As of August 2020, the S&P 500 index had lost ** percent of its value due to the COVID-19 pandemic. However, the Great Crash, which began with Black Tuesday, remains the most significant loss in value in its history. That market crash lasted for 300 months and wiped ** percent off the index value.

📊 BBRI Stock Price Dataset (2020–2025)

This dataset contains historical stock price data for Bank Rakyat Indonesia Tbk (BBRI.JK), one of the largest banks in Indonesia and a key component of the IDX Composite (IHSG). The data spans 8th August 2020 to 15th August 2025, covering five years of daily trading activity on the Indonesia Stock Exchange (IDX).

📅 Date Range

• Start Date: 2020-08-08
• End Date: 2025-08-15

📂 Dataset Content

The dataset includes the following columns: 1. Date — Trading date 2. Open — Opening stock price 3. High — Highest stock price during the trading day 4. Low — Lowest stock price during the trading day 5. Close — Closing stock price 6. Volume — Number of shares traded

✅ Source

The dataset was collected using the Yahoo Finance API via the yfinance Python library.

🔎 Use Cases

This dataset is suitable for: - Stock price analysis and visualization - Technical analysis (moving averages, RSI, MACD, etc.) - Machine learning models for forecasting and pattern recognition - Financial research on Indonesian banking sector stocks - Portfolio optimization and risk analysis

⚠️ Disclaimer

This dataset is provided for educational and research purposes only. It should not be considered financial advice. Please verify all results with official sources before making investment decisions.

The Dow Jones Industrial Average (DJIA) index dropped around ***** points in the four weeks from February 12 to March 11, 2020, but has since recovered and peaked at ********* points as of November 24, 2024. In February 2020 - just prior to the global coronavirus (COVID-19) pandemic, the DJIA index stood at a little over ****** points. U.S. markets suffer as virus spreads The COVID-19 pandemic triggered a turbulent period for stock markets – the S&P 500 and Nasdaq Composite also recorded dramatic drops. At the start of February, some analysts remained optimistic that the outbreak would ease. However, the increased spread of the virus started to hit investor confidence, prompting a record plunge in the stock markets. The Dow dropped by more than ***** points in the week from February 21 to February 28, which was a fall of **** percent – its worst percentage loss in a week since October 2008. Stock markets offer valuable economic insights The Dow Jones Industrial Average is a stock market index that monitors the share prices of the 30 largest companies in the United States. By studying the performance of the listed companies, analysts can gauge the strength of the domestic economy. If investors are confident in a company’s future, they will buy its stocks. The uncertainty of the coronavirus sparked fears of an economic crisis, and many traders decided that investment during the pandemic was too risky.

📊 BMRI Stock Price Dataset (2020–2025)

This dataset contains historical stock price data for Bank Mandiri Tbk (BMRI.JK), one of the largest banks in Indonesia and a key component of the IDX Composite (IHSG). The data spans 8th August 2020 to 15th August 2025, covering five years of daily trading activity on the Indonesia Stock Exchange (IDX).

📅 Date Range

• Start Date: 2020-08-08
• End Date: 2025-08-15

📂 Dataset Content

The dataset includes the following columns: 1. Date — Trading date 2. Open — Opening stock price 3. High — Highest stock price during the trading day 4. Low — Lowest stock price during the trading day 5. Close — Closing stock price 6. Volume — Number of shares traded

✅ Source

The dataset was collected using the Yahoo Finance API via the yfinance Python library.

🔎 Use Cases

This dataset is suitable for: - Stock price analysis and visualization - Technical analysis (moving averages, RSI, MACD, etc.) - Machine learning models for forecasting and pattern recognition - Financial research on Indonesian banking sector stocks - Portfolio optimization and risk analysis

⚠️ Disclaimer

This dataset is provided for educational and research purposes only. It should not be considered financial advice. Please verify all results with official sources before making investment decisions.

Dataset providing prices of 30 VN30 stocks, from August 1, 2020 to August 1, 2025.

1. VCB: Ngân hàng TMCP Ngoại thương Việt Nam (Vietcombank)
2. VIC: Tập đoàn Vingroup
3. VHM: Công ty CP Vinhomes
4. BID: Ngân hàng TMCP Đầu tư và Phát triển Việt Nam (BIDV)
5. TCB: Ngân hàng TMCP Kỹ thương Việt Nam (Techcombank)
6. CTG: Ngân hàng TMCP Công Thương Việt Nam (VietinBank)
7. VPB: Ngân hàng TMCP Việt Nam Thịnh Vượng (VPBank)
8. HPG: Tập đoàn Hòa Phát
9. MBB: Ngân hàng TMCP Quân Đội (MBBank)
10. GAS: Tổng công ty Khí Việt Nam – CTCP (PV Gas)
11. FPT: Công ty CP FPT
12. VNM: Công ty CP Sữa Việt Nam (Vinamilk)
13. ACB: Ngân hàng TMCP Á Châu (ACB)
14. GVR: Tập đoàn Công nghiệp Cao su Việt Nam (VRG)
15. MSN: Tập đoàn Masan (Masan Group)
16. MWG: Công ty CP Đầu tư Thế giới Di động
17. LPB: Ngân hàng TMCP Bưu điện Liên Việt (LPBank)
18. STB: Ngân hàng TMCP Sài Gòn Thương Tín (Sacombank)
19. HDB: Ngân hàng TMCP Phát triển TP.HCM (HDBank)
20. SHB: Ngân hàng TMCP Sài Gòn – Hà Nội (SHB)
21. BCM: Tổng Công ty Phát triển Đô thị Kinh Bắc – CTCP (Becamex IDC)
22. SSI: Công ty CP Chứng khoán Sài Gòn (Saigon Securities)
23. VRE: Công ty CP Vincom Retail
24. VJC: Công ty CP Hàng không Vietjet (Vietjet Air)
25. SAB: Tổng Công ty CP Bia – Rượu – Nước giải khát Sài Gòn (Sabeco)
26. VIB: Ngân hàng TMCP Quốc tế Việt Nam (VIB)
27. SSB: Ngân hàng TMCP Đông Nam Á (SeABank)
28. TPB: Ngân hàng TMCP Tiên Phong (TPBank)
29. PLX: Tập đoàn Xăng dầu Việt Nam (Petrolimex – PLX)
30. BVH: Tập đoàn Bảo Việt (Bao Viet Holdings)

Copyright (c) 2025 Thinh Vu @ Vnstock. Source of dataset: TCBS: https://tcinvest.tcbs.com.vn/tc-price SSI iBoard (provided by FinnTrade): https://iboard.ssi.com.vn/

Indexes included in the Russell U.S. Index Series Russell 3000®: The Russell 3000 Index measures the performance of the largest 3,000 U.S. companies representing approximately 98% of the investable U.S. equity market. Russell 1000®: The Russell 1000 Index measures the performance of the large-cap segment of the U.S. equity universe. It is a subset of the Russell 3000 Index and includes approximately 1,000 of the largest securities based on a combination of their market cap and current index membership. The Russell 1000 represents approximately 91% of the U.S. market. Russell 2000®: The Russell 2000 Index measures the performance of the small-cap segment of the U.S. equity universe. The Russell 2000 Index is a subset of the Russell 3000 Index representing approximately 9% of the total market capitalization of that index. It includes approximately 2,000 of the smallest securities based on a combination of their market cap and current index membership. Index Inception Dates Russell 1000® Index (1/1979) Russell 1000® Growth Index (1/1979) Russell 1000® Value Index (1/1979) Russell 2000® Index (1/1979) Russell 2000® Growth Index (1/1979) Russell 2000® Value Index (1/1979) Russell 2500™ Index (4/2003) Russell 2500™ Growth Index (4/2003) Russell 2500™ Value Index (4/2003) Russell 3000® Index (1/1979) Russell 3000® Growth Index (1/1979) Russell 3000® Value Index (1/1979) Russell Midcap® Index (1/1986) Russell Midcap® Growth Index (1/1987) Russell Midcap® Value Index (1/1987) Russell Small Cap Completeness Index (4/2003) Russell Small Cap Completeness Growth Index (4/2003) Russell Small Cap Completeness Value Index (4/2003) Russell Top 200® Index (7/1996) Russell Top 200® Growth Index (7/2001) Russell Top 200® Value Index (7/2001) Monthly Files included in the Russell U.S. Index Series Monthly Closing Files – RGS These holdings files reflect the official closing positions for all constituents of the 21 U.S. Russell Indexes at month-end back to December 1986 and at quarter-end from September 1986 back to December 1978. Security level information such as returns, market values, sector and industry classifications, and security weights are included in the file. Files are fixed-width text files and have a naming convention of H_yyyymmdd_RGS.txt. Monthly Closing Files – ICB These holdings files reflect the official closing positions for all constituents of the 21 U.S. Russell Indexes at month-end back to January 2010. Security level information such as returns, market values, sector and industry classifications, and security weights are included in the file. Files are comma delimited text files and have a naming convention of H_yyyymmdd.csv. Monthly Contribution to Return by RGS Files These files provide contribution to return using RGS as of the end of the month for each of the 21 U.S. Russell Indexes back to August 2008. Files are tab delimited text files and have a naming convention of CTR_MONTHLY_RGS_yyyymmdd.txt.. Monthly Contribution to Return by ICB Files These files provide contribution to return using ICB as of the end of the month for each of the 21 U.S. Russell Indexes back to August 2020. Files are comma delimited text files and have a naming convention of CTR_MONTHLY_yyyymmdd.csv. Monthly RGS Sector Weights Files These files provide monthly Russell Global Sector (RGS) weights for all 21 US Indexes at month-end back to November 2009. Files are comma delimited text files and have a naming convention of SWH_RGS_ALL_yyyymmdd.txt. Monthly ICB Sector Weights Files These files provide monthly Industrial Classification Benchmark (ICB) weights for all 21 US Indexes at month-end back to March 2020. Files are comma delimited text files and have a naming convention of SWH_ALL_yyyymmdd.csv. Note: In August 2020 FTSE Russell transitioned to ICB classification from the RGS classification. All data from September, 2020 is only available using ICB Classification. Data is current to 2024.

The value of the DJIA index amounted to ****** at the end of June 2025, up from ********* at the end of March 2020. Global panic about the coronavirus epidemic caused the drop in March 2020, which was the worst drop since the collapse of Lehman Brothers in 2008. Dow Jones Industrial Average index – additional information The Dow Jones Industrial Average index is a price-weighted average of 30 of the largest American publicly traded companies on New York Stock Exchange and NASDAQ, and includes companies like Goldman Sachs, IBM and Walt Disney. This index is considered to be a barometer of the state of the American economy. DJIA index was created in 1986 by Charles Dow. Along with the NASDAQ 100 and S&P 500 indices, it is amongst the most well-known and used stock indexes in the world. The year that the 2018 financial crisis unfolded was one of the worst years of the Dow. It was also in 2008 that some of the largest ever recorded losses of the Dow Jones Index based on single-day points were registered. On September 29, 2008, for instance, the Dow had a loss of ****** points, one of the largest single-day losses of all times. The best years in the history of the index still are 1915, when the index value increased by ***** percent in one year, and 1933, year when the index registered a growth of ***** percent.

Context

Facebook is a company that literally every kid is aware of. Its a household name. People from various age groups are there on this social media website. It has helped many in connecting with different people and also has helped some of the investors by earning them a good amount of money. This data set contains the details of the stock of Facebook Inc.

Content

This data set has 7 columns with all the necessary values such as opening price of the stock, the closing price of it, its highest in the day and much more. It has date wise data of the stock starting from 2012 to 2020(August).

Companies are grouped into four categories, namely: radiated with headquarters, radiated without headquarters, not radiated with headquarters and not radiated without headquarters. There is also a nomenclature with company statuses.

Context

Pheewwwww........ This is my first Data set Upload, kudos to that. I have been wondering around on internet for getting historical stock data, but it was cumbersome task. I have decided to scrape this data and here it is now after 20 days struggle. I am a newbie in this vast data science field and little help giving bronze, silver or gold medal would be kind of you.(Although I am totally unaware about how this work or how to get this medal :) )

Content

Here I have uploaded Tikers.xlsx file in which list of companies is given. In Data Folder Company wise folder is created and in each folder there is csv file which consist seven column, namely : Date,Open,High,Low,Close,Adj Close,Volume

Data is from 1st Jan,1996 to 7th Aug,2020.

I am still learning about Tasks and Kernels, So I would be uploading and updating data set from time to time, Stay tune for that.

Any Suggestion regarding this data? Contact me on dipnmodi@gmail.com I have my resume site : Resume

Acknowledgements

A big thank you goes to Yahoo Finance , I have scrape this data entirely from them. Second thanks goes to Samir for making wonderful list of Tickers which I have used in my program. Also A huge thanks goes to Aarefa for being my motivation.

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered? Honestly I don't know what to except, Still I will try. - I want to identify highly volatile companies(In terms of stock price movement) - I also want to identify market movers, by average volume of stock. - I also want to apply different ML models to each company to do technical analysis.

And lastly anything you want to do since it is GPL 2.0 licence and I support freedom.

List of Newly Incorporated/Registered Companies and Companies which have changed Names in 2020 ( 10 August 2020 to 16 August 2020)

Index: OMX Iceland Stock Exchange: Telecommunications data was reported at 2,293.100 18Dec2012=1000 in Aug 2020. This records an increase from the previous number of 2,095.550 18Dec2012=1000 for Jul 2020. Index: OMX Iceland Stock Exchange: Telecommunications data is updated monthly, averaging 1,470.370 18Dec2012=1000 from Dec 2012 (Median) to Aug 2020, with 93 observations. The data reached an all-time high of 2,293.100 18Dec2012=1000 in Aug 2020 and a record low of 793.480 18Dec2012=1000 in Aug 2013. Index: OMX Iceland Stock Exchange: Telecommunications data remains active status in CEIC and is reported by Iceland Stock Exchange. The data is categorized under Global Database’s Iceland – Table IS.Z001: Iceland Stock Exchange: Index.

Brazil Loans: Stock: Non Financial Corporations: Credit Operations Overdue for More than 90 Days: House Finance System - SFH: Paraná data was reported at 17,846.700 BRL in Aug 2020. This records a decrease from the previous number of 8,160,587.450 BRL for Jul 2020. Brazil Loans: Stock: Non Financial Corporations: Credit Operations Overdue for More than 90 Days: House Finance System - SFH: Paraná data is updated monthly, averaging 17,801,725.560 BRL from Apr 2015 (Median) to Aug 2020, with 60 observations. The data reached an all-time high of 98,013,355.190 BRL in Jan 2019 and a record low of 17,846.700 BRL in Aug 2020. Brazil Loans: Stock: Non Financial Corporations: Credit Operations Overdue for More than 90 Days: House Finance System - SFH: Paraná data remains active status in CEIC and is reported by Central Bank of Brazil. The data is categorized under Brazil Premium Database’s Monetary – Table BR.KAB094: Loans: Stock: Non Financial Corporations: Credit Operations Overdue for More than 90 Days: House Finance System - SFH. [COVID-19-IMPACT]

Common-Stock Time Series for Shenzhen Terca Technology Co Ltd. Shenzhen Dawei Innovation Technology Co., Ltd. engages in the manufacturing of automobiles and smart terminals business in China. The company offers smart terminal products in the fields of communication equipment and accessories, computers, and other electronic equipment; and automotive retarders under the Teerjia brand name. It also provides new energy special vehicles; and semiconductor memory products, such as NAND and DRAM storage. The company was formerly known as Shenzhen Terca Technology Co., Ltd and changed its name to Shenzhen Dawei Innovation Technology Co., Ltd. in August 2020. Shenzhen Dawei Innovation Technology Co., Ltd. was founded in 2000 and is headquartered in Shenzhen, China.

Brazil Loans: Stock: Non Financial Corporations: Default: House Finance System - SFH: Paraná data was reported at 0.000 % in Aug 2020. This records a decrease from the previous number of 1.600 % for Jul 2020. Brazil Loans: Stock: Non Financial Corporations: Default: House Finance System - SFH: Paraná data is updated monthly, averaging 1.850 % from Feb 2016 (Median) to Aug 2020, with 55 observations. The data reached an all-time high of 13.480 % in Jun 2019 and a record low of 0.000 % in Aug 2020. Brazil Loans: Stock: Non Financial Corporations: Default: House Finance System - SFH: Paraná data remains active status in CEIC and is reported by Central Bank of Brazil. The data is categorized under Brazil Premium Database’s Monetary – Table BR.KAB112: Loans: Stock: Non Financial Corporations: Default: House Finance System - SFH. [COVID-19-IMPACT]

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Context

Numerous people had asked me to study the role retail trading plays in driving asset prices. Using this as my inspiration, I found a dataset with hourly tick data for ~9,000 stocks and another one with hourly Robinhood user participation data (aka how many Robinhood users own a stock in a particular time period) . Here you will not only find the data used to perform my research, but also a copy of the notebook I ended up using. Excited to see what the community does with this!

Content

2 major sources were used to acquire this data: - Stooq - While not written in English, this website hosts numerous free stock tick datasets. I was able to directionally confirm accuracy of the data vs what my personal brokerage account reported over this time period. I cannot speak to the preciseness of this data. - RobinTrack - This website collects Robinhood user participation data for stocks that trade on their platform. Per Bloomberg, it does appear Robinhood will stop providing access to this data in the near future (as of August 2020)

Additionally, you can find the notebook I used to prepare the research for my article here

The data covers the time period between September 2019 and July 2020.

Understanding the Files

I originally tried to input this information directly in the Data Explorer section but Kaggle kept bugging out.

Robinhood_Master_v1.csv

This is the master dataframe that includes hourly tick and Robinhood user participation data for ~9,000 stocks going back ~1 year - #: Index column; it can be ignored - Clean_Datetime: This column can also be ignored. - Close: Closing price for the stock noted in the Ticker column during this row's time period - High: Highest price reached for the stock noted in the Ticker column during this row's time period - Low: Lowest price reached for the stock noted in the Ticker column during this row's time period - Close: Closing price for the stock noted in the Ticker column during this row's time period - Open: Opening price for the stock noted in the Ticker column during this row's time period - OpenInt: This column can be ignored - its almost all 0 - Ticker: The stock ticker analyzed in a given row. For example, if this shows 'AAPL' then this row is reporting data on Apple stock. - users _ holding _ first: The initial amount of Robinhood users who owned the stock noted in the Ticker column during this row's time period - users _ holding _ last: The final amount of Robinhood users who owned the stock noted in the Ticker column during this row's time period - users _ holding _ max: The highest amount of Robinhood users who owned the stock noted in the Ticker column during this row's time period - users _ holding _ min: The lowest amount of Robinhood users who owned the stock noted in the Ticker column during this row's time period

df_apple_final.csv

This is the pre-processed dataframe that includes the cleaned predictors I used for my Apple time series modeling. All columns (except "y", "Clean _ Datetime _ PST" and "ds") were shifted back 1 day. The idea here is that all predictors need to occur on or before the target data. Otherwise, you end up using future data to predict the past. I'll only describe columns below that are not also found in the master dataframe. - users _ holding _ 1D _ change: the day-over-day change in Robinhood stock ownership for Apple - users _ holding _ 13D _ change: the 13 day change in Robinhood stock ownership for Apple - Open 6D_change: the 6 day change in Apple’s stock market opening price - Open 13D_change: the 13 day change in Apple’s stock market opening price - SPY users _ holding _ 1D _ change: the day-over-day change in Robinhood stock ownership for SPY - SPY Open 1D _ change: the day-over-day change in SPY’s stock market opening price - SPY Open 13D _ change: the 13 day change in SPY’s stock market opening price

custom_functions.py

In my notebook, I had to create a couple custom functions to run the graphs used there (this file is explicitly imported into my notebook with all the other python libraries). If you want to run my notebook, make sure it can find this file so it can run these functions.

Online searches for stocks to invest in during the coronavirus pandemic underwent the biggest year-over-year growth in online searches. Between March and August 2020, the number U.S. online searches regarding stock investments increased by 240 percent compared to the previous year. Online searches related to mortgages grew 80 percent.

View monthly updates and historical trends for S&P 500 Monthly Return. from United States. Source: Standard and Poor's. Track economic data with YCharts a…

China Livestock: Number: MoM: Pig Stock data was reported at 4.700 % in Aug 2020. This records a decrease from the previous number of 4.800 % for Jul 2020. China Livestock: Number: MoM: Pig Stock data is updated monthly, averaging -0.100 % from Jan 2009 (Median) to Aug 2020, with 134 observations. The data reached an all-time high of 4.800 % in Jul 2020 and a record low of -9.800 % in Aug 2019. China Livestock: Number: MoM: Pig Stock data remains active status in CEIC and is reported by Ministry of Agriculture and Rural Affairs. The data is categorized under China Premium Database’s Agriculture Sector – Table CN.RID: Number of Livestock: Pig Stock.

Jordan Amman Stock Exchange: Index: Free Float Weighted Index: Technology & Communications data was reported at 763.800 NA in Oct 2024. This records an increase from the previous number of 754.570 NA for Sep 2024. Jordan Amman Stock Exchange: Index: Free Float Weighted Index: Technology & Communications data is updated monthly, averaging 626.795 NA from Jul 2013 (Median) to Oct 2024, with 135 observations. The data reached an all-time high of 1,356.652 NA in Dec 2013 and a record low of 396.780 NA in Aug 2020. Jordan Amman Stock Exchange: Index: Free Float Weighted Index: Technology & Communications data remains active status in CEIC and is reported by Exchange Data International Limited. The data is categorized under Global Database’s Jordan – Table JO.EDI.SE: Amman Stock Exchange: Monthly.