Discussing the article: "Generative Adversarial Networks (GANs) for Synthetic Data in Financial Modeling (Part 1): Introduction to GANs and Synthetic Data in Financial Modeling"

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Check out the new article: Generative Adversarial Networks (GANs) for Synthetic Data in Financial Modeling (Part 1): Introduction to GANs and Synthetic Data in Financial Modeling.

This article introduces traders to Generative Adversarial Networks (GANs) for generating Synthetic Financial data, addressing data limitations in model training. It covers GAN basics, python and MQL5 code implementations, and practical applications in finance, empowering traders to enhance model accuracy and robustness through synthetic data.

GANs are simply the two neural networks - the Generator and the Discriminator-that play an adversarial game: Here's a breakdown of these components. 

  • Generator: By the word Generator, the intent here is to train an algorithm to mimic actual data. It works with random noise as an input and over time tends to produce data samples that are more realistic. In trading terms, the Generator would give out fake price movement or trading volume sequences that resemble real sequences. 

  • Discriminator: The role of the Discriminator is to decide which data out of the structured data and synthesized data is genuine. Each data sample is then assessed on its likelihood of being original data or synthesized data. As a result, in a training process, the Discriminator increases in ability to classify the input as real data, thus encouraging the Generator to advance in generating the data.

Let's now look at the Adversarial process since it is the very adversarial aspect of GANs that makes them so powerful. Here's how the two networks interact during the training process:

  • Step 1: The Generator creates a batch of synthetic data samples through noise. 
  • Step 2: The Discriminator takes in the real data as well as the Synthetic data from the Generator. It assigns possibilities, or in other words it "passes judgment" in the authenticity of each sample. 
  • Step 3: In the next interactions, based on the Discriminator's feedback, the weight of the Generator is adjusted to generate more realistic data. 
  • Step 4: The discriminator also changes its weight to better distinguish real data from fake data. 


    Author: LiviaObongo

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