Win Rate vs Payoff: Two Shapes of a Profitable Tradereric

This post opens the second track of the ASX order book series: the business side of trading. The first track ended with measuring order book imbalance in code. Now we look at the numbers that decide whether any of that craft actually pays.

Every serious journal I read posted the same dashboard metrics: number of trades, win rate, average win, average loss, the ratio between them, commissions, net profit. And the most interesting thing in those dashboards wasn't any single number. It was the shape the numbers made together, and how differently the traders were shaped.

The only formula that matters

Expectancy is the average amount you make per trade:

Copy expectancy = (win rate × average win) - (loss rate × average loss)

Two related numbers come up constantly:

Copy payoff ratio  = average win / average loss
profit factor = gross profit / gross loss = (win rate × average win) / (loss rate × average loss)

A profit factor above 1 means you make money before costs. And the breakeven win rate for any payoff is:

Copy breakeven win rate = 1 / (1 + payoff ratio)

That table explains most of the confusion beginners have. A 70% win rate sounds fantastic. With a payoff of 0.4 it loses money. A 35% win rate sounds terrible. With a payoff of 3 it's an excellent business.

Every trader whose journal I read learned some version of that lesson, usually by staring at a dashboard with a lovely win rate and a disappointing bottom line.

The two shapes

Across the journals, profitable traders clustered into two very different shapes. Here they are with synthetic numbers.

Both are profitable. Per trade, the sniper makes more. Per month, the harvester makes far more, because volume multiplies a small edge. But that volume comes with a catch that we'll see in the next post: every one of those 150 trades pays brokerage and spread.

They feel completely different

The numbers above hide the experience of trading them.

The harvester is green most days. Losing streaks are short. The psychological challenge is boredom, precision and the occasional big loss that wipes out a week of small wins. Their biggest danger is one oversized, averaged-down loser.

The sniper is wrong more often than right. Losing streaks of six, eight, ten trades are normal. The psychological challenge is staying patient and sized correctly through long dry spells, and actually holding winners long enough to collect the big payoff. Their biggest danger is cutting winners early out of relief, which destroys the payoff that makes the whole thing work.

Here's a rough sense of how long losing streaks get, assuming independent trades.

If you're built like a sniper and expect to feel like a harvester, you'll abandon a perfectly good strategy in the middle of a normal losing streak.

You can simulate it

The fastest way to build intuition is to simulate your own numbers.

Copy import numpy as np

def simulate(win_rate, avg_win, avg_loss, trades=250, runs=10_000, seed=1):
    rng = np.random.default_rng(seed)
    wins = rng.random((runs, trades)) < win_rate
    pnl = np.where(wins, avg_win, -avg_loss)
    equity = pnl.cumsum(axis=1)
    drawdown = (np.maximum.accumulate(equity, axis=1) - equity).max(axis=1)
    return {
        "median_final_R": float(np.median(equity[:, -1])),
        "p5_final_R": float(np.percentile(equity[:, -1], 5)),
        "median_max_drawdown_R": float(np.median(drawdown)),
    }

print(simulate(0.75, 1.1, 1.0))
print(simulate(0.42, 3.2, 1.0))

Run that with your real win rate and payoff and you'll get a realistic picture of the drawdowns you should expect. Most people are surprised by how deep they are.

Improving payoff without wrecking win rate

A common arc for harvesters goes like this: a very high win rate for a long time, but a payoff stuck around 1, so the edge per trade is tiny and costs eat a big share of it. The usual diagnosis, once they look honestly at the data, is that they aren't taking enough risk on their best setups.

What changed their numbers wasn't a new setup. It was management:

The win rate barely moved. The average win grew meaningfully. And because expectancy is win rate times average win, the whole business changed shape.

The sniper's equivalent problem runs the other way: improve win rate by being more selective, without cutting winners. Several journals found that a hard cap on the number of trades per day did exactly that. (That's a whole post later in the series.)

What to track

If you keep a journal, track these at a minimum, overall and per setup:

That last metric matters more than it looks. In nearly every journal, the months that went badly weren't months where the averages were bad. They were months with one loss many times larger than the average.