OpenBB/website/content/platform/usage/historical_prices.md

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Historical Prices	4	This page provides an introduction to financial statement data available in the OpenBB Platform. This includes quarterly and annual reports, along with metrics and ratios by company. This guide provides examples for using the variety of sources.	stocks companies prices historical ohlc intraday intervals market data

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Historical market prices typically come in the form of OHLC+V - open, high, low, close, volume. There may be additional fields returned by a provider, but those are the expected columns. Granularity and amount of historical data will vary by provider and subscription status. Visit their websites to understand what your entitlements are.

:::info These examples will assume that the OpenBB Platform is initialized in a Python session.

from openbb import obb
import pandas as pd

:::

Historical OHLC

The historical function is located under a submodule for each asset type. In the openbb-equity module.

help(obb.equity.price.historical)

• This endpoint has the most number of providers out of any function. At the time of writing, choices are:

['alpha_vantage', 'cboe', 'fmp', 'intrinio', 'polygon', 'tiingo', 'yfinance']

• Common parameters have been standardized across all sources, start_date, end_date, interval.

• The default interval will be 1d.

• The depth of historical data and choices for granularity will vary by provider and subscription status. Refer to the website and documentation of each source understand your specific entitlements.

• Despite being in the equity module, it's might be possible to get other asset types, like currencies or crypto, from the same endpoint.

• For demonstration purposes, we will use the openbb-yfinance data extension.

df_daily = obb.equity.price.historical(symbol = "spy", provider="yfinance")
df_daily.to_df().head(1)

date	open	high	low	close	volume	dividends	stock splits	capital gains
2022-11-22	396.63	400.07	395.15	399.9	60429000	0	0	0

To load the entire history available from a source, pick a starting date well beyond what it might be. For example, 1900-01-01

df_daily =(
  obb.equity.price.historical(symbol = "spy", start_date = "1990-01-01", provider="yfinance")
  .to_df()
)
df_daily.head(1)

date	open	high	low	close	volume	dividends	stock splits	capital gains
1993-01-29	43.97	43.97	43.75	43.94	1003200	0	0	0

Intervals

The intervals are entered according to this pattern:

• 1m = One Minute
• 1h = One Hour
• 1d = One Day
• 1W = One Week
• 1M = One Month

The date for monthly value is the first or last, depending on the provider. This can be easily resampled from daily data.

df_monthly = (
  obb.equity.price.historical("spy", start_date="1990-01-01", interval="1M", provider="yfinance")
  .to_df()
)
df_monthly.tail(2)

date	open	high	low	close	volume	dividends	stock splits	capital gains
2023-10-01	426.62	438.14	409.21	418.2	1999149700	0	0	0
2023-11-01	419.2	456.38	418.65	455.02	1161239576	0	0	0

Resample a Time Series

yfinance returns the monthly data for the first day of each month. Let's resample it to take from the last, using the daily information captured in the previous cells.

(
    df_daily[["open", "high", "low", "close", "volume"]]
    .resample("M")
    .agg(
        {"open": "first", "high": "max", "low": "min", "close": "last", "volume": "sum"}
    ).tail(2)
)

date	open	high	low	close	volume
2023-10-31	426.62	438.14	409.21	418.2	1999149700
2023-11-30	419.2	456.38	418.65	455.02	1210484176

We can see that the current month's total volume is higher when we resample the daily time series. It is difficult to know where the discrepancy lays, and it may just be a temporary glitch. However, we can verify that the total volume, according to YahooFinance, is the number we just sampled.

:::note If you are following along, the results will not match exactly what is displayed here. :::

df_daily.loc["2023-11-01":].sum()["volume"]

1210484176

Differences Between Sources

To demonstrate the difference between sources, let's compare values for daily volume from several sources.

# Collect the data

yahoo = obb.equity.price.historical("spy", provider="yfinance").to_df()
alphavantage = obb.equity.price.historical("spy", provider = "alpha_vantage").to_df()
intrinio = obb.equity.price.historical("spy", provider="intrinio").to_df()
fmp = obb.equity.price.historical("spy", provider="fmp").to_df()

# Make a new DataFrame with just the volume columns
compare = pd.DataFrame()
compare["AV Volume"] = alphavantage["volume"].tail(10)
compare["FMP Volume"] = fmp["volume"].tail(10)
compare["Intrinio Volume"] = intrinio["volume"].tail(10)
compare["Yahoo Volume"] = yahoo["volume"].tail(10)

compare

date	AV Volume	FMP Volume	Intrinio Volume	Yahoo Volume
2023-11-09	83174417	83071417	83174417	83174400
2023-11-10	89558054	89558054	89558054	89462200
2023-11-13	52236068	52192568	52236068	52236100
2023-11-14	97176935	97130503	97176935	97176900
2023-11-15	77327573	77327573	77327573	77327600
2023-11-16	66665797	66654468	66665797	66665800
2023-11-17	83193902	83193902	83193902	83133200
2023-11-20	70055633	69614633	70055633	69936200
2023-11-21	49244639	49244639	49244639	49244600
2023-11-22	59446573	59313820	58205780	59394900

Other Types of Symbols

Other types of assets and ticker symbols can be loaded from obb.equity.price.historical(), below are some examples but not an exhaustive list.

Share Classes

Some sources use - as the distinction between a share class, e.g., BRK-A and BRK-B. Other formats include:

• A period: BRK.A
• A slash: BRK/A
• No separator, the share class becomes the fourth or fifth letter.

obb.equity.price.historical("brk.b", provider="polygon")

obb.equity.price.historical("brk-b", provider="fmp")

While some providers handle the different formats on their end, others do not. This is something to consider when no results are returned from one source. 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.

Regional Identifiers

With providers supporting market data from multiple jurisdictions, the most common method for requesting data outside of US-listings is to append a suffix to the ticker symbol (e.g., RELIANCE.NS). Formats may be unique to a provider, so it is best to review the source's documentation for an overview of their specific conventions. This page on Yahoo describes how they format symbols, which many others follow to some degree.

Indices

Sources will have their own treatment of these symbols, some examples are:

• YahooFinance/FMP/CBOE: ^RUT
• Polygon: I:NDX

obb.equity.price.historical("^RUT", provider="cboe").to_df().tail(1)

date	open	high	low	close	volume
2023-11-22	1796.37	1804.96	1785.93	1792.92	0

obb.equity.price.historical("^RUT", provider="fmp").to_df().tail(1)

date	open	high	low	close	volume	vwap	label	adj_close	unadjusted_volume	change	change_percent	change_over_time
2023-11-22	1792.51	1803.12	1789.88	1795.54	0	1796.18	November 22, 23	1795.54	0	3.02893	0.16898	0.0016898

:::info For an endpoint geared more specifically towards indices, try obb.index.market() :::

Currencies

FX symbols face the same dilemma as share classes, there are several variations of the same symbol.

• YahooFinance: EURUSD=X
• Polygon: C:EURUSD
• AlphaVantage/FMP: EURUSD

:::info The symbol prefixes are handled internally when obb.currency.price.historical() is used, enter as a pair with no extra characters. :::

obb.equity.price.historical("EURUSD=X", provider="yfinance").to_df().tail(1)

date	open	high	low	close	volume	dividends	stock splits
2023-11-22	1.0918	1.0923	1.0855	1.0918	0	0	0

obb.equity.price.historical("C:EURUSD", provider="polygon").to_df().tail(1)

date	open	high	low	close	volume	vwap	transactions
2023-11-21	1.09168	1.0923	1.0851	1.0888	155827	1.0893	155827

Crypto

Similar, but different to FX tickers.

• YahooFinance: BTC-USD
• Polygon: X:BTCUSD
• AlphaVantage/FMP: BTCUSD

:::info The symbol prefixes are handled internally when obb.crypto.price.historical() is used, enter as a pair with no extra characters and placing the fiat currency second. :::

obb.equity.price.historical("X:BTCUSD", provider="polygon").to_df().tail(1)

date	open	high	low	close	volume	vwap	transactions
2023-11-21	35756	37900	35633	37433.8	30411.4	36841.5	464907

As noted above, X: or other prefixes are not required when using the crypto version of this same endpoint.

obb.crypto.price.historical("BTCUSD", provider="polygon").to_df().tail(1)

date	open	high	low	close	volume	vwap	transactions
2023-11-21	35756	37900	35633	37433.8	30411.4	36841.5	464907

Futures

Historical prices for the continuation chart, can be fetched by the fmp or yfinance data extensions. Individual active contracts are returned by yfinance.

• Continuous front-month: CL=F
• December 2023 contract: CLZ24.NYM
• March 2024 contract: CLH24.NYM

Individual contracts will require knowing which of the CME venues the future is listed on. ["NYM", "NYB", "CME", "CBT"].

obb.equity.price.historical("CL=F", provider="fmp").to_df().tail(1)

date	open	high	low	close	volume	vwap	label	adj_close	unadjusted_volume	change	change_percent	change_over_time
2023-11-22	77.77	77.97	73.79	76.78	368686	76.18	November 22, 23	76.78	368686	-0.99	-1.27	-0.0127

obb.equity.price.historical("CLZ24.NYM", provider="yfinance").to_df().tail(1)

date	open	high	low	close	volume	dividends	stock splits
2023-11-22	74.07	74.07	73.41	73.46	610	0	0

Options

Individual options contracts are also loadable from openbb.equity.price.historical().

• YahooFinance: SPY241220P00400000
• Polygon: O:SPY241220P00400000

obb.equity.price.historical("SPY241220P00400000", provider="yfinance").to_df().tail(1)

date	open	high	low	close	volume	dividends	stock splits
2023-11-22 00:00:00	10.5	10.82	10.25	10.61	77	0	0

obb.equity.price.historical("O:SPY241220P00400000", provider="polygon").to_df().tail(1)

date	open	high	low	close	volume	vwap	transactions
2023-11-20	10.9	10.95	10.75	10.75	17	10.8376	10