Do simple trading strategies still work?

By John, builder of Wise Apple · Updated August 27, 2026

You tested a 20/50 moving-average cross on a chart, it looked fine, and then someone in the thread said every indicator has a 50% win rate and you should forget all of them. Both of you are partly right, and neither of you has run the test that settles it.

Every few weeks r/algotrading relitigates the same argument. One side: simple rules — EMA crosses, RSI pullbacks, Bollinger reversion, breakouts — are all anyone needs, and complexity is where people go to overfit. The other side: indicators are transforms of price, there's no information in them, and anything built on one is a coin flip with extra steps. The threads get hundreds of comments and end where they started.

This page gives the honest answer, which is more useful than either side's: some simple rules genuinely worked, most of that edge has been competed away, and the simple strategies that survive are simple in a different place than the entry. Then it gives you the procedure to find out which of those applies to the rule on your screen.

What the research actually found

This is one of the few questions in trading with a real literature. Brock, Lakonishok and LeBaron's 1992 paper "Simple Technical Trading Rules and the Stochastic Properties of Stock Returns" tested moving-average and trading-range-breakout rules on the Dow Jones index from 1897 to 1986 and found that buy signals were followed by higher returns than sell signals, by margins that were hard to explain with the usual statistical models of prices. It was a careful paper and it's still cited as evidence that the rules did something.

Then Sullivan, Timmermann and White (1999) asked the question Brock and his co-authors couldn't: how many rules had the technical-analysis tradition tried before settling on the ones that got tested? They applied White's "Reality Check" (2000) — a bootstrap that adjusts for the number of rules searched — to a universe of thousands of rules. The best rule still looked good over the original period after adjustment. On the ten years of data after the original study, its out-of-sample performance was much weaker. Aronson's Evidence-Based Technical Analysis (2007) ran a similar exercise on thousands of rules on the S&P 500 and found none that survived the data-snooping adjustment.

That is the whole answer to "did simple rules work?" Yes, some did. The interesting question is why, and what that tells you about what still might.

Why a single-indicator entry is close to a coin flip

An indicator is a function of past prices. A moving average is a weighted sum of them; RSI is a ratio of recent up-moves to down-moves; Bollinger Bands are a rolling mean and standard deviation. None of them contains information that wasn't already in the price series. That doesn't make them useless — a good transform can make a pattern easier to act on — but it puts a ceiling on what any one of them can know.

Now add the market's side of it. If a 14-period RSI dropping below 30 on a liquid stock reliably predicted a bounce, the people with the fastest systems would buy a few seconds before you, and the price would already reflect it by the time your bar closes. Widely known, cheap-to-compute signals on liquid instruments are the first edges to be arbitraged, because they're the easiest to find. The trading-edge guide puts it as a rule: if you can't name who is paying you and why, you have a pattern, not an edge. A raw RSI level has no one paying you.

So the r/algotrading claim that "all indicators have a 50% win rate" is roughly right as a statement about entries taken alone on liquid instruments at a random exit — and it misses where the strategies that survive actually keep their edge.

Where the survivors keep their edge

In our experience, when a simple strategy holds up out of sample, the indicator is rarely the reason. It's one of these five:

The exit

Two strategies with the same entry and different exits are different strategies. A trend-following MA cross with a wide trailing stop is a bet on rare large moves; the same cross with a fixed 1% target is a bet on nothing in particular. Most of the arithmetic that decides whether a rule has positive expectancy — the ratio of average win to average loss the win-rate guide walks through — is set by the exit, not the entry. The exits guide covers how to test that separately.

The regime filter

Mean reversion works in ranges and dies in trends; breakouts do the opposite. A simple rule with a simple condition on top — only fade when the longer-term trend is flat, only buy breakouts when volatility has been compressed — is still simple, and it often keeps the part of the edge the raw rule loses. It's also where overfitting starts, because a filter is a parameter.

The holding period

Institutions can't hold a position for six weeks on a signal that fires twice a year; the mandate, the risk limits and the reporting cycle won't allow it. A retail account can. Momentum at the three-to-twelve-month horizon — Jegadeesh and Titman (1993) — is the best-documented simple effect in the literature, and it persists partly because the horizon is awkward for the people best placed to arbitrage it.

The instrument

The same rule on a liquid index ETF and on a thinly traded small-cap or an altcoin are different tests. Smaller, less covered instruments keep simple patterns longer because fewer people are looking. They also cost more to trade, which is why the next item matters.

The costs

A simple rule that trades often is a simple rule that pays the spread often. Many rules that look positive before costs are negative after them, and the ones that survive tend to be the ones that trade rarely. The costs guide has the multiplier test: if scaling costs by 1.5× kills the strategy, the edge was the cost model.

The rule families, honestly

Note what the last column keeps asking: not "does the indicator work" but "does this rule, with this exit, on this instrument, after costs, beat a fair benchmark out of sample." That's the only version of the question that has an answer.

"The indicator has no edge" versus "this rule has no edge"

It helps to separate two claims people run together. The first: a given indicator, used as an entry with no other structure, carries no exploitable information on liquid instruments. That's probably true and it's what the coin-flip crowd means. The second: any strategy that uses that indicator is worthless. That doesn't follow. The indicator can be a perfectly good component — a regime filter, a feature in a model, a way of expressing "the move is stretched" — inside a rule whose edge comes from somewhere else.

This is also how machine-learning models use them. A model given raw price does badly; a model given a few sensible transforms — distance from a moving average, a normalised range, a return over several horizons — does better, not because any one transform predicts anything but because together they describe the situation. The feature engineering guide covers that side.

How to test a simple rule before you post it

Roughly once a month someone backtests a strategy from a popular video and reports the results. They're usually poor, and the interesting part is that the tester often had to make a dozen decisions the video never mentioned — fill timing, costs, the exit, the instrument. Here's the procedure, so your test means something whether the result is good or bad:

1. Write the rule down completely before you look at results. Entry, exit, stop, sizing, instrument, timeframe. If the video left the exit out, you're choosing it — write that down too.
2. Fill on the next bar, never the signal bar. A rule that buys at the close of the bar that generated the signal is the most common way a simple backtest lies; the leakage guide explains why.
3. Charge realistic costs and run the multiplier test.
4. Count the trades. A rule that fires forty times in ten years hasn't been tested yet, whatever the numbers say — the sample-size guide has the error bars.
5. Sweep the parameters and look for a plateau. If 20/50 works and 19/48 doesn't, you found noise. The overfitting guide has the four tests.
6. Benchmark it. Against buy-and-hold for the same period and against the same exit with random entries. A rule that doesn't beat a random entry with its own exit is an exit strategy, not an entry strategy.
7. Hold out the last few years and look once.

So what should you actually build?

• Keep the entry simple and spend your complexity on the exit and the filter. That's where the arithmetic is decided and it's where survivors keep their edge.
• Prefer rules with a reason. "Institutions can't hold this long" is a reason. "RSI is below 30" is a description.
• Trade less. Every simple rule looks better at lower frequency once costs are charged.
• Look where fewer people look. Smaller instruments, longer horizons, awkward holding periods.
• Accept that the honest result is usually modest. Simple strategies that survive out of sample tend to have small edges and long flat stretches. The report-reading guide is about recognising a modest real edge when you see one, instead of throwing it out for a spectacular fake.

How this looks in Wise Apple

Any rule on this page can be written in WiseApple Script, a Pine-like language with 26 built-ins, and either tested on its own or fed to a model as a feature — the output of any script can be a feature. Next-bar fills, the fee, slippage and market-impact models, and Walk-Forward Window out-of-sample testing are on by default, so the seven-step procedure above is mostly the default configuration. The report shows the trade count, the HODL benchmark and the out-of-sample metrics side by side, and every trade is inspectable candle by candle. It tests one instrument at a time and places no trades; it's early software from one builder.