Fees and leverage are often overlooked in algorithmic trading, yet they largely determine whether a strategy works outside of backtests. Many systems appear profitable until real world execution costs, liquidation mechanics, and leverage constraints are applied, at which point the expected edge disappears.
Fees quietly compound over time, especially for strategies with frequent trades and small profit margins. Even modest taker fees can turn a marginally profitable system unviable once slippage and spreads are included. Transparent fee structures help with modeling, but sudden liquidity changes or fee adjustments can still materially affect performance and must be stress-tested.
Leverage should be viewed as a risk-scaling tool rather than a profit enhancer. Moderate leverage can improve capital efficiency, but higher leverage sharply reduces tolerance for execution errors and volatility. With very high leverage caps, such as a 500x limit on platforms like Bitget TradFi, capital efficiency can benefit certain short duration or tightly controlled strategies, but liquidation risk becomes extremely sensitive to fees, latency, and micro movements.
From a balanced perspective, high leverage flexibility combined with clear fees can be useful for disciplined, well modeled algorithms, while posing significant risks for poorly tested systems. Ultimately, sustainable algo performance depends less on maximum leverage or headline fees and more on robust execution modeling, realistic assumptions, and strict risk control.