’m a retail futures trader. Over the past few months I ran a systematic backtest of basically every idea I’d ever picked up from YouTube, Reddit, and trading courses, on 21 months of 15-minute MNQ data plus 5-, 3- and 1-minute data for the recent months. I used AI to write and check the code, real data, out-of-sample checks, costs included — and I made it show the work every time, because I learned the hard way that an AI asked for a trade log will happily invent one if you don’t make it load the file first. Posting the conclusions because I wish someone had shown me these numbers five years ago.
1. Every bar is a coin flip. Measurably.
After a green 15-minute bar, the next bar is green 50.5% of the time. After a red one, 51.9%. After five reds in a row — the “it’s due for a bounce” setup — 56%. After seven reds, 46% (it kept falling). I tested every colour sequence up to five bars, with bar size added, with volume added, with wicks added, with Doris and hammers and engulfings and three-bar reversals and morning stars. The most lopsided cell out of hundreds was 54% in-sample and regressed to 51% out-of-sample. Morning-vs-afternoon, streaks of hours, streaks of sessions — same thing. The run-length distribution of green and red bars matches a fair coin to the third decimal.
2. The indicators don’t work either. None of them.
RSI (two lengths), MACD and its histogram, Bollinger bands, stochastic, CCI, distance from moving averages, momentum at three horizons, ATR ratios, 20-bar highs and lows — nineteen indicators, each tested on its own and then together. Pick the direction that worked in the first half of the data, apply it to the second half: eighteen of nineteen lose money. The in-sample winners were the worst out of sample. “Indicator agreement” — trade only when 60%, 70%, 90% of them point the same way — doesn’t help, because they’re all measuring the same thing (recent price) and agreeing with themselves. A logistic regression on all of them was 65% accurate in training and 42% in testing. A gradient-boosted model: 90% in training, 48% in testing. That gap is the signature of fitting noise, and it’s what every indicator strategy looks like when you’re honest about the split.
3. The slight edge that exists is real and worthless to you.
Statistically the market is not a perfect coin: with 40,000 bars you can prove that. The dependence is about one basis point per bar. A retail round trip on MNQ costs roughly 0.8 bp in commission and another 0.8 in slippage. So the edge is the same size as the fee. A firm paying a tenth of that, co-located, filling at the touch with rebates, can harvest the same one basis point thousands of times a day. That’s literally what HFT and market-making is: eating the crumbs that are mathematically too small for you to pick up. By the time a 15-minute candle closes, the inefficiency inside it has already been taken.
4. A very few things have a slightly better edge. They still don’t work for small accounts.
Out of roughly forty ideas tested over these months, a handful survived out-of-sample checks with win rates in the low 60s instead of 50. I’m not going to describe them, partly because they’re small and partly because the details don’t change the point. What they have in common is that they trade rarely — a few times a week at most — and they earn, per micro contract, about what a part-time job pays per hour if you count the screen time. Every one of them had losing streaks of six or more and at least one quarter in the red. The best one lost 2.4R over three straight weeks right after the backtest said it was the best thing I’d found.
That’s the part nobody tells you. An edge that pays out over 300 trades needs an account that can sit through the 40-trade stretch where it doesn’t. On a $5,000 account, a −$1,300 drawdown isn’t survivable psychologically even when it’s survivable mathematically — you quit at the bottom, and you’re arguably right to. These strategies are real, and they’re built for people who can treat a quarter of losses as noise. If you can’t, the math isn’t on your side even when the strategy is.
5. The ones who make money from retail trading are not retail traders.
The broker makes money on every one of your 78 five-minute bars whether you win or lose. The prop firm makes money on the evaluation fee, which is why 90% of people fail the evaluation and the firm is fine with that. The course seller makes money on the course; the strategy in it was tested on 40 days of data and reported with a p-value of 0.11 that nobody read. And the big players — the ones whose option positioning actually moves the market — are perfectly happy for a million retail accounts to generate noise, because noise is where their size disappears.
6. What I actually learned.
If you insist on trading: trade rarely, trade levels not candles or indicators, log every trade, demand out-of-sample tests, and assume any strategy with fewer than 200 trades behind it is a story — including the ones an AI hands you, unless you watched it compute them. Then ask yourself honestly whether the size of edge that’s actually available is worth the hours and the drawdowns, because the people telling you it’s bigger than that are selling something.
Not financial advice. Just 41,000 bars and a few months of my life.
