Algorithmic trading eliminates human error, provides a high level of autonomy and allows perfect time management, among other virtues. Working with strategies will allow you to focus according to your needs. There is a wide variety and types of algorithmic trading strategies in operation and constantly new ones are created or improvements on existing ones are developed. Even so, the central strategies of what these algorithms do can be segmented into different categories, such as price action strategy, technical analysis strategy, combined strategy, index rebalancing strategies, High Frequency Arbitrage Trading Strategies, mean reversion trading strategies and Machine learning and Artificial Intelligence trading strategies.
One of the most used programming languages to carry out these strategies is Python, due to its versatility and large existing community, which allows you to learn quickly, program without great difficulties because it is aimed at new Python programmers and there is a lot of content on Python libraries which makes it easy to apply to solve complex problems such as algorithmic trading strategies.
The price action algorithmic trading strategies will analyze a variable as a contrast parameter, being able to evaluate opening and closing, or maximum and minimum of a candlestick chart, which deconstructed has 4 data (the opening prices, maximum, minimum and closing). The price will be set at the previous points of the 4 defined data and the bot will launch a buy or sell order if similar values are reached.
A combined algorithmic trading strategy incorporates technical analysis and price action to be able to verify hypotheses about price fluctuations by analyzing charts with indicators, and thus the bot can execute buy or sell orders based on that information.
When creating a combined trading strategy, the price of the stock in the underlying market must be analyzed. For this it is necessary to understand what movements in the price of an asset we want to identify through the different technical indicators to implement.
In this type of strategy, you must determine if you are going to invest long or short, and at what time you want the algorithm to operate. A combined strategy can be set up according to the market, the time frame, the size of the trade and the different technical indicators that the algorithm is designed to use.
Another type of strategies used in algorithmic trading are arbitrage strategies. Then, Mean Reversion Trading Strategies is the return of a market price to the historical average price. The support of this type of strategy is based on a mathematical model whose premise is that the high or low price of an asset is temporary and that it will return to its historical average during a certain period of time.
The technical trading indicators already mentioned above, like moving averages and Bollinger bands, are used in mean reversion trading strategies, since a moving average provides the average historical price of an asset, and on the other hand, Bollinger bands allow us to identify, using the standard deviation as a measure of its volatility, if a market has moved too far from the average.
A different and new form of algorithmic trading is the use of machine learning and artificial intelligence (AI). With machine learning and AI trading strategies, the trading bot is updated as it goes.
In this area of speculative strategy there are a large number of segments and subsections, and you can speculate with Artificial Intelligence (AI), with indicators, mixing both, with sentimental, and dozens of types. Therefore, it encompasses a little of everything described in this article.
In any case, whenever we talk about speculation, we mean the same thing: a prediction about what will happen and taking a position on it. For example, we may believe that something is going to go up, regardless of what method we do it through: AI, Indicators, a data feed, a twitter feed, etc.
If we believe that a future price is going to go up, we will take a buying position, and then that same speculative algorithm that you have defined in your code is a take profit, that will then decide when it has won enough, and in this way, it will execute the position closure.
