Note: If you aren’t a trader, this will seem like a foreign language to you. I’m not going into detail – that will take me the rest of the year to explain, so excuse the jargon.

One pain point is that STT is between 1.7 and 2.5 basis points (only on sell part) for a two-way transaction. That’s for futures and cash respectively. If you add transaction fees and stamp dity, you end up paying between 2 and 3 basis points per transaction overall. Brokerage - if you are a member this can be zero, but at retail end you might get 1/2 basis point to 2 basis points as additional cost. That means you need 5-6 basis points for a market making transaction, which is high considering most liquid bid-asks are not quite that much.

VWAP algos are easily doable in liquid stocks (Cash market) but then after the top 30 stock, every stock trades less than $5 million (25 cr.) a day. That means you can't move much volume on VWAP, and you don't get a deep order book to hit market. VWAP in India mostly means the weighted average of the last 30 min of trading, unlike in the western world – funds want last 30 min VWAP because it’s what the closing price prints.

You can do classic arb algos, like cash/future, basket arb or BSE/NSE. But there is quite a bit of competition here with software from the likes of FT and Greek occupying the classic arb space.

You can also do stat-arb or pairs or such, but pairing is a pain in that you don't get margin offsets. Still, there are useful mid-freq strategies – not necessarily high frequency – that might work. What’s important is to not get muddled up in the mathematics or see pair correlations where they don’t exist. For instance there is no point pairing an Real Estate company along with an IT exporter, no matter how correlated their prices seem to be. This seems obvious but it really is not; a number of people get confused.

Yesterday, in the US, the Nasdaq was down while the Dow was up 1%.A number of high-freq pair trades must have just died – the pair is well traded, even if the indices are weighted on completely different industries.

Option strategies can be made to work - from traditional triangles to risk or stat based like butterflies, IV arb etc. but these have risk associated with execution (you will almost never get a decent spread on bid-ask). Plus, liquidity is low in non Nifty options, so cash deployable is probably <10cr. Additionally in India, you have the issue of options exercisable in cash, meaning your offset position is naked in case you hold an ITM option that gets exercised at 5 pm (you can do nothing)

Still, things have improved. From tick-bunching to real-time ticks, and co-location, NSE is providing some support. They are also penalizing order-junkies by putting a small cost on order-exec ratio >100 and such.

If you consider “algo” trading as purely something that’s generated by an algorithm with no human input, then yes, algo trading can definitely work. I’m doing quant work in generating trades myself, and I’ve had success in the past (though I choose to execute manually, and have pretty high costs). Quant work can be in technicals – price and volume only – or by adding fundamentals (earnings, news) and finally by mixing in macro (sector, country, allocation, money flows); whatever mix you choose, the algo should be able to say buy X shares in Y company and so on.

A number of people think algo = high frequency trading only. High frequency trading will work best in exchanges like the US ones where they give market makers rebates. India has no rebates, and further transaction charges like STT, so high-freq has a bigger barrier.

This is an interesting field, but as in all fields, timing is important. I don’t know if this is the greatest time to invest in it, but in the long term, quant and algo work will be useful, even if as a worthwhile alternative to discretionary trading.

This post is written by Deepak Shenoy, at Capital Mind.

(Above is the daily equity curve, marked to market, including all commissions – which are unfortunately too high. The green curve is the portfolio normalized to 100, and the orange curve is the Nifty through the entire series, normalized to 100)

I’ve only had to take four trades, two long and two short. I’ve had no signal since the 22nd as the system does not throw up a signal when option values are too low to cover brokerage costs. Now I must retest that system using further dated futures and options – a task I have decided to use Python for and am learning the concepts of that scripting language.

This system’s been quite decent on back-testing; About three years is shown here:

(182 trades, 1 Jan 2007 to 6 Nov 2009; I still need to finish the test till December)

I have about five years of data but some of the instruments used weren’t liquid at all in 2005, so it is unreliable. The system has seen a drawdown (peak to trough) of about 18% – but it’s likely to see something as much as 30%, from another statistical analysis test I’ve done. (See Carstens’ ultra-useful tool, which we modified to be more rigorous)

On the back test the system has seen about 55% gains compounded annually (242% flat), and with a 30% potential drawdown the  of the system the “system heat” (risk adjusted return or return/drawdown) is greater than one. 

It’s done about 2% on one month of live testing with real money behind it; will have to wait a few more months, increasing money allocated each month as I gain confidence. Note: not revealing the details of this system here; and the purpose is not to sell the system or it’s output as tips. This is just a log entry.

This post is written by Deepak Shenoy, at Capital Mind.