The Challenge of Curve Fitting in Forex Bots
If you are currently an Algorithmic Trader considering trading with a Forex Bot, or you've traded with a Forex robot in the past, then you'll want to read this entire article. In this article, I'm going to reveal the number one problem with Forex bots, and most importantly, three solutions that can help you address it.
Reading this article will likely save you thousands of dollars that might otherwise be spent on phony black box magical trading bots. It will also educate you on how to optimize your existing bot or algorithm for better performance over an extended period.
You should consider my advice because I've been trading with Forex bots for the past six years. During this time, I've encountered both the positive and negative aspects of using Forex bots, testing dozens of different Forex algorithms along the way.
At this point, we have what I believe is perhaps the most successful algorithmic Forex trading group on the internet. We've successfully helped over 500 individuals become professional funded prop traders using Forex bots. So, needless to say, we have valuable insights on this topic. Now, let's dive in.
My Experience with Forex Bots
Let me share with you how I came to discover the number one problem with Forex bots. Essentially, my journey began by seeking out a Forex bot with an impressive historical backtest performance. I did this not once, but twice, investing a substantial $10,000 in each instance for what seemed like incredible bots.
The initial results were quite promising. For the first two to three weeks, the performance was outstanding, and I was raking in profits. I was filled with excitement, waking up each morning to see profits rolling in.
However, this positive streak was short-lived. Suddenly, it seemed as though the bot had completely stopped performing. Almost every day, I found myself in the red, and within the first month, I had surpassed the maximum drawdown limit.
This stark contrast to the backtest results spanning three years left me bewildered. I began to wonder if the first bot I tried was simply not up to the task.
So, undeterred, I embarked on a quest to find a Forex bot with even more impressive backtest results. It boasted thousands of percentage gains with an incredibly low percent drawdown, and it had maintained its performance for over 10 years. I figured the odds of this one working were exceedingly high.
Understanding Curve Fitting and Over Fitting
This is how I stumbled upon what is known as "Curve Fitting" or "Over Fitting," which stands as the primary issue with forex bots. To better understand these terms, let's draw an analogy with a lock and key.
Imagine you have a lock at your front door, and you possess the perfect key for it. The key fits seamlessly into the lock and opens the door effortlessly. Similarly, when you have a trading strategy finely tuned to current market conditions, it works like a charm.
However, if you were to take that same key and attempt to use it on your neighbor's house, which has a different lock, chances are it won't work. This analogy mirrors the situation where a trading bot or algorithm excels in one market environment but fails when introduced to a new one.
Curve Fitting occurs when you tailor your trading strategy excessively to past market conditions, often neglecting to consider how future market conditions may differ.
When developing your trading bot or algorithm, focusing too much on past performance may hinder its future performance when it's applied beyond the historical data or what we call "out of sample," which I'll explain shortly.
Why does "Over Fitting" or "Curve Fitting" happen so frequently? Mainly because newcomers to algorithmic trading tend to prioritize trading strategies with the best backtest or historical performance, which is not as significant as it might seem.
While historical performance provides some insight into how a strategy could perform, it doesn't guarantee future results. "Over Fitting" or "Curve Fitting" can also occur when traders incorporate an excess of indicators or make their strategy overly complex, attempting to account for every past market variable.
Often, in such cases, the strategy becomes too tightly aligned with historical data, making it vulnerable to small random market fluctuations instead of capturing the broader market trend.
Strategies to Overcome Curve Fitting
Before delving into the three strategies to overcome "Curve Fitting" or "Over Fitting," it's crucial to understand why this issue poses a problem. Imagine, for instance, that we create a weather forecasting model trained exclusively on a tropical climate. Let's say it's tailored specifically to the climate of South Florida.
Now, if you were to take that same weather model and apply it to a completely different environment, such as the desert in Arizona, attempting to predict the weather there with a model exclusively trained on tropical weather, the chances of accurate predictions are very slim.
Similarly, an overfitted forex bot may excel in specific conditions for which it was trained but fall short in different market environments. The significant issue with "Over Fitting" or "Curve Fitting" is that it assumes the future market will resemble or mirror past market conditions, which is an unreliable assumption considering the ever-changing nature of the financial markets.
Effective Approaches to Address Curve Fitting
Now that you have a better understanding of what "Curve Fitting" or "Over Fitting" is and the issues associated with it, let's discuss how to address this problem. We've identified three effective approaches.
1. Out of Sample Testing: Imagine learning to drive. Before hitting the live road, your parents took you to a parking lot. This controlled environment allowed you to handle the vehicle, similar to how a forex bot or algorithm operates in historical data. Out of sample testing involves examining your strategy in periods outside the initial dataset. For instance, if you built your strategy using data from 2023, you'd test it in 2022, 2021, and 2020. This ensures the strategy's effectiveness in diverse market conditions.
2. Simplify Model Design: A common misconception is that complex models with numerous indicators perform better. However, we've found that simplicity often leads to future success. Think of it as a Swiss army knife—not perfect for every task but versatile enough to handle various situations. Similarly, a simple trading algorithm, while not the best tool for every scenario, can perform well across different market conditions due to its adaptability.
3. Continuous Optimization ("Curve Fitting" on the Fly): Remember those bots with impressive track records that worked exceptionally well for a brief period before faltering? We discovered that continuous optimization, or "curve fitting" on the fly, during these peak performance periods is highly effective. By regularly optimizing the strategy, you can prolong its peak performance, leading to remarkable results. This approach has proven successful for our community.