What it means
A number is useful only when you know what it measures. A quant score converts selected numerical inputs into a common scale so that different stocks, or a stock and a basket, can be assessed consistently. For example, a liquidity score concerns ease of trading, while an execution score concerns estimated conditions for placing a trade. They are not interchangeable.
A score of 80 does not, by itself, mean an 80% chance of success. It is a rating on a scale, not a probability. Its interpretation depends on the scoring method: which inputs are included, how they are transformed, their weights and the direction of the scale.
How it is calculated
There is no universal quant-score formula. A simple illustrative method first converts each input to a comparable 0–100 rating, then takes a weighted average:
Quant score = (w1 × s1 + w2 × s2 + ... + wn × sn) / (w1 + w2 + ... + wn)
Here, s1 through sn are input ratings, each measured in score points from 0 to 100. w1 through wn are nonnegative, unitless weights; at least one must be positive. The result is also measured in score points from 0 to 100. If the weights sum to 1, the denominator equals 1.
Raw inputs often have different units: bid-ask spread may be expressed as a percentage, daily volatility as a percentage per day, and participation as a percentage of daily volume. They must be converted using stated rules before averaging. A formula should also specify whether a higher rating means more favorable conditions or more risk. The weighted average above is an example, not PreTrAIde’s disclosed scoring formula.
Worked example
Suppose a hypothetical scoring method combines two equally weighted input ratings. It defines higher points as more favorable trading conditions. After converting the raw measurements to 0–100 ratings, a hypothetical stock has:
- Spread rating: 80 points.
- Participation rating: 60 points.
- Weight for each rating: 1.
Step 1: Multiply each rating by its weight: 1 × 80 = 80 and 1 × 60 = 60.
Step 2: Add the weighted ratings: 80 + 60 = 140.
Step 3: Divide by the total weight: 140 / (1 + 1) = 70.
The illustrative quant score is 70 out of 100. It means 70 points under this particular method—not a 70% probability of a profitable trade. Changing the conversion rules or weights could change the score even if the underlying stock and proposed order stay the same.
Why it matters before you trade
Pre-trade analysis asks whether a proposed order’s size and conditions fit the available market information before an order is placed. Quant scores can make measurable considerations easier to compare across lines in a basket. They can also help distinguish a concern about liquidity from one about volatility or execution.
A single number necessarily compresses information. Check what sits behind it: a large order relative to daily volume, a wider estimated spread or elevated daily volatility can matter for different reasons. A basket-level score can also conceal variation among its stocks. Scores are decision-support inputs, not instructions to trade.
How PreTrAIde uses it
PreTrAIde provides 0–100 quant scores for liquidity, volatility and execution, both per stock and for the basket. Its quantitative layer is deterministic and reproducible. It estimates inputs including spread, participation in daily volume, market impact and daily volatility, and also produces liquidity and data-quality scoring.
Its AI agents reason about each stock and the basket using quant scores, news and analyst ratings. A PreTrade analysis returns a GO, NO-GO or MODIFY verdict, a separate model-estimated success probability, a risk assessment, a suggested execution strategy and plain-language reasoning. The success probability is between 5% and 95%; it is not the quant score, a guarantee or a forecast of returns. PreTrAIde is analysis software: users place any trades themselves through their own broker.
Common mistakes
- Reading score points as percentages. A 75-point score is not automatically a 75% likelihood of any outcome.
- Comparing unlike categories. A liquidity score and a volatility score describe different properties.
- Assuming every provider uses the same formula. Inputs, weights, scales and basket aggregation methods can differ.
- Ignoring the underlying measurements. Similar overall scores can mask different spreads, order sizes or volatility.
- Treating a score as a recommendation. It summarizes selected data; it does not decide whether a trade suits an investor.