{ "cells": [ { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "# Downloading Historical Price Data Using the OpenBB SDK\n", "\n", "This notebook demonstrates the different ways to approach loading historical price data using the OpenBB SDK. There are obvious methods, and some less conventional ones. To begin, let's look at the Stocks module, but first, we need to initialize the notebook with the import statements block.\n", "\n", "## Import Statements" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "from openbb_terminal.sdk import openbb" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## The Stocks Module\n", "\n", "The `load` function is likely the first place to reach when requesting daily, or intraday, historical price data. `openbb.stocks.load()` is a versatile data aggregator, and not only for stocks.\n", "\n", "### openbb.stocks.load()\n", "\n", "The input parameters for the function:\n", "\n", "```python\n", "Parameters\n", "----------\n", "symbol: str\n", " Ticker to get data\n", "start_date: str or datetime, optional\n", " Start date to get data from with. - datetime or string format (YYYY-MM-DD)\n", "interval: int\n", " Interval (in minutes) to get data 1, 5, 15, 30, 60 or 1440\n", "end_date: str or datetime, optional\n", " End date to get data from with. - datetime or string format (YYYY-MM-DD)\n", "prepost: bool\n", " Pre and After hours data\n", "source: str\n", " Source of data extracted\n", "weekly: bool\n", " Flag to get weekly data\n", "monthly: bool\n", " Flag to get monthly data\n", "verbose: bool\n", " Display verbose information on what was the symbol that was loaded\n", "```\n", "\n", "\n", "Set `verbose = False` to silence the console print. Execute the two cells below to demonstrate." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Loading Daily data for SPY with starting period 2020-05-18.\n",
       "
\n" ], "text/plain": [ "Loading Daily data for SPY with starting period 2020-05-18.\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df_daily = openbb.stocks.load(symbol = 'spy')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "df_daily = openbb.stocks.load(symbol = 'spy', verbose = False)" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "To load the entire history available from a source, pick a starting date well beyond what it might be. For example, `1900-01-01`" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Loading Daily data for SPY with starting period 1993-01-29.\n",
       "
\n" ], "text/plain": [ "Loading Daily data for SPY with starting period 1993-01-29.\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df_daily = openbb.stocks.load(symbol = 'spy', start_date = '1990-01-01')" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "Weekly and monthly intervals are selected with either argument as `True`. The `load` function can be used as an input parameter to functions, like `candle`. This draws a chart without needing to load anything into memory." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "displaylogo": false, "plotlyServerURL": "https://plot.ly", "scrollZoom": true }, "data": [ { "close": [ 305.04913330078125, 290.59619140625, 294.8279724121094, 287.8835754394531, 299.56976318359375, 304.71240234375, 308.6750183105469, 307.8690185546875, 313.28033447265625, 321.0039367675781, 323.1818542480469, 325.7148132324219, 336.3647155761719, 328.6795349121094, 320.51458740234375, 317.242919921875, 316.663330078125, 321.585693359375, 334.1181335449219, 334.5419921875, 333.0874328613281, 314.5536193847656, 337.30670166015625, 344.9552001953125, 342.286865234375, 350.3207702636719, 356.27392578125, 352.85418701171875, 355.6284484863281, 356.9703063964844, 361.6911315917969, 368.83056640625, 363.4518737792969, 370.3977966308594, 358.0054016113281, 375.0703125, 379.8395690917969, 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"source": [ "openbb.stocks.candle(\n", " data = openbb.stocks.load(symbol = 'spy', start_date = '2013-06-01', monthly = True, verbose = False),\n", " symbol = \"SPY Monthly\",\n", " ha = True,\n", ")" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "The default settings retrieve daily, intraday data is selected using the `interval` argument, with a choice of: `[1, 5, 15, 30, 60]`. Include pre/post market data by setting the argument, `prepost = True`. The amount of history for each interval, and level of market coverage, will vary by the source, which is selectable via the `source` argument. The block below packs an object with all intervals." ] }, { "cell_type": "code", "execution_count": 121, "metadata": {}, "outputs": [], "source": [ "from datetime import datetime,timedelta\n", "\n", "def load_historical(symbol: str = \"\", start_date: str = \"\", end_date: str = \"\", source: str = \"\", prepost: bool = False) -> object:\n", "\n", " if symbol == \"\":\n", " print(\"Please enter a ticker symbol\")\n", " if start_date == \"\":\n", " start_date = (datetime.now() - timedelta(weeks = 104)).date()\n", " if end_date == \"\":\n", " end_date = datetime.now().date()\n", " if source == \"\":\n", " source = \"YahooFinance\"\n", "\n", " class HistoricalPrices:\n", " def __init__(self) -> None:\n", " self.one: pd.DataFrame = openbb.stocks.load(\n", " symbol=symbol, start_date=start_date, end_date=end_date, interval = 1, source=source, verbose = False, prepost = prepost\n", " ).convert_dtypes()\n", " self.five: pd.DataFrame = openbb.stocks.load(\n", " symbol=symbol, start_date=start_date, end_date=end_date, interval = 5, source = source, verbose = False, prepost = prepost\n", " ).convert_dtypes()\n", " self.fifteen: pd.DataFrame = openbb.stocks.load(\n", " symbol=symbol, start_date=start_date, end_date=end_date, interval = 15, verbose = False, prepost = prepost\n", " ).convert_dtypes()\n", " self.thirty: pd.DataFrame = openbb.stocks.load(\n", " symbol=symbol, start_date=start_date, end_date=end_date, interval = 30, verbose = False, prepost = prepost\n", " ).convert_dtypes()\n", " self.sixty: pd.DataFrame = openbb.stocks.load(\n", " symbol=symbol, start_date=start_date, end_date=end_date, interval = 60, verbose = False, prepost = prepost\n", " ).convert_dtypes()\n", " self.daily: pd.DataFrame = openbb.stocks.load(\n", " symbol=symbol, start_date=start_date, end_date=end_date, source = source, verbose = False\n", " ).convert_dtypes()\n", " self.weekly: pd.DataFrame = openbb.stocks.load(\n", " symbol=symbol, start_date=start_date, end_date=end_date, source = source, weekly = True, verbose = False\n", " ).convert_dtypes()\n", " self.monthly: pd.DataFrame = openbb.stocks.load(\n", " symbol=symbol, start_date=start_date, end_date=end_date, source=source, monthly = True, verbose = False\n", " ).convert_dtypes()\n", " prices = HistoricalPrices()\n", "\n", " return prices" ] }, { "cell_type": "code", "execution_count": 122, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "displaylogo": false, "plotlyServerURL": "https://plot.ly", "scrollZoom": true }, "data": [ { "close": [ 411.5015869140625, 410.8399963378906, 410.3299865722656, 411.0199890136719, 411.57000732421875, 411.4212951660156, 411.8901062011719, 411.9800109863281, 411.6600036621094, 411.9800109863281, 411.7900085449219, 411.94500732421875, 412.18798828125, 412.760009765625, 413.2349853515625, 412.30999755859375, 411.8299865722656, 412.29998779296875, 412.6077880859375, 412.2391052246094, 412.59600830078125, 412.42999267578125, 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1, "tickfont": { "size": 14 }, "ticks": "outside", "title": { "standoff": 20 }, "zeroline": false, "zerolinecolor": "#283442", "zerolinewidth": 2 }, "yaxis": { "anchor": "x", "automargin": true, "fixedrange": false, "gridcolor": "#283442", "linecolor": "#F5EFF3", "mirror": true, "showgrid": true, "showline": true, "side": "right", "tick0": 0.5, "ticks": "outside", "title": { "standoff": 20 }, "zeroline": false, "zerolinecolor": "#283442", "zerolinewidth": 2 } } }, "title": { "text": " SPY - Fifteen Minute Candles", "x": 0.5, "xanchor": "center", "y": 0.98, "yanchor": "top" }, "xaxis": { "anchor": "y", "domain": [ 0, 0.94 ], "matches": "x3", "rangebreaks": [ { "bounds": [ 16, 9.5 ], "pattern": "hour" }, { "bounds": [ "sat", "mon" ] }, { "values": [] } ], "showticklabels": true }, "yaxis": { "anchor": "x", "automargin": true, "autorange": true, "domain": [ 0, 1 ], "fixedrange": false, "layer": "above traces", "nticks": 15, "side": "right", "tickfont": { "size": 16 }, "type": "linear" }, "yaxis2": { "anchor": "x", "fixedrange": true, "nticks": 10, "overlaying": "y", "range": [ 0, 119409934 ], "showgrid": false, "showline": false, "side": "left", "tickfont": { "size": 13 }, "tickvals": [ 3000000, 6000000, 9000000, 12000000 ], "zeroline": false } } } }, "metadata": {}, "output_type": "display_data" } ], "source": [ "prices = load_historical(\"SPY\")\n", "openbb.stocks.candle(data = prices.fifteen[\"2023-05-15\":], symbol = \"SPY - Fifteen Minute Candles\")" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "To demonstrate the difference between sources, let's compare values for daily volume from several sources." ] }, { "cell_type": "code", "execution_count": 113, "metadata": {}, "outputs": [], "source": [ "# Yahoo was the source for the previously loaded data.\n", "yahoo = prices\n", "\n", "polygon = load_historical(\"SPY\", source = \"Polygon\")\n", "alphavantage = load_historical(\"SPY\", source = \"AlphaVantage\")" ] }, { "cell_type": 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2023-05-23 16:00:00546340546340<NA>
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" ], "text/plain": [ " AlphaVantage Volume Polygon Volume Yahoo Volume\n", "date \n", "2023-05-23 15:51:00 485786 485786 485808\n", "2023-05-23 15:52:00 644560 644560 644023\n", "2023-05-23 15:53:00 368111 368111 368608\n", "2023-05-23 15:54:00 684340 684340 684180\n", "2023-05-23 15:55:00 773708 773708 773810\n", "2023-05-23 15:56:00 476353 476353 476358\n", "2023-05-23 15:57:00 694285 694285 694322\n", "2023-05-23 15:58:00 1009322 1009322 1009331\n", "2023-05-23 15:59:00 2831803 2831803 2814414\n", "2023-05-23 16:00:00 546340 546340 " ] }, "execution_count": 115, "metadata": {}, "output_type": "execute_result" } ], "source": [ "compare_intraday = pd.DataFrame()\n", "compare_intraday[\"AlphaVantage Volume\"] = alphavantage.one[\"Volume\"]\n", "compare_intraday[\"Polygon Volume\"] = polygon.one[\"Volume\"]\n", "compare_intraday[\"Yahoo Volume\"] = yahoo.one[\"Volume\"]\n", "compare_intraday.tail(10)" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## Other Types of Symbols\n", "\n", "Other types of assets and ticker symbols can be loaded from `openbb.stocks.load()`, below are some examples but not an exhaustive list.\n", "\n", "### Share Classes\n", "\n", "Some sources use `-` as the distinction between a share class, e.g., `BRK-A` and `BRK-B`. Other formats include:\n", "\n", "- A period: `BRK.A`\n", "- A slash: `BRK/A`\n", "- No separator, the share class becomes the fourth or fifth letter.\n", "\n", "Some may even use a combination, or accept multiple variations. Sometimes there is no real logic behind the additional characters, `GOOGL` vs. `GOOG`. These are known unknown variables of ticker symbology, what's good for one source may return errors from another. \n", "\n", "### Indexes\n", "\n", "Sources will have their own treatment of these symbols, some examples are:\n", "\n", "- YahooFinance: ^IXIC\n", "- FRED (not the `load` function): `openbb.economy.fred([\"NASDAQCOM\"])`\n", "- Polygon: I:NDX\n", "\n", "### Currencies\n", "\n", "FX symbols face the same dilemna as share classes, there are several variations of the same symbol.\n", "\n", "- YahooFinance: `EURUSD=X`\n", "- Polygon: `C:EURUSD`\n", "- AlphaVantage: `EURUSD`\n", "\n", "### Crypto\n", "\n", "Similar, but different to FX tickers.\n", "\n", "- YahooFinance: `BTC-USD`\n", "- Polygon: `X:BTCUSD`\n", "- AlphaVantage: `BTCUSD`\n", "\n", "### Futures\n", "\n", "Historical prices for active contracts, and the continuous front-month contracts, are able to be loaded via `YahooFinance`.\n", "\n", "- Continuous front-month: `CL=F`\n", "- December 2023 contract: `CLZ23.NYM`\n", "- March 2024 contract: `CLH23.NYM`\n", "\n", "Individual contracts will require knowing which of the CME venues the future is listed on. `[\"NYM\", \"NYB\", \"CME\", \"CBT\"]`. Use, `openbb.futures.search()`, to determine which.\n", "\n", "### Options\n", "\n", "Individual options contracts are also loadable from `openbb.stocks.load()`.\n", "\n", "- YahooFinance: `SPY230616P00400000`\n", "- Polygon: `O:SPY230616P00400000`" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "What the `load` function **cannot** do is batch download. For this type of functionality, we can go elsewhere in the SDK.\n", "\n", "### openbb.stocks.ca.hist()\n", "\n", "This function, within the Comparison Analysis sub-module, offers the ability to return data for multiple tickers as a single Pandas DataFrame, and targeting a single data point. Choices are: open, high, low, close, adjust close, volume, and return. It is chosen by using the optional argument, `candle_type`, with the first letter of the candle type as the value - `a` for Adjusted Close. The list of tickers must be encapsulated with square brackets, [], and quotation marks, `\"\"`, for each individual symbol." ] }, { "cell_type": "code", "execution_count": 126, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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METAAAPLAMZNNFLXGOOGMSFTTSLA
Date
2023-05-19245.639999175.160004116.250000365.359985123.250000318.339996180.139999
2023-05-22248.320007174.199997115.010002363.010010125.870003321.179993188.869995
2023-05-23246.740005171.559998114.989998355.989990123.290001315.260010185.770004
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" ], "text/plain": [ " META AAPL AMZN NFLX GOOG \\\n", "Date \n", "2023-05-19 245.639999 175.160004 116.250000 365.359985 123.250000 \n", "2023-05-22 248.320007 174.199997 115.010002 363.010010 125.870003 \n", "2023-05-23 246.740005 171.559998 114.989998 355.989990 123.290001 \n", "\n", " MSFT TSLA \n", "Date \n", "2023-05-19 318.339996 180.139999 \n", "2023-05-22 321.179993 188.869995 \n", "2023-05-23 315.260010 185.770004 " ] }, "execution_count": 126, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tickers = [\"META\", \"AAPL\", \"AMZN\", \"NFLX\", \"GOOG\", \"MSFT\", \"TSLA\"]\n", "df_compare = openbb.stocks.ca.hist(similar = tickers, start_date = \"2023-01-01\", candle_type = 'a')\n", "\n", "df_compare.tail(3)" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## The Economy Module\n", "\n", "It may not be an obvious choice, but it can get the job done in a similar way as `openbb.stocks.ca.hist()`.\n", "\n", "### openbb.economy.index()\n", "\n", "This function can be coaxed into completing the same task, with slight differences being the `column` argument, and `returns` is a boolean choice for calculating the cumulative returns over the entire history." ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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METAAAPLAMZNNFLXGOOGMSFTTSLA
Date
2023-05-18246.850006175.050003118.150002371.290009123.519997318.519989176.889999
2023-05-19245.639999175.160004116.250000365.359985123.250000318.339996180.139999
2023-05-22251.149994173.779999114.544998366.720001126.144997321.709991184.540100
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" ], "text/plain": [ " META AAPL AMZN NFLX GOOG \\\n", "Date \n", "2023-05-18 246.850006 175.050003 118.150002 371.290009 123.519997 \n", "2023-05-19 245.639999 175.160004 116.250000 365.359985 123.250000 \n", "2023-05-22 251.149994 173.779999 114.544998 366.720001 126.144997 \n", "\n", " MSFT TSLA \n", "Date \n", "2023-05-18 318.519989 176.889999 \n", "2023-05-19 318.339996 180.139999 \n", "2023-05-22 321.709991 184.540100 " ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_compare2 = openbb.economy.index(indices = tickers, column = 'Adj Close')\n", "\n", "df_compare2.tail(3)" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "Some daily historical prices, or index levels, are part of the FRED library. For example, the Nasdaq Composite Index." ] }, { "cell_type": "code", "execution_count": 128, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'SP500': {'title': 'S&P 500', 'units': 'Index'}}\n" ] }, { "data": { "text/html": [ "
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SP500
2023-05-234145.58
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" ], "text/plain": [ " SP500\n", "2023-05-23 4145.58" ] }, "execution_count": 128, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sp500 = openbb.economy.fred([\"SP500\"])\n", "\n", "# Note that the return from FRED is a Tuple, and the second element is the Series ID and description.\n", "\n", "print(sp500[1])\n", "\n", "sp500[0].tail(1)" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "This endpoint also has a fast method for plotting series, simply add `_chart` to the command." ] }, { "cell_type": "code", "execution_count": 131, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "displaylogo": false, "plotlyServerURL": "https://plot.ly", "scrollZoom": true }, "data": [ { "hovertemplate": "%{y}", "name": "Dow Jones Composite Average (Index) [x 10K]", "showlegend": true, "type": "scatter", "x": [ "2013-05-24T00:00:00", "2013-05-28T00:00:00", "2013-05-29T00:00:00", "2013-05-30T00:00:00", 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], "type": "date" } } } }, "metadata": {}, "output_type": "display_data" } ], "source": [ "openbb.economy.fred_chart([\"DJCA\", \"NASDAQCOM\"])" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## The Crypto Module\n", "\n", "Similarly, the `crypto` module has its own nuances.\n", "\n", "### openbb.crypto.load()\n", "\n", "This version of the `load` function has three sources: `YahooFinance`, `CoinGecko`, and `CCXT`. Selecting `CCXT` will activate the `--exchange` argument, which allows the user to target a specific venue. \n", "\n", "Choices of exchange are: [\n", "\n", " ace,alpaca,ascendex,bequant,bigone,binance,binancecoinm,binanceus,binanceusdm,bit2c,bitbank,bitbay,bitcoincom,\n", " bitfinex,bitfinex2,bitflyer,bitforex,bitget,bithumb,bitmart,bitmex,bitopro,bitpanda,bitrue,bitso,bitstamp,bitstamp1,\n", " bittrex,bitvavo,bkex,bl3p,btcalpha,btcbox,btcex,btcmarkets,btctradeua,btcturk,buda,bybit,cex,coinbase,coinbaseprime,coinbasepro,\n", " coincheck,coinex,coinfalcon,coinmate,coinone,coinspot,cryptocom,currencycom,delta,deribit,digifinex,exmo,flowbtc,fmfwio,gate,\n", " gateio,gemini,hitbtc,hitbtc3,hollaex,huobi,huobijp,huobipro,idex,independentreserve,indodax,itbit,kraken,krakenfutures,kucoin,\n", " kucoinfutures,kuna,latoken,lbank,lbank2,lykke,mercado,mexc,mexc3,ndax,novadax,oceanex,okcoin,okex,okex5,okx,paymium,phemex,\n", " poloniex,poloniexfutures,probit,ripio,stex,tidex,timex,tokocrypto,upbit,wavesexchange,wazirx,whitebit,woo,yobit,zaif,zb,zonda\n", "]\n", "\n", "**Note**: Asset coverage and history varies widely by exchange." ] }, { "cell_type": "code", "execution_count": 149, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "displaylogo": false, "plotlyServerURL": "https://plot.ly", "scrollZoom": true }, "data": [ { "hovertemplate": "%{y}", "mode": "lines", "name": "", "showlegend": false, "type": "scatter", "x": [ "2013-12-27T00:00:00", "2013-12-28T00:00:00", "2013-12-29T00:00:00", "2013-12-30T00:00:00", "2013-12-31T00:00:00", "2014-01-01T00:00:00", "2014-01-02T00:00:00", "2014-01-03T00:00:00", "2014-01-04T00:00:00", "2014-01-05T00:00:00", "2014-01-06T00:00:00", "2014-01-07T00:00:00", "2014-01-08T00:00:00", "2014-01-09T00:00:00", "2014-01-10T00:00:00", "2014-01-11T00:00:00", "2014-01-12T00:00:00", "2014-01-13T00:00:00", "2014-01-14T00:00:00", 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"outside", "title": { "standoff": 20 }, "zeroline": false, "zerolinecolor": "#283442", "zerolinewidth": 2 }, "yaxis": { "anchor": "x", "automargin": true, "fixedrange": false, "gridcolor": "#283442", "linecolor": "#F5EFF3", "mirror": true, "showgrid": true, "showline": true, "side": "right", "tick0": 0.5, "ticks": "outside", "title": { "standoff": 20 }, "zeroline": false, "zerolinecolor": "#283442", "zerolinewidth": 2 } } }, "title": { "text": " Daily BTC/USDT" }, "xaxis": { "tickformatstops": [ { "dtickrange": [ null, 604800000 ], "value": "%Y-%m-%d" }, { "dtickrange": [ 604800000, "M1" ], "value": "%Y-%m-%d" }, { "dtickrange": [ "M1", null ], "value": "%Y-%m-%d" } ], "type": "date" }, "yaxis": { "title": { "text": "Close" }, "type": "log" } } } }, "metadata": {}, "output_type": "display_data" } ], "source": [ "btc_usdt = openbb.crypto.load(symbol = 'BTC', to_symbol = 'USDT', source = 'CCXT', exchange = 'bitcoincom', start_date = '2010-01-01')\n", "openbb.qa.line(data = btc_usdt[\"Close\"], title = \" Daily BTC/USDT\", log_y = True)" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "### openbb.crypto.dd.mt()\n", "\n", "This function returns crpyto time-series data from Messari, selectable by `timeseries_id`. Find the appropriate value with, `openbb.crypto.dd.get_mt()`. " ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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TitleDescriptionRequires Paid KeySources
id
sply.total.issTotal IssuanceThe sum USD value of all new native units issu...FalseCoinmetrics
sply.circCirculating SupplyThe circulating supply acknowledges that token...False
txn.fee.avgAverage Transaction FeesThe USD value of the mean fee per transaction ...FalseCoinmetrics
txn.tfr.val.adj.ntvAdjusted Transaction Volume (Native Units)The sum of native units transferred between di...FalseCoinmetrics
txn.tfr.erc721.cntERC-721 Transfer CountThe sum count of ERC-721 transfers in that int...FalseCoinmetrics
...............
mcap.circCirculating MarketcapThe circulating marketcap is the price of the ...FalseMessari,Kaiko
cg.sply.circCirculating Supply (CoinGecko)The circulating supply acknowledges that token...False
real.volReal VolumeIt is well known that many exchanges conduct w...FalseKaiko
twitter.followersTwitter FollowersThe number of followers of the asset's primary...FalseCoinGecko
reddit.subscribersReddit SubscribersThe number of subscribers on the asset's prima...FalseReddit
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61 rows × 4 columns

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" ], "text/plain": [ " Title \\\n", "id \n", "sply.total.iss Total Issuance \n", "sply.circ Circulating Supply \n", "txn.fee.avg Average Transaction Fees \n", "txn.tfr.val.adj.ntv Adjusted Transaction Volume (Native Units) \n", "txn.tfr.erc721.cnt ERC-721 Transfer Count \n", "... ... \n", "mcap.circ Circulating Marketcap \n", "cg.sply.circ Circulating Supply (CoinGecko) \n", "real.vol Real Volume \n", "twitter.followers Twitter Followers \n", "reddit.subscribers Reddit Subscribers \n", "\n", " Description \\\n", "id \n", "sply.total.iss The sum USD value of all new native units issu... \n", "sply.circ The circulating supply acknowledges that token... \n", "txn.fee.avg The USD value of the mean fee per transaction ... \n", "txn.tfr.val.adj.ntv The sum of native units transferred between di... \n", "txn.tfr.erc721.cnt The sum count of ERC-721 transfers in that int... \n", "... ... \n", "mcap.circ The circulating marketcap is the price of the ... \n", "cg.sply.circ The circulating supply acknowledges that token... \n", "real.vol It is well known that many exchanges conduct w... \n", "twitter.followers The number of followers of the asset's primary... \n", "reddit.subscribers The number of subscribers on the asset's prima... \n", "\n", " Requires Paid Key Sources \n", "id \n", "sply.total.iss False Coinmetrics \n", "sply.circ False \n", "txn.fee.avg False Coinmetrics \n", "txn.tfr.val.adj.ntv False Coinmetrics \n", "txn.tfr.erc721.cnt False Coinmetrics \n", "... ... ... \n", "mcap.circ False Messari,Kaiko \n", "cg.sply.circ False \n", "real.vol False Kaiko \n", "twitter.followers False CoinGecko \n", "reddit.subscribers False Reddit \n", "\n", "[61 rows x 4 columns]" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "openbb.crypto.dd.get_mt()" ] }, { "cell_type": "code", "execution_count": 157, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "displaylogo": false, "plotlyServerURL": 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"tickformatstops": [ { "dtickrange": [ null, 604800000 ], "value": "%Y-%m-%d" }, { "dtickrange": [ 604800000, "M1" ], "value": "%Y-%m-%d" }, { "dtickrange": [ "M1", null ], "value": "%Y-%m-%d" } ], "type": "date" }, "yaxis": { "title": { "text": "close" }, "type": "log" } } } }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data = openbb.crypto.dd.mt('eth', timeseries_id='price', start_date = '2010-01-01', interval = '1w')[0]\n", "openbb.qa.line(data = data[\"close\"], log_y = True, title = \"ETH/USD - Weekly\")" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "These examples represent only a few methods for fetching historical price data. Explore the contents of each module to find more!" ] } ], "metadata": { "kernelspec": { "display_name": "obb", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.8" }, "orig_nbformat": 4 }, "nbformat": 4, "nbformat_minor": 2 }