Why technical indicators cannot prove 90% odds

Stacking indicators can make a trade look unusually convincing. RSI turns higher, MACD crosses, the price moves above a regression line and a moving average confirms the direction. When several signals point the same way, it is tempting to say the probability of success has become very high.

The problem is that three indicators are not automatically three independent pieces of evidence. They may be different calculations built from the same price history. Agreement can still be useful, but it does not justify attaching a number such as “90 per cent” without a properly defined and tested process.

More indicators do not automatically mean more independent evidence.

Three signals may still be one source of information

RSI measures the balance of recent gains and losses. MACD compares moving averages of the same price series. A regression line estimates the direction or relationship within that price series. Their formulas differ, but their inputs overlap heavily.

This matters because probabilities do not compound simply because several indicators agree. If three independent tests each added genuinely new information, combining them might improve confidence. If all three react to the same trend, however, the second and third signals may mostly repeat what the first one already detected.

Think of three weather apps that all use the same forecasting model. Seeing the same rain forecast three times does not give you three independent forecasts. Technical indicators can have the same problem: the chart looks more crowded while the underlying evidence has barely changed.

Inputs that come from somewhere else may add more information. Trading volume, company earnings, liquidity conditions and market structure are not automatically independent either, but they at least require a different question from three transformations of closing prices. The test is not how many lines appear on the chart. It is how much genuinely new information each line contributes.

A probability needs a denominator

Before accepting a claim that a setup works 90 per cent of the time, ask what was actually measured.

Was it tested on every signal or only selected examples? On ASX shares, broad-market ETFs, US equities or one unusually strong chart? Was the entry taken at the closing price, the next session’s open or a later price that would not have been available in real time? How long was each position held? What counted as success? How many trades were included?

Without those details, “90 per cent” is not a probability that an investor can evaluate. It is a number without a denominator.

Win rate is also only one part of a strategy. A method that wins often can still lose money if its occasional losses are much larger than its typical gains. A more useful starting point is expectancy: the average gain from winning trades, minus the average loss from losing trades, adjusted for how often each occurs and for trading costs.

Why backtests often look better than live trading

Backtesting is valuable, but it is easy to produce a result that is cleaner than the experience an investor will have in the market. The most common problems are:

• Data snooping. If enough indicators, settings and entry rules are tested, something will eventually look impressive by chance.
• Overfitting. A rule can be tuned so precisely to past data that it captures historical noise rather than a repeatable relationship.
• Look-ahead bias. A test may accidentally use information that would not have been available when the trade decision was made.
• Survivorship bias. Testing only securities that still exist today can exclude failed, delisted or acquired companies and make the historical universe look stronger.
• The execution gap. Brokerage, bid–ask spreads, slippage, liquidity and tax treatment can change a paper result materially. A rule tested on a highly liquid ETF may not transfer unchanged to a thinly traded small-cap share.

The data-snooping problem is not theoretical. Sullivan, Timmermann and White’s study examined how apparent technical-rule performance changes when researchers account for the full universe of rules that was searched. CFA Institute’s Backtesting & Simulation reading likewise treats survivorship bias, look-ahead bias, structural breaks, fat tails and realistic implementation as central parts of a credible test.

What would weaken this view

The counter-signal is a technical rule that survives a process designed to expose false confidence. The view would weaken if the rule and thresholds were fixed before testing, the full set of attempted rules and failed cases were disclosed, and the result remained useful on unseen data and in other markets.

The result would also need to retain a positive expectancy after realistic brokerage, spreads and slippage, while keeping drawdowns and losing streaks within the stated risk limits. Those are observable conditions that would change the view. A selected chart or one successful period is not enough.

Seven questions to ask before believing a high-probability claim

A useful claim should survive questions such as:

1. Were the signal rules, trade timing and exit rules fixed before the results were examined?
2. Are success, failure, holding period and sample size defined clearly?
3. Does the test disclose the full set of securities, periods and rule variations that were tried?
4. Were the data used to develop the rule separated from the data used to validate it?
5. Were brokerage, spreads, slippage and other implementation costs included?
6. Does the report show expectancy, drawdown and losing streaks as well as win rate?
7. Does the result remain useful across different markets, volatility environments and trend conditions?

If several of these answers are missing, the percentage should be treated as an untested hypothesis or a marketing claim, not as a dependable estimate.

What technical indicators are actually good for

None of this makes indicators useless. Their real value is more practical than predictive.

Indicators can turn a vague impression into a repeatable rule. They can help an investor define when a trend is strengthening, when momentum is fading, where a position should be reduced and how much risk to take. Used this way, an indicator supports decision discipline rather than pretending to remove uncertainty.

The distinction matters. “RSI, MACD and the regression line agree, therefore this trade has a 90 per cent chance of success” is an unsupported probability claim. “When these conditions occur, I will enter at a defined time, risk a defined amount and exit under a defined rule” is a process that can be tested.

Market regimes change what a signal means

A technical rule does not operate in a vacuum. Trend-following signals can behave differently in a persistent trend than in a choppy, range-bound market. A signal tested during calm conditions may perform differently when volatility rises, liquidity thins or correlations move sharply higher.

That is why a single backtest average can conceal more than it reveals. Investors should examine how the rule behaved across distinct environments, not only across one long period. The aim is to understand when the process has historically been useful, when it has struggled and how much loss can occur before that becomes obvious.

This is the same discipline used in a broader market-regime framework : define the environment, identify the mechanism and state what evidence would invalidate the working view.

A more credible testing standard

A sensible process does not need to be complicated, but it should be difficult to fool.

Start with a clear hypothesis. Write the entry, exit and position-sizing rules before looking at the final result. Separate the period used to develop the idea from the period used to test it. Include realistic costs. Report the distribution of outcomes, not only the average or win rate. Test other securities and different market conditions. Finally, observe the rule prospectively, on paper or at very small size, before relying on it.

The goal is not to prove that a chart pattern is certain. It is to find out whether the process has a positive expectancy, whether the evidence is robust enough to matter and whether the drawdowns are tolerable for the person using it.

When several indicators line up, begin by counting independent inputs rather than signals. Then ask whether the rules were fixed in advance, whether the result survived unseen data and whether costs and changing market regimes were included. That will not produce the certainty promised by a “90 per cent setup”, but it gives an investor something more useful: a repeatable decision process with limits that can be measured.

The publication’s standards for sourcing, uncertainty and review are set out in the research methodology .