Leveraging LSTM and LLM Models for Stock Price Prediction (2024)

Manthandeshpande

Published in

Accredian

Implementation Using Advanced AI Techniques:

Leveraging LSTM and LLM Models for Stock Price Prediction (6)

Let’s now focus on the usefulness of employing cutting-edge AI techniques to predict stock prices. We will concentrate on two approaches: Language Models (LMs) and Long Short-Term Memory (LSTM) networks.

Long Short-Term Memory (LSTM) Networks:

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Recurrent neural networks (RNNs) of the long-short-term dependency (LSTM) type are intended to process and forecast sequential data. When it comes to predicting stock prices, long short-term memory (LSTM) models examine past price data to find patterns and trends that could affect future price changes.

Steps in Implementation:

Data Collection: Using financial data APIs like Yahoo Finance, we start by gathering historical stock price information for the target asset.

Preprocessing of the Data: The raw data is cleaned up to eliminate noise, deal with missing values, and standardize the values to a common scale.

Model Design: Based on the features of the dataset, such as the quantity of input features, hidden layers, and activation functions, we create an LSTM architecture.

Training: By maximizing accuracy and minimizing prediction error, the parameters of the LSTM model are optimized through the use of historical price sequences.

Evaluation: To gauge the performance and generalization abilities of the trained model, it is tested on a different validation dataset.

2. Language Models (LMs) for Stock Price Prediction:

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Language models have proven adept at producing coherent text based on learned contexts; this is especially true of transformer-based models such as GPT (Generative Pre-trained Transformer). We can train a pre-trained LM to produce believable future price trajectories by fine-tuning it on historical stock price data.

Implementation Steps:

Adjusting Pre-trained LM: Using historical stock price data, we adjust the parameters of a pre-trained GPT model to uncover underlying patterns and trends.
Training Process: To reduce prediction errors, the model’s weights are updated via gradient descent optimization during the fine-tuning phase.
Future Price Generation: After being trained, the LM can use the patterns it has discovered in the data to produce a series of future price predictions.

An example of a trending language model (LLM) that could be used to forecast future stock prices based on historical data is GPT (Generative Pre-trained Transformer). Using historical stock price data, we will refine a pre-trained GPT model and use it to predict future prices.

First, you’ll need to install the transformers library by running:

pip install transformers

Importing necessary libraries for various tasks:

import yfinance as yf
import numpy as np
import matplotlib.pyplot as plt
import torch
from transformers import GPT2LMHeadModel, GPT2Tokenizer

Downloading and Preprocessing Data

ticker = “^NSEI”: This line sets the variable ticker to the symbol of the Nifty 50 index on the National Stock Exchange of India.

start_date = “2023–01–01” and end_date = “2023–06–08”: These lines define the start and end dates for which historical stock price data will be downloaded. In this case, it is from January 1, 2023, to June 8, 2023.

data = yf.download(ticker, start=start_date, end=end_date): In this, the yfinance library downloads historical stock price data for the specified ticker (Nifty 50 index) within the given time range. The data is stored in a Pandas DataFrame, and various information about the stock (Open, High, Low, Close, Volume, etc.) is collected.

ticker = "^NSEI"
start_date = "2023-01-01"
end_date = "2023-06-08"
data = yf.download(ticker, start=start_date, end=end_date)prices = data["Close"].tolist()

Fine-tuning GPT model on Historical Data

tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
model = GPT2LMHeadModel.from_pretrained("gpt2")

Here, the GPT-2 tokenizer and pre-trained model are loaded from the transformers library.

Tokenizing Historical Prices and Fine-tuning the Model

encoded_prices = tokenizer.encode(" ".join([str(price) for price in prices]), return_tensors="pt")model.train()
optimizer = torch.optim.Adam(model.parameters(), lr=5e-5)
model.resize_token_embeddings(len(tokenizer))
for _ in range(3):
 model.zero_grad()
 outputs = model(encoded_prices, labels=encoded_prices)
 loss = outputs.loss
 loss.backward()
 optimizer.step()

The historical stock prices are tokenized and used to fine-tune the GPT-2 model. The model is trained for three epochs using an Adam optimizer

Generating Future Prices

generated = model.generate(encoded_prices, max_length=len(encoded_prices) + 10, temperature=1.0, num_return_sequences=1)
generated_prices = tokenizer.decode(generated[0], skip_special_tokens=True).split()

The fine-tuned model is then used to generate future stock prices based on the historical data.

Plotting the Results

plt.figure(figsize=(12, 6))
plt.plot(data.index, prices, label="Historical Prices")
plt.plot(data.index[-1] + pd.to_timedelta(np.arange(1, len(generated_prices) + 1), 'D'), [float(price) for price in generated_prices[len(prices):]], "g^", label="Predicted Prices")
plt.xlabel("Date")
plt.ylabel("Stock Price")
plt.title(f"{ticker} - Historical and Predicted Stock Prices (GPT)")
plt.legend()
plt.xticks(rotation=45)
plt.tight_layout()
plt.show()

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Using historical stock price data, this code refines a pre-trained GPT model to produce future prices. Lastly, a graph with the projected and historical prices is plotted. The predictions might be more accurate if the GPT model architecture and fine-tuning parameters are changed.

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There is a lot of potential to increase the accuracy of stock price predictions by utilizing cutting-edge AI techniques like Language Models and LSTM networks. Cutting-edge technologies and domain knowledge can provide investors with important insights into market dynamics and help them make better decisions. AI will surely be crucial in determining how financial markets develop in the future as they continue to advance.

Happy coding :)