Algorithmic trading, algo trading or automated trading refers to the use of computers to generate trading signals, send orders and manage portfolios. The computer uses advanced and complex mathematical models and formulas to make high-speed decisions and transactions in the global financial market. Similar to manual trading, sophisticated online platforms are used by algorithms to make decisions and transactions in the financial markets. However, since the use of fast computer programs and complex algorithms are used to create and determine investment strategies there are better opportunities for optimal returns.
Algorithmic trading is substantially more efficient than a discretionary approach. With a fully automated system there is no need for an individual or team to be constantly monitoring the markets for price action or news input. This help freeing up time for investors as the computer already analyse and deploy its algorithm automatically. Furthermore by automating the risk management and position sizing process, by considering a stable of systematic strategies, it is necessary to automatically adjust leverage and risk factors dynamically, directly responding to market dynamics in real-time. This is not possible in a discretionary world, as a trader is unable to continuously compute risk and must take occasional breaks from monitoring the market.
One of the primary element of an automated trading system is that there is no subsequent discretionary input. This refers to modification of trades at the point of execution or while in a position. Fear and greed can be overwhelming motivators when carrying out discretionary trading. In the context of systematic trading it is rare that discretionary input improves the performance of a strategy. That being said, it is certainly possible for systematic strategies to stop being profitable due to regime shifts or other external factors. In this instance judgement is required to modify parameters of the strategy or to retire it. Note that this process is still devoid of interfering with individual investments.
Systematic strategies provide statistical information on both historical and current performance. In particular it is possible to determine equity growth, risk, trading frequency and a variety of other metrics. This allows an “apples to apples” comparison between various strategies such that capital can be allocated optimally. This is in contrast to the case where only profit & loss information is tracked in a discretionary setting, since it masks potential drawdown risk.
This is a corollary of the efficiency advantage discussed above. Strategies that operate at higher frequencies over many markets become possible in an automated setting. Indeed, some of the most profitable trading strategies operate at the ultra-high frequency domain on limit order book data. These strategies are simply impossible for a human to carry out.
Algorithmic trading has attracted a lot of attention and hype over the last few years. In this section we will examine some of the common fears and myths that have attached themselves to this topic. Broadly speaking, these may be categorised into three main issues, namely safety, performance and usefulness.
Some of the most common concerns about algorithmic trading are:
There can be no doubt that algorithmic trading has transformed the world’s financial markets. Computers are ideally suited to working in complex markets since they can easily monitor the order books of a range of investment possibilities. If everyone use algorithms for their trading then clearly some self-reinforcement on trading patterns will occur, much like if every investor used the same technical analysis. The very reason why the financial market functioning is because investors have a diverse range of opinions. Therefore, investors target different prices and use a range of alternative trading strategies and there is no reason why algorithmic trading should alter this diversity. Algorithmic trading will certainly have a major impact. For instance, good algorithms may prove to be more efficient and also open avenues for investment, expanding the potential of multi asset investments
Another common fear when using investment algorithms is that it has become commoditised. However, being commoditised is not altogether a bad thing. It is important to have a certain level of standardisation. Having similarities in algorithms can help the financial market become more liquid and thus result in more investment opportunities for investors to profit on.
Up to now people are still questioning the success of algorithmic trading. However, most algorithms go through a series of sanity checks, so that these algorithms can be tested. Algorithms which were once only able to cope with liquid assets or small orders are now much more versatile. With time algorithms evolve to become better and provide investors with greater choice in terms of investment assets that can be bought or sold on the financial market.
If you look at it from the outside, an algorithm is just a set of instructions or rules. These set of rules are then used on the financial market to automate the execution of orders without any human intervention. In everyday trading, far more complex trading algorithms are used to generate algorithmic trading strategies. All the algorithmic trading strategies that are being used today can be classified broadly into the following categories:
Assuming that there is a particular trend in the market, there are specific algorithms designed to that trend. Further to this assumption, the markets fall within the week. Now, momentum or trend following algorithms will determine if this trend is going to continue or if it will change in the coming weeks. Accordingly, the algorithms will make the next move.
Here investors can also take benefit from the pricing inefficiencies that may exist for the same underlying across various trading destinations or instrument types. These strategies can be market neutral and used by hedge funds and proprietary traders widely.
Statistical arbitrage (often abbreviated as Stat Arb or StatArb) is a class of short-term financial trading strategies that employ mean reversion models involving broadly diversified portfolios of securities (a few to hundreds) held for short periods of time (generally seconds to days). These strategies are supported by substantial mathematical, computational, and trading, platforms.
It’s exciting to go for automation aided by computers with the goal of making money effortlessly. But one must make sure the system is thoroughly tested and required limits are set. The algorithms that members from Global Markets Club have access to, have been back tested to ensure that the right strategies are implemented and in a foolproof manner. Traders should keep the benefits and risks in mind when investing and take precautions to limit their exposure to the issues. Cautious use and thorough testing of algorithms can create profitable opportunities.