Tweeting stock picks .. using FnO tricks – part 2

Hi there !

Over the last month the markets have seemed to be turning up. And we’ve been trying to do some stock picks of late using simple F&O data analysis with no rocket science:). We have been tweeting these stock picks on,  and oh boy ! are we happy or what –> 8 out of last 10 picks given nice returns.

Just thought we should share these ideas with the myfno community & look forward to your views so we can all build F&O knowledge along the way

If you’ve been following us on twitter, hope you would have liked the below stock picks , if not, there should be more to come to choose from:) Wish us luck !


Just to reiterate & to add a disclaimer here –> we love to design & build great softwares & are NOT in the business of stock recommendation / advisory. We are doing this stock picking purely at a goodwill and to showcase what the power of F&O data could do. 

Sector Analysis of FnO data – How to see the bigger picture ?

One of the best ways to pick a winning trade is through a top down approach . First identify the sector showing a trade opportunity and then look for the best stock in it.

In MyFnO, we have always strived to figure out how we can deliver that extra edge using F&O data.

And today we are proud to introduce to be seen for the FIRST time anywheresectorTrends
Sectorwise Charts with aggregated F&O data of price, oi, volume, rollover, basis, iv, pcr, delivery
(P.S. avaliable in PLATINUM plan only)


Computation methodology used ->

  1. For Open Interest (OI), Future Volume, Delivery, we calculate in Value terms (INR cr) and add up the Total of all the sector components so that they can be compared on a common base.
  2. For others like basis,iv,pcr,rollover we take the Average at any tick point of all the sector constituents
  3. For price we compute a simple average index where the initial price of each stock in the sector is rationalized to base 100

This sectoral data can now be beautifully analyzed across various modules in MyFnO

Our favourite one is the “Trends” page where you can get a clear understanding of the action happening across sectors in one screen all together.  Click below image to see full screen

Sector Trends
Sector Trends

Now you can also see a full Sector Chart for the first time with aggregated F&O data & Technical indicators combined together in one screen !



In Markets page , you can also see Sector wise TopX gainers/losers across aggregated sector data like price%, oi% , avg premium/disc %, avg Rollovers % / delivery% etc.


In Sectors page itself you can see % changes of various F&O aggregrated parameters across any time frame/interval.. be it Days/week/Month or any custom N days.. or even intraday of 5/10/15/hourly changes. so you know where the action is taking place. You can also use this interval chg in other screens like Markets, Heatmap, etc. whereever applicable


In Changes page you can see smooth trends with colored heatmap where it is then easy to identify regions of sector weakness/ strengths and turnarounds



In Ranges page you can lookout for sectors where F&O data reaching extremes of the period ranges and especially if there is a gap or divergence of 2 parameters like price & oi


In Compare page you can actually compare for eg. F&O data like Sector avg Rollover % across ‘monthly’ time frame in a easy to analyse graph



So as you can see there are various ways to see the bigger picture in action happening across sectors. It is all out there for you to see. Just waiting for you to spot it !

Unravel the mystery behind rollover costs

We have all heard about rollovers & some even the rollover cost, but how do we interpret & how do we see if it signals a change in price trend

the definitions we’ve already laid out in our post on our post–>
F&O data … the basic fundas

Essentially rollover% is an indication of how much % of positions is getting rolled over to the next month ( be it long or short) .. and a higher rollover% indicates that the base direction for the next expiry action is set strongly by the bulls/bears prior to its beginning itself.

And whether the price trend reversal will be up or down can be seen clearly by a positive/negative or a change in the rollover cost which essentially in the price of next month future – current month future expressed as a %percentage premium/discount

So wondering how the rollover% and especially the rollover cost works out during the expiry / rollover week and what it gives a hint of ?

Well, what better way to showcase it in various scenarios than through a single ‘selfie’ to understand pic  –>

the mystery behind rollover costs

the above pic is is for a month trend.. to see expiry day trend you can choose daterange as ‘1d’,  so that you can see if there are any last day rollcost changes/reversals happening in long/short buildups , indicating where the buy/sell pressure is taking place.

to see what how you could have made money on the last trading day of the expiry  see this example–>

rollcost on the expiry day itself!

To view this live in one-single click you can directly goto our directly created ‘Rollcost’ view template in the ‘Trends’ module –>

The trick i used to find this turnaround stock

I like to see stocks which are near their high /low range . And not only in terms of price , but also other F&O parameters, especially Open Interest (OI) and Delivery.

Why ? because OI itself is like a pressure cooker, there is a limit to how high can it go, and at the tipping point either the bulls or the bears will eventually give in, and the price would flow in the reverse direction. And if coupled with high delivery volumes, it is like a sure shot .

But how do identify if these parameters are near their high/low ranges from so many F&O stocks in a quick & easy way? For this the “Ranges” screen in the MyFnO app comes real handy.

The trick of finding such stocks is shown in our one-page pic –>

NOTE: I personally like to choose ‘percentile‘ form of data as that gives me how many % of times the value has been below the current value


F&O data – the basic fundas

The Basics (not the usual price/volume):

  1. Open Interest (OI) :
    in simple terms, this is the net number of long/short positions outstanding at any given point of time. For each buyer of an F&O contract there must be a seller.

    From the time the buyer or seller opens the F&O contract (call/put/future) until the counter-party closes it, that contract is considered 'open'.

    A large open interest indicates more activity and liquidity for the contract. The more the buildup the heavier the counter becomes, and depending on who comes out stronger, the bulls or the bears, the price follows.

  2. Basis (premium/discount) :
    is the difference of the Future price over the underlying spot price. It essentially is a bullish/bearish indicator of whether the buyer is willing to pay extra premium & expects the price to rise in future, or whether the short-seller is selling at a discount thinking the price is going to fall in the future. When expressed as a percentage it is called the basis% and is calculated as

    basis% = 100*(future-spot)/spot.

    CoC (cost of carry) is nothing but the basis computed in annualized terms, as good as the rate of interest for carrying the position forward.

    Coc=(basis% *365) / (days-to-expiry)

  3. Near (1), Next (2), Far (3), Long (4) : the data for the 3 month’s series we can see break up as

    near: for current month(1)
    next: for month 2
    far: month 3
    long: for long-dated contracts beyond the 3 regular months

  4. Rollover : is a close estimate of how many future positions are actually being carried over to the next month series, (but no one actually knows the actual figure, it is just a notional assumption). The formula we use is :

    Rollover% = 100 * (OI_Next(2) + OI_Far(3) )/ (OI_future of all 1+2+3 months combined)

  5. RollCost: is essentially the difference in the next & current month ‘Futures’ price only  i.e. the premium/discount the buyers/sellers are willing to pay to roll their positions to the next month. A positive rollcost generally indicates that a bullish position is being rolled or built for the next month and a negative rollcost indicates a bearish one

    RollCost% = 100 * (Price_Next(2) - Price_Near(1) )/ (Price_Near)

  6. Implied Volatility (IV) : in simple terms it is how much volatility the market is expecting in the future ( vis-à-vis the Historical Volatility HV which is calculated from the past price movements). A higher IV means people expecting a lot of volatility & are thus willing to pay a higher price / premium in options to protect their interests. A lower volatility means people are getting comfortable with current market scenario.
    For IV we use black-scholes formula to calculate IV for each strike, using futures price for underlying & zero interest rate ( since all are European options). Then we apply a volume-weight and calculate overall IV of a symbol through a volume-weighted avg of IVs across strikes to arrive at one common IV for that stock/symbol i.e.

    IVsum = (IV1*Qty1) + (IV2*Qty2) + ... (IVn * Qtyn)
    QtySum = ( Qty1 + Qty2 + ........... QtyN)
    IVavg = IVsum / QtySum
    where 1,2,…N represent individual strike contracts

  7. PutCall Ratio (PCR) : a barometer for investor sentiment, it is the ratio of the open-interest positions of Puts to Calls.

    PCR_OI = OI_Puts / OI_Calls

    A very high PCR can trigger a fall and a very low PCR can trigger a rise in in the markets. There is also a PCR for volume which is a ratio of puts traded to calls traded, representing bullish/bearish sentiment for traders.

  8. Delivery : it is the positions carried over trading sessions in the cash market of all exchanges combined

    (i.e. it goes into the demat account of the trader’s portfolio).

    Delivery is to cash market like OI is to derivatives market. Spikes in deliveries are indicators that major price action can happen from that point, as it forms a support or resistance, depending on whether big positions have been built up or offloaded.

    What we show in is 3 unique things –>
    1.  BSE + NSE delivery qty combined
    2.  delivery% of the total both exchanges traded quantity to get an overall pic
    3.  delivery %change  vis-a-vis the previous day.. so any delivery spikes can be visible if delivery_chg>100%

all-in-one F&O data
all-in-one F&O data