Backtesting is the general method for seeing how well a strategy or model would have done ex-post. It assesses the viability of a trading strategy by discovering how it would behave using historical data. If backtesting works, traders and analysts may have the confidence to employ it going forward. Backtesting can be an important step in optimizing your trading strategy. We can say it is a term used in modeling to refer to testing a predictive model on historical data.
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How does it work?
Generally, analysts use it as a way to test and compare various trading methods without risking much money. The theory which is used is that if their strategy performed badly in the past, it is unlikely to perform well in the future (vice-versa). Two main components looked at backtesting are the overall profitability and risk taken. However, it will look at the performance of a strategy to many different factors. A backtest which is successful will show the traders a strategy that is proven to give positive results. While the market never moves in a similar pattern, this test believes that the stocks move in near similar patterns as they did in the past. A backtest is normally coded by the programmer, running a simulation on the trading strategy that uses historical data from stocks, bonds, and other financial instruments. The backtest facilitator measures the model returns in multiple datasets.
There are different approaches available for Backtesting, including the use of platforms and simulators to test the strategy. One can also create a model using Excel VBA and evaluate it later with Python. There is also a large range of vendor-developed back-testing platforms available on the market that can be very effective in back-testing automated strategies, but some research is required to determine which ones can meet the requirements.
Suppose you are an analyst at an investment bank and you’ve been given a task to backtest a strategy against a set of historical data given to you. The strategy is that buying a stock if it hits a 90-day low. The first step would be to choose unbiased historical data. Then apply the strategy to the data. This would help you in simplifying the research performed in creating the strategy. The team will decide whether the backtest is enough to do the investment.
- Helps in estimating potential profits or losses based on historical data.
- If executed correctly, backtesting will determine under what conditions it will manifest a persistent edge of an idea.
- Recognition of patterns that tend to repeat themselves within a certain course of time.
- A deeper understanding of the trading system and more precise decision making.
- If the prototype framework is traded live with minimum risk, real outcomes can be compared with backtesting results to see whether the transactions can be performed as expected.
- One of the main drawbacks of backtesting is that it can lead the trader to overconfidence about a technique on how it has been achieved in the past.
- Some strategies require specific spread conditions, meaning that those strategies may not be as effective during live trading.
- The trader may see a significant difference between the results of backtesting and the real results of live trading.
- Summer and winter time changes may cause confusion and mismatch price and history figures for specific charts.
- Often backtesting would not take into consideration the human component of the execution of a set of trading rules.
- Trading live means dealing with volatile market prices, therefore backtesting results may not matter.
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Backtesting is one of the most significant parts of developing a trading system and the process is an essential part of a complete trading plan. . Whenever made and deciphered properly, it can assist brokers with enhancing and improving techniques, find technical and theoretical flaws, just as gaining confidence in their strategy before applying it to the real-world markets.
Author – Priyanshu Ahuja
About the author – I’m a first-year student from City Premier College, Nagpur, pursuing BBA. My interest includes financial markets and investment domain.