Outline what we follow when developing our algorithmic trading systems.

• An algorithmic trading system is a set of rules that seek to best model repeatable non-random behaviour in markets.
• Define the behaviour, determine the conditions in which the behaviour persists, and then data mine and create code that best fits this non-random behaviour.

1. What is an algorithmic trading strategy?

When we are day trading, we have three key elements to every position: entry price, stop price, and target price. What determines each of these factors is a human decision based on an estimation that our entry will make a profit. Some traders have a set plan, others trade from their gut, but regardless of trading style, each entry, either consciously or subconsciously, is based on definite rules. We aim to model this market behavior and quantify it into a series of rules. Whether you are working on technical analysis, fundamental news, or price action, we aim to break down your entries into a series of rules that can then be tested against past data or data on various markets.

2. Process of creating our algorithmic trading system

For all our algorithmic trading systems, we follow this basic approach:

Define:

• All futures algorithmic trading systems are based on finding and pulling a fundamental truth about the market. Define what fundamental truth you’ll be going after. All markets have a tendency to trend beyond random. There’s more than one way to skin a cat, meaning there is no right or wrong method as long as you are trying to expose a market inefficiency that shows some repeatable behavior.

Determine Conditions:

• Determine the conditions under which the defined truth tends to occur. For example, let’s take trend-following truth: Following this approach, we will ask how to measure a trend. Since most trends occur randomly, we need a trend that is beyond a confidence level of randomness. Does this trending tendency beyond random exhibit the same degree of persistence beyond one year? Two years? Five years? If not, is there some point at which the persistence beyond random occurs every year? If so, does it also persist at the same frequency for 5, 10, or 50 different markets? If so, you’ve discovered a fundamental truth/market inefficacy which we can exploit.

Mine the data and create your code

• The next stage is to write code necessary to fit and create rules to model this behaviour. Once our basic coded rules are in process, we then determine how well it maps against the behavior. After you’re satisfied you’ve developed a satisfactory method for mining the behaviour, you can do an edge test to see if it happens beyond random. If not, use Monte Carlo simulations to determine confidence levels for trading the method. Determine at what confidence level you’ll stop trading. Examine the drawdown versus the profit. Is it worth risking any money on this? If so, allocate money using a money management scheme.

Creating our algorithmic trading system – Coding our rules

Some details regarding creating our algorithmic trading rules. There are three parts to our trade: entry, stop, and target. When we develop an algorithmic trading system, we look for an edge. An edge is a statistical estimation that our trade will be profitable over 1000 trades. It is the same as how a casino operates in blackjack. A casino works on an edge basis and knows that over 1000 hands of blackjack, they have the probability of winning in their favor – this is a mathematical and statistical absolute.

• Entry: For a winning system, we want our entry to have a statistical edge. When we enter a trade, we expect that our entry has some predictive power in our favor.
• Stop: When we create a stop for our entry, we expect it to have some predictive power that the odds of our success for our trade have diminished. We either trail stop to lock in gains or stop out to ensure we don’t lose any more capital.
• Target: This has some predictive ability to determine that the market is most likely to either stall or reverse at this point. We expect our target to accurately predict, based on the market action, that it has given us a sign or quantitative data to tell us to exit at this point.

When we create an algorithmic trading system, we don’t want our entry, stop, or target to be static. This means we can’t just choose a stop based on our account size as this has zero predictive power over current market conditions. Static stops and targets mean our system is not accurately assessing the current market and quantitative data.

We use various strategies and methodologies to ensure our trading systems remain robust and effective in different market conditions. By continuously refining our approaches and leveraging advanced techniques like algorithmic swing trading, we aim to stay ahead in the competitive landscape of trading.

Explore more about how we create and optimize our trading systems here.