As promised, here is a new test/discussion thread, which is a new step in our exploratory journey on trading and scalping/hedging methodologies. This time we will use the Portfolio margin method, as we have seen that the RegT in practice is not viable (and, of course, it does not make sense at all when trading decent capital).
Tomorrow I will start the actual trading application and I will also be illustrating all the new numerous features I have been adding in recent times to the pre-existing work.
To make the discussion self-contained, let's recap the basic principles we have been proposing. We apply scalping/hedging techniques in such a way as to preserve the past trading information, in order to create "order clouds" which are "internally consistent", and no attempt to "predict locally" or to create so called "signals" is made. In the long term perspective, and in this thread with reference to leveraged ETFs, we simply ride the (long term) drifts which these instruments typically exhibit. This is mostly public information, readily apparent behavior, that is also pointed out on the instrument prospects.
[Clearly, the concept of structural drift could also be replaced with some long term "forecasting model". However, at least in this thread, we do not do that, to avoid a "contamination" with subjective or rather "arbitrary" factors, which could (and certainly will, sooner or later) damage the confidence of the fund manager, once for some reason (typically a DD) he lose "faith" in the model or starts doubting its foundations.]
The main innovation we have been proposing so far is the use of the "past trading information" to statistically unbalance the proportion of positively closed trades in our favor and push the (weighted) avg of buy orders below the avg of sell orders. We have already explained that in previous test runs, along the role of the "players" (that is, units containing information about single past orders, and equipped with the capability to close automatically based on rules provided by the fund manager) and their "superposition" (which means that the concept of single consecutive trades does not exists in our approach, but we reason on terms of the so-called "order clouds" or "player clouds").
In this thread we also add a new conceptual element which was not previously present, and that I will explain now shortly, and will probably clearer later, as we proceed with a practical application.
New conceptual elements
We have seen that the "player" concept is a rather convenient way to store the past trading information of our order cloud. The update rules of the players are relatively straightforward and we have seen them in previous posts: the avg of an open player fluctuates up and down according to fills (open/close) related to the player.
Now, a conceptual issue is that, once a player is closed (in profit), it is removed completely from the system, and we just focus on the remaining players which are still "locked" in a "losing" situation (price smaller than the player's avg for BUY players or price larger than the player's avg for SELL players).
After some experiments and some thinking, it become apparent that we are actually "throwing away" some information which should, instead, be retained and used. In particular, we are discarding the information relative to the players which have been closed, while only retaining the information about the losing players:
Past Trading Information =
Information stored in temporarily "losing" players + Information coming from already closed players
It becomes apparent, particularly from the applications, that we need to also incorporate such information in our trading game, or else what will happen in practice is that the "pressure" of losing players,without the information of the positively closed trades might induce an "excess" of hedging action and also an excess of sizing which may not be beneficial to performances, or in any case unnecessary. I am aware that if you are reading this, at this point you probably get only a fuzzy idea of what I mean, and actually it has taken some time to me too to fully realized this point, but with some practice, the concept would be more clear.
Now, the "technical" problem is: how do we incorporate in the "scalping/hedging game" the information relative to the closed scalps, so that the remaining players reflect such contributions. This is a question I have been pondering lately and I have resolved that one technical solution could be simply provided by a generalization of the mechanism have been using for instance to correct the players' averages after a futures rollover, to adjust according to the possible contango or backwardation. I will explain how this works during the practical trading application, so it would be much more clear what I am proposing and the motivation.
Trading methodology and plan
This thread is clearly not intended for retail traders, but for funds or relatively large private investors. We will not do "betting" (where the term is intended in a general sense: for instance trading forex based on so-called "signals" is considered gambling in this broader sense. Or similarly, to use the options for volatility or directional "bets", etc.). Our previous tests (which in early times even included trading without any form of "stops") have shown clearly that the hedging methods are crucial and that it is actually impossible to trade meaningfully without a suitable and very methodical approach to hedging (which must also include the use of options, when appropriate).
The main sources of risk we need to protect against are:
- Moves unfavorable respect to current position, temporary price crash (of other form of sudden price variation)
- Open gaps
- Structural drifts, decay, contango, etc. (long term moves against the prevalent position)
- Possible temporary trading limitation of the instrument (eg., non shortable, etc.)
We will achieve that through the following means:
- "Player superposition" (most effective, when it is possible to trade)
- "Options structures" (necessary against gaps, long term unfavorable moves, and to take care of the psychological aspects). We will study several different option configurations (in a next post I will make a review of some possible option "structures").
- Trading "suspension", position "flattening", "play for time" or wait appropriate moves against possible drifts before engaging or reengaging, when it appears appropriate to do so.
In any case, some good risk capital is needed. For ETFs, is also needed to use the portfolio margin method, as we have explored the significant problems created by the RegT margin method. Futures could be traded in a similar way to what we will show here (apart the typical drifts of the leveraged ETFs), but the risk capital should obviously be much larger, according to the respective multipliers of the instruments used (while the margin requirements may be relatively smaller, still the DD and related psychological consequences are mainly determined by the notional value of the position.)
Temporal horizon
Do not be misled by our usage of the term "scalping", which we adopt, and extend, to merely contrast the term "hedging" and to mean the generic action of taking profit (no matter how small or big it is). We are not after small or short term profits. Open orders can last any amount of time, from seconds to years, whatever it is necessary.
We are looking at results over several months.
Patience and capability to hold psychologically during DD are, therefore, crucial factors, as well as the capability to take the "right" profits after long DD, without "rushing" (closing with relatively too small rewards). In this sense, the 2 main factors, apart all the methodological and technical considerations, are: capital and psychological resilience.
Drawdown (DD)
Drawdown is actually an integral part of this non-predictive approach and, fundamentally, the potential source itself of future profits, along with the efficiency of the scalping/hedging mechanism. The methodological challenge is obviously to keep it under control within reasonable limits an, then get the "deserved" profit (our goal is to stay within a max of 50%).
Tomorrow I will start the actual trading application and I will also be illustrating all the new numerous features I have been adding in recent times to the pre-existing work.
To make the discussion self-contained, let's recap the basic principles we have been proposing. We apply scalping/hedging techniques in such a way as to preserve the past trading information, in order to create "order clouds" which are "internally consistent", and no attempt to "predict locally" or to create so called "signals" is made. In the long term perspective, and in this thread with reference to leveraged ETFs, we simply ride the (long term) drifts which these instruments typically exhibit. This is mostly public information, readily apparent behavior, that is also pointed out on the instrument prospects.
[Clearly, the concept of structural drift could also be replaced with some long term "forecasting model". However, at least in this thread, we do not do that, to avoid a "contamination" with subjective or rather "arbitrary" factors, which could (and certainly will, sooner or later) damage the confidence of the fund manager, once for some reason (typically a DD) he lose "faith" in the model or starts doubting its foundations.]
The main innovation we have been proposing so far is the use of the "past trading information" to statistically unbalance the proportion of positively closed trades in our favor and push the (weighted) avg of buy orders below the avg of sell orders. We have already explained that in previous test runs, along the role of the "players" (that is, units containing information about single past orders, and equipped with the capability to close automatically based on rules provided by the fund manager) and their "superposition" (which means that the concept of single consecutive trades does not exists in our approach, but we reason on terms of the so-called "order clouds" or "player clouds").
In this thread we also add a new conceptual element which was not previously present, and that I will explain now shortly, and will probably clearer later, as we proceed with a practical application.
New conceptual elements
We have seen that the "player" concept is a rather convenient way to store the past trading information of our order cloud. The update rules of the players are relatively straightforward and we have seen them in previous posts: the avg of an open player fluctuates up and down according to fills (open/close) related to the player.
Now, a conceptual issue is that, once a player is closed (in profit), it is removed completely from the system, and we just focus on the remaining players which are still "locked" in a "losing" situation (price smaller than the player's avg for BUY players or price larger than the player's avg for SELL players).
After some experiments and some thinking, it become apparent that we are actually "throwing away" some information which should, instead, be retained and used. In particular, we are discarding the information relative to the players which have been closed, while only retaining the information about the losing players:
Past Trading Information =
Information stored in temporarily "losing" players + Information coming from already closed players
It becomes apparent, particularly from the applications, that we need to also incorporate such information in our trading game, or else what will happen in practice is that the "pressure" of losing players,without the information of the positively closed trades might induce an "excess" of hedging action and also an excess of sizing which may not be beneficial to performances, or in any case unnecessary. I am aware that if you are reading this, at this point you probably get only a fuzzy idea of what I mean, and actually it has taken some time to me too to fully realized this point, but with some practice, the concept would be more clear.
Now, the "technical" problem is: how do we incorporate in the "scalping/hedging game" the information relative to the closed scalps, so that the remaining players reflect such contributions. This is a question I have been pondering lately and I have resolved that one technical solution could be simply provided by a generalization of the mechanism have been using for instance to correct the players' averages after a futures rollover, to adjust according to the possible contango or backwardation. I will explain how this works during the practical trading application, so it would be much more clear what I am proposing and the motivation.
Trading methodology and plan
This thread is clearly not intended for retail traders, but for funds or relatively large private investors. We will not do "betting" (where the term is intended in a general sense: for instance trading forex based on so-called "signals" is considered gambling in this broader sense. Or similarly, to use the options for volatility or directional "bets", etc.). Our previous tests (which in early times even included trading without any form of "stops") have shown clearly that the hedging methods are crucial and that it is actually impossible to trade meaningfully without a suitable and very methodical approach to hedging (which must also include the use of options, when appropriate).
The main sources of risk we need to protect against are:
- Moves unfavorable respect to current position, temporary price crash (of other form of sudden price variation)
- Open gaps
- Structural drifts, decay, contango, etc. (long term moves against the prevalent position)
- Possible temporary trading limitation of the instrument (eg., non shortable, etc.)
We will achieve that through the following means:
- "Player superposition" (most effective, when it is possible to trade)
- "Options structures" (necessary against gaps, long term unfavorable moves, and to take care of the psychological aspects). We will study several different option configurations (in a next post I will make a review of some possible option "structures").
- Trading "suspension", position "flattening", "play for time" or wait appropriate moves against possible drifts before engaging or reengaging, when it appears appropriate to do so.
In any case, some good risk capital is needed. For ETFs, is also needed to use the portfolio margin method, as we have explored the significant problems created by the RegT margin method. Futures could be traded in a similar way to what we will show here (apart the typical drifts of the leveraged ETFs), but the risk capital should obviously be much larger, according to the respective multipliers of the instruments used (while the margin requirements may be relatively smaller, still the DD and related psychological consequences are mainly determined by the notional value of the position.)
Temporal horizon
Do not be misled by our usage of the term "scalping", which we adopt, and extend, to merely contrast the term "hedging" and to mean the generic action of taking profit (no matter how small or big it is). We are not after small or short term profits. Open orders can last any amount of time, from seconds to years, whatever it is necessary.
We are looking at results over several months.
Patience and capability to hold psychologically during DD are, therefore, crucial factors, as well as the capability to take the "right" profits after long DD, without "rushing" (closing with relatively too small rewards). In this sense, the 2 main factors, apart all the methodological and technical considerations, are: capital and psychological resilience.
Drawdown (DD)
Drawdown is actually an integral part of this non-predictive approach and, fundamentally, the potential source itself of future profits, along with the efficiency of the scalping/hedging mechanism. The methodological challenge is obviously to keep it under control within reasonable limits an, then get the "deserved" profit (our goal is to stay within a max of 50%).
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