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.

conda env create -f environment.yml
conda activate stock-prediction

If you prefer pip inside an existing env:

pip install -r requirements.txt

Jupyter users should select the stock-prediction kernel. If it does not appear:

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) 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 library in Python. This library is designed specifically for downloading relevant information on a given ticker symbol from the Yahoo Finance 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.

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": 2011171,
    "beta": 1.068946,
    "beta3Year": null,
    "bid": 1432.16,
    "bidSize": 3000,
    "bookValue": 297.759,
    "category": null,
    "circulatingSupply": null,
    "city": "Mountain View",
    "companyOfficers": [],
    "country": "United States",
    "currency": "USD",
    "dateShortInterest": 1592179200,
    "dayHigh": 1441.19,
    "dayLow": 1409.82,
    "dividendRate": null,
    "dividendYield": null,
    "earningsQuarterlyGrowth": 0.027,
    "enterpriseToEbitda": 17.899,
    "enterpriseToRevenue": 5.187,
    "enterpriseValue": 864533741568,
    "exDividendDate": null,
    "exchange": "NMS",
    "exchangeTimezoneName": "America/New_York",
    "exchangeTimezoneShortName": "EDT",
    "expireDate": null,
    "fiftyDayAverage": 1417.009,
    "fiftyTwoWeekHigh": 1532.106,
    "fiftyTwoWeekLow": 1013.536,
    "fiveYearAverageReturn": null,
    "fiveYearAvgDividendYield": null,
    "floatShares": 613293304,
    "forwardEps": 55.05,
    "forwardPE": 26.028149,
    "fromCurrency": null,
    "fullTimeEmployees": 123048,
    "fundFamily": null,
    "fundInceptionDate": null,
    "gmtOffSetMilliseconds": "-14400000",
    "heldPercentInsiders": 0.05746,
    "heldPercentInstitutions": 0.7062,
    "industry": "Internet Content & Information",
    "isEsgPopulated": false,
    "lastCapGain": null,
    "lastDividendValue": null,
    "lastFiscalYearEnd": 1577750400,
    "lastMarket": null,
    "lastSplitDate": 1430092800,
    "lastSplitFactor": "10000000:10000000",
    "legalType": null,
    "longBusinessSummary": "Alphabet Inc. provides online advertising services in the United States, Europe, the Middle East, Africa, the Asia-Pacific, Canada, and Latin America. It offers performance and brand advertising services. The company operates through Google and Other Bets segments. The Google segment offers products, such as Ads, Android, Chrome, Google Cloud, Google Maps, Google Play, Hardware, Search, and YouTube, as well as technical infrastructure. It also offers digital content, cloud services, hardware devices, and other miscellaneous products and services. The Other Bets segment includes businesses, including Access, Calico, CapitalG, GV, Verily, Waymo, and X, as well as Internet and television services. Alphabet Inc. was founded in 1998 and is headquartered in Mountain View, California.",
    "longName": "Alphabet Inc.",
    "market": "us_market",
    "marketCap": 979650805760,
    "maxAge": 1,
    "maxSupply": null,
    "messageBoardId": "finmb_29096",
    "morningStarOverallRating": null,
    "morningStarRiskRating": null,
    "mostRecentQuarter": 1585612800,
    "navPrice": null,
    "netIncomeToCommon": 34522001408,
    "nextFiscalYearEnd": 1640908800,
    "open": 1411.1,
    "openInterest": null,
    "payoutRatio": 0,
    "pegRatio": 4.38,
    "phone": "650-253-0000",
    "previousClose": 1413.61,
    "priceHint": 2,
    "priceToBook": 4.812112,
    "priceToSalesTrailing12Months": 5.87754,
    "profitMargins": 0.20712,
    "quoteType": "EQUITY",
    "regularMarketDayHigh": 1441.19,
    "regularMarketDayLow": 1409.82,
    "regularMarketOpen": 1411.1,
    "regularMarketPreviousClose": 1413.61,
    "regularMarketPrice": 1411.1,
    "regularMarketVolume": 1084440,
    "revenueQuarterlyGrowth": null,
    "sector": "Communication Services",
    "sharesOutstanding": 336161984,
    "sharesPercentSharesOut": 0.0049,
    "sharesShort": 3371476,
    "sharesShortPreviousMonthDate": 1589500800,
    "sharesShortPriorMonth": 3462105,
    "shortName": "Alphabet Inc.",
    "shortPercentOfFloat": null,
    "shortRatio": 1.9,
    "startDate": null,
    "state": "CA",
    "strikePrice": null,
    "symbol": "GOOG",
    "threeYearAverageReturn": null,
    "toCurrency": null,
    "totalAssets": null,
    "tradeable": false,
    "trailingAnnualDividendRate": null,
    "trailingAnnualDividendYield": null,
    "trailingEps": 49.572,
    "trailingPE": 28.904415,
    "twoHundredDayAverage": 1352.9939,
    "volume": 1084440,
    "volume24Hr": null,
    "volumeAllCurrencies": null,
    "website": "http://www.abc.xyz",
    "yield": null,
    "ytdReturn": null,
    "zip": "94043"
}

ISIN
-

Major Holders
        0                                      1
0   5.75%        % of Shares Held by All Insider
1  70.62%       % of Shares Held by Institutions
2  74.93%        % of Float Held by Institutions
3    3304  Number of Institutions Holding Shares

Institutional Holders
                            Holder    Shares Date Reported   % Out        Value
0       Vanguard Group, Inc. (The)  23162950    2020-03-30  0.0687  26934109889
1                   Blackrock Inc.  20264225    2020-03-30  0.0601  23563443472
2    Price (T.Rowe) Associates Inc  12520058    2020-03-30  0.0371  14558448642
3         State Street Corporation  11814026    2020-03-30  0.0350  13737467573
4                         FMR, LLC   8331868    2020-03-30  0.0247   9688379429
5  Capital International Investors   4555880    2020-03-30  0.0135   5297622822
6    Geode Capital Management, LLC   4403934    2020-03-30  0.0131   5120938494
7       Northern Trust Corporation   4017009    2020-03-30  0.0119   4671018235
8        JP Morgan Chase & Company   3707376    2020-03-30  0.0110   4310973886
9          AllianceBernstein, L.P.   3483382    2020-03-30  0.0103   4050511423

Dividents
Series([], Name: Dividends, dtype: int64)

Splits
Date
2014-03-27    2.002
2015-04-27    1.000
Name: Stock Splits, dtype: float64

Actions
            Dividends  Stock Splits
Date
2014-03-27        0.0         2.002
2015-04-27        0.0         1.000

Calendar
Empty DataFrame
Columns: []
Index: [Earnings Date, Earnings Average, Earnings Low, Earnings High, Revenue Average, Revenue Low, Revenue High]

Recommendations
                                         Firm    To Grade    From Grade Action
Date
2012-03-14 15:28:00                Oxen Group        Hold                 init
2012-03-28 06:29:00                 Citigroup         Buy                 main
2012-04-03 08:45:00  Global E