Algorithmic trading system design

Do you know specifics of the instrument you want to trade? For example, if you trade live cattle futures, do you know how to avoid having 40, pounds of live cattle delivered to your front yard? I doubt it has ever happened to a trader, but it is certainly possible. The more you know about trading in general, the easier the algo trading process will be. A second skill is being good at math. You should have a good understanding of financial calculations, basic statistics and computing trading performance metrics. A related skill is being good with Excel or other data manipulation software such as Matlab.

You will be using such software a lot to supplement your trading strategy analysis, so the better off you are at math, the better you will be at algo trading.

The third important skill is to know how to run your chosen trading platform. This seems like a basic skill, but I always tell traders that they should keep learning their platform until they can fool it — i. By being skilled enough to trick the software, you can avoid many rookie and intermediate level mistakes. Being able to follow an established scientific approach to trading system development is a third skill every good algo trader has.

To create solid trading systems, you have to have a sound process for designing, developing and testing your algo strategies.

It is not as simple as just programming and trading. If you do not have the skills or ability to follow a set process, algo trading might not be for you. The final skill you need to have algo trading success is arguably the most important - programming ability. Remember a while back when I discussed trading software? Well, a key part of knowing which piece of software to use is knowing your programming abilities. The key is to be proficient in whatever programming language is required. Successful algo traders program hundreds or even thousands of trading systems over the course of a year.

That is because most trading systems are worthless — they lose money in the long run. Can you imagine paying someone to program worthless strategies for you? So, programming ability is well worth your time if you want to be a successful algo trader. Before I discuss a solid, proven process to developing profitable algo trading systems, it is worth pointing out some of the things NOT to do.

Almost every new algo trader falls into these pitfalls, but with a little forewarning, you can easily avoid them. Speaking from personal experience, steering around these traps will save you a lot of money. First, since many algo traders have programming, science and math backgrounds, they believe that their models need to be complicated. After all, financial markets are complex beasts, and more trading rules and variables should be better able to model that behavior.

More rules and variables are not better at all. Yes, complicated models will fit historic data better, but financial markets are noisy. Many times, having a lot of rules just models the noise better, not the actual underlying market signal. Most professional algo traders have simple models, since those tend to work the best going forward on unseen data.

Once a trading system model is complete, the second pitfall becomes an issue: optimizing. And just because your computer can run a million backtest iterations an hour does not mean you should. Optimizing is great for creating awesome backtests, but remember most of the market data is just noise. A trading strategy optimized for a noisy historical price signal does not translate well to future performance.

A third pitfall is related to the first two pitfalls: building a great backtest. When you are developing an algo system, the only feedback you get on how good it may be is via the historical backtest. So naturally most traders attempt to make the backtest as perfect as possible.

An experienced algo trader, however, remembers that the backtest does not matter nearly as much as real time performance. Yes, a backtest should be profitable, but when you find yourself trying to improve the backtest performance, you are in danger of falling into this trap. Be wary of any historical result that just looks too good to be true.

But almost without exception, those great strategies fall apart in real time. Maybe it was due to a programming error, over-optimization or tricking the strategy backtest engine, but having a healthy dose a skepticism at the outset keeps you away from strategies like this. Once you avoid the common pitfalls in algo trading, it is time to develop strategies in a controlled, repeatable process. The steps I use to create a strategy are given below. The process starts with goals and objectives. Like driving a car to a destination, you have to know where you want to end up before you begin.

Identify the market you want to trade, and also the annual return and drawdown you desire. You can have more goals than that, so that is really the bare minimum. Having solid goals and objectives will help you know when you should be satisfied with the trading algo you created, and will help you avoid many of the pitfalls described earlier. Tailor-made implementations meet even the most discerning client needs. AlgoTrader is a multi-asset class platform providing a single FIX connection to over liquidity venues. All major markets and asset classes are supported including a market-leading range of spot and derivative digital and crypto assets.

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Abstract This paper provides an overview of research and development in algorithmic trading and discusses key issues involved in the current effort on its improvement, which would be of great value to traders and investors.

Algorithmic Trading System Design & Implementation

References 1. AMD White Paper 3. Journal of Finance, , 97— Article Google Scholar 4.

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