also if you are a bit intimidated by math (which is totally normal), the best way to learn is through programming. It really makes math interactive and fun. You kind of kill 2 birds with one stone (learning math and programming). R/Python/Matlab, choose one (they each have their own pros and cons) and start learning from datacamp for $20 a month. MIT opencourse is another great place to learn. You get a $10k a year education for a big donut (0). Hop on the math forums, get involved and sooner than later you will be up to speed.
ajensen, Thank you for these ideas. I'm embarrassed to say I wasn't familiar with the Sharpe ratio. It gives us a way to evaluate our returns based on the risk we are taking, a valuable tool indeed. Using the risk-free rate of .0234 (yesterday's T-bill rate) my ratio comes out at 1.09. That's not exactly killing it, but it does seem to indicate that I have a return slightly above what I should be getting based on the amount of risk I'm taking. SPY Sharp is .27 as of end of November. I need to do some further study to better interpret this result.
Careful of cherry picking time-frames with Sharpe ratios. Almost by definition the Sharpe ratio of a broad based index like the S&P 500 approaches 1 over time. If you cut things into short enough time-frames they'll diverge, but again almost by definition that is noise. If you're really thinking portfolios and statistics you'll think of SPY as a Sharpe of 1.
@ironchef & @TheBigShort , great discussion! I've been thinking about the advantages we have as retail traders recently; I made a thread about it a while ago. I think a commonality all trading efforts have is at the end of the day, we are engaged in trying to optimize the risks we hold, like trading relative value, and/or we are trying to find pockets of non-random behavior to take advantage of. Having a rational, quantitative approach is invaluable for sustainability and consistency. And particularly as retails, this can be paired with a higher-level discretion in choosing when to press a strategy or edge harder or not, since we're our own boss, and usually small enough to exit a position very quickly if need be. I see this in business, the guys that have both the confidence to aggressively leverage an advantage and the agility to adjust when the context starts to shift so they aren't caught over-extended are the ones that build wealth really quickly. For example, if someone had the conviction to put $100,000 in TQQQ at any point in the next several years following GFC they'd be pushing toward $2mm. You win; now diversify and preserve your capital. Hard to have both the guts and intuition of when to put risk on and correct sensitivity when to take it off. This is a crude example obv, but the paradigm should be what we're on the lookout for. And of course, we want to keep aware of survivorship bias inherent in such analyses, but it's still far from a roulette wheel imo. rallymode made a great post on this a few years back. The absence of a force multiplier, not the lurking tail-risk, is the biggest drawback of grinding away at mediocre-ecpectancy options strategies, imo.
Wrong! There is no reason to assume the Sharpe ratio of the S&P 500 approaches 1 over time. In fact, if you assume historical volatility of about 15%, a Sharpe ratio of 1 means that average annual stock market excess returns have 15%. They have been much lower.
It actually does, calculate it over the last 50 years. That aside, remember the numerator is the portfolio return minus the risk free rate. Your comment assumes the risk free rate was always in the low single digits like today.
Thank you for the excellent suggestions. Of the three, R; Python; MATLAB, which is easier? Once upon a time in college I learned simple programming using FORTRAN. But have not programed for years. Recently, some of you here were kind enough to help with VBA in Excel (thank you userque). But that is a cumbersome way to calculate stuff. So, I just signed up for a Coursera class to learn programming in MATLAB. Very difficult, hard to teach an old dog new tricks. Regards,
As an electrical engineer I found MATLAB very easy to learn and use. The problem is that while it's perfect for EE type problems it's not really a full-on programming language so, for example, you wouldn't want to use it to code in any kind of trading algorithm ever. I haven't done a lot with R but my understanding of it is that it is similar. Python is a real language and if you learned it, you would find it much easier to transition to something like C++ if you ever wanted to do some serious coding, although personal preference I'd start with java instead of Python. Final note, you can get Python for free but MATLAB is somewhat expensive if you're not a student. So to summarize, if you just want to work out the math behind options I'd highly recommend MATLAB. If you want to keep your options open for programming to accomplish actual tasks like trading, Python would be the best of your choices.
I never dug into it but I remember listening to a podcast with Ernest Chan and he mentioned a home licence of Matlab. They don’t advertise it but think it’s like 100$ vs 2K plus they advertise for commercial use.
Magic, I really appreciate your post. Here, you just brought up a point that has been bugging me for the longest time: Risk adjusted returns. Usually people referred to as the Sharpe ratio. I have two questions for you: 1. If I have a method that produces the same risk adjusted return as SPY but a CAGR return that is 2x SPY. Folks here said they are equivalent. Are they? 2. In selecting which instrument to put my bet, should time be a factor? When one integrates over a long time frame, say 10, 20, 30 years, even with the same Sharpe, wouldn't the 2x instrument give me a higher absolute return with the same probability? Here is a quote from an article I read: 3. It is then consistent with financial advisers' advices for investing: The longer the time horizon the higher the risk you should take. But what about trading? How do I deal with risks as a trader?