Systematic Trading, backtesting, etc., may sound like a current generation fad, but they are clearly not.

As far back as 1990s, several big funds, quant funds have used backtesting/exploration, etc., to develop systems to trade.

Only recently it has become viable for retail.
Until late 2010s, we didn't have faster internet speeds, access to better data, low cost brokers, access to ease-of-use programming languages/tools to backtest strategies thoroughly.

MATLAB was complicated. Excel was limited without VBA. VBA was not everyone's cup of tea.
Python going mainstream as a programming language alongside R programming made it possible for many people to start backtesting their ideas.

And proliferation of several helpful tools/libraries in Python has also helped several people move fast in backtesting.
Earlier, you could only manually backtest ideas. It takes 3-4 months to test an idea thoroughly, and that too only one variation.

Today, you can test 100s of ideas in the same day provided you can code them up.
So, don't consider systematic trading a fad. It's the way you can survive as a trader in the future.

If you want to trade for a living, better start to embrace systematic trading, at least as a % of your portfolio of trades.

Thank me later.

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More from @theBuoyantMan

12 Jan
When you break down the lives of extremely successful people, you find that their success didn't come from an earth shattering break-through.

Rather, it came from obsessing about a simple idea, fanatically.

Time for a thread about one such idea. 👇👇👇
1/ During world war II, a eleven year old kid checks out a book from the nearest library. The book's title was "1000 ways to make 1000 dollars".

After he reads that book, he makes a statement that by the time he was 35 years old, he'd be a millionaire. Image
2/ By the age of 35, his net worth was roughly 7-8 million dollars. The library was Omaha public library. That kid was Warren Buffett.

How he got there is an interesting story.

As soon as he reads that book, he decides he should have 1000 dollars before he finishes school. Image
Read 23 tweets
11 Jan
To avoid this, do a monte carlo analysis and find out the probabilities of range of drawdowns and work with leverage on your system accordingly.

For ex: a system I trade currently, has a historical maxDD of 4.76% and recently it hit a maxDD of about 5.5%.
On conducting monte carlo analysis of the system, I understood that the probability of maxDD to be below 5% is only ~3%.

There was about 61% probability of the maxDD to be between 5-10%

and ~2% probability that it could be around 20-40%.

You need to be aware of these.
Once you know the odds of a certain range of maxDD happening, then you can confidently deploy your strategy.

You'd also face drawdowns that lie within your comfortable range instead of being misled by just the historical maxDD as it happened in the series of trades historically.
Read 4 tweets
10 Jan
One of the parameters you use to understand your trading strategy performance historically before deploying it is called the RoMaD.

Return over Max Drawdown.

It's essentially (annual return % / max DD % )

Let me explain each part of this.
Average return % :

If your system started with $100,000 in 2010, and ended 2020 with $200,000, that's a 7.18% annual return over 10 years.

Maximum Drawdown % :

If portfolio falls from 150k to 120k, that's a 20% drawdown. Amongst all such drawdowns, maximum value is taken.
Return over Max Drawdown will then be

7.18% / 20% = 0.359

The RoMaD value is expressed as a ratio.

*This helps answer the question*:

Am I willing to accept an occasional drawdown of X% in order to generate an average return of Y%?
Read 6 tweets
8 Jan
One of the systems I have been forward testing would have gone from 3 lakhs on September 1st 2020 to 13.4 lakhs by December end.

Needless to say, I was shocked and surprised.

Time for a thread 👇
I have been forward testing a system on BankNifty for last four months.

I always forward test with 1 lot or the smallest size possible.

This is to make up for lack of clean data in the backtesting process.
I wanted to see what would have happened if I'd aggressively compounded the system.

I have been testing this with 2xATM option buying (for every 1x futures).

So, I took the results of the one lot test (after commissions and everything else is deducted) and applied it.
Read 12 tweets
6 Jan
Back in 2017 Sep, I stashed away some money in BTC on a particular crypto platform.

I vowed to never withdraw it, or look at it for 10 years (Taking a leaf out of @dmuthuk's playbook)

It will either be the best investment of my life or turn out a dud.

Let's see.
Initially, I had spread out my investment across BTC and few altcoins including Ethereum.

Within a few months, once bitcoin went to 20k and then crashed to 4-5k range, I cashed out all altcoins and put everything into bitcoin.
Now it may seem like it was a good move investment wise.

But no, this is pure DUMB LUCK.

There was no investment thesis.

I didn't (and still don't fully) understand Bitcoin/Blockchain.

For those who want to understand, their whitepaper + code is the place to start.
Read 4 tweets
1 Jan
Are you planning on handing over your money to an algo trading platform?

Maybe you want to subscribe to a strategy or two and make insane returns.

Here's why you shouldn't do it, and save your money instead.
1/ Maybe you want to have a strategy development company deploy such algorithms on your account with respective brokers.

Or the service itself has strategies that you just subscribe to and authorize your trading account with, and it will automatically trade your money.
2/ When it comes to @Tradetron1 there are a slew of issues.

Let's keep aside the lack of transparency and the total regulatory non-compliance for a moment.

First off, with most of the strategies, the backtests are BOGUS.
Read 37 tweets

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