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What Is a Good Profit Factor? Benchmarks and Formula

July 17, 2026 · Agenttrading · Last updated July 2026

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01 THESIS · AS A TESTABLE RULE

02 EVIDENCE · FUNDAMENTALS

03 BACKTEST · GROWTH OF $10,000
Strategy Buy & hold

04 RISK · IN PLAIN ENGLISH

05 VERDICT · HISTORICAL, NOT PREDICTIVE

Past performance does not guarantee future results. Educational analysis only, not financial advice.

Profit factor is gross profit divided by gross loss: the total money your winning trades made, split by the total your losers gave back. Below 1 the strategy loses money. Around 1.5 is respectable. Above 2 is strong but deserves scrutiny, because on a small sample a high reading is often luck or overfitting, not edge.

That single ratio tells you whether a rule takes out more than it puts at risk, in one number you can read at a glance. It is one of the most quoted stats in trading, and also one of the easiest to fool yourself with, so it is worth understanding exactly what it does and does not say.

What is a good profit factor?

A good profit factor is generally above 1.5 across a large sample of trades, with anything under 1 losing money and readings above 2 counting as strong. Those are rough bands, not laws. The right threshold depends on your trade count, your costs, and how the strategy behaves when markets turn, not on the number alone.

The benchmark table below is the whole idea in five rows. Read it as a spectrum, and remember that every value assumes costs have already been charged. A profit factor measured on a costless backtest is a fantasy number.

Profit factorInterpretation
Below 1.0Losing system. Gross losses exceed gross profits. No amount of position sizing fixes this.
1.0 to 1.25Barely positive. Thin edge, easily erased by higher costs or a bad stretch.
1.25 to 1.5Workable. A real but modest edge that needs discipline to keep.
1.5 to 2.0Respectable. A solid edge across a decent sample, the range most durable systems live in.
Above 2.0Strong, and suspicious. Excellent if it holds across many trades and conditions; usually overfit if it came from a short, hand-picked period.

Notice that the top band comes with a warning attached. In trading, a stat that looks too good is a prompt to investigate, not to celebrate. More on that below.

What is the formula for profit factor?

The formula is one line: Profit factor = gross profit / gross loss. Gross profit is the sum of every winning trade's gain. Gross loss is the sum of every losing trade's loss, taken as a positive number. Divide the first by the second and you have the ratio of money made to money lost across the whole record.

A worked example makes it concrete. Say a strategy took 100 trades. The winners together made $8,000. The losers together gave back $5,000. Profit factor is 8,000 / 5,000 = 1.6. That means for every $1 the system lost, it earned $1.60. The 60 cents of surplus per dollar risked is the edge.

MetricValue
Total trades100
Gross profit (sum of all winners)$8,000
Gross loss (sum of all losers)$5,000
Profit factor8,000 / 5,000 = 1.6
Net profit$3,000

A useful sanity check: a profit factor of exactly 1 is break-even, because gross profit equals gross loss and net profit is zero. Anything you see above 1 is the part that actually pays you, and anything below 1 is the part quietly costing you.

Is a profit factor of 2 good?

A profit factor of 2 is very good on paper, meaning the system earned twice what it lost, but whether it is real depends almost entirely on the sample behind it. Across several hundred trades and multiple market regimes, a 2.0 is genuinely strong. Across 20 cherry-picked trades in one trending year, it is close to meaningless.

Here is the trap. The fewer trades you have, the wilder the ratio swings. A single large winner can lift a 30-trade record from 1.3 to 2.1, and removing that one trade drops it back. So the same value, 2.0, can describe a rugged edge or a statistical accident. The number does not tell you which. The trade count and the conditions do.

Treat a 2.0 as a question, not an answer. Ask how many trades produced it, whether the profit came from a handful of outliers, and whether the test charged realistic costs. If it survives all three, it is impressive. If it fails any of them, it is a screenshot, not a strategy.

What is the difference between profit factor and win rate?

Profit factor measures the size of your wins against your losses. Win rate measures how often you win, and says nothing about size. They answer different questions, and a strategy can look great on one and terrible on the other, which is exactly why quoting either alone is misleading.

Think about a trend-following rule that wins only 35% of the time. That sounds bad. But if the average winner is four times the average loser, the gross profit still dwarfs the gross loss, and the profit factor can sit comfortably above 1.8. Low win rate, strong profit factor. The opposite happens too: a system that wins 85% of the time but lets a few losers run huge can post a win rate that dazzles and a profit factor under 1.

The two are linked through the payoff ratio (average win divided by average loss). Roughly, profit factor equals the win/loss odds multiplied by the payoff ratio. That is why you can trade either lever. A high win rate can carry a poor payoff, and a big payoff can carry a low win rate, and the profit factor is where both show up in one figure. If you want the frequency side of the picture in depth, the win rate guide covers the break-even math trade by trade.

Can profit factor be misleading?

Yes, and in predictable ways. Profit factor is misleading whenever the sample is small, the results are overfit, one trade dominates the total, or the test ignored costs. In each case the ratio can look strong while the underlying edge is weak or imaginary. The number is honest; the way people generate it often is not.

The four failure modes worth memorizing:

  1. Small samples. Under 30 trades, ignore the profit factor. Between 30 and 100, treat it as a hint. Real evidence starts above 100 trades spread across different market conditions. Any ratio built on a handful of trades is noise wearing a decimal point.
  2. Overfitting. Tune enough parameters against one slice of history and you can manufacture a profit factor of 3 that collapses to 0.9 the moment new data arrives. A curve fit to the past describes the past. The test that matters is out-of-sample.
  3. Outlier dependence. If removing your single best trade tanks the ratio, the edge lives in that one trade, not the rule. Check whether the profit is broad or concentrated. Broad is durable. Concentrated is luck you may not repeat.
  4. Ignored costs. A profit factor computed without commissions and slippage flatters every high-frequency system. Charge a realistic cost per trade, around 0.1% for liquid US stocks, and thin edges near 1.1 often fall below 1 entirely.

None of this means the metric is useless. It means the metric is only as trustworthy as the test that produced it. A profit factor of 1.6 from a large, out-of-sample, cost-charged backtest tells you far more than a 3.0 from a short, optimized, frictionless one.

How profit factor fits with the other numbers

Profit factor is one lens, not the whole view. It tells you the ratio of reward to risk taken across all trades, but it says nothing about the path. A rule with a healthy 1.7 can still put you through a 45% drawdown that no real person would sit through, and a drawdown you abandon has an edge of zero. Pair profit factor with maximum drawdown, expectancy per trade, and the longest losing streak before you trust it.

It also pairs naturally with expectancy. Profit factor tells you the ratio; expectancy tells you the dollars per trade. Reading them together keeps you from optimizing one at the expense of the other.

Getting your real profit factor rather than a rule of thumb

You can compute profit factor on your own trades with a spreadsheet, and you should. But your own trades are a small, biased sample: the ones you actually took, in the market you happened to trade, minus the entries you skipped when you were nervous. That sample will not tell you the profit factor of the rule itself.

A backtest will. State the idea in plain English, such as "buy SPY when the 50-day moving average crosses above the 200-day and sell on the reverse cross", and Agenttrading restates it as an explicit rule and shows it to you before running anything. Then it tests across 20+ years of split- and dividend-adjusted daily data with a 0.1% cost per trade charged by default, so the profit factor comes out of a sample large enough to argue with, alongside the drawdown, the trade count, and a risk panel. The verdict at the end is honest: HELD UP, MIXED, or UNDERPERFORMED. Plans start at $19 per month, with no execution and no brokerage connection.

To see how the number is generated, backtesting software explains the engine and the assumptions behind it, and the trading strategy tester lets you run a rule and read its profit factor, win rate, and drawdown side by side.

Past performance does not guarantee future results. For educational and informational purposes only. Not financial advice. Consult a licensed advisor. Agenttrading does not execute trades, connect to a brokerage, or provide personalized investment advice.

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Past performance does not guarantee future results. For educational and informational purposes only. Not financial advice. Consult a licensed advisor.