Win Rate Is the Wrong Number to Watch: Profit Factor and Expectancy Decide If You Survive

October 2026 · 9 min read · ChartWise Team

A trader posted his last 100 trades on r/Daytrading on October 19 with a conclusion worth pinning up: the win rate looked fine, the profit factor was under 1, because the average loss ran bigger than the average win. One number said he was doing well. The other said he was paying for the privilege of trading. Both were true at the same time, which is exactly the problem. Win rate tells you how often you are right. Profit factor and expectancy tell you whether being right that often pays. The second pair of numbers decides if you survive, and this post walks through the math with dollar figures.

What 100 Trades Actually Said

The thread is worth reconstructing because the sample was honest: 100 real trades, a win rate the trader was happy with, and a profit factor under 1.0. Under 1.0 means the gross losses exceeded the gross wins, so every dollar of profit was paid for with more than a dollar of losses somewhere else. The trader's own diagnosis was the size mismatch: the average loss had crept larger than the average win. Nothing about the entry logic changed. The exits did, which is where most accounts actually die.

Two other October threads rounded out the picture. On October 20, r/Forex ran a simulation study with a result that looks impossible on first read: a strategy that finished the seven-year test near breakeven, around -0.15% a year, still put the account through a 16.74% peak-to-trough drawdown, with a worst single day of -4.42%. A system that makes no money can still hurt you like a bad one. The next day, r/Daytrading produced a +0.23R winner that its poster called more instructive than many of his winning trades, because the trade's MAE showed exactly how far price traveled against the position before working. That is a review habit, and it belongs in the same toolkit as expectancy.

Run the three threads together and one theme falls out: traders who keep track of the right numbers converge on the same three fixes, and none of the three is "win more often."

The Only Two Equations That Matter

Expectancy per trade, in dollars:

Expectancy = (win% × average win) − (loss% × average loss)

Profit factor, for the whole sample:

Profit factor = gross profit ÷ gross loss

Win rate appears inside the first equation and nowhere else. That is its entire job. A 30% win rate with a 3-to-1 reward-to-risk ratio has positive expectancy: 0.30 × 3R minus 0.70 × 1R equals +0.2R per trade, which compounds into something real over a few hundred trades. A 75% win rate at 1:1 pays 0.5R per trade before costs and can still be a fine system. The number on its own tells you nothing until you know what the wins and losses weighed.

Here is the cleanest demonstration I know, and it takes twenty trades. Trade both of these at $10,000, one risk unit at a time. System A wins $150 and loses $100. System B wins $100 and loses $150. Both win exactly half their trades, and to keep it brutal, both run the same sequence: two wins, then two losses, five times over.

Twenty trades later, A has $10,500 and B has $9,500. Same win rate, same sequence, a $1,000 gap, and the gap is one thousand dollars per twenty trades forever. Profit factor for A is $3,000 ÷ $2,000 = 1.5. For B it is $2,000 ÷ $3,000 = 0.67. Expectancy for A is +$25 a trade; B is at -$25. Win rate, the statistic both accounts would quote first, was 50% for both and predicted nothing.

What Good Numbers Actually Look Like

The thread that asked long-term profitable traders for a realistic profit factor settled in a range worth memorizing. Sustained, repeatable profit factor lands between roughly 1.3 and 1.5 for a lot of professionals, and the traders in that thread argued anything above 2.0 usually means one of three things: a sample too small to trust, a regime that flattered the system, or hidden tail risk that has not billed yet. One poster with roughly 700 live trades behind him reported a 1.58 and called himself proud of it, which is the right reaction. A boring 1.4 sustained for a year is worth more than a 2.3 that lived for six weeks.

That last row is its own October story. A backtester rebuilt a strategy video that showed 100 trades at a 56% win rate, 1.5 reward-to-risk, and about +40% return. Over 16 years and more than 1,700 trades, the same rules produced -23% total, a 39% win rate, and a 36% max drawdown. The video's hundred trades were in the data. They were a lucky stretch inside a losing system. Sample size is not pedantry; it is the difference between an edge and an anecdote.

The Three Fixes That Moved the Needle

The 100-trade thread listed what actually changed his numbers, and the list is better than most paid courses:

1. The stop gets set before entry, and size comes from it

Stop first, then position size from the distance and the risk budget, never the other way around. This is where the average-loss creep gets caught: an unsized trade grows its loss in whatever increment the market feels like delivering that day. Sizing off the stop pins every loss at roughly the same R, which is what makes expectancy arithmetic possible in the first place. We covered the sizing side in the position sizing mistakes breakdown.

2. A hard daily loss limit, and walking away when it hits

The worst single trade of a bad day is almost never the last one. The daily cap converts a cascade into a cost. Prop traders already live under one; the trick is treating the firm's limit as a ceiling to sit far below rather than a target to approach, which is the whole argument in the daily loss limit guide. The -4.42% worst day from the simulation is what one unprotected session looks like.

3. Tag every trade by setup, then cut the negative ones

This is the journal's real job. An aggregate profit factor over "my trading" hides the split: one setup at a 1.8 profit factor quietly paying for three at 0.7. Tagging every trade and reading expectancy per setup turns that fog into a short list. Cutting one bleeding setup does more for next month's numbers than any new indicator, and the tools for it live in the MT4/MT5 auto-import journal, where the tags and expectancy math are already wired.

All three fixes are process rules, all three are enforceable, and the win rate never appears. That is not a coincidence. Win rate is the number that feels like skill. Profit factor is the number that pays the rent.

Execution Rules

This week:

• Compute profit factor and average win/loss on your last 50 trades. If profit factor is under 1.0, stop adding size and diagnose before the next entry, because the account is funding a lesson you have not read yet.
• If the average loss exceeds the average win at a win rate under about 55%, write down why. Sometimes it is a trailing-stop system and the arithmetic still works. Usually it is exits drifting, and the drift is the finding.
• Add two columns to the journal: setup tag, and R multiple. Expectancy per setup is a five-minute calculation once those exist, and it ends the "is this strategy broken or am I just in a drawdown" argument with data instead of mood.

Ongoing:

• Review by path, not by return: max drawdown, worst day, longest streak. The breakeven simulation that still lost 16.74% peak to trough is the standing reminder that a flat line can hurt.
• Log MAE on losers, the way the +0.23R trade thread did. Where price traveled against your position before the stop tells you whether the stop is in the market's noise or in the trade's failure zone.
• Let the cap be mechanical. ChartWise's journal computes expectancy and profit factor per setup tag automatically from imported MT4/MT5 fills, and Equity Guard enforces the daily stop so the walking-away part stops depending on willpower, which the rules-breaking post covers in full.

Frequently Asked Questions

Is a 50% win rate good in trading?

On its own it means nothing, and that is the point of this whole discussion. At 50% winners, the average win minus the average loss decides everything: $150 against $100 makes +$25 per trade, $100 against $150 loses $25 per trade, same win rate. Fifty percent with a 2-to-1 reward-to-risk ratio is a strong system. Fifty percent with 1-to-1 after costs is a slow leak. Ask what the wins weigh before asking how often they arrive.

What is profit factor and what is a good number?

Gross profit divided by gross loss over a sample of trades. Above 1.0 the system makes money. Long-term profitable traders on r/Daytrading describe 1.3 to 1.5 as genuinely solid and repeatable, with 1.6 to 2.0 strong. Numbers sustained above 2.0 deserve suspicion: small sample, a friendly regime, or tail risk that has not shown up yet. One trader with roughly 700 live trades reported a 1.58 and was right to be proud of it.

How is expectancy calculated in trading?

Expectancy = (win% × average win) − (loss% × average loss), computed in R or in dollars over a meaningful sample. Example: 45% winners at 2R against 1R gives (0.45 × 2) − (0.55 × 1) = +0.35R per trade, so risking $250 per trade expects about +$87.50 each over time. A negative expectancy system cannot be fixed by sizing up, since sizing up only scales the leak.

Why did my strategy backtest well but fail live?

The October backtest autopsy is the cleanest case: a video showed 100 trades, 56% win rate, 1.5 reward-to-risk, +40% return. Rebuilt over 16 years and 1,700+ trades, the identical rules produced -23%, a 39% win rate, and a 36% max drawdown. The hundred trades were real; they were a lucky stretch inside a negative-expectancy system. Before trusting any backtest, extend the sample, include costs and realistic fills, and check that the good stretch is not the whole sample, which is also why the 575-trade management backtest reports its losers in full.

What metrics should I track instead of win rate?

Profit factor and expectancy per trade for the edge, maximum drawdown and worst day for the path, longest losing streak for survivability, and expectancy per setup tag to catch the one setup bleeding inside an aggregate that looks fine. Add MAE on losers to audit stop placement. That set fits on one journal page and answers every question win rate pretends to answer.

The Short Version

• Win rate is half an equation: 50% winners with $150 wins and $100 losses makes +$25 a trade, the same 50% with $100 wins and $150 losses burns -$25 a trade
• Profit factor (gross win ÷ gross loss) is the ranking number: 1.3-1.5 is solid professional territory, sustained 2.0+ deserves an audit, under 1.0 means the account is paying you to trade it
• The Oct 19 thread's three fixes: stop set before entry with size derived from it, a hard daily loss cap, cutting negative-expectancy setups by tag; none of the three touches win rate
• Judge systems on the path: a near-breakeven 2017-2023 simulation still drew down 16.74% with a -4.42% worst day, and a 100-trade +40% sample became -23% over 1,700 trades
• Tag every trade by setup, compute expectancy per tag, log MAE on losers, and let ChartWise's journal do the math while Equity Guard enforces the daily cap