Why charts don't predict price

Every day tens of thousands of people open charts and draw rectangles on them. Demand zones, order blocks, imbalances, waves, divergences. Behind it is an understandable hope: if you learn to see the shapes that few people see, you can guess where the price will go.

Part one. What the science says

Technical analysis is almost three centuries old. Japanese rice traders were drawing candlestick charts back in the eighteenth century; the technique reached the West only in the twentieth. Over that time a huge body of tests has piled up — and, unfortunately for the industry, it is not encouraging.

The most complete review of this literature was compiled by Cheol-Ho Park and Scott Irwin: "The Profitability of Technical Analysis: A Review", a report of the AgMAS project at the University of Illinois. They gathered dozens of empirical tests of technical trading systems across very different markets — stocks, currencies, futures — and summed them up.

The summary is this. Profitability in speculative markets held up "at least until the early 1990s", and after that it faded away. In other words, all the findings belong to the era when orders were placed by voice and moving averages were computed on paper. And almost all the positive results, in the review's own assessment, suffer from the same illnesses: data snooping, choosing the rules after the fact, and underestimating transaction costs.

The first of these deserves an explanation, because it is the main trap of the whole industry. If you try a thousand rules on the same history, the best of them will look brilliant — simply by the laws of probability, even if all thousand are empty. That is how most "working" strategies are born: not because someone found a regularity, but because someone kept searching long enough.

Candlestick patterns did no better. Piyapas Tharavanij and co-authors tested bullish and bearish reversal candlestick patterns on the Stock Exchange of Thailand — "Profitability of Candlestick Charting Patterns in the Stock Exchange of Thailand", in the journal SAGE Open. The conclusion comes in two parts. On their own: for most patterns the average return is statistically indistinguishable from zero, and for the few where a difference was found, a return of 0.07–0.84% sits next to a standard deviation of four to seven percent — that is, the signal drowned in the noise around it. And the second part, more painful: filtering by the stochastic oscillator, RSI or the money flow index "in general does not increase either profitability or prediction accuracy". The popular recipe "pattern plus indicator confirmation" does not pass the test.

But the most interesting study appeared recently — and it is the closest to what a retail trader does today.

In 2026 Mathias Mesfin published "Structural Limits of OHLCV-Based Intraday Momentum Signals in MNQ Futures: A Systematic Falsification Study". He took the Nasdaq 100 future, 947 trading days of five-minute data for 2021–2025, and tested fourteen families of intraday momentum signals. Each had to pass five conditions at once: a t-statistic of at least 2.0 on out-of-sample data, at least thirty trades in every test window, a positive return after realistic costs, the same direction in 2023, 2024 and 2025 separately, and a permutation test with a significance level below 0.001.

None passed.¹

But what matters more than the fact itself is where exactly everything broke. Eleven of the fourteen families died in the same place: their gross return was between 0.07 and 1.50 points, while getting in and out costs about two points. So the signal exists. It even points in the right direction. It is simply smaller than what it costs to use it. This is the most common and least noticeable way to lose money on charts: not being wrong about the direction, but being right by an amount smaller than the commission.

Three more families cleared the costs but fell on robustness: the long breakout of the morning range gave a t-statistic of 0.88, the reversal based on a volatility-and-volume classifier gave 1.26, and the short gap continuation earned a solid 16.53 points gross with a t-statistic of 1.46 and fell apart on the requirement to work in each of the three years separately. A familiar story: a strategy that "worked" usually worked in one particular year.

And what the author did at the end deserves separate respect. He ran two signals through the same grinder that he knew in advance had an edge — and they passed: t-statistics of 3.11 and 4.30. This is called a positive control, and it answers the main objection to any negative study: "maybe your method just can't see anything". It can. It did not see precisely what was being tested.

Part two. What works

If it all ended here, the article would be pointless. But the same research has a second half too — about what does pass the test.

The things that work are few, and they share a property we will come back to.

Trend following

The longest confirmation of any that exists. A group of researchers from Capital Fund Management — Lempérière, Deremble, Seager, Potters and Bouchaud — collected data spanning two centuries: "Two Centuries of Trend Following". Spot prices of commodities and indices since 1800, government bond yields since 1918, futures since 1960. The conclusion: the ten-year return of trend following "has never been negative in two centuries".

But this coin has a heavy reverse side, and the authors do not hide it. The typical length of a drawdown, they write, equals one divided by the square of the Sharpe ratio — in years. For a strategy with a Sharpe of 0.7 that means two-year drawdowns as the norm. For 0.5 — four years. Think about it: a correct strategy that has worked for two hundred years can give you nothing but losses and explanations for four years in a row. No amount of discipline replaces understanding this number in advance.

There is a third side too, the most unpleasant one. Geetesh Bhardwaj, Gary Gorton and Geert Rouwenhorst studied how much of this premium reaches the investor — a paper with a telling title, "Fooling Some of the People All of the Time: The Inefficient Performance and Persistence of Commodity Trading Advisors". Managers of futures funds earned 5.4% of gross excess return over 1994–2007, while the investor received 85 basis points — eight tenths of a percent over Treasury bills, statistically indistinguishable from zero. The premium was there. It was taken away in fees.

The volatility premium

The most persistent of those documented. Peter Carr and Liuren Wu in "Variance Risk Premiums" (Review of Financial Studies) showed that the variance risk premium on the S&P 500 index is persistently negative: investors accept a negative expected return just to insure themselves against rising volatility. The other side of the same trade — selling volatility — has historically delivered very high returns per unit of risk.

And the main thing: it is clear why this does not disappear under the pressure of arbitrageurs. It is not a pricing error that can be corrected. It is payment for risk. The option seller gets money for taking on the risk of a crash that hedgers buy their way out of. The risk is real — and so is the payment. Anyone who was selling volatility in February 2018 or in March 2020 found out exactly what they had been paid for all the years before.

Carry

In crypto this is the funding rate on perpetual contracts. The mechanism here is extremely transparent, and it is the only one on this list that is checked not by a backtest but by a receipt.

A perpetual contract has no expiry, so the exchange ties it to the spot price with money: every eight hours one side pays the other. If the contract is above the index, those who are long pay; if it is below, those who are short pay. This is not an estimate and not a model, it is a published transfer of funds with a history for every instrument. Whoever bought the asset on spot and sold the perpetual contract receives these payments and does not depend on the direction of the price. How the rate is built and the logic of its calculation are discussed, for example, in "Designing Funding Rates for Perpetual Futures in Cryptocurrency Markets".

But the premium here depends on the regime more than is usually thought. When the market is overheated with longs, the funding rate shoots up; when capital comes into this trade, it compresses toward the yield of ordinary dollar instruments — and then the whole point of the construction disappears: the same money could have been held in dollars with no exchange risk, no leverage and no risk of the short leg being liquidated. Carry is not a money-printing machine but payment for being willing to hold a position that, at the critical moment, there may be no one to hand over to.

Part three. Why one thing works and another doesn't

The difference between the two lists is not accidental.

Everything that works is payment for taking on risk, not a reward for recognizing shapes.

Selling volatility, you take on the risk of a crash that others pay to avoid. Following the trend, you accept the risk of long reversals and four-year drawdowns that most people cannot psychologically endure. In carry — the risk that the rate flips at the worst possible moment and the short leg gets wiped out by a margin call.

Nobody pays you for having seen a rectangle on a chart. They pay you for holding a risk that others find uncomfortable to hold.

The second point follows from this.

Why discoveries disappear

Even real regularities fade once people find out about them.

The classic test of this was done by David McLean and Jeffrey Pontiff — "Does Academic Research Destroy Stock Return Predictability?", Journal of Finance. They took 97 characteristics that had been shown in leading academic journals to predict stock returns and looked at what happened to them afterwards. Out of sample, returns turned out to be 26% lower. After publication — 58% lower. The difference between these numbers, 32%, is the price of publication itself: that much was taken by the people who read the paper. An alpha of five percent in sample turns into roughly three point four after people find out about it.

A later paper by Antoine Falck, Adam Rej and David Thesmar — "Why and How Systematic Strategies Decay" — refines the picture and adds a worrying detail. The year of publication by itself explains 30% of the variation in decay, and with each passing year the decay of newly published factors grows by five percentage points. Capital rushes to a discovered inefficiency faster than before. What was found in 2005 lived for years. What is found today dies within months.

Now apply this to the methods that have spread across YouTube in millions of views. If there was anything in them, it went first — and went long before you heard about them.

Part four. The numbers they don't mention in courses

Since we have taken on uncomfortable facts, here are these too. They are not about methods but about people.

Fernando Chague, Rodrigo De-Losso and Bruno Giovannetti obtained complete regulatory data on retail accounts in Brazilian futures — "Day Trading for a Living?". Of the 19,646 people who started trading, 1,551 made it to three hundred or more trading sessions — seven point nine percent. Of these persistent ones, 97% lost money. 1.1% earned more than the Brazilian minimum wage. More than a bank teller's salary — half a percent. Eight people out of one and a half thousand.

And the key sentence from the same paper, which deserves separate attention: "there is no evidence of learning by day traders over the periods in which they trade". People do not get better with practice. Three hundred days at the terminal do not turn into a skill — they turn into three hundred days of commissions.

Taiwan, a different era and a different market, the same result. Brad Barber, Yi-Tsung Lee, Yu-Jane Liu and Terrance Odean went through fifteen years of complete data for the entire market, 1992–2006: "The Cross-Section of Speculator Skill: Evidence from Day Trading", Journal of Financial Markets. Their wording: "less than one percent of the day trader population is able to predictably and reliably earn positive abnormal returns net of fees". In absolute numbers — about four thousand people out of roughly four hundred and fifty thousand trading in a year.

Note: these four thousand exist. Skill in speculation is not a myth; it is measurable and repeatable. It is just that its distribution looks not like "learn and you can", but like the distribution of skill in professional sports. The only difference is that in the second case nobody sells courses promising you a spot in the NBA starting lineup in three months.

What to do with all this

We do not think chart markup is useless. It is useful — but not in the role in which it is usually sold.

An unfilled gap on a chart is a fact. A level from which the price reversed twice is a fact. An extreme that the price went through is a fact. These are landmarks: where to put a stop, where to set a target, where the price has already been. A map of the terrain.

What a map does not do is tell you where the car will go.

The difference between these two things is the whole difference between a tool and a promise. A tool saves time and structures the view. A promise sells better, but is not kept.

We chose the first. That is why our markup module has no probability percentages, no words "signal" and "forecast", and it has a state "no clean zones here" — instead of drawing something at any cost.

And we publish the numbers from this article because we believe the user has the right to know what exactly they are holding in their hands.

Sources

1. Park C.-H., Irwin S. The Profitability of Technical Analysis: A Review. AgMAS Project Research Report 2004-04, University of Illinois. ageconsearch.umn.edu/record/37487 — a consolidated review of empirical tests of technical trading systems.
2. Tharavanij P., Siraprapasiri V., Rajchamaha K. Profitability of Candlestick Charting Patterns in the Stock Exchange of Thailand. SAGE Open, 2017. doi:10.1177/2158244017736799 — candlestick reversal patterns with and without indicator filters.
3. Mesfin M. Structural Limits of OHLCV-Based Intraday Momentum Signals in MNQ Futures: A Systematic Falsification Study. arXiv:2605.04004, 2026. arxiv.org/abs/2605.04004 — fourteen signal families, 947 days of five-minute data, none passed the five criteria.
4. Lempérière Y., Deremble C., Seager P., Potters M., Bouchaud J.-P. Two Centuries of Trend Following. arXiv:1404.3274, 2014. arxiv.org/abs/1404.3274 — trend following on data since 1800; drawdown length as 1/S².
5. Bhardwaj G., Gorton G., Rouwenhorst K. G. Fooling Some of the People All of the Time: The Inefficient Performance and Persistence of Commodity Trading Advisors. NBER Working Paper 14424. nber.org/papers/w14424 — 5.4% before fees versus 85 basis points after, 1994–2007.
6. Carr P., Wu L. Variance Risk Premiums. Review of Financial Studies, 22(3), 2009. ideas.repec.org — a persistently negative variance risk premium on the S&P 500.
7. Designing Funding Rates for Perpetual Futures in Cryptocurrency Markets. arXiv:2506.08573. arxiv.org/abs/2506.08573 — how the funding rate of perpetual contracts is built.
8. McLean R. D., Pontiff J. Does Academic Research Destroy Stock Return Predictability? Journal of Finance. PDF — 97 characteristics: −26% out of sample, −58% after publication.
9. Falck A., Rej A., Thesmar D. Why and How Systematic Strategies Decay. Capital Fund Management, 2021. cfm.com — the year of publication explains 30% of the variation in decay, plus 5 percentage points for each year.
10. Chague F., De-Losso R., Giovannetti B. Day Trading for a Living? SSRN 3423101 — Brazilian futures accounts: 97% losing among those who lasted 300+ sessions, 1.1% above the minimum wage, no learning.
11. Barber B., Lee Y.-T., Liu Y.-J., Odean T. The Cross-Section of Speculator Skill: Evidence from Day Trading. Journal of Financial Markets, 18, 2014. escholarship.org — Taiwan 1992–2006, less than 1% of day traders predictably profitable.