Data & Backtesting

Question 1

What is backtesting?

Answer 1

Testing a trading strategy on historical data to evaluate performance.

Question 2

Why backtest before going live?

Answer 2

To confirm your edge and avoid costly real-time mistakes.

Question 3

What does “in-sample data” mean?

Answer 3

Data used to create or optimize the strategy.

Question 4

What does “out-of-sample data” mean?

Answer 4

Fresh data used to test if the strategy holds up beyond the sample period.

Question 5

What’s a forward test (paper trading)?

Answer 5

Running the strategy in real-time with no risk to confirm results.

Question 6

What is win rate?

Answer 6

The percentage of trades that are profitable.

Question 7

What is average R per trade?

Answer 7

The average multiple of risk returned (e.g., +1.5R).

Question 8

What is profit factor?

Answer 8

Gross profit ÷ gross loss. (>1 = profitable).

Question 9

What is max drawdown?

Answer 9

Largest peak-to-trough equity drop.

Question 10

Why track expectancy?

Answer 10

Shows if the strategy makes money over many trades.

Question 11

Why does bad data ruin backtests?

Answer 11

Missing ticks or incorrect candles distort results.

Question 12

What timeframe should you backtest?

Answer 12

The one you trade on, plus higher timeframe context.

Question 13

Why test multiple market conditions?

Answer 13

Strategies behave differently in trends, ranges, and high volatility.

Question 14

What is curve fitting?

Answer 14

Over-optimizing to past data, making the strategy fail live.

Question 15

How do you prevent curve fitting?

Answer 15

Keep rules simple, test out-of-sample data, avoid endless tweaks.

Question 16

Why include commissions and slippage in backtests?

Answer 16

To reflect real-world costs of trading.

Question 17

What is slippage?

Answer 17

The difference between expected and actual fill price.

Question 18

Why simulate stop-loss and take-profit orders?

Answer 18

To test realistic execution conditions.

Question 19

What’s walk-forward testing?

Answer 19

Repeatedly testing a strategy on new unseen data to confirm robustness.

Question 20

What is Monte Carlo simulation in trading?

Answer 20

Randomizing trade sequences to test how strategy handles variance.

Question 21

Why should you log each backtest trade?

Answer 21

To spot recurring patterns and mistakes.

Question 22

What’s a minimum sample size for backtesting validity?

Answer 22

At least 100–200 trades.

Question 23

Why compare strategies across instruments (MES, MNQ, MGC)?

Answer 23

To see which contract best fits the edge.

Question 24

Why keep strategy rules mechanical in backtests?

Answer 24

To avoid bias — rules must be repeatable.

Question 25

What’s the ultimate goal of backtesting?

Answer 25

To build confidence and discipline to trade the edge live.