Curve Fitting Trading Strategies – (Examples and Backtest)

Curve fitting is when a strategy or edge is not fit to market behavior, but market noise, leading to failure in live trading. Curve fitting is overoptimization that is unlikely to fit into future unknown data.

You have probably read about curve fitting in trading books, trading magazines, and social media. The danger of Curve fitting is ubiquitous when designing trading strategies. It has the potential to ruin your trading career if not dealt with correctly and can be hard to notice, even for traders with decades of experience. In this article, we will learn what curve fitting is, and why you should try to avoid it.

(Before we go on we’d like to mention that we have a backtesting course that covers all aspects of how to backtest.)

Part I: What is curve fitting?

Backtesting in search of edges

When we perform backtests, we analyze data in search of recurrent patterns that have predictive potential. In other words, we want to know if the tested pattern can tell us when the market is prone to going up or down so that we can be in the market only when it is favorable to us. If we succeed to find patterns that we think mirror market behavior, we have a trading edge.

Since our trading business is, or at least should be entirely reliant on edges, their quality and robustness dictate how well we cope with carving out profits in the market. Therefore, it is critical that our edges continue to work well also into the future if we want to make any money.

A thought experiment

Insinuate that your observation of what you presume is an edge, is flawed and holds no merit. Insinuate that you cannot know if your edge will continue to deliver going forward and that an overwhelming majority of patterns you call edges, will not work at all.

Quite scary, isn’t it, when you are about to risk your own money on those very edges?

Well, this is not a thought experiment. It is reality.

The harsh truth about observations of market behavior

When we search for edges in the markets, most of what we assume is an edge, will be outright garbage! True edges are hard to find, and in your search, you will sometimes be completely certain that you have an edge ready to trade, only to see it fall apart completely once exposed to new market data. This is one of the aspects that makes trading so hard for beginners to succeed in, and that needs to be overcome before risking real money!

Asking a question

Now that we know about the tendencies of the markets to deceive us into believing in false edges, it is time for us to ask a question to understand why this is, and what it has to do with curve fitting.

The question reads as follows:

Of all patterns we observe, only a few are true edges. How come that some of the observations we make are edges and others are not?

Or the same question veiled in other words:

How come some observations are true edges and others are curve fit?

To answer this, let us begin by learning a few lessons:

Lesson 1: Markets are mostly random

The first thing every trader needs to grasp to be able to understand the concept of curve fitting, is that a majority of market action is random noise. Most market activity simply cannot be derived from any form of analysis and needs to be accepted as nothing else than random market noise.

Lesson 2: Most people want explanations, even to the inexplicable

We as humans have an urge to explain everything we see and experience. By doing so we bring order to a chaotic world, at the cost of quite often lying to ourselves. This tendency among humans can often be observed when financial news media covers recent market activity. The expert may ascribe soaring markets to some recent event, which seems perfectly reasonable. However, once the market turns around, so do often the experts explaining the downturn with the very same arguments.

In such cases, it is apparent that humans like to fit explanations to reality and not the other way around since both of our expert´s comments cannot be true at the same time.

The severe fallback of this inclination of the human mind is that reality is not very inclined to conform to our description of it. Curve fit edges will not hold, regardless of what reason we ascribe to its logic.

Lesson 3: Correlation does not equal causality – market behavior and market data are not the same

The third and last lesson we must learn before we can grasp the concept of curve fitting is that market data and market behavior are not the same.

Market behavior is non-random price action that holds predictive value, while market data consists of market behavior AND market noise combined. The consequence of this is that what seems to work in the backtest carried out on market data, cannot be taken for true market behavior before being put under scrutiny. It may very well be a result of randomness, thus holding no value going forward.

So, what is curve fitting?

Using what we now know from the three lessons in this article, we may define what we mean by curve fitting. Our definition reads as follows:

Curve fitting is when random market noise forms haphazard patterns in price data, that are later viewed and considered an edge, despite being a prod