In crypto it often feels like “everything follows the leader.” Sometimes that’s true—during strong impulses the whole market can move in sync. But for trading decisions you need something measurable: which assets actually move together, on which timeframe, and whether there is a leader–follower delay.
Crypto correlation is a control tool. It helps you:
- select follower coins that statistically track a market leader;
- understand when “diversification” is only on paper;
- manage portfolio risk and avoid stacking the same exposure across many symbols;
- generate cleaner ideas for manual trading and systematic workflows.
This article explains what correlation in crypto is, why it changes, and how to use a crypto correlation screener and a free crypto correlation table with timeframe sorting and liquidity filtering. It also breaks down the correlation metrics available in our tool: Pearson, Spearman, direction agreement, best lag, and lag correlation.
What is crypto correlation
Crypto correlation describes how consistently two assets move together over a chosen time window.
The correlation coefficient typically ranges from -1 to +1:
— +1 means the assets move in the same direction most of the time;
— 0 means there is no stable relationship;
— -1 means they tend to move in opposite directions.
A key point: correlation does not explain “why.” It only measures “how often and how strongly” the co-movement appears on the selected timeframe.
Correlation is not causation, and it changes by market regime
Correlation is not fixed forever. It shifts with market regime:
— during panic and impulse phases, the market often becomes more synchronized;
— during ranges and rotations, relationships weaken or break;
— individual coins can decouple due to their own catalysts or liquidity conditions.
That is why a practical approach is to read correlation on a specific timeframe and within a defined observation window, not as a lifetime average.
Correlation indicator vs crypto correlation screener
A correlation indicator usually compares one asset to another on a chart. It’s useful, but limited to a single pair.
A crypto correlation screener solves a different problem: it scans the market and shows which symbols are currently most correlated to a selected leader, with sorting and filters. This speeds up decision-making for both manual traders and systematic strategies.
Our free crypto correlation table
In our project we provide a free crypto correlation table that shows relationships between many crypto assets and a selected leader. You choose a leader, set a timeframe, apply a liquidity filter, and then sort the market by correlation strength and movement agreement.
For leader assets, the most practical choices are the core market drivers: BTC, ETH, and SOL.
Correlation metrics in the tool: what each one means
The table includes several correlation and alignment metrics. They answer different questions, so they are best used together.
- Pearson correlation
- Pearson measures linear correlation between the leader’s returns and the asset’s returns. It works well when the relationship is close to “leader up → follower up” in a relatively straight-line sense.
Practical use: a high Pearson often means the asset tracks the leader both in direction and in a more proportional way.
- Spearman correlation
- Spearman is rank correlation. It detects monotonic relationships even when the connection is not strictly linear. Some coins “follow the idea” of the leader’s move, but with different shape and amplitude. Spearman helps capture those cases.
Practical use: a high Spearman is useful when structure matches, even if the movement is not perfectly linear.
- Direction agreement (Sign accuracy)
- This metric answers a very practical question: how often does the asset move in the same direction as the leader on the chosen timeframe.
Practical use: for many trading workflows, direction agreement is more actionable than a pure coefficient.
- Best lag
- Lag is the time shift that maximizes the relationship. In other words, the tool can show whether the leader tends to move first and the follower tends to react later.
Practical use: this supports leader–follower logic. If a follower consistently reacts with a delay, it can improve confirmation and timing.
- Lag correlation
- This is the correlation value after applying the best lag. It helps differentiate a true delayed follower from a symbol that only matches by coincidence when measured “at the same time.”
Practical use: a strong lag correlation suggests the asset follows the leader with a consistent delay.
The table also includes practical context fields:
- Leader: the chosen leader;
- TF (m): timeframe in minutes;
- Turnover 24h: a liquidity filter to avoid misleading stats on thin symbols;
- Price change 24h: a quick regime reference;
- Window end: the end of the calculation window for recency checks.
How to use a crypto correlation screener: a practical workflow
Step 1. Pick a leader
BTC, ETH, or SOL are usually the most useful leaders depending on market regime.
Step 2. Choose a timeframe
Short timeframes show fast, current relationships. Longer timeframes show more stable patterns. The shorter the horizon, the faster the picture can change.
Step 3. Apply a 24h turnover filter
Turnover is basic risk control. Thin markets can show “beautiful” coefficients that fail in real execution.
Step 4. Sort by Pearson or Spearman
Use Pearson for more linear tracking. Use Spearman for structural following that may not be linear.
Step 5. Validate direction agreement and lag
High correlation without solid direction agreement often means unstable co-movement. Lag metrics help you understand whether the asset is a true follower.
Step 6. Make the trading decision
For manual trading, this creates a list of follower assets aligned with the leader’s current regime. For systematic workflows, it becomes a pre-trade quality filter.
How to use crypto correlation in manual trading
- Selecting follower coins around a leader move
- When a leader enters an impulse phase, you can focus on assets with strong correlation and solid liquidity. This reduces random trades.
- Exposing “fake diversification”
- Ten coins is not diversification if all of them are highly correlated. A correlation matrix view helps you see when your portfolio is effectively one bet across multiple tickers.
- Regime confirmation
- When correlations rise broadly, the market is in “all move together” mode. When correlations break, rotations and dispersion increase, and your approach should adapt.
- Pairs and relative workflows
- Correlation can help find asset pairs that usually move together and then temporarily diverge. It is not a guarantee, but it is a strong idea filter.
How to use crypto correlation for systematic workflows
For trading bots and systematic strategies, correlation is a quality filter, not decoration. You pick a leader, define follower-selection rules, and only then apply entries and position management.
Practical takeaway: automation works best when it does not have to guess which symbols are worth trading. A correlation table helps exclude assets that do not follow the leader or fail liquidity constraints.
Common mistakes with crypto correlation
- reading coefficients without timeframe and without a defined window;
- ignoring liquidity and turnover;
- treating correlation as a prediction rather than a measurement;
- relying on Pearson or Spearman alone without checking direction agreement;
- ignoring lag: many assets follow, but not simultaneously.
Conclusion
Crypto correlation is a practical tool for systematic trading. It helps you select follower assets, manage risk, control correlated exposure, and understand who drives the market regime.
Our free crypto correlation screener and correlation table provide an actionable market view: leader selection, timeframe sorting, turnover filtering, multiple correlation types, direction agreement, and lead–lag metrics. This is useful for both beginners and experienced traders—whether you trade manually or build automated workflows. Free after registration.