A Theory of Market Action: Part IV - The Theory

[madspeculator 10]

So far, we have made a case for a good theory to provide a framework for trading model research (Part I of this series), and provided a survey and critique of the existing theory of supply and demand (Part II of this series), and the theory of value (Part III of this series). In this final post, we discuss the theory of Market Action, and tools that can be created to measure it.

 

Theory of Markets Action

 

The “Kyle” model proposed in the field of market microstructure motivates this theory of Market Action. However, the reader should be aware that these two models are vastly different.

 

The theory of Market Action is based on the following tenants (the conditions and consequence of each tenant are listed below it):

1.

There are two categories of traders: liquidity providers (market makers), and liquidity takers (market takers)

.

a. Existence of two categories does not prevent individual market participants from switching between being a liquidity provider and liquidity taker based on their trading needs;

b. A consequence of this tenant is that competition exists only within members of each category and never between categories.

2.

Both the category of traders are profit motivated

.

a. There is no quest for value in the market.

3.

Liquidity providers react to order-flow from liquidity takers

.

a. Liquidity takers, on the other hand, trade for variety of reasons (“Mill Process”)

b. This, along with the next tenant, is the reason for price movements.

c. This implies that liquidity providers might capitalize at times of weak order-flow from liquidity takers to make a profit (e.g. triggering stops)

d. However, when there is a strong order flow, going against such order flow will be to the detriment of the liquidity provider.

4.

To liquidity providers, inventory management is of paramount importance

.

a. Inventory poses risk to liquidity providers; so, it is in the interest of liquidity providers to balance their inventory as soon as possible.

b. The areas of accumulation and distribution are results of liquidity providers managing their inventory in response to anticipated or actual order-flow (from liquidity takers).

c. The formation of high volume price (PoC or HVP) is also the result of this inventory management process.

d. Liquidity providers usually increase their chance of balancing their inventory when price reaches certain points that are of interest to liquidity takers.

i. This is the reason for price hitting certain well-known targets like previous day open, high, low, close; high volume price, PoC, etc.

ii. However, competition within liquidity providers tends to destroy tradable patterns or prevent price from hitting those well-known targets.

 

There are four parameters to this theory; they are: demand and supply of liquidity provider, and demand and supply of liquidity takers.

 

Since this theory depends heavily on liquidity provider inventory, we need to explain what this “inventory” really is. This brief digression will help us explain and define the term “inventory”:

 

Let us assume that a trader needs to transact a large transaction. There are three ways this trader can accomplish her goal:

 

(i) By means of a market order. Since the trader’s transaction will move the market, the average price received by the trader for her transaction might not be optimal. So, this might not be the preferred method.

(ii) By means of a limit order. This is a little better than a market order, but the trader has to be content with not getting her entire order filled (if the market moves away, because other traders notice the “support” or “resistance”, then the order will not be completely filled). This is not an acceptable situation.

(iii) By means of some algorithm to execute this order (a.k.a algo trading or program trading). The providers of these algorithms, usually the big banks or hedge funds, guarantee complete “fill” of the traders’ orders at a “benchmark” price (the details of this are not essential for our present purpose). This methodology of trade execution is usually preferable to the large trader. These algorithms, in the process of filling a trader’s order, end up buying or selling more quantity that is really needed. This excess quantity, in the hands of the algo-trading providers, then becomes “inventory”. It is this excess inventory that the liquidity providers (in this case, the algo-trading providers, not the trade whose order is being executed) need to manage. [Note: We are not concerned about client orders that might in a bank’s book. We are only concerned about the excess inventory these banks acquire in the process of filling such client orders]

 

An astute reader will immediately recognize that the Wyckoff theory of markets is nothing but a sub-set of this theory – Wyckoffian parameters are either higher order (cause, effect, effort, result) or aggregate (demand and supply) of this theory’s parameters. This theory strives to explain a lot more of the observable phenomena in the markets, which remain unanswered by Wyckoff’s theory.

 

A note on the applicability of this theory: The author is confident that this theory can be applicable in all markets where T&S and volume information is dispersed. However, the author has tested this theory only in limit-order markets (an example of a limit-order market would be the ES on Globex; most of the non-option electronic markets are either limit-order markets or hybrid).

 

Tools of measurement

 

All existing tools discussed under Wyckoff section can be used to measure the theory’s parameters either in aggregate or indirectly (using second- and higher-order parameters).

 

Volume- and market-profile tools can be used to identify (measure) liquidity providers’ inventory adjustment zones (a second order parameter, which presents itself as a bulge in the profile). [Please note the first standard deviation from PoC has not practical value.] It must be noted that a profile could contain multiple inventory adjustment zones (double distribution days) or could include multiple inventory adjustment zones that occur around the same price range but at different times (i.e., the profile could show just one “value area” but in reality could be multiple inventory adjustment zones spread out temporally but in the same price range).

 

However, the advantage of this theory is that new tools can be developed to further one’s understanding of the market, and more specifically, the operations of the liquidity providers – a very valuable information to a trader. Three examples of such measuring tools are presented here:

 

1.

Reference is made to this thread by UrmeBlume

(UB). The author is not aware of any particular details of UB’s work, but UB’s explanation of his work makes the author believe that he could be measuring the activities of liquidity providers and liquidity takers.

2.

FulcurmTrader’s (FT) thread

gives the author the impression that FT is tracking the activities of liquidity providers.

3. The author’s own work presented below tries to estimate the demand and supply of both the liquidity providers and liquidity takers. By monitoring the demand and supply of liquidity providers, the author is able to notice the inventory adjustment process without the aid of the profile (which has a tendency to include a lot of noise, therefore less accurate – a major problem which is a result of the profile’s construction technique). Measurements of parameters for different time frames and periods are captured in the attached charts (first two are 30-min charts, and the last two are day charts). NIP measures the inventory held by liquidity providers, and NOP measures imbalance in the liquidity takers’ order flow. The sum of these two parameters is the “real” order flow. All other volume is due to the activities of the algorithms (which the author considers to be noise). [As the motivation for showing these charts is to provide examples of possible new methods of measuring the theory’s parameters, the author will refrain from analyzing these charts, although such an urge is very much present.] A quick note on these charts: These charts were not handpicked. The author used whatever data was available on his laptop to create these charts for this article.

 

Concluding comments

 

As can be seen, the extent to which one can research and extract parameters depends on the quality of the theory of markets one subscribes to. This is by no means the one and only applicable theory to market action. What is important is having a theory as a base for one’s research. Should you not have a theory that challenges you to go that extra-mile to generate your edge, please feel free to use the one presented here, of course, only if you are comfortable with its tenants.

 

The author does believe that the tenants of this theory are strong enough that it will survive the test of time, and that new tools will be invented in the future that will provide more accurate measurements of the parameters presented in this theory, thereby making the present day tools outdated.

 

If one decides to undertake research based on the above-mentioned theory, please note that one cannot split ‘bid’ and ‘ask’ as is proposed/done in the industry today in order to measure all of this theory’s parameters. One has to perform the analysis from the perspective of a liquidity provider (Ask the question: If I were to end up with inventory as a result of me trading a client’s order, how will I trade? This, I hope, will lead you to an acceptable answer, as it did to me). Once one figures this out, the land is theirs to conquer!

 

It is suffice to say that there is no holy grail in trading, and this author doesn’t consider the author’s tool to be the holy grail either. As was said earlier in this article, the lack of accuracy in measuring a theory’s parameters will always make trading a game of probabilities; a good tool, however, can give one the much-needed “edge”.

 

Hope your research helps you create a new tool of measurement, which might result in you harvesting multiple trading systems.

 

A short note on why I wrote this article

 

My training in both hard and soft sciences, and my exposure both to the “buy-side” and “sell-side” of the business made me evaluate, question and/or reject the theories that underlie the publicly available measuring tools. When it was time for me to develop a “quantitative” model that could be used to generate trading strategy(s), I had to create a theory that was capable of explaining all observable phenomena in the market. The knowledge I gained during this process was invaluable; I wanted to share that knowledge and provide a helping hand to those interested in “quantitative research” by giving them a stepping-stone for their work – a stepping-stone that was not available to me when I started my work.

 

I am not a vendor; I don’t plan to be one; and, no part of my work is for sale. Although I might be able to answer questions related to this proposed theory or the research process, I am, due to various constraints, unable to answer inquires related to (or disclose) the construction of my measuring tool.

 

For those interested in the tools I use to perform my research: I use the statistical package R. I do my research using R for two reasons: (1) it has good data manipulation and visualization capabilities. The graphs were created using ggplot2 package in R. R also has the ability to connect to Gobi to visualize multiple-variable/multivariate data; and (2) it helps me perform statistical analysis on new trading strategies with ease.

 

Thank your for coming along for the ride; hope you got something useful from it.

[madspeculator 10]

I haven't figured out how to edit the post. So, I am posting a correction, although small, very important.

 

Under the "Tools of Measurement" Section in author's work, I said:

"The sum of these two parameters is the “real” order flow. All other volume is due to the activities of the algorithms (which the author considers to be noise)."

 

This should read:

 

The sum of these two parameters is the “real” order flow

imbalance

. All other volume is due to the activities of the algorithms (which the author considers to be noise)

or balanced trade

.

[Kiwi 16]

Interesting.

 

Have you yet done any work comparing the predictive efficacy of your current imbalance measures with other approaches?

[gooni 10]

These algorithms, in the process of filling a trader’s order, end up buying or selling more quantity that is really needed. This excess quantity, in the hands of the algo-trading providers, then becomes “inventory”.

 

As I suspect this may be key to determining when liquidity providers have accumulated excess inventory, would you mind explaining why there is the assumption that filling a large order may result in excess?

[BlowFish 10]

Great stuff. I am not sure where to start, I have a couple of observations and questions. Just for fun lets start from the end with practical application and work backwards:). Having accepted the validity of some of the core concepts you present in this model that does not seem inappropriate.

 

You write :-

The author is confident that this theory can be applicable in all markets where T&S and volume information is dispersed.

 

and

If one decides to undertake research based on the above-mentioned theory, please note that one cannot split ‘bid’ and ‘ask’ as is proposed/done in the industry today in order to measure all of this theory’s parameters.

 

This seems to strongly suggest to me that you are not using nay of the established algorithms (based on where a trade transacted compared to best bid/ask) to gauge 'net inventory from providers' and 'net orderflow from takers'? (as an aside shouldn't that be NIP and NOT? I have obviously not identified the acronyms correctly ).

 

Put another way am I right in thinking you do not require quote data for the algorithms you use to classify trades?

 

Cheers.

Edited January 27, 2010 by BlowFish

[madspeculator 10]

Interesting.

 

Have you yet done any work comparing the predictive efficacy of your current imbalance measures with other approaches?

 

Kiwi:

 

Yes, I have done some comparative studies to determine the "usability" of my measures. I use the term "usability" instead of "predictability" for the following reason: In trading, people use the word "predictability" to mean prices going in the direction they expect the price to go. However, I define "predictability" to mean either (a) price going in the expected direction; or (b) price meandering around the area where the price first started. The reason I define "predictability" as above is because this event, which I call "positives", provides very little risk to a trade. However, the "false positive" (price going in the opposite direction than what was predicted), in my mind, is the true measure of risk for a trading model; my risk management is a function of my false positive analysis.

 

Having said the above, my false positive percentages for raw measurement on delta relationships, value-area relationships, value-area and shape of profile relationships, and vwap-hvp (PoC) relationships hover around 50%, with vwap-hvp being around 45% (a lower false positive percentage is better). My measurements of imbalance gives me a false positive percentage in the low 30s. I used about 2 years of tick-data for this analysis.

 

Note: I did not overlay other statistical transforms to the raw measurements to determine false positive percentage. Although that is what practitioners do in the industry to get an "edge", I was not mining for a trading model, and hence was not in my interest.

 

Hope this answers your question.

[madspeculator 10]

As I suspect this may be key to determining when liquidity providers have accumulated excess inventory, would you mind explaining why there is the assumption that filling a large order may result in excess?

 

Gooni:

 

There is an old adage in the "street": "One has to sell some to buy some". This adage is true even for algos. If one is "selling some" to meet a client's buy order, that "selling some" becomes excess inventory.

 

Note: I also try to track how much, in the above example, is being bought. Sometimes, banks buy for themselves instead of buying for clients. I have no way of differentiating that, so I assume that all purchases are for the bank and proceed with my analysis (not a great assumption but seems to help my analysis).

[madspeculator 10]

Put another way am I right in thinking you do not require quote data for the algorithms you use to classify trades?

 

Blowfish:

 

In a tightly traded limit-order book market like ES on Globex, one doesn't need the quote data as long as one is also tracking indicative bids-asks quotes (indicative bids-asks quotes seldom happens during RTH in ES, unless there is a major event during RTH -- like FOMC announcements. These are helpful for overnight though).

 

In other markets like Nasdaq, which is a quote-driven market, monitoring quotes are helpful, but only for "jumps". Otherwise, the inherent delay between the T&S reporting and its corresponding quotes (in other words, quotes might have changed before the T&S for the previous quote is being sent out) might unnecessarily complicate the analysis. Just my opinion and experience!

[pmwhite 10]

Thank you for detailing your theory of market action. I have a few questions for clarification:

 

b. A consequence of this tenant is that competition exists only within members of each category and never between categories.

 

I'm trying to understand if this particular tenant is crucial to the whole of the theory, or is more of an observation based on empirical evidence. Does b lead to the implication that liquidity providers are represented solely in the limit order book and market orders represent mostly liquidity takers with the exception being when excess inventory is being disposed of by liquidity providers via market order? Essentially I'm asking, does placing a limit order that is not immediately marketable make the order placer a liquidity provider under your definition?

 

a. There is no quest for value in the market.

 

Is it possible that the profit motive by both categories of traders could unintentionally lead to a composite market quest for value? Does the profit motive of the operators necessarily disprove the value seeking hypothesis of markets? Could value/efficiency/utility result as an unintended result?

 

What is the horizontal scale on the charts you posted? Transactions?

 

Is the delta shown on the charts representative of the industry standard way of splitting bid and ask transactions?

 

I have more questions but this will do for now. Thanks in advance for your responses!

[BlowFish 10]

Pardon my ignorance but could you clarify what you mean by indicative? My understanding is that the term would be used for something like spot FX where you have access to a subset of the data sources and get quotes indicative of the average.

 

My goal is to make some progress towards a working algorithm! And I absolutely agree about matching time and sales with current quotes. Unless you are provided with quote changes absolutely synchronously with T&S that path is fraught with issues (as I have discovered)!

[clmacdougall 11]

I think the questions which I have for you may be too simplistic but if you could answer it I could more easily understand your writings.

How do you physically identify support or resistance in the now, via your market understanding. Or do you look to identify either?

Do you believe in or trade based upon a perception of value at all? Thanks in advance.

[madspeculator 10]

I'm trying to understand if this particular tenant is crucial to the whole of the theory, or is more of an observation based on empirical evidence. Does b lead to the implication that liquidity providers are represented solely in the limit order book and market orders represent mostly liquidity takers with the exception being when excess inventory is being disposed of by liquidity providers via market order? Essentially I'm asking, does placing a limit order that is not immediately marketable make the order placer a liquidity provider under your definition?

pmwhite please understand that a trader becomes a liquidity provider due to her actions: placing a limit order. It is not the around way around: A liquidity provider does not place a limit order. In other words, there is no "liquidity provider" or "liquidity taker" category until the trader's (who is category-less) action creates one. This is a very small distinction albeit a very important one.

 

Now, 1.b is a technicality that is required to make sure that a trader can be both a "liquidity provider" and a "liquidity taker" at the same time. If this were to be the case, then competition between the categories should not be present.

 

No, this should not affect your analysis.

 

Is it possible that the profit motive by both categories of traders could unintentionally lead to a composite market quest for value? Does the profit motive of the operators necessarily disprove the value seeking hypothesis of markets? Could value/efficiency/utility result as an unintended result?

Everything is possible. However, I am yet to find proof of such value. Also, new research in finance and economics is questioning the existence of value as we presently understand it.

 

What is the horizontal scale on the charts you posted? Transactions?

Yes, they are transactions.

 

Is the delta shown on the charts representative of the industry standard way of splitting bid and ask transactions?

Yes.

[madspeculator 10]

Pardon my ignorance but could you clarify what you mean by indicative? My understanding is that the term would be used for something like spot FX where you have access to a subset of the data sources and get quotes indicative of the average.

 

Very similar. If you look at the T&S data from CME for Globex, you will see a "I" indicating that this is not a true transaction but a indicative quote. I am not sure which data feeds expose this field, but I know one can extract this information from a FIX connection.

 

However, don't get bogged down by this if you are starting out. ES and other markets are tight enough that you should be able to proceed with your work without quote information. Even in other markets, the error in classification is going to be so small that it might not be worth the time and effort to get "perfect" data.

[madspeculator 10]

How do you physically identify support or resistance in the now, via your market understanding. Or do you look to identify either?

I don't "identify" support or resistance levels before hand. I look at order-flow and go with order flow. Support and resistance, according to me, is created when a large group of traders enter the market as per their trading system. This is reflected in the order-flow imbalance. Since I don't know which trading system is going to kick-in when, I don't want to be in the business of "identifying" support and resistance.

Do you believe in or trade based upon a perception of value at all?

This is a loaded question! I believe in trades based on value when it comes to arb. trading (of which index and other futures play an important part; a futures trader should understand how such trades are done and the impact such trades have on the market even if she is not going to do such trades). However, I don't believe in "value" as defined by the MP theory.

[clmacdougall 11]

I don't "identify" support or resistance levels before hand. I look at order-flow and go with order flow. Support and resistance, according to me, is created when a large group of traders enter the market as per their trading system. This is reflected in the order-flow imbalance. Since I don't know which trading system is going to kick-in when, I don't want to be in the business of "identifying" support and resistance.

 

This is a loaded question! I believe in trades based on value when it comes to arb. trading (of which index and other futures play an important part; a futures trader should understand how such trades are done and the impact such trades have on the market even if she is not going to do such trades). However, I don't believe in "value" as defined by the MP theory.

 

Would I be correct if I said that the basis of this type of trading is identifying momentum through order flow (commercial activity) and that in reality it leaves you not believing in any type of market structure. And if this is true, what context are you left to trade with other than searching for opposing momentum at which point you exit the trade.

I'm not trying to put it down, but to understand in simple terms what it is you are doing.

 

Do you believe in any type of market structure/context other than uncovering an orderflow imbalance, which aids you in selecting your trades?

[madspeculator 10]

Would I be correct if I said that the basis of this type of trading is identifying momentum through order flow (commercial activity) and that in reality it leaves you not believing in any type of market structure.

I'm not trying to put it down, but to understand in simple terms what it is you are doing.

 

Do you believe in any type of market structure/context other than uncovering an orderflow imbalance, which aids you in selecting your trades?

 

clmacdougall:

 

You used the word "market structure". I don't know how you define this word, but the way I define this word is thus: An existence of underlying structure in the market that can be determined. Further, as an extension to this definition, such market structures can only be determined ex-post since we are using the [past] data in our analysis.

 

Now that "market structure" has been defined, I will attempt to answer your question: Yes, I do believe in a market structure -- a structure where people trade for various reasons, and that order-flow captures such activity. Can I use it to "predict" future actions? No, I can't because I don't know what will happen in the future (please refer to my response to Kiwi's question on how I use these measurements). As you can see the observable supports and resistances in the market are due to people's actions.

 

And if this is true, what context are you left to trade with other than searching for opposing momentum at which point you exit the trade.

This part of your question make me think that you think traders automatically take off or put on trades when prices come to points of pre-identified support or resistance. Please correct me if my assumption is incorrect. I know of no professional trader who does that. In fact, they all look at order-flow at those points to determine their course of action. I am doing nothing different.

 

NOTE: I don't have to wait for the opposing order flow imbalance to come in in-order to take off my trades; I can take them off when the existing order flow imbalance weakens. To paraphrase Steidlmayer, it is the order flow imbalance that moves markets.

 

Hope this answers your question.

[BlowFish 10]

Very similar. If you look at the T&S data from CME for Globex, you will see a "I" indicating that this is not a true transaction but a indicative quote. I am not sure which data feeds expose this field, but I know one can extract this information from a FIX connection.

 

However, don't get bogged down by this if you are starting out. ES and other markets are tight enough that you should be able to proceed with your work without quote information. Even in other markets, the error in classification is going to be so small that it might not be worth the time and effort to get "perfect" data.

 

Ahh I see, I did not realise there was an 'indicator' column further investigation of other flags that FIX provides reveal....

 

The Price column displays the price at which the transaction occurred. Some prices are followed by an "A" after the price indicating an offer/ask price which occurred at or below the previous last. A "B" after the price indicates a bid which occurred at or above the previous last. Prior to regular session hours, an indicative price may be displayed with a volume of zero in the size column.

 

I presume you look at A or B to determine whether buy side/sell side liquidity has been taken?

[BlowFish 10]

 

If one decides to undertake research based on the above-mentioned theory, please note that one cannot split ‘bid’ and ‘ask’ as is proposed/done in the industry today in order to measure all of this theory’s parameters. One has to perform the analysis from the perspective of a liquidity provider (Ask the question: If I were to end up with inventory as a result of me trading a client’s order, how will I trade? This, I hope, will lead you to an acceptable answer, as it did to me). Once one figures this out, the land is theirs to conquer!

 

 

The obvious answer (well to me so it might be half baked) is that they would try to offset the position to other side liquidity takers. If that is not possible they would demand inventory from other side liquidity providers. I guess one might assume the very fact they have ended up with an inventory imbalance would mean they might swiftly move to step two, demanding liquidity?

 

Isn't this a fundamental issue when there is an intermediary the intent liquidity requirements of the original participant is obscured?

[pmwhite 10]

pmwhite please understand that a trader becomes a liquidity provider due to her actions: placing a limit order. It is not the around way around: A liquidity provider does not place a limit order. In other words, there is no "liquidity provider" or "liquidity taker" category until the trader's (who is category-less) action creates one. This is a very small distinction albeit a very important one.

 

Now, 1.b is a technicality that is required to make sure that a trader can be both a "liquidity provider" and a "liquidity taker" at the same time. If this were to be the case, then competition between the categories should not be present.

 

Thanks for your reply. The definition of liquidity provider and taker is consistent with what I have read in the market microstructure literature. If the definition hinges on the type of order placed: limit for liquidity provider and market for liquidity taker, then at least one additional case needs to be resolved (at least in my mind). Let's say trader A places a marketable limit order to buy at the offer price. The order is partially filled and the remaining amount sits as the new bid price and size. Is trader A a liquidity taker (including the unfilled amount) because A's order consumed the liquidity at the offer, or both a taker and a liquidity provider because the remainder of A's order resides on the bid and thus new liquidity is created by A? Or is trader A simply a liquidity provider (even though liquidity was consumed) because a limit order was utilized? How would you analyze the hypothetical situation? TIA

[gooni 10]

Furthermore, is every transaction then a transaction between a liquidity provider and a liquidity taker? If so, I'm not sure how there can be an imbalance between the two. Surely I'm missing something..

[pmwhite 10]

I'm trying to refresh my memory of what "industry standard" bid/ask splitting might look like. It's my understanding that charting programs attribute ask transacted volume into the ask or buy column and bid transacted volume into the bid or sell column. Is this consistent with your understanding of how it's typically split between bid and ask?

[Karish 10]

madspeculator , Thanks for your educating post,

 

In relation to liquidity takers / providers you said that:

 

2. FulcurmTrader’s (FT) thread gives the author the impression that FT is tracking the activities of liquidity providers.

 

 

FT is using CVD to spot liquidity zone where the CVD measures the market orders (aggressive traders order flow) in the market,

 

Is it correct to say that CVD is measuring the liquidity takers (commercials?) as the market order takes liquidity out of the Limit order book pool. But once they took the resting limit orders, they are left with the inventory which happens to be the liquidity provider once the market is visiting this zone afterward

 

Thanks

Karish

[BlowFish 10]

I'm trying to refresh my memory of what "industry standard" bid/ask splitting might look like. It's my understanding that charting programs attribute ask transacted volume into the ask or buy column and bid transacted volume into the bid or sell column. Is this consistent with your understanding of how it's typically split between bid and ask?

 

That's pretty much the standard way of doing it with the participants requiring immediacy (market orders) taking liquidity from those offering liquidity (limit orders).

[BlowFish 10]

Thanks for your reply. The definition of liquidity provider and taker is consistent with what I have read in the market microstructure literature. If the definition hinges on the type of order placed: limit for liquidity provider and market for liquidity taker, then at least one additional case needs to be resolved (at least in my mind). Let's say trader A places a marketable limit order to buy at the offer price. The order is partially filled and the remaining amount sits as the new bid price and size. Is trader A a liquidity taker (including the unfilled amount) because A's order consumed the liquidity at the offer, or both a taker and a liquidity provider because the remainder of A's order resides on the bid and thus new liquidity is created by A? Or is trader A simply a liquidity provider (even though liquidity was consumed) because a limit order was utilized? How would you analyze the hypothetical situation? TIA

 

In that situation it seems sensible to assign the portion that executes immediately as taking liquidity and the balance as providing it (if it executes). There are numerous situations like this for example when two non-marketable limit orders are crossed within the spread, then both orders could effectively provide liquidity but, because of their timing, the second one reduces liquidity. I think it is probably academic as it will all depend on how the exchange reports the trade.

[madspeculator 10]

The obvious answer (well to me so it might be half baked) is that they would try to offset the position to other side liquidity takers. If that is not possible they would demand inventory from other side liquidity providers. I guess one might assume the very fact they have ended up with an inventory imbalance would mean they might swiftly move to step two, demanding liquidity?

 

BlowFish:

 

This is exactly how client order execution algos work! You might be on to something here. Now, go one step further and think about how index arbs programs might work, and how index option market makers might trade index futuers (these two steps are necessary only if you trade index futures or futures that might be used for hedging other financial instruments), and you will get the "full" picture!

 

Isn't this a fundamental issue when there is an intermediary the intent liquidity requirements of the original participant is obscured?

 

Yes. This is the reason why in an earlier post (I think in reply to pmwhite) I said, I assume that all the "purchase" is for the bank although such an assumption is not necessarily valid.