Martingale vs anti-martingale in trading
Why I do not use martingale sizing, why percent-at-stop sizing behaves a little like anti-martingale, and why I would choose anti-martingale if forced.
- Martingale vs anti-martingale in trading
- Trailing stop losses in trading
- When to take profit
- Fixed vs percentage vs ATR stop losses
- What leverage is for in trading
- Should my quant bot always be running?
- Trading Basics
- Trading Research
- Trading Psychology
- Trading Infrastructure
- Trading Signals
- Commodity Research
- AI Trade Review
- Signal Readiness
- Risk Capacity
- Forecasting
- Trade Flow
- Trade Quality
- Trading Roles
- Platform Architecture
- Product Thinking
- Product Notes
What martingale means
A martingale-style system increases position size after losses.
The classic version is simple and dangerous: lose one trade, then double the next trade, and keep doing that until a win arrives and recovers the previous losses.
It sounds clean in theory because one eventual win can appear to repair the whole sequence. In markets, the dangerous part is the word eventual.
My opinion
My strong opinion is that martingale is not a good idea for trading.
The reason is boring but brutal: there is usually not enough money to keep doubling forever.
Accounts have limits. Brokers have margin requirements. Prop firms have drawdown rules. Markets can trend against you longer than your spreadsheet can stay cute.
Loss streaks are real
A strategy can be profitable and still have loss streaks.
Martingale turns those streaks into a sizing crisis. Each loss demands a larger next trade, so the system becomes most aggressive exactly when the account is already under pressure.
That is the part I dislike most. A bad sequence should make the system more careful, not force it to bet heavier just to emotionally recover.
Anti-martingale
Anti-martingale sizing does the opposite.
Instead of increasing after losses, the system increases size when it is winning or when account equity grows, and reduces size when the account is shrinking.
That makes more intuitive sense to me because the system scales with strength and contracts during stress.
My sizing
I do not intentionally use martingale or anti-martingale as a named strategy.
But I do use percentage-at-stop sizing, and that creates an indirect anti-martingale effect.
If the account grows, the same percentage risk is a larger currency amount. If the account shrinks, the same percentage risk becomes smaller. The sizing follows the account instead of trying to force the account back to a previous high.
Risk range
I usually risk somewhere between 0.2% and 1% per trade.
That number is based on the distance to the stop loss, the account size, and the risk percentage. The stop distance matters because a wider stop needs a smaller position to keep the same account risk.
For me, this is much cleaner than doubling after a loss. The question becomes: how much am I willing to lose if this one trade is wrong?
If forced to choose
If I had to choose between martingale and anti-martingale, I would choose anti-martingale.
It still needs rules and testing, but at least it respects the account state. It scales up after strength and scales down after weakness.
Martingale does the opposite: it turns weakness into a reason to increase exposure, and that is exactly the kind of logic that can destroy an account during the wrong streak.
Where Quantarya fits
Quantarya should make sizing logic visible in the trade record.
Each trade should show the account equity, risk percentage, stop-loss distance, position size, leverage, final outcome, drawdown, drawup, and whether the sizing changed after previous wins or losses.
That would make it obvious if a strategy is quietly drifting into martingale behavior instead of following a stable risk plan.
Practical takeaway
I do not use martingale, and I would be very careful with anything that increases size after losses.
My own approach is closer to indirect anti-martingale because risk is based on a percentage of the account at the stop loss, usually between 0.2% and 1% per trade.
This is not financial advice. It is a process opinion: never let a recovery fantasy become a position-sizing rule.
Christian Weiss
Christian has worked in software engineering, data platforms, and cloud infrastructure for over a decade. He currently works on large-scale AWS-based data platforms and writes about software engineering, trading systems, automation, and the lessons learned while building Quantarya. He is also a hobby quant and the founder of Quantarya.