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We started something strange recently. You know the Outliers? We like the outliers. We find them fascinating, even if we wrote the algorithms to generate them. So we figured something: Why don’t we just take some of the outliers – just stocks picked off the list that gets displayed – and buy them when they’re breaking out?

The answer has been incredible. 22% in 62 days.

This is a portfolio that chases “Mad Momentum“. Very short term. Very crazy. Very risky. But the results are here, at least the initial ones.

First, see the state as of now.

This is a portfolio of Rs. 250,000, which we “experimented” with. In Slack, all entries and exits were updated (#experimental-action and #experimentals).

Outlier Trades

What did we do?

The idea was simple.

• Go to the Outliers Page.

• Look at Outliers with grades above 5.

• Prefer stocks making new all-time highs.

• Put 10% in each stock.

• Remove a stock that weakens. Try not to lose too much money.

• Replace it with a strong stock.

There wasn’t that much of a look at financials. There were some liquidity criteria of no-ultra-smallcaps, no-BSE-only stocks etc. But largely the stocks were identified on the basis of a chart only.

Give Us An Example.

Ok. Here’s one we got in very recently. A break out in Torrent Power. This has been Grade 8+.

And earlier, one in Orient Bell Ceramics. The strong breakout (near Grade 10), took the stock up from 300 to 375 and then back to 340. We were out in 6 days at 340 – a 11% return.

We don’t really have to care about not getting back into a stock. Every entry is like a new one. We had a phenomenal run in Tinplate, with three trades as the stock broke out and made new highs. Just that stock returned 58% in those three trades together.

There are losses too! And we’re very transparent about them – there’s no point highlighting only the winners. We keep a track of everything.

The Statistics

Here’s the statistics we track. We’ve had 50 trades. Rs. 25,000 allocated per trade. (Only recently, when we crossed a 20% return have we bumped it up to Rs. 30,000 per trade.)


We only have a 55% win-loss ratio. Out of 49 trades, we “won” only in 27. That’s close to a coin-toss!

Yet, we made 22%, but how? The answer is: win more when you win, lose less when you lose.

The biggest winner we had was 35% (GNFC, ongoing). The biggest loser? A 9% loss in TRIDENT.

And the distribution of losses is like this:

This is the real lesson: Cut your losers fast, let your winners run. You notice we have no losers after around 10-12 days. There are stocks which hurt us by moving back up when we get out. No problem. No regrets. You walk away because you cut losses short.

This concept works well when the market has momentum. It will hurt when the market starts losing steam. And then, we’ll have to cut positions or keep them loose.

What happens to the portfolio? We will keep experimenting with it. After all, the market is at all time highs, so everything would have done well. Such a portfolio should see losses when the markets reverse, and we will have to see then if there are stocks that still stand out as positive outliers. Keep checking in #experimentals and #experimental-action on Slack.

Note: This is not advice. We are creating a demonstration portfolio only to show the power of outliers. Any actual trades will be very risky, even if the stats above look exciting. Please also note that we don’t check fundamentals when we add or remove stocks – this is purely a way to understand how to play the madness in the moves that the markets are seeing.

Oh, and don’t forget to check Capitalmind SNAP Outliers.

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Note: This is not portfolio advice. Consider this a very risky portfolio and proceed at your own risk. At Capitalmind Premium the reason we have a portfolio is to demonstrate our commitment to our analysis, and we track it closely. It is not meant to be a recommendation for anyone in particular, primarily because we don’t know your risk profile.

Holdings: Analyst and family do own some of the positions listed above. Please assume we are biased.

Now, tell them about it: