1. Trading Systems: Introduction
  2. Trading Systems: What Is A Trading System?
  3. Trading Systems: Different Markets and Types
  4. Trading Systems: Components of a Trading System
  5. Trading Systems: Building a Trading System
  6. Trading Systems: Other Considerations

You should now be familiar with some common elements that make up a trading system, the advantages and disadvantages of using them, some of the different markets and strategies that can be used to build them, and the basic components of a trading system. We also looked at how to build a basic moving average crossover trading system from scratch.

Let's take a look at some other important considerations before you get started.

Common Mistakes

Trading systems may seem relatively straightforward to develop, but great performance in a simulation doesn't always translate to great live trading results. There are many different reasons for these failures, including those beyond the control of the developer, but there are some common mistakes to look out for when developing trading systems.

Some of the most common mistakes include:

  • Using the Wrong Data - Many trading system developers are tempted to use the closing price for equities when calculating a technical factor. Unfortunately, these traders don't place trades after the close, so the slight look-ahead bias can skew backtesting results.
  • Making Unrealistic Trades - Some trading systems allocate large amounts of capital to small companies, which causes a host of issues when translating the results to live trading. Some companies may have a limited market cap or volume, while your trades may actually impact the price of others.
  • Curvefitting the Model - Many trading systems are involve training a model with sample data, but there's a limit to that training. If you train too much, then the model is curve-fit to that dataset and may not work as well on different data sets. If you train too little, then the strategy may not be fully optimized. The goal should be to train a system up to the point at which it's high performing and generic enough to use anywhere.
  • Improper Training - Many trading systems, such as neural network models, involve training a model with a dataset to help it learn. When backtesting these trading systems, it's important to use a different dataset than the training dataset.

The best way to avoid these problems is often to paper trade a strategy before committing real capital. That way, you can determine how the system performs in real life and weed out any instances where these biases could have made an impact.

Helpful Resources

Popular Brokers

  • InteractiveBrokers
  • TradeStation


  • Tradecision

Programming Languages

  • Java
  • .NET
  • VB
  • Python
  • R
  • MatLabĀ®

Online Platforms

  • Quantopian
  • QuantConnect
  • QuadMod

Looking Ahead

You should now have a basic understanding of trading systems and how to develop them, as well as access to some of the resources that you can use to learn more.

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