Part 3

So here it is. I think that trade effectively, consistently and profitably you have to really know your instrument. Intimately. Personally. Like a spouse, sibling or best friend. If you don’t have this familiarity, you won’t have the instinct or intuition needed to act when you should or to sit tight when doing nothing is the right thing. So if you trade oil you better know a little something about the global oil market. If you trade an equities index, you really need to know what drives the underlying stocks which make up that index.

You also should know a little something, in my oh-so-humble opinion, about the technical structure of the market you trade. In formal economics, market structure refers to the firms which produce an identical product. Though for this specific context, structure refers to the participants in a given market and their observable activity. In other words, I think knowing something about how the participants actually execute trades has real merit. Trades made, or ticks, are the building blocks of a our kind of market structure. And like with any physical structure such as a car or skyscraper, some insight into how a thing is built can go far toward helping you develop or refine that understanding of and intuition for a market. I’m talking here about the gut instinct needed to become the best trader you can be.

In “real” research, this could be classified as a longitudinal study, meaning one that spans multiple points in time with the same subjects. It’s a study I revisit every few months. Over the last 18 months, there have been some striking changes to the structure of most of the instruments in this study and so I thought it was time to freshen this one up. If you have not had a chance to read parts one and two, you may want to go ahead and at least skim them now. Go ahead, we’ll wait.

Back already? Good. Let’s get started.

Methodology: As in the previous two parts,  I collected a set of recent RTH tick data for each of the instruments, then I broke that tick data down by trade size. Said another way, I sort though each and every transaction for the time period and toss each of them into a bucket according to the size of the tick. If this is not clear there’s an elaboration in the methodology section of the original study (part 1) down there at the bottom.

I’ll discuss this a a bit in each instrument section, but the first thing that is worth noting is that in most cases overall volume is down from previous published samplings. Because I wanted to keep the sample size very close to the same for each instrument for an apples-to-apples comparison, I had to vary the number of days included in the samples. In the previous installments, all instruments had the same number of sessions.

So now as before, for each instrument the top 10 trade sizes – listed by frequency – are shown. Also as before, for additional color and comparison, I’ve also included data for some notable big trades.

Period: Varies, see each instrument section below.

Instruments in Study*: ES, NQ, YM, CL, 6E, ZB.

 

[iconbox title=”Results – ES” icon=”x-office-spreadsheet.png”]

For ease of comparison, the values are expressed as part 3 versus part 2.

  • Sample period: August 27 – September 12 2014 (13 sessions vs 9 sessions)
  • Total ticks in sample: 2,979,388 vs 2,937,238
  • Number of unique trade sizes: 142 vs 505
  • Percentage of ticks in the 10 most frequent trade sizes: 93.55 vs 92.76%
Lot Size     Frequency      % of Ticks     Change from Part 2
1            1498590        50.30%         -5.44%
2            566412         19.01%         +6.67%
3            209165         7.02%          +0.54%
5            159050         5.34%          +0.54%
4            133280         4.47%          +0.95%
10           57631          1.93%          -0.65%
6            54413          1.83%          +0.17%
8            40296          1.35%          +0.27%
7            39408          1.32%          +0.41%
9            28969          0.97%          N/A

  • Notable Big Lot Trades
Lot Size     Frequency      % of Ticks     Change from Part 2
50           5694           0.19%          -0.06%
100          1577           0.05%          -0.03%       
200          224            0.01%           0.00%
250          271            0.01%           0.00%
500          0              0.00%           0.00%
1000         0              0.00%           0.00%

Discussion: Wow. Overall session volumes are definitely down. Both samples yielded nearly 3 million trades though in part 2 the sample included 31% fewer sessions. What else? Most notable are 2 factors above: 2-lots are gaining in popularity to be sure and seemingly at the expense of 1 lots;  and also the conspicuous absence of very large lots. As a long time ES trader, in years past 500 and 1000 lots were blue moons and comets, known but rarely seen. Now large lots are in same class as unicorns, UFOs, Sasquatch and Nessie. Relegated largely to myth.

OK, so that’s interesting and all, but what’s it mean? Well, to me what is for sure is that the ES market participants have changed, the level of participation has changed, the ways and means used to participate has changed as well. Reasonable traders can disagree, but I believe that effectively zero trades are hand-executed by large (sometimes called Other Time Frame in TPO parlance) participants. Instead, orders are executed algorithmically now (not talking about HFT, that’s another issue altogether).

Why is that? Well, as far as I can figure there is nothing but informational disadvantage for a large participant with 1000 lots to trade to place a 1000 lot order. To this participant’s informational “enemies” the participant has just announced their presence, made their intentions known and opened themselves up to order book games and possibly disadvantageous prices. I mean, why would you ever do this in a world where 500 2-lot orders (for example) can be executed just as about as efficiently as a single 1000 lot order? With 500 2-lots executed at a tight range of prices over a small amount of time, our large participant can achieve a desired theoretical average price and get they business done without really ever being noticed. All on the down-low. We’re talking special forces stealth.

Lastly, when it comes to the advantages of small lots, you have to understand the exchange’s matching engine has a God-like influence on how trades are appear on the tape. In the case of the CME (and most other futures around the world) we can only know how trades were filled, not how they were ordered. You may not even be able to know anything about an exchanges matching system, though some exchanges are pretty transparent about their matching algorithms (CME in particular). Regardless of whether I like how trades are matched, as a quantitative trader I need to find edges in the data I have, not the data I wish I had. But I digress (a little)…

I also suspect some of the decrease in 1-lots and increase in 2-lots may be due to many small traders finally understanding the mathematical disadvantage of 1-lot trading. Meaning the outcome of a 1-lot trade is binary. You win or you lose. You can never scratch on a net basis (because of trading costs), and you have no way to manage your risk other than not trading at all. That and 1-lots require your entries (and exits too) to be inspired works of art and science, precisely and perfectly timed. First time. Every time.

Like before, I still think it remains a #BIGWIN for the little trade. Over time the numbers show that volume has been skewing toward smaller lots (less than 10) in the ES. I’ve said it before and I’ll say it again, volume is volume. Now more than ever you should consider decreasing reliance on seeing big trades as an edge. Maybe instead watch for volume surges of all sizes to detect possibly opportune market activity.

[/iconbox]

 

[iconbox title=”Results – NQ” icon=”x-office-spreadsheet.png”]

For ease of comparison, the values are expressed as part 3 versus part 2.

  • Sample period: September 3 – September 12 2014 (8 sessions vs 9 sessions)
  • Total ticks in sample: 893,092 vs 829,719
  • Number of unique trade sizes: 48 vs 86
  • Percentage of ticks in the 10 most frequent trade sizes: 99.47 vs 99.34%
Lot Size     Frequency      % of Ticks     Change from Part 2
1            566678         63.45%         -17.32%
2            178613         20.00%         +11.13%
3            58253          6.52%          +2.76%
4            40498          4.53%          +2.73%
5            24590          2.75%          +1.03%
6            6161           0.69%          -0.31%
7            4569           0.51%          +0.08%
10           3553           0.40%          +0.01%
8            3428           0.38%          -0.01%
9            2000           0.22%          +0.01%

  • Notable Big Lot Trades
Lot Size     Frequency      % of Ticks     Change from Part 2
20           283            0.03%          -0.03%
50           28             0.00%          -0.01%       
100          5              0.00%          0.00% 

Discussion: Here we see a similar pattern with small trades on the rise, though in the NQ overall volume is pretty much the same as we saw part 2. Not the big drop we’ve seen in the ES, 8 versus 9 sessions to achieve a comparable sample size.  And also like with the ES, what I find striking is the biggish decrease in diversity of the trade sizes. The number of unique trade sizes is nearly half of what is was in our previous NQ sample. And check out the big increase in 2-lots with even more dramatic once drop in 1-lots. Hole-eeee cow.

OK, so again that’s cool and all. But so what? Well, to me the most likely explanation is the Rise of the Machines, Terminator style. If you know anything about engineered systems, you know the archenemy of reliability is complexity. So systems are generally engineered to be as simple as possible, and no simpler (Einstein said that). One of the ways for an algorithmic trading system to reduce complexity is to favor a small number of trade sizes. Again, there is no way (for me) to know for sure, but based on the evidence I can observe, I have to conclude that the order flow is dominated now more than ever by the machines, and by fewer distinct algorithms if not fewer distinct participants.

Still a #BIGWIN for the small trade, and for decreasing diversity.  As in the ES, large trades are now practically legend. All the action is concentrated around a small set of sizes, 1-5 lots specifically.

[/iconbox]

 

[iconbox title=”Results – YM” icon=”x-office-spreadsheet.png”]

For ease of comparison, the values below are expressed as part 3 versus part 2.

  • Sample period: August 29 – September 12 2014 (11 sessions vs 9 sessions)
  • Total ticks in sample: 530,495 vs 533,764
  • Number of unique trade sizes: 33 vs 41
  • Percentage of ticks in the 10 most frequent trade sizes: 99.75% vs 99.87%
Lot Size     Frequency      % of Ticks     Change from Part 2
1            364978         68.80%         -17.47%
2            93309          17.59%         +9.40%
3            33616          6.34%          +4.11%
4            20574          3.88%          +2.52%
5            9467           1.78%          +0.64%
6            2543           0.48%          +0.21%
7            1645           0.31%          +0.18%
8            1425           0.27%          +0.15%
10           818            0.15%          +0.05%
9            791            0.15%          +0.15%

  • Notable Big Lot Trades
Lot Size     Frequency      % of Ticks     Change from Part 2
20           58             0.12%          +0.11%

 

Discussion: Hmmm… decreasing overall volume, decrease in size diversity, significant increase in small trade sizes and the conspicuous decrease in 1-lots. What could it all mean? Well, I could probably just copy-and-paste the discussion from the ES and NQ above, but I won’t. What I will say is that the similarity of the structural changes mean that not only are the same dynamics in play for all 3 CME index futures, but I might even go so far as to say that the same participants are creating these shared dynamics. If it’s really true, and this I leave for you to see, good reader, then an edge which works in the ES, NQ or YM might work in all 3 of these instruments. Hint, hint.

So suffice to say “ditto above.” Small trades rule. All others drool, as we used to say in junior high. ;-}

[/iconbox]

 

[iconbox title=”Results – CL” icon=”x-office-spreadsheet.png”]

For ease of comparison, the values below are expressed as part 3 versus part 2.

  • Sample period: August 25 – September 12 2014 (15 sessions vs 9 sessions)
  • Total ticks in sample: 1,557,716 vs 1,565,664
  • Number of unique trade sizes: 41 vs 83
  • Percentage of ticks in the 10 most frequent trade sizes: 99.78% vs 99.75%
Lot Size     Frequency      % of Ticks     Change from Part 2
1            1344733        86.33%         -0.36%
2            142037         9.12%          -0.93%
3            32600          2.09%          +0.02%
4            14404          0.92%          -0.28%
5            10346          0.66%          -0.22%
6            3219           0.21%          -0.02%
10           2446           0.16%          0.00%
7            1800           0.12%          -0.01%
8            1524           0.10%          -0.02%
9            1104           0.07%          -0.01%

  • Notable Big Lot Trades
Lot Size     Frequency      % of Ticks     Change from Part 2
20           352            0.02%          0.00%
50           115            0.01%          0.00%       
100          3              0.00%          0.00% 

 

Discussion: Per-session volume is down by a pretty substantial amount but at the same time the breakdown of trade sizes is for all intents and purposes goes unchanged. There were some variances, as you can see, but they are so small as to be irrelevant, methinks. Unlike the equity indexes, I think that what this shows is that the participants in the WTI market are largely the same as when part 2 was written, there is just fewer of them or they are just participating less overall. It’s also quite possible that the change in volume was due to substantial geopolitical issues in play at this time. As we all know the energy market is particularly sensitive to those.

In any case, as before, tiny trades still trump the tape.

[/iconbox]

 

[iconbox title=”Results – 6E” icon=”x-office-spreadsheet.png”]

For ease of comparison, the values below are expressed as part 3 versus part 2.

  • Sample period: August 7 – September 12 2014 (27 sessions vs 9 sessions)
  • Total ticks in sample: 1,400,985 vs 1,366,073
  • Number of unique trade sizes: 60 vs 80
  • Percentage of ticks in the 10 most frequent trade sizes: 99.12 vs 99.56%
Lot Size     Frequency      % of Ticks     Change from Part 2
1            804209         57.40%         -17.28%
2            353934         25.26%         +11.64%
3            86694          6.19%          +2.35%
4            55392          3.95%          +1.50%
5            37049          2.64%          +0.42%
8            21950          1.57%          +0.27%
6            14240          1.02%          +0.33%
7            8536           0.61%          -0.08%
10           3880           0.28%          +0.10%
9            2822           0.20%          +0.09%

  • Notable Big Lot Trades
Lot Size     Frequency      % of Ticks     Change from Part 2
20           629            0.04%          +0.01%
50           76             0.01%          +0.01%       
100          0              0.00%          0.00%

 

Discussion: Per-session volume in this sample was way, way down. It took 3x as many sessions to achieve a similar sample size. Of course, lower volume at this moment in time could also be geopolitically influenced, just as with CL. But still, as in the equities futures the 2-lot is up and the 1-lot significantly down, as was overall diversity of trade size. Again, I interpret this as a sea change in the market participants, or at the very least a sea change in how and how much they participate. At the bottom line though it was once more a #BIGWIN for the atomic-size trade.

[/iconbox]

 

[iconbox title=”Results – ZB” icon=”x-office-spreadsheet.png”]

For ease of comparison, the values below are expressed as part 3 versus part 2.

  • Sample period: August 30 – September 12 2014 (11 sessions vs 9 sessions)
  • Total ticks in sample: 640,147 vs 605,377
  • Number of unique trade sizes: 90 vs 217
  • Percentage of ticks in the 10 most frequent trade sizes: 92.66 vs 96.61%
Lot Size     Frequency      % of Ticks     Change from Part 2
1            297519         46.48%          +7.61%
2            102940         16.08%          -19.90%
3            53840          8.41%           +1.99%
4            44567          6.96%           +1.84%
5            36700          5.73%           +1.51%
6            20200          3.16%           +0.62%
12           9856           1.54%           +0.81%
10           9759           1.52%           +0.00%
8            8996           1.41%           +0.72%
7            8774           1.37%           +0.71%

  • Notable Big Lot Trades
Lot Size     Frequency      % of Ticks     Change from Part 2
20           2237           0.35%          -0.07%
50           611            0.00%          -0.08%       
100          188            0.00%          -0.03% 

 

Discussion: What sticks out like the proverbial sore thumb with the 30-year US Treasure bond is its polar inverse change from the other instruments. One-lots have increased while 2-lots have decreased, all the while the overall volume the same, mas o menos. Go figure. To be frank I’m really not sure at all what to make of this as I don’t regularly watch either the 10-year or the 30-year bond. Obviously, something has changed though in terms of participants, the nature of their participation, or perhaps both. Some additional digging may be in order…

[/iconbox]

 

Alright. Enough numbers already. If you’ve made it this far and your eyes are still free of glaze, I’d say a tip ‘o the Stetson and a hearty congratulations are in order. Maybe even a cold one, if it’s that time of day. It’s thick stuff, this kind of research. Hopefully this was interesting, informative and maybe even entertaining for you.

Clearly, the original premise that big sizes, in the aggregate, simply don’t move a market has even more ooomph now than in the past. Don’t get carried away by the apparent audacity of that statement. Of course big size can clean out the order book and move price very far very fast. But the point here is that now even more than ever, swing after swing, trend after retracement after trend, the little trade gets the big jobs done.

Question is, how will you turn this knowledge to your advantage? Until next time, trade ’em well.

 

*TF was excluded from this installment because of technical issues.

[hr]

Part 2

I originally published this study back in late August 2011, and at the time it was met with quite a bit of controversy. Since that time it has been read thousands of times by traders all over the globe. Not that I really expect influence the at-large trading community’s beliefs about market structure, but I still read every week on blogs, forums and twitter about how a trader can tell what the big boys in Instrument X are doing because of all the monstrous lots coming across the tape.

I asked the question in the original study (you can read the unedited original below) and I’ll ask it again – does size move markets? Intuition, and maybe even common sense says “of course.” But the empirical evidence replies with an emphatic “not really.”

Things get interesting when intuition/conventional wisdom and evidence so strongly disagree, so I decided to gently kick the hornet’s nest again and repeat the study. But this time I’ve expanded the coverage to additional instruments.

Methodology: Just as before, I collected the most recent 9 days’ worth of RTH tick data for each of the instruments below, then I broke that tick data down by trade size. That is, I sort though each and every transaction for the time period and toss each of them into a bucket according to the size of the tick**. If this is not clear there is additional elaboration in the methodology section of the original study down there a little ways.

Then, for each instrument the top 10 trade sizes – by frequency – are shown. This time though, for additional color, I’ve also added data for the notable big trades.

Period: The date range for this study for all instruments is January 3-14, 2012.

Instruments in Study: ES, NQ, YM, TF, CL, 6E, ZB

Here we go…

[iconbox title=”Results – ES” icon=”x-office-spreadsheet.png”]

  • Total ticks in sample: 2,937,238
  • Number of unique trade sizes: 505
  • Percentage of ticks in the 10 most frequent trade sizes: 92.76%
Lot Size     Frequency      % of Total Ticks
1            1637092        55.74%
2            362344         12.34%
4            198892         6.77%
3            190286         6.48%
5            128818         4.39%
10           75871          2.58%
6            48870          1.66%
8            31840          1.08%
7            26749          0.91%
15           23779          0.81%
  • Notable Big Lot Trades
Lot Size     Frequency      % of Total Ticks
50           7211           0.25%
100          2375           0.08%
200          326            0.01%
250          271            0.01%
500          36             0.00%
1000         6              0.00%

Conclusion: It’s still a big win for the little trade. Small lots at high frequency move the ES. 1 – 4 lots dominate the trade. The most frequent big-lot trade could only muster an measly quarter of a percentage of the total trades.[/iconbox]

[iconbox title=”Results – NQ” icon=”x-office-spreadsheet.png”]

  • Total ticks in sample: 829,719
  • Number of unique trade sizes: 86
  • Percentage of ticks in the 10 most frequent trade sizes: 99.34%
Lot Size     Frequency      % of Total Ticks
1	     670133	    80.77%
2	     73635	    8.87%
3	     31200	    3.76%
4	     14911	    1.80%
5	     14306	    1.72%
6	     8316	    1.00%
7	     3599	    0.43%
10	     3240	    0.39%
8	     3201	    0.39%
9	     1768	    0.21%
  • Notable Big Lot Trades
Lot Size     Frequency      % of Total Ticks
20           480            0.06%
50	     48	            0.01%
100	     5	            0.00%

Conclusion: An even stronger win for the small trade. The most frequent big-lot trade only accounts for six one-hundredths of a percent of the total trades.[/iconbox]

[iconbox title=”Results – YM” icon=”x-office-spreadsheet.png”]

  • Total ticks in sample: 533,764
  • Number of unique trade sizes: 41
  • Percentage of ticks in the 10 most frequent trade sizes: 99.87%
Lot Size     Frequency      % of Total Ticks
1            460463         86.27%
2            43701          8.19%
3            11908          2.23%
4            7249           1.36%
5            6062           1.14%
6            1436           0.27%
7            686            0.13%
8            648            0.12%
10           513            0.10%
9            331            0.06%
  • Notable Big Lot Trades
Lot Size     Frequency      % of Total Ticks
20           58             0.01%

Conclusion: Same as the NQ, overwhelmingly dominated by small trades.[/iconbox]

[iconbox title=”Results – TF” icon=”x-office-spreadsheet.png”]

  • Total ticks in sample: 735,118
  • Number of unique trade sizes: 56
  • Percentage of ticks in the 10 most frequent trade sizes: 99.82%
Lot Size     Frequency      % of Total Ticks
1            606389         82.49%
2            84453          11.49%
3            19292          2.62%
4            10132          1.38%
5            7283           0.99%
6            2325           0.32%
7            1467           0.20%
10           981            0.13%
  • Notable Big Lot Trades
Lot Size     Frequency      % of Total Ticks
20           146            0.02%
50           1              0.00%

Conclusion: Ditto the NQ and YM. The TF is overwhelmingly dominated by small trades.[/iconbox]

[iconbox title=”Results – CL” icon=”x-office-spreadsheet.png”]

  • Total ticks in sample: 1,565,664
  • Number of unique trade sizes: 83
  • Percentage of ticks in the 10 most frequent trade sizes: 99.75%
Lot Size     Frequency      % of Total Ticks
1	     1357235        86.69%
2            128208         8.19%
3            32473          2.07%
4            18823          1.20%
5            13822          0.88%
6            3594           0.23%
10           2434           0.16%
7            2023           0.13%
8            1835           0.12%
9            1196           0.08%
  • Notable Big Lot Trades
Lot Size     Frequency      % of Total Ticks
20           304	    0.02%
25	     161	    0.01%
50	     36	            0.00%
100	     4	            0.00%

Conclusion: Ditto the above yet again.[/iconbox]

 

[iconbox title=”Results – 6E” icon=”x-office-spreadsheet.png”]

  • Total ticks in sample: 1,366,073
  • Number of unique trade sizes: 80
  • Percentage of ticks in the 10 most frequent trade sizes: 99.56%
Lot Size     Frequency      % of Total Ticks
1            1020162        74.68%
2            186113         13.62%
3            52435          3.84%
4            33491          2.45%
5            30278          2.22%
8            17780          1.30%
6            9421           0.69%
7            6462           0.47%
10           2396           0.18%
9            1462           0.11%
  • Notable Big Lot Trades
Lot Size     Frequency      % of Total Ticks
20           357            0.03%
25           232            0.02%
50           21             0.00%
100          5              0.00%

Conclusion: Once more… say it with me… “ditto.”[/iconbox]

 

[iconbox title=”Results – ZB” icon=”x-office-spreadsheet.png”]

  • Total ticks in sample: 605,377
  • Number of unique trade sizes: 217
  • Percentage of ticks in the 10 most frequent trade sizes: 96.61%
Lot Size     Frequency      % of Total Ticks
1            235328         38.87%
2            217828         35.98%
3            38756          6.40%
4            30999          5.12%
5            25533          4.22%
6            15406          2.54%
10           8311           1.37%
12           4627           0.76%
8            4206           0.69%
7            4002           0.66%
  • Notable Big Lot Trades
Lot Size     Frequency      % of Total Ticks
20           2518           0.42%
25           1006           0.17%
30           468            0.08%
40           236            0.04%
50           495            0.08%
60           95             0.02%
100          157            0.03%

Conclusion: Though the most-frequent trade sizes are small, the ZB shows more diversity in the trade size distribution. There could be any number of reasons for this, but the point here is that this is one instrument where you might be able to reliably glean some relevant information about the order flow from the size and frequency of the individual trades in a session. The outliers may be important here, in other words.[/iconbox]

 —

So I’ll ask the question again. Based on the evidence, do you still think it’s true that size moves markets? Overall, the only conclusion the evidence supports – in my analysis – is not really. In most cases, big trades just don’t happen frequently enough to be significant in the overall price action, even at important prices. And let me pose what I think are even more interesting questions than the study’s main hypothesis…

What if the big trades are often just a red herring? What if the PhDs who created the algorithms that execute most of the volume in the futures markets today knew you were watching for big trades to forecast what is likely to happen next in the price action? What if every now and then they executed big trades to obfuscate or throw you off the scent of their real intentions?

Paranoia? Or a new edge? To quote the late and great lyricist Kurt Cobain from Nirvana… just because you’re paranoid don’t mean they’re not after you.

Trade ’em well, amigos.

[hr]

Part 1

I’ve been asked quite a bit lately about adding options in the Acme packs to filter out trades below certain size, and the rationale for this request is almost unanimously based on the assumption that size moves markets.

But is that true? Or maybe a better way to frame the question is how true is it?

Well, lets find out. I have a study that I run periodically to analyze how volume is behaving. And I’ve seen it change pretty significantly even over the last couple of years. Want to know what it looks like now?

Methodology: I collect the most recent 10 days’ worth of RTH tick data for my selected instruments, then I break that tick data down by trade size. That is, I sort though each and every transaction for the time period and toss each of them into a bucket according to the size of the tick**.

If that’s not clear, here’s an analogy. Imagine a giant pile of trade receipts on a concrete floor with you standing waist deep in them. All around you are plastic 5 gallon buckets, each labelled with a different number – the size of a trade. One bucket says 3, another says 4, and another says 425 and so on. Your job is to toss each trade receipt (tick) in the correct bucket according to how many contracts were traded on that tick, and finally add up the number of ticks in each bucket. This is the frequency column in the tables below.

Sound really tedious? Well it is, and that’s why they invented computers. I have some software that does the sorting for me in under a second. Standing around on a cold concrete floor tossing little bits of paper into buckets all day sucks.

Period: The time period I choose for this study is August 15-25, 2011.

So here we go…

[iconbox title=”Results – ES 09-11″ icon=”x-office-spreadsheet.png”]

  • Total ticks in sample: 5,241,972
  • Number of unique trade sizes: 535
  • Percentage of ticks in the 10 most frequent trade sizes: 92.59%
Lot Size     Frequency      % of Total Ticks
1            3002240        57.27
2            728580         13.90
3            344016         6.56
5            248496         4.74
4            170145         3.25
10           104831         2.00
6            78389          1.50
8            73530          1.40
7            57614          1.10
9            45666          0.87
---------------------------------
             4853507        92.59%

Conclusion: It’s a big win for the little trade. Small lots at high frequency move the ES. Trades of more than 10 contracts account for less than 8% of the ticks in the sample.[/iconbox]

[hr]

[iconbox title=”Results – NQ 09-11″ icon=”x-office-spreadsheet.png”]

  • Total ticks in sample: 1,401,118
  • Number of unique trade sizes: 111
  • Percentage of ticks in the 10 most frequent trade sizes: 99.23%
Lot Size     Frequency      % of Total Ticks
1            1037545        74.05
2            197507         14.10
3            59180          4.22
4            32531          2.32
5            25563          1.82
6            12656          0.90
8            8680           0.62
7            6669           0.48
10           5785           0.41
9            4196           0.30
---------------------------------
             1390312        99.23%

Conclusion: It’s an even bigger win for the little trade. Small lots at high frequency move the NQ. No doubt about it.[/iconbox]

[hr]

[iconbox title=”Results – CL 10-11″ icon=”x-office-spreadsheet.png”]

  • Total ticks in sample: 1,203,814
  • Number of unique trade sizes: 85
  • Percentage of ticks in the 10 most frequent trade sizes: 99.65%
Lot Size  Frequency      % of Total Ticks
1         1027656        85.37
2         106535         8.85
3         25081          2.08
4         17962          1.49
5         10289          0.85
6         5470           0.45
10        2004           0.17
7         1997           0.17
8         1477           0.12
9         1081           0.09
---------------------------------
          1199552        99.65%

Conclusion: Again, no doubt about it. Small lots at high frequency move the CL.[/iconbox]

[hr]

**This study was performed using ticks from DTN IQ Feed, which is not the even the “rawest” feed possible. As I understand it the “rawest” feed comes directly from the CME, which as of about 18 months ago is itself slightly aggregated. So in reality, the small lots are probably even more frequent than this study shows, though I have no way to quantify it.