I'm a bit skeptical of what you claim because it is so different from my approach to becoming proficient at pieces mathematics: usually I will work through progressively more-complicated problems in excruciating detail. I don't claim that this is the most efficient method, and it would be nice to find such an approach, just that memorization methods have usually lead to me agreeing with the mathematics, rather than really understanding it.
But maybe you are using Anki differently to how I expect. How exactly do you review cards? Do you look at a prompt until you can say verbatim what is on the card? Or if not verbatim what tolerance do you have for missing details/small mistakes?
The attribution I have seen for the bull market is that investors are bullish on a return to normal via widespread vaccine distribution. If that is the case it follows that the current market is highly dependent on investor sentiment, and that a rapid, negative change in the short-to-moderate term outlook (due to the rise in a new, more-proboematic variant) will decrease the market.
However, the above line of logic is easy to follow and any investor who made or lost a lot of money last March will be on the lookout for the same thing to happen. So, the chanc...
One could just wait until 'the market' (pick an ETF on your favourite index) drops by x%, buy back in (or buy calls) and cash out ~ a year or so later. This would have been a good trade in late March/early April and has a couple of pros: limited downside, relatively unsophisticated (i.e. easy to execute and plan), clear entry and exit signals. The cons are a lack of precision (I suspect a more targeted bet on e.g. vol could make more money, maybe buy the ATM straddle?), and that the low leverage.
There is a rich field of research on statistical laws (such as the CLT) for deterministic systems, which I think might interest various commenters. Here one starts with a randomly chosen point x in some state space X, some dynamics T on X, and a real (or complex) valued observable function g :X -> R and considers the statistics of Y_n = g(T^n(x)) for n > 0 (i.e. we start with X, apply T some number of times and then take a 'measurement' of the system via g). In some interesting circumstances these (completely deterministic) systems satisfy a CLT. Spe...
Regarding the topic of your last paragraph (how can we have choice in a deterministic universe): this is something Gary Drescher discusses extensively in his book.
Firstly, he points out that determinism does not imply that choice is necessarily futile. Our 'choices' only happen because we engage in some kind of decision or choice making process. Even though the choice may be fixed in advance, it is still only taken because we engage in this process.
Additionally, Gary proposes the notion of a subjunctive means-end link (a means-end link is a method of ident...
I don't think it's a fair deduction to conclude that Goldbach's conjecture is "probably true" based on a estimate of the measure (or probability) of the set of possible counter examples being small. The conjecture is either true or false, but more to the point I think you are using the words probability and probable in two different ways (the measure theoretic sense, and in the sense of uncertainty about the truth value of a statement), which obfuscates (at least to me) what exactly the conclusion of your argument is.
There is of co...
How do you find doing problems/exercises from these textbooks when you have prepared using Anki? And are you finding that earlier material seems obvious when reread?
Sorry if this is all coming across as critical and /or doubtful. I've tried to use Anki for theory before and dismally failed; the success you claim is very exciting and I'm trying to understand where I was going wrong.
So far I think I have focused too much on creating cards that can be memorised exactly (formulae and what-not), rather than having general concept cards that are used to develop fluency and familiarity (and later understanding), which sounds like what you are doing.