利用 LSTM 和 LLM 模型进行股票价格预测

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Leveraging LSTM and LLM Models for Stock Price Prediction

利用 LSTM 和 LLM 模型进行股票价格预测

Introduction

引言

Investors are continuously looking for methods to obtain an advantage and make wise decisions in the fast-paced financial markets of today. While it's still difficult to predict stock prices with 100% accuracy, advances in artificial intelligence (AI) have made it possible to analyze historical data and spot possible trends. This article investigates the prediction of stock prices using state-of-the-art artificial intelligence techniques, namely Language Models (LMs) and Long Short-Term Memory (LSTM) networks. Investors can make better investment decisions and obtain a deeper understanding of market dynamics by utilizing these technologies.

在当今快节奏的金融市场中,投资者不断寻找获得优势和做出明智决策的方法。虽然仍难以100%准确地预测股票价格,但人工智能(AI)的进步使分析历史数据和发现可能的趋势成为可能。本文探讨了使用最先进的人工智能技术,即语言模型(LM)和长短期记忆(LSTM)网络来预测股票价格。通过利用这些技术,投资者可以做出更好的投资决策,并更深入地了解市场动态。

Understanding Stock Markets

了解股票市场

Prior to exploring the nuances of stock price forecasting, let us take a quick look at stocks and their role in the financial system. Equities, commonly referred to as shares or stocks, are ownership stakes in a business. Acquiring stocks entitles individuals or institutions to a portion of the company's assets as well as a share of its profits. A number of variables, such as business performance, investor mood, prevailing economic conditions, and geopolitical developments, affect stock prices. Investors trying to negotiate the intricacies of the stock market must comprehend these dynamics.

在探讨股票价格预测的细微差别之前,让我们快速了解一下股票及其在金融体系中的作用。股票,通常被称为股份或股票,是企业的所有权份额。购买股票使个人或机构有权获得公司部分资产以及利润份额。许多变量,如企业业绩、投资者情绪、当前经济状况和地缘政治发展,都会影响股票价格。试图驾驭股票市场复杂性的投资者必须理解这些动态。

Implementation Using Advanced AI Techniques

使用高级AI技术的实现

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.

现在让我们关注使用尖端AI技术预测股票价格的有效性。我们将集中讨论两种方法:语言模型(LM)和长短期记忆(LSTM)网络。

Long Short-Term Memory (LSTM) Networks

长短期记忆(LSTM)网络

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.

长短期依赖(LSTM)类型的循环神经网络(RNN)旨在处理和预测顺序数据。在预测股票价格方面,长短期记忆(LSTM)模型检查过去的价格数据,以找到可能影响未来价格变化的模式和趋势。

Steps in Implementation:

实现步骤:

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

• 数据收集:使用雅虎财经等金融数据API,我们首先收集目标资产的历史股价信息。

• 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.

• 数据预处理:对原始数据进行清理,以消除噪音,处理缺失值,并将值标准化为统一的尺度。

• 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.

• 模型设计:根据数据集的特征,如输入特征的数量、隐藏层和激活函数,我们创建一个 LSTM 架构。

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

• 训练:通过使用历史价格序列最大化准确性和最小化预测误差,优化 LSTM 模型的参数。

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

• 评估:为了衡量训练模型的性能和泛化能力,在不同的验证数据集上对其进行测试。

Language Models (LMs) for Stock Price Prediction

用于股票价格预测的语言模型(LM)

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.

语言模型已被证明善于根据学习到的上下文产生连贯的文本;这对于基于transformer的模型如GPT(生成式预训练变换器)尤其如此。我们可以在历史股价数据上微调预训练的LM,使其生成可信的未来价格轨迹。

Implementation Steps:

实现步骤:

• 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.
• 调整预训练的LM:使用历史股价数据,我们调整预训练GPT模型的参数,以发现潜在的模式和趋势。
• Training Process: To reduce prediction errors, the model's weights are updated via gradient descent optimization during the fine-tuning phase.
• 训练过程:为了减少预测误差,在微调阶段通过梯度下降优化更新模型的权重。
• 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.
• 未来价格生成:经过训练后,LM可以利用它在数据中发现的模式生成一系列未来价格预测。

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.

一个可用于根据历史数据预测未来股价的趋势语言模型(LLM)例子是GPT(生成式预训练变换器)。使用历史股价数据,我们将优化预训练的GPT模型,并用它来预测未来价格。

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

首先,你需要通过运行以下命令安装transformers库:

        
            

          pip install transformers
        
      

Importing necessary libraries for various tasks:

为各种任务导入必要的库:

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

Downloading and Preprocessing Data

下载和预处理数据

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

ticker = "^NSEI":这一行将变量ticker设置为印度国家证券交易所Nifty 50指数的代码。

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.

start_date = "2023-01-01"和end_date = "2023-06-08":这些行定义了将下载历史股价数据的开始和结束日期。在本例中,是从2023年1月1日到2023年6月8日。

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.

data = yf.download(ticker, start=start_date, end=end_date):在这里,yfinance库下载给定时间范围内指定股票代码(Nifty 50指数)的历史股价数据。数据存储在Pandas DataFrame中,收集股票的各种信息(开盘价、最高价、最低价、收盘价、成交量等)。

          
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
      

在历史数据上微调GPT模型

          
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.

在这里,从transformers库中加载GPT-2分词器和预训练模型。

Tokenizing Historical Prices and Fine-tuning the Model

对历史价格进行分词并微调模型

          
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

历史股价被分词并用于微调GPT-2模型。使用Adam优化器对模型进行三个epoch的训练

Generating Future Prices

生成未来价格

          
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.

然后使用微调后的模型根据历史数据生成未来的股票价格。

Plotting the Results

绘制结果

          
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()
      

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.

该代码使用历史股价数据优化预训练的GPT模型以生成未来价格。最后,绘制了预测价格和历史价格的图表。如果更改GPT模型架构和微调参数,预测可能会更准确。

Conclusion

结论

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.

通过利用语言模型和LSTM网络等尖端AI技术,有很大潜力提高股价预测的准确性。尖端技术和领域知识可以为投资者提供对市场动态的重要见解,并帮助他们做出更好的决策。随着AI的不断发展,它必将在决定未来金融市场的发展方面发挥关键作用。

Happy coding :)

编码愉快 :)

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