🐳 Watchlists + @unusual_whales 🐳

Unusual Whales is an incredible tool that can take anyone's trading to the next level. It presents a ton of data, which can often be hard to synthesize.

This thread will provide a simplistic approach to analyzing and utilizing flow data. 🌊

MAHARAANI TARARANI: THE QUEEN OF MARATHAS

Rani Tarabai was the brave daughter-in-law of Chhatrapati Shivaji and one of the greatest medieval Monarchs India ever had.
She was born in 1675 to one of the bravest commander-in-chiefs of Shivaji's Army named Hambir Rao Mohite.

Tarabai was just 8yrs old when she was married to Shivaji's younger son, Rajaram. Mughals and Marathas were constantly at war with each other. Shivaji was crowned the ruler of the Independent Maratha Swarajya in 1674 which was established by him.

However,the great leader died in 1680&the Maratha Samrajya went into a complete&total chaos.Marathas saw some very tough times ahead. In 1689,when Raigad Fort fell to the Mughal Army of over 15000,Shivaji's first wife Saibai and his elder son Sambhaji were captured&put to death.

Sambhaji's wife and son Shahu were taken prisoners to the Mughal court. During the same battle, Shivaji's second son Rajaram(Tarabai's husband) , who was the heir to the throne now, managed to escape using a disguise. He made his way to the Gingee Fort in Tamil Nadu.

Mughal General Zulfikar Khan, however, followed them to Gingee laying siege to the Gingee Fort. From 1690-1698,This was the longest siege by a Mughal Army of any Fort which stands testimony to the military and diplomatic credentials of Rani Tarabai.

ULTIMATE GUIDE TO 21 and 200 Moving Average
SUNDAY THREAD 🔖
A trend Following System That will Help You Improve Trading immensely.

RETWEET AND SHARE 🔄
@kuttrapali26 @AmitabhJha3
#stocks #StockMarket
1/n

Moving averages
EMA and SMA
1. Exponential Moving Average (EMA) is similar to Simple Moving Average (SMA), measuring trend direction over a period of time.
2. However, whereas SMA simply calculates an average of price data, EMA applies more weight to data that is more current
2/

21 EMA : SHORT TERM TREND
1. Calculates the Average Price of past 21 days
2. A stock above 21 ema is in strong momentum , which will give pullbacks to 21 ema again and again.
3. Stock above 21 ema should never be shorted so as to stay with the trend
3/n

3. A stock which moves 10-15% above 21 ema will be too extended and hence will generally pullback
Stocks take dynamic support on 21 ema
4. You can Short only if its too extended like 15 to 20% from 21 ema ( Not advised )
#ADANIPOWER:
#pnbhousing
4/n

1. Whenever 21 ema is sloping upwards its called uptrend
2. Whenever 21 ema is sloping downwards its called dowtrend
3. Whenever 21 ema is sideways its called consolidation
⏫Buy in uptrend
⏬Sell in downtrend
Examples:
5/n

1 There's a chasm between an NLP technology that works well in the research lab and something that works for applications that real people use. This was eye-opening when I started my career, and every time I talk to an NLP engineer at @textio, it continues to strike me even now.

2 Research conditions are theoretical and/or idealized. A huge problem for so-called NLP or AI startups with highly credentialed academic founders is that they bring limited knowledge of what it takes to build real products outside the lab.

3 A product is ultimately a thing that people pay for - not just cool technology or user experience. But I’m not even talking about knowledge gaps in go-to-market work. I'm talking purely technical gaps: how you go from science project to performant + delightful user experience.

4 Most commoditized NLP packages solve well-understood problems in standard ways that sacrifice either precision or performance. In a research lab, this is not usually a hard trade-off; in general, no one is using what you make, so performance is less important than precision.

5 In software, when you’re making something for real people to use, these tradeoffs are a big deal. Especially if you’re asking those people to pay for what you’ve made (can’t get away from that pesky GTM thinking). They expect quality, which includes precision AND performance.

1/ The first 18 months of starting a company is often life or death. I must've made 5 different companies that each failed within 9 mo. 😭 Each time the company failed I figured out what I could do better. Eventually startup #6 got to $40K/mo by month 18. Here’s what I learned...

2/ Stay focused! Ignore things that are a waste of time: meetups & conferences, meetings with no clear agenda, fundraising if you're not fundraising, reading lots of tech media articles, etc. Every week should feel like significant progress in the first year.

3/ Your first 5 hires will be the difference between life or death. Choose carefully. Be picky. Many of the things we do at the company still are a result of those early hires' legacy. Have fun as a tight knit team. It will change & evolve as you get bigger so enjoy this moment.

4/ Growth may be flat for the first 9 months. It's gonna be okay. Almost every company has experienced this: Airbnb had to sell cereal in-between, Slack failed as a gaming company first, Tesla sold only 147 cars after 6 years! You probably won't be an overnight success either.

5/ In the beginning, do customer support yourself. You will learn a lot about why your product sucks. I did 5,000+ support tickets when it was the two of us. Delight customers & fix things fast while you learn. It will help you build an amazing intuition about your customers.