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Predicting stock prices using a TensorFlow LSTM (long short-term memory) neural network for times series forecasting
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| 2026-08-04 | 685 |
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| 2026-08-06 | 686 |
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# Stock prediction using deep neural learning
Predicting stock prices can be a challenging task as it often does not follow any specific pattern. However, deep neural learning can be used to identify patterns through machine learning. One of the most effective techniques for series forecasting is using LSTM (long short-term memory) networks, which are a type of recurrent neural network (RNN) capable of remembering information over a long period of time. This makes them extremely useful for predicting stock prices.
This TensorFlow implementation of an LSTM neural network can be used for time series forecasting. **Successful prediction of a stock's future price can yield significant profits for investors**.
## Quickstart (Conda)
This project uses Python 3.12 and TensorFlow 2.18.1.
```cmd
conda env create -f environment.yml
conda activate stock-prediction
```
If you prefer pip inside an existing env:
```cmd
pip install -r requirements.txt
```
Jupyter users should select the `stock-prediction` kernel. If it does not appear:
```cmd
python -m ipykernel install --user --name stock-prediction --display-name "stock-prediction"
```
## 1) Introduction
Predicting stock prices is a complex task, as it is influenced by various factors such as market trends, political events, and economic indicators. The fluctuations in the stock prices are driven by the forces of supply and demand, which can be unpredictable at times. To identify patterns and trends in stock prices, deep learning techniques can be used for machine learning. Long short-term memory ([LSTM](https://www.researchgate.net/publication/13853244_Long_Short-term_Memory)) is a type of recurrent neural network (RNN) that is specifically designed for sequence modeling and prediction. LSTM is capable of retaining information over an extended period of time, making it an ideal approach for predicting stock prices. As a result, RNNs are well-suited to time series data, where they process data step-by-step, maintaining an internal state where they store the information they have seen so far in a compressed form. Accurate prediction of a stock's future price can provide significant financial gain to investors.
## 2) Stock Market Data
To gather the necessary market data for our stock prediction model, we will utilize the [yFinance](https://aroussi.com/post/python-yahoo-finance) library in Python. This library is designed specifically for downloading relevant information on a given [ticker symbol](https://www.investopedia.com/terms/t/tickersymbol.asp) from the [Yahoo Finance](https://finance.yahoo.com/quote/GOOG?p=GOOG) Finance webpage. By using yFinance, we can easily access the latest market data and incorporate it into our model.
For our purposes, we will be using the ticker symbol "GOOG", which is a well-known technology company. Here's an example screenshot of the ticker symbol on the Yahoo Finance page:
### 2.1) Market Info Download
To download the data info, we will need the `yFinance` library installed and then we will only need to perform the following operation to download all the relevant information of a given Stock using its ticker symbol. Please make sure you use the latest version of the library (`pip install yfinance==0.2.54`) as I have seen errors with previous versions.
Below is the output from the [download_market_data_info.py] file that is able to download financial data from Yahoo Finance.
```cmd
C:\Users\thund\Source\Repos\stock-prediction-deep-neural-learning>python download_market_data_info.py
Info
{
"52WeekChange": 0.26037383,
"SandP52WeekChange": 0.034871936,
"address1": "1600 Amphitheatre Parkway",
"algorithm": null,
"annualHoldingsTurnover": null,
"annualReportExpenseRatio": null,
"ask": 1432.77,
"askSize": 1400,
"averageDailyVolume10Day": 2011171,
"averageVolume": 1857809,
"averageVolume10days": 201117Excerpt of 41,707 characters
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matched fp:c46e55732f1723cd, llm:topics: lstm, time-series, stock-prediction, machine-learning; description/readme: TensorFlow LSTM neural network for time series forecasting of stock prices
matched fp:c46e55732f1723cd, llm:topics: lstm, time-series, stock-prediction, machine-learning; description/readme: TensorFlow LSTM neural network for time series forecasting of stock prices