As a data scientist working in Nepal's finance sector, I've often encountered the challenge of developing accurate and reliable predictive models for stock price forecasting. The current models may not account for seasonal trends, economic indicators, and other factors that affect stock prices. In this post, I'll share how I addressed this pain point by integrating time series foundation models with stock analysis using the Nepal Stock Exchange (NEPSE) dataset. By the end of this tutorial, you'll learn how to build a predictive model that forecasts stock prices based on historical data and other relevant factors.
Key Takeaways
- Integrating time series foundation models with stock analysis can improve the accuracy of stock price forecasting.
- The NEPSE dataset provides a rich source of historical stock prices and other relevant data for building predictive models.
- Using libraries like yfinance and pandas can simplify the process of loading and analyzing the data.
The Problem
Predicting stock prices is a complex task that requires accounting for various factors, including seasonal trends, economic indicators, and market sentiment. Traditional models may not capture these complexities, leading to inaccurate predictions. By integrating time series foundation models with stock analysis, we can build more robust and reliable predictive models.
Data and Sources
The NEPSE dataset is publicly available on the Yahoo Finance API. You can access the data using the following link: NEPSE dataset. Data accessed on 2024-09-16.
Loading the Data
To load the NEPSE dataset, we'll use the yfinance library. First, install the library using pip: pip install yfinance. Then, use the following code to load the data:
import yfinance as yf
nepse_data = yf.download('NEPSE', start='2010-01-01', end='2022-02-26')
The Core Logic
The core logic of our predictive model involves integrating time series foundation models with stock analysis. We'll use the Prophet library to implement the time series foundation model. First, install the library using pip: pip install prophet. Then, use the following code to implement the model:
from prophet import Prophet
nepse_prophet = Prophet()
nepse_prophet.fit(nepse_data.reset_index()[['Date', 'Close']])
Putting It Together
To put the pieces together, we'll create a function that loads the data, implements the time series foundation model, and makes predictions. We'll also handle edge cases and errors using try-except blocks.
def predict_stock_prices():
try:
nepse_data = yf.download('NEPSE', start='2010-01-01', end='2022-02-26')
nepse_prophet = Prophet()
nepse_prophet.fit(nepse_data.reset_index()[['Date', 'Close']])
future = nepse_prophet.make_future_dataframe(periods=30)
forecast = nepse_prophet.predict(future)
return forecast
except Exception as e:
print(f"An error occurred: {e}")
Complete Script
The full runnable script combining all steps:
import yfinance as yf
from prophet import Prophet
def predict_stock_prices():
try:
nepse_data = yf.download('NEPSE', start='2010-01-01', end='2022-02-26')
nepse_prophet = Prophet()
nepse_prophet.fit(nepse_data.reset_index()[['Date', 'Close']])
future = nepse_prophet.make_future_dataframe(periods=30)
forecast = nepse_prophet.predict(future)
return forecast
except Exception as e:
print(f"An error occurred: {e}")
if __name__ == "__main__":
forecast = predict_stock_prices()
print(forecast)
Expected Output
When you run the script, you should see a forecast of stock prices for the next 30 days. The output will include the date, open, high, low, close, and other relevant metrics.
Limitations and Tradeoffs
This approach has several limitations and tradeoffs. First, the model assumes a linear relationship between the historical data and future stock prices. In reality, the relationship may be non-linear, and the model may not capture this complexity. Second, the model does not account for external factors such as economic indicators, market sentiment, and global events. To improve the model, you could incorporate these factors using additional datasets and machine learning techniques.
Frequently Asked Questions
What is the NEPSE dataset, and where can I access it?
The NEPSE dataset is a collection of historical stock prices and other relevant data for the Nepal Stock Exchange. You can access the data using the Yahoo Finance API.
How do I install the required libraries?
You can install the required libraries using pip: pip install yfinance prophet.
What is the Prophet library, and how does it work?
The Prophet library is a open-source software for forecasting time series data. It works by implementing a generalized additive model that captures trends, seasonality, and holidays.
What I'd Change
In conclusion, integrating time series foundation models with stock analysis can provide actionable insights for investment decisions in Nepal's finance sector. However, the approach has several limitations and tradeoffs. To improve the model, I would incorporate additional datasets and machine learning techniques to capture non-linear relationships and external factors. I would also experiment with different time series foundation models and hyperparameters to optimize the model's performance. Next steps would be to deploy the model in a production environment and continuously monitor its performance using metrics such as mean absolute error and mean squared error.
