The 3-5-7 rule is mostly sound, but its 3% risk-per-trade cap is looser than the data supports. Accounts risking over 2% were profitable at 15.0%, against 24.5% for the under-0.5% group.

Analysis of 500,000+ trader accounts shows accounts keeping risk per trade under 0.5% were profitable at 24.5%, well above every higher-risk group. Here is what the data says about how much to risk.

Only about 7% of prop firm traders ever reach a payout, and most fail in the first week on a daily loss limit breach. Analysis of 500,000+ accounts shows the behavioral patterns that end challenges, and how to spot them before you pay the fee.

Traders argue over risk-reward ratio versus win rate using made-up examples. We checked 500,000+ real accounts. Risk-reward separates the profitable from the rest more sharply than win rate, and most traders sit on the wrong side of it.

At the Online Trading Expo Hong Kong 2026, Jonas Schleypen hosted an AMA with Dr. Ken Ip on AI regulation, systemic risk, and flash crashes. The biggest takeaway? The industry's AI obsession with prediction misses where AI delivers the most value.

You checked the charts. Read the analysis. Consulted multiple sources. And you were still wrong, because you selected those sources precisely because they agreed with you. That is confirmation bias in trading.

Not all AI trading tools improve your trading. Some just track it. The difference between an AI that shows you a chart and one that changes your behaviour is the difference between data and coaching.

The trade is in profit. You close it early because it feels like the reversal is coming. It keeps running. Pessimism bias explains why traders consistently leave money on the table.

A winning trade is not the same as a good trade. Outcome bias convinces traders otherwise, leading to constant strategy changes and decisions driven by luck rather than logic.