@Iplaywithgerms This paper gives documentation on software (with causal reasoning, assumptions reviewed in appendix) for a parametric approach to estimating either "total effects" or "controlled direct effects" with competing events and time-varying

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)

My simple Approach towards Breakout trading :

1. Scan stocks on the basis of FTDB Scanner.

2. Buy stocks with EMA-9> EMA-21> EMA-63 > EMA 200 & Volume 200% up

3. maximum SL of 1.25X ( ATR )

4. I do breakout trading only in Nifty -500 stocks

#StockMarket
#trading
#Nifty

HELLO: If you pee and every time you finish, your urine looks like this picture, you may have a serious medical illness.

Foamy urine especially if it is persistent and noticeable over time may mean you have a serious kidney problem.

Let’s talk about this today...
RT for others.

What are the features of a normal urine?

A normal urine should have a light yellow colour, painless, odourless (or faint smell), steady stream and should ideally not foam when you pee.

When there’s something wrong, then you see changes in all the above normal features of urine.

For instance if urine becomes very deep yellow, it may mean you are dehydrated;

If urine becomes painful or smells really really bad, it may mean you have an infection;

If urine has a poor stream, especially in a man, it may mean a prostate problem like cancer.

Do you get me?

So today let’s talk about Foamy Urine.

What are the things that can cause urine to suddenly be foamy. As I said in the first tweet, it MAY mean a serious medical illness like a serious kidney problem.

First things first- it does NOT always mean a problem but it may as well.

Now what are the causes of Foamy Urine:

1. SPEED-
If you pee really fast or with a lot of force, it may make your urine more foamy than usual. The speed and force makes the urine generate a lot of bubbles which tend to almost quickly disappear as it is formed in the toilet bowl.

This post is pretty bizarre, but it manages to hit on so many false beliefs that I've seen hurt junior data scientists that it deserves some explicit

(1) The notion that R is well-suited to "building web applications" seems totally out of left field. I don't feel like most R loyalists think this is a good idea, but it's worth calling out that no normal company will be glad you wrote your entire web app in R.

(2) It is true that Python had some issues historically with the 2-to-3 transition, but it's not such a big deal these days. On the flip side, I have found interesting R code that doesn't run in modern R interpreters because of changes in core operations (e.g. assignment syntax).

(3) "Most of the time we only need a latest, working interpreter with the latest packages to run the code" -- this is where things get real and reveal some things that hurt data scientists. If this sentence is true, it's likely because you don't share code with coworkers.

(3) Really is a broader issue in data science: people only think of what they need to do their work if no one else existed and code was never maintained. Junior data scientists almost always operate on projects they start from scratch and don't have to maintain for long.

Kolaru Pathigam-Thevaram hymn composed by ThiruGnana Sambandar- #Thread

In Tamil Kol means planets,Aru means cut- set of songs in Kolaru Pathigam addresses all Navagrahas& last 2 lines of every song with end a plea to Bhagwan Shiva to reduce/erase the ill effects of Navagrahas

கோளறு பதிகம் - This pathigam was sung by Thirugnana Sambandar at Thirumaraikadu Kshetram -Vedaranyam,Tamilnadu

Thirugnana Sambandar & Appar(Thirunavukkarasar) travelled together to various Shiva Alayam to spread shivam & bhakti among people.They both reached Thirumaraikadu.

Thirumaraikadu(Vedaranyam) -
Appar & Sambandar were asked to enter the temple through side entrance.The main door was locked by Vedas many years ago.Appar&Sambandar sang devotional hymns praising Shiva, after which the gates opened & closed.After which main door opened for all.

After darshan of Bhagwan Shiva at Thirumaraikkadu,Sambandar received a message to travel to Madurai.
During that time, Samanam was on the rise in the Pandya Kingdom.The Pandya King converted to Samanam.The Queen Mangaiyarkkarasi was a staunch Shiva devotee and was upset.

Kularchirai Nayanar ,Chief Minister of Pandya kingdom was in agony by the turn of events.Queen requested Kulachirai Nayanar to escort Thiru gnana Sambandar to Madurai.Kularchirai Nayanar met Thiru gnana Sambandar at Thirumaraikkadu & requested him to visit Madurai.