uTrade Algos

How to Improve the Probability of Backtesting Strategy

April 3, 2023
Reading Time: 5 minutes

Want to increase your chances of success in the markets? Backtesting can help by testing your trading strategies on historical data. Follow our tips on how to effectively conduct backtesting, including using multiple data sets, considering relevant time periods and samples, and avoiding bias and data dredging. This valuable tool is useful for both systematic and discretionary traders and can help you make informed decisions about your strategies.

There are far too many factors involved when it comes to making money in the markets. Several strategies are thought of, but few will actually give you the ‘Eureka!’ moment. Therefore, traders should practice backtesting before the implementation of any strategy using historical data – be it automatic or manual trading – to boost the probability of success. Since we cannot see or predict the future, especially when so many variables are involved, the next best thing we can do is to know how a model behaves when certain past data sets are input into it. Backtesting, thus, is fast emerging as an essential tool. 

What is Backtesting?

What is Backtesting

Backtesting Feature on uTrade algos

Backtesting is the process which sees how well a strategy or model would have done ex-post. With the help of this, by using a set of historical data, one can check the viability of a trading strategy. If a trading strategy works during backtesting, one can also be confident of implementing it going forward. Added to this, if too many conditions are not fitted into the model, it can also prove to be effective in future scenarios. Hence, both systematic and discretionary traders have begun to consider it as a very useful tool. 

Why Backtesting?

Backtesting helps one to find how a particular strategy will pan out in the future. This is done using historical data to check how it behaves with these data sets. The theory behind this is that if it has worked well/poorly in the past, it will do so in the future as well. That is, if​​ a well-conducted backtest gives a positive result, it means that the model at hand is sound enough to be used and is likely to yield profits when implemented. On the other hand, if the strategy gives suboptimal results, it should be rejected and not used in reality, as it will most likely result in losses.  

Things to Note While Checking if the Trading Strategy Works

Things to keep in mind while Backtesting
Using More Than One Data Set

Backtesting uses historical data. Hence, using only a single data set is not advisable. To see the viability of a model, one should test it on different time periods or use out-of-sample data to confirm its potential viability.

Using the Correct Sample
Relevant time period :

When backtesting, the sample data must be chosen from a relevant time period which reflects the market conditions accurately. Only then can one judge if the backtest results represent a fluke or a sound trading decision.

Relevant data set :

The historical data set that is being used should have a true representative sample of stocks, which includes those of companies that eventually went bankrupt, were sold, or were liquidated. If data from only those companies are used which are still in existence in the present time, then the results that the test will yield will be artificially high returns.

All Trading Costs Should be Considered

No matter how insignificant, when backtesting, all trading costs should be taken into consideration. Else the results can show a profit/loss, which is not a true representation. Hence, when the model is being prepared, a trader must ensure that his backtesting software takes into account all costs.

Conduct Scenario Analysis 

Often, so as to ensure that the backtested results are the correct strategy, traders also use scenario analysis. This is a process wherein hypothetical data is used to see what the outcomes are. This helps a trader to find out how the values of a portfolio will change in case of the occurrence of an unfavourable event. The results of this, together with the backtested one, are then analysed together to come to a decision on whether a strategy is viable or not. 

Avoid Bias

If backtesting has to provide meaningful results, the testing has to be done where no bias creeps in. This means the development of the strategy should happen without relying on the data used in backtesting – the model should be built using data that is different from the ones that are being used for testing. Else, the results of the test will mean nothing. 

Avoid Data Dredging

In data dredging, traders use the same data set to test several hypothetical strategies. This might result in giving outcomes that show success during the testing, whereas they are likely to fail in real-time markets since many invalid strategies might land up beating the market over a given period of time. One way to avoid this is by picking a strategy that works for a given time period when an in-sample is used and then using that in a different time period or an out-of-sample. If the results then, too, stay the same, then the strategy can be considered to be a viable one. 

Avoid Over-optimisation

Optimising a model makes it more efficient. After an algorithm is designed, beta testing happens, wherein parameters are added and/or removed so that the model works best. However, often, over-optimisation occurs, which makes the strategy not reflect the picture accurately when data is input into it. Excessive adjustment of the variables of an algorithm is what leads to over-optimisation. Often this is done so that the program’s performance is maximised. In the process, though, it no longer reflects the market picture accurately.

‍Summarizing why Backtesting your trading strategies is necessay

There are always factors like luck, randomness, trading psychology, and unpredictable transaction costs, which can make the real-time results far different from what backtesting revealed. Having said this, backtesting has come to play a significant part in a trader’s strategy development. If performed while keeping in mind the aforementioned tips, it will help in creating sound strategies that can be used in live markets. 

Backtesting is the process of testing a trading strategy or model using historical data to check its viability in the future.

Conducting backtesting can boost the probability of success for both systematic and discretionary traders.

It’s essential to use more than one data set, choose a relevant time period, consider all trading costs, and avoid bias and over-optimization while conducting backtesting.

Backtesting, along with scenario analysis, can help traders find out how a strategy will perform in different scenarios.

Backtesting results can help create sound strategies that can be used in live markets.

Backtesting has limitations and does not account for factors like luck, randomness, and unpredictable transaction costs.Traders must keep these limitations in mind while developing a trading strategy.

Explore our platform to learn more about trading strategies and how backtesting can help improve your trading performance.

Frequently Asked Questions

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uTrade Algo’s proprietary features—advanced strategy form, one of the fastest algorithmic trading backtesting engines, and pre-made strategies—help you level up your derivatives trading experience

The dashboard is a summarised view of how well your portfolios are doing, with fields such as Total P&L, Margin Available, Actively Traded Underlyings, Portfolio Name, and Respective Underlyings, etc. Use it to quickly gauge your algo trading strategy performance.

You can sign up with uTrade Algos and start using our algo trading software instantly. Please make sure to connect your Share India trading account with us as it’s essential for you to be able to trade in the live markets. Watch our explainer series to get started with your account.

While algo trading has been in use for decades now for a variety of purposes, its presence has been mainly limited to big institutions. With uTrade Algos you get institutional grade features at a marginal cost so that everyone can experience the power of algos and trade like a pro.

On uTrade Algos, beginners can start by subscribing to pre-built algos by industry experts, called uTrade Originals. The more advanced traders can create their own algo-enabled portfolios, with our no-code and easy-to-use order form, equipped with tons of features such as robust risk management, pre-made algorithmic trading strategy templates, payoff graphs, options chain, and a lot more.

From single-leg strategies to complex portfolios, with upto five strategies, each strategy having up to six legs, uTrade Algos gives one enough freedom to create almost any auto trading strategy one likes. What’s more, is that there are pre-built algos by industry experts for complete beginners and pre-made strategy templates for those who want to try their hand at strategy creation.

An interesting feature that uTrade Algos is bringing to the table is a set of pre-built algorithms curated by top-ranking industry experts who have seen the financial markets inside out. These algorithms, called uTrade Originals, will be available for subscribers on the platform.

Algos have the capability to fire orders to the exchange in milliseconds, a speed which is impossible in manual trading. That is why traders leverage the power of algo trading to make their efforts more streamlined and efficient. You can try uTrade Algos for free for 7 days!

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Knowledge Centre & Stories of Success

In the ever-evolving landscape of algorithmic trading in India, AI has emerged as a game-changer, changing the way trading strategies are developed, executed, and optimised. At uTrade Algos, we recognise its significance and are eager to share the multifaceted role of AI in algorithmic trading platforms within the dynamic realm of financial markets.

In the fast-paced world of algorithmic trading, on various algo trading platforms, market making plays a pivotal role in ensuring liquidity, facilitating efficient price discovery, and maintaining orderly markets. This blog delves into the fundamentals of market making, exploring its principles, strategies, and execution in algorithmic trading.

In today's ‘fast’ financial markets, every second counts. For traders seeking to gain an edge in this high-speed environment, optimising execution algorithms on algo trading platforms for low latency trading is paramount. In this guide, we'll delve into the intricacies of low-latency trading and explore strategies to optimise execution algorithms for maximum efficiency. It is our endeavour at uTrade Algos to assist you in understanding these complexities and empower you with the tools and knowledge needed to thrive in the fast-paced world of algo trading in India and across the globe.

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