Back testing and Optimizing Trading Systems: An Essential Approach to Successful Investing

Investing in financial markets can be akin to navigating a vast, stormy ocean. The unpredictable waves of price fluctuations, the sudden gush of market news, and the unrelenting undercurrents of economic fundamentals can easily throw even the most seasoned investors off balance. It is here that systematic trading strategies come into play, offering a disciplined approach to investing and the promise of consistent returns. And the bedrock of these strategies? Back testing and optimization.

What is Back testing?

Back testing is a method used to evaluate the viability of a trading strategy by applying it to historical data. It is like a time machine, allowing us to travel back to past market conditions and see how our strategy would have performed. We would input a set of rules - conditions for entering and exiting trades, portfolio management, and risk controls - and run these rules on the chosen dataset. The resulting performance metrics give us an indication of how the strategy might perform in the future.

The core assumption in back testing is that history, to some extent, repeats itself. Therefore, a strategy that performed well in the past is likely to do so in the future. This, of course, comes with its own caveats, but more on that later.

Back testing Software and Programming Languages

While there are numerous back testing software solutions available, such as MetaTrader, NinjaTrader, and Backtrader, learning to code your own back testing system offers unparalleled flexibility. Python, with its powerful libraries like Pandas, NumPy, and Matplotlib, is a popular choice for this task.

What is Optimization?

After back testing, we often find that our strategy works well under certain market conditions but not so well in others. This is where optimization steps in. Optimization is the process of fine-tuning the parameters of our trading strategy to improve its performance. This could involve adjusting the values of technical indicators, refining the entry/exit criteria, or tweaking the position sizing rules.

The Art and Science of Optimization

While optimization may sound like a magic wand that can turn any mediocre strategy into a money-making machine, it’s not that simple. Over-optimization, for instance, is a common pitfall. It refers to an excessive curve fitting to historical data, which makes the strategy perform exceptionally well on the back test but poorly in real trading. 

To avoid this trap, we should optimize our strategy based on sound financial theories and not merely on the pursuit of the highest back test returns. We should also use out-of-sample testing, where the optimized strategy is tested on a different dataset from the one used for optimization.

Forward Testing

Another essential step in the optimization process is forward testing or paper trading. In this phase, we run the optimized strategy on live market data but without risking real money. This can give us further confidence in our strategy before we start live trading.

The Power of Robustness

A crucial objective in back testing and optimizing trading strategies is to build a robust system. A robust trading system is one that can withstand various market conditions and is not overly sensitive to changes in parameters. It is more likely to deliver consistent performance over the long term, which is the ultimate goal for most systematic traders and investors.

Backtesting and optimization are indispensable tools in the arsenal of systematic traders. They allow us to assess the potential of our trading strategy scientifically and enhance its performance in a disciplined manner. Yet, we must remember that these tools are not crystal balls that can predict the future. They are based on past data and come with their own set of assumptions and limitations.

What these tools do offer, however, is an effective way to learn from the past, adapt to the present, and prepare for the future. They enable us to sail confidently on the choppy waters of financial markets, guided not by gut feelings, but by data, analysis, and systematic decision-making.

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