End-of-day pricing and portfolio valuation

Overview

This example shows how to use the Historical client to calculate the end-of-day (EOD) value of a hypothetical portfolio. To do this we will request the closing price for each symbol on a given date and use this price to determine the value of our portfolio.

OHLCV-1d schema

We'll demonstrate this example using the OHLCV-1d schema. The OHLCV family of schemas contain the opening, high, low and closing prices as well as the aggregated volume of trades within a time interval. Since we are interested in end-of-day evaluation, we'll use an interval of one day, which is specified by the suffix -1d.

Many users will prefer to use the official daily settlement prices found in the statistics schema for this purpose.

Example

Python

PythonC++Rust

import databento as db # A hypothetical portfolio mapping symbols to a quantity of shares portfolio = { "AAPL": 200, "AMZN": 200, "GOOG": 100, "META": 100, "NFLX": 114, } # Create a historical client client = db.Historical("YOUR_API_KEY") # Request OHLCV-1d data data = client.timeseries.get_range( dataset="XNAS.ITCH", start="2022-09-19", symbols=list(portfolio.keys()), stype_in="raw_symbol", schema="ohlcv-1d", ) # Convert to DataFrame eod_data = data.to_df() eod_data = eod_data.set_index("symbol") # Sum the products of the close prices and portfolio quantities eod_evaluation = sum( eod_data.at[symbol, "close"] * quantity for symbol, quantity in portfolio.items() ) print(f"${eod_evaluation:,.2f}") 

Result

Python

PythonC++Rust

$109,235.90