If you’ve ever tried developing a trading bot, you know that it’s not as simple as it sounds. In fact, it can be quite challenging, especially if you’re a new programmer. Even if you’re an experienced programmer, there are still a few potential mistakes that you could make that would make you want to give up on your bot-building endeavors. Here are three of the most common mistakes traders make when developing trading bots.

These mistakes include:

1. not having a clear trading strategy,
2. not correctly backtesting the trading strategy, and
3. not understanding the limitations of trading bots.

By avoiding these mistakes, traders can develop trading bots that fit their needs and help them achieve their trading goals.

Clear Trading Strategy

Without a clear trading strategy, it’s very difficult to code your bot in a way that will be profitable and consistent. A trading strategy doesn’t have to be complicated, but it does need to be clear to both you and to your computer.

A clear trading strategy is painfully specific. To your computer something like an Ichimoku chart is just an aggregation of pixels meaning nothing. You have to translate that screen into specific data point events, and quanta that a computer can understand.

Lots of times, traders will try to build one super bot that can do everything in every market condition, and as a result, the bot ends up being too complex and too difficult to use. Instead of trying to build a “jack of all trades” bot, it’s much better to focus on one specific strategy for one specific market condition and build a bot that can execute that one strategy in that one specific market condition well.

You can build trading bots in layers, testing and improving each individual layer before adding the next one. This will make your bot much simpler and easier to use, and it will be more likely to be profitable in the long run. Each layer can become its own individual framework that you could use to propagate additional bots with different characteristics for the different market conditions — trending, breakout and consolidation.

Backtesting

Backtesting is the process of testing a trading strategy on historical data to ensure that it is effective and doing what you intended. Correct backtesting includes not over fitting the data to produce good results that come from a few anomalies, e.g. big profit trades from earnings surges, or good results that come from highly correlated underlyings.

Purposeful backtesting also means running the bot through historical periods that show all the different market conditions. A trending strategy bot that shows a good winning % and low drawdowns in a particularly favorable test data set may become a disaster when deployed in a range bound market.

There is no hard and fast rule about the minimum number of trades your bot should make during backtesting before you pull it off the assembly line and send it out the door. Are 50 enough? Are 100?

Use your charting software to visually inspect exactly where your buys, sells and exits occur on the historical charts. That will give so much more effective feedback than mere statistics and ratios that you get from a backtesting program.

Limitations

Trading bots have several unavoidable limitations. First, they are only as good as the trading strategy that they are based on and the clarity which with the strategy was coded. If the trading strategy or the coding is not efficacious for the intended market conditions, the bot will not be effective.

Trading bots can only trade based on the information that they are given. This means that if there is incorrect or incomplete information, the bot will make incorrect trades. It makes sense to check often on the integrity of the data you have used for backtesting and that which you will be using during implementation.

Science fiction stories aside, bots are not able to think at all, never mind creatively, and your trading bot will never adapt on its own to changing market conditions. A trading bot that performs well in high volatility conditions is likely to over-trade at a loss in consolidations.

The biggest and most important limitation of all is that most retail trading bots are designed to focus only on the timing and execution of trades. Money management and risk tolerance are far too important to treat them as an afterthought in bot development.

The best way to learn how to develop a trading bot is to find resources created by experienced bot developers. These resources can include books, blog posts, YouTube videos, and even courses that teach you how to code a bot. Once you have a basic understanding of how to code a bot, you can begin to develop your own money management rules and trading strategy and code them into a trading bot you’ll be happy with.