Ethereum's price history suggests that that crypto was worth more in 2025 than during late 2021, although nowhere near the highest price recorded. Much like Bitcoin (BTC), the price of ETH went up in 2021 but for different reasons altogether: Ethereum, for instance, hit the news when a digital art piece was sold as the world's most expensive NFT for over 38,000 ETH - or 69.3 million U.S. dollars. Unlike Bitcoin, of which the price growth was fueled by the IPO of the U.S.'s biggest crypto trader, Coinbase, the rally on Ethereum came from technological developments that caused much excitement among traders. First, the so-called 'Berlin update' rolled out on the Ethereum network in April 2021, an update that would eventually lead to the Ethereum Merge in 2022 and reduced ETH gas prices - or reduced transaction fees. The collapse of FTX in late 2022, however, changed much for the cryptocurrency. As of July 30, 2025, Ethereum was worth 3,788.6 U.S. dollars - significantly less than the 4,400 U.S. dollars by the end of 2021.Ethereum's future and the DeFi industryPrice developments on Ethereum are difficult to predict but cannot be seen without the world of DeFi, or decentralized finance. This industry used technology to remove intermediaries between parties in a financial transaction. One example includes crypto wallets such as Coinbase Wallet that grew in popularity recently, with other examples including smart contractor Uniswap, Maker (responsible for stablecoin DAI), moneylender Dharma and market protocol Compound. Ethereum's future developments are tied with this industry: Unlike Bitcoin and Ripple, Ethereum is technically not a currency but an open-source software platform for blockchain applications, with Ether being the cryptocurrency that is used inside the Ethereum network. Essentially, Ethereum facilitates DeFi, meaning that if DeFi does well, so does Ethereum.NFTs: the most well-known application of EthereumNFTs or non-fungible tokens, grew nearly tenfold between 2018 and 2020, as can be seen in the market cap of NFTs worldwide. These digital blockchain assets can essentially function as a unique code connected to a digital file, allowing to distinguish the original file from any potential copies. This application is especially prominent in crypto art, although there are other applications: gaming, sports, and collectibles are other segments where NFT sales occur.

Context

Things like Block chain, Bitcoin, Bitcoin cash, Ethereum, Ripple etc are constantly coming in the news articles I read. So I wanted to understand more about it and this post helped me get started. Once the basics are done, the data scientist inside me started raising questions like:

1. How many cryptocurrencies are there and what are their prices and valuations?
2. Why is there a sudden surge in the interest in recent days?

So what next? Now that we have the price data, I wanted to dig a little more about the factors affecting the price of coins. I started of with Bitcoin and there are quite a few parameters which affect the price of Bitcoin. Thanks to Blockchain Info, I was able to get quite a few parameters on once in two day basis.

This will help understand the other factors related to Bitcoin price and also help one make future predictions in a better way than just using the historical price.

Content

The dataset has one csv file for each currency. Price history is available on a daily basis from April 28, 2013. This dataset has the historical price information of some of the top crypto currencies by market capitalization.

• Date : date of observation
• Open : Opening price on the given day
• High : Highest price on the given day
• Low : Lowest price on the given day
• Close : Closing price on the given day
• Volume : Volume of transactions on the given day
• Market Cap : Market capitalization in USD

Acknowledgements

This data is taken from coinmarketcap and it is free to use the data.

Cover Image : Photo by Thomas Malama on Unsplash

Inspiration

Some of the questions which could be inferred from this dataset are:

1. How did the historical prices / market capitalizations of various currencies change over time?
2. Predicting the future price of the currencies
3. Which currencies are more volatile and which ones are more stable?
4. How does the price fluctuations of currencies correlate with each other?
5. Seasonal trend in the price fluctuations

This dataset presents an extensive record of daily historical stock prices for Tesla, Inc. (TSLA), one of the world’s most innovative and closely watched electric vehicle and clean energy companies. The data was sourced from Yahoo Finance, a widely used and trusted provider of financial market data, and covers a significant period spanning from Tesla’s initial public offering (IPO) to the most recent date available at the time of extraction.

The dataset includes critical trading metrics for each market day, such as the opening price, highest and lowest prices of the day, closing price, adjusted closing price (accounting for dividends and splits), and total trading volume. This rich dataset supports a variety of use cases, including financial market analysis, investment research, time series forecasting, development and backtesting of trading algorithms, and educational projects in data science and finance.

Dataset Features

• Date: The calendar date for each trading session (in YYYY-MM-DD format)
• Open: The opening price of TSLA shares at the start of the trading day
• High: The highest price reached during the trading session
• Low: The lowest price reached during the trading session
• Close: The last price at which the stock traded during the day
• Adj Close: The closing price adjusted for corporate actions (splits, dividends, etc.)
• Volume: The total number of TSLA shares traded on that day

Source and Collection Details

• Source: Yahoo Finance - Tesla (TSLA) Historical Data
• Collection Method: Data was downloaded using Yahoo Finance's CSV export feature for accuracy and completeness.
• Time Range: Covers from Tesla’s IPO (June 2010) to the most recent available trading day.
• Data Integrity: Minimal cleaning was performed—dates were standardized, and any duplicate or empty rows were removed; all values remain as originally reported by Yahoo Finance.

Example Use Cases

• Stock Price Prediction: Train and test time series models (ARIMA, LSTM, Prophet, etc.) to forecast Tesla’s stock prices.
• Algorithmic Trading: Backtest and evaluate trading strategies using historical price and volume data.
• Market Trend Analysis: Analyze price trends, volatility, and return rates over different periods.
• Event Study: Investigate the impact of major announcements (e.g., product launches, earnings releases) on TSLA stock price.
• Educational Projects: Use as a hands-on resource for learning finance, statistics, or machine learning.

License & Acknowledgments

• Intended Use: This dataset is provided for academic, research, and personal projects. For commercial or investment use, please verify data accuracy and consult Yahoo Finance’s terms of use.
• Acknowledgment: Data sourced from Yahoo Finance. All trademarks and copyrights belong to their respective owners.

Platinum rose to 1,316.40 USD/t.oz on August 1, 2025, up 1.74% from the previous day. Over the past month, Platinum's price has fallen 6.74%, but it is still 37.27% higher than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Platinum - values, historical data, forecasts and news - updated on August of 2025.

Analysis of ‘Crypto-data-part1’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/tusharsarkar/cryptodatapart1 on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

Things like Block chain, Bitcoin, Bitcoin cash, Ethereum, Ripple etc are constantly coming in the news articles I read. So I wanted to understand more about it and this post helped me get started. Once the basics are done, the data scientist inside me started raising questions like:

How many cryptocurrencies are there and what are their prices and valuations? Why is there a sudden surge in the interest in recent days? So what next? Now that we have the price data, I wanted to dig a little more about the factors affecting the price of coins. I started of with Bitcoin and there are quite a few parameters which affect the price of Bitcoin. Thanks to Blockchain Info, I was able to get quite a few parameters on once in two day basis.

This will help understand the other factors related to Bitcoin price and also help one make future predictions in a better way than just using the historical price.

Content

The dataset has one csv file for each currency. Price history is available on a daily basis from April 28, 2013. This dataset has the historical price information of some of the top crypto currencies by market capitalization.

Date : date of observation Open : Opening price on the given day High : Highest price on the given day Low : Lowest price on the given day Close : Closing price on the given day Volume : Volume of transactions on the given day

--- Original source retains full ownership of the source dataset ---

A "spread" can have multiple meanings, but it generally implies a difference between two comparable measures. These can be differences across space, across time, or across anything with a similar attribute. For example, in the stock market, there is a spread between the highest price a buyer is willing to pay and the lowest price a seller is willing to accept.

In this dataset, spread refers to differences in prices between two locations, an origin (e.g., Illinois, Iowa, etc.) and a destination (e.g., Louisiana Gulf, Pacific Northwest, etc.). Mathematically, it is the destination price minus the origin price.

Price spreads are closely linked to transportation. They tend to reflect the costs of moving goods from one point to another, all else constant. Fluctuations in spreads can change the flow of goods (where it may be more profitable to ship to a different location), as well as indicate changes in transportation availability (e.g., disruptions). For more information on how price spreads are linked to transportation, see the story, "Grain Prices, Basis, and Transportation" (https://agtransport.usda.gov/stories/s/sjmk-tkh6).

This is one of three companion datasets. The other two are grain prices (https://agtransport.usda.gov/d/g92w-8cn7) and grain basis (https://agtransport.usda.gov/d/v85y-3hep). These datasets are separate, because the coverage lengths differ and missing values are removed (e.g., there needs to be a cash price and a futures price to have a basis price, and there needs to be both an origin and a destination to have a price spread).

The origin and destination prices come from the grain prices dataset.

This dataset offers a comprehensive historical record of Netflix’s stock price movements, capturing the company’s financial journey from its early days to its position as a global streaming giant.

From its IPO in May 2002, Netflix (Ticker: NFLX) has transformed from a DVD rental service to a powerhouse in on-demand digital content. With its disruptive innovation, strategic shifts, and global expansion, Netflix has seen dramatic shifts in stock prices, reflecting not just market trends but also cultural impact. This dataset provides a window into that evolution.

What’s Included?

Each row in this dataset represents daily trading activity on the stock market and includes the following columns:

• Date – The trading day (from 2002 onward)
• Open – Stock price when the market opened
• High – Highest trading price of the day
• Low – Lowest trading price of the day
• Close – Final price at market close
• Adj Close – Closing price adjusted for splits and dividends
• Volume – Number of shares traded that day

The data is structured in CSV format and is clean, easy to use, and ready for immediate analysis.

Why Use This Dataset?

Whether you're learning data science, building a financial model, or exploring machine learning in the real world, this dataset is a goldmine of insights. Netflix's market history includes:

• Periods of explosive growth during digital transformation
• Volatility during market crashes and global events (e.g., 2008, COVID-19)
• Strategic pivots such as the shift to original content
• Market reactions to earnings, acquisitions, and subscriber milestones

This makes the dataset ideal for:

• Time-series forecasting (ARIMA, Prophet, LSTM)
• Technical and trend analysis (moving averages, RSI, Bollinger Bands)
• Predictive modeling with machine learning
• Investment simulation projects
• Stock market visualization and storytelling
• Financial dashboards (Tableau, Power BI, Streamlit, etc.)

Who Can Use It?

This dataset is designed for:

• Aspiring data scientists practicing EDA and modeling
• Financial analysts and traders exploring trends
• AI researchers working on time-series models
• Students building ML projects
• Developers creating stock visualization tools
• Kaggle competitors seeking real-world datasets

Data Source & Credits

The dataset is derived from publicly available historical stock price data, such as Yahoo Finance, and has been cleaned and organized for educational and research purposes. It is continuously maintained to ensure accuracy.

Start Exploring

Netflix’s rise is more than just a business story — it’s a data-driven journey. With this dataset, you can analyze the company’s stock behavior, train models to predict future trends, or simply visualize how tech reshapes the market.

Crypto Trading and Technical Indicators

Understanding the Market Dynamics of 600 Popular Cryptocurrencies

By [source]

About this dataset

This dataset provides an unprecedented overview of the crypto industry, offering comprehensive market analysis of more than 600 well-known cryptocurrencies. The data contained in this dataset is extremely up-to-date, ranging from trading statuses, price movements and volatility levels to technical indicators and market capitalization. Perfect for those interested in cryptocurrency trading, technical analysis or investing, this data can be used to facilitate informed decisions and fulfill respective requirements.
The 35 columns present in this dataset enable users to gain a greater understanding into price movements and distinguish between various levels of volatility. It also allows users to analyze oscillator ratings for each crypto asset listed within for accurate risk management. Banks, investors, data analysts as well as crypto exchanges could all benefit from utilizing this powerful dataset; its ability to provide a top level summary into the entire crypto industry has earned it a valuable place among the cryptocurrency community worldwide

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset provides comprehensive market analysis of more than 600 popular cryptocurrencies, including trading prices, volatility, technical indicators, and market capitalization. In this guide, we'll cover what kind of information you can obtain from the dataset, how to use it effectively to gain insight into the crypto industry, and how to analyze the results in order to make informed decisions regarding cryptocurrency trading.

The dataset consists of 35 columns that are divided into two main categories: Market Information and Technical Indicators. The Market Information section contains data about each cryptocurrency's price performance – including change percentages for 1 week/1 month/3 months/6 months/1 year – as well as its fully diluted market capitalization (FD Mkt Cap), traded volume (Traded Vol), last trading price in USD (Last_y), available coins (Avail Coins), total coins created with a max supply(Total Coins) and its respective rating out of 5 stars by moving averages(Moving Averages Rating). The Technical Indicators section includes data pertaining to oscillator ratings (Oscillators Rating) such as Average Directional Index (ADX), Awesome Oscillator(AO), Average True Range(ATR) , Commodity Channel Index20(CCI20) etc., moving averages such as Simple Moving Average 20 days /50 days / 200 days (SMA20/ SMA50 / SMA200) , Bollinger Bands upper & lower limit lines comprised of standard deviations known as BB Up & BB Low respectively , Momentum(MOM ), Relative Strength Index14 day time frame indicator denoted by RSI14 , Macd level & signal line along with Stochitic %K &%D indicators.

With all that knowledge now let’s take a look at some tips on how you can analyse this data effectively. For example: finding safety ranks based on volatility scores or locatingcryptocurrencies whose MACD line has recently crossed over its signal line thus giving buy signals or vice versa giving sell signals also mining through various time frames using multiple technical indicators like ADX +CCI20+RSI14 etc can help spot potential trends which may be indicative for a particular currency . Also moving averages assessments over several time periods might be useful for calculating trend based values in order for possible bullish or bearish orientations . Furthermore when examining long term trends across multiple currencies it might be suitable carry out simple comparisons between certain columns from one currency against

Research Ideas

• Utilizing the price movements and technical indicators, investors can analyze the different crypto industry trends and develop strategies to apply them in their portfolios.
• Governmental institutions and banks can use this dataset to monitor the industry’s activity from country to country, helping create regulatory policies when necessary.
• Crypto exchanges can design algorithms based on this data set which will assist with detecting any manipulation or malicious activities in trades occurring in their platform

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - Y...

This dataset automatic update every day. Contained S&P 500, ETF, FX & Crypto which is over 4000 assets. Included history open price, high, low, close, volume, dividends and stock splits. Date files over 1GB!!!

• if the data less than 1GB, please use previous version

About this dataset

Stock market has become of the wonderful place to make money. Many loss and many gains. Many have tried to predict the price of a stock but fails miserably. Those who say they're able to do so, are the one who hide their biggest losses. If stock price cannot be determined by price alone, then there might be other way to predict it, or say to invest it in the "better" way. Otherwise Warren Buffet wouldn't as rich as he is now by luck alone. But who says we cannot play around with it and create our standard of investing in stock?

How to use this dataset

EDA RNN to predict future price Trend identifier Classifier Stock Recommendation

Features

Other Information

You also could use dataset outside this one. This dataset present all public company data in Indonesia. Might be helpful to do certain task, e.g. classification for the industry, etc.

Acknowledgement

Yahoo Finance

1) Data Introduction • The S&P 500 stock data is a tabular stock market dataset of daily stock price information (market, high price, low price, closing price, trading volume, etc.) for the last five years (the latest data is until February 2018) of all companies in the S&P 500 index.

2) Data Utilization (1) S&P 500 stock data has characteristics that: • Each row contains key stock metrics such as date, open, high, low, close, volume, and stock ticker name. • Data is provided as individual stock files and all stock integrated files, so it can be used for various analysis purposes. (2) S&P 500 stock data can be used to: • Stock Price Forecasting and Investment Strategy Development: Using historical stock price data, a variety of investment strategies and forecasting models can be developed, including time series forecasting, volatility analysis, and moving averages. • Market Trends and Corporate Comparison Analysis: It can be used to visualize stock price fluctuations across stocks, compare performance between stocks, analyze market trends, optimize portfolios, and more.

• Only market data, eg. stock price, transaction amount, turnover etc. No enterprise data or valuation data included.
• Only China A-stock is included, B/H stock or ETF or HK listed is out of scope.
• In China stock market, the small capital stock price fluctuate heavily. It is not effective for long term analysis or modeling. This dataset only contains the stocks whose market capital larger than 3 billion CNY (eqv 500m USD)
• The data set are split into 4 csv files by market capital at snapshot 2021-12-31 approximately. Please combine them together if you don't have preference on the market capital breakdown.
• In the label files, label_f{}t{} means whether the stock price change exceed the threshold(t) in future(f) days. eg. label_f5t10 means the price raises over 10% in 5 days after current date(row date).

• This is the Historical Stock Market Data of five major Big Tech companies: NVIDIA (NVDA), Apple (AAPL), Microsoft (MSFT), Google (GOOGL), and Amazon (AMZN) over a 15 years from January 1, 2010 to January 1, 2025.
• It includes daily stock data with opening and closing prices, highs, lows and trading volume.
• This dataset serves as a valuable resource for analyzing long term growth trends, volatility and market behavior of leading tech giants.
• By analyzing this dataset, we can gain a deeper understanding of NVDA, AAPL, MSFT, GOOGL, and AMZN's historical stock behavior over 15 years and make predictions about their future performance.

Columns Description:

1. Date: The trading date of the stock data entry.
2. Close_AAPL: Apple’s stock price at market close at the end of the trading days.
3. Close_AMZN: Amazon’s stock price at market close at the end of the trading days.
4. Close_GOOGL: Google’s stock price at market close at the end of the trading days.
5. Close_MSFT: Microsoft’s stock price at the end of the trading days.
6. Close_NVDA: NVIDIA’s stock price at the end of the trading days.
7. High_AAPL: The highest price of Apple’s stock reached during the trading days.
8. High_AMZN: The highest price of Amazon’s stock reached during the trading days.
9. High_GOOGL: The highest price of Google’s stock reached during the trading days.
10. High_MSFT: The highest price of Microsoft’s stock reached during the trading days.
11. High_NVDA: The highest price of NVIDIA’s stock reached during the trading days.
12. Low_AAPL: The lowest price of Apple’s stock reached during the trading days.
13. Low_AMZN: The lowest price of Amazon’s stock reached during the trading days.
14. Low_GOOGL: The lowest price of Google’s stock reached during the trading days.
15. Low_MSFT: The lowest price of Microsoft’s stock reached during the trading days.
16. Low_NVDA: The lowest price NVIDIA’s stock reached during the trading days.
17. Open_AAPL: Apple’s opening stock price at the beginning of the trading days.
18. Open_AMZN: Amazon’s opening stock price at the beginning of the trading days.
19. Open_GOOGL: Google’s opening stock price at the beginning of the trading days.
20. Open_MSFT: Microsoft’s opening stock price at the beginning of the trading days.
21. Open_NVDA: NVIDIA’s opening stock price at the beginning of the trading days.
22. Volume_AAPL: The number of shares traded of Apple’s stock during the trading days.
23. Volume_AMZN: The number of shares traded of Amazon’s stock during the trading days.
24. Volume_GOOGL: The number of shares traded of Google’s stock during the trading days.
25. Volume_MSFT: The number of shares traded of Microsoft’s stock during the trading days.
26. Volume_NVDA: The number of shares traded of NVIDIA’s stock during the trading days.

Usefulness of Data:

1. Trend Analysis: This dataset can be used for the analysis of long term stock price trends for major 5 tech companies. By analyzing this dataset and taking deep insights about the data and stock patterns over 15 years, investors can identify potential opportunities.
2. Volatility and Risk Assessment: The data helps to assess the volatility of 5 big tech companies' stocks by comparing highs and lows and provides the management strategies to the investors.
3. Predictive Modeling: With stock prices, this dataset can be used for developing predictive models such as forecasting future stock prices using techniques such as ARIMA, SARIMAX, or Deep Learning Models.
4. Comparative Analysis: By analyzing this Dataset, researchers and analysts can compare the performance of NVIDIA, Apple, Microsoft, Google, and Amazon over 15 years, which helps to identify trends in the stock market and relative growth between these companies.
5. Market Behavior Understanding: By analyzing how each stock reacts to major market events (e.g., earnings reports & macroeconomic changes, etc.), we can understand the companies' growth & patterns.

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Germany Consumer Price Index (CPI): Weights: Communications: TO: Smartwatch, Fitness Tracker etc data was reported at 1.280 Per 1000 in 2023. This stayed constant from the previous number of 1.280 Per 1000 for 2022. Germany Consumer Price Index (CPI): Weights: Communications: TO: Smartwatch, Fitness Tracker etc data is updated yearly, averaging 1.280 Per 1000 from Dec 2020 (Median) to 2023, with 4 observations. The data reached an all-time high of 1.280 Per 1000 in 2023 and a record low of 1.280 Per 1000 in 2023. Germany Consumer Price Index (CPI): Weights: Communications: TO: Smartwatch, Fitness Tracker etc data remains active status in CEIC and is reported by Statistisches Bundesamt. The data is categorized under Global Database’s Germany – Table DE.I032: Consumer Price Index: Weights: Annual.

Ethereum network fees paid to miners whenever a payment transaction is initiated on the blockchain more than ***** times between October 2020 and March 2021. These transaction fees - commonly denoted as gas or Gwei - were considered to be very low up to 2020, when the Ethereum network started to cope with increasing amounts as well as more complex transactions. This coincided with the growing importance of Decentralized Finance or DeFi, with more services essentially putting more strain on the cryptocurrency's network. The consequence is that Ethereum gas price increased for all users, especially for NFT transactions across various segments.

Wholesale Price Index: Imports: Food Processed Products, Vegetable Oil, etc data was reported at 200.940 2000=100 in Dec 2008. This stayed constant from the previous number of 200.940 2000=100 for Nov 2008. Wholesale Price Index: Imports: Food Processed Products, Vegetable Oil, etc data is updated monthly, averaging 129.400 2000=100 from Jan 2000 (Median) to Dec 2008, with 108 observations. The data reached an all-time high of 200.940 2000=100 in Dec 2008 and a record low of 94.800 2000=100 in Aug 2000. Wholesale Price Index: Imports: Food Processed Products, Vegetable Oil, etc data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Indonesia Premium Database’s Inflation – Table ID.IB005: Wholesale Price Index: by Sector: Imports.

Context

Stock market data can be interesting to analyze and as a further incentive, strong predictive models can have large financial payoff. The amount of financial data on the web is seemingly endless. A large and well structured dataset on a wide array of companies can be hard to come by. Here I provide a dataset with historical stock prices (last 5 years) for all companies currently found on the S&P 500 index.

The script I used to acquire all of these .csv files can be found in this GitHub repository In the future if you wish for a more up to date dataset, this can be used to acquire new versions of the .csv files.

Content

The data is presented in a couple of formats to suit different individual's needs or computational limitations. I have included files containing 5 years of stock data (in the all_stocks_5yr.csv and corresponding folder) and a smaller version of the dataset (all_stocks_1yr.csv) with only the past year's stock data for those wishing to use something more manageable in size.

The folder individual_stocks_5yr contains files of data for individual stocks, labelled by their stock ticker name. The all_stocks_5yr.csv and all_stocks_1yr.csv contain this same data, presented in merged .csv files. Depending on the intended use (graphing, modelling etc.) the user may prefer one of these given formats.

All the files have the following columns: Date - in format: yy-mm-dd Open - price of the stock at market open (this is NYSE data so all in USD) High - Highest price reached in the day Low Close - Lowest price reached in the day Volume - Number of shares traded Name - the stock's ticker name

Acknowledgements

I scraped this data from Google finance using the python library 'pandas_datareader'. Special thanks to Kaggle, Github and The Market.

Inspiration

This dataset lends itself to a some very interesting visualizations. One can look at simple things like how prices change over time, graph an compare multiple stocks at once, or generate and graph new metrics from the data provided. From these data informative stock stats such as volatility and moving averages can be easily calculated. The million dollar question is: can you develop a model that can beat the market and allow you to make statistically informed trades!

Wholesale Price Index: Manufacturing: Manufacture of Processed and Preserved Fish, Water Biota, etc: Dried/Salted Squid data was reported at 126.100 2018=100 in Dec 2023. This records an increase from the previous number of 124.830 2018=100 for Nov 2023. Wholesale Price Index: Manufacturing: Manufacture of Processed and Preserved Fish, Water Biota, etc: Dried/Salted Squid data is updated monthly, averaging 95.895 2018=100 from Jan 2020 (Median) to Dec 2023, with 48 observations. The data reached an all-time high of 126.100 2018=100 in Dec 2023 and a record low of 80.810 2018=100 in May 2020. Wholesale Price Index: Manufacturing: Manufacture of Processed and Preserved Fish, Water Biota, etc: Dried/Salted Squid data remains active status in CEIC and is reported by Statistics Indonesia. The data is categorized under Indonesia Premium Database’s Inflation – Table ID.IB010: Wholesale Price Index: by Sector: Manufacturing (Discontinued).

The Global Precious metals Market size is expected to grow at a CAGR of 5.6% during the forecast period by 2028. The growth can be attributed to industrial applications such as jewelry and medical devices which are anticipated to increase demand for gold and silver medal, respectively over the forecast period.

Precious metals are a group of elements that have been used for centuries to create some of the most beautiful and highest quality objects in history. Gold, silver, platinum, and palladium are precious metals while copper is not typically categorized as a precious metal because it is so abundant on earth. The beauty of precious metals is not their only value. They are also used in applications that range from industry to financial services and even consumer goods such as jewelry.

On the basis of Type, the market is segmented into Gold, Silver Metal, Platinum Group Metals.

Gold:

Gold is a chemical element with the symbol Au and an atomic number of 79. It has been a highly sought-after precious metal for coinage, jewelry, and other arts since the beginning of recorded history. The metal occurs as nuggets or grains in rocks, underground veins, and in alluvial deposits. Gold is dense, soft, shiny and the most malleable and ductile pure metal known to man which means it can be beaten into thin sheets (0.0000001 mm) but not dissolved by any acid.

Silver Metal:

The term ‘silver metal’ is used to refer to the silver-rich alloy of metals. The alloys are also known as Ag or sterling, which has high purity and includes copper, nickel, zinc, etc. Silver metal is increasingly preferred over other precious metals due to its low cost in comparison with gold and platinum group metals (PGM).

Platinum Group Metals:

Platinum Group Metals are a group of metals that have been traditionally used in the industry, but lately, they have become popular for other applications as well. The main features of Platinum Group Metals are that they can be worked into almost any shape or form and their price remains stable even when the market becomes very volatile.

On the basis of Application, the market is segmented into Industry, Consumer Sector, Financial Sector.

Industry:

In the industry sector, precious metals are used in many different aspects of production. This includes anything from electronics to car manufacturing and even healthcare applications. A lot of technology devices require these materials for their construction, most notably smartphones. Smartphone manufacturers use gold foils on circuit boards due to their low electrical resistance properties that help with signal transmission between components.

Consumer Sector:

Precious metals are used in various consumer electronics. The use of gold, silver, and platinum for manufacturing electronic equipment has increased due to the increasing penetration of smartphones, tablets, etc., which have grown rapidly over the past decade or so. The consumer sector is expected to be the largest market for precious metals, and this trend will continue over the forecast period.

Financial Sector:

Gold and silver are used in a range of financial products, from coins to bars. Gold has been held as an asset for thousands of years because it is scarce, durable, liquid, and does not need any other material to produce jewelry or be shaped into ornaments. Silver's physical characteristics have also resulted in its widespread use in industry: being highly conductive means that silver metal dramatically lowers energy costs when compared with traditional materials such as a copper wire.

On the basis of Region, the market is segmented into North America, Latin America, Europe, Asia Pacific, and Middle East & Africa.

The North American market is expected to account for the largest share, due to its increasing demand from several end-use industries. The U.S., which contributes a major share of this region’s revenue, has been witnessing an upsurge in public and private investment opportunities across different sectors such as the oil & gas exploration and the construction industry. This trend is likely to boost the availability of raw materials required by downstream segments including Gold Jewelry manufacturer companies, thereby driving growth over the next few years. The Latin American market is estimated to be the fastest-growing during the forecast period. The Asia Pacific will witness significant growth due to factors such as developments in the mining industr

Analysis of ‘Time Series Forecasting with Yahoo Stock Price ’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/arashnic/time-series-forecasting-with-yahoo-stock-price on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

Stocks and financial instrument trading is a lucrative proposition. Stock markets across the world facilitate such trades and thus wealth exchanges hands. Stock prices move up and down all the time and having ability to predict its movement has immense potential to make one rich. Stock price prediction has kept people interested from a long time. There are hypothesis like the Efficient Market Hypothesis, which says that it is almost impossible to beat the market consistently and there are others which disagree with it.

There are a number of known approaches and new research going on to find the magic formula to make you rich. One of the traditional methods is the time series forecasting. Fundamental analysis is another method where numerous performance ratios are analyzed to assess a given stock. On the emerging front, there are neural networks, genetic algorithms, and ensembling techniques.

Another challenging problem in stock price prediction is Black Swan Event, unpredictable events that cause stock market turbulence. These are events that occur from time to time, are unpredictable and often come with little or no warning.

A black swan event is an event that is completely unexpected and cannot be predicted. Unexpected events are generally referred to as black swans when they have significant consequences, though an event with few consequences might also be a black swan event. It may or may not be possible to provide explanations for the occurrence after the fact – but not before. In complex systems, like economies, markets and weather systems, there are often several causes. After such an event, many of the explanations for its occurrence will be overly simplistic.

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New bleeding age state-of-the-art deep learning models stock predictions is overcoming such obstacles e.g. "Transformer and Time Embeddings". An objectives are to apply these novel models to forecast stock price.

Content

Stock price prediction is the task of forecasting the future value of a given stock. Given the historical daily close price for S&P 500 Index, prepare and compare forecasting solutions. S&P 500 or Standard and Poor's 500 index is an index comprising of 500 stocks from different sectors of US economy and is an indicator of US equities. Other such indices are the Dow 30, NIFTY 50, Nikkei 225, etc. For the purpose of understanding, we are utilizing S&P500 index, concepts, and knowledge can be applied to other stocks as well.

Dataset

The historical stock price information is also publicly available. For our current use case, we will utilize the pandas_datareader library to get the required S&P 500 index history using Yahoo Finance databases. We utilize the closing price information from the dataset available though other information such as opening price, adjusted closing price, etc., are also available. We prepare a utility function get_raw_data() to extract required information in a pandas dataframe. The function takes index ticker name as input. For S&P 500 index, the ticker name is ^GSPC. The following snippet uses the utility function to get the required data.(See Simple LSTM Regression)

Features and Terminology: In stock trading, the high and low refer to the maximum and minimum prices in a given time period. Open and close are the prices at which a stock began and ended trading in the same period. Volume is the total amount of trading activity. Adjusted values factor in corporate actions such as dividends, stock splits, and new share issuance.

Starter Kernel(s)

• Simple LSTM Regression

Acknowledgements

Mining and updating of this dateset will depend upon Yahoo Finance .

Inspiration

Sort of variation of sequence modeling and bleeding age e.g. attention can be applied for research and forecasting

Some Readings

• Applications of deep learning in stock market prediction: recent progress
• Stock predictions with state-of-the-art Transformer and Time Embeddings
• https://arxiv.org/pdf/1712.02136.pdf
• https://catanacapital.com/blog/unpredictable-black-swan-event-stock-market/
• https://par.nsf.gov/servlets/purl/10149715
• https://arxiv.org/pdf/1712.02136.pdf

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--- Original source retains full ownership of the source dataset ---