Algorithmic trading

The creation of a trading strategy requires a combination of financial capabilities, programming ones and data analysis.

Here are some of the general steps to create algorithmic trading.

  1. Define your trading goals: before starting to plan a strategy, it is important to define your goals. For example, you may want to focus on a specific market of activity class, such as shares or currencies, or you may want to aim to a specific risk-income relationship;
  2. Collect and analyze data: once you have defined your trading goals, you need to collect and analyze relevant market data to identify potential trading opportunities. This can involve the use of technical analysis tools or fundamental techniques to study prices tendencies, market trends and other economic factors;
  3. Develop a trading algorithm: based on your market data analysis, you can create a trading algorithm which includes standardized rules to determine entry and exit. This can provoke the use of programming languages, such as Python or Java, to codify the logic of your trading strategy;
  4. Execute the algorithm backtest: before implementing your trading algorithm on live, you need to execute the backtest using historical data to value its abilities. This will help identify any weaknesses or flaws in your strategy so you can perfect it for better performance;
  5. Implement and monitor your strategy: after having tested and perfected your trading algorithm, you can implement it in your live trading process and monitor the performance. This will involve the monitoring of your performance and your strategy over time, making the needed amends and constantly analyzing market data to identify new opportunities.

Surely, the creation of an algorithmic trading results to be a complex process in which it is important to have a solid knowledge of the markets and of trading strategies, including risk management, to reduce possible losses as much as possible.

Which are the differences between automated algorithmic trading systems and those which use artificial intelligence?

Both AI trading systems and automated algorithmic trading systems are built to execute operations on financial markets without human intervention, but are different in the approach towards decisional process.

The automated algorithmic trading systems are based on rules which use predefined algorithms to analyze market data and to execute operations based on specific criteria such as price changes, volumes or technical indicators. These systems are generally programmed by human traders and require manual updates to adapt to the ever changing market conditions.

On the other hand, AI trading systems use automated learning algorithms, machine learning, to analyze big quantities of data and learn from models and market trends. They can adapt to changing market conditions and make decisions based on data in real time, which allow them to identify opportunities and risks which would not be evident to human traders. AI trading systems can also incorporate more than one source of data, such as news, social media and economic indicators, to make more informed trading decisions.

In synthesis, the automated algorithmic trading systems are based on rules and on predefined criteria, while AI trading bases itself on data and can adapt to changing conditions in real time.

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