When I was first learning to trade, I was obsessed with win rate. If I could just get my win rate above 60%, I figured everything else would take care of itself. More wins than losses, simple math. What could go wrong?

A lot, it turns out.

The number that actually determines whether you make it as a trader isn't win rate. It's something called risk of ruin, the probability that a string of losses wipes out your account before your strategy has enough trades to prove itself. And here's the part that took me a while to fully internalize: you can have a genuinely profitable system, positive expected value on every trade, and still blow up your account if you're sizing positions wrong or if your account isn't large enough to absorb a bad stretch while the edge plays out. The market has no idea you have a good strategy. It will happily hand you eight losers in a row and check back in later.

Start Here: A Coin Toss That Breaks You

Before we get into trading, let me give you the clearest version of this concept I've ever seen. Imagine a coin that's slightly rigged in your favor: 60% heads, 40% tails. Every time it lands heads, you win $1. Every time it lands tails, you lose $1. You'd take that bet all day. The math says you should make money over time.

Now here's the twist. You have $10 in your pocket and you're betting $5 per flip. Even with a coin that favors you, there's a very real chance you hit two tails in a row before the edge asserts itself, and you're out. Broke. Done. The coin was always in your favor. You just ran out of money before you could prove it.

That's risk of ruin. It's not about whether your edge is real. It's about whether you have enough runway to let the edge play out. Reduce the bet to $1 per flip with the same $10, and suddenly the math works comfortably in your favor over time. Same coin, same edge, very different outcome. The only thing that changed was how much you put on each flip.

Casinos understand this intuitively, which is why the house limits are what they are. A whale betting $500,000 on a single roulette spin is a genuine risk to the house even though the house has the edge. Spread it across a thousand $500 bets and the house sleeps fine. The math is the same. The sizing changes everything.

Now Bring That Into Trading

Say you have a system with a 55% win rate and a 1:1 reward-to-risk ratio. That's real edge. Over enough trades, you make money. Now say you're risking 20% of your account on each trade because you feel confident in the setup.

With a 45% loss rate, losing 10 trades in a row sounds like a rare disaster. Over a 200-trade career, it's close to a certainty that you'll hit it at least once. Ten consecutive 20% losses leaves you with 10.7% of what you started with. The strategy still works in aggregate. You're just not around for the aggregate anymore.

There's a formula for this. A simplified version looks like:

Plug in 55% win rate (Edge = 0.10), $10,000 account, $500 per trade. Risk of ruin: about 13.5%. Now change the trade size to $2,000. Same strategy, same edge, same win rate, and risk of ruin jumps above 60%. The strategy didn't change. The sizing did. That's the lesson I keep coming back to: your strategy determines your edge, and your sizing determines whether you're still trading.

What We're Seeing in Our Own Live Accounts Right Now

I'm not going to just talk about this in the abstract. We're currently running Maya across three live accounts of different sizes, and the past two months of market correction have given us a real-world stress test of everything I'm describing here.

The smaller two accounts are down. The large account is up on the year despite going through the same correction. Same algorithm, same trades, same market. The difference is entirely about what happened when the market started recovering.

Maya's algo trades a roughly $30,000 account internally. At peak deployment it only has about $9,000-$10,000 actually committed in open trades, and on average closer to $4,000. That means at any given point, the majority of the capital is sitting in reserve, not because it's being conservative, but because that's how the position sizing works across a ~50 stock watchlist with 40 DTE spreads that open and close on their own schedule. When the market corrected and then started recovering, the larger account had fresh capital available to deploy into new trades as conditions improved. It could put capital to work during the recovery, which is exactly when the best setups appear. The smaller accounts were already near full utilization relative to their size, and had less room to maneuver.

This is a live demonstration of what risk of ruin looks like in practice. The $2,500 account hasn't blown up. But it's exposed to the possibility of a bad stretch early in its history before the edge has had 200+ trades to assert itself. The $85,000 account absorbed an $8,000 drawdown, kept trading, and came out ahead. Same strategy. Different survival characteristics.

One More Layer: Debit Spreads Take Time to Show Their Hand

There's another dimension to this that's worth understanding if you trade options the way Maya does. We use bull call spreads (debit spreads), meaning we pay premium upfront and wait for the trade to mature in our favor. Maya typically opens positions around 40 DTE (days to expiration). The real profit on these trades usually doesn't materialize until about 30 days in, when the underlying has had time to move and time decay has started working on the spread's value.

Here's what that looks like in practice. Below are the 17 open Maya positions right now as I'm writing this. Notice the pattern: the trades opened in mid-April (around 25-30 days ago) are the ones showing the biggest gains. NVDA entered April 13 is up 83%. NVDA entered April 16 is up 70%. PANW entered April 24 is up 79%. The trades opened in early May are still early in the cycle, mostly flat or slightly down. The portfolio is net positive by over $800 across all open positions, but that gain is invisible to the casual observer who just looks at account balance on any given day.

A note on scale: the table above shows 1-contract positions, which is how Maya's own ~$30k reference account runs. The real $85k live account we're testing runs more contracts per trade. Pulling directly from that live TradeStation account right now: it's sitting at $87,500 in equity with +$2,122.50 in net unrealized gains across all open positions. CRWD alone is carrying +$1,150 of that, NVDA is adding +$738.50 across two spreads, and QQQ, IVV, and SPY are all in the green. Same strategy, bigger scale, and those profits are sitting in open positions that haven't closed yet.

The trades that are 25-30 days old are the ones doing the heavy lifting. The ones opened last week are still warming up. This is why looking at an account balance on any single day and drawing conclusions from it is like reading a book through the middle chapter and deciding whether you like the ending. The story isn't done yet.

This is another reason account size matters so much in this style of trading. A smaller account that looks down 15% right now may be holding several trades that are 20 days into their 40-day cycle, unrealized gains that haven't shown up yet. A larger account has more of those in-flight positions at any given time, so the smoothing effect is greater and the paper drawdowns look less dramatic relative to the eventual outcome.

Why the Nasdaq Bounced 28% and Your Spread Didn't

If you've been watching the markets over the past few weeks, you've seen something remarkable. The Nasdaq recovered roughly 28% from its lows in about 28 days. And if you're running a defined-risk strategy like Maya's and looking at your account, you might be wondering why the numbers don't reflect that.

The honest answer is that this strategy wasn't built to capture violent snapback rallies. It was built to survive them and profit from sustained trends. Those are very different things, and understanding why matters a lot for staying patient through exactly this kind of moment.

If you bought naked calls at the bottom of that correction, you might have seen 10x returns in a week. You'd also have been completely wiped out if the bounce hadn't come, or if it came two weeks later after your options expired worthless. That's the trade-off: unlimited upside, unlimited risk, and zero margin for being wrong on timing. It's genuinely exciting when it works. It's account-ending when it doesn't.

Where this strategy really earns its keep is on a sustained, slow, grinding trend upward over 2-3 months. When a stock moves steadily from $190 to $230 over a quarter, opening new 40-day spreads every few weeks and letting them mature creates a compounding rhythm that adds up quietly. It's not exciting to watch. It doesn't make for dramatic screenshots. But it compounds in a way that a pure snapback trade doesn't, because each cycle closes profitably and the next cycle opens with the full account intact.

The correction we just came through was brutal. The recovery has been fast. What's coming next, if the market can hold these levels and grind higher, is exactly the environment where this strategy does its best work. The trades that were opened in April at the lows are already showing 64-83% returns. The ones opened in the past week are just getting started. If the next two months look like a slow, sustained recovery, the table above is going to look very different, and so will the smaller accounts.

What to Ask About Any Strategy You're Evaluating

Whether you're looking at your own system or evaluating someone else's, the questions I'd focus on are these: What's the maximum loss per trade as a percentage of the account? What does a 10-trade losing streak look like in dollar terms? Does the strategy have a structured exit when a trade goes wrong, or does it rely on the trader making a good decision under pressure? And, critically, is the account large enough that a bad stretch early on doesn't end the experiment before the sample size is meaningful?

Those answers matter more than the win rate. A 45% win rate with tight, defined risk and disciplined exits will outlast a 65% win rate with sloppy sizing every time. The math is patient. It will find the weak points in a system eventually. The question is whether you've thought about them first.

Risk of ruin is how you think about them first.