How Much to Risk Per Trade: Position Sizing, Expectancy & the Kelly Criterion

🧠 Why Position Sizing and Risk Per Trade Matter

How much to risk per trade? It’s a deceptively simple question — but one that separates traders who survive from those who blow up. Position sizing, expectancy, and risk management form the core of long-term trading success.

But I like to think about it on a broader level. Nearly every life decision we make can be broken down into estimates of risk and reward — and based on those, we can decide how much to bet on being right.

Most people lean toward high win-rate, low-reward paths. Fewer choose low win-rate but high-reward endeavors. But regardless of the route, what often determines success is how much they risk per trade when they believe they’re right.

Let’s bring this to life with a few examples.

🎲 Dice Rolls and Risk-to-Reward Thinking

Imagine you’re betting on a single number in a roll of a six-sided die. How much would you risk if:

• You win 3x your bet?

• You win 6x?

• You win 10x?

Now think of your time like money. If a job gives you a 90% chance of earning 10% of what a business could, while a business has a 10% chance of earning 20x, how much of your time should go to each?

Is position sizing more tied to R-multiple? To win-rate? Or is the real answer: expectancy?

(If you’re unsure about expectancy, check out the previous issues where we break it down:

• Expected Value in Trading: Measuring Your Strategy’s Real Potential 

• Exploring Expected Value: The Psychology and Math of Big Wins vs. Frequent Wins 

Let’s take it further.

Say you have $10,000. How much should you risk per trade if you have:

• A 40% chance of turning it into $30,000?

• A 90% chance of turning it into $13,300?

Is the classic 1–2% risk rule really appropriate in all cases?

💡What is the Kelly Criterion and Why Traders Use It

Before we answer that, we need to explore an elegant formula — the Kelly Criterion.

I first came across it thanks to Michael from Magnelibra, who recommended Fortune’s Formula by William Poundstone. (Great read — highly recommended.)

To simplify: a brilliant mind figured out how to size bets optimally to beat the casino at blackjack by using card counting. That formula became the Kelly Criterion — a way to calculate the optimal bet size (as a % of your bankroll) based on your edge and odds.

Warren Buffett, in his early years, would often allocate 60%+ of his capital to a single stock. Why? Because he knew his edge — and he used Kelly logic.

As Charlie Munger often said:

Kelly tells you exactly how much to risk per trade based on your system’s win-rate and reward-to-risk ratio. That’s why it’s a foundational idea in trading risk management.

That’s what this is about.

🧪 How Much to Risk Per Trade — Kelly Criterion Examples

Using a risk per trade calculator like the one on ClockTrades can help you avoid emotional or inconsistent position sizing.

1. Roll of a die — win 3x if correct

Bad bet. Expectancy is negative. Don’t take it.

2. Roll of a die — win 6x if correct

Surprise! This is right around where betting 1–2% starts making sense.

It’s likely where trading educators got their “safe risk” numbers from.

In other words: if your system isn’t performing better than a dice roll with a 6R return, you’re statistically worse than a gambler.

3. Roll of a die — win 10x if correct

Now we’re talking. Kelly would suggest risking ~7.6% per bet here.

Why? Because even though you’re only winning ~16% of the time, the payoff is so high the math works.

🕰️ Time Allocation as a Position Size Metaphor

Let’s bring this back to life decisions.

If a business has a 10% chance of earning a 20x return, its expectancy is 1.1R.

If you value your time like money, you’d expect to get 1.1 hours back for every hour invested.

Kelly tells you to allocate 5.5% of your time to this business.

Now say your job has a 90% chance of earning just 10% of that. Expectancy is ~0.1R.

Kelly would say: spend ~40% of your time on the job.

In a 24-hour day, that means:

• 9.6 hours on your job

• 1.3 hours on the business

The ratio? Spend 7.38x more time on your job than your business — if those odds and rewards are accurate.

❓ How to Calculate Optimal Risk Per Trade (Homework)

So back to our earlier question:

Use the Kelly Criterion to figure it out. (Feel free to drop your answers in the comments.)

🧠 So… does Kelly feel natural?

So… does Kelly sizing feel natural to you? Does it change how you think about position sizing in trading?

What’s that 1–2% risk rule?

It mirrors the Kelly bet for situations with barely positive expectancy (like rolling a die for 6x payout).

So beginner traders are basically being told:

But when Warren Buffett saw a company trading at a 30% discount and estimated a 90%+ chance of realizing that value — it’s no wonder he bet big.

Kelly supports that. The higher the edge and certainty, the more you should commit.

⚠️ Drawdowns, Overfitting, and the Limits of Kelly

Kelly does produce optimal long-term growth, but it can also come with major drawdowns.

The real danger?

Using Kelly on backtested systems that are too optimistic.

If your live results don’t match, the risk of ruin gets very real.

But we’ll talk about that — and the importance of good research — in Monte Carlo Trading Simulation: How Traders Can Measure Risk, Ruin, and Time to Profit.

But until then it is better first to understand the power of compound interest which we explore in

• Next read: How Compound Interest Builds Trading Wealth Over Time 

If you’re just beginning this journey with us and enjoyed this issue, you might want to start with the Why I Write This (and for Whom) — or jump straight into the Expected Value in Trading: Measuring Your Strategy’s Real Potential where we break down the key concepts behind the tools we explore here.

Kamil — Markets & Manners