A market table becomes useful only when you read its columns as one connected story. Price tells you what one unit costs. Market cap estimates the value of the circulating network. Supply explains why those two numbers differ. Volume shows current trading activity. Percent changes describe direction over specific windows. None of those measurements is a verdict by itself.

The goal of this guide is not to memorize definitions. It is to build a repeatable read: What kind of asset is this, how much participation supports the move, and is the move broad enough to deserve attention? CryptoScreener supplies the evidence; your job is to keep the comparisons fair.

Use market data to narrow a question, not to manufacture certainty. Every value reflects the latest successful local data sync and can be missing or stale.

Price is the cost of one coin or token in the quote currency, usually USD. A $0.05 token is not automatically “cheaper” than a $2,000 coin. Unit price depends heavily on how many units exist. If Project A has one billion circulating tokens at $1, its market cap is roughly $1 billion. Project B can trade at $100 with only one million circulating units and have a much smaller $100 million market cap.

Use the price column when the unit itself matters: defining a trading range, checking how far a coin moved, or comparing the same asset through time. Use market cap—not unit price—when comparing economic size across different assets.

• Useful question: “Which coins between $1 and $10 meet my other conditions?”
• Weak conclusion: “Coins under $1 have more upside.”
• Check: Pair price with circulating supply, market cap, and liquidity before interpreting it.

Market capitalization is approximately price × circulating supply. It gives unlike assets a common size measure and drives market-cap rank. In CryptoScreener, presets divide the universe into mega caps ($10B+), large caps ($1B–$10B), mid caps ($100M–$1B), small caps ($10M–$100M), micro caps ($1M–$10M), and nano caps (below $1M).

Size changes the character of a screen. Large assets often have deeper markets and need more capital to move sharply. Smaller assets can move faster but may carry greater liquidity, pricing, and concentration risk. These are tendencies, not guarantees.

Rank is useful for creating a stable comparison universe such as the top 50 or top 500. A market-cap range is useful when the size itself is part of the thesis. Do not mix a mega-cap leader and a thin micro cap, compare their percentage moves, and assume the opportunities are equivalent.

Circulating supply is the amount estimated to be available in the market now. Total supply includes units that exist but may not circulate. Max supply is the stated upper limit when one exists. Fully diluted valuation (FDV) estimates value using a broader future supply measure, commonly price multiplied by max or total supply.

A wide gap between market cap and FDV can signal potential dilution: more tokens may enter circulation through unlocks, emissions, rewards, or treasury distributions. That does not prove price will fall, but it changes what “size” means. Two projects with the same market cap can have very different future supply pressure.

Supply fields can be unavailable or vendor-dependent. Treat a missing max supply as unknown, not unlimited. When dilution matters, use the table to identify the question, then verify the token schedule and unlock mechanics from primary project documentation.

24-hour volume estimates how much trading occurred during the last day. Higher volume usually means more participation, but raw volume favors large assets. A $100 million day is ordinary for a very large coin and extraordinary for a $120 million project.

Volume-to-market-cap (Vol/Mcap) normalizes activity by size. A ratio of 0.20 means reported 24-hour volume equals about 20% of market cap. Higher ratios can identify attention, rotation, or unusually active trading. They can also reflect churn, fragmented venues, incentives, or unreliable volume. “High attention” is a prompt to investigate—not a quality badge.

For a cleaner momentum screen, combine a minimum market cap or rank with minimum volume, then inspect Vol/Mcap. This avoids letting tiny, thin names dominate simply because a small trade produced a large percentage move.

The 1h, 24h, 7d, and 30d columns measure change over different lookback windows. They overlap, so they are not independent votes. The one-hour move describes immediate pressure; 24h describes the current day; 7d adds short-term context; 30d provides a broader swing view.

Read the shape, not just the largest number. A coin at +2% for 1h, +8% for 24h, and +20% for 7d is positive across windows but may already be extended. A coin at +3% for 1h, -2% for 24h, and -15% for 7d may be bouncing inside a larger decline. The same green one-hour cell means something different in each case.

Percentage change says direction and magnitude, not tradability or cause. Confirm that the move has enough volume, fits the size bucket, and is shared by more than one coin when making a claim about a theme.

Imagine a filtered list of 30 mid-cap assets. Twenty are up over 24h, but only six are positive across 1h, 24h, and 7d. Median daily trading is moderate, two coins account for most of the market cap, and the strongest gainers have very low volume.

A weak read would be: “Mid caps are bullish because most are green today.” A fuller read is: “Daily breadth is positive, but multi-window confirmation is limited. The list is concentrated, and the headline gainers have weak participation, so the move looks selective rather than broadly established.” That statement uses direction, participation, size, and concentration together.

Now change one fact: twelve coins become positive across all three windows while volume expands across the middle of the list. The conclusion can strengthen because participation—not merely the top return—improved.

1. Define the universe with rank, market cap, category, exchange, or named coins.
2. Choose the change window that matches the question.
3. Check volume and Vol/Mcap so participation is visible.
4. Inspect supply and FDV when dilution could matter.
5. Look for breadth: is the pattern shared or driven by a few names?
6. Keep missing fields visible in your reasoning; do not silently treat them as zero.
7. Save the filter as a screen only when you can explain why every condition belongs.

The strongest screen is not the one with the most filters. It is the one whose result set answers a clear question and remains interpretable when the market changes.