Technical Analysis vs Quantitative Analysis

Michael Whitman · Trading since 2017, full-time since 2020 · Published 2026-09-09 · Updated 2026-10-04 · Reviewed 2026-10-04

Both approaches work from the same price history. Technical analysis interprets it through a trained eye; quantitative analysis writes a rule, runs it, and then checks it on data the rule never saw. Run on seventy years of the S&P 500, a simple chart rule passed the first kind of test convincingly and failed the second.

What each one is

Technical analysis reads charts through patterns, levels, indicators and trend, interpreted by a person. Technical analysis covers the tradition, and price action covers its most judgment-heavy end.

Quantitative analysis writes a rule precisely enough to run mechanically, measures what happened, and keeps part of the history back to check the result on. Quantitative analysis covers the method.

One produces readings and the other produces claims. A chart reading describes what someone sees. A quantitative claim is a specification that anyone can run, get the same numbers from, and disagree with.

Where they differ

Whether the claim can be rerun. Both start from the same chart, yet a chart reading depends on which level, which pattern and how strictly each was judged, so two people can see different things in one chart. A written rule gives the same answer every time it is run, which is what makes disagreement useful.

How each one guards against luck. A chart reader’s sense that a pattern is reliable comes from the cases remembered. A quantitative test can compare a result with what shuffled data produces, and with what the rule did on a later period.

What each one adapts to. A person can weigh something that has never happened before, while a rule cannot, and will apply itself to conditions nobody had in mind when it was written.

How each one is usually shown. Chart lessons are mostly marked up after the outcome, whereas a quantitative result is supposed to arrive with its in-sample and out-of-sample numbers side by side.

The test, written as a rule

The chart idea: “the market behaves better above its moving average.” Written as a rule: on each day, note whether the S&P 500 closed above its N-day simple average, then compare the average next-day change after closes above with the average after closes at or below. That gap is the statistic, in percent per day, for 25 lengths: 10, 20 and so on up to 250 days.

The split. Every measured day from 26 Feb 1958 to 1 Oct 2026 has all 25 averages available. The first half, to 17 Jun 1992 (8,632 days), is where a researcher would pick the best length. The second half, 18 Jun 1992 onward (8,632 days), is the check.

The shuffle. To see what the best of 25 rules looks like on data with no order in it, the first half’s daily changes were put in random order 1,000 times, a price path was rebuilt from each, and the best of the 25 gaps was recorded.

A rule that passed one test and failed the other

In the first half, every one of the 25 lengths showed a positive gap. The best was the 10-day average: next-day changes after closes above it averaged 0.1221% a day more than after closes below.

The shuffles never matched it. Across 1,000 reordered histories, the median best-of-25 gap was 0.0189% and the 90th percentile 0.0385%; none reached 0.1221%. By that test the first-half result was not luck. It also shows how easily noise flatters a search: in 87.6% of the shuffles, the best of 25 lengths still came out above zero.

In the second half the same 10-day rule ranked 25th of 25, with a gap of -0.0593% a day. Only three lengths (220, 230 and 240 days) stayed above zero, the best at 0.0049%. The rank correlation between the two halves’ 25 results was -0.735: the lengths that looked best early tended to look worst later.

Which one to use: Technical Analysis or Quantitative Analysis?

Use a quantitative test when the question is whether something held up, and insist on the part of the test run on data the rule was not chosen from. The first half’s result above was real for its period and still did not carry forward.

Use judgment rather than a backtest when the situation is new, such as an event no rule was specified for. A person who sees that conditions have changed is doing work no backtest does.

Use both by writing your chart reading down as a rule. The exact level, the exact condition and what would disprove it turn a reading into something that can be run, which anyone willing to be that precise can do.

And when a pattern looks obvious, test it on a later period before trusting it. The obviousness comes from the data you already looked at.

Why the rule turned around

Next-day persistence on the S&P 500 changed sign over the decades. The correlation between one day’s change and the next was 0.1532 in the 1960s and 0.2487 in the 1970s, so up days tended to follow up days, which is what a short moving-average rule picks up. It fell to 0.0531 in the 1980s and 0.0171 in the 1990s, and turned negative: -0.0878 in the 2000s, -0.0471 in the 2010s and -0.1661 from 2020 to 2026.

So the market the rule was found in was not the market it was applied to. A shuffle test asks whether a result could come from the same numbers in random order. It cannot ask whether the process producing the numbers will stay the same, which is the question only a later period answers.

The original data

Sample: S&P 500 daily closes from 4 Mar 1957, the day the 500-stock index began, to 2 Oct 2026 (Yahoo Finance, ^GSPC). Measured days need 250 closes before them for the longest average, so the test starts on 26 Feb 1958; the last measured day is 1 Oct 2026, whose next-day change runs to 2 Oct.

Shuffle test: 1,000 random reorderings of the 1958-1992 daily changes (fixed seed, so it reruns exactly); best-of-25 gap median 0.0189%, 90th percentile 0.0385%, highest 0.0605%, so none of the 1,000 reached the real 0.1221%.

Next-day correlation by decade: 1957-59 0.0967 (714 pairs), 1960s 0.1532, 1970s 0.2487, 1980s 0.0531, 1990s 0.0171, 2000s -0.0878, 2010s -0.0471, 2020-2026 -0.1661; over the whole span -0.0079. The pair counts are in the S&P 500 next-day correlation file.

What this does not show. The gaps are averages of next-day changes before any cost; nothing here is a trading result, and none of the 25 rules is offered as one. All 25 lengths, both halves, are in the S&P 500 moving-average split file.

When it fails

The characteristic technical failure is confusing familiarity with evidence. Someone who has looked at charts for years has seen a setup resolve well many times and remembers those cases better than the ones where nothing happened. The confidence is genuine and the sample was chosen by memory. Counting is the remedy, and the count has to include the cases that were forgotten.

The characteristic quantitative failure is stopping at the in-sample result. The 10-day rule above had the best first-half number and a result no shuffle matched, and it was the worst of the 25 afterward.

A third is testing many variations and reporting the winner. The best of 25 beat zero in 87.6% of shuffled histories.

A fourth is applying a rule in conditions it was never built for.

And a fifth is treating either as a complete method, since neither sets a position size or a loss limit by itself.

Related

Technical analysis covers the interpretive tradition on the left of this comparison. Quantitative analysis covers writing and testing a rule, including the split used here. And price action covers the most judgment-heavy way of reading the same charts.

This page is educational, not financial advice. Test every idea on your own charts before risking money.