Unlocking the Future of Stock Predictions with GPLVMs
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Chapter 1: Introduction to Stock Market Predictions
In the ever-changing landscape of finance, the ability to accurately forecast stock trends can significantly impact investment strategies. Among the various predictive models, Gaussian Process Latent Variable Models (GPLVMs) are emerging as a powerful tool. These models can untangle the complexities and fluctuations of financial markets, potentially transforming our methodologies for stock predictions.
Section 1.1: Understanding GPLVMs
Gaussian Process Latent Variable Models are sophisticated non-linear generative probabilistic models adept at recognizing complex patterns in high-dimensional datasets. While they have found extensive applications in fields such as bioinformatics and computer vision, their potential in financial forecasting remains largely underexplored.
Subsection 1.1.1: Applications of GPLVMs
This article seeks to delve into the promising use cases of GPLVMs in stock market trend prediction. By unveiling previously hidden patterns and variables, these models enhance our comprehension of the volatile stock market environment, thereby improving prediction accuracy.
Chapter 2: Real-World Applications of GPLVMs
As we traverse the exciting terrain of stock market forecasting, we will explore various real-world applications of GPLVMs. These insights will equip you with the knowledge needed to utilize these robust models effectively.
The video titled "Stock Price Prediction Using Monte Carlo Methods and Matlab" provides a practical overview of how Monte Carlo methods can be leveraged for stock price prediction. It serves as a valuable resource for understanding how these techniques can complement GPLVMs in financial forecasting.
Section 2.1: Getting Started with Python
Let's dive into coding with a fundamental setup.
import warnings
warnings.simplefilter('ignore')
By importing the warnings module and configuring it to ignore minor warnings, we can focus on the critical aspects of our analysis without distractions.
Next, we set up the necessary libraries and functions to facilitate our modeling efforts.
%load_ext autoreload
%autoreload 2
from functions.helper_functions import (StanModel_cache, vb, model_dict)
run_in_parallel = False
This code snippet loads additional functionalities for automatically reloading modules, which is essential for streamlining our workflow.
Section 2.2: Data Preparation
To begin our analysis, we need to load and preprocess our data.
N = 70 # max: 120
data = pd.read_csv('example_data/stock_data_17_18.csv', index_col='Date', parse_dates=['Date']).iloc[:,:N]
stock_list = data.columns
print("number of nan's: {}".format(data.isna().sum().sum()))
print("shape data: {}".format(data.shape))
display(data.head())
This code reads stock data from a CSV file, filtering to the first N columns, and checks for missing values, providing a snapshot of the dataset.
Section 2.3: Visualizing Stock Data
For visual representation, if N is less than or equal to 30, we can generate plots to visualize stock prices and returns:
if N <= 30:
fig = plt.figure(figsize=(20,10))
ax = fig.add_subplot(121)
np.cumprod(1 + data, axis=0).plot(ax=ax, title='Stock Price')
np.cumprod(1 + data.mean(axis=1)).plot(ax=ax, label='Mean', color='black')
ax.legend()
ax = fig.add_subplot(122)
data.plot(ax=ax, title='Returns')
plt.show()
This block of code visualizes cumulative stock prices and returns, providing valuable insights into historical trends.
Chapter 3: Building and Training Models
To create a robust predictive model, we will write and compile the necessary Stan code.
file = "functions/stan_gplvm_finance_loo.stan"
with open(file) as f:
stan_code = f.read()
stan_model = StanModel_cache(model_code=stan_code)
This code reads the Stan model file and caches it for efficient access, preparing us for the modeling process.
# Model Calculation Function
def run_calc(model_name, Q, num):
import pandas as pd
import numpy as np
data_dict = {'N': N, 'D': D, 'Q': Q, 'Y': Y, 'model_number': model_dict[model_name]}
n_error, should_break, n_error_max = 0, False, 5
while n_error < n_error_max:
# Model training logic goes here
The run_calc function is defined to perform the calculations based on the specified model parameters.
As we progress through this journey, we will uncover how GPLVMs can elevate our stock market predictions to new heights, transforming how we approach financial forecasting.