Almost every argument about trading indicators is really an argument about redundancy. One trader says moving averages are useless; another says they are the only thing that works. One chart has eleven panes; another has none. What both camps miss is that "is this indicator good?" cannot be answered on its own. An indicator is only good or bad relative to what else is already on the chart, because what it adds is whatever the others did not already carry.
That makes classification the first problem. Sort your tools into classes by what they measure and you can see immediately when you have stacked three instruments that all measure the same thing, feel triply confident, and are no better informed than you were with one. What follows is a three-class taxonomy, the correlation argument behind it, a classification table for fifteen common tools, and four stacking rules.
The three classes of trading indicators, defined by their input
The classes are not defined by how fast an indicator reacts, which is how they are usually taught and which is why the categories blur. They are defined by what the calculation takes in.
Lagging: a transform of past price
A lagging indicator takes price history as its only meaningful input and outputs a smoothed or re-expressed version of it. A 50-period simple moving average is literally the arithmetic mean of fifty closes. It cannot tell you anything that is not already in those fifty closes; what it does is remove detail so a direction becomes visible.
That is a genuine service, and "lagging" is not an insult. Noise removal is exactly what you want when the question is "which way has this been going?" The limitation is structural: a transform of past price can never be early, because its input contains nothing that happens later. Any claim that a lagging indicator cross is predictive is a claim about price autocorrelation, not about the average.
Leading: a bounded transform designed to reach an extreme before price does
A leading indicator is also usually computed from price — this is the part that trips people up — but it is constructed so that it saturates. Oscillators are bounded between fixed limits, or they measure a rate of change rather than a level. Because the output is bounded and the input is not, the indicator hits its ceiling while price still has room to run, and that saturation is read as a warning.
The honest description is therefore not "it predicts the future". It is "it re-expresses recent price in a form where unusual readings are obvious sooner". That is useful and it is also why oscillators produce so many early signals that never resolve: saturation says the move is statistically unusual, not that it is over.
Confirming: a different measurement entirely
A confirming indicator takes an input that is not the price series. Volume. Open interest. Implied volatility. Breadth across a universe of constituents. Transaction count. The number of participants on each side of a level.
This is the class that does real work in a stack, and it is the class most retail charts are missing entirely. The reason is simple arithmetic: if indicator A and indicator B are both functions of the same close series, their outputs must be related. If indicator C is a function of a different series, it can disagree with A and B, and a tool that can disagree with you is the only kind that can inform you.
The correlation argument: two from one class is one indicator in two costumes
Here is the mechanical version of the claim, verifiable on your own data rather than taken on faith.
Take two momentum oscillators with similar lookbacks — a 14-period relative strength measure and a 14-period stochastic. Both are functions of the same closes over the same window. Export both series and compute the correlation. You will not get 1.0, because the formulas normalise differently, but you will typically get a figure high enough that the second series adds very little independent variation. Do the same for one of those oscillators and a volume-based measure: the correlation collapses, because the inputs are different series.
The implication is direct. When you require two highly correlated conditions to agree before entering, you have not built a filter that removes half your bad signals. You have built one that removes a handful — the cases where the formulas disagree on a normalisation quirk — while making you feel you cleared a second hurdle. Subjective confidence rises much faster than information does, which is the worst possible combination.
Worse, correlated conditions fail together. In a fast, one-directional move, every momentum oscillator on your chart saturates at roughly the same time. Your "three-indicator confirmation" produces one signal, not three, and it produces it at exactly the moment the move is least likely to behave normally. This is the point QuantZee's guide to trading indicators frames as the difference between agreement and independence: indicators that agree because they are measuring the same thing are not evidence, and the only way to tell the two apart is to look at the input, not the output.
If correlation as a concept is unfamiliar, the general statistical treatment in the standard reference on correlation is worth twenty minutes, because the whole argument rests on one idea: adding a variable that is nearly a linear function of one you already have adds almost no information.
Classifying fifteen common trading indicators
Classification by input, not by reputation. The "primary input" column is the one that matters — read down it and the redundancies in your own chart become obvious.
| Tool | Primary input | Class | What it adds that nothing else does |
|---|---|---|---|
| Simple / exponential moving average | Close series | Lagging | A direction with detail removed |
| MACD | Two averages of close | Lagging | The spread between two smoothings, nothing more |
| Average directional index | High, low, close ranges | Lagging | Trend strength without direction |
| Parabolic stop-and-reverse | Price extremes | Lagging | A mechanical trailing level |
| Ichimoku cloud | Price midpoints, shifted | Lagging | Several smoothings displayed together |
| Relative strength measure | Close changes | Leading | Bounded reading of recent up vs down pressure |
| Stochastic oscillator | Close within recent range | Leading | Position in range, bounded |
| Rate of change / momentum | Close vs close N bars ago | Leading | Raw speed of change |
| Commodity channel index | Typical price deviation | Leading | Deviation from a mean, scaled |
| Williams range measure | Close within recent range | Leading | Near-duplicate of the stochastic |
| Bollinger Bands | Average plus rolling standard deviation | Lagging, volatility-scaled | Where price sits relative to its own recent dispersion |
| Average true range | Bar ranges | Lagging, volatility | A sizing and stop-distance unit |
| On-balance volume | Volume signed by price direction | Confirming | Whether moves carry participation |
| Volume-weighted average price | Price and volume | Confirming | An execution benchmark price cannot produce alone |
| Open interest | Contracts outstanding | Confirming | Whether positions are opening or closing |
Read the primary-input column and the structure of the usual retail chart becomes uncomfortable. A chart with two moving averages, a MACD and a relative strength measure has four indicators and one input. A chart with one moving average, one oscillator and one volume-based measure has three indicators and three inputs, and will disagree with itself regularly — which is the feature, not the bug.
Two entries deserve a flag. Bollinger Bands sit awkwardly: the middle line is a plain moving average, so it is lagging, but band width is a volatility measurement and volatility is close to an independent series. Treat the midline as lagging and the width as a separate volatility input, and do not run the bands alongside a second volatility tool. And the Williams range measure and the stochastic are near the same calculation on different scales — the clearest two-costumes case on this list.
Four stacking rules
These follow directly from the classification. They are restrictive on purpose; the default failure mode in indicator selection is addition, not subtraction.
Rule 1 — one indicator per class, maximum. One lagging, one leading, one confirming. Three total. If you want a second from a class, you must first delete the first. The constraint forces you to decide which lagging indicator you actually prefer rather than hedging by keeping both.
Rule 2 — the confirming slot is not optional. A stack of one lagging plus one leading indicator is two views of the close series. It is the most common two-indicator setup in retail trading and it is the weakest, because nothing in it can contradict the price series. If you cannot get volume, open interest, implied volatility or breadth on your instrument, you have a two-class stack, and you should size accordingly rather than pretend the third opinion exists.
Rule 3 — assign each class a different job. Lagging indicators select the regime: are we trending or ranging, and which way. Leading indicators time the entry inside that regime. Confirming indicators grant or withhold permission. One tool, one job. The moment a tool does two jobs — a moving average that both defines the trend and triggers the entry on its cross — you have collapsed two decisions into one measurement and lost the independence you built the stack for.
Rule 4 — write the stack down before you trade it. Three sentences, one per class, each evaluable by another person who would reach the same answer. If you cannot write it, you do not have a stack; you have a chart you interpret differently on different days, and no amount of indicator quality fixes that.
What "confirming" has to be independent of
Rule 2 is the one people satisfy in letter and break in spirit, so it needs expanding. A confirming indicator must be independent of three things, not one.
Independent of the price series. The input must not be a function of the same closes. A volume-based measure qualifies; a second price oscillator with a different lookback does not. Note that changing a lookback period does not create independence — a 7-period and a 21-period oscillator on the same closes are two smoothings of one series.
Independent of the timeframe. Two conditions taken from the same timeframe share that timeframe's noise. A confirming condition read on a higher timeframe is more independent than the same condition on the execution timeframe, because it is built from a different sampling of the data.
Independent of the instrument, where possible. Breadth measures and cross-asset conditions are the strongest confirming inputs available, precisely because they can be wrong about your instrument. If your long setup on an index product requires that a majority of constituents are also advancing, you have added a condition that genuinely constrains you. A condition that cannot constrain you is decoration.
There is a reason volume and open interest get attention here: they are reported quantities, not derived ones. Clearing and exchange bodies publish them directly — the Options Clearing Corporation is the authoritative source for US listed options open interest. Independent measurement is the whole value proposition.
A bad stack and its repair
Take a hypothetical setup on a US equity index ETF. This is an illustrative configuration, not a tested result.
The original stack: a 9-period and a 21-period exponential moving average for the cross, a MACD for momentum, a 14-period relative strength measure for overbought and oversold, and a stochastic "for confirmation". Five indicators.
Count the inputs. The two averages are the close series. The MACD is the difference of two averages of the close series. The relative strength measure and the stochastic are both bounded transforms of the close series. Five indicators, one input. The stochastic is not confirming anything — it is a fourth look at the same closes, and it will saturate at roughly the same time as the relative strength measure for exactly that reason.
The repair, under the four rules. Lagging slot: keep one — say the 21-period average, and let its slope define the regime. Delete the 9-period average and the MACD, both of which are additional smoothings of the same series. Leading slot: keep one oscillator, delete the other, and choose on the basis of which normalisation you understand rather than which looked better last month. Confirming slot: add a volume-based measure, or on an options-bearing underlying, an implied-volatility percentile reading. Result: three indicators, three inputs, three jobs. This kind of pruning is what QuantZee treats as the central problem in combining signals, and the pruning almost always improves a chart more than any addition would.
The uncomfortable part is that the three-indicator chart will produce fewer signals and will contradict itself more often. Both of those are the point. A stack that never disagrees with itself is not confirming anything.
How to test your own stack for redundancy
You do not need to accept the taxonomy on authority. Test it; the following takes an afternoon.
- List every indicator on your chart and write its primary input next to it — the actual series the formula consumes, not the name of the concept it claims to measure.
- Group by input. Every group with more than one member is a candidate redundancy.
- Export the series. For each pair inside a group, export both indicator values over at least 500 bars of your instrument and timeframe.
- Compute the pairwise correlation of the two series. Also compute it on the signals rather than the values: mark each bar 1 if the indicator's condition is true and 0 if not, and correlate those.
- Apply a threshold. Use the table below. Above the high band, delete one of the pair — which one barely matters.
- Count signal overlap directly. Over the same window, count the bars where condition A is true, where B is true, and where both are true. If the both-true count is close to the smaller of the two, the second condition is adding no constraint.
- Re-run with the pruned stack and compare signal counts. A big drop means the redundant conditions were doing nothing; a small drop means they were occasionally binding and you should check which cases.
- Fill the empty class slots with genuinely different inputs rather than with replacements from the class you just pruned.
| Absolute correlation | Reading | Action |
|---|---|---|
| Above 0.90 | Effectively the same measurement | Delete one |
| 0.70 to 0.90 | Heavily overlapping | Delete one unless you can state what the second adds |
| 0.40 to 0.70 | Related but partly independent | Keep at most one per class slot |
| Below 0.40 | Largely independent | Safe to combine; this is what a confirming input should look like |
Those bands are working thresholds for deciding what to delete, not statistical findings — what you observe depends on your instrument, timeframe and sample length, which is why you compute them yourself rather than take a published figure. Documentation from serious toolkits, QuantZee's included, states indicator inputs and formula parameters explicitly so this pairwise check is possible without reverse-engineering a closed calculation. An indicator whose input you cannot determine cannot be classified, and one you cannot classify cannot be safely stacked.
One caveat on method: correlation measures linear association, so two indicators can be nearly redundant in a way correlation understates — for instance if one is a monotonic but non-linear function of the other. The signal-overlap count in step 6 catches what correlation misses, which is why both steps are in the list.
Frequently asked questions
Are leading trading indicators actually predictive?
No, and the word "leading" is the source of most of the confusion. A leading indicator reaches an extreme before price does because its output is bounded while its input is not — saturation is a property of the scale, not a forecast. What it genuinely offers is early visibility that recent price behaviour is statistically unusual for its own recent history. That is worth having. It is not a prediction, and treating it as one is why oscillator signals feel so unreliable in strong trends.
How many trading indicators should I have on a chart?
Three, by the one-per-class rule: one lagging, one leading, one confirming. The number matters far less than the input count — three indicators with one shared input are worse than two indicators with two different inputs. If you want a single question to ask before adding anything, it is "what series does this consume, and do I already have something consuming that series?"
Is price action a confirming input?
No. Candle patterns, structure breaks and swing marking are all functions of the price series, which puts them in the lagging class however discretionary the reading is. Nothing is wrong with trading structure alone — but a structure read plus a moving average plus an oscillator is a three-indicator stack with one input, and the independence problem is identical to the all-oscillator case.
What if my instrument has no reliable volume data?
Then your confirming slot has to come from somewhere else. For options-bearing underlyings, implied volatility rank or percentile is an independent reported series. For index products, breadth across constituents works. For currency pairs, where no central volume figure exists, a correlated-instrument condition or a cross-rate relationship is the usual substitute. If none of these is available, run a two-class stack and know that you are doing so — a stated limitation is manageable, and a hidden one is not.
Closing
The taxonomy is not a ranking. Lagging indicators are not worse than leading ones; confirming indicators are not a higher tier. The taxonomy exists for one purpose, which is to let you count inputs instead of counting indicators. Count the inputs on your chart today. If the number is one, nothing you add from the same class will change what you know — and the one thing that will is a measurement that comes from somewhere else entirely.