Moufid ABAYOMIMoufid ABAYOMINovember 27, 2018


The purpose of the article is to determine if Bitcoin is a speculative bubble or a monetary innovation. The results of our study show that Bitcoin is a monetary innovation that has suffered several speculative bubbles due to:

that it is not yet regulated;

the increased public interest;

and media interest.

Indeed, Bitcoin is a decentralized digital money system that is mainly acquired for speculative purposes or to protect savings wherever monetary inflation threatens it. It is the first decentralized cryptocurrency created in 2009 by a mysterious being named Satoshi Nakamoto. It is based on blockChain technology, which is a technology for storing and transmitting information. It is a huge database containing the history of all the exchanges made between its users since its creation.

It does not fully meet the requirement of a sovereign currency. For example, it is decentralized and not under the control of a central bank. In addition, it is oriented towards disinflation, unlike inflationary sovereign currencies.

When it was created, it was technically priced at $0 before reaching an all-time high of $18,800 in December 2017. At that time, it became a new asset class on which investment banks, investment funds, hedge funds create products to attract their clients. It is used in the real economy as a means of payment, a safe haven and a hedging instrument for some investors. Bitcoin supply is controlled by miners and buyers who see in it technological potential.

Demand, on the other hand, is created by specialized investors and the novice public. Since its creation, it has suffered market anomalies, currency platform hacking, exit-scam during ICOs and price manipulation due to trading algorithms.

It then becomes a subject at major international summits. Several consultations were conducted by financial institutions, including European, Asian, American and global financial institutions. Regulators and countries differ on its regulation and taxation. In terms of perspective, we think:

  • the regulation by states and financial institutions of cryptocurrencies in general, and of it and blockChain in particular;
  • that there will be the creation of new sovereign cryptocurrencies;
  • the creation of bitcoin-indexed derivatives such as warrants, turbos, structured products, swaps and transactions such as loan-borrowing, repo etc……..

All this will make it possible to stabilize the economy around Bitcoin, to control its extreme volatility so that there will no longer be a succession of speculative bubbles. It will also help to explore the potential of blockChain technology. As for its value, its price was below $5,000 in the last quarter of 2018.


Alexandre CAMPOSAlexandre CAMPOSJuly 28, 2018


The human or animal side of trading rooms remained paramount in the trading world. Nowadays, IT and mathematics are omnipresent in the front office. In this war against time, some market players are increasingly denouncing a real “arms race”, making it impossible for small players to intervene and thus creating distortions of competition.

The Hight frequency trader or high frequency trading uses powerful computers including algorithms to select and operate minute market movements with an order of time close to one millisecond. We can have up to 1000 executions per second.

The aim being to take advantage of very small price differences on securities system values, it is a form of scalping. This form of trading, which has developed strongly in recent years, is generating a great deal of interest, as is the case for Goldman Sachs, which no longer has a trader in New York, but also a lot of questions for managers, investors and, above all, the AMF. Indeed, it is difficult for the AMF to establish this ratio because not all HF traders are market members and the AMF has direct access only to the identity of the market members responsible for orders and transactions, not end customers.

The debate is great around this apparent form of trading. Some believe that it provides liquidity via market-making and arbitrage as well as a certain market efficiency through a balance of prices between the market and related values.
Denouncers denounce a “ghost” liquidity, the permanent instability of the order book would introduce a structural uncertainty into trading (an order is already obsolete at the time it is sent) that is an obstacle to efficiency.

high frequency trading

Scheme : Source AMF

In addition, market authorities are starting to raise their voices. For example, the AMF recently reported a damning report on this type of trading, denouncing in particular the threats of “market integrity” when trading strategies are diverted from their original purpose and used for market manipulation purposes. 

We now note the appearance in recent years of a system similar to high frequency trading: Robots advisors.

From FinTech (Financial Technology), supported by the AMF and ACPR, it marks a break for individuals regarding their investments and advisors.

Note that robots are already well established in market finance as stated above. For market finance players, these robots are an undeniable reality taking full part in the digital era (BigData).

The banks have therefore opened this possibility to retail investors in order to retain unprofitable customers at a lower cost, attract new customers via an automated and fluidized delegation of transactions through these robots and their algorithms.

The possibilities are numerous for portfolio management, market definition and risk profile allow to let the robot free to process orders automatically. Who will then be held responsible for mismanagement, loss of capital or poor arbitration. The bank, the quant or the investor? of many questions remains present concerning these attractive robots, eager for profit but even more so most inefficient on fraudulent sites.

In short, robots allow financial players to trade at high frequency, allowing automatic execution on thousands of orders on a daily basis, but they require constant vigilance and highly qualified people in mathematics and IT to push the limits of finance a little further each day. This system was an opening to Fintech and a development of the banking and financial offer for individual investors through robots able to manage a portfolio of assets in purchase/sale in an algorithmic way.

Maxime LEMaxime LEJuly 19, 2018


High Frequency Trading THF or HFT: High-Frequency Trading, are the high-speed execution of financial transactions made by computer algorithms. This is one of the categories of the “automatic trading” based on statistical decision, which increasingly manages stock market data in the manner of a Big Data that has become inaccessible to traditional human and banking analysis.

These virtual market operators can thus execute transactions on financial markets: stock exchanges or over-the-counter markets in a few microseconds. While the THF transaction speed of the THF was still 20 milliseconds at the end of the 2010 decade, it increased to 113 microseconds in 2011.

High Frequency Trading

According to La Tribune, the THF now dominates market activities. To the point of shading traditional investors, and likely to be more regulated in the future. With a market share of 60% on Euronext and nearly 90% in the United States, THF has become a must in recent years. No wonder that investments in IT infrastructure for the THF reached $1.5 billion in the United States in 2013.

The THF is therefore one of the most advanced research topics in finance, not to mention that it represents major challenges for regulators, particularly the AMF. Thus, it is necessary to maintain this algorithmic trading? What are its advantages? Where is she going to go?

A model that calculates profits. While some people are delighted with the rise of high-frequency trading, such as Goldman Sachs, which has practically no more human traders in New York but no more IT engineers, this development constitutes a formidable competition with conventional investors, who place their orders much less quickly than algorithms.

The high-frequency trading model is based on a Nash equilibrium, the teacher-researcher draws the following conclusions:” The higher the speed, the more liquid the market, which improves price quality. However, speeding can be detrimental to the benefit of THF, as speed is a time competition.

In other traders, profits fall with speed. Finally, price volatility decreases with the THF, but increases with traditional traders“. He added: “high-frequency trading makes infinitely more than others. For small carriers, there is no point in gaining speed against the THF. They should focus on longer-term strategies rather than intraday transactions“.

But like any success, the regulations are to come: currently governed by the European Directive MIF 1, the THF will be supervised by MIF 2 from January 2018. This device will make it possible to identify the orders placed by each algorithm and thus help researchers in their future work. And we will suspect that many more regulations are coming for THF if it continues to generate more profits.

Thus, the future of THF has a bright future as have technological developments with the Big Data, La Blockchain for instance. But only if they are not overly regulated, otherwise other new technologies will emerge as a virtuous circle.

Maxime LEMaxime LEJuly 19, 2018


According to the best brokers, the trading robots are a dazzling success, on forex, stocks or even binary options. The trading robot is a computer program that is dedicated to the practice of trading. The latter automatically gives orders based on market trend signals.

robot trading

It is therefore a trading tool based on the technical study of statistics made available to stock market participants. This robot is continuously operational, 24 hours a day, so it makes investments in place of the trader without the latter having to constantly monitor the prices of its assets.

If trading robots are a great success with traders at all levels and with individuals interested in the world of trading, it is above all thanks to the platform MetaTrader 4.

For more than thirty years, professional traders in financial institutions such as banks or investment funds have been using de autonomous trading algorithms. But until the arrival of the MetaTrader 4 platform, their use was not accessible to the general public.

This new trading platform, thanks to its integrated programming language, allows all its users, whether experienced or novice traders, to code and develop their own algorithms, while being able to test or launch them in real time by attaching them to the platform’s graphics.

The best robot trading

As you will surely have understood, the best trading robot is the one whose strategy you master perfectly. That’s why there is not one but the best trading robots. There are as many traders as there are strategies, and each trader with an autonomous algorithm, which follows his strategy to the letter, without psychological bias, without the risk of overtrading, and without tiring, will be fully satisfied satisfied.

This way he will know when to activate it, when to deactivate it, and he will know how to improve his strategy because trading is an activity in which we constantly learn and improve.

Not all brokers allow their clients to use trading algorithms, some brokers allow them but slow down the execution of orders which can make a viable strategy lose out.

At Admiral Markets you can develop your algorithms with confidence, test them in a secure environment thanks to the demonstration accounts made available to customers. But above all, your robot will benefit from the same trading conditions as a trader who enters his positions manually, so your robot will benefit from optimal conditions.

But what future for robot traders? It is essential to understand that no automatic trading software can guarantee a 100% rate of winning trades. It is also important to remember that past performance does not guarantee future results.

AvatarThibault MEYNADIERJune 18, 2018


The digital revolution, which can be defined as all new technologies; Big data, Artificial Intelligence, Machine learning, BlockChain, or the Internet is tending to develop strongly in the financial world (digital finance) and this in all professions.

Whether from a task automation point of view, information processing in the back and middle office professions, or from the front office side with powerful calculation methods to perform predictive analyses, arbitrations, or automate transactions or even from the asset management side with robo-advisory. Everything goes through it.

digital finance

Despite all these innovations and major changes, it now seems difficult to measure the real impact of the digital revolution on market finance, unlike other sectors that have been profoundly changed by this revolution. So how can we understand digital technology in market finance? (difficulty and levers)

Initially, the dematerialization of finance is in fact an old process that is already anchored on the markets, such as the SWIFT system for bank transfers that appeared in the early 2000s. The famous “MTF” trading platforms were also created shortly before the 2008 economic crisis.

All this means that the major players in the financial markets were already used to digital technology and in particular process automation. Unlike many other sectors, digital technology has not disrupted finance. But it can be said that in recent years, innovation in the digital sector has grown significantly and individuals have much higher requirements in terms of speed of transactions in particular.

Moreover, the emergence of Startups specialized in finance, known as “fintechs”, are not only direct competitors of major credit institutions, as one might think, but most of them are in fact there to develop new technological models to improve the existing services of the major banks to which they are attached.

Fintechs are appreciated by large banks because they have more appropriate human resources, they are more efficient, more flexible, and the speed of project execution is higher than large groups.

The blockChain, for its part, is a real technological breakthrough, more and more present over the past 4 years, it constitutes a real breakthrough lever for large financial companies. Indeed, the field of action of this technology could profoundly disrupt market finance by eliminating intermediaries (compensation, transactions, bank syndicates, for example).

The increasingly strict and restrictive regulation of market finance over the past ten years has also helped to reduce the scope for innovative companies and the emergence of new technologies in this sector.

Thus, all these technological upheavals may prove in the coming years to be an important growth driver for market finance, which has seen its revenues decline since the repeated crises. This could simplify finance, reduce costs and thus increase the profitability of the sector, or even generate new jobs.

Dario PETROVSKIDario PETROVSKIJanuary 26, 2018


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

« Les Nouveaux Loups de Wall Street »




Big Data’s main potential is to help companies improve their operations in the sense that they’re able to make faster, better and more intelligent decisions by collecting and analyzing the data with the aim of gaining a useful edge and increasing revenues. Are finance and Trading exempt from this massive improvement in Data Science?

Big Data… What’s that?

Big data has been used since the 1990’s, with the computer scientist John Mashey making it more popular. It includes data sets (A specific collection of data) with sizes exceeding the ability of commonly used tools. Its main philosophy involves encompassing data, be it unstructured in the form of texts and images, semi-structured or structured in the form of numerical inputs and tables. In order for it to work, Big data requires a set of techniques with more advanced integration forms, able to reveal insights from the massively scaled, complex and diverse collected data sets.

big data

By the 21st century, researches and reports have associated Big data and its data growth challenges with a stack of characteristics.

  • Volume: Represents the quantity of generated or stored data. The value and potential insights are often determined by the size of the collected data.
  • Velocity: Represents the speed at which the data processing is done. A threshold is often set in order to meet the requirements and challenges lying in the path of a company’s growth.
  • Variety: Represents the nature and type of data used. Classifying the collected data will often help the analysts determine an effective use of the resulting insights.
  • Veracity: Accurate analysis heavily depends on the quality of data sets. As such, captured data are forgone an extensive veracity analysis with the aim of increasing the insights’ performance.
  • Machine Learning: Often used as an introductory operation to Big Data, it explores the study and implementation of algorithms able to learn from and make predictions on data. In other words, machines are given the ability to learn without being explicitly programmed.

As such, Big data begins to have a predominant role in feeding computers and servers thriving on useful knowledge, enabling the companies to maintain a competitive edge in their respective environments.

That’s great… And what about Financial Trading?

Like any other forms of trading, financial trading is all about buying and selling financial instruments, be it shares, forex, bonds or derivatives in the form of CFDs, futures, swaps and options. It doesn’t matter which financial instrument is involved, the outcome should be common: To make a profit, which is easier said than done! In financial markets, millions of firms, individuals and even governments simultaneously tend to attempt making profits from trading.

However, with all these traders colliding against one another, the prices of the instruments tend to move in a rather random pace, making it very hard to predict the future prices, with the conventional methods at least. Some markets tend to be very volatile in the sense that not only it is moving a lot and bringing more profit opportunities, but also increasing the risk

…Which bring us to the enigma of risk! No matter what instrument is being traded, who’s trading it or where the trade is taking place, it is all about balancing the potential profit against the involved risk.

Big Data and Financial Trading… How do they correlate?

Good question. As financial markets tend to be some of the most dynamic entities to exist, the trading methods must follow the same dynamism in order to consistently generate profits. As such, traders will consistently develop trading methods that are temporarily profitable for the corresponding market conditions and constraints. But what will happen if the conditions change? The methods will ultimately show their failures.

This leads us to the infamous enigma of traders: Is there a way to build and implement a system able to consistently calculate the optimal probability of executing profitable trades? We all know that it has become almost impossible for the trader to keep up with the overwhelming surge of incoming data from market analysis, especially with the use of classic methods involving market monitoring.

This is where Big data analytics come to the rescue. Traders are starting to switch from the classic, manual trading strategies to what we call to this day, Quantitative Trading. Exactly as its name states, it consists of trading strategies based on quantitative analysis, which by itself relies on mathematical computations and number crunching with the hope of identifying trading opportunities. As quantitative trading is effective for extremely large-in-size transactions, it is mostly used by Hedge Funds and financial institutions. That doesn’t matter anymore since even individual investors are getting used to it!

For now, let’s break the quantitative trading down. The very first things a trader needs are data inputs. For a quantitative analyst, the most commonly used inputs are the price and volume. Next, the trader is prone to select the technique they wish to use, such as high-frequency trading or statistical arbitrages, and then couple it with the quantitative tools like the moving averages, stochastic indicators and oscillators.

But here’s where it gets more complicated. The trader creates their mathematical models and then develops a computer program able to simulate the model with the help of historical data. Of course, depending on the obtained results, the model may forgo backtests and optimizations, and once validated, the model is hence implemented in real-time markets. This leads us to understand how quantitative trading works best: It uses all the possible analogies, patterns and trends in order to predict the outcome of a specific event, which in our case is the future price.

Based on the Big data’s characteristics we’ve already mentioned above, financial organizations and retail traders are finally able to extract a great deal of information, helping them in their trading decisions.

Quantitative traders, rejoice! Thanks to the predictive capabilities Big data has recently given, historical data (Prices) can easily be crunched with the advanced techniques of Machine Learning and Artificial Intelligence, and then be explored to identify patterns allowing the traders to refrain from order punching and switch to the more creative aspect of estimation. This will notoriously help the trader park their capital at the right time and place.

A simple proof of Big Data being extremely useful for automated trading is the fact it is widely used by the biggest financial institutions like J.P Morgan, which are, for the record, mass-recruiting Data scientists who perfectly understand Machine Learning and Data analysis using Big Data.

Going even further, some financial institutions have begun to use the sentimental analysis technique, which by itself is a form of data mining. Also known as opinion mining, it involves computationally identifying and categorizing opinions (Buy at a specific timespan, Sell at a specific timespan, Indifferent, Waiting for the market to move, etc) usually expressed in the form of texts. The aim is to properly determine whether a specific population of traders’ attitude towards a specific financial instrument at a specific timespan is positive, negative or neutral. This technique can show some very interesting results when coupled with the previously stated predictive models using Big Data.

In a nutshell

Big data is starting to show its notoriety when it comes to Quantitative and High-Frequency trading, whether done by financial firms or private investors. As firms receive petabytes of live tick data from electronic transactions and feed them to the dedicated server, they are used as historical inputs for developing quantitative models and algorithms based on the obtained trade decisions.

As mouth-watering as it may sound, it also presents imperfections. Of course, not only big data and machine learning have drastically reduced the margins of error caused by human decisions, but have also made it possible to trade more accurately, and thus dramatically impact the way transactions are executed. However, traders need to understand that not all the market scenarios can be predicted or at least recreated.

You could have all the possible data sets, coupled with the best patterns generated by Big data, and then use the best quantitative model there exists, but still end up with a trade loss! This can be explained by the incompleteness of Big data patterns, in the sense that they do not include the sudden market surges caused by human errors and/or false rumors. Nevertheless, it won’t be very long before Big data becomes a mainstream necessity for financial institutions… Or has it already?

Edouard CHANSAVANGEdouard CHANSAVANGJanuary 22, 2018


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”.


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.


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.


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.


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.


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)?


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).


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.


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?


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):


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.”


Baptiste BRUNEAUBaptiste BRUNEAUJanuary 9, 2018



First of all, let’s go back to the fundamentals (monero rate), for several years now crypto-currencies have existed. Today, and given the importance that this type of currency is taking on in our financial system, I think it makes sense to focus on it. A cryptomonnaie is a 100% electronic, virtual and encrypted currency.

monero rate

The encryption of these virtual currencies is done via a file storage system called Blockchain. A Blockchain is comparable to a public transaction ledger. It is an information storage and transmission technology that allows fast and secure transactions to be carried out while saving on operations.

To return to cryptomonnaies, it is important to know that behind each of them lies a well-defined project. Each of the existing cryptomonnaies was founded by fulfilling a well-defined objective. In the following paragraph we will look at the Monero’s.

Monero, “Secure, Private and Untraceable”

Among more than 1000 cryptomonnaies, only a few ten stand out. We’ve all heard about the famous Bitcoin that just crossed the $10,000 mark or the Ethereum, the Dash and so on… A cryptomonnaise is making a name for itself in the intriguing world of virtual money, the Monero. But why? Why?

Created in April 2014, the Monero rate is a peer-to-peer virtual currency (see monero course) that unlike the vast majority of new cryptomonnaies is not a Bitcoin clone. That is, it is not developed on the source code of the latter. The Monero is built from a protocol called CryptoNote (Source code with constant evolution). This virtual currency is characterized by three elements which are as follows:

– First, it uses a system called “Ring signatures” (Circular Signatures). This cryptographic process consists in electronically signing a message anonymously.

– Then, the RCT (Ring Confidential Transactions), it is a question of splitting each transferred sum into several “packages”, this makes it possible to disclose the transaction amount in order to make it less traceable. In addition, recently this technology may mask the amount of the transaction.

– Finally, the most important feature of the Monero rate is the “Stealth Adresses” component. This technique ensures the confidentiality of the parties involved in the transaction (recipient and sender). Thanks to a stealth address a sender can take the public address of a recipient and make it a unique address, which means that only one address is registered in the blockchain.

These three characteristics make Monero rate an anonymous, untraceable and therefore secure currency. The advantage of these elements is that Monero exchanges are impossible to follow on the Blockchain unlike Bitcoin.

Bitcoin is less secure than Monero because we can access its blockchain, we can see all the transactions there so we can deduce the origin and destination of the transfer. Concerning the Monero, a user will not be able to know the content or history of another portfolio, unless its owner authorizes it by giving it a special key.

Could the Monero rate the Bitcoin?

The Monero has several advantages over the Bitcoin, here they are:

Monero’s extraction algorithm is superior to Bitcoin’s: ‘Mining’ is the name given to the execution of a program on a computer that verifies and processes the cryptocurrency transactions that other people report to the global network.

The Bitcoin algorithm runs much faster on custom extraction chips (ASIC – high-performance and very expensive computer) than on standard PCs and laptops. This means that it is almost totally unnecessary for an ordinary computer user to attempt to participate in the Bitcoin extraction process, and leads to a relative concentration of miners in countries with the lowest electricity costs.

On the other hand, the Monero mining algorithm has been specifically designed so that ASICs will not have too much advantage over ordinary computers belonging to the general public. Those who do this will receive Moneros in exchange for running the software. As no special mining equipment is required, this means that it would be easy for anyone to download a Monero portfolio to simply click on a single button to start exploration on their computer.

– Block size (Blockchain components) – When transactions are announced on the Monero or Bitcoin networks, they appear as part of a “block”. Monero blocks are produced on average every 2 minutes and Bitcoin blocks are produced on average every 10 minutes. Bitcoin blocks have a maximum size, so if there is no space, your transaction will be delayed.

If you desperately need your transaction to be included in a Bitcoin block, you will need to increase the transaction fees you pay to the Bitcoin network. Monero, however, was designed to have an automatically adaptive block size limit. This means that it will automatically be able to manage future increases in transaction volume by automatically increasing the block size to accommodate higher future transaction volumes.

– An impressive integration of the I2P (Invisible Internet Project) layer into Monero is under development. This will add even more privacy protection during the transaction in Monero. I2P The purpose of this technology is to make your payments untraceable.

– The quality of the Monero project’s R&D team and their design objectives are very impressive, with more than 180 engineers contributing to the development of this currency.

So can the Monero compete with Bitcoin or not?

cours monero
Golden bitcoins on PC keyboard

I think that from a technical point of view, yes. The source code of the Monero is more advanced than that of the Bitcoin as we have seen previously and the execution protocol is constantly evolving. Moreover, its network is not yet saturated, as may be the case with the Bitcoin, which has a problem of scaling up.

There is a risk of saturation and consequently of longer and longer payment transfers (sometimes more than 30 hours to receive Bitcoins). Concerning Monero the network is not saturated (with many empty blocks per day due to its anonymous nature). It is impossible to say which transactions have been processed or not. This is perhaps Monero’s greatest challenge, to ensure that his anonymity does not hinder his progress to scale.

There is also a major question about the future of cryptocurrencies, from a political and regulatory point of view. No one is certain that governments will let governments operate freely without control, regulation or taxation of virtual currencies.


The only disadvantage is that thanks to or because of this anonymity, the Monero is widely used in illegal (or at least underground) markets, says Darknet, so it can jeopardize his reputation. Despite this, this currency will continue to develop so I think there is reason to be interested in it and to be optimistic about the future of this project.

A certain number of individuals are already seeing the Bitcoin collapse (bubble phenomenon) and give way to another currency such as the Monero, which claims to be more confidential.

Personally, I think that cryptomonnaies are very interesting technologies that have certain limitations. The blockchain is a step forward not to be overlooked.

As a reminder, the disadvantage of the Monero is its image linked to illegal markets, however it should be remembered that the main use of Bitcoin at its creation was the purchase on the Darknet. Ideally, a currency should be created that identifies individuals and therefore eliminates all illegal uses while maintaining the advantages of the Blockchain (see monero rate).

I think the Monero is the Beta version of the Bitcoin. Moreover, it looks a lot like Bitcoin in 2010. Is it this activity on the Darknet that caused the significant increase in Bitcoin? Will the Monero have the same route as the Bitcoin?  According to some researchers, the Monero would be the best replacement for the Bitcoin in the event of a Bubble.

Thanks to the research I was able to do to write this article, I was able to discover another virtual currency that is all the more interesting because the Monero, it is the Ripple. The Ripple plans to replace the SWIFT transfer, so many banking institutions are already interested in it. This currency will be the subject of my next article (see monero rate).

NB: Cryptocurrencies are extremely risky investments. This article does not constitute investment advice, please do your own in-depth research (see monero course) in case you wish to invest. ”monero rate”



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