Welcome to our guide on predicting stock prices using powerful machine learning techniques! In this article, we will walk you through the process of using LSTM (Long Short-Term Memory) Recurrent Neural Networks (RNNs) for forecasting the stock prices of Apple Inc. Let’s dive in!
Dataset Overview
We utilized a dataset obtained from Yahoo Finance, encapsulating crucial stock data of Apple Inc. from January 3, 2011, to August 13, 2017, spanning a total of 1664 rows. This dataset includes:
- Open Prices
- High Prices
- Low Prices
- Closing Prices
For the purpose of our analysis, we will focus on the OHLC average—this is the average value of Open, High, Low, and Closing prices.
Data Pre-processing
Before diving into modeling, we need to prepare our data:
- Convert the dataset into a single column for the OHLC average.
- Transform this single-column data into a two-column time series dataset, where the first column holds the stock price at time t and the second column contains the stock price at time t+1.
- Normalize all values to fit within the range of 0 to 1 to facilitate better model performance.
Building the LSTM Model
Now, let’s visualize our model’s architecture through an analogy: imagine you are building a multi-layer cake. Each layer has its own function but they all work together to create a delicious final product. Here’s how our LSTM model stacks up:
- Two sequential LSTM layers represent the crucial parts that capture temporal dependencies in stock prices.
- A single dense layer acts like the frosting that binds everything together, aiding in making the final predictions.
We use Keras, a powerful deep learning library, to construct this RNN. As this is a regression task, we apply a linear activation function in the final layer to ensure smooth outputs.
Training the Model
For our training process:
- 75% of our dataset is allocated for training the model.
- We employ the Adagrad optimizer, which is like a well-tuned fuel system for a race car—allowing for faster convergence to the optimal solution.
Below is an example of how the training process looks:
Testing and Results
After training, we evaluate our model using the RMSE (Root Mean Square Error) metric as the testing accuracy. We also compare the OHLC average, HLC average, and Closing price as follows:
Additionally, after training, the fitted curve against the original stock prices looks like this:
Conclusions and Observations
The difference among the OHLC average, HLC average, and closing value turned out to be minimal, justifying the decision to utilize only the OHLC average for our model. The training and testing RMSE values were 1.24 and 1.37 respectively, a fairly decent performance for predicting future stock prices.
For instance, the stock price for the last day in our dataset was 158.8745. Utilizing our model, we predicted the prices for the next two days as 160.3230 and 160.9240, while the actual prices recorded on August 14th and 15th, 2017 were 159.2075 and 159.8325 respectively. This showcases our model’s predictive capability for future values!
This modeling work can significantly benefit quantitative traders by aiding them in making well-informed decisions.
Troubleshooting
If you encounter issues during the modeling process, consider the following troubleshooting steps:
- Ensure your dataset is clean and free from missing values for accurate predictions.
- Examine the normalization of your data, as improper scaling can lead to faulty model interpretations.
- Check for compatibility issues in library versions, especially when using Python 2.7.
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Final Words
At **[fxis.ai](https://fxis.ai)**, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.
