When developing an algorithmic trading system, it’s crucial that you know how to assess its performance. This article outlines key tests you can perform on the system to make sure it is profitable and efficient.
Historical tests and walkforward testing can both provide invaluable tools for gauging the performance of your system, although backtest results must be used with caution as they often contain hindsight bias and overoptimization.
Historical testing is one of the best ways to assess the performance of an algorithmic trading system, helping identify any potential issues and making your strategy more efficient while increasing overall preparedness for trading.
Many trading platforms enable their users to backtest their strategies quickly and easily on historical data sets, providing a useful way for testing new ideas quickly and cost effectively.
Some platforms provide optimization studies, enabling traders to select the optimal inputs for an algorithmic trading strategy. To conduct an optimization study, enter your range for each input and allow the software to “do the math”; ultimately this allows for optimal inputs that would have led to optimal results.
Out-of-sample (OOS) testing is another form of strategy development testing which should be considered, to detect any overfitting or curve fitting within its performance. This helps detect any strategies which overfit or curve fit when developed over time.
OOS testing can be conducted using various types of data, such as price and volume. The goal of OOS testing is to ascertain whether a trading strategy can accurately forecast price movements and generate profits in the market.
When conducting OOS testing, it’s vitally important that the data used represent all aspects of a security’s history in order to avoid potential issues during live trading.
OOS testing often exposes strategies to overfitting, which can wreak havoc with their performance in live markets. Overfitting occurs when trading strategies see price anomalies that do not actually exist – an indicator of poor analysis that could lead to system failure in real trading conditions.
To prevent this from occurring, it is advisable to set aside a portion of historical data specifically for testing purposes. This will enable you to properly design, estimate, tweak and optimize a trading strategy on in-sample data before moving it out onto OOS data.
Walk forward tests provide an effective means to evaluate the performance of an algorithmic trading system. They enable you to determine whether or not it’s ready for real-time trading and can withstand time.
Backtest results, on the other hand, tend to be filled with hindsight bias and over-optimization; walk forward testing provides more realistic representations of market behavior than backtest results can. Since random patterns do not persist across data sets from different periods, any strategy which relies solely on random noise should no longer perform well when tested against new data sets.
The walk forward optimization process includes performing several in-sample and out-of-sample test runs over various time segments to optimize each segment using parameters chosen in an earlier optimization phase.
Once all in-sample and out-of-sample tests have been completed, the optimal parameter combinations are used to optimize new data segments – this process continues until all in-sample and out-of-sample segments have been optimized.
Walk forward testing has one additional benefit – taking into account market volatility – which enables traders to ensure that their trading system adapts with market personality and dynamics as they shift.
As part of the walk forward optimization process, results of walk forward optimization can also be compared with actual trading data from real live accounts to assess its robustness and help traders create strategies more likely to perform well in the market.
To begin performing walk forward optimizations, all that’s necessary to get started is selecting your fitness function of choice on the “Setup > Walk-Forward Optimizer” screen and setting your preferred number of in-sample and out-of-sample training windows.
Backtesting is an efficient way to gauge the performance of an algorithmic trading system. By comparing its results against historical information, backtesting enables you to accurately gauge if your strategy works or needs adjustments based on real results.
Backtesting can be carried out in various ways, with software analyzing historical market data being the most prevalent approach. This software allows you to simulate trades using identical trading indicators and strategies as in reality.
Backtest your system using popular programming languages like Python, R, or Matlab – popular among financial industry users for their powerful libraries for data analysis – Python, R or Matlab are three such popular choices which provide great libraries to analyze data in depth. With such tools at your disposal you can conduct various statistical analysis such as finding key patterns to assessing new information’s impact.
Backtesting trading strategies is essential to their effectiveness, so when conducting backtests you should test them across various timeframes and markets to help determine their viability as well as identify any problems which might reduce its efficacy.
Backtesting should include accounting for slippage and bid/ask spread effects, which can greatly alter a strategy’s profitability. When backtesting it is imperative that models include variables for such variables.
Additionally, it is also crucial that your strategy can produce profits across a wide variety of time frames and markets in order to reach the maximum Sharpe ratio.
As a beginner to backtesting, it is a good idea to begin by manually reviewing recent chart timeframes and prior trades in order to identify any problems in your strategy as well as understand its profitability in the past.
Build a backtesting engine capable of processing all of the data and implementing your strategy. You can do this using many languages, with Python and MATLAB being widely-used programs that offer free open-source access allowing for the creation of various analytical models.
Risk management is a core element of any project, and especially relevant when developing algorithmic trading systems. An effective risk management process includes identifying risks, mitigating them where possible and keeping an eye on progress to ensure the project stays on schedule.
Risks are any potential threats that could thwart a project’s success and should be identified and managed as quickly as possible.
Once risks have been identified, they should be prioritized and ranked. Lower-level risks that pose only minimal threats should be prioritized lower while high-impact risks that pose the greatest threat should take priority.
After ranking risks, teams can begin brainstorming potential solutions. These could include restricting access to certain information or changing procedures; providing all parties involved with clear expectations regarding compliance or explaining consequences if compliance fails; etc.
A company’s risk management plan must also outline all processes and controls required to effectively mitigate its risks, such as training employees to work on the project with certifications that match up and communicating with clients or customers when something goes amiss.
Plan for Resolving or Mitigating Risks Once risks have been identified and prioritized for resolution or mitigation, an action plan should also be developed that helps prevent them from reoccurring and ensures the project remains on track to reach its goals.
No matter if it’s identified manually or via risk management solution, any risks identified should be thoroughly analyzed and evaluated as soon as possible using methods like interviews, surveys and focus groups.
Review and compare results with other information to assess likelihood and impact. These analyses can also be used to identify and rank risks according to severity.
Risks with high severity but low frequency should be handled first, while those that present greater damage should be managed later.
Analyzing and assessing risks can take time, but the effort will more than pay off in the end. Doing so could help avoid creating an algorithmic trading system which crashes within minutes; or aid organizations in making informed decisions for their future and avoid costly errors.