PSEI: Predicting Stock Prices With CNNs

Hey guys! Ever wondered how to predict the Philippine Stock Exchange Index (PSEi)? It's a question on everyone's mind! The stock market, with its ups and downs, is a fascinating world. And, if you're like me, you are always searching for new ways to forecast where it's headed. In this article, we'll dive into how we can use Convolutional Neural Networks (CNNs) to predict the PSEi stock price. It's an exciting journey, and the potential rewards are pretty sweet. We will see the process from data collection to model training and evaluation. It's going to be a fun ride, and I'll break everything down so that you can follow along.

The Allure of Stock Price Prediction

So, why bother trying to predict stock prices? Well, the answer is pretty straightforward: potential profits. If you can accurately forecast the PSEi, you can make informed investment decisions. Being able to anticipate market movements can lead to strategic trading and ultimately, financial gains. But it's not just about the money, there's a certain thrill in trying to crack the code of the market. It's like solving a complex puzzle.

We all know that the stock market is influenced by a gazillion of factors, from global economics to local news, even social media buzz! That's why it is so difficult, and a big reason why artificial intelligence (AI), specifically CNNs, is so promising. CNNs are brilliant at spotting patterns in data. In our case, the data is the historical stock prices. CNNs can analyze vast amounts of data, recognize trends, and make predictions.

Of course, predicting the stock market is always a challenge. The market can be very unpredictable. But, by using tools such as CNNs, we can increase our chances of making accurate predictions. It is also important to remember that these are just predictions, so we have to manage our expectations. No method is perfect, but with the right approach, we can still gain valuable insights. The goal here is to give you a head start and give you a better understanding of the stock market. With the potential rewards, it's definitely worth exploring. Are you ready?

Data Acquisition and Preparation

Okay, let's roll up our sleeves and dive into the practical stuff: data acquisition and preparation. This step is super important. It sets the foundation for our entire prediction model. Garbage in, garbage out, right? So, we need to make sure we're starting with clean, reliable data. The PSEi stock price data can be collected from different financial data providers. One of the most popular sources is Yahoo Finance, which offers historical stock prices for free. You can download the data in CSV format, which is easily accessible. You can also use other data providers like Bloomberg or Refinitiv. If you want to dive in deep, you may use paid ones for more detailed info.

Gathering the Raw Materials

First, we need to gather our data. We need to collect historical data of the PSEi stock prices. We want the daily closing prices, which is the most common data point used for analysis. We'll also collect other relevant data, such as trading volume. The more data you collect, the better, but starting with at least a few years' worth of daily data is good. We also have to consider external data. This could include news sentiment, economic indicators, and other factors that might influence the market.

Once we have our data, we have to prepare it. It's almost never perfect from the source. The data might have missing values, outliers, or inconsistencies. We have to make sure the data is in the right format. We have to clean it, and make sure that it's consistent. This will significantly impact the accuracy of our predictions. So, what steps do we have to do?

Cleaning and Preprocessing

Handling Missing Data: If there are any missing values, we have to address them. The easiest way is to remove rows with missing data or fill them with the mean or median of the existing data. Be careful with this, because it could skew your results. If there is too much data missing, you may have to remove that column, or search for other sources. There are many ways to fill missing values and the best way is to pick the one that fits your data and your needs.
Outlier Detection: Outliers, which are extreme values that can skew our analysis, have to be identified and handled. They can be due to errors in data entry or unexpected market events. We can handle them by removing the outliers, or adjusting them with a more suitable value.
Data Transformation: We'll need to transform the data so it's suitable for our CNN model. We can do this with normalization or standardization. Normalization brings the data to a specific range, like 0 to 1. Standardization, which makes the data have a mean of 0 and a standard deviation of 1, is another good way to transform the data. Normalization is a good choice if we know the bounds of our data, while standardization is good when our data has some outliers.
Feature Engineering: This is where we create new features from the existing data. For example, we might create moving averages, which smooth out price fluctuations and highlight trends. We can also create other technical indicators like the Relative Strength Index (RSI). These can all improve the performance of our model.

By following these steps, we ensure our data is clean, consistent, and ready for our CNN model. This initial step is so important, because a well-prepared dataset is critical for accurate predictions. So, take your time here. Make sure that you are doing everything right before moving forward.

Building the CNN Model

Alright, it's time to build the heart of our PSEi stock price prediction system: the Convolutional Neural Network (CNN). It's a type of neural network that's exceptionally good at spotting patterns in data. CNNs are widely used in image recognition, but they can be applied to time series data such as stock prices. The key to creating a successful CNN model is to understand its architecture and how each component contributes to the prediction process. Let's break it down.

Understanding CNN Architecture

CNNs consist of layers that extract features from the data. The most important layers are the convolutional layers, pooling layers, and fully connected layers. Let's see how they work:

Convolutional Layers: These layers are the workhorses of the CNN. They apply filters to the input data to extract features. The filters slide across the data, and perform a mathematical operation to capture local patterns.
Pooling Layers: Pooling layers reduce the dimensionality of the data and make the model more robust to variations in the input. The most common type of pooling is max pooling, which selects the maximum value within a certain region. These reduce the computational load and help prevent overfitting.
Fully Connected Layers: These layers are at the end of the CNN and take the features extracted by the convolutional and pooling layers and use them to make predictions. In these layers, every neuron is connected to every neuron in the previous layer.

By stacking these layers, we create a network that can automatically learn and recognize the most important patterns in our data.

Setting Up the Model in Python

Let's get our hands dirty and implement a CNN model in Python. We'll use popular libraries like TensorFlow and Keras, which make it super easy to build and train neural networks. I am assuming you have a basic knowledge of Python and some experience with machine learning. This should be enough to follow along.

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Importing Libraries: First, we import the necessary libraries. This includes TensorFlow and Keras for building the model, and any other libraries we need for data manipulation and analysis, such as NumPy and Pandas.
Defining the Model: We define the structure of our CNN. We'll start with convolutional layers to extract features, followed by pooling layers to reduce dimensionality. We'll then add fully connected layers for making predictions. We can experiment with different numbers of layers, filters, and neurons to optimize performance.
Compiling the Model: Once we've defined the structure, we compile the model. During compilation, we specify the optimizer, loss function, and evaluation metrics. The optimizer is an algorithm that adjusts the model's parameters during training.
Training the Model: Now comes the training. We feed the model our prepared data and let it learn. We split our data into training and validation sets. We also define the number of epochs and batch size. An epoch is one complete pass through the entire dataset.
Evaluating the Model: After training, we evaluate the model on a separate test set to assess its performance. We use the evaluation metrics we specified during compilation, such as mean squared error (MSE) or mean absolute error (MAE), to see how well our model has learned to predict the PSEi stock prices.

By following these steps, we'll create a powerful CNN model capable of predicting the PSEi stock prices. The process can be tricky at first, but with a little practice and the right tools, you'll be well on your way to building a great model.

Training and Evaluation

Now, let's get down to the exciting parts: training and evaluation. Once our CNN model is built, we have to prepare it to learn from our data. This involves training the model and evaluating its performance. This is where we see if all our hard work has paid off. So, let's get into it.

Training the CNN Model

Training the model is the process of feeding it the data and adjusting its internal parameters to minimize the difference between the predicted values and the actual values. We have to divide our dataset into training, validation, and testing sets. We use the training set to train the model, the validation set to monitor the training process and prevent overfitting, and the test set to evaluate the final model.

Data Splitting: First, split your data into training, validation, and testing sets. A good split is often 70% for training, 15% for validation, and 15% for testing. Make sure your data is shuffled randomly before you split the data. This will make your model more robust and prevent it from learning patterns from the order of your data.
Choosing Hyperparameters: Hyperparameters are settings that we specify before training the model, such as the learning rate, the number of epochs, and the batch size. They affect how well our model learns, and they also affect how long it takes to train. So, we have to choose them carefully. Experimenting with different values will improve performance.
Training the Model: Finally, we train the model using the training data. We feed the data to the model in batches, and the model adjusts its weights and biases to minimize the loss function. You can track the progress of the training through the training and validation loss curves. If the training loss decreases and the validation loss remains stable, it's a good sign that the model is learning well.

Evaluating the Model's Performance

Once the model is trained, we need to evaluate its performance. This is very important. We need to determine how well it predicts the PSEi stock prices. We'll use the test set to evaluate our model. We will use the metrics to understand the quality of the model.

Selecting Evaluation Metrics: There are different metrics that you can use to evaluate the performance of your model. Some of the most common metrics are the Mean Squared Error (MSE), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE). Each metric measures the difference between the predicted values and the actual values in different ways. The choice of the metric depends on the application.
Generating Predictions: We make predictions using the test data and compare them to the actual values.
Analyzing Results: Finally, analyze your results. Look at the metrics and the visualizations to identify any patterns. Look for areas of strength and weakness. If the results are good, you can use the model to make predictions on future data. If the results are not great, you need to revisit the model, your data, and your preprocessing steps to improve it.

By carefully training and evaluating your CNN model, you'll be able to create a model that accurately predicts the PSEi stock prices. Remember that model training is an iterative process. You may have to experiment with different hyperparameters and architectures before you get the results you want. But it's all part of the fun!

Fine-tuning and Improving the Model

So, you have trained your model, evaluated it, and now it's time to fine-tune it and improve its performance. Fine-tuning is an iterative process where we tweak the model's architecture, hyperparameters, and data preprocessing techniques to make it more accurate and robust. You will be surprised by how much improvement you can achieve with these steps. This is where you really get to see your work pay off. Let's see some tips!

Hyperparameter Tuning

Hyperparameter tuning involves finding the best combination of hyperparameter values for our model. Hyperparameters are settings we specify before training. This impacts how the model learns. Common hyperparameters to tune include learning rate, batch size, the number of layers, and the number of filters in convolutional layers.

Grid Search: Try out every possible combination of hyperparameters. This approach is thorough but can be computationally expensive if you have a lot of hyperparameters.
Random Search: Randomly sample the hyperparameter space. This is often more efficient than grid search, especially when you have many hyperparameters.
Automated Tuning: Use tools like Keras Tuner or Hyperopt, which automate the hyperparameter tuning process. These tools test multiple combinations of hyperparameters. They also use the results to suggest the next best combinations.

Advanced Techniques for Enhancement

There are also more advanced techniques you can use to enhance your model:

Regularization: Prevent overfitting by adding regularization techniques, such as dropout.
Data Augmentation: Increase the size and diversity of your training data. This can include creating new data points from the existing ones.
Ensemble Methods: Combine predictions from multiple models. This is often an effective way to improve overall accuracy.

These techniques will help improve model accuracy and make the model more robust. Keep in mind that fine-tuning is an iterative process. It requires experimentation and patience. Keep trying and don't get discouraged!

Conclusion: Your PSEi Prediction Journey

Alright, we've come to the end of our journey through PSEi stock price prediction using CNNs. We've covered everything from data acquisition and preprocessing to model building, training, evaluation, and fine-tuning. It's been quite the ride, right? Building a stock price prediction model requires careful planning, diligent execution, and a willingness to learn. You will encounter challenges, but the rewards can be significant.

Key Takeaways

Data is King: Clean, well-prepared data is the foundation of any successful prediction model. So, take your time with it!
CNNs are Powerful: CNNs are exceptionally good at pattern recognition, making them ideal for time series data such as stock prices.
Experiment and Iterate: Model building is an iterative process. You have to be willing to experiment, adjust, and learn from your mistakes.
Stay Updated: The financial markets are constantly evolving. So, keep up-to-date with new tools, methods, and market trends.

Final Thoughts

So, what's next? Well, now that you've got the foundation, it's time to build your own model. Start by gathering data, building your CNN, training, evaluating, and fine-tuning your model. Remember to keep experimenting, and don't be afraid to try new things. The journey of predicting stock prices is challenging, but also incredibly rewarding. Embrace the process, keep learning, and who knows, maybe you'll be the one to accurately predict the next big move in the PSEi! Good luck, and happy predicting!