The scenario in trading today is not as it was 20 years back. 20 years back systematic or algorithmic trading was almost non-existent. Today it's growing in numbers.

Throughout the last 20-30 years, traders have had to evolve to adapt to the changing technology and market.
It's clear what the next 20 years look like. By next 20 years, almost 90% of global markets will become algorithmic and systematically driven. Already we see discretionary prop funds withering away. Very few are surviving and barely scraping to get by.
How do you prepare yourselves for trading as a lifelong career?

No other go. You have to learn programming, learn math, statistics, and adapt to the moving technology goalposts. Otherwise you will face competition from those who do learn those things.
If you don't want to learn any of these, you can simply resign yourself from trading, invest the money in good businesses and earn through dividends and share price growth. That method of making a living in the market won't go out of fashion. But even there you have competition.
The edge in value investing or any form of long-term investing is psychology + quantitative analysis + qualitative analysis.

Quantitative analysis wise you can't beat those with the information advantage - the big funds, investors, etc., with state of the art tech.
These people/funds with cutting edge tech, analytics, etc., will make sure all the quantitative analysis based information is priced in, coz it will be.

Qualitative analysis is then doing all the field work, talking to management, customers, suppliers, vendors, etc.
Doing the ground work is where the edge already is, and in having strong psychology, being aware of your biases, and holding through the difficult periods, being steadfast in your conviction, etc.

All these will come into play if you go into the long-term investing way.
But these things are outside of the quantitative analysis. Even for long-term investing, to do quantitative analysis, you need to understand at least basic statistics, and to analyse many companies and screen out unfavorables, you should use programming. Otherwise it's tedious.
Slowly in the next two decades, you will see intraday game getting crowded by algos, and Indian market will get way more efficient than it is now, with way more participants, liquidity, etc.

You'll either compete with systematic traders, or you'll be pushed out of lower tf.
That's what's been happening in the last few many years. Algos have come to dominate scalping, so scalpers are slowly going out of business. Slowly, algos will come to work with the 5-15m timeframes as companies like Tower Research have started India focused mid-frequency ops.
You'll eventually have to come out of those timeframes crowded by algos and work with timeframes where there will be lesser competition - like 30m, 1h, etc. Slowly your returns will decline too. Otherwise you'll have to take leverage which SEBI won't allow you to take.
If you have to take leverage, you can only go to bank and apply for a personal loan or something else, which is dangerous. So, you'll eventually end up playing swing trading in cash or in futures. Even in those timeframes, systematic trend followers will dominate.
Discretionary trading has its place. But slowly discretionary traders will get pushed out of lower timeframes into higher timeframes. This is my guess based on the pattern I am observing in the last 20 years of evolution of stock markets.
Indian markets may not have dark pools or orderflow selling as a predominant thing right now. But already there's a lot of spoofing by institutions going on. It will eventually be algos competing with each other rather than people in the lower timeframes.
So, either adapt or perish. That's the message for today. Starting now, slowly build your math and programming skills. It's not difficult. 1-2 hours a day should help you get there. Worst case excel + vba, best case - C++ and Python with a bit of R programming should help.
The reason I don't suggest amibroker-afl above others is because it's a proprietary tool and language. No one knows when Amibroker will go out of existence. It's always better to have command over one programming language and build your own stack for backtesting & live trading.

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More from @theBuoyantMan

4 Nov
If I were to start my trading journey in the markets from scratch now, this is what I would do:

1) Learn market microstructure.

Trading & Exchanges - by Larry Harris

2) Learn the basics of price, volume, volatility.

Price Action by Laurentiu Damir
3) Learn Python

- Udacity, Coursera courses.
- TutorialsPoint
- Python Docs
- Books

4) Learn Pandas, Backtrader, and other requisite libraries (numpy, scipy, ta-lib, etc) using relevant documentation.
5) Build a proper backtesting engine with scalability in mind.

6) Scour through forums, books, papers, fintwit, etc., for strategies and test everything I can.

7) Learn and pick up skills related to backtesting and statistical analysis along the way.
Read 5 tweets
4 Nov
Pidilite is a sticky business.

- Very few products.
- Growth at scale
- Very little debt required to keep growing
- Strong cashflows.
Based on this framework ->

Pidilite has the following:

1) Sustainable competitive advantage: it is the market leader in the segment. The products are synonymous with the brand names.
2) Consistent (better if increasing) and high gross margins.

The gross margins are well above 40% (in recent years well above 50%). Image
Read 11 tweets
3 Nov
Currently, there's a message all around social media that college is for suckers.

India hasn't improved all that much to accommodate people with skills but without degree in the job market. It's very rare to find companies like that.

Moral: You're better off going to college.
If you can afford to, go to US/Canada or Europe for your bachelor's. Otherwise, do a master's degree abroad after bachelor's.

Treat the degree as a milestone that gets your foot in the door. Make sure the degree matches what you are interested in.
If you're an average joe, your life can get better through incremental improvement. Don't depend on your college for a job though. Build the skills in the field of your choice while pursuing your degree (hopefully both are aligned) and go vertically deep.
Read 6 tweets
2 Nov
To truly learn how to deal with data at scale,

Get a regular 4-8gb ram laptop/desktop with a good fibernet connection.

Your task is to

- Fetch live tick data from the broker
- Store all the ticks you're able to receive
- Design the storage for efficiency & ease of use
- Also design a separate data store of the same tickdata optimising for speed
- Collect tick data of one instrument and do this.
- Find a way to collect live tick data of multiple instruments.
- Use the stored tick data for backtesting
- Optimise the backtesting process & speed
This also involves optimising the data store. If you're able to accomplish this at scale with your laptop, with say 400-500gb of data over a period of time maybe, you'll acquire the relevant skills along the way to appear for a HFT firm's interview.
Read 8 tweets
2 Nov
The bikes I remember fondly from my childhood:

1. Bajaj Caliber - hoodibaba ad
2. TVS Victor - Actor vijay's vehicle in a movie
3. Yamaha RX100 / Suzuki 100
4. TVS Star Sport - Surya's vehicle in Sillunu oru Kadhal
5. Yamaha Enticer / Bajaj Avenger
6. Hero Honda Splendor
The bikes I remember fondly of, since I rode them

1. Bajaj Discover
2. Bajaj Pulsar 180 (back when it was introduced, a friend's).
3. Hero Honda Karizma
4. TVS Apache
5. Hero Honda Street
6. Bajaj Sunny
Bajaj used to lead the market in performance oriented vehicles and also had Discover, CT100 and Platina lead the budget segment.

TVS and Honda captured the unisex models market (Activa, Jupiter, Scooty, Pep, etc.,)

After Hero split, they captured the budget bikes market.
Read 4 tweets
1 Nov
How many of you have cold messaged people on LinkedIn - for referrals, job offers, or information/research? Did you manage to get what you want?
My story: I have extensively used Linkedin to reach out to people, to ask them questions. I got two clients through Linkedin for freelancing projects.

When I was applying for Master's program, I messaged 4-5 people per university per program (totaling about 200+ people).
And out of the 200+ people I messaged, with no mutual connections at all, at least 60-70 replied, spoke, and helped out enthusiastically responding to any question I had. At least 20 of them were very sweet and very patient with everything.
Read 13 tweets

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