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AI Trading: the Guide!

AI Trading: Definition

AI trading, also known as algorithmic trading or algo trading. It’s a method of executing transactions on the financial markets using computer algorithms. These algorithms analyze larger or smaller amounts of data — such as historical price movements, market trends, and economic indicators — in order to identify patterns and make trading decisions.

Recently, the use of artificial intelligence in the markets has gained ground thanks to its ability to analyze large amounts of data quickly and accurately, which lets it identify patterns and make decisions faster than any human could ever hope to.

Trading-focused AIs have evolved considerably over the years, using increasingly sophisticated machine-learning algorithms. The use of AI in trading has quite simply allowed traders to make better decisions by analyzing large amounts of data quickly and accurately.

On top of that, AI has enabled some traders to automate their trading strategies, allowing them to seize market opportunities 24 hours a day, 7 days a week. And I’ve already given you a taste of this in the first part!

Of course, artificial intelligence is used on traditional markets as well as in cryptocurrency trading.

AI Trading: the Main Uses and Technologies

1. machine learning,
2. natural language processing
3. massive-data analysis: big data
4. Automatic order execution

Machine-learning algorithms are used to analyze large amounts of data in order to identify patterns and make longer-term trading decisions.

Natural language processing makes it possible to analyze news articles and other information sources in order to identify market trends and opportunities.

Massive-data analysis (big data) is used to analyze large amounts of data in order to identify patterns and trends in the market.

Beyond these 3 main aspects, AI trading platforms also use advanced algorithms to execute transactions automatically. These algorithms are designed to capitalize on market opportunities the moment they arise, letting traders make better decisions without delay and thereby increase their profitability.

Overall, AI trading is a fast-moving field that offers traders a whole range of advantages. By using advanced algorithms and technologies, traders can analyze large amounts of data quickly and accurately, identify market trends and opportunities, and automate their trading strategies to capitalize on market opportunities 24 hours a day, 7 days a week.

AI Trading: the Advantages

There are many reasons why investors and traders may be drawn to artificial intelligence — we’ve already mentioned a few, but it can go much further! Here are some of the specific benefits of using artificial intelligence in trading:

• Reduced research time
• Automation
• Better forecasts and reporting
• Cost reduction
• Scalable technology
• No emotions
• Fast trade execution
• Effective backtesting

All in all, AI trading — trading assisted by artificial intelligence — brings tremendous added value to the financial markets!

Algo Trading: the Different Types of Algorithmic Trading

Quantitative Analysis

Quantitative analysis can be applied to both technical and fundamental analysis. It’s a popular approach to algorithmic trading. It involves using mathematical models and statistical techniques to identify patterns and trends in market data. A trader can use these models to develop trading strategies that exploit market inefficiencies and other opportunities.

For example, regression analysis is a very common quantitative-analysis technique. In fact, if you went to business school, the term should ring a bell — especially if I say “linear regression,” right?!

Regression analysis is a statistical approach used to explore the connection between dependent and independent variables. In other words, it serves to analyze how the independent variable influences the dependent variable.

This technique involves analyzing the relationship — even the correlation — between two or more variables in order to identify patterns and trends and capitalize on the projections!

High-Frequency Trading

High-frequency trading (HFT) is a type of algorithmic trading that involves executing transactions at very high speed.

HFT strategies rely on sophisticated algo trading, but also on ultra-high-speed data-transfer networks, in order to execute transactions within fractions of a second. This speed is essential, which

HFT strategies are designed to profit from small price movements in the market. As such, for a trader, using HFT to execute large transaction volumes quickly and efficiently can also cut fees considerably, making it possible to turn a profit in niches that wouldn’t be profitable otherwise.

Indeed, High Frequency Trading, in essence, “means”:

1. Large transaction volume
2. A very high number of orders
3. Low but steady profitability

So it’s essential not to get it wrong when it comes to calculating the break-even point!

I dug up a documentary I watched a while back on high-frequency trading. If this is a topic you’re passionate about, it’s a solid watch for understanding what’s at stake with this strategy!

“In 2 minutes, there can be up to 2 million transactions”

Arbitrage

Arbitrage strategies consist of profiting from price differences between two or more markets.

One of the most common arbitrage strategies is statistical arbitrage. This strategy consists of identifying assets that are mispriced relative to one another, then buying and selling those assets to make a profit.

Statistical arbitrage is designed to capitalize on market inefficiencies and other opportunities.

This term — if I’ve read my audience right — you’ve probably heard more than once, so we won’t dwell on it; let’s just recall a solid definition! Arbitrage was an incredibly lucrative activity as recently as 2 or 3 years ago on the crypto market. In fact, for those who still remember, that’s how SBF started building his fortune through Alameda Research!

Today, arbitrage on the crypto market is probably still profitable, but it has certainly become far more competitive!

In the past, all it took was being registered on a few crypto platforms around the world, holding substantial funds, buying on the platforms where a market imbalance leaned toward supply and selling on the platforms where the imbalance leaned the other way.

Wars, for example, have in recent years created significant arbitrage opportunities on local platforms for anyone — but it’s also a profession, and some people do nothing else!

As a result, in a market whose competitiveness is growing exponentially, AI trading has become indispensable to the practice of arbitrage on the financial markets.

AI Trading and Machine Learning

Predictive Models

Predictive models have almost certainly been used ever since people had information, data, and a method to interpret them. That said, modern predictive modeling reportedly began in the 1940s, when governments used the first computers to analyze weather data, among other things…

Over the following decades, software and hardware capabilities grew, making it possible to store large amounts of data and access it more easily for analysis.

Internet connectivity has made it easier for anyone with access to accumulate, share, and examine vast amounts of data. As a result, modeling has expanded to cover almost every area of business and finance. For example, it’s perfectly common in the marketing world to see predictive modeling used. And of course — this is the part that interests us — financial analysts also use it to estimate trends and events on the stock market.

Uses of Predictive Models

Predictive analytics uses predictors, or identified features, to build models with the goal of reaching an outcome.

Predictive analytics can be used in hundreds, even thousands, of ways. For example, investors use it to spot trends in the stock market or in individual securities that could signal investment opportunities or decision points.

One of the models most commonly used by investors is the moving average of an investment, which smooths out price fluctuations to help them identify trends over a defined period. In addition, autoregression is used to establish a correlation between the past values of an investment or an index and its future values.

Predictive modeling is also a valuable risk-management tool for investors, since it makes it possible to identify the potential outcomes of various scenarios. For example, the data can be adjusted to predict what might happen if a fundamental condition were to change.

The Various Forms of Predictive Modeling

There are several distinct forms of predictive modeling that can be used to analyze most datasets in order to uncover insights about upcoming events.

Classification Predictive Models

Classification models draw on machine learning to sort data into categories or classes according to criteria defined by the user. There are several types of classification algorithms, a few examples of which are below:

Logistic regression: An estimate of whether an event will occur, usually a binary classification such as a yes-or-no answer.

Decision trees: A series of yes/no, if/else, or other binary outcomes presented in a visualization called a decision tree.

Decision tree: An algorithm that combines unrelated decision trees using classification and regression.

Neural networks: Machine-learning models that comb through enormous volumes of data to detect correlations that only emerge after analyzing millions of data points.

Naive Bayes: a modeling system that relies on Bayes’ theorem to establish conditional probability.

Clustering (Grouping) Models

Clustering is a technique for grouping data points together. Cluster analysts therefore assume that data belonging to similar groups should share the same characteristics, and that data belonging to different groups should have very different properties. The most common clustering algorithms are as follows:

• K-Means / Mean: K-means is a modeling technique that uses clusters to identify the central tendencies of different groups of data.
• Mean-Shift: In the Mean-Shift model, the algorithm shifts the mean of a group so as to identify the “bubbles,” or maxima, of a density function. When the points are plotted on a graph, the data appears to cluster around central points called centroids.
• DBSCAN, or Density-based Spatial Clustering With Noise : DBSCAN is an algorithm that groups data points based on an established distance between them. This model builds relationships between the different groups in order to screen out outliers. That said, there are other, more specialized models dedicated to that!

Outlier Models

It’s perfectly normal for a data series to contain outliers. The problem is that these exceptional values can skew our predictions in a fairly unwarranted way. That’s why it’s important to be able to remove outliers.

• Isolation Forest: An algorithm that detects the few, deviant data points within a sample.
• Minimum Covariance Determinant (MCD): Covariance is the relationship of change between two variables. The MCD measures the mean and covariance of a dataset in a way that minimizes the influence of outliers on the data.
• LOF, or Local Outlier Factor : An algorithm that identifies the nearest data points and assigns them scores, which makes it possible to flag the most distant points as outliers.

Time-Series Models

Commonly used before other types of modeling, time-series modeling uses historical data to forecast events.

Here are 2 of the most common time-series models:

• ARIMA: The Autoregressive Integrated Moving Average model uses autoregression, integration (differences between observations), and moving averages to forecast trends or outcomes.
• Moving Average: The moving average uses the average over a given period — for example the MA50 and the MA200, with 50 and 200 days respectively — in order to smooth out fluctuations.