How Genetic Algorithms and Machine Learning Apply to Investments

Learn how genetic algorithms and machine learning can help hedge fund organizations manage a business. As well as bolster investor confidence and improve profitability.

As a hedge fund shareholder, you certainly want the best for your organization, right?

For instance, you want to generate effective AUM, NAV, and share value reports to improve investor confidence as a manager.

Or enable your company to produce maximum profits as a trader or employee, etc.

Well, it doesn’t need to be that difficult.

This article looks at how genetic algorithms (GA) and machine learning (ML) can help hedge fund organizations. For instance, to manage a business, boost investor confidence and increase profitability.

Let me walk you through these.


Modern machine learning and back-testing; how quant hedge funds use it

1. Manage funds and make investment decisions

First off, hedge fund companies require sound investment decisions to enable profitability.

As such, over 56% of hedge fund managers use AI and ML when making investment decisions. And their percentage is expected to increase sharply over time.

They do so since investment algorithms are effective as they aren’t affected by opinions, emotions, and judgments like their human counterparts.

“Most of the hedge fund managers surveyed are leveraging advanced algorithms and human judgment to deliver smarter investment decisions.” This is according to Barclay Hedge founder and President Sol Waksman in his July 2018 statement.

2. Perform quantitative analysis

Similarly, hedge funds often use modern machine learning and back-testing to analyze their quant models. Machine learning has done a lot to help them improve financial trading. They do so to ensure that they’re in top form or of the highest quality.

Here, the models get tested using historical data to evaluate their profitability. And their risks before the organizations invest real money.

According to Insight FactSet, hedge funds can use ML to find patterns in data. As a result, it allows models that explain stock performance based on different factors, such as company activity and pricing.

Besides that, integrate advanced back-tests to test their algorithms from time to time to ensure that they’re in tiptop condition.

Methods of Algo-trading, machine learning tests, back-tests

1. Algo-trading approaches

Hedge funds often prefer Algo-trading strategies over human traders or analytics as they generate profits faster.

Some of the commonly used approaches include:

2. Trend-following strategies

These methods typically observe price shifts, channel flare-ups, moving averages, and associated technical indicators to decide. For instance, issue a purchase command when an asset’s price rises. And give out a sell order when the asset’s price falls.

However, they do not call for price forecasts or predictions, making them easy to implement.

3. Mathematical Model-based Strategies

Unlike trend-following approaches, these methods use time-tested and proven mathematical prototypes to enable combination-based trading.

Here, a method instructs a quant model to buy or sell stock when a specified mathematical condition is met. Or withdraw or deposit particular sums of money. An example of such is the delta-neutral trading approach.

Machine learning tests

As a routine, hedge funds usually test new and incorporated ML’s to determine their effectiveness to maintain competitive advantage.

To do so, they typically use the following evaluations:

1. Pre-train tests

They’re mainly carried out early on when developing a new ML to identify bugs to avoid needless training.

They include:

  • Tests that check an organization’s ML model output shape to ensure that it corresponds with the labels in its dataset
  • Evaluations that check for label disclosure between an ML’s training and validation datasets, etc.

What makes the pre-tests unique is that they do not require trained parameters.

2. Post-train tests

The primary aim of these tests is to cross-examine the logic gained during training and showcase how the models are performing—as behavioral reports.

3. Invariance tests

They’re typically carried out to explain the sets of perturbations used. In addition, the tests are implemented to examine the consistency of the model predictions.


Hedge funds usually carry out back-tests on historical data to:

  1. Test if the quant models are working effectively
  2. Evaluate previous trading days or go through historical data to train new models
  3. Collect statistical data regarding the likelihood of the opening gaps getting closed within trading sessions

That said; some of the commonly used historical data analysis methods used by hedge funds include the use of:

  1. Custom software such as Python and R;
  2. Robust trading platforms, such as MetaTrader 5 for hedge funds;
  3. Efficient back-testing software.

Genetic algorithm use case

Testing Expert Advisors on multiple currencies

Hedge fund organizations mostly use a Strategy Test to test and boost their trading techniques (Expert Advisors) before engaging in business.

This reduces the chances of making losses during actual trading.

Here, an Expert Advisor with its initial variables is first run on history data during the testing phase. And subsequently, run using different parameters during the optimization phase to identify the most suitable combination.

This reduces the chances of making losses during actual trading.

Final thoughts

As you can see, genetic algorithms and machine learning are being extensively used by investment organizations, such as hedge funds to improve profitability.

You can also tap into this revolutionary field by implementing similar strategies.

For instance:

  • Understand and implement ML when making investment decisions
  • Learn and use Algo-trading approaches


  •  Routinely test your system’s ML capabilities

Doing so can significantly revamp your hedge fund business.

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