I don’t know how many of you have developed a European RS, but for some reason, the use of open vs. close doesn’t work the same way in Europe.
Can anyone confirm this?
For me it works the same: next open is best, next close is worst.
I made a spreadsheet to compare sell and buy transactions - If you make a copy of the spreadsheet you can copy/past your own transactions in to it.
Thank you! I will test it!
Do I understand your findings correctly that the best idea is to close the sellers at the open, and buy the buys at the close?
In my sample data it looks like the best buy is on the previous close, best sell in next open (with a small margin to previous close).
When I click on Rebalance now for a live strategy, it says “Recommendations will be based on fundamental data as of 2023-08-26.”, that’s two days ago. In a simulation, do we simulate on fresh data from today, or two day old data (as in live strategies)?
TL;DR: I hate munging data a little less since ChatGPT came along. And do not let this fool you. I suck as programmer. I did not get inner and outer merge right. I called it “external merge” and did not have the right use, for example. 
Here are the results for 20 years of data for the sim above. ChatGPT allowed me to upload the entire file:
Difference of open compared to close (buy then sell). I.e. buy open and sell open just as Yuval says above (for my model too); Or for buys the close price is higher than the open and for sells the close is lower than the open (on average over 20 years). We have the same result FOR A SIM RUN ON MONDAY:
So I do not know why the columns do not line up but I was pretty sure they would not. Maybe this is important and Yuval can tell us why if Whycliffes wants to know.
Above I mentioned an “external merge” the correct term would have been ‘outer merge’ and I really needed an inner merge. Above was not correct.
One needs to do an’ inner merge.’ ChatGPT walked me through that. And BTW, you can upload the full 20 years of transactions.
Code:
// Perform an inner merge on the ‘Date’ and ‘Symbol’ columns
merged_df = pd.merge(tran_df[[‘Date_open’, ‘Symbol_open’, ‘Open’, ‘Price_open’]], tran_df[[‘Date_close’, ‘Symbol_close’, ‘Close’, ‘Price_close’]],
left_on=[‘Date_open’, ‘Symbol_open’], right_on=[‘Date_close’, ‘Symbol_close’], how=‘inner’)
// Extract the buy and sell rows from the merged dataframe
merged_buy = merged_df[merged_df[‘Open’] == ‘BUY’]
merged_sell = merged_df[merged_df[‘Open’] == ‘SELL’]
// Reset the index
merged_buy = merged_buy.reset_index(drop=True)
merged_sell = merged_sell.reset_index(drop=True)
// Compute the percentage difference in buy prices and sell prices
buy_price_diff_merged = ((merged_buy[‘Price_close’] - merged_buy[‘Price_open’]) / merged_buy[‘Price_open’]) * 100
sell_price_diff_merged = ((merged_sell[‘Price_close’] - merged_sell[‘Price_open’]) / merged_sell[‘Price_open’]) * 100
buy_price_diff_merged.mean(), sell_price_diff_merged.mean()
Jim
You will not get the exact same transactions if the account size differs over time, that’s why I made the spreadsheet, to only compare the transactions that matches.
I have used your spreadsheet on a model with 100 positions of the S&P 500. The model has over 21,000 transactions. Your sheet returns that it is best to buy at the next open and sell at the next close.
| BUY transaction difference | ||
|---|---|---|
| 0.07% | If positive, better to buy in the open | If negative, better to buy at Next Close |
| SELL transaction difference | ||
| 0.04% | If negative, better to sell in the open | If Positive, better sell at next close |
Is that trades on Mondays or any day?
All trades on 1st trading day of the week.
Did this ever get fully resolved? I’m running a strategy that is showing absurd differences (~18% CAGR) whether I run Next Open or Next Close. Average of High/Low/2x Close is only slightly better than Next Close.
Next close:
Next open:
Rebalance is bi-weekly. Obviously if I dial that out to monthly and beyond the spread narrows, but that’s due to less turnover. The two photos above are product of me literally only toggling between open/close execution.
It also begs the question, which one does the ranking system go off of? We can’t change it there.
Just to follow up...I've done about 125 trades since that comment and recorded my live execution prices versus the modeled Next Open and Next Close prices. What I've found is that the average slippage compared to Next Close is less. So when backtesting with the new Next Open and Next Close slippage figures independently, the performance stats come out closer to even. I was doing $10-15k per position without much patience (often times mobile) and clocked in slippage compared to Next Open at ~0.5% and ~0.2% compared to Next Close. I discovered a rule for myself that I don't enter the position if the price opens 4-5% in the green that day. That really helped my average once I did that after about 25 trades. If I throw out the first 25ish trades before I implemented that rule, slippage decreased.
There's something else going on here that my earlier replies might have missed.
When a new earnings report is issued, on the Monday after the report is issued the stock will frequently go up or down according to how good the report was. If your simulation is liable to agree with the market on the quality of the report, then--whether you're selling or buying--it'll be somewhat advantageous to start trading early in the day.
One way to test this is to lag your trades by, say, two weeks. Let's say you're running a 20-stock sim. Put as a buy rule RankPosPrev (2) < 21. As a sell rule use RankPosPrev(2) > 60. Then run the sim both with next open and next close and see if there's a difference. If there's still a large difference, then my explanation can't be correct.