I’m intrigued by the latest technological advancements and have been exploring various new technologies, particularly those related to trading. Moving forward, I plan to document my findings and experiments with neural networks through a series of blog posts, where I’ll share my knowledge 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, 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 on the human brain. It’s composed of a series of interconnected units or nodes (akin to neurons) that process information by responding 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 the learned patterns from data.
Why Neural Networks is So Popular?
The popularity of neural networks notably surged around 2012, following the success of AlexNet, a deep convolutional neural network, in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC). The surge in neural networks’ popularity was further amplified by the development of models like GPT (Generative Pre-trained Transformer) and its successors, including ChatGPT. Launched by OpenAI, ChatGPT showcased the power of neural networks in natural language processing, understanding, and generation. The combination of more sophisticated models, larger datasets, and faster hardware enabled previously unachievable performance, sparking a renaissance in AI research and applications.
This architecture is a single-layer perceptron, which is the simplest form of a neural network. . It is mostly used for educational purposes and simple binary classification tasks. Modern neural networks often consist of many such layers stacked together, forming a deep network capable of learning very complex patterns and functions.learn more about the components of single-layer perceptron
- Inputs (X1, X2, X3): These are the input nodes, representing the data that is fed into the network. Each node corresponds to a feature in your dataset.
- Weights (W1, W2, W3): Each input has an associated weight. These weights are parameters that the neural network will learn during training. They determine the strength and sign of the input’s influence on the output – whether a particular feature has a strong or weak influence, and whether it is positive or negative.
- Bias (b): The bias allows the model to fit the best line for the data by shifting the line up or down. It is also a learnable parameter and is analogous to the intercept in linear regression.
- Summation (Σ): This is the process where each input is multiplied by its respective weight, all these products are added together, and the bias is then added to this sum. Mathematically, this is often represented as
Σ(Wi * Xi) + b, where
Xiare the weights and inputs, respectively, and
bis the bias.
- Activation (σ): The result of the summation is then passed through an activation function. The activation function introduces non-linearity to the model, allowing it to learn more complex patterns. Common activation functions include sigmoid, ReLU, and tanh. The symbol
σtypically represents the sigmoid function, which squashes the input values to be between 0 and 1.
- Output (ŷ): This is the final output of the network after the activation function has been applied. It represents the prediction or decision made by the network based on the input data.
Visualizing Neural Networks
Understanding Weights and Biases
The strength of connections between these nodes is determined by ‘weights’. In trading, consider weights as factors influencing investment decisions. For instance, one weight might represent the impact of market volatility, while another could represent economic indicators.
Biases, on the other hand, are akin to the trader’s intuition or gut feeling. In a neural network, they allow each node to adjust its output independently, adding an extra layer of sophistication to decision-making.
The Role of Activation Functions
Neural networks use activation functions to decide what information should be passed through the network. Think of these as filters that screen the most relevant information for trading decisions. They help the network learn complex patterns beyond simple linear relationships, crucial for understanding intricate market dynamics.
Training the Network: Learning from Data
Training is where the magic happens. It’s the process of feeding market data into the network, allowing it to learn and adapt. Here’s how it works:
- Feeding 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: The predictions are compared against real outcomes, and the difference (error) is calculated.
- Adjusting and Improving: Using techniques like backpropagation and gradient descent, the network adjusts its weights and biases to minimize this error, learning from its mistakes.
What is Forward Propagation in Neural Networks?
During forward propagation, the neural network takes in input data, processes it through its layers, and produces an output. The weighted connections and biases are used to calculate the output of each neuron through the activation functions. This output is compared to the expected output to measure how well the network is performing.
What is Backpropagation in Neural Networks?
Backpropagation is where the learning takes place. It’s the mechanism that enables the neural network to improve over time. Here’s how it works:
- Error Calculation: The difference between the network’s output and the expected output is calculated. This is the error or loss.
- Error Propagation: The error is propagated backward through the network, layer by layer. This step determines how much each neuron contributed to the error.
- Weight and Bias Adjustments: The weights and biases are adjusted in a way that reduces the error. Neurons that contributed more to the error receive more significant adjustments.
- 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 Traders Care?
The use of neural networks in trading comes with several advantages. They can process and analyze data at a speed and scale unattainable for human traders, providing insights based on the analysis of vast datasets. This leads to more accurate predictions and the ability to identify profitable trading opportunities. Furthermore, neural networks can continuously learn and adapt to new data, improving their predictions over time.
Understanding neural networks is crucial for traders in today’s digital age. These networks can:
- Analyze vast amounts of market data more efficiently than traditional methods.
- Uncover complex, non-linear patterns and relationships in financial markets.
- Enhance decision-making processes with predictive analytics.
- Adapt to new data, making them suitable for dynamic market conditions.
Neural networks represent a significant leap forward in trading technology. By understanding and leveraging their power, traders can gain deeper insights into market dynamics, forecast trends with greater accuracy, and make more informed decisions. As the financial world becomes increasingly data-centric, staying ahead in the game means embracing and understanding technologies like neural networks.
In the next tutorial, we will learn about deep neural networks and the Python libraries to implement deep neural network concepts with basic workflow.