February 1, 2024
4 minutes reading
I am interested in the latest technological developments and have explored various new technologies, especially those related to commerce. Moving forward, I plan to document my findings and experiments with neural networks through a series of blog posts where I will share my learnings and experiments.
This article provides a light and accessible introduction to neural networks for traders, explaining what they are, how they work, what they can be used for and demystifying neural networks by explaining their fundamental concepts in a trader friendly way.
What is a neural network?
At its core, a neural network is a computer system modeled after the human brain. It consists of a series of interconnected units or nodes (similar to neurons) that process information in response to external inputs. These responses are then passed on to other nodes, creating a complex network of information flow. Neural networks make decisions through a combination of mathematical operations, iterative training, and learning patterns from data.
Why are neural networks so popular?
The popularity of neural networks increased significantly around 2012, after the success of AlexNet, a deep convolutional neural network, in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC). The rise in popularity of neural networks was further boosted by the development of models such as GPT (Generative Pre-trained Transformer) and its successors, including ChatGPT. ChatGPT, which was started by OpenAI, demonstrated the power of neural networks in processing, understanding and generating natural language. The combination of more sophisticated models, larger data sets, and faster hardware has enabled previously unattainable performance, sparking a renaissance in AI research and applications.
Visualization of neural networks
Understanding weights and biases
The strength of the connections between these nodes is determined by the “weights”. In trading, consider weights as factors influencing investment decisions. For example, one weight might represent the impact of market volatility, while another could represent economic indicators.
Biases, on the other hand, are similar to a trader’s intuition or instinct. In a neural network, they allow each node to independently adjust its output, adding an extra layer of complexity to decision-making.
The role of activation functions
Neural networks use activation functions to decide what information to pass through the network. Think of them as filters that screen for the most relevant information for trading decisions. They help the network learn complex patterns beyond simple linear relationships, vital to understanding complex market dynamics.
Training the Network: Learning from Data
Training is where the magic happens. It is the process of feeding market data to the network, allowing it to learn and adapt. This is how it works:
- Feed data: The network is exposed to historical market data, including price movements, trading volumes and economic indicators.
- Making Predictions: Based on its current weights and biases, the network makes predictions. For traders, this could be about future price movements.
- Learning from mistakes: Predictions are compared with actual results and the difference (error) is calculated.
- Adaptation and improvement: Using techniques such as backpropagation and gradient descent, the network adjusts its weights and biases to minimize this error by learning from its mistakes.
What is forward propagation in neural networks?
In forward propagation, the neural network takes input data, processes it through its layers, and produces an output. Weighted connections and biases are used to calculate the output of each neuron through activation functions. This output is compared to the expected output to measure how well the network is performing.
What is backpropagation in neural networks?
Back propagation is where the learning takes place. It is the mechanism that allows the neural network to improve over time. This is how it works:
- Error calculation: Calculates the difference between the network output and the expected output. This is the error or loss.
- Error propagation: The error is propagated backwards through the network, layer by layer. This step determines how much each neuron contributed to the error.
- Weight and bias settings: Weights and biases are adjusted in a way that reduces error. Neurons that contributed the most to the error receive more significant adaptations.
- Repeat: Steps 1-3 are repeated for many different examples from the training data. Over time, the neural network learns to adjust its weights and biases to minimize errors, improving its ability to make predictions.
Why should marketers care?
The use of neural networks in trading has many advantages. They can process and analyze data at a speed and scale not possible for human traffickers, providing insights based on the analysis of huge data sets. This leads to more accurate forecasts and the ability to identify profitable trading opportunities. In addition, neural networks can continuously learn and adapt to new data, improving their predictions over time.
Understanding neural networks is vital for marketers in today’s digital age. These networks can:
- Analyze massive amounts of market data more efficiently than traditional methods.
- Discover complex, non-linear patterns and relationships in financial markets.
- Improve decision-making processes with predictive analytics.
- They adapt to new data, making them suitable for dynamic market conditions.
Neural networks represent a major leap forward in trading technology. By understanding and leveraging their power, traders can gain deeper insights into market dynamics, predict trends more accurately, and make more informed decisions. As the financial world becomes increasingly data-centric, staying ahead of the game means embracing and understanding technologies like neural networks.
In the next tutorial, we will learn about deep neural networks and Python libraries for implementing deep neural network concepts with a basic workflow.
