Understanding Expected Value in Trading Decisions
Trading without understanding expected value is basically just guessing with money. Most retail traders focus on whether they won or lost individual trades instead of whether their overall approach actually makes sense mathematically. This causes consistent losses even when people occasionally hit big wins that feel amazing but don’t fix the underlying problem.
What Expected Value Actually Means
Expected value calculates what happens on average when something random repeats a bunch of times. In trading that’s what a strategy returns per trade over hundreds or thousands of executions, not what happens on any single trade. A strategy with positive expected value makes money long-term even when lots of individual trades lose, which feels wrong emotionally but that’s how probability works.
The math is straightforward. Multiply each outcome by its probability, add everything together. If a trade has 60% chance of making €100 and 40% chance of losing €80, expected value is €28 per trade. Sounds simple except traders rarely know their actual win rates or profit ratios until they’ve tracked way more data than they think they need. Casinos get this better than traders do. Every game has calculated house edge, they know exactly what they’ll make over thousands of bets even though individual players might win short-term. Professional traders think like casinos, retail traders think like gamblers hoping for lucky streaks to continue forever.
Probability Requires Sample Size
Flip a coin ten times and getting seven heads doesn’t mean the coin is biased. Small samples produce weird results, that’s just variance. Trading works the same way but traders make big decisions after tiny sample sizes and convince themselves they’ve discovered something. A strategy needs at least 100 trades before probabilities become somewhat reliable, more if the win rate is extreme. Most retail traders never execute 100 trades with the same strategy though. They switch after ten losing trades, convinced the system doesn’t work. Maybe it doesn’t, maybe it’s just a normal losing streak that would reverse with more data.
Statistical significance matters in trading like it matters anywhere probability is involved. Analytical platforms in various fields demonstrate this, like https://101rtp.com/ie which analyzes return rates and probabilities in gaming systems showing how large sample sizes reveal true expected outcomes versus short-term noise. Traders need similar rigor but rarely apply it.
Risk-Reward Ratios Don’t Tell the Whole Story
Trading educators talk about risk-reward ratios constantly. “Always aim for 2:1 or 3:1” like it’s magic or something. But a 3:1 risk-reward trade that only wins 20% of the time has negative expected value, you’re losing money over time. A 1:1 trade that wins 65% of the time has positive expected value even though the ratio looks worse. The combination matters, not just one number by itself.
Most retail strategies have terrible win rates disguised by occasional big wins that feel incredible. Traders remember the one time they made €500, completely forget about twenty trades where they lost €50 each. That’s €1000 in losses versus €500 in gains but it feels like winning because the big win is memorable and recent.
Professional traders track everything in spreadsheets and calculate actual expected value from real performance data. Retail traders trust feelings and remember what they want to remember, which is basically a recipe for losing money consistently.
Variance Can Destroy Even Good Strategies
Positive expected value doesn’t guarantee profits over any specific timeframe. Results fluctuate around the expected value, sometimes really dramatically. A trader with a strategy showing positive expected value over 1000 trades might lose money over the next 100 just from bad luck hitting at the wrong time. This destroys people psychologically. They develop something profitable, hit a variance-driven losing streak, abandon it right before it turns around. Happens all the time in retail trading, like constantly. The math works but the trader’s psychology or risk management fails first.
Professional poker players understand variance because they experience it every session. Good players lose sometimes, bad players win sometimes, variance hides skill over short timeframes. Same in trading but most traders don’t have the bankroll management or mental framework to survive those periods when everything goes wrong despite doing everything right.
Conclusion
Retail traders evaluate based on today’s P&L or this week’s results. They make emotional decisions based on recent outcomes, constantly switching strategies instead of gathering enough data to know if anything works. This guarantees they never develop real edge because they abandon things before statistical significance appears.
The transition from amateur to professional trading is mostly psychological rather than technical. Learning to think in probabilities instead of individual outcomes, accepting variance instead of fighting it, sizing positions based on mathematics instead of how confident you feel about a trade. Most traders never make that shift even after years of trying because emotional patterns run too deep and feel too true in the moment to override with logic.
