Can You Actually Make Money on Prediction Markets? Here's Our Real P&L

Let us get the uncomfortable question out of the way first: can you actually make money trading prediction markets? The answer is yes — but with caveats that most promotional content about these platforms conveniently ignores. We trade prediction markets daily with automated systems and real capital, and the honest picture is more nuanced than “buy low, sell high, collect profits.”

This article is not a sales pitch. It is an honest accounting of where money is made, where it is lost, and what it actually takes to be profitable in this space.

The Reality of Prediction Market P&L

Here is what most people get wrong about prediction market profitability: they assume the money is made by predicting outcomes correctly. It is not — at least not primarily.

The majority of our profitable trades are exits, not settlements. We buy a contract at $0.40, the market moves in our favor, and we sell at $0.55 or $0.60 before the event even settles. The $0.15 to $0.20 per contract profit on the exit is more reliable and more frequent than waiting for a binary $1.00 or $0.00 settlement.

Settlement wins do contribute to P&L, but they are inherently volatile. You can have a streak of correct predictions followed by a streak of losses, and the binary nature of the outcomes means there is no partial credit. You are either right and collect $1.00, or wrong and get $0.00.

Exit strategies — taking profits when the market moves in your direction — smooth out that volatility. They convert prediction market trading from a series of coin flips into something closer to systematic trading, where edge is captured incrementally over hundreds of small trades.

Where the Money Actually Comes From

After analyzing thousands of our own trades across weather, economics, S&P 500, and crypto contracts, the profitable patterns are clear:

1. Information Speed Advantages

Markets misprice when new information arrives and participants are slow to update their positions. Weather forecast updates, economic data releases, and breaking news all create windows where contract prices lag behind the new reality. If you can process information faster than the median participant — ideally with automated systems monitoring data feeds — you can buy or sell before the market fully adjusts.

This is our single largest source of edge. It is not about being smarter than the market. It is about being faster at incorporating the latest data.

2. Probability Model Edges

Some events have quantifiable probability distributions that the market consistently misprices. Weather is the best example: if you build a calibrated model that converts NWS forecast data into probability estimates, you will find contracts where the market price diverges from your model’s estimate by 10 to 20 cents or more. These are real, repeatable edges.

The catch is that building and maintaining the model takes real work. You need to backtest against historical data, calibrate your forecast error estimates by city and season, and continuously validate that your model is still accurate. This is not something you can do casually.

3. Exit Timing

Knowing when to sell is as important as knowing when to buy. A contract that moves from $0.40 to $0.65 in your favor represents a 62.5% return on capital. If your model says the true probability is $0.70, you might hold for the extra $0.05 — but that extra $0.05 comes with the risk of a reversal. In many cases, taking the sure profit at $0.65 is the higher expected value play when you account for fees and the opportunity cost of capital tied up in the position.

We target exit thresholds based on net return after fees. When a position hits our target, we sell. No second-guessing, no hoping for more. This discipline is the difference between a strategy that compounds and one that gives back its gains.

The Fee Reality

Fees are the silent killer of prediction market profitability. On Kalshi, fees typically run around 7 cents round-trip per contract. That means every trade starts in a 7-cent hole that you need to climb out of before you see any profit.

Let us do the math on a concrete example. You buy a contract at $0.45 and your model says the true probability is 60%. The expected settlement value is $0.60. Your expected gross profit is $0.15 per contract. After the 7-cent round-trip fee, your expected net profit is roughly $0.08 per contract. That is still positive, but it is barely half of what it looked like before fees.

Now consider a smaller edge. You buy at $0.50 with a model estimate of 55%. Expected gross: $0.05. After fees: roughly negative $0.02. A trade that looked profitable before fees is actually a loser.

This is why we emphasize that you need meaningful edges — 10 cents or more after fees — to justify entering a position. Small edges get eaten by transaction costs. If you are not disciplined about this, your account will bleed out through fees regardless of how good your predictions are.

Bankroll Requirements

How much capital do you need to trade prediction markets seriously? It depends on your goals, but here are some realistic benchmarks:

• Casual exploration ($50 to $200): Enough to learn the mechanics, place a few trades, and understand how the platform works. Do not expect meaningful returns at this level.
• Active trading ($500 to $2,000): Enough to run a small portfolio of positions across multiple market categories. At this level, you can begin to see whether your analytical approach generates consistent edge.
• Serious trading ($2,000 to $10,000): Enough to diversify across many simultaneous positions, absorb losing streaks without going bust, and generate returns that justify the time investment.

The Kelly criterion — a mathematical framework for optimal position sizing — is essential at any bankroll level. It tells you how much to risk on each trade based on your edge and the odds. Without it, even a profitable strategy can go bust through overleveraging. Most experienced prediction market traders use a fractional Kelly approach, betting a fraction of what the full Kelly criterion suggests to reduce variance.

Realistic Return Expectations

Anyone promising 100% annual returns on prediction markets is either lying or taking on enormous risk. Here is what we consider realistic for different trader profiles:

• Informed casual trader: 5% to 15% annualized return on capital, assuming disciplined trade selection and position sizing. This is after fees and accounts for losing streaks.
• Systematic trader with automation: 15% to 40% annualized, depending on the number of markets traded, speed of execution, and quality of probability models. The upper end requires significant infrastructure investment.
• Most participants: Zero or slightly negative. The majority of prediction market traders break even or lose money, just as in any other form of trading. Fees and behavioral biases (holding losers too long, selling winners too early) account for most of the losses.

These numbers are not glamorous, but they are honest. A 20% annualized return on a $5,000 bankroll is $1,000 per year. That is real money, but it is not quitting-your-job money. The traders who do well at this treat it as a serious part-time activity or run it alongside other trading strategies.

Comparison to Other Trading

How does prediction market profitability compare to other forms of trading?

vs. Stock Trading

Stock trading offers the advantage of long-term market drift (the S&P 500 returns roughly 10% annually over long periods). Prediction markets have no equivalent drift — every contract settles at $0 or $1, and the market’s average pricing is roughly efficient. This means prediction market trading is pure alpha generation, with no beta to ride. It is harder, but the returns are uncorrelated with the stock market, which has diversification value.

vs. Options Trading

Options trading offers more instruments, more strategies, and deeper liquidity — but also more complexity and more ways to lose money. Event contracts are simpler and have defined risk by construction, but offer fewer strategic possibilities. For traders who find options overwhelming, prediction markets are a more accessible entry point into probability-based trading.

vs. Sports Betting

The house edge in sports betting (typically 5% to 10% vigorish) is larger than prediction market fees for most trade sizes. Prediction markets also let you exit positions before settlement, which sports betting generally does not. However, sports betting has more liquidity in mainstream events and a much larger participant base. The analytical frameworks overlap significantly — both are fundamentally about identifying mispriced probabilities. Check our detailed comparison for the full breakdown.

What Kind of Edge Actually Exists?

The edges in prediction markets fall into a few categories:

• Data processing speed: Being faster than the median participant at incorporating new information. This is the most scalable edge and the most amenable to automation.
• Model calibration: Having better probability estimates than the market for quantifiable events (weather, economic data). This requires statistical skill and ongoing model maintenance.
• Behavioral exploitation: Markets systematically misprice certain types of events. Longshots tend to be overpriced (people love lottery tickets). Near-certainties tend to be underpriced. Mean-reverting events get trended. These biases create exploitable patterns.
• Timing and execution: Buying and selling at better prices by using limit orders, trading during high-liquidity periods, and avoiding market orders into thin books.

None of these edges are large. We are talking about 5 to 15 cents per contract on average, before fees. But applied consistently across hundreds of trades per month with proper position sizing, they compound into meaningful returns.

Time Investment Required

This is the factor most people underestimate. Trading prediction markets profitably is not passive income. Even with automation, it requires:

• Model development and backtesting: Dozens of hours upfront, plus ongoing maintenance.
• Market monitoring: Checking positions, reviewing new market listings, evaluating opportunities.
• System maintenance: If you automate, keeping your systems running, debugging issues, and adapting to platform changes.
• Record keeping: Tracking P&L, analyzing which strategies are working, and cutting those that are not.

Realistically, an active prediction market trader spends 5 to 15 hours per week on the activity. If your returns are $200 per month on a $5,000 bankroll, you are earning $13 to $40 per hour for your time. That is not nothing, but it is not a windfall either.

The Honest Bottom Line

Can you make money on prediction markets? Yes. Will you? Probably not, at least not initially. The learning curve is real, the edges are thin, and fees punish undisciplined trading.

But if you approach it with realistic expectations — treating it as a skill to develop rather than a get-rich-quick scheme — there is genuine opportunity. The markets are young, still growing, and less efficient than mature financial markets. Retail traders with good analytical frameworks and proper risk management can generate consistent, if modest, returns.

Start small, track everything, and focus on markets where you have a genuine informational or analytical advantage. Do not trade every contract that looks interesting. If you want a framework for identifying those opportunities, our strategy guide for retail traders is the place to start.

The traders who succeed in prediction markets are the ones who treat it like a business: measure the inputs, track the outputs, cut what is not working, and scale what is. It is not glamorous. But it works.