You are too good and clever in making this happen !
May be, but some of your statements are really good.
Good Luck and Enjoy the windfall ! Just want to give you a feedback whatever I felt ! No idea to join this thread.
Jordan Peterson: What kind of job fits you?
The last few sentences that he said,
āPlenty of position for people who are creative, fast on the feet and super smartā
āIn fact, those people are going to have all the monies. Is already happening in a great degreeā
Boy, wuqijun, manch, Jil, tomato, marcus are going to have all the monies. Can you guys spare me a few pretty pennies.
I obviously donāt have all the monies cause I am still out looking for more. 
Itās interesting. I thought machine learning would be further along in taking large data sets and organizing it in meaningful way for business metrics and decisions. Itās not even remotely close yet. Companies have more data than ever, but they are struggling to use the data to make their business better. Tableau helps with visualizing it, but you have to structure the data and create relationships first. I see most humans struggle badly with it. The ability to turn data into something useful is becoming super critical. Everyone should take some basic classes on relational databases.
When people say machine learning, they really are running a SQL query with a group by 
Why relational? Big data database is non-relational right? Like Hadoop and Cassandra.
That is knowledge and wisdom. Doubt many have it.
You are too modest. With millions in aapl stock, 2 Cupertino homes, several Austin rentals, and a Singaporean villa, you are the envy of the forum.
I have no idea how you structure data into a coherent format without relationships. Without getting it into a usable format data is just noise. Itās like an orchestra with everyone playing randomly. Thereās a reason why thereās sections, harmonies, etc for music to sound good.
I didnāt realize I can put machine learning on my resume. Thatās awesome. Haha.
relational database is orthogonal. meant to reduce storage space. the ārelationsā in RDBMS are mostly useless in generating knowledge. we donāt think orthogonally. humans think in objects
I was thinking of relationships in terms of having 6 tables with data and how do you join it all together then group it, so it can be useful. Most people just see lots of data and get overwhelmed by it.
The biggest bottleneck is technology known to technical people while management (decision making) belongs to non-technical persons. When both are merged to single person/group, they make it big.
It is happening in reality, but not heard mostly. Here are the some examples of success.
There is a big algotrading community is here, lot of examples are there https://www.quandl.com/
HFT - High Frequency Trading is very common with big firms and they are making volatility in the market and successful mostly. I was told they make money during bullish as well as bearish times.
Algorithmic trading is widespread, with some technical community, but they do not project or tell outside.
Technical trading was there in the past. Here is an interesting book (very old) and famous one.
https://www.amazon.com/gp/product/1946963062
There is a living legend of Mathematician turned into HedgeFund Billionarre using Algotradings
True HFT just front runs other orders due to have better data access. If you are willing to pay $100.00/share and someone is willing to sell for $99.90, they step between you two and pocket the $0.10. It should be illegal for them to have access to that data.
Algo trading is interesting. The ones Iāve heard of are pretty simple. The watch twitter data to see if a company is suddenly getting a ton of positive or negative mentions and trade on it. One trades based on if Trump makes a positive or negative tweet about a company. Thereās also a bunch of algo trading based on technical analysis. All they did was automate what the human was doing in terms of watching moving averages, RSI indicator, or whatever your technical measure of choice is. I donāt consider any of that machine learning though. Humans realize those patterns are there.
I want something that discovers patterns and correlations that we arenāt even aware of or thinking about. Tell me bug spray sales in Brazil can predict how the farming season in China will be. Thatās a huge advantage thatāll take others forever to figure out. Thatās the type of stuff ML should be capable of delivering.
My friend works (investment division program manager) one of the insurance companies, he used to mention this to me. There are plenty of such programs are running at wall street hedge funds, big institutions, insurance companies. They pay $300/hr for those technical consultantsā¦and most of them are specific business/tasks. Those software, such as Bloomberg terminals (subscription is $24000/month !) or its derivatives, are expensive, and focused on big firms.
Sound more like data mining
Read the book or pdf copy (Reminiscences of a Stock Operator), the author used to outsmart many brokers using his pattern knowledge (those days). Now,everything is turned into algorithms.
One of the forums, someone suggested having discrete time trading, like every second, orders get updated, and submitted.
Having such knowledge is wonderful but you donāt really need it. Personally Iāve never had such advantage but I still laugh all the way to the bank with my stock portfolio. The trick, of course, is the looked-down-upon-but-so-very-effective buy and hold for the long term.
Even though I have not bought at the low of Feb/Mar for F10s, is already up 15% from my average purchase price. Many folds of the cost of dim sum with manch. Hail F10s. But only meagre sum invested 
didnāt follow wuqijun adviceā¦ refuse to admit is my fault, is his poor teaching. DCA purchasing of good stocks work well.
Thatās fine why be so hard on yourself. Havenāt I said you are the envy of this forum?
Not good enough. I need to beat tomatoās goal. And time is running out.