Paper trading and backtesting serve different purposes. A backtest evaluates defined rules against historical data, while paper trading observes how a process behaves in current market conditions without using real capital. AI StockScanner connects both forms of validation to the same research workflow.
What backtesting can and cannot show
A backtest can reveal how a rule set behaved across historical samples, including trade count, win rate, return, maximum drawdown and profit factor. It can also expose unstable strategies that depend on a narrow period or unrealistic assumptions.
Historical results are not proof of future performance. Overfitting, survivorship bias, data errors, commissions, slippage and regime changes can make a strategy look stronger than it is.
- Use realistic commissions and slippage.
- Test different market regimes and date ranges.
- Review drawdown, not only total return.
- Require a meaningful sample size.
- Validate the rules again with paper trading.
Paper trading tests the operating process
Paper trading allows users to create simulated positions, monitor open profit or loss, sell fully or partially and review completed trades. It is useful for testing entries, exits, position sizing and record-keeping under current conditions.
Simulated fills may not match real execution. Liquidity, queue position, spread changes and emotional pressure are different when capital is at risk.
Connect validation to the trade journal
A journal should record the original thesis, entry conditions, invalidation level, risk budget, outcome and lessons. Connecting this information to the scanner and AI Trade Coach makes it easier to identify repeated process errors.
Frequently asked questions
No. Backtesting applies rules to historical data. Paper trading simulates current-market decisions and order management without using real money.
Trade count, win rate, net return, maximum drawdown, profit factor, average trade, exposure and sensitivity to commission or slippage are important starting points.
Yes. Market regimes change, historical data can contain bias, and real execution introduces spreads, slippage, latency and psychological pressure.
The platform includes a paper-trading workspace for simulated positions and order review. Exact features depend on the installed version and account permissions.