Intro, skippable if seen previous posts: The Information Ratio is a way to look at risk adjusted return. It's returns divided by volatility, but with extra steps for comparing with a benchmark. An asset might have a lot more gain and but only a bit more risk, or the other way around. With the portfolio calculator, just copy to your google drive and enter the symbols and percentages.

Common uses:

• Gaining more insight into a coin/token;
• Comparing coins/tokens of a similar risk level;
• More common with portfolios than single asset.

Higher number is better, negative number is worse than benchmark. High IR can still be too risky/not risky enough for your preference.

Individual cryptocurrencies: Full Year Data | Half Year Data

Raw data: Full Year | Half Year

Portfolio calculator: Full Year Data | Half Year Data

Last day on file: 2021-12-26

Previous posts: Individual cryptocurrencies | Portfolio calculator

(IR is based on averages, so no huge differences other than expanding from top 300 to top 500)

Not all coins in top 500 have been around for a full year, so there's a table done with the last 6 months of data. The benchmark used is CCI30, the crypto index.

(Coins/tokens outside top5 are in the google sheets)

12 Month's Data, Top 5 per risk group

High Risk (highest 25% of coins)

Med Risk

|Symbol|N

I want you post your sharpe ratio.

No poof needed.

My sharpe is 1.96

https://preview.redd.it/qnc7mfn0n2981.jpg?width=1920&format=pjpg&auto=webp&s=2d6ecee54e100b23945ae2635802cccdfdeb8482

I got the code from here:

https://github.com/AchillesJJ/DSR

And modified it like this:

  def dsr(net_worth):

    returns = np.diff(net_worth)/net_worth[:-1]
    pct = returns
    
    A = np.mean(pct)
    B = np.mean(pct**2)
    delta_A = pct[-1] - A
    delta_B = pct[-1]**2 - B
    Dt = (B*delta_A - 0.5*A*delta_B) / (B-A**2)**(3/2)

    return Dt 

Is this how DSR is calculated?

EDIT: added VET and XTZ

Sharpe Ratio

The Sharpe Ratio is a quick number which takes the returns, puts it in context with its volatility, and then compares it with a risk-free investment (eg. US Treasury Bills). It's also only one way to do risk adjusted returns.

If two tokens/coins give you the same returns, then the one with lower volatility is less risky and gives a higher Sharpe Ratio.

Here's roughly what the numbers correspond to:

• < 0 -- Probably meh
• 0-1 -- Probably okay
• &gt;1 -- Probably above average

The use of Sharpe Ratio can get more complicated so here are two Benjamin Cowen videos on Sharpe Ratio and MPT:

Bitcoin: Modern Portfolio Theory and the Sharpe Ratio

Investing $1,000 in Crypto?

I calculated the Sharpe Ratio twice, once using data since May (since my SHIB data only goes back that far). And once using data for 1 year.

Woobull has Sharpe ratios for BTC and ETH calculated using 4 years of data. There are slight differences in how different people calculate the Sharpe Ratio, as well as the returns of US Treasury Bills year to year.

Sharpe - Using Data Since 2020-10-19

Sharpe - Using Data Since 1st May

Thoughts

Both DOGE and SHIB did better than I expected. But DOGE's Sharpe Ratio dropped in the May table.

The 1 year table is higher quality data.

It's interesting that Grayscale's large cap fund has slightly more ADA than SOL.

Methodology Details

Google sheet for all the math

Top 10 coins from posts like this, by u/BradlyL and his wife.

XL

My first post here!

Here's a basic introduction to Sharpe ratios I wrote up and thought might be helpful for any beginners here. My background is mostly in statistical arbitrage at hedge funds so this is meant to be more for practitioners compared to the mostly academic explanations out there. I've always been interested in writing/education as well so this is my first attempt! Let me what you guys think.

Two hypothetical investments below returned the same ~40%. Which is better?

https://preview.redd.it/wr7lxlxy8ek71.png?width=724&format=png&auto=webp&s=1e8c8b137234d3a358e1629926fa1712be2c12c8

Since returns are identical, we must look elsewhere to answer this. One place to look that is different between the two is the path each took to reach that ~40%.

BLACK certainly “wiggles” more. You probably already intuitively know that the wiggles are bad. Here are a few reasons why.

First, it makes BLACK much more painful to hold. Sure, sometimes it’s beating GREEN (Sept 2019). But other times, it underperforms horrendously (July 2020). And because of prospect theory / loss aversion (see Kahneman) we know that sadness from losses > happiness from gains. The losses will outweigh the gains and makes any holding experience very unpleasant. Anyone who has actually traded can confirm.

Second, after wiggling around so much, doesn’t it almost seem “lucky” that BLACK returned the same as GREEN? And in the next moment, it feels like BLACK could be anywhere, whereas GREEN seems it would make steady gains. So the wiggles also make us less confident in our investment going forward.

Third, what happens if we suddenly needed cash. BLACK wiggles so much it could be down when we need the money, and we would be forced to close the position at a loss.

By now many will recognize that the “wiggles” here represent risk, and we can answer our initial question more explicitly: BLACK is a worse investment than GREEN, despite having returned the same, because it took on much more risk to get that return.

We need a performance metric that reflects this. Simple % returns fails.

Risk-Adjusted Investment Performance

First, we must quantify risk. Our risk metric should capture our intuition that if the returns graph wiggles a lot like BLACK, there is more uncertainty/pain/luck involved; thus, it is riskier.

One of the simplest and most common ways to do this is by calculating the standard deviation of returns, known as volatility.

https:

I cannot find the original post but a couple of days ago a poster had said that they had built a strategy that simply bought the stocks with the best performing sharpe ratio and after a few months of forward testing was seeing some success. I found the idea interesting and went out and built and backtested it.

The Process

I used the yahoo_fin library to get data and utilised their tickers_dow() function to get the list of DJIA stocks for the backtest in monthly interval from 2011 till today.

I then calculated a 12mo rolling sharpe ratio for all stocks in the index:

https://preview.redd.it/q16607eymae71.png?width=1097&format=png&auto=webp&s=f676468b87b90c4121ea98375adbcff45797126c

Using the rolling sharpe I selected the top 5 stocks in the index with the best rolling sharpe ratio each month, which would be my portfolio going into the NEXT month. Below are sample of portfolios, note for simplicity, portfolios were equally weighted:

https://preview.redd.it/veyycm7xpae71.png?width=548&format=png&auto=webp&s=c0149019ec5fa85ef38a7140f0eb5bf04958e5d6

interesting to note that trade frequency was very low and the portfolio rarely fully turned itself over, instead only adding/removing a couple of stocks each month:

https://preview.redd.it/o80kp33cqae71.png?width=1291&format=png&auto=webp&s=7d521b166f48d4974c8f6f52a104b554d656d710

frequency graph of above:

https://preview.redd.it/kogm1pphqae71.png?width=484&format=png&auto=webp&s=2889a03fa054cc90080e458b1856a42db82001b3

Results

Results were a little lacklustre as even with transaction costs set to 0 the strategy failed to beat the benchmark (DJIA returns 178%) over the back-tested period:

https://preview.redd.it/784tknj1rae71.png?width=1290&format=png&auto=webp&s=072ddc84a4436f34339ad3ebe0c18e8a902e3d7e

While disappointing there does seem to be some method behind the madness and I could see with some adjustments there's potential. Adjustments, such as:

- increase/decrease rolling look-back period

- increase/decrease number of stocks held

- increase/decrease minimum hold time

- add position sizing/portfolio optimisation

- TP/SL targets

Thanks for reading!

I suppose I'm not that afraid of announcing this "strategy" because I assume every trader and their mother has probably considered this one time or another. I also don't see how it would hurt sharing this... if more people buy these stocks, the price ought to go up even further right? Anyways I'm just a hobbyist looking for a slight edge over an index fund. Backtests suggest this simple strategy could get me there.

Maybe this isn't considered "algotrading" because I don't really need to "daytrade" or trade in seconds/milliseconds to deploy this kind of strategy?

What am I missing? Is there any reason this is a bad idea? I assume an experienced trader can do way better than this, but for the hobbyist? Sounds decent to me.

A quick google and you will see its calculated a lot of different ways. I know the formula is (rp - rf )/ std rp

Using python syntax (and assuming rf = 0) I've seen it written as:

sr = np.mean(rp) / (np.sqrt(256) * np.std(rp))

sr = ( np.mean(rp) / np.std(rp) ) * (256 **0.5)

sr = np.mean(rp) * 256 / (np.std(rp) * 16) << this is actually from trading evolved anyone can build killer trading strategies in python by Andreas Clenow

as well as

sr = rp / np.std(rp)

and I am sure i have seen many others.

I was wondering if someone could explain which is the correct method and ideally why it is that way rather than some of the others

Banks with the price change above 50% with the best sharpe ratio in the last 12 months:

Filters applied in the screener:

Minimum market value: $1B Avg.

Volume: $100K

Minimum Performance: 50%

Industry: All applied banks

Order by Performance: 1 year sharpe

CM Canadian Imperial Bank of Commerce

BMO Bank of Montreal

SI Silvergate Capital Corporation

TBK Triumph Bancorp, Inc.

CUBI Customers Bancorp, Inc.

LOB Live Oak Bancshares, Inc.

SIVB SVB Financial Group

WAL Western Alliance Bancorporation

BHLB Berkshire Hills Bancorp, Inc.

BNS The Bank of Nova Scotia

CASH Meta Financial Group, Inc.

CIT CIT Group Inc.

PNFP Pinnacle Financial Partners, Inc.

BBVA Banco Bilbao Vizcaya Argentaria, S.A.

BPOP Popular, Inc.

WFC Wells Fargo & Company

TBBK The Bancorp, Inc.

AX Axos Financial, Inc.

FBP First BanCorp.

https://www.dataeconomicis.com/screener

In terms (a) which type of averaging method is it using for the return of asset? (b) where is risk free rate coming from, and is it unchanged throughout entire backtest period?

I would assume that it's using the same timeframe as the current chart because I saw earlier someone posting saying they working on a script to find the Sharpe ratio sourced at a higher tf

Can't find this post, was a good one.

Someone giving a breakdown on expected Sharpe Ratio based on the type of strategy, going from amateur retail TA to Quantitative hedged stuff.

Single indicator: 0.75
Array of indicators: 0.9
Quantitative mean reversion on basket: 1.2
etc etc

I hope the title isn't too confusing. There is a stock market investment competition I am planning to enter where the winner is chosen by ranking investors' Sharpe ratio (where returns are calculated as a coefficient of the risk free rate) over the course of a calendar month and picking the investor with the highest ratio. The winner is given a fixed prize.

However, this competition is run every calendar month. I'd love to win every month, but doing so would require that I achieve outsized returns every month. Hence, I would like to settle for winning some of the time. If I can find an investment plan with a monthly Sharpe ratio that spikes occasionally, then that would give me a good chance of winning. An illustration of what I mean can be found here; I would like to find an investment who's 30 day Sharpe ratio vs time graph looks something like the one on the right (i.e. occasionally has dramatic increases) in comparison to the one on the left which has a lower maximum Sharpe ratio.

Because the prize for winning is fixed, I don't really care about the long term sharpe ratio of the investment; I can invest with an amount of money so small that winning even once, assuming all my other investment attempts go to 0, will leave me with a net gain.

Is there a sector/investment vehicle of some kind that is known to have sharp spikes in monthly Sharpe ratio? How would I find such an investment? Thank you!

Most traders use the Sharpe Ratio on individual stocks. This Bloomberg article suggest using it on your portfolio instead.

https://www.bloomberg.com/news/articles/2021-04-27/how-to-build-a-portfolio-that-outperforms-for-a-century?cmpid=BBD042821_MKT&utm_medium=email&utm_source=newsletter&utm_term=210428&utm_campaign=markets

I’m running some portfolio backtesting with IWDA 88% and EMIM 12% and the sharpe ratio by putting a short period of time is above 1 but with long period of time is under 1 (by putting 10 years is around 0.5). Is it normal? I have tested many ETFs and I wasn’t able to find a portfolio with a low correlation value between ETFs and a high sharpe ratio.

I've got the blu-ray set, I'm getting Sharpe's Peril and Sharpe's Challenge on blu ray (because it isnt included), then I read Sharpe's Challenge on blu was the cut version so I am getting the DVD version of that, then I read that all the aspect ratio isn't correct so I have ordered the 15 disc DVD set (because apparently the bitrate is higher and its better quality than the 8 disc version?). Are any other episodes condensed or just Sharpe's Challenge?

Wowzer!!

Does anyone know which episodes (if any) are correct aspect ratio on the blu rays? I know you get more width on some episodes but its cropped top and bottom, but as the filming and series progressed did it change aspect ratio from 4:3 to 16:9?

Does anyone have a solid idea what happened? I've read loads about it but there's this other thing about it being anamorphic 4:3 (which I think is square with widescreen bars top and bottom making it 4:3 ratio, instead of actual full screen, square pic 4:3).

Also when they created the blu rays, was it made widescreen scene by scene or just processed with the full image not moving so the framing was not adjusted?

Anyone who knows, it would be great for this additional info :)

You just enter your portfolio symbols and % and it gives you the risk adjusted returns. Works for 1-12 coin or token portfolios. Includes most top 300 coins by market cap. Has a 12 month version and a 6 month version since not all top 300s have been around for a year.

Edit: Fixed a big bug, day was shifted by one.

Google Sheet: 12 Month | 6 Month

Copy to your google drive or download as excel file to use.

Here is a clip of it in action: https://i.imgur.com/kVmaLz5.mp4

Information Ratio is similar to Sharpe Ratio but uses a benchmark, I used CCI30.

For the IR of individual coins/tokens check these sheets:

IR google sheets: 12 Month | 6 Month

Notes:

Higher number is better, benchmark is an IR of 0.

Higher volatility is more risky.

Investopedia on IR

Not financial advice

Edit: data is from coingecko

Edit2: made the results square a bit more clear

Edit3: btw if you make a BTC ETH 60:50 portfolio, it doesn't break. It'll just think you have $60 of BTC and $50 of ETH and do the math with that.

Intro, skippable if seen previous posts: The Information Ratio is a way to look at risk adjusted return. It's returns divided by volatility, but with extra steps for comparing with a benchmark. An asset might have a lot more gain and but only a bit more risk, or the other way around. With the portfolio calculator, just copy to your google drive and enter the symbols and percentages.

Common uses:

• Gaining more insight into a coin/token;
• Comparing coins/tokens of a similar risk level;
• More common with portfolios than single asset.

Higher number is better, negative number is worse than benchmark. High IR can still be too risky/not risky enough for your preference.

Individual cryptocurrencies: Full Year Data | Half Year Data

Raw data: Full Year | Half Year

Portfolio calculator: Full Year Data | Half Year Data

Last day on file: 2021-12-26

Previous posts: Individual cryptocurrencies | Portfolio calculator

(IR is based on averages, so no huge differences)

Not all coins in top 500 have been around for a full year, so there's a table done with the last 6 months of data. The benchmark used is CCI30, the crypto index.

12 Month's Data, Top 5 per risk group

High Risk (highest 25% of coins)

Med Risk

You just enter your portfolio symbols and % and it gives you the risk adjusted returns. Works for 1-12 coin or token portfolios. Includes most top 300 coins by market cap. Has a 12 month version and a 6 month version since not all top 300s have been around for a year.

Edit: Fixed a big bug, day was shifted by one.

Google Sheet: 12 Month | 6 Month

Copy to your google drive or download as excel file to use.

Here is a clip of it in action: https://i.imgur.com/kVmaLz5.mp4

Information Ratio is similar to Sharpe Ratio but uses a benchmark, I used CCI30.

For the IR of individual coins/tokens check these sheets:

IR google sheets: 12 Month | 6 Month

Notes:

Higher number is better, benchmark is an IR of 0.

Higher volatility is more risky.

Investopedia on IR

Not financial advice

Data is from coingecko

If you make a BTC ETH 60:50 portfolio, it doesn't break. It'll just think you have $60 of BTC and $50 of ETH and do the math with that.

My first post here!

Here's a basic introduction to Sharpe ratios (risk-adjusted returns) I wrote up and thought might be helpful for any beginner traders here. I’ve spent my career as a quant running statistical arbitrage books at hedge funds so this is meant to be more for practitioners compared to the mostly academic explanations out there. I've always been interested in writing/education as well, and this is my first attempt! Let me what you guys think.

Two hypothetical investments below returned the same ~40%. Which is better?

https://preview.redd.it/8kiqoosjltl71.png?width=725&format=png&auto=webp&s=39267960eb04087dc15a8931d01fc38f1bb6b2fb

Since returns are identical, we must look elsewhere to answer this. One place to look that is different between the two is the path each took to reach that ~40%.

BLACK certainly “wiggles” more. You probably already intuitively know that the wiggles are bad. Here are a few reasons why.

First, it makes BLACK much more painful to hold. Sure, sometimes it’s beating GREEN (Sept 2019). But other times, it underperforms horrendously (July 2020). And because of prospect theory / loss aversion (see Kahneman) we know that sadness from losses > happiness from gains. The losses will outweigh the gains and makes any holding experience very unpleasant. Anyone who has actually traded can confirm.

Second, after wiggling around so much, doesn’t it almost seem “lucky” that BLACK returned the same as GREEN? And in the next moment, it feels like BLACK could be anywhere, whereas GREEN seems it would make steady gains. So the wiggles also make us less confident in our investment going forward.

Third, what happens if we suddenly needed cash. BLACK wiggles so much it could be down when we need the money, and we would be forced to close the position at a loss.

By now many will recognize that the “wiggles” here represent risk, and we can answer our initial question more explicitly: BLACK is a worse investment than GREEN, despite having returned the same, because it took on much more risk to get that return.

We need a performance metric that reflects this. Simple % returns fails.

Risk-Adjusted Investment Performance

First, we must quantify risk. Our risk metric should capture our intuition that if the returns graph wiggles a lot like BLACK, there is more uncertainty/pain/luck involved; thus, it is riskier.

One of the simplest and most common ways to do this is by calculating the standard deviation of