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The standard technique for determining how well a strategy or model would have performed ex-post. Backtesting examines the performance of a trading strategy using past data to determine its viability. If backtesting is successful, traders and analysts might feel confident using it in the future.

Realizing the Value of Backtesting
Backtesting is a method for simulating trading strategies with historical data in order to generate outcomes, examine risk, and determine profitability prior to risking real money.
A successful backtesting method gives investors confidence that the approach has solid underpinnings and will likely generate returns when put into practice in the real world. A well-executed backtest that produces inferior results, on the other hand, will cause traders to reconsider the approach.
A trading strategy can be backtested as long as it can be measured. In order to get their ideas into a testable form, some traders and investors may hire programmers and coding experts. A programmer must typically implement the concept in the platform's proprietary language.
To tweak the system, a trader can use user-defined input variables, which can be incorporated by the programmer. Variables are added to the system, such as moving average crossovers using different time periods. With the help of backtesting, the trader can learn which moving average lengths would have yielded the best results based on the past data.

Best Kind of Backtesting
The best kind of backtesting uses sample data from a time period that's both relevant and long enough to cover a wide range of market situations. This allows one to determine with greater certainty whether the backtest's positive results reflect luck or legitimate trading potential.
First include equities from companies that went bankrupt, were sold, or were liquidated to ensure a realistic historical data set. If instead we were to use only equities that are currently trading, we would get inflated results in our backtesting.
All trading costs, no matter how small, should be factored into a backtest, as they might pile up throughout the backtesting period and distort the strategy's apparent profitability. The backtesting software that traders use should factor in these expenses.
Further evidence of a system's efficiency can be found through forward performance and out-of-sample testing, which can expose a system's flaws before actual money is at stake. For a trading system to be viable, there must be a high degree of consistency across backtesting, forward performance and out-of-sample findings.

Differentiating Past Performance Testing from Future Predictive Testing
As an alternative to using only in-sample data to judge a trading system's efficacy, forward performance testing (or paper trading) gives investors access to additional, real-world data. Forward performance testing mimics real-world trading by applying the system's logic in a real market environment. In this type of trading, no actual purchases or sales are made; instead, all transactions are recorded in a computer and analyzed for potential gains or losses before being erased.
Forward performance testing relies heavily on adhering to the system's logic to the letter; without doing so, an accurate evaluation of this stage becomes extremely challenging, if not impossible. Traders should be forthright about all trade entries and exits, and should not engage in practices like cherry-picking deals or omitting a trade on paper without offering a valid reason (such as it didn't fit the backtesting criteria). It's important to keep track of and assess whether or not the trade would have been made had the procedure been followed.

Scenario Analysis and Backtesting
In contrast to backtesting, which uses actual historical data, scenario analysis employs hypothetical data to model different possible scenarios in order to assess for fit or success.
Scenario analysis, by way of illustration, can be used to simulate the effects of various changes, such as a shift in the interest rate, on the value of a portfolio's securities.
Estimating the value of a portfolio after an unfavorable event can be done with the use of scenario analysis, which can also be used to look at a worst-case scenario.

Dangers Associated with Backtesting
Traders must construct and test their strategies in good faith, free from bias, if backtesting is to yield useful results. This necessitates that the strategy be created independently of the results of any backtesting that may have been conducted.
This is far more difficult than it sounds. Most trading methods are constructed using past market data. They need to be very careful to choose data sets that are different from the ones used to train the models. If not, the backtest will likely return inflated numbers that don't tell the whole story.
Similarly, traders should steer clear of data dredging, a practice that involves testing a large number of hypothetical strategies on the same set of data and yielding results that fail in real-time markets due to the prevalence of invalid methods that would outperform the market over a particular period of time by pure coincidence.
Using a winning technique during the in-sample period and then backtesting it along with data from an out-of-sample period can help counteract the bias that arises from just considering data from the most recent period. Comparable findings from in-sample and out-of-sample backtests are indicative of a model's validity.

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