Stock Trading Bot

Introduction

As technology has evolved, so too have the means with which trading is a possibility. The rapid pace of development within the space of the internet has allowed algorithmic trading to become a possibility. Now, while several traders have sought to establish themselves as algorithmic traders, they often have a hard time coding their trading robots properly. It is easy to find misleading information pertaining to algorithmic trading which is responsible for skewered results.

Read on to learn more about stock trading bots and how they help fix such issues.

Understanding What Trading Robots Are

In simple terms, an algorithmic trading bot refers to a computer code which is capable of generating and executing buy and sell signals within financial markets. The primary features of such bots include the following.

• Entry rules which help make apparent the time to buy or sell
• Exit rules which outline when to close a current position
• Position sizing – these rules determine the amount needed to be bought or sold

Apart from requiring an internet connection and a computer to partake in algorithmic trading, traders need to make use of an electronic trading platform.

Algorithmic Trading Strategies

Prior to developing an algorithmic trading strategy, it is important to draw attention to some of the key traits each of these strategies should employ. 

• For starters, the strategy should be fundamentally sound from an economic as well as market stance.
• Viable statistical models incorporating mathematics should be used to develop the strategy.
• Traders must understand what information their robot ought to capture. In order for this to be a possibility, a trader’s robot should be able to capture spottable, regular market efficiencies. This is important as algorithmic trading strategies make use of fixed rules that take advantage of market behaviour. A single inefficiency in the market is insufficient to create a strategy around it.

The following strategies that algorithmic trading robots ought to consider include but aren’t limited to the following.

• Fundamental analysis – This could be carried out by considering revenue data or notes on earnings releases.
• Macroeconomic news – Take for example interest rate changes.
• Technical analysis – Moving averages, for example, are pertinent here.
• Statistical analysis - A relevant example here looks at co-integration or correlation.
• The microstructure of the market is equally viable.

Preliminary research pertains to the development of a strategy that best suits a trader’s personal characteristics. Here, elements such as time commitment, personal risk profile and trading capital are relevant.

Following this it is possible to zoom in on frequently occurring market inefficiencies. Once this is identified, it is possible to code a trading robot that is aligned with a trader’s personal characteristics.

The Relevance of Backtesting and Optimization

Backtesting revolves around validating a trader’s trading robot which involves cross-checking a code to ensure it does what the trader wants it to do. This also means the trader must understand how the strategy will perform across different time periods, market conditions and varied asset classes.

Once a trader codes a robot that works, they must seek to maximize its performance while reducing the overfitting bias. In order to maximize the trading robot’s performance, the trader must first select a viable performance measure that is capable of capturing the risk and reward elements along with consistency.

An overfitting bias arises in instances of a trading bot being too closely based on previous data. This sort of a bot will give its traders the impression of a high performance however, as the future never is able to mirror the past in its entirety, this strategy might fail. By training with additional data, removing unnecessary input elements and making the model simpler can prevent overfitting.

Live Execution

Provided the aforementioned factors are considered and applied, traders can carry out live trades with real money. That being said, they must be prepared to face the emotional highs and lows and be willing to address technical issues. This includes the ability to select the right broker and implement mechanisms that are capable of managing market risks as well as operational risks.

Equally important is the ability to verify the performance associated with the robot and whether it is similar to what was experienced during the testing stage.

Following this, trade bots must be monitored to ensure that the market efficiency owing to which a robot was designed is still functional.

Conclusion

Provided inexperienced traders follow a regimented and strict set of guidelines they can acquire success. That being said, aspirational traders must always keep their expectations in check.

While algorithmic trading can be rewarding, its success lies in the ability to understand. Courses or teachers that guarantee high rewards without an adequate understanding should be viewed with caution.

Frequently Asked Questions

Q1. What is an algorithmic trading robot?
A1. An algorithmic trading bot refers to a computer code which is capable of generating and executing buy and sell signals within financial markets.

Q2. Name some features associated with a trading robot?
A2. Features associated with a trading bot include entry rules, exit rules and position sizing.