For every trader, the question is how he makes his trading decisions. Two fundamental approaches can be followed for this. On the one hand, this is the discretionary approach, where decisions are made intuitively. The alternative approach is the systematic trading approach using trading systems.
In a discretionary approach, the trader intuitively makes his trading decisions based on the data available to him. As a rule, trading decisions are based on fundamental analysis or technical or quantitative models of analysis. However, these models are used only as support. Some discretionary traders rely solely on gut instincts to make their trading decisions. These traders usually have years of experience in the financial markets. They interpret the market behavior as well as the current news situation and make their trading decisions based on this information.
Automatic trading systems are computerized systems used to make trading decisions. These decisions are based on a predetermined set of rules, so such approaches are also referred to as algorithmic trading or as robots. The implementation of trade decisions is usually automated, which means that as soon as the algorithm generates a buy signal, this is automatically sent to the broker. In most cases, a set of rules is included in this process, which calculates the position size based on risk parameters. These systematic trading approaches can be classified into different types due to the way they make their trading decisions.
Trend following strategies try to invest in an existing or beginning trend. In an uptrend, this approach attempts to take advantage of the trend for as long as possible and closes a position only when the trend is no longer intact. The criterion when a trend is no longer valid is defined within the rules. Trend following approaches usually have a comparatively low trading frequency and a hit rate that is usually below 50%. Characteristic of these approaches is that most of the profit is earned through a few trades. These are the trades that succeed in positioning in a trend that lasts a very long time and is associated with a significant increase or decrease. Such a trend can persist for long-term oriented trend followers over several years. But there are also short-term trend-following strategies that operate in much shorter periods of time.
For systematic trading approaches based on breakout strategies, an area is defined in advance by a channel. If the prices leave the selected channel, the trading systems initiate trades in the direction of this breakout. This approach was followed, for example, by the so-called "turtle traders" in the early 1980s. They define the trading range of the last 55 days and as soon as the prices have broken above the upper limit of this range the financial instrument has been bought. In an outbreak below the lower limit, a sale was made accordingly. An essential factor in this strategy was the determination of the position size by means of a fixed set of rules. An outbreak strategy can also be short-term oriented and defined as a pure intraday trading strategy. In the opening range breakout approach, the trade margin of the first minutes of trading determines the upper and lower limits of the channel, and the breakout from this range is used to enter a position.
Countertrend strategies are based on the approach to determine the approaching end of an existing trend by determining reversal points, and then position themselves against the existing trend. In contrast to trend following strategies, these approaches anticipate the end of a trend. In a rising market, countertrend strategies try to set a point in time for a short position. This trade succeeds when a downtrend develops, or at least a price reset occurs.
Computer-based trading systems are not equally suitable for every investor. Unless the trader has defined the rules of a systematic trading strategy, the decision process of when a position is opened and when closed is not transparent to the trader. Therefore, such trading strategies are also referred to as "black box" strategies. It therefore needs a lot of confidence in the developers of such a trading strategy.
If a trader develops his own trading system, he can avoid the black box problem, but this requires programming skills and experience with the software used to implement the strategy. A faulty programming can quickly lead to losses.
Furthermore, technical problems can lead to disruptions that can have a negative impact on the trading result. Depending on the level of professionalism of the participant, the use of redundant techniques can substantially or almost eliminate the likelihood of failure. An important aspect of this is the uninterrupted connection to the broker. Because with a broken connection, the strategy can no longer be implemented correctly.
The technical stability of a trading strategy is put to the test, in particular during the publication of important news. Here it can come within a short time to significant price fluctuations. Even during so-called "flash crashes", a trading strategy with insufficient implementation may no longer be able to correctly implement the set of rules.
Another issue that can affect the reliability of an automated trading strategy is the pricing of the strategy. Some real-time data providers may receive incorrect price data. These can then result in the policy of the strategy to make transactions that would not have happened with correct data. Therefore, it is advisable to select only high-quality course data providers and to use techniques for filtering incorrect course data.
The rules and regulations of a systematic trading strategy are drawn up and evaluated on the basis of historical price trends or other data. For the future success of a strategy, the selection of this data is crucial. Because the strategy will make better decisions in the future, the more similar the future price data are to those of the past. For example, if a set of rules is created with data that only contains a bull market with rising prices, then the strategy in a bear market of falling prices can later get into trouble and fail to deliver the expected results. While this example may still be very obvious, elements of the data on buy and sell decisions that are not so obvious can also be relevant in the set of rules. Such changes in price developments are also called structural breaks.
It also uses automated trading strategies that interpret news. However, the algorithms are so far only able to interpret these messages really limited. Rather, in the publication of economic data, only the key figure is compared with the expected value and then completed a predetermined transaction. The actual interpretation of the key figure and the classification into macroeconomic contexts is still reserved for the merchant.
A well-defined set of rules and the automatic implementation of this logic also contains significant benefits for the trade.
A fundamental advantage of automated trading systems lies in the fact that they have more control over the assets invested in trading. Trading capital is subject to a permanent risk controlling process through fixed parameters.
Discrimination trading can be very emotional. Due to stress-related anxiety and greed, decisions are often no longer rational. It is difficult for many traders to sell a position which was still clearly in profit and then has given back some of these profits. He hesitates. He is waiting. If the profits continue to melt or if the position even falls into the loss zone, it is all the more difficult to make the decision to close the position. An automated trading system does not know this inhibiting hesitation. Once the criterion for closing a position is met, the order is placed with the broker and the position is closed. Stress and boredom are other emotions that can influence discretionary trading.
A key advantage of a fixed algorithm is that it can be tested. Once a trade idea is found, it can be simulated based on historical data. Often it turns out that this idea was indeed profitable in the current market environment, but if you go back only a few days or weeks, it shows that the trade idea in this period would have produced horrendous losses. Once a successful set of rules has been found, it can be systematically simulated in real time in a test phase before it is implemented in real trading. If there is no fixed set of rules, one or the other loss-making trade will be overlooked in the visual examination of a supposed logic.
Another advantage of mechanically implemented trading strategies is the avoidance of input errors during manual order placement. The confusion between price and order size is often the reason for significant losses or even market distortions. It has already been reported more frequently in the news that a "fat finger" was responsible for price distortions. This means nothing else than that a trader mistyped when entering an order.
By using an automated strategy, you can also achieve significant productivity gains. The robot usually works completely independently. The activity of the trader is limited only to the monitoring of the robot and gives the trader time.
The use of trading systems for the management of foreign assets also offers the investor a decisive advantage. If an investor invests in the fortunes of an asset manager or fund manager, the investment style may change as soon as the fund manager is replaced. This is usually not experienced by a investor or too late. When investing in an algorithmic strategy, the investor does not have that risk. He can be sure that the strategy will be implemented exactly in accordance with the rules, even if the person responsible for the leave is ill or otherwise prevented.