Automated Trading Systems

The Benefits Of Automated Futures Trading

Automated futures trading, also referred to as algorithmic trading or black box trading, has many benefits. Automated futures trading provides a futures trader buy and sell signals based on predetermined logic or “rules”.

An automated futures trading system is not required to transmit orders to a futures broker or commodities exchange; however many automated trading systems are capable of doing that. Minimally, an automated futures trading analyzes price data and notifies the futures trader when a buy or a sell signal has occurred. Automated futures trading systems should have one or more money management components built into them. Maximum risk for a trade should be known at the time the trade signal is generated.

Building an automated trading system requires some degree of software development. Depending on the nature of the trading system, software development can be extremely complex, or it can be trivial. Some systems are simple enough in design such that Microsoft will suffice as a development tool for creating an automated trading system.

One of the most important benefits of using an automated trading system is the ability to optimize the system. Optimization is a critical concept and enables the futures trader to fine tune and hone the automated trading system so that it produces maximum profits with minimal draw-down. Note that optimization will vary, depending on the underlying rules of the system.

Consider a simple system that states that a market will test or trade at its daily pivot point within the first hour of trading. A futures trader using this system is likely to ask such questions as, how often does this theorem prove to be true? If it is true 64% of the time, it is only natural to probe ways of optimizing this system. For example, suppose we modified the automated trading systems to test that a market will test or trade to its daily pivot point within the first thirty minutes of trading.

In the above example, optimization is in the form of modifying a time frame for an event that we believe will occur with some degree of probability; however, we are trying to determine the most optimal time frame in which the event will occur.

It is obvious that the inputs that are changed through the process of optimization will vary, based on the underlying theory being traded. The trader must be provided with the ability to optimize their automated trading systems by having access to change one or more input parameters that are relevant to the theorem being tested and traded.

Let’s consider an automated trading system that attempts to predict how far a market will trade as a result of a correlated event. For example, suppose a futures market trades higher than its previous day’s high and we feel that, as a result of doing so, the futures market will trade the same distance above yesterday’s high as it had traded below it during the current session.

In this trading system, the most obvious input parameter require to optimized the automated system would be the price parameter; specifically, how much momentum will the market provide, as it trades above the previous day’s high. We chose, as a starting point, that the market may trade the same distance above yesterday’s high as it had traded below it during the current session.

Suppose we find this phenomenon to hold true 40% of the time. We might be inclined to optimized our automated trading system by accessing an input parameter that decreases the amount of follow through – for example, suppose we wanted to optimize our system and test the frequency or probability that the futures market will trade above yesterday’s high by an amount equal to %50 that it had traded below it, during the current session. For example, if yesterday’s high was 100, and in the current session we traded at 80, then %50 of this difference would be 10 points. By including an input parameter the futures trade can use to optimized the automated trading system, we can test the probability of the market trading 10 points higher than its previous high (versus a full %100, or 20 points, above yesterday’s high).

In contrast to the first example, this example uses price as an input parameter used to optimize the automated trading system. In the prior example, time frame was used to optimized the trading system.

Many automated trading systems used stop loss inputs to help optimize trading results. Being able to modify the stop loss level to optimize an automated trading system should be a pre-requisite for any trading program.

As you can see, each idea intended to become an automated trading system has specific characteristics that need to be considered when thinking about optimizing the system.

In summary, a futures trader conceives an idea; software programs are used to create an automated trading system based on the rules of the system; money management principles are built into the system; optimization criteria defined and tools are supplied to the futures trader that let them optimize the automated trading system through modifying input values relevant to the automated trading system being used.