Text files of historical data available!

Here is large-cap growth.

Here is small-cap value.

Here is the gold file

Once again, there is only data for managed futures starting in 1980, so I had ChatGPT extrapolate the existing data back to 1928 ... better than nothing.

Here are some charts from Paul Merriman. 90 years (30 x 3) return of S&P500, US large value, small balanced and small value.

@hecht790

Thanks for sharing that, though I’m not sure how to use it within Pralana. Pralana lets you define custom asset types as long as you can supply annual return histories. That’s why I posted TXT files with year-by-year performance for multiple asset classes—to make historical modeling possible beyond Pralana’s limited built-in datasets.

I’m not a strong fan of Monte Carlo, which assigns random returns to each asset across thousands of runs; assets rarely all rise or fall together, so many simulated paths are unrealistic. By loading actual histories for my user-defined assets, the historical backtest feels more credible. I can also start the test in a known “worst” retirement year to stress sequence-of-returns risk and specific economic regimes. Bottom line: the historical approach is more robust and gives me greater confidence after seeing how the portfolio behaves across real-world conditions.

@boomdaddy3

You are right. You cannot run historical analysis without yearly data. I also agree that Monte Carlo is limited, and correlation is a problem.

I currently have 12 assets in PRC and do not have the historical data for them. I used to have two: Stocks and Bonds. I ran the historical and monte Carlo, changed parameters to test edge conditions, used different scenarios, to see if I’m OK. My goal is the basic PRC goal, maximize saving, I am not looking to maximize expenses.

Once I felt that my overall saving was OK or better than OK, I tried using PRC to optimize my investments. I added more granularity to my PRC assets. Asset Allocation is a personal decision and Pralana Mode-2 does a good job of tracking the overall ratio of the 12 assets. Asset Location has to do with tax optimization. PRC is not trying to optimize it, so the user needs to optimize it himself by changing the location of the assets and observing the impact on the saving. For that the user does not need historical data just average return (and additional data: dividends, Qualified dividends and foreign tax paid).

I hope that in the future PRC will add an Asset Location optimizer. It is important since once a taxable asset accumulates large LTCG (and I have some assets that grew by over 50% in the last 2 years) it is very difficult to change it without paying significant taxes. So, people should pay attention to their Asset Location early in investment life.

Posted by: @boomdaddy3

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I’m not a strong fan of Monte Carlo, which assigns random returns to each asset across thousands of runs; assets rarely all rise or fall together, so many simulated paths are unrealistic. By loading actual histories for my user-defined assets, the historical backtest feels more credible. I can also start the test in a known “worst” retirement year to stress sequence-of-returns risk and specific economic regimes. Bottom line: the historical approach is more robust and gives me greater confidence after seeing how the portfolio behaves across real-world conditions.

Just fyi I've requested (wish list) Pralana also offer a bootstrapped version of historical for just this reason, and because it doesn't show autocorrelation (return to mean). Bootstrap is where you take blocks of actual historical data but randomly re-arrange them. The bootstrapped would have many of the benefits of monte carlo but also maintain some of the autocorrelation within assets and also the correlations between assets. i think the topic is too esoteric to get general support, but just putting it on your radar.

@hecht790 I used ChatGPT to collect and format the data I provided above. In some cases, I had to go where it directed and download a file, which I then uploaded to ChatGPT for processing. I believe you can do the same. I'm curious - what are your 12 asset classes?

@boomdaddy3

Thank you, I will try ChatGPT.

Here are my 12 assets:

• Cash
• US Treasury Bonds (Short, Intermediate and TIPS)
• US Large Cap Blend
• US Large Cap Value
• US Small Cap Blend
• US Small Cap Value
• US REIT
• International Large Cap Blend
• International Large Cap Value
• International Small Cap Blend
• International Small Cap Value
• International Emerging Market

@hecht790 Good luck - you may have to ask multiple times/ways to get what you need. I have already posted US Small Cap Value. You can use that information (Fama/French is the source). Cash, 3-month, & 10-year treasuries already exist in PRC (Cash has a 0 return always). Please share your tables as text files (formatted like mine above) so other people can use them as a resource. Let me know if you cannot develop any and I can try to help. These files are useful for everyone who wants to use historical simulations.

Here is a commodity historical data file using the SDCI ETF data where available, otherwise uses Gorton–Rouwenhorst Equal-Weighted converted to total return by adding Ken French's monthly RF to the GR's monthly excess returns, then compounding to calendar-year returns (courtesy of ChatGPT).

Enjoy!

Another good source of data is the Bogleheads returns spreadsheet. https://www.bogleheads.org/wiki/Simba%27s_backtesting_spreadsheet .

@jkandell thank you,

I used Simba to create the historical input for my 10 equity assets. Attache is an Excel file with the data. International data starts only in 1975 or even later. To convert it to text, create a 96x2 table (one column is the years and the other is the return) and in Table Layout convert the table to text with comas (I did it in Word) and then past the text into PRC.