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GithubStock 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.
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==0.2.33) 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,
"logo_url": "https://logo.clearbit.com/abc.xyz",
"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 Equities Research Overweight main
2012-04-05 06:34:00 Deutsche Bank Buy main
2012-04-09 06:03:00 Pivotal Research Buy main
2012-04-10 11:32:00 UBS Buy main
2012-04-13 06:16:00 Deutsche Bank Buy main
2012-04-13 06:18:00 Jefferies Buy main
2012-04-13 06:37:00 PiperJaffray Overweight main
2012-04-13 06:38:00 Goldman Sachs Neutral main
2012-04-13 06:41:00 JP Morgan Overweight main
2012-04-13 06:51:00 Oppenheimer Outperform main
2012-04-13 07:13:00 Benchmark Hold main
2012-04-13 08:46:00 BMO Capital Outperform main
2012-04-16 06:52:00 Hilliard Lyons Buy main
2012-06-06 06:17:00 Deutsche Bank Buy main
2012-06-06 06:56:00 JP Morgan Overweight main
2012-06-22 06:15:00 Citigroup Buy main
2012-07-13 05:57:00 Wedbush Neutral init
2012-07-17 09:33:00 Outperform main
2012-07-20 06:43:00 Benchmark Hold main
2012-07-20 06:54:00 Deutsche Bank Buy main
2012-07-20 06:59:00 Bank of America Buy main
2012-08-13 05:49:00 Morgan Stanley Overweight Equal-Weight up
2012-09-17 06:07:00 Global Equities Research Overweight main
2012-09-21 06:28:00 Cantor Fitzgerald Buy init
2012-09-24 06:11:00 Citigroup Buy main
2012-09-24 09:05:00 Pivotal Research Buy main
2012-09-25 07:20:00 Capstone Buy main
2012-09-26 05:48:00 Canaccord Genuity Buy main
... ... ... ... ...
2017-10-27 19:29:31 UBS Buy main
2018-02-02 14:04:52 PiperJaffray Overweight Overweight main
2018-04-24 11:43:49 JP Morgan Overweight Overweight main
2018-04-24 12:24:37 Deutsche Bank Buy Buy main
2018-05-05 14:00:37 B. Riley FBR Buy main
2018-07-13 13:49:13 Cowen & Co. Outperform Outperform main
2018-07-24 11:50:55 Cowen & Co. Outperform Outperform main
2018-07-24 13:33:47 Raymond James Outperform Outperform main
2018-10-23 11:18:00 Deutsche Bank Buy Buy main
2018-10-26 15:17:08 Raymond James Outperform Outperform main
2019-01-23 12:55:04 Deutsche Bank Buy Buy main
2019-02-05 12:55:12 Deutsche Bank Buy Buy main
2019-02-05 13:18:47 PiperJaffray Overweight Overweight main
2019-05-15 12:34:54 Deutsche Bank Buy main
2019-10-23 12:58:59 Credit Suisse Outperform main
2019-10-29 11:58:09 Raymond James Outperform main
2019-10-29 14:15:40 Deutsche Bank Buy main
2019-10-29 15:48:29 UBS Buy main
2020-01-06 11:22:07 Pivotal Research Buy Hold up
2020-01-17 13:01:48 UBS Buy main
2020-02-04 12:26:56 Piper Sandler Overweight main
2020-02-04 12:41:00 Raymond James Outperform main
2020-02-04 14:00:36 Deutsche Bank Buy main
2020-02-06 11:34:20 CFRA Strong Buy main
2020-03-18 13:52:51 JP Morgan Overweight main
2020-03-30 13:26:16 UBS Buy main
2020-04-17 13:01:41 Oppenheimer Outperform main
2020-04-20 19:29:50 Credit Suisse Outperform main
2020-04-29 14:01:51 UBS Buy main
2020-05-05 12:44:16 Deutsche Bank Buy main
[219 rows x 4 columns]
Earnings
Empty DataFrame
Columns: [Open, High, Low, Close, Adj Close, Volume]
Index: []
Quarterly Earnings
Empty DataFrame
Columns: [Open, High, Low, Close,
