Backtesting and Automated Execution in Algorithmic Trading
Even while this blog focuses on certain strategy categories rather than generic backtesting procedures, there are a number of crucial factors and typical errors that must be solved before moving on to other strategies. Backtesting a strategy carelessly without being careful to avoid these dangers would result in a meaningless backtest. Even worse, it will be deceptive and could result in large losses.
It is plausible to doubt the statistical significance of these figures as backtesting usually entails calculating an expected return and other statistical metrics of a strategy’s success. We will go over several approaches to estimating statistical significance using Monte Carlo simulations and hypothesis testing. Generally speaking, the statistical significance increases with the number of round trip trades in the backtest. However, a backtest is not always predictive of future returns, even if it is executed precisely, free of errors, and has high statistical significance. A few significant historical examples will be emphasized to illustrate how regime changes can ruin everything.
The choice of a software platform for backtesting is also an important consideration and needs to be tackled early on. A good choice not only will vastly increase your productivity, it will also allow you to backtest the broadest possible spectrum of strategies in the broadest variety of asset classes.
Additionally, it will lessen or completely eliminate the likelihood of falling into the previously listed dangers. We will also discuss why selecting a good automated execution platform is frequently linked to selecting a good backtesting platform, as the optimal platform frequently contains both features.
The Importance of Backtesting :
Feeding your trading strategy with historical data to see how it would have performed is known as backtesting. We’re hoping that its past performance will provide insight into how it will perform in the future. If you have created a strategy from start, you will undoubtedly want to know how it has performed, thus the significance of this procedure is clear. Independently backtesting the technique is crucial, even if you read about it in a publication and you believe the author did not misrepresent its claimed performance. This is due to multiple factors.
Often, the profitability of a strategy depends sensitively on the details of implementation. For example, are the stock orders supposed to be sent as market-on-open orders or as market orders just after the open? Are we supposed to send in an order for the E-mini Standard & Poor’s (S&P) 500 future just before the 4:00 p.m. stock market closing time, or just before the 4:15 p.m. futures market closing time? Are we supposed to use the bid or ask price to trigger a trade, or are we supposed to use the last price? All these details tend to be glossed over in a published article, often justifiably so lest they distract from the main idea, but they can affect the profitability of a live-traded strategy significantly. The only way to pin down these details exactly, so as to implement them in our own automated execution system, is to backtest the strategy ourselves. In fact, ideally, our backtesting program can be transformed into an automated execution program by the push of a button to ensure the exact implementation of details.
After implementing every aspect of a strategy as a backtest program, we may examine them closely and search for potential problems with the strategy or the backtesting procedure. For instance, have we considered the fact that certain stocks were difficult to borrow and could not be readily shorted at any reasonable scale when backtesting a stock portfolio strategy with both long and short positions? Have we ensured that the closing prices of the two markets happen simultaneously when backtesting an intermarket pair-trading technique in futures? Although the entire list of potential problems is lengthy and laborious, I will highlight some of the most prevalent ones in the section under “Common Pitfalls of Backtesting.” Often, each market and each strategy presents its own very specific set of pitfalls. Usually, a pitfall tends to inflate the backtest performance of a strategy relative to its actual performance in the past, which is particularly dangerous.
Even if we have satisfied ourselves that we have understood and implemented every detail of a strategy in a backtesting program, and that there is no pitfall that we can discover, backtesting a published strategy can still yield important benefits.
True out-of-sample testing can be carried out in the time after publication by backtesting a published approach. The method may have only been effective on a small collection of data, therefore one must be cautious if that out-of-sample performance turns out to be subpar. Actually, many don’t realize how significant this point is. The backtest results were “verified with out-of-sample data,” as many writers will state in their studies. However, the authors could have simply adjusted a few parameters or made significant adjustments to the model to make the findings appear favorable with the “out-of-sample” data if the out-of-sample testing results were subpar. Hence, until a method is publicized and finalized, true out-of-sample testing cannot actually start.
Finally, we can frequently uncover methods to refine and improve a strategy to make it less hazardous or more profitable by backtesting it ourselves. Trading backtesting should adhere to the “scientific method.” We should begin with a hypothesis on an arbitrage opportunity, which may be derived from public research or our own market sense. Then, using a backtest, we confirm or disprove this theory. We can adjust our hypothesis and try again if the backtest results are insufficient.
The success of a strategy is frequently highly sensitive to specifics, as I have stressed, and even minor adjustments to these aspects can result in significant gains. Changing the look-back period for calculating the moving average or placing orders at the open instead of the close are two examples of these adjustments. By backtesting a strategy, we can test every aspect of it.
Read Also; Backtest Quantitative Trading Strategies
