Learn about the steps to train and deploy a machine learning model, from building Intuitive AI Dataset to Autonomous AI to execute flawlessly
Use R to train and deploy machine learning models to predict future stock prices accurately. Learn about the steps to train and deploy a machine learning model, from building Intuitive AI Dataset to Autonomous AI to execute flawlessly according to validated hyperparameters. This article demonstrates the model working in real-time and the outcome.
R is one of the most widely used statistical computing and machine learning programming languages. Data scientists love it, including me, especially for the rich world of packages from tidyverse, an opinionated collection of R packages for data science. Besides the tidyverse, there are over 18,000 open-source packages on CRAN, the package repository for R.
You can use RStudio, available as a desktop version, or on the Google Cloud Marketplace, a popular Integrated Development Environment (IDE) used by data professionals for visualization and machine learning model development.
Once we build a model, a recurring question is: "How do I deploy models written in the R language to production in a scalable, reliable, and low-maintenance way?" You can use Google Vertex AI to train and deploy machine learning models built with R; however, you cannot use Vertex AI for investing.
Hence, this article discusses the value of using machine learning models on autonomous AI to invest successfully in the stock market, showing practical results.
To showcase this process, we develop and train an existing model, which we will adopt to predict Roblox stock prices daily since IPO.
Initially, we expect the dataset to give mediocre results given the non-existence of reliable historical data. Therefore, the first step will be establishing a reliable dataset upon which we can extrapolate, aiming to achieve higher accuracy.
Our experience tells us that it can take 90 to 180 days on average to build a model that can eventually deliver 100% accurate signals.
Before considering deploying an autonomous trading model for Roblox, we must build the Intuitive AI Dataset using hyperparameters.
A hyperparameter is a parameter of a machine-learning model that is set before training. A data scientist and an expert in trading choose the hyperparameters and their values. However, we will not populate the data set with the universe of hyperparameters; otherwise, we will get inaccurate results.
So, time is unknown. Likewise, RP5, RP6, SP5, and SP6 are impossible to be known. Therefore, we must start by populating the first layer only, RP1, SP1, R1, PT1, and so forth.
Note that each layer has less weight than the next. So, for example, SP1 is less relevant than SP2, SP3 up to SP6.
Note that the number of layers in a neural network constitutes a significant hyperparameter. Another is the expected level of complexity in achieving reliable predictions.
As stated before, to get H(n), we need to solve H(n-1). To advance in the equation,
In many cases, we can use a virtual trading model instead of a robot to prevent actual losses.
Training the model allows us to find the optimal set of values for the ground layer and to advance to the next one, which we will refer to hereafter as endpoints. Once we reach the highest ranking H(n), we will reverse direction to find H(n-1). This step is imperative for us to predict future H (n-1) endpoints without knowing them.
We will illustrate how it works by investing in Roblox, demonstrating 100% accuracy when defining H(n) values and subsequently defining H(n-1).
Let's define a hyperparameter H(n) in real-time. First, log in to ClickUp AI, ai.clickup.com. Then, navigate to the free plan, where you will find the corresponding task. We set RBLX at $51.8 with an instruction to sell and go short associated with a trigger event, Roblox earnings report. The event serves the purpose of changing H(n) status.
Now that we have created a newly 100% accurate endpoint, we can accurately predict the next one, H(n-1), using an n-class sub-set, for instance, R (n), H(n), C(n), and BCS. Once H(n-1) is validated, we can move to the next step of tuning the hyperparameter PT, often referred to as the price target.
So, in the following video, and article, we explain in real-time how we learned about $47 being the next action price level before Roblox's share price reached that endpoint.
Article: Alex Vieira Sells Roblox Shares $47 Today Increasing Short Position on FedEx.
Note that we used the newly created endpoint to define and increase significantly the weight of certain hyperparameters. By doing it, we automatically increased the potential outcome. We will explain this matter in another article.
The previous steps need to be validated to learn about the potential outcome, i.e., net profit. For instance, in Roblox's case, we predicted that a share price crash to $30 to be inevitable. To make it explicit, using dollar figures, an implosion from $52 down to $30 results in $60 million under a low-risk scenario. Considering that the same model has been accurately used to predict Roblox's share price top for $137, it is a simple task to calculate the implicit risk, accumulated net balance, and needed position sizing.
For your reference Roblox's share price closed at $26 in the most recent trading session.
In this blog post, you have gone through the necessary steps to train and deploy an R model, from Intuitive AI Dataset to Autonomous AI, being the latter used to execute flawlessly investing in Roblox since IPO. In addition, you learned that Autonomous AI only works with an Intuitive AI Dataset or, ultimately, with a team of scientists that could successfully populate and update data. Hence, a trading expert must explain in easy-to-use familiar vocabulary to end-users the actions they need to execute once the model finds out H(n) values. Furthermore, he shall provide easy-to-use real-time visual instructions using a stock chart or tape so that any inexperienced user can execute them. The process described here, can be used elsewhere, for instance, the case of Tesla's stock crash addressed in Intuitive Q4 2022 release notes.
