Algorithmic Trading Software – Empower Your Investment With AI

This algorithmic trading software article was written by Erica McGillicuddy, Analyst at I Know First.

algorithmic trading
(Source: List A Token)

Summary

  • Algorithmic trading software automatically takes a variety of factors into account to determine what stocks to buy and sell
  • Algorithmic trading software platforms such as Quantopian help users learn about writing algorithms, and give them a variety of tools including the ability to backtest
  • I Know First indicators can contribute to an algorithm so that it accounts for traditional stock statistics as well as I Know First algorithmic forecasts

What is Algo Trading Software?

Algorithmic trading utilizes process and rules-based algorithms to automatically execute programmed trading instructions. These algorithms take into account price, timing, volume, target quantity, delay time, and more. They can make decisions regarding the buying and selling of assets on their own, and advanced programs are able to implement machine learning for functions such as the scanning of current articles for keywords to make decisions based on news seconds after it is released. However, it is important to exercise caution in algorithmic trading through backtesting and watching the algorithm’s results to achieve the highest upside.

algorithmic trading
(Source: Investopedia)

When done cautiously, algorithmic trading has tons of upside. Trades are made instantly at the most desirable prices subject to the market, based on the rules that have been set for the algorithm. Unlike a human, programs can simultaneously keep themselves updated on a variety of market conditions. Algorithmic trading also reduces time spent, risk of manual error, and takes out the emotional factor. The rules can be backtested based on historical data to get an idea of how well it will play out in the current market.

Algorithmic trading can be a huge asset to I Know First subscribers as it reduces the daily time commitment to actively trade. Once coded, the algorithm should consider I Know First’s signal and predictability values and base buy and sell decisions off of how you weigh those values.

algorithmic trading platform
(Source: LearnBonds)

Do You Need to be a Coding Expert?

As algorithmic trading is becoming increasingly popular, there are a variety of platforms to choose from that make the practice of algorithmic trading more accessible to individual traders. Some of the most prevalent algorithmic trading software platforms are Quantopian, QuantConnect, Quantiacs, WorldQuant, and Numerai, all of which run on Python. These companies crowd source code from amateur coders to develop the most successful algorithms. The amateur coders are motivated to share their code because the coders who build the most profitable code receive compensation.

While you by no means need to be a coding pro to use algorithmic trading software, it is important to have an understanding of the basics. Quantopian, QuantConnect, and Quantiacs are more optimal for less experienced coders than WorldQuant and Numerai which prioritize their code-building tournaments. QuantConnect offers an online bootcamp, and Quantiacs offers tutorials and sample code, but Quantopian has the most extensive offerings for beginners.

algorithmic trading
(Source: Medium)

Quantopian states that, “We don’t expect you to be a Python expert, but you should have a basic working knowledge of the language.” It provides a free comprehensive library of lectures that start with the basics and increase in complexity and detail, allowing users to gain that knowledge. Quantopian also offers recipes which consist of snippets of code that users can implement in their own programs.

What Are the Basics?

Before building any code, it is essential that you learn some of the common functions for the platform’s API. This may include how to:

  • Identify the current price of an asset
  • Order and sell a specific amount of shares
  • Determine your cash availability
  • Find out how many shares of an asset you have
algorithmic trading backtesting
(Source: Quantopian)

Once you have learned the basics of Python and the platform’s API, the next step is putting your knowledge to use. There are two main steps to creating an algorithm: writing and backtesting. The writing stage involves writing code and adding snippets of example code that will follow the rules that you have set for trading. When this is complete and the errors have been fixed, you move on to the backtesting stage. This involves testing the algorithm using historical and real-time data to determine if it will likely be effective in the current market. In order to properly test effectiveness, backtesting must be done over many time horizons, assets, and market conditions. While backtesting is valuable and can be used as a gauge, it is crucial not to adjust too much based on it. The future has many similarities to the past, but it is not identical so if your algorithm is heavily based on the past, it will not meet expectations. Check out this I Know First article for more detail on how to get the most out of backtesting.

How Can I Know First be Used for Algorithmic Trading?

I Know First’s algorithm produces signal and predictability indicators associated with every stock in a forecast. The signal indicates the direction of growth that is anticipated, and the predictability indicator represents the accuracy of past predictions for that stock. You can learn more about these indicators here. The values of these indicators can be implemented into algorithmic trading to achieve investor goals.

algorithmic trading steps

The diagram above emphasizes the general steps to follow in algorithmic trading. I will go through a simple example and how I Know First forecasts can help in building the most effective algorithms. Let’s say that a trader’s goal is to increase their returns through purchases of conservative stocks on long-term horizons, and aggressive stocks on short-term horizons. I Know First offers a risk conscious and aggressive stocks package which include the top 10 conservative and aggressive stock picks for the long and short positions every day. The forecast offers six time horizons: 3 days, 7 days, 14 days, 1 month, 3 months, and 1 year.

One of the most basic strategies would be to automatically buy the top however many stocks in the daily forecast, and sell them when the time horizon is met. Alternatively, this trader could program their algorithm to disregard any stock with a predictability less than 0.5, and any stock which doesn’t have a strong signal across all six time horizons. The values for signal and predictability of each remaining stock could then be multiplied to create a new scale of comparison. If this value exceeds 100 then the stock is bought, and if it is less than 100 it is not. This is just one example of how to apply I Know First forecasts to achieve your trading strategies, however there are many more ways that are more specific to your goals. High efficiency algorithms that are more complex may even use AI to self-adjust the weight of the signal and predictability values. More information regarding implementation of I Know First forecasts in algorithmic trading can be found here.

Conclusion

Algorithmic trading is a big part of the future of trading. It reduces manual errors, the psychological factor, and daily time spent trading. Algorithmic trading also allows for in depth backtesting which tests out a plan for trading before using it in the live market. There is a range of algorithmic trading platforms that offer different strengths, allowing you to select the platform that best suits your trading needs.

Incorporating the I Know First indicators with traditional algorithmic trading strategies, I Know First traders will have an advantage thanks to the additional information they can implement in their code. I Know First will be releasing another algorithmic trading article shortly, which will focus on more advanced algorithmic trading strategies.

I Know First Past Success with Risk Conscious Stocks

I Know First has a history of strong predictions in its risk conscious package. This November 18, 2018 1-Year Conservative Stock Forecast correctly predicted 9 out of 10 movements and saw an average return of 16.96%. This is the perfect example of a forecast that would be used in the example above, as it predicts conservative stocks on a long time horizon.

Defensive Stocks

This July 8, 2020 14-Day Aggressive Stock Forecast would also be used in this strategy as it predicts aggressive stocks on a short time horizon. This forecast accurately predicted 8 out of 10 stock movements and had an average return of 21.98%.

Stock Scanner
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Please note-for trading decisions use the most recent forecast