Alexandre CAMPOSJuly 28, 2018
Fotolia_194839680_XS-1.jpg

7min160

Le côté humain ou animal des salles de marchés demeuraient primordiale dans le monde du trading. De nos jours, l’informatique et les mathématiques sont omniprésentes en front office. Dans cette guerre contre le temps, certains acteurs du marché dénoncent de plus en plus une véritable « course à l’armement », rendant impossible l’intervention de petits acteurs et créant ainsi des distorsions de concurrence.
The Hight frequency Trader ou le trading haute fréquence utilise de puissants ordinateurs incluant des algorithmes permettant de sélectionner et opérer d’infime mouvement de marché avec un ordre de temps proche de la milliseconde. On peut avoir jusqu’à 1000 exécutions à la seconde.
Le but étant de tirer profit de très faible écart de prix sur les valeurs des systèmes de titres, c’est une forme de scalping. Cette forme de Trading qui s’est fortement développé depuis quelques années suscite énormément d’engouement, comme pour Goldman Sachs qui n’a quasiment plus de trader à New York, mais aussi beaucoup de questions pour les gestionnaires, les investisseurs et surtout l’AMF. En effet, il est difficile pour l’AMF d’établir ce ratio car les traders HF ne sont pas tous membres de marché et l’AMF n’a accès en direct qu’à l’identité des membres de marché responsables des ordres et des transactions, non des clients finaux.
Le débat est grand autour de cette forme apparente de Trading. Certains estiment qu’il apporte de la liquidité via le market-making et l’arbitrage ainsi qu’une certaine efficience de marché par le biais d’un équilibre de prix entre place et valeurs liées.
Ses détracteurs dénoncent une liquidité « fantôme », l’instabilité permanente du carnet d’ordres introduirait une incertitude structurelle dans le trading (un ordre est déjà obsolète au moment où il est envoyé) qui est un obstacle à l’éfficience.

Schéma : Source AMF

De plus, les autorités de marché commencent à hausser le ton. Ainsi, l’AMF a récemment fait état d’un rapport accablant sur ce type de trading, dénonçant notamment les menaces « d’intégrité du marché dès lors que les stratégies de trading sont détournées de leur objectif initial pour être utilisées à des fins de manipulation de marché ». 
Nous notons dorénavant l’apparition depuis quelques années l’apparition d’un système similaire au trading haute fréquence : Les robots advisors.
Provenant pour la plupart et surtout en France de FinTech (Financial Technology), soutenus par l’AMF et l’ACPR, cela marque une rupture pour les particuliers concernant leurs investissements et leurs conseillés.
Notons que les robots sont déjà bien établis en finance de marché comme énoncé ci-dessus. Pour les acteurs de la finance de marché, ces robots sont une réalité indéniable prenant entièrement partie dans l’ère du digital (BigData).
Les banques ont donc ouvert cette possibilité aux investisseurs particuliers afin de retenir à moindre cout les clients peu rentables, attirer de nouveaux clients via une délégation des transactions automatisé et fluidifié à travers ces robots et leurs algorithmes.
Les possibilités sont nombreuses pour une gestion de portefeuille, une définition du marché et du profil de risque permet alors de laisser libre cours au robot qui traitera les ordres automatiquement. Qui sera pris alors pour responsable d’une mauvaise gestion, une perte de capital ou un mauvais arbitrage. La banque, le quant ou l’investisseur ? de nombreuse question restes présente concernant ces robots attrayant, avide de gain mais encore plus la plupart inefficient sur des sites frauduleux.
En résumé, les robots permettent pour les acteurs financiers un trading haute fréquence permettant une exécution automatique sur des milliers d’ordres au quotidien, néanmoins il nécessite une vigilance de tous les instants ainsi que des personnes hautement qualifiées en mathématiques et en informatique pour repousser un peu plus chaque jour les limites de la finance. Ce système fut une ouverture aux Fintech et un développement de l’offre bancaire et financière pour les investisseurs particuliers à travers des robots pouvant gérer un portefeuille d’actif en achat/vente de façon algorithmique.


Alassane MBENGUEJuly 19, 2018
Fotolia_73481319_XS.jpg

12min170

L’effet de levier peut être présenté très rapidement dans son principe. Son mécanisme tient à deux propositions :
La rentabilité des fonds propres est accrue par un recours à l’endettement financier si et seulement si la rentabilité des actifs est supérieure au coût des dettes ;
Cette augmentation de rentabilité a pour contrepartie une augmentation du risque financier de l’entreprise, qui croît avec le niveau des dettes de l’entreprise.
Principe de l’effet de levier
L’effet de levier explique le taux de la rentabilité des capitaux propres en fonction du taux de rentabilité de l’actif économique et du cout de la dette.
L’ensemble des capitaux apportés par les préteurs et les actionnaires finance l’ensembles des emplois, c’est-à-dire l’actif économique ; ces emplois dégagent un résultat d’exploitation qui se répartit ensuite entre les frais financiers (rémunération des prêteurs) et le résultat net revenant aux actionnaires.
En fait lorsque l’on compare la rentabilité des capitaux propres et la rentabilité économique (après impôt pour être homogène), on s’aperçoit qu’elles ne sont séparées que par l’impact de la structure financière.
On appelle effet de levier la différence entre la rentabilité des capitaux propres et la rentabilité économique économique.
L’effet de levier explique comment il est possible de réaliser une rentabilité des capitaux propres supérieure à la rentabilité économique.
Mais attention, l’effet de levier peut jouer dans les deux sens : s’il peut accroitre la rentabilité des capitaux propres par rapport à la rentabilité économique, il peut aussi, dans certains cas, la minorer. Le rêve devient un cauchemar.
Les limites des taux de l’effet de levier
Partant d’une taulogie comptable, la formule de l’effet de levier est nécessairement juste, même si certains chiffres sont manifestement des aberrations. Ainsi, le coût de la dette calculé comme le rapport des charges financières nettes des produits financiers sur l’endettement net au bilan pourra être de façon évidente trop élevé (ou trop faible). Cela indique simplement que l’endettement net figurant au bilan ne correspond pas à l’endettement moyen, que l’entreprise est beaucoup plus endettée que cela (ou beaucoup moins) ou qu’il y a un phénomène de saisonnalité ou une opération financière (augmentation de capital) qui a été réalisée en cours d’année.
Il faut donc se méfier des taux d’intérêt apparents lorsqu’ils sont visiblement aberrants et de l’effet de levier ainsi calculé.
La rentabilité économique ou la rentabilité des capitaux propres sont des taux de rentabilité comparable ex-post ; en aucun manière ils ne peuvent correspondre aux exigences de rentabilité ex-ante des actionnaires ou de l’ensemble des pourvoyeurs de fonds.
L’intérêt de l’effet de levier
Stratégie caricaturale des années 1960, ou actuellement en chine dans la sidérurgie, la stratégie de fuite en avant est particulièrement bien adaptée dans un contexte de forte croissance. Cette stratégie a une double caractéristique : forts investissements pour augmenter la taille de l’outil industriel et faibles marges pour conquérir des parts de marché et faire tourner l’outil de production. Bien évidemment, la rentabilité économique est faible (faibles marges et forts investissements), mais le recours inévitable à la l’endettement (la faiblesse des marges entraine des flux sécrétés par l’exploitation insuffisants pour couvrir les investissement important) permet de gonfler la rentabilité des capitaux propres par le mécanisme de l’effet de levier. Ce d’autant plus que le cout réel de la dette est faible ou négatif en raison de l’inflation. Cependant, la rentabilité des capitaux propres est très instable, elle peut brutalement chuter lorsque le taux de croissance de l’activité se ralentit.
Ce fut typiquement la stratégie de Suntech, le leader mondial chinois des panneaux solaires, qui lui a permis de s’imposer sur son marché, de « descendre sa courbe d’expérience » diraient les consultants, mais qui fut aussi la source de sa faillite en 2013.
L’intérêt de l’effet de levier est donc essentiellement pédagogique : comprendre comment se partage la rentabilité des capitaux propres entre la rentabilité de l’outil industriel et commercial et une pure construction financière (l’effet de levier).
L’effet de levier n’a qu’un intérêt limité en finance car il ne crée pas de valeur sauf dans deux cas très particuliers :
Inflation constante, le taux d’intérêt réel (inflation déduite) est négatif et conduit à la spoliation des créanciers remboursés en monnaie de singe pour plus grand bonheur des actionnaires.
D’un endettement très lourd (cas des sociétés en LBO, qui pousse les dirigeants à être particulièrement performants pour que l’entreprise soit à même, par ses flux de trésorerie, de faire face au lourd poids de son endettement qui a alors peu près le rôle du fouet dans les villas de l’antiquité !
Une globalisation abusive de la dette
Un point qui n’est que rarement mentionné concerne la nature des capitaux empruntés des dettes. Implicitement, les auteurs entendent “Total des dettes financières”. Or, pour valider ce modèle, on doit retenir l’ensemble des dettes (dettes financières, dettes d’exploitation, dettes hors exploitation). En effet, comme la rentabilité économique est calculée sur le total de l’Actif, cela implique que l’on doive raisonner sur le total du Passif. Les fonds propres, les dettes financières et les dettes non financières (dettes fournisseurs et assimilés, dettes fiscales et sociales, autres dettes).
On est donc confronté à une alternative dont les termes ne sont satisfaisants ni l’un ni l’autre :
– si l’on retient comme indicateur l’ensemble des dettes de l’entreprise, on incorpore des dettes qui ne portent pas intérêt (dettes d’exploitation et dettes hors exploitation) et donc le calcul d’un coût moyen de la dette est faussé ;
– si l’on ne retient que les dettes financières, le calcul d’un coût moyen de la dette aura un sens, mais cela introduit une nouvelle hypothèse :
on suppose que Total du Passif = Capitaux Propres + Dettes financières.
En d’autres termes, on suppose qu’il n’existe pas de dettes non financières. En tout état de cause, on procède à une globalisation de la dette indépendamment de son origine et de son coût, et surtout sans tenir aucunement compte de son terme. Dans ces conditions, cela expose le modèle à une sensibilité aux fluctuations de la dette d’exploitation. Or, en matière de politique financière, les directeurs financiers jouent régulièrement sur le montant de leurs dettes d’exploitation pour résoudre leurs problèmes de financement.
Ceci peut s’expliquer, après coup, par le modèle lui-même :
La rentabilité financière d’une entreprise est égale à sa rentabilité économique augmentée du différentiel entre le rendement des actifs et le coût moyen des dettes multiplié par le coefficient d’endettement. Par simple lecture, on comprend que tant que r – i > 1 l’entreprise a intérêt a s’endetter.
Tant qu’une entreprise trouve des actifs dont le taux de rentabilité est supérieur au taux de financement des dettes, l’entreprise a intérêt à se financer par de la dette.
A fonds propres égaux, on accumule ainsi de la richesse marginale sans avoir à accroître l’investissement des actionnaires. On peut alors très logiquement chercher à optimiser le levier :
si l’on recherche un rendement d’actif fort, la rentabilité se maximisera avec l’endettement, et dès lors que l’endettement est gratuit, la maximisation sera optimale :
on aura donc intérêt à préférer de la dette fournisseur gratuite. Le passif optimal sera constitué de peu de fonds propres et d’un maximum de dettes fournisseur. Cela peut conduire à négliger la constitution d’un fonds de roulement, et à de la transformation : financement des emplois longs par des ressources très courtes. Certes, le caractère permanent du crédit fournisseur peut corriger ce risque de transformation, mais les bilans générés par une politique de ce type demanderont une gestion stricte du Besoin en Fonds de Roulement, tout en présentant un risque financier très fort : tout aléa sur les ventes, donc toute volatilité sur les achats, se répercutera immédiatement sur la structure du passif et conduira l’entreprise à l’illiquidité, une crise de trésorerie non résolue. La maximisation de la rentabilité pour l’actionnaire s’accompagne alors d’un risque fort. Ceci nous ramène à une logique économique fondamentale : la juste rémunération du risque encouru.
Source:
VERNIIMEN
ZONE BOURSE
LE FINANCIER
YAHOO FINANCE
LECTURE DES ARTICLES ET MEMOIRE
WIKIPEDIA


Dario PETROVSKIJanuary 26, 2018
image1-1.png

11min18630

Today, it takes 350 milliseconds to complete 7,000 transactions. This is the time a man takes to blink. A transaction can be done in 5 microseconds or 0.000005 seconds. These numbers show that speed has dramatically changed the way financial transactions operate. This speed could be reached following the various technological evolutions. Increased competition in the financial sector resulting of financial deregulation in the 2000s and technological evolutions has led to high frequency trading. Financial actors entering this competition are constantly looking for innovations to reduce time differences in their financial transactions.

As a result, a new war is emerging among practitioners of high-frequency transactions. The speed of information and transactions becomes the main element of the core of high frequency trading. It is on this aspect that the financial players will base themselves in order to stay alive in this fierce competition.

Optical fiber to reach high speed
The search for speed is not new today. Indeed, markets already had means and tools to facilitate and improve the speed of transactions. Telegraph, phone or roller skates, all means was good to save time on transactions and be the first to achieve the best transaction.

The invention of Thomas Peterffy in 1987 completely revolutionized the markets. The program that buys and sells shares automatically has saved a lot of time on transactions. The number of transactions is growing considerably. But the tool that will completely revolutionize the financial sector and allow the rise of high-frequency trading is fiber optics. To show you the importance of its use for financial transactions, we will take the example of Brad Katsuyama who will be the first to condemn the way that fiber optics is used by high frequency traders.

What Brad is going to show is how the stock exchanges are connected to each other by these fibers. He succeeds in appropriating the map of New York by an operator by making him believe that he wants to be his client. The map reveals the different fibers existing in New York and what surprises him is the fibers connecting these stock exchanges.

Stock exchanges in New York unevenly linked by fibers

high frequency tradingBrad Katsuyama places his orders from the south of Manhattan. Its establishment is connected to an optical fiber at normal frequency. When his orders pass through the various stock exchanges, certain financial actors can intercept his orders and manipulate them. These actors are attached to optical fibers allowing high frequency. Thus, as soon as Brad will place an order, high frequency traders will be able to intercept the information of these orders before they are executed.

So, they will manipulate the markets in order to raise stock prices and resell them to Brad. On the map above, we can see where Brad’s orders (blue tracings) and the path of high frequency traders (red tracings) go. Brad observes, therefore, that his shortcomings in the markets are due to the unequal manner in which he is attached to the New York’s Stock Exchanges.

Telecommunication networks have become a key element in high frequency trading. Every year in November, Chicago gather telecom companies to introduce new technologies and building new fibers to gain speed. This is the “big date” for high frequency traders. Some telecommunications companies are creating high-tech “VIP” networks called dark fiber by professionals.

The price to pay for high frequency traders to attach to these high-speed fibers is very high. It varies between $ 3 and $ 5 million a year, knowing that the cost of installing fiber is an average of $ 300 million. The price of attachment to fiber is lower compared to the benefits that high frequency trading can generate.

In the summer of 2010, a telecommunication company directly connected New York to Chicago with a 1200-km private optical fiber. The information then takes 9 milliseconds to cross a third of the United States. This optical fiber has caused some problems for some regions. Sunbury is a city of nearly 10,000 people in the state of Pennsylvania. This region is considered “amish”, that is, it is opposed to technological progress.

When Sunbury Mayor David Persing signed the contract for the fiber installation, he mentioned a facility used for telecommunication. The use for high frequency trading is therefore not mentioned in the contract. The passage of this fiber into the city allows Sunbury to raise $ 14,000 a year. Thousands of contracts are signed with municipalities, individuals and businesses with land along the route. Certain confidentiality clauses are included in the contracts in order to maintain the secrecy of use of the fiber.

Even if fiber is a very useful tool for high frequency traders to gain more and more speed in their transactions, there are alternatives to gain a few milliseconds more.

…and the waves to be even faster
Optical fiber is the basic tool for the passage of information in the context of high frequency trading. But a discovery was made by a telecommunication engineer. Stéphane Tyc is a specialist in radio waves. It specifies the fact that the optical fiber must overlap between the different establishments present in its path.

He thinks that he can gain milliseconds through the air because the waves can go straight contrary to fiber optics. In 2011, he began working on connecting antennas on the road from New York to Chicago. With this installation, it will enable high-frequency traders to earn 1 millisecond over the fiber by reaching a speed of 8 milliseconds back and forth on this route.

Stéphane Tyc also aims to bring together the two largest stock exchanges in Europe. He wants to install a network of antennas to make the information flow at very high speed between London and Frankfort. Its installation must be as precise as possible because every microsecond gained is important in its work.

Antennas must therefore be installed with extreme precision. Moreover, he gains speed by putting his antennas as high as possible. So, he goes looking for height like a church in London where he places his antenna in the steeple or the third tallest building in Chicago on the road from New York to Chicago. The rents to be able to settle in height turn around several tens of thousands of euros per year.

In January 2013, Jump Trading buys an old tower 243 meters high for the price of 5 million euros to install its antennas to pass data between the stock exchanges in Frankfurt and London. This pylon once belonged to the US Army. The Belgian state hoped to reap at least 300,000 euros through the auction of this pylon.

The investment to connect to these antennas is very expensive but it allows to gain more and more speed in the passage of transactions. But there is an even more beneficial technique for high frequency traders. Some companies settle directly inside the stock exchanges. To be able to place their own computers closer to the general server and gain another thousandths of a second, these companies must pay nearly $ 30,000 per month. This method is called colocation. The “roommates” are installed at an equal distance to the nearest millimeter facing the server.

In the sector, there is a real race to speed. High frequency traders are constantly looking for new techniques and ways to earn milliseconds or even microseconds to be the fastest in the market. These actors are ready to spend unimaginable sums to appropriate the necessary tools in order to stay ahead of the race. Speed ​​has become an essential element, even the heart of high frequency trading today.

It is impossible for a human to list high frequency transactions and process their information. The speed has reached a level inappropriate to the primary function of the financial markets. But this practice has become a big problem for some actors and for the financial authorities. It has also acted directly on the markets and caused many dysfunctions and great fears. She has been repeatedly denounced and has been the source of many debates

References:

Contrepoints.org
Numerama.com
Lesechos.fr
« Les Nouveaux Loups de Wall Street »

 


Edouard CHANSAVANGJanuary 22, 2018
Fotolia_194839680_XS.jpg

43min14720

Elements of strategic trading guideline

In this article, I woud like to share with you a few elements of some thoughts on probability and trading that built up through my observations of the markets over the years. I would not delve into complex mathematical concepts or formulae here but simply refer to intuitive statistics and probability. It is not my goal to deliver definitive trading edges as I would merely explain some ways to ultimately elaborate some through technical analyses and possible implementation into complex algorithmic trading systems.

Before going further, I would like to make a very quick and simple recap about probability space and some parallels with real life and trading examples.
In a probability space, the probability that an outcome occurs is comprised between 0 and 1
The probability of an event that will necessarily occur is 1. An event that is impossible has a probability of 0 to happen.

  • Example 1 (Real life)

For instance, our lifespan is positive and this is obviously true. A negative lifespan is impossible and therefore its probability is 0.

  • Example 1 (Trading)

The price of a stock cannot be less than 0. This event is impossible, however a trading account can be negative if leverage is used and that a gap appears in a direction adverse to that of our position.
This first instance brings up a 
first important notion, that is “integration“, meaning that a trader must incorporate different elements in its scope that are outside of the limits of the initial intrinsic market (in this case, integration of leverage skewed potential risks and profits and ultimately created new possibilities that were not initially possible).

  • Exemple 2 (Real life)

In real life, we are mostly certain to “die” with a probability of 0,999…999…; and the limit tends toward 1 given enough time.

  • Exemple 2 (Trading)

Depending on the markets we are dealing with, if we do nothing over a long period of time, it is more likely that we lose everything even though potential gains are theoretically infinite (ie. end of financial markets, bankruptcies of brokers and/or banks …). We would not be able to benefit from it anyway since we would not be here anymore (see previous example).

Nonetheless, it is possible to manage dynamically our trading (profits/losses, positions …) and this is where the notion of “flexibility kicks in.

It is preferable to speak in terms of probability space when it comes to trading, because nothing is 100% sure in the markets, if not this claim itself.

We are never free from Black Swans and one of the main objectives of the trader is to find a trading edge in any market condition that would allow him or her to “play with chaos”.

STRATEGY AND PERFORMANCE

Repeating the same action over and over can lead to very different results that can be counted and used to create statistics.

What worked in the past is not bound to work again in the future. Past performance is not necessarily representative of future performance. This is also an issue when models that are overfitted are being backtested lead to results that cannot be positively expected all time in the future.
Out of sample data and time series must be analyzed, especially when other algorithms might eventually find out what our strategies are and integrate our model into theirs.

There are many questions of interest that can be tackled: how is for instance short term “noise” impacting longer time frames and the latter impacting those same moves that appear at first to be very chaotic? There are indeed some correlations and patterns that are difficult to detect and define but that seem to be existent.

I would like to address next a particular point that can drastically increase profits if used cautiously and correctly.

Reverse position

What is the probability that we perform a winning operation. Likewise, what are the odds that we recoup our losses or even that we generate some profit from this reverse position? Various vital questions must also be considered: should we be correct to change our mind, when should we secure our gains from that reverse position? Can we only partially recover from our losses or is it possible to breakeven? Different psychological impacts have to taken into consideration by artificial intelligence in scalping/intraday trading, including whipsaws and bull/bear traps.

We should admit and accept to be wrong and be flexible. We need to become systematically flexible because the market is always “right”. Hence the notion of  
flexibility” and systematicity.

TECHNICAL ANALYSIS


Example of adaptation to western technical analysis (use of trendlines, mathematical indicators …)

Supports and resistances: what is the probability that they are hit? How many “hits” have to appear so that the chance of a breakout increases?

We need to pay attention to the fact that the price doesn’t always stop at the same value. We should setup arbitrary zones of resistance and support (ex: percentage …).

In terms of probability, we can ask ourselves what are the odds that the price remains inside a range after 4 contacts with extreme band levels? 5 contact points? 6 contact points? On the contrary, how should we interpret that if we witness 10 hits at those key levels? Are we simply observing a singular event or should we completely questioning ourselves (we are maybe seeing things that “do not exist”)? One simple example would be to consider a security which is being sold at 101 then bought at 100, to be later sold again at 101.

The process is repeated 4 times. Someone who would be aware of the concept might believe there is a resistance and support at those specific levels or even better, a range. This could well be the case for a scalper, but this price action could be insignificant to a day trader who would not even consider that a pattern has been drawn. Depending on the situations (here, the time frame among other things), things can be perceived differently.

Therefore, it is crucial to focus on the notion of interpretation and to dynamically adjust our methods in order to increase our probability to succeed and/or to avoid to being stopped. In fine, even some tiny tweaks could greatly impart our P&L.

Other examples

What is the probability that a moving average (ex: EMA20, 100 etc.) is touched over a defined period? Broken through for more than two days? A month? A year? Ten years? Many false penetrations and signals could indeed happen, and only the combination of different confirmations could correctly strengthen our opinion. A trader should also learn to recognize and use arbitrary yet popular elements of technical analysis (sets of patterns, indicators …). Would they increase the odds of “self-fulfilling prophecy”?

What is the probability that a fractal pattern appears over a given period?

Example of probability independence and conditioning

In order for a pullback or a throwback to materialize, is it a prerequisite that price pierces through resistance or support lines (as long as a resistance or support area has not been gone through because of a price increase/decrease or a gap, this probability is 0 because this could not possibly happen if the price below/above). This depicts that many events are conditioned by other ones (cf. Bayes’ Theorem).

What is also the probability that a technical pattern transforms into another one? For instance, that a triangle becomes a rectangle? Needless to say, for this situation to appear, the triangle must already exist in first hand.

Dynamism, agility and mastery of the basics and statistics are fundamental.

VALIDATION / INVALIDATION

A trader must bear in mind that invalidation is as important if not more important than validation itself in every kinds of settings (patterns, strategies…). One must think about the best options available: Do we have in this case to reverse our open positions? Should we simply close our positions and take our profits or losses? Should we wait and pray instead? Is the market clearly telling us that our position is wrong? Success rate has to be constantly assessed with flexibility and free from biases. We also face the possibility to be tricked and whipsawed so validation and invalidation depends on criteria that could be either systematic or arbitrary.

Time could also serve as a stop-loss. For instance, an intraday bullish flag that stretches too much over a long period of time could signal that the pattern and current targets are invalidated, or at least for the time being. Indeed, traders and algorithms often “keep in memory” targets that seem to be existent one of particular shorter time frame but that would only be “perfectly” reached much later (the following day, in a week, a month …). Analysis of this kind of delay is very important to remember as it could also add to trading edges.

A second target becomes valid id if and only if the first one has been hit. That being said, this is also dependent upon the strength of the moves: Was the objective attained fortuitously? Were the momentum and/or market conviction strong? Could be it be due to some kind of overreaction or simply to the triggering of stop-loss orders? Are the moves resembling to a bull or a bear trap?

Many breakouts could be false breakouts (cf. bull traps and bear traps).


Special case: volatility

When a key level (resistance, support line …) is being pierced downright with low volatility in certain markets, the probability that we head further in the direction of the “breakout” gets larger in the given time frame and period of time. On the other hand, some analysts advocate that large volatility is preferable to clear a particular level.

MONEY MANAGEMENT

What is the probability that our trading account could survive over a certain period if a given amount of leverage is used (ex: full leverage, under leverage)? What are our odds to become a millionaire if we start with a account with 30 bucks? Billionaire?
How fluctuating is our success rate and how can we improve it depending on our trading style (very short term, short term, midterm, long term). We need to involve a set of different factors such as psychological effects, position size, leverage …).

It is vital to identify our profile across different market conditions and time horizons. We can also get sort the data and get the skewness (measure of symmetry or rather the asymmetry) and kurtosis (heavy-tailed or light-tailed in comparison with a normal distribution).
We need to understand whether we are performing in a consistent manner or in an erratic and instable way (small gains, small losses? Small gains, large losses? Large gains, small losses? Large gains, small losses?) in order to determine our odds to reach our goals over the long haul.

NON-RANDOM WALK

We could think of “random walks” that wouldn’t in fact be that random on the grounds of self-fulfilling events that would be carried out once a certain set of conditions are met.

Mean reversion: Many statisticians agree that the best predictor of trading price is holds to price average. However, several markets do not reach their previous price level from where they are deemed to be overvalued before a very long period of time.

Probability and market biases: Declines in price are in general faster and more violent compared to price increases. Bubbles also show several statistics such as increased steepening. Yet, this is sometimes what others try to make us believe like in a game of poker, prompting many to open an inverse position (cf. contrarian strategies). Some arbitragists are knowingly surfing this wave on purpose and inflating further bubbles. It is also a strategy used by some professional traders who are attempting to trade what they believe to be excess momentum and buy “overbought” products instead of short-selling them.

What we know for sure is that trees do not grow to the sky. It is also possible that we have to deal with all kinds of cycles even though we cannot not how long they can last. How many consecutive ups and downs can we have? This would certainly depend on different elements, such as the product itself, its history and different cycles. What is the probability that a new game of poker is started and that new “normals” are emerging?

What is the probability that a stop-loss is hit depending on our position or even that a CFD broker traps a CFD trader by playing against him or her (ex: B route)?

PSYCHOLOGY

What are the different factors that impact our probability to become euphoric, petrified, optimist, pessimist or angry?

Long? Short? Flat? We all possess this bias and it is quite interesting to know the probability that it would change. Equity markets are structurally bullish and should “normally” keep growing for a long time. In times of panic, momentum is naturally much higher. If we do nothing, we only waste time and maybe some opportunities, but the best trade could well be to not pull the trigger at all and wait.

Under which market conditions should a scalper become a day trader or hold his or her positions in swing trading or investing mode (see volatility)? Some funds specialized in algorithmic scalping might not also have the leisure to be that flexible owing to their systematic strategies unless they integrate more dynamism and adaptability in their algorithms (ie: Artificial Intelligence).

Depending on personal objectives and financial capacities, it could be preferable to make long-term investments or speculations with some or all of the available capital when some particular market conditions are being met. 

What is the likelihood that a certain price, if reached, would attract investors or speculators? We need to take into account the speed and momentum with which we reached those targets, along with indicators and other elements in which a significant number of actors believe. We can also rely on the alignment of planets (we shouldn’t exclude spurious factors that could be randomly correlated to the result we desire to achieve).

RISK AND UNCERTAINTY

A trade should generally be jumped in when there is a high probability trade setup. We cannot be sure to always win over a certain period of time and a certain number of trades.

We might as well possess a solid strategy but not be able to be filled at the price we want due to lag time and our position in the queue of an order bookSlippage can also frequently occur in particular when market orders are thrown in illiquid markets or during periods of price shocks (ex: well defined stop-loss orders are set but no buyer is willing to take our position in case of a sharp decline when we are long the market). We also need to pay attention to busted trades and non-reviewable trading ranges.

Obviously, there are many other exogenous and endogenous issues that have to be dealt with. “Fat fingers” could appear, Iceberg orders must be accounted for (implementation of strategies using VWAP – Volume Weighted Average Price) and so on.

MISCELLANEOUS

What is the probabiliy that a trader in possession of a confidential information performs insider trading? What are also the odds that a broker would do front-running or that a high-frequency trader use illegal spoofing techniques and that her or she gets caught by the SEC or any similar entity?

CONCLUSION

You’ll find below a summary diagram of the different components to be aware of when it comes to probability in trading. Please note that this outline is merely based on personal experience and observations and is certainly non-comprehensive. I simply hope that this guideline will provide some food for thoughts (click to enlarge):

trading

Guideline overview

Nothing is never certain in a game of poker. As Warren Buffett once apparently said, “If after ten minutes at the poker table you do not know who the patsy is – you are the patsy”. However, like in a game of chess (or even better, a game of Go), we can analyze the different possible moves from our opponents ranging from the most probable to the least probable ones. That way, we can become prepared and able to react properly according to the scenario that is unfolding in front of us and the constraints that we have.

Therefore, we must take into consideration several dimensions and dynamics that keep changing incessantly in real time. Black Swans are also very important and interesting to analyze. Through repeated observation of price action and price reactions to different possible situations, it is likely that we detect numerous elements that are not bound to randomness or coincidences.

If I had to sum up everything in a single sentence, it would be “Once in position, a trader should become mostly a risk manager.”

 


Sébastien CAUDRONDecember 30, 2017
Fotolia_43686650_XS.jpg

8min130

Cela fait plus de 5 ans que les matières premières agricoles, que sont le soja, le mais ou encore le blé, sont en chute libre, perdant plus de 50% depuis leurs sommets de 2011 et 2012.
Quelle en est la raison ? Qu’est ce qui pèse sur leur prix ? En 2018, allons-nous faire face une hausse des prix des matières premières agricoles ?
Les analystes de Merrill Lynch ont analysé le cycle économique en le découpant en 4 phases : Reflation, recovery, overheat phase, stagflation.
Tout d’abord, si l’on regarde de plus près le cycle économique de Merrill Lynch, et en analysant comment se comporte le marché à l’heure actuelle, nous sommes en phrase 3 (overheat). Mais les marchés semblent pricer la phase 2 de recovery : Une hausse du PIB avec faible inflation. Pourtant les données macroéconomiques sont belles et bien présentes : l’Inflation en Angleterre est en nette hausse et supérieur à 3%, au Canada elle est au dessus des 2%, aux USA et en Europe elle se rapproche des 2%. Ces hausses de l’inflation ont incités les banques centrales à augmenter leur taux directeur (Angleterre, Canada et USA) ce qui confirme bien l’hypothèse que le marché ne price pas correctement cette tendance à la hausse de l’inflation.

Deuxièmement, jetons un œil à l’économie la plus importante de cette dernière décennie : La Chine.
C’est à partir du début des années 2000 que la Chine a entamé l’ascension de son PIB, tirant les économies mondiales et le prix des matières premières (agricoles, soft et énergétiques) à la hausse dû à une forte demande intérieure. Le PIB par habitant chinois, augmentant de façon exponentielle, a incité les chinois à importer et consommer plus de matières premières.
Dès 2011/2012 on a pu observer un ralentissement de la croissance chinoise, entrainant une baisse de la demande intérieure et donc une chute des prix des matières premières agricoles jusqu’à ce jour.
Si la Chine continue de ralentir, comment les matières premières peuvent elles reprendre des couleurs ? La réponse est : l’Inde
L’inde va faire partie des grandes économies développées (devant la France en 2018). Les besoins en matières premières seront grandissantes, comme cela le fut avec la Chine. Le PIB par habitant de l’Inde est exactement au niveau de celui de la Chine au début des années 2000, et comme le mentionne Goehring & Rozencwajg Associates au « delà d’un PIB 2000 dollars par habitant la demande intérieure augment et les besoins en matières premières sont décuplés ». En conséquent, une forte demande de matières premières, principalement agricoles, viendra de l’Inde dans les années à venir.
Pour finir, d’après l’analyse de Bloomberg « Les Hedge funds ne sont guère optimistes pour le début de l’année 2018. Les gérants de fonds maintiennent leurs paris baissiers pour le soja, le blé et le maïs fin décembre », Megan Durisin, reporter Bloomberg.
Ce type de sentiment baissier ne nous rappelle t’il pas l’analyse de certaines banques qui prévoyaient un euro à parité avec le dollar, ou un pétrole à 20 dollars le baril ? Pour information, l’euro contre le dollar à pris 15% sans atteindre la parité, et le pétrole a atteint les 60 dollars le baril sans descendre sous les 25 dollars le baril.
Au vu de tout ce qui a été mentionné, à savoir la hausse de l’inflation, l’arrivée de l’Inde en tant que puissance économique mondiale remplaçant la Chine, et un sentiment terriblement baissier sur ces matières premières, on peut déjà imaginer un brillant avenir pour le soja, le blé et la mais.

Sébastien CAUDRON

 
Main sources :
Bloomberg : https://www.bloomberg.com/news/articles/2017-12-29/crop-gloom-drags-on-as-grains-post-worst-losing-streak-since-92
GRA fund : http://www.gorozen.com/#/index/
Banque mondiale: https://donnees.banquemondiale.org/indicateur/NY.GDP.MKTP.KD.ZG
Boursorama : http://www.boursorama.com/actualites/puissance-economique-la-france-depassee-par-l-inde-des-2018-e172d61aa92a9f8d6829d7cea300dec2