The Hidden Costs of Paper Trading: What 6 Weeks of Data Showed

As developers, we're often tempted to test our trading strategies using paper trading platforms. These tools allow us to simulate trades without risking any real money. But what happens when we start analyzing the data from these simulations? Do they accurately reflect what would happen in the real world? I ran a 6-week experiment simulating trades across multiple strategies on various assets using TradeSight, my Python paper trading bot. The results were eye-opening. Slippage: The Silent Killer When executing trades, brokers incur slippage due to market conditions and order books. Paper trading platforms often assume perfect fill rates or don't account for this spread at all. In reality, even the best traders can expect some level of slippage. Here's how I started accounting for it in Python: import yfinance as yf # Simulate slippage: assume 0.05% on each fill def apply_slippage ( price , direction = ' buy ' , slippage_pct = 0.0005 ): if direction == ' buy ' : return price * ( 1 + sli