Identifying Your Own Personal Preferences for Trading
In order to be a successful trader - either discretionally or algorithmically - it is necessary to ask yourself some honest questions. Trading provides you with the ability to lose money at an alarming rate, so it is necessary to "know thyself" as much as it is necessary to understand your chosen strategy.
The most important consideration in trading is being aware of your own personality. Trading, and algorithmic trading in particular, requires a significant degree of discipline, patience and emotional detachment. Since you are letting an algorithm perform your trading for you, it is necessary to be resolved not to interfere with the strategy when it is being executed. This can be extremely difficult, especially in periods of extended drawdown. However, many strategies that have been shown to be highly profitable in a backtest can be ruined by simple interference. Understand that if you wish to enter the world of algorithmic trading you will be emotionally tested and that in order to be successful, it is necessary to work through these difficulties!
The next consideration is one of time. Do you have a full time job? Do you work part time? Do you work from home or have a long commute each day? These questions will help determine the frequency of the strategy that you should seek. For those of you in full time employment, an intraday futures strategy may not be appropriate (at least until it is fully automated!). Your time constraints will also dictate the methodology of the strategy. If your strategy is frequently traded and reliant on expensive news feeds (such as a Bloomberg terminal) you will clearly have to be realistic about your ability to successfully run this while at the office! For those of you with a lot of time, or the skills to automate your strategy, you may wish to look into a more technical high-frequency trading (HFT) strategy.
A belief is that it is necessary to carry out continual research into your trading strategies to maintain a consistently profitable portfolio. Few strategies stay "under the radar" forever. Hence a significant portion of the time allocated to trading will be in carrying out ongoing research. Ask yourself whether you are prepared to do this, as it can be the difference between strong profitability or a slow decline towards losses.
You also need to consider your trading capital. The generally accepted ideal minimum amount for a quantitative strategy is 50,000 USD (approximately £35,000 for us in the UK). If I was starting again, I would begin with a larger amount, probably nearer 100,000 USD (approximately £70,000). This is because transaction costs can be extremely expensive for mid- to high-frequency strategies and it is necessary to have sufficient capital to absorb them in times of drawdown. If you are considering beginning with less than 10,000 USD then you will need to restrict yourself to low-frequency strategies, trading in one or two assets, as transaction costs will rapidly eat into your returns. Interactive Brokers, which is one of the friendliest brokers to those with programming skills, due to its API, has a retail account minimum of 10,000 USD.
Programming skill is an important factor in creating an automated algorithmic trading strategy. Being knowledgeable in a programming language such as C++, Java, C#, Python or R will enable you to create the end-to-end data storage, backtest engine and execution system yourself. This has a number of advantages, chief of which is the ability to be completely aware of all aspects of the trading infrastructure. It also allows you to explore the higher frequency strategies as you will be in full control of your "technology stack". While this means that you can test your own software and eliminate bugs, it also means more time spent coding up infrastructure and less on implementing strategies, at least in the earlier part of your algo trading career. You may find that you are comfortable trading in Excel or MATLAB and can outsource the development of other components.
You need to ask yourself what you hope to achieve by algorithmic trading. Are you interested in a regular income, whereby you hope to draw earnings from your trading account? Or, are you interested in a long-term capital gain and can afford to trade without the need to drawdown funds? Income dependence will dictate the frequency of your strategy. More regular income withdrawals will require a higher frequency trading strategy with less volatility (i.e. a higher Sharpe ratio). Long-term traders can afford a more sedate trading frequency.
Finally, do not be deluded by the notion of becoming extremely wealthy in a short space of time! Algo trading is NOT a get-rich-quick scheme - if anything it can be a become-poor-quick scheme. It takes significant discipline, research, diligence and patience to be successful at algorithmic trading. It can take months, if not years, to generate consistent profitability.
Sourcing Algorithmic Trading Ideas
Despite common perceptions to the contrary, it is actually quite straightforward to locate profitable trading strategies in the public domain. Never have trading ideas been more readily available than they are today. Academic finance journals, pre-print servers, trading blogs, trading forums, weekly trading magazines and specialist texts provide thousands of trading strategies with which to base your ideas upon.
Our goal as quantitative trading researchers is to establish a strategy pipeline that will provide us with a stream of ongoing trading ideas. Ideally we want to create a methodical approach to sourcing, evaluating and implementing strategies that we come across. The aims of the pipeline are to generate a consistent quantity of new ideas and to provide us with a framework for rejecting the majority of these ideas with the minimum of emotional consideration.
We must be extremely careful not to let cognitive biases influence our decision making methodology. This could be as simple as having a preference for one asset class over another (gold and other precious metals come to mind) because they are perceived as more exotic. Our goal should always be to find consistently profitable strategies, with positive expectation. The choice of asset class should be based on other considerations, such as trading capital constraints, brokerage fees and leverage capabilities.
If you are completely unfamiliar with the concept of a trading strategy then the first place to look is with established textbooks. Classic texts provide a wide range of simpler, more straightforward ideas, with which to familiarise yourself with quantitative trading. Here is a selection that I recommend for those who are new to quantitative trading, which gradually become more sophisticated as you work through the list:
- Quantitative Trading: How to Build Your Own Algorithmic Trading Business (Wiley Trading) - Ernest Chan
- Algorithmic Trading and DMA: An introduction to direct access trading strategies - Barry Johnson
- Option Volatility & Pricing: Advanced Trading Strategies and Techniques - Sheldon Natenberg
- Volatility Trading - Euan Sinclair
- Trading and Exchanges: Market Microstructure for Practitioners - Larry Harris
