Understanding Backtesting Implementation in Automated Trading Systems


Backtesting is a vital instrument that, as a financial trader, you will find useful when dealing with portfolios of strategies trading multiple symbols from various exchanges.

When you purchase a commodity, you would indeed check the brand’s history, offered features, and see if it’s a worthwhile investment. Automated trading systems follow the same principle and backtesting software is the ideal way to compare the trading strategies’ viability to employ the most successful ones.

What is Backtesting?

Backtesting is a trading strategy that involves applying historical data to determine its accuracy without risk capital.

The implementation leads to a set of trade signals, each with associated profit or loss. While you continue to backtest the strategy in question, the profit or loss data gets accumulated over time, which helps you assess the trading scheme’s success.

The tool is quite useful for eliminating strategies that don’t meet your performance needs, optimization, and verification to check for incorrect implementation.

Working Principle

The total profitability and risk levels are the two main factors considered during backtesting a strategy. But the tool also looks at other aspects related to the overall trading technique performance. 

The core theory is that if the strategy has not performed well in the past, it is most unlikely to perform well in the future. 

A successful backtest on a sophisticated algorithmic trading platform will indeed show you a strategy with proven historic positive results. However, the market never behaves in the same manner; the backtesting software assumes that stocks show similar trends as they did earlier.

Backtesting Implementation

You can engage an institutional-grade research and execution platform, on which a programmer can code a backtest and run the simulation on a trading strategy. Leading experts use C# and Python for coding, using a built-in IDE or Visual Studio 2019. Ideally, in automated trading, they use event-driven backtesting to connect to a real-time market feed, thus generating new trade signals.

The other crucial part of the implementation is to get the model tested across different market conditions for complete performance assessment. Accordingly, experts tweak the related variables for optimization for various backtesting evaluation parameters.

Backtesting Measures to Evaluate a Trading Strategy

The total profit or loss is a critical parameter that can help you determine whether the trading system actually can benefit you or not. The portfolio’s total returns over a given time frame and volatility, which is the dispersion of returns on the portfolio, are the other backtesting measures to consider.

You can also get the backtest results optimized against the risk-adjusted returns and market exposure, the parameters associated with trading risks.

Backtesting Features Available for an Automated Trading System

On a top algorithmic trading platform, you can backtest a portfolio of strategies using multiple cores and utilizing up to 100% of CPU. You can work with large data sets and view an extensive performance summary that includes over 70 critical metrics for every symbol traded by the strategies.

While viewing profit distribution for every PnL% is possible, you can also check MAE and MFE distributions for every MAE% and MFE%, respectively.

And above all, you get access to dozens of performance graphs, such as Equity Curve and Drawdown, that work as excellent visual aids for your analysis.

Biases to Consider

The qualified expert you hire should keep you aware of certain biases that can drastically impact your backtesting results. The most common types include optimization, look-ahead, and survivorship biases.

Though these biases are almost impossible to eliminate from algorithmic trading, specialists aim to minimize them, to make informed decisions about the strategies.

Backtesting has proved to be one of the top advantages of algorithmic trading, as testing potential trading strategies becomes possible before implementing them in the live market. Engage a specialist who offers a top-class platform, covering a full cycle of algorithmic trading with a lucrative backtesting software. Rest assured, you can have a successful strategy development, backtesting, optimization, and thus profitable live trading.