Calculating Close-to-Open Stock Returns with a Price Shift

Summary

The document defines the close-to-open return for each stock on day t as that day’s opening price divided by the prior day’s close, minus one. The question asks how to calculate this daily across multiple stocks without a loop, using separate data frames for opening and closing prices.

The answer points to a pandas example that divides adjusted close prices by adjusted opening prices shifted by five trading days, then subtracts one. This illustrates how a shift aligns prices from different dates for a multi-day return calculation. However, the example calculates a different interval and direction from the requested prior-close-to-current-open return. The document offers no direct implementation for that formula, nor discussion of missing data, corporate actions, or return validation.

Key ideas

• Close-to-open return compares the current session’s open with the previous session’s close.
• The return is expressed as the price ratio minus one.
• Shifting a price series aligns observations from different trading dates.
• The example uses adjusted prices and a five-day shift, so it does not directly implement the question’s one-day close-to-open calculation.

Tags

# Pandas: Close-to-Open return on stocks


# Pandas: Close-to-Open return on stocks












I am trying to daily calculate the close-to-open return for j stocks for t days. Is there anyway I can calculate without using a for loop? I have one Dataframe for daily close prices and one for daily open. I am using python.

If it is close-to-close return, I am able to use the pct_change, not sure for close-to-open.

```
RCO(t,j) = SO(t,j)/SC(t-1,j)-1
```

where RCO is the returns on day t for stock j, SO is the opening price and SC is the previous days closing price.

## Answer by Helin (score 4)

https://quant.stackexchange.com/a/39433

Please refer to this code example, which calculates open-to-close returns over the past five trading days.

```
import pandas_datareader.data as web
symbol = 'WIKI/AAPL'
df = web.DataReader(symbol, 'quandl', '2018-01-01', '2018-03-31')
df['AdjClose'] / df['AdjOpen'].shift(5) - 1  # change 5 to the desired interval
```

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