As others have said, "Average return is just one statistic". When trading, losses hit harder than wins. Go up 50% then down 50% and you're not even, you're down 25%. The degree of overestimation from this mean return -> "annualized return" calculation depends on what the returns distribution looks like.
Here's the calculation used in main.js line 77 applied to a very extreme unrealistic example. I simulated 253 days of return percentages from a uniform distribution between -5.5% and 5.6%, and then the actual total return percent, calculated in R
In reality the actual numbers are likely to be not nearly as different as this example. I chose uniformly distributed returns with a wide range to make the reason against this calculation very obvious. Here's an example return distribution where there's hardly any difference. Normal returns with average of 0.085% and standard deviation of .05 i.e. daily_gain <- rnorm(n, .085/100, .05/100) gives
For good measure here's one in the middle where your returns are normally distributed with an average of 0.35% and a sd of .2%, but then you have on average 10 bad days a year where returns are 5 percentage points lower than that distribution i.e. daily_gain <- rnorm(n, .35/100, .2/100) - rbinom(n, 1, 10/n)*.05 gives
It's listed as being "encrypted in storage" and "encrypted in transit", which is a fancy way of saying "we use encrypted disks and TLS, all of which we have the keys for".
Further down on the page, they have a list of things that are end-to-end encrypted. iCloud Backups are not in that list.
Hi Peter, thanks for doing this.
I'm on L1B, and I was surprised to receive 221(g) while renewing my visa.
I was told to wait for 2-6+ months for a response and crossed my existing visa that expires end of the year.
If my visa expires before hearing back, does that mean I lose my job in the US? It's a European company so they might offer me to transfer back to the UK.
Do I have any rights in such a situation? like ask for a reason for this, or perhaps a severance pay? can I still visit my spouse (in MA) on an ESTA given this is considered a visa rejection for now?
This is excellent advice, I'd like to emphasize one point that gets missed a lot, change the game.
The basis for the negativity here is a "score" which you, the OP, have scored in a way that makes you feel like a loser. Try not to get suckered into that. It is really hard to do but write down all of the things you aspire to. It can be trivia knowledge it can be computer languages known, it can be chemical constants memorized it can be states capitals. Doesn't matter what but it has to be honest and come from inside you.
You might say, "I want to be a valued employee." (a bit self serving but its an example). Now you need want to find out an objective way to evaluate your value. Talk to your boss, talk to your peers, talk to others. Ask them what do they consider valuable in employees. Collect all of that data and write it down, now rank it based on your internal values. What do you consider makes you a valuable employee. If there are things that aren't on the list write those down. Do one last checked with your boss and peers about what you think the top 10 things are that make someone valuable to see how close you are to consensus. Now for each of the things on your list, write down three things; first a way that value is demonstrated, second a way that value is diminished, and third an activity that you can practice that will contribute positively to that value.
It may seem like a crazy thing to spend your time on but the key is that you will have turned a fuzzy thing into something that can guide your actions. And if you ever want to know if you're valuable you can go through the list and see the things you've done to contribute to your value. And when you're thinking about what to get done you can prioritize by your value structure. This is basically a away for you to convert an implicit (and ever changing) score, into an explicit and measurable thing.
Here's the calculation used in main.js line 77 applied to a very extreme unrealistic example. I simulated 253 days of return percentages from a uniform distribution between -5.5% and 5.6%, and then the actual total return percent, calculated in R
Edit:In reality the actual numbers are likely to be not nearly as different as this example. I chose uniformly distributed returns with a wide range to make the reason against this calculation very obvious. Here's an example return distribution where there's hardly any difference. Normal returns with average of 0.085% and standard deviation of .05 i.e. daily_gain <- rnorm(n, .085/100, .05/100) gives
For good measure here's one in the middle where your returns are normally distributed with an average of 0.35% and a sd of .2%, but then you have on average 10 bad days a year where returns are 5 percentage points lower than that distribution i.e. daily_gain <- rnorm(n, .35/100, .2/100) - rbinom(n, 1, 10/n)*.05 gives