30,
    "computed_at": "2026-09-02T10:00:00+00:00"
  }
]

Data fields

Field	Type	Description
asset_a	string	First asset in the pair (ticker symbol)
asset_b	string	Second asset in the pair (ticker symbol)
correlation	number	Pearson correlation coefficient (-1.0 to +1.0)
interpretation	string	Human-readable label for the correlation strength
lookback_days	integer	Number of days of price history used in computation
computed_at	string	ISO 8601 timestamp of when the matrix was computed

Correlation interpretation guide

Range	Interpretation	Portfolio implication
0.8–1.0	very_high	Assets move almost identically — little diversification benefit
0.6–0.8	high	Similar direction with some divergence
0.4–0.6	moderate	Partial co-movement — moderate diversification
0.2–0.4	low	Mostly independent — good diversification candidates
0.0–0.2	near_zero	Effectively uncorrelated — strong diversification
Negative	inverse	One asset tends to rise when the other falls

Cost estimation

Pay-Per-Event pricing — flat $0.008 per run.

Schedule	Daily Cost	Monthly Cost
Once per day	$0.008	$0.24
Every 4 hours	$0.048	$1.44
Every hour	$0.192	$5.76
Weekly (Mon/Fri)	—	$0.07

Running a Bitcoin correlation tracker on a daily schedule for a full year costs under $3 — compared to hundreds per month for institutional data providers offering similar correlation analytics.

FAQ

Which assets are included in the matrix by default? The default asset list covers 15 major tokens: BTC, ETH, SOL, BNB, AVAX, LINK, UNI, AAVE, ARB, OP, SUI, INJ, NEAR, TAO, and WIF. This spans large-cap L1s, DeFi blue chips, L2 tokens, and high-beta assets — giving a representative cross-section of the crypto portfolio correlation landscape.

How is the correlation calculated? The actor fetches daily closing prices from CoinGecko for the specified lookback window and computes Pearson correlation coefficients on the log returns (not raw prices). Log returns are standard in quantitative finance as they are stationary and additive across time periods, making the resulting correlations more reliable for portfolio analysis.

Why does the correlation change between lookback windows? Crypto correlations are regime-dependent. During bull markets, most assets correlate strongly (0.7–0.95). During bear markets or sector-specific events, correlations can drop significantly as capital rotates selectively. Running the actor with 7-day, 30-day, and 90-day windows simultaneously reveals whether current correlations are a short-term anomaly or a structural shift.

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Related actors

• Tokenomics Analyzer — Token economic design scores to combine with correlation data for portfolio construction
• Altcoin BTC-Relative Performance — 90-day per-pair performance to cross-reference with correlation readings
• CoinMarketCap Global Metrics — Market regime (Fear & Greed, dominance) to contextualize why correlations shift

Feedback

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Disclaimer: This actor aggregates publicly available price data for informational and analytical purposes only. Correlation statistics are backward-looking and do not predict future price relationships. Portfolio construction decisions should incorporate additional risk factors beyond correlation alone. This is not financial advice.