Business
Successful algo trading in 2025 requires accounting for real-world execution costs, such as slippage, spreads, and commissions, which are often absent from historical backtests. Unmodeled costs can inflate performance metrics by 10-15%. For accurate real execution, algo trading strategies should simulate jumps using tick-level data, incorporate broker-specific fees and spreads, and test across various liquidity types. Tools like Elitealgo simplify the integration of these costs, aligning backtests with live trading conditions. This ensures algo trading strategies are robust for real-time markets, preventing unexpected losses when transitioning from historical simulations to real sensor data.