| Because - management fees, margins, and available capital can easily be modelled properly - you can easily set up constraints (like no fractional trading for your S&P example) - you can set up a proper point-in-time database to avoid snooping (especially if you're using earnings reports or other fundamental data which is often actually released _after_ it's published release date...) - you can set up regime-shifting simulation environments (various market conditions) - you can avoid over-fitting if you're back-testing (with dozens of techniques, most notably : test once and forget about parameter optimization) I would say that with paper trading and back-testing the serious problems are that : - your orders don't show up on the book so no one sees and reacts to your limit orders - your "filled" orders don't affect the book, so you're not affecting liquidity, so the market doesn't change in response to your trading - your bot has no access to market micro-structure strategies and conditional orders (and if you want to trade fast or are placing big trades you need them) These are the problems that make any simulation unrealistic, and they are fundamental. It's shadowboxing, which is not entirely devoid of value, but which is certainly insufficient on its own. (I've worked as a quant developing strategies for several funds these past 15 years) |