Buy Historical Stock Data

Here you can select data packages you are interested in or contact us with your custom order requirements. Click on the \"Details\" link below each package name to find more information about the files that are included. Press the \"Continue\" button to place an order or to get a quote.

You can download free minute and tick historical data and use them for your analysis. If you have any existing data, we encourage you to compare it with our data to check for any discrepancies. We are also providing this data so you can see what the purchased data will look like.

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We have made it very easy for you to get the historical data you need. Our automated ordering system will process your order online and, in most cases, allow you to download products immediately or after no more than an hour. Most custom orders take no longer than 24 hours to process. Credit card payments are processed within seconds, and clients receive their products without delay.

FirstRate Data is a leading provider of high-resolution intraday stock market, crypto, futures and FX data. We source our historical stock data directly from major exchanges and fully adjusted for both splits and dividends. Futures and ETF datasets are also sourced from co-located servers in major exchanges. All datasets are rigorously tested for accuracy. Our cost-effective, historical intraday data solutions are research-ready and used by traders, hedge funds and academic institutions. We offer 1-minute, 5-minute, 30-minute, 1-hour, and 1-day intraday stock data as well as intraday futures, ETFs, and FX data going back 15 years, and tick data going back 10 years.

As of March 2023 we offer historical 1-minute, 5-minute, 30-minute, and 1-hour intraday bars for 7412 stock tickers (including 187 delisted tickers) starting 2005. All tickers listed on NYSE and Nasdaq with market capitalizations above $25M are included, as well as all tickers in Dow Jones Industrials, S&P500, Nasdaq100 and Russell 3000 indices (tickers previously included in the indices as well as delisted tickers are also included). For ETFs, we provide historical 1-minute data for the most active 850 tickers back to 200.

Each 1-minute bar has OHLCV (open/high/low/close/volume) data which is aggregated from trades executed on major exchanges as well as four dark pools. For tick data, each tick contains the timestamp, trade price, volume and exchange code (please see our stock tick data page for details on tick data.

For futures, we carry 1-minute, 5-minute, 30-minute, 1-hour, and 1-day historical bars for the most active 150 contracts (as of March 2023) starting back to 2007. We provide both individual futures contracts as well as a continuous futures series with prices adjusted for the price gaps from rolling contracts (this series is best suited to long-timeframe backtesting of futures trading strategies).

We offer data by both individual ticker and by bundles. Bundles aggregate multiple intraday data sets for a specific niche - for example our Complete Stocks Bundle contains all 7000+ tickers we carry.

Data can easily be converted to TradeStation, MetaStock, NinjaTrader, AmiBroker, Wealth-Lab formats, and for use in many other popular trading software applications as well as popular analysis tools/languages such as python, pandas and R / R Studio. Files for stocks and ETFs include out-of-hours trades. For minute by minute data, bars with zero volumes (ie no trades) are excluded to reduce filesizes. For efficient downloading we offer an API for downloading data in our bundles.

The STOCKHISTORY function retrieves historical data about a financial instrument and loads it as an array, which will spill if it's the final result of a formula. This means that Excel will dynamically create the appropriate sized array range when you press ENTER.

Function returns historical price data about the financial instrument corresponding to this value. Enter a ticker symbol in double quotes (e.g., \"MSFT\") or a reference to a cell containing the Stocks data type. This will pull data from the default exchange for the instrument. You can also refer to a specific exchange by entering a 4-character ISO market identifier code (MIC), followed by a colon, followed by the ticker symbol (e.g., \"XNAS:MSFT\"). Learn more about our data sources.

The earliest date for which data is retrieved. Note that if interval is not 0 (daily), the first data point may be earlier than the start_date provided - it will be the first date of the period requested.

Please note that while some financial instruments may be available as Stocks data types, the historical information will not be available. For example, this is the case for most popular Index Funds including the S&P 500.

If you want to get the highest high over a 3-month period, it is faster to use a monthly interval than a daily or weekly interval. For example, =MAX(STOCKHISTORY(\"XNAS:MSFT\", \"1/1/2022\", \"3/1/2022\", 2, 0, 3)) will calculate the maximum value of 3 datapoints (one for each month), data only with no headers, for the highest trading value for each month. If instead the formula used a weekly or daily interval, you would get the same result but there would be many more datapoints used in the calculation which can lead to reduced performance.

If you want to see a 52-week high or low, it is often faster to use a Stocks data type, which has those properties readily available. For example, convert \"xnas:msft\" to a stock data type in cell A1, and in cell B1 you can write the formula =A1.[52 week high] to get the value. You can also configure your workbook to automatically refresh that value as described here.

STOCKHISTORY, in showing historical data, generally only updates after a trading day completes. This means that you cannot use STOCKHISTORY to get data for today's trading details until after the market has closed or after the day has completed depending on the market.

If you use STOCKHISTORY with a function that automatically updates (like TODAY) and if your workbook has automatic calculation enabled, then STOCKHISTORY will automatically refresh the data when you open the workbook. This update will happen in the background, and you can edit your workbook as desired while this update is underway. If your workbook uses large number of STOCKHISTORY function calls, this background update will continue as long as needed to update the data. You may close your workbook at any time during this process if you wish.

The stock information provided is for informational purposes only and is not intended for trading purposes. The stock information and charts are provided by Tickertech, a third party service, and Apple does not provide information to this service.

Costco Wholesale has adopted Direct Registration, a book-entry form of stock ownership. When you purchase Costco Common Stock through the direct stock purchase plan, a stock certificate will not be issued, unless specifically requested.

With the rapid development of the financial market, many professional traders use technical indicators to analyze the stock market. As one of these technical indicators, moving average convergence divergence (MACD) is widely applied by many investors. MACD is a momentum indicator derived from the exponential moving average (EMA) or exponentially weighted moving average (EWMA), which reacts more significantly to recent price changes than the simple moving average (SMA). Traders find the analysis of 12- and 26-day EMA very useful and insightful for determining buy-and-sell points. The purpose of this study is to develop an effective method for predicting the stock price trend. Typically, the traditional EMA is calculated using a fixed weight; however, in this study, we use a changing weight based on the historical volatility. We denote the historical volatility index as HVIX and the new MACD as MACD-HVIX. We test the stability of MACD-HVIX and compare it with that of MACD. Furthermore, the validity of the MACD-HVIX index is tested by using the trend recognition accuracy. We compare the accuracy between a MACD histogram and a MACD-HVIX histogram and find that the accuracy of using MACD-HVIX histogram is 55.55% higher than that of the MACD histogram when we use the buy-and-sell strategy. When we use the buy-and-hold strategy for 5 and 10 days, the prediction accuracy of MACD-HVIX is 33.33% and 12% higher than that of the traditional MACD strategy, respectively. We found that the new indicator is more stable. Therefore, the improved stock price forecasting model can predict the trend of stock prices and help investors augment their return in the stock market.

Securities investment is a financial activity influenced by many factors such as politics, economy, and psychology of investors. Its process of change is nonlinear and multifractal [1]. The stock market has high-risk characteristics; i.e., if the stock price volatility is excessive or the stability is low, the risk is uncontrollable. Financial asset returns in the short term are persistent; however, those in the long term will be reversed [2].

We will introduce the concept of moving average convergence divergence (MACD) and help the readers understand its principle and application in Section 2. Although the MACD oscillator is one of the most popular technical indicators, it is a lagging indicator. In Section 3, we propose an improved model called MACD-HVIX to deal with the lag factor. In Section 4, data for empirical research are described. Finally, in Section 5, we develop a trading strategy using MACD-HVIX and employ actual market data to verify its val