Free advice - get out at the breakout failure

~ price doesn't follow through
~ candles have long upper wicks/close near the lows
~ volume on down days - high
~ price comes back with all of the above within the range

This was a 10,000 candidate by many handles

Bayer Cropscience

🕵️‍♂️ How Google's PageRank algorithm works

The PageRank algorithm gives each page a rating of its
importance, which is a recursively defined measure of importance, based on if important pages link to it.
It's recursive because the importance of a page refers back to the importance of other pages that link to it

Here's how it works in practice:
1⃣ We start with some pages and crawl them for links

2⃣ Each page has 1/N points (where N as the total number of pages)

3⃣ Add points to each page for the amount of links to it, divided by the number
of links emanating from the sources of these links

Shocking fact: Millennial men are less likely to work than any other age and gender demographic in America.

Today, there are 500,000 young men missing from the U.S. workforce.

Research suggests video games & improved leisure tech plays a role in the problem. 👇 Thread:

Following the 2007 to 2009 recession, 25 to 34 year old men exited high school with fewer middle-skill job opportunities than years prior.

During this time, we saw an increased number of men living with parents & choosing unemployment over lower paying jobs.

It's estimated that 24M millennials live w/ their parents.

1 in 4 living in their parents’ home neither go to school nor work.

What's more surprising? 9 in 10 who lived with their parents a year ago are still living there w/ no plans to leave.

Economists are calling millennial men a lost generation.

According to economist David Dorn:

“If you get to the point where you’re turning 30, you’ve never held a real job and you don’t have a college education, then it is very hard to recover at that point.”

Economists suggest this choosiness is a generational trait.

Forbes interview w/ a high school educated man:

"I’m very quick to get frustrated when people refuse to pay me what I’m worth."
“People feel that they have choice nowadays, and they

Light travels at about 1 foot per nanosecond.

Hold your hand up 12 inches from your face: you’re seeing your hand as it was a nanosecond ago. Everything you look at is, to one degree or another, in the past. The farther away in space, the more ancient in time.

You can’t see the Sun as it is now, but you can see it as it was about 8 minutes ago. You can’t see Alpha Centauri now, but you can see it 4.4 years ago. You can see the Andromeda Galaxy as it was 2.5 million years in the past. And so on.

With powerful telescopes, we can see galaxies whose light has been traveling to us for more than 13 billion years. We see them shining in a universe that’s still young, where gravity has just begun to pull matter together into stars and galaxies.

We can see something even more distant, and more ancient, than the first galaxies. If we peer out far enough, in between the galaxies, we can see parts of the Universe that are so far away, it has taken the light from that distance almost the entire age of the cosmos to reach us.

Important paper from Google on large batch optimization. They do impressively careful experiments measuring # iterations needed to achieve target validation error at various batch sizes. The main "surprise" is the lack of surprises. [thread]

https://t.co/7QIx5CFdfJ

The paper is a good example of lots of elements of good experimental design. They validate their metric by showing lots of variants give consistent results. They tune hyperparamters separately for each condition, check that optimum isn't at the endpoints, and measure sensitivity.

They have separate experiments where the hold fixed # iterations and # epochs, which (as they explain) measure very different things. They avoid confounds, such as batch norm's artificial dependence between batch size and regularization strength.

When the experiments are done carefully enough, the results are remarkably consistent between different datasets and architectures. Qualitatively, MNIST behaves just like ImageNet.

Importantly, they don't find any evidence for a "sharp/flat optima" effect whereby better optimization leads to worse final results. They have a good discussion of experimental artifacts/confounds in past papers where such effects were reported.

How you can filter stocks for Intraday trades - 🧵

As a kid, we would do homework before school the next day - you have to do homework here too. A specific sector performs on a particular day and studying things a day before will help you spot that particular sector. (1/11)

To prepare for the next trading day, you got to check the Index charts of all sectors. You got to keep an eye on a specific Index if you see a few factors:

- If any Index closed exactly at the resistance, you can get a trade on either side on the next day. (2/11)

- If it gave a rejection candle at resistance.

- If any Index closed exactly at support, you can get a trade on either side the following day.

- If there was a bullish signal at the support.

- If any Index gave a breakout or breakdown and then gave a good closing. (3/11)

- If any Index has taken support at 50 or 200 EMA.

Once you spot the Index, look at all the stocks in that particular Index. Pick at least 3-4 stocks from that Index and add them to your watchlist for the next day. It's ideal to check both higher and lower timeframes. (4/11)

What you should do next day? (When I am unable to check Indices and stocks a day before, I sometimes directly start with this process.)

- Start with checking the top gainers/losers list. I observe market from 9:15 to 9:45am to check the sectors performing well on that day.(5/11)