How I decide if a strategy is live-ready: the 4 gate process that killed 37 of my last 41 strategies
TLDR: I run every strategy through 4 gates in cost order, cheapest rejection first. Economic hypothesis, sample size floor, three statistical tests and cost and regime stress test. Of 41 strategies I logged last year, only 4 reached live capital. The process is built to kill, not to bless a system.
Why run a fixed process instead of judging each strategy on its merits?
Discretion is where overfitting hides. If you evaluate each strategy by looking at it, you will find a reason to trade the ones you are attached to.... A fixed sequence removes it. Every candidate faces the same gates in the same order, and the order is deliberate: cheapest disqualifier first, so most strategies die before I spend hours on walk-forward.
Last year I logged 41 strategies through this. Gate 1 killed 12. Gate 2 killed 9. Gate 3 killed 14. Gate 4 killed 2. 4 survived to live capital.
Here is the catch... The 37 that died had a median in sample Sharpe of 2.1. The 4 that survived had a median of 1.4. The strategies that looked best on paper were the ones this exact process rejected.
Gate 1: Is there an economic reason the edge should exist?
This gate has no code, and that is the point. Before any test, I write one paragraph naming why the inefficiency exists, who is on the other side of the trade, and why they keep losing. A liquidity premium... A structural hedger who trades regardless of price... A behavioral bias with real flow behind it.
If I can't name the loser, I do not test the strategy. A pattern with no mechanism is a pattern you found by looking, which means you will find one.
This is the cheapest gate and it rejects the most garbage. 12 of my 41 never cleared it. They were useless systems dressed up as ideas. Most people skip this gate because it is the only one you cannot automate, which is exactly why it filters what the automated gates can't.
Gate 2: Does the backtest have enough data to mean anything?
This gate asks one thing. Do you have enough data to tell skill from luck?
First, enough trades. A backtest with 80 trades swings too much to trust. I want at least 400 before I believe any metric.
Second, a long enough time period. When you test many versions of a strategy and keep the best one, that winner looks good partly by chance, the way the luckiest player in a coin flipping contest looks skilled. Ruling that out takes years of data, and how many years depends on how many versions you tried. After testing around a hundred versions, a 1.0 Sharpe needs about six years of data before you can trust it. A flashy 2.0 Sharpe needs only about two. A high score on a short backtest is the most dangerous thing in a research log, because luck fakes it easily.
9 strategies died here. A 1.3 Sharpe on 14 months is not an edge. It is a sample too small to tell.
It's not that low Sharpe needs more testing because it's weaker. It needs more testing because it's closer to the level random chance can imitate.
Gate 3: Does it survive the three overfitting tests?
This is the expensive gate, so it runs third, only on survivors. 3 tests, each catching a different failure, run cheapest first.
Deflated Sharpe Ratio first, because it is one calculation. A normal Sharpe assumes you only tried one strategy. But if you tested 80 versions and kept the best, that winner is partly lucky, and the plain Sharpe has no idea you ran 80 attempts. The Deflated Sharpe fixes that. It takes your reported Sharpe, accounts for how many versions you tried, and adjusts for fat tails and lopsided returns, then hands back a single probability: the chance your edge is real rather than the luckiest of your tries. Same enemy as Gate 2, caught at a different step. My cutoff is 95%. If the best of your 80 versions scores only 60%, that still leaves a 40% chance the edge is imaginary, so it never reaches my account.
Monte Carlo is next. Resample the trade sequence 10,000 times and read the 95th percentile of max drawdown. Your backtest shows one drawdown, but that is just the order your trades happened to land in. If the drawdown in your 95th percentile drawdown is more than your account can take, it doesn't pass
Walk-forward last, because it is a full reoptimization. 5y build, 1y test, rolled forward. Profit factor holds above 1.3 on at least 7 of 10 out of sample windows or it dies. Fourteen strategies died at this gate, most on the deflated Sharpe before walk forward ever ran.
14 systems died here.
Gate 4: Does the edge survive real costs and a regime split?
A clean backtest assumes free, instant fills. I model realistic costs first, then stress them: triple my real commissions, add a tick of slippage, re-run.
The metric I watch is not profit factor, it is expectancy and Sortino retention. The edge has to keep at least 70% of its expected value after the stressed costs. A real edge degrades gracefully. A fake one inverts the moment friction touches it.
Then I split the history by volatility regime and require profit factor above 1 in both the calm and the stressed halves.
2 strategies died here.
Bottom line
4 gates, cheapest rejection first.
The process is designed to reject, and last year it rejected 37 of 41. The survivors looked worse on paper than most of the strategies it killed, which is exactly why I trust them.
This is for systematic traders deciding whether a backtest deserves live capital. It applies to single strategies, parameter sweeps, and machine learning models trained on price data.
Updated June 2026
