Option Trading Strategy – QQQ Diagonal Put Spreads

A ‘diagonal put spread’ is when puts are bought and sold on the same security, and both the strike price and expiration date are different on the two legs.

E.g., buy a 9 month put on QQQ at 135, and sell a 3 month put on QQQ at 145.  In the strategy analyzed this month, another 3 month put on QQQ is sold when the first 3 month put expires, and a third 3 month put on QQQ is sold when the second 3 month put expires.  In this way, the long put protects against arbitrarily large losses when the market drops (though money is lost when the market drops), and profit is made off of selling put option premium.

Take a look at my analysis of how QQQ diagonal put spreads have worked out over the past 10 years.  I’d especially like to hear from anyone who has been using this strategy on non-index options.

I appreciate all the emails I receive to discuss investing, so if there’s anything you’re interested in please send me an email:

joseph AT StockMarketMovement.com

Option Trading Strategy – Buy QQQ Sell SPY

This month’s strategy is buying quarterly QQQ calls and selling quarterly SPY calls.  You can follow the link to see how this strategy has played out historically.

The idea behind this strategy is that the NASDAQ and SP500 tend to move together, but QQQ is more volatile and makes bigger moves.  Selling SPY partially offsets the cost of buying QQQ when the market is down, and QQQ outperforms SPY when the market is up.  At least that’s the theory, you can see how that compares to reality.

I appreciate all the emails I receive to discuss stock (/ option) trading, so if there’s anything you’re interested in please send me an email:

joseph AT StockMarketMovement.com

Option Trading Strategy – Buy Yearly Index Long Call Options

This month’s strategy is buying yearly index long call options.  You can follow the link to see how this strategy has played out historically.

The idea behind this strategy is to buy an index option that’s a little more than 12 months out, so that any profits will be long term capital gains rather than short term.

Options in this strategy are purchased 5% In The Money.  This is a trade-off between purchasing the options At The Money, which has a higher time premium but doesn’t have exposure to intrinsic value loss, and buying options deeply In The Money, which have a lower time premium but do have exposure to substantial intrinsic value loss.  Options could also be purchased Out of The Money, lowering the option premium, but then profit is only made if the index rises by more than the sum of (percent Out of The Money + option premium).  I’ve found 5% In The Money to be a reasonable trade-off.

As always, I appreciate all the emails I receive to discuss stock (/ option) trading, so if there’s anything you’re interested in please send me an email:

joseph AT StockMarketMovement.com

Option Trading Strategy – Buy Yearly Index Long Call Spreads

This month’s strategy is buying yearly index long call spreads.

In the variant of this strategy that I describe, profit is made primarily off of the difference between the option premium collected on the short end, minus the option premium paid on the long end.  The way it’s structured means profit is made even in a relatively flat market.

As always, I appreciate all the emails I receive to discuss stock (/ option) trading, so if there’s anything you’re interested in please send me an email:

joseph AT StockMarketMovement.com

Option Trading Strategy – Buy Monthly QQQ Options

The most common questions I get are about options trading.  Usually it’s from people looking for something to do with their ‘fun money’ (or ‘Vegas money’), not for investing.  I’ve gotten enough feedback, in fact, that I’ve started a new section on the website so they’re easy to find (rather than looking through old posts).

This month’s strategy is fairly simple, buying monthly call options on QQQ (the NASDAQ ETF).

If you need a refresher on options, Investopedia has a good summary.

I do appreciate all the emails I receive to discuss stock (/ option) trading, so if there’s anything you’re interested in please send me an email:

joseph AT StockMarketMovement.com

 

Quantopian Challenge

I’m going to take a small detour this month to discuss the Quantopian Challenge.  Quantopian runs an online investment algorithm contest.  You code your algorithm on their website (using the Python language and their libraries), and enter it into a monthly contest where you compete against other algorithm writers for the ‘best’ algorithm.

‘Best’ is subjective, of course.  Return on investment is an obvious criterion, but it isn’t the only one.  They’re also looking for a low correlation compared to the SP500 index (low Beta), use of hedging (having both long and short positions), and other criteria.  In fact, they don’t tell you exactly what all of the criteria are, or how they’re weighted to reach a final score.  But it is this final score that they use to declare the ‘best’ algorithm.

No actual money is traded up to this point, so you don’t have to worry about having capital (or losing it).  The interesting part is that Quantopian also runs a hedge fund, which does trade real money.  If they like your algorithm’s results, they may give it a capital allocation from their hedge fund, and give you a percentage of any profit it generates while they’re using it (but not any losses, so again, nothing to worry about there).  So, for example, they might trade $2 million of their fund using your algorithm.

It’s a clever idea.  They believe they can get a fairly large number of sophisticated, but non-professional, investors to write algorithms with the attributes they’re looking for, and in return they pay said investors a percentage of profits they generate.

Last Fall I spent some time coding one of my existing algorithms into their system and entered it into a contest.  Contests start once a month, and run for six months, so the first contest just ended.  My entry came in 11th place.  At the end there were about 320 algorithms in the contest.  There were more at the beginning, about 500 if I remember correctly.  The contest that just started in March has 1500 entries, so I guess it’s getting more popular.

(NOTE: When I looked at the just-ended contest earlier today it said I came in 12th, now it says 11th.  I have no idea why.  Maybe in a few weeks I’ll break into the top 3.)

The same algorithm automatically gets entered every month now.  I’ll work on tweaking it a bit, and see if I can top my existing algorithm.  You  can enter up to 3 algorithms at a time, so no need to shut down my current algorithm.  And I’ll update you on how I’m doing.

If you decide to enter the contest, please drop me an email and let me know.  I won’t ask for any details (or provide any of mine), but I do like to talk to people about investment strategies.

How To Use This Website

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I’m putting options trading strategies here, which is what I get the most requests for.  These pages include historical results of various options trading strategies.

I have analyses on generating lifetime income here.

Miscellaneous analyses that don’t below anywhere else are here.

If you use the content or ideas on this website, either on your electronic or printed material, please attribute it by including a plainly visible link back to this website.

For questions, comments, or to discuss research ideas or collaboration, you can contact me at:

joseph AT StockMarketMovement.com

 

Volume Based Indicator Analysis Part 4

This will be my final post on volume-based stock market indicators, at least for a little while.

If you recall, in my previous post I formed a weighted average of the previous 15 days of (daily volume), and used that to predict relative price performance over the next week.  The results were encouraging, but not great.  In this post I’m going look at some variations on the original.  I’ll put the original graph of results here for easy reference:

The first variation is to use an equal weighting of the most recent 6 days, rather than a decreasing weighting of the previous 15 days.

This really isn’t much different.  There’s a little more weight on positions 1 and 2 than previously, but position 0 had fewer occurrences.  It’s interesting that a drastic change like going from decreasing weight with age to constant weight has so little effect on results.

Next, we’ll go back to scaled weights, but put the maximum weight on the day prior to the most recent, rather than the most recent day.

What stands out in this graph isn’t so much the performance of ranks 0-2, but rather the performance of ranks 26-27.  They’ve been suppressed, leaving rank 28 with many occurrences all by itself.  Still, there isn’t much here to say this would make a good indicator.

The next graph is a difference between the 3 day average and the 10 day average.  I.e., each is a constant-weight average of the most recent N days, and we subtract them.

Now, that is an improvement!  Rank 0 occurrences improved from the 350-375 range to over 450.  Rank 28 occurrences improved from about 325 to over 400.

Those are pretty strong signals.  It seems that volume suddenly increasing or decreasing over a couple of  days is correlated with stock price moving up over the next week.

The correlation is probably strong enough that you could trade on it, but it’s also possible to use this as one metric of a more involved indicator, which is one of the things I’m working on.

Volume Based Indicator Analysis Part 3

This post continues my analysis of volume-based stock market indicators.

If you recall, in my previous posts I formed a weighted average of (daily volume * percent price change from previous day).  In this post I’m going to analyze a similar indicator, but instead use a weighted average of just (daily volume).  I.e., the percent price change from the previous day will not be factored into this indicator.

The methodology followed is also the same as that used before, which I’ll describe briefly here.

We want to know if the daily trading volume of a stock gives any indication of its price movement over the next five trading days.  For each of 29 stocks, I calculate the weighted average of the previous 15 days of trading volume.  The most recent trading day gets the most weight, trading volume from 15 days prior gets the least weight.  This weighted average is then divided by a simple average of the previous 15 days of trading volume.

The division by a simple average of the trading volume normalizes the indicator across stocks, some of which may have naturally high or low trading volumes.  The use of a weighted average allows us to determine if volume has been increasing or decreasing over the previous 15 trading days.

The 29 stocks are then ranked, each day, based on the volume indicator calculated above.  A rank of 0 means it had the highest indicator value, a rank of 28 means it had the lowest indicator value.  Finally, a count is kept for each rank as to how many days a stock with that rank had the best performance over the next five trading days.

By looking at a graph of these rank counts, we should be able to tell if this volume-based indicator has any probabilistic predictive value for stock price change over the next five trading days.  I.e., if there is no predictive value, each rank would be expected to have approximately the same number of best performance occurrences over the next five days (with a little randomness thrown in).  If there is some predictive value, one or more ranks should have unusually high (or low) counts.

A graph of this indicator is shown below:

That looks pretty random.  There does, however, seems to be a correlation between having a rank of 0 (increasing trading volume) and relative price performance over the next five trading days.  As before, with the indicator based on (daily volume * percent price change from previous day), the correlation isn’t strong, but there is one.

I do want to emphasize that what’s being tracked here is ‘relative’ price performance over the next five trading days, not absolute price performance.  That means if all of the stocks went down over the next five trading days, the best relative performance is the stock with the smallest price decrease.  But if one or more of the stocks went up, the best relative performance is the stock with the greatest price increase.

We’ll look at variations of this indicator in future posts to see if we can do better, but this is encouraging for a first attempt.

 

 

 

Volume Based Indicator Analysis Part 2

As I mentioned in my earlier post, there were several places where I could have taken one of a number of directions, but I chose just one and moved on. In this post I’m going to go over some of the other experiments I’ve performed since then.
Recall that I used a weighting factor, where the most recent date received a maximum weight (15), and the weight for each date prior to that was decremented by one per day. This choice of weights was arbitrary, so in this post I’m going to discuss the effect of choosing these weights differently.
The alternative weight schemes I’m going to cover are:

  1. Multiplying each weight by a constant for earlier dates
  2. Choosing a date other than the most recent one to have the maximum weight
  3. Use a constant weight for the most recent N dates, then multiply the weight by a factor for earlier dates
  4. Use a constant weight for the most recent N dates, and don’t use any earlier dates

I actually tested quite a few more, but so far these have the best results so I’ll only go over them.

 

This was the result of the original experiment that we’re trying to beat:

volume_based_indicator_nov_2016_

For the first experiment, the best result came from multiplying the weighting factor by 0.85 each day. So the most recent date was weighted 15, one day prior was 12.75, two days prior was 10.8375, etc. Here’s a graph of the results:

volume_based_indicator_dec_2016_g0-85

We’ll compare this directly to the earlier results later, but if you’re thinking it’s not much different, you’re right. There’s a slightly higher peak, but for the most part the results are very similar.
Moving on to the second experiment, choosing a date other than the most recent, it turns out that slightly better results are obtained by giving the largest weight to two days ago, rather than the most recent date:

volume_based_indicator_dec_2016_2nddaypeak

For the third experiment, we’ll use the same maximum weight for the most recent three days, then apply a multiplication factor of 0.7 for each day prior to that:

volume_based_indicator_dec_2016_const3days

Finally, for the fourth experiment, we’ll weight the four most recent days equally, and assign a zero weight to all days prior to that:

volume_based_indicator_dec_2016_4days

And now for the denouement, we’ll look at all five of them together:

volume_based_indicator_dec_2016_all

The most surprising thing to me about the above graph is how consistent it is. I spent a lot of time tweaking each of these and kept just the one that did the best. And they each have pretty much the same distribution.
I’ll probably spend time looking for other ways to tweak this to see if I can find significantly better results, but it’s possible this is all the stock prediction information content there is in the combination of trading volume multiplied by percentage change.