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HFT and illiquidity – Part 4 (English Language)

The below article continues a series dedicated to High Frequency Trading (HFT) and program trading, from which I derive my trading edge. In previous articles I have briefly explained what HFT and Program Trading are. A few weeks ago I have started a new series focusing on market illiquidity produced by the ever increasing presence of HFT. HFT is really dangerous for markets’ health. Hereunder the list of articles published so far:

Recently I have introduced an new series titled: “HFT and illiquidity” (find here part 1, part 2 and part 3)  focusing specifically on the problem of illiquidity, tightly connected to market crashes, caused by the overwhelming presence of High Frequency Trading (HFT). In the last article of this series below I will briefly report on the social process known as normalization of deviance and how it affects market stability.

‘Some researchers propose the “Flash Crash” event in the US financial markets on 6 May 2010 is in fact an instance of a “normal failure”. Such failures have previously been identified in other complex engineered systems and are major system-level failures that become almost certain as the complexity and interconnectedness of the system increases. Previous examples of normal failures include the accident that crippled the Apollo 13 moon mission, the nuclear-power accidents at Three Mile Island and Chernobyl, and the losses of the two US space-shuttles, Challenger and Columbia.

Researches argue that major systemic failures in the financial markets, at a national or global scale, can be expected in the future, unless appropriate steps are taken. The key factor in this belief is the natural human tendency to engage in a process that is called “normalization of deviance”. How can we easily explain that? Let’s say that some deviant event occurs that was previously thought to be highly likely to lead to a disastrous failure. When this event presents itself and it then happens that actually no disaster occurs, there is a tendency to revise the agreed opinion on the danger posed by the deviant event, assuming that in fact it is normal: so the “deviance” becomes “normalized”.

The fact that no disaster has yet occurred is taken as evidence that no disaster is likely if the same circumstances occur again in future. This line of reasoning is wrong, but it is only broken when a disaster does occur, confirming the original assessment of the threat posed by the deviant event.

As a reaction to the “Flash Crash”, exchanges have tightened the circuit-breaker mechanisms. But these mechanisms in each of the world’s major trading hubs are not harmonized, exposing arbitrage opportunities for exploiting differences. Moreover, computer and telecommunications systems can still fail, or be sabotaged by those who oppose the system, and the systemic effects of those failures may not have been fully thought through.

The new circuit breakers that were introduced will probably help managing adverse events. But there are no guarantees that another event, just as unprecedented, just as severe, and just as fast (or faster) than the Flash Crash cannot happen in future. Normalization of deviance can be a very deep-running, pernicious process. Regulators are not trusted because they were not able to foresee and mitigate the causes of the sub-prime crisis and the next market failure may well have roots in other aspect of the system, maybe a failure of risky technology that, like the Flash Crash, has no clear precedent.

The dangers posed by normalization of deviance and normal failures are if anything heightened in the technology-enabled global financial markets and that is because the globally interconnected network of human and computer traders, or what is known in the academic literature as a socio-technical system-of-systems, i.e., an interconnected mesh of people and adaptive computer systems interacting with one another, where the global system is composed of constituent entities that are themselves entire independent systems, with no single overall management or coordination.

Such systems are so radically different from traditional engineered systems that there is very little established science or engineering teaching that allows us to understand how to manage and control such super-systems. Research thus far provides no direct evidence that high frequency computer based trading has increased volatility. But, in certain specific circumstances, self-reinforcing feedback loops within well-intentioned management and control processes can amplify internal risks and lead to undesired interactions and outcomes. These feedback loops can involve risk-management systems, and can be driven by changes in market volume or volatility, by market news, and by delays in distributing reference data. A second cause of market instability is social: normalisation of deviance, a process recognised as a major threat in the engineering of safety-critical systems such aeroplanes and spacecraft, can also affect the engineering of computer based trading systems.’

The above article appeared on my free Newsletter sent out on Sunday, December the 2nd  along with other information typically including: a weekly review for the Dollar Index, the Euro-Dollar cross, and the S&P500 index, other forex pairs, commodities futures (FibStalker View on Currencies) and stocks, articles on my trading method, market commentaries and HFT/Program Trading articles like the one you have just read. Please, register here to receive the free weekly newsletter.

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HFT and illiquidity – Part 3 (English Language)

The present article continues a series dedicated to High Frequency Trading (HFT) and program trading, from which I derive my trading edge. In previous articles I have briefly explained what HFT and program trading are. Two weeks ago I have started a new series focusing on market illiquidity produced by the ever increasing presence of HFT. HFT is really dangerous for markets’ health. Hereunder the list of articles published so far:

‘Recently I have introduced an new series titled: “HFT and illiquidity” (part 1 and part 2) focusing specifically on the problem of illiquidity, tightly connected to market crashes, caused by the overwhelming presence of High Frequency Trading (HFT). In the below article I dwell a bit more the feedback loops responsible for market instability. In the next article I will analyze the social process known as normalisation of deviance and how it affects market stability.

Changes and fluctuations in market values are always to be expected, but if a change is sufficiently large or unexpected that it fundamentally impairs the saving/investment process, eroding confidence, then that change can be considered a financial stability event. For example, despite being an intra-day event, the “Flash Crash” of May the 6th, 2010, when the US equity market dropped by 600 points in 5 minutes and then regained almost all of the losses within 30 minutes, helped eroding confidence in stock markets sufficiently to be followed by several months of outflows from retail mutual funds in the US.

Computer based trading (CBT) may adopt liquidity-consuming (aggressive) or liquidity-supplying (passive) trading styles. We focus on the aggressive algorithms. Even if market daily volume is large, the second-by-second volume may not be. For instance, even a daily turnover of more than $4 trillion in the foreign exchange market on average corresponds to only $2.7 million second-by-second volume for major currency pairs like Euro-Dollar. Even in such a huge market, a sufficiently large order can temporarily sway prices, depending on how many other orders are in the market (the “depth” of the market) at that moment in time.

As far as financial stability is concerned, a significant aspect is the nonlinear dynamics. Put simply, this is how a system changes over time: it is nonlinear if a given change in one variable may either lead to a small change in another variable or to a large change in that other variable, depending on the current level of the first variable. The complexity of financial system and the network of interactions between agents (firms, individual, regulator, market exchanges, programs, etc.) also makes the dynamics more complicated. The problem is that complex nonlinear dynamics of networked systems is, in comparison to other fields, in its infancy with regards to concrete predictions and reliably statements. This makes regulators work very difficult.

Market crashes have been present since decades, like the story of a “Flash-Crash” type event in 1962; however the 1987 crash offers a good illustration for the sort of systemic events that mechanical rule-following –implemented in HFT – is able to generate: that market decline was portfolio-insurance-led. In order to hedge their risks, as stock indices dropped, portfolio insurers were required to adjust their holding of stocks used to balance risk. However, the values of those the stocks were used to calculate the value of the index and selling stocks depressed prices, and that pushed the index even lower; this then caused another adjustment of the stocks holdings, which pushed the index even lower still. This positive feedback loop, i.e. the effects of a small change looping back on themselves and triggering a bigger change, which again loops back, and so on, had a profoundly damaging effect, leading to major share sell-offs. This example shows that it seems more likely that, despite all its benefits, HFT and CBT may lead to more obviously nonlinear financial system in which crises and critical events are more likely to occur, even in the absence of frequent external fundamental shocks.

Financial market instability could be implied by:

  1. increased sensitivity where financial dynamics become sufficiently non-linear so that widely different outcomes can result from only small changes to one or more current variables;
  2. informational issues where the information structure can exacerbate or reduce market swings. For instance malicious agents could diffuse information to coordinate and create a ’bank-run‘ on an institution, a security or a currency if a given publicly observed signal is bad enough;
  3. endogenous risks, related to the emergence of positive, mutually reinforcing and pernicious feedback loops, similar to that illustrated in the case of the 1987 crash.

There are different feedback loops that can contribute to the endogenous risk cause for market instability. These loops include:

  • Risk feedback loop, whereas some financial institutions are hit by a loss that forces them to lower the risk they hold on their books, and that requires selling risky securities. A small initial fundamental shock can lead to disproportionate forced sales. Versions of this loop apply to HFT market makers: given the tight position and risk limits HFT operate under, losses and an increase in risk lead them to reduce their inventories, thereby depressing prices, creating further losses and risk, closing the loop.
  • Volume feedback loop, HFT algorithms may directly create feedback effects via their tendency to hold small positions for short time periods and then pass the “hot potato” to other HFTs algorithms generating very large, fictious volumes but the overall net position hardly changed at all. Financial instruments are circulating rapidly within the system, and this increase in volume triggers other algorithms which are instructed to sell more aggressively in higher volume markets, closing the loop.
  • Shallowness feedback loop, whereas an initial increase of volatility, for instance due to news, widens the spread. With everything else constant, incoming market orders are more able to move the market reference price and increase volatility, which in turn feeds back into yet more dispersed quotes, and the loop is closed.
  • News feedback loop, whereas some HFT systems include a news listener component that scans headlines for tags and acts upon them immediately. For instance HFTs buy or sell depending on where prices are relative to the HFT’s own perceived fair value; if the transactions of HFT systems are reported in news feeds, and picked up on by other HFT systems this can lead to similar trades and the loop is closed.
  • Delay feedback loop, whereas in a lower move a small quote lag in a market can push HFT into routing orders and bidding in the most attractive market, regardless of the fact that actual bids were lower. A second feedback loop then reinforces the first one: as delays creep in and grow, the increased flurry of activity arising from the previous feedback loop can cause further misalignments in bid/ask time stamps, closing and amplifying the pricing feedback loop.
  • Index feedback loop, whereas extreme volatility of the individual component securities spilled over into the ETF (exchange-traded fund) markets and led to pause pause their market making activities. Thus the illiquid/unreal ETF prices for aggregates provide false systematic factor signals, feeding back into the pricing of individual securities, and thereby closing the loop.’

The above article appeared on my free Newsletter sent out on Sunday, November the 18th along with other information typically including: a weekly review for the Euro-Dollar cross, and S&P500 and other forex pairs, indices or commodities futures (FibStalker View on Currencies) and stocks, articles on my trading method, market commentaries and HFT/Program Trading articles like the one you have just read. Please, register here to receive the free weekly newsletter.

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HFT and illiquidity – Part 2 (English Language)

The present article continues a series dedicated to High Frequency Trading (HFT) and program trading, from which I derive my trading edge. In previous articles I have explained briefly what HFT and program trading are. Last week I have started a new series focusing on market illiquidity produced by the ever increasing presence of HFT. HFT is really dangerous for markets’ health. Hereunder the list of articles published so far:

‘As anticipated last week I have introduced an new series titled: “HFT and illiquidity” (part 1) that focus specifically on the problem of illiquidity, tightly connected to market crashes, caused by the overwhelming presence of High Frequency Trading (HFT). In the below article I will report on the results of some scientific-economic researches. In subsequent articles I will delve into some of the aspects and will provide some conclusions on this topic.

Computer based trading has transformed how financial markets operate. The volume of financial products traded through computer automated trading taking place at high speed and with little human involvement has increased dramatically in the past few years. For example, today, over one third of UK equity trading volume is generated through high frequency automated computer trading while in the US this figure is closer to three-quarters. Whilst the prevalence of computer based trading is not disputed, there are diverse views on the risks and benefits which it brings today, and how these could develop in the future. As anticipated, gaining a better understanding of these issues is critical as they affect the health of the financial services sector and the wider economies this serves. The increasingly rapid changes in financial markets mean that foresight is vital if a resilient regulatory framework is to be put in place. Following are three outcomes from some economic researches.

Outcome 1 – Economic research thus far provides no direct evidence that high frequency computer based trading has increased volatility. However, in specific circumstances, a key type of mechanism can lead to significant instability in financial markets with computer based trading (CBT): self-reinforcing feedback loops (the effect of a small change looping back on itself and triggering a bigger change, which again loops back and so on) within well-intentioned management and control processes can amplify internal risks and lead to undesired interactions and outcomes. The feedback loops can involve risk-management systems, and can be driven by changes in market volume or volatility, by market news, and by delays in distributing reference data. A second cause of instability is social: a process known as normalisation of deviance, where unexpected and risky events come to be seen as ever more normal (e.g. extremely rapid crashes, like the Flash Crash in 2010), until a disaster occurs.

Outcome 2 – Overall, liquidity has improved, transaction costs are lower, and market efficiency has not been harmed by computerised trading in regular market conditions. The nature of market making (i.e. the task of quoting both a buy and a sell price in a financial instrument or commodity held in inventory, hoping to make a profit on the bid-offer spread) has changed, shifting from designated providers to opportunistic traders. High frequency traders now provide the bulk of liquidity, but their use of limited capital combined with ultra-fast speed creates the potential for periodic illiquidity. Computer –driven portfolio rebalancing and deterministic algorithms create predictability in order flows. This allows greater market efficiency, but also new forms of market manipulation. Technological advances in extracting news will generate more demand for high frequency trading, while increased participation in this will limit its profitability.

Outcome 3 – Ongoing advances in the sophistication of ’robot‘ automated trading technology, and reductions in the cost of that technology, are set to continue for the foreseeable future. Today’s markets involve human traders interacting with large numbers of robot trading systems, yet there is very little scientific understanding of how such markets can behave. For time-critical aspects of automated trading, readily customisable, special-purpose silicon chips offer major increases in speed; where time is less of an issue, remotely-accessed ’cloud‘ computing services, offer even greater reductions in cost. Future trading robots will be able to adapt and learn with little human involvement in their design. Far fewer human traders will be needed in the major financial markets of the future.

In conclusion, there are researches putting focus on the causes of instability, both technical and social; some researches focus on the benefit of increased liquidity, but as we have seen in the first part of this series, the portion of human market participants reacts to the presence of HTF with a size back of their trades. Lastly, a thir group of researchers focuses on the technology advances indicating that the phenomen could even develop with less and less participation from humans. Next week I will continue to dwell on the outcomes illustrated so far.’

The above article appeared on my free Newsletter sent out last Sunday, November the 11th along with other information typically including: a weekly review for the Euro-Dollar cross, and S&P500 and other Forex, indices or commodities futures (FibStalker View on Currencies), articles on my trading method, market commentaries and HFT/Program Trading articles like the one you just read. Please, register here to receive the free weekly newsletter.

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HFT and illiquidity – Part 1 (English Language)

The present article continues a series dedicated to High Frequency Trading (HFT) and program trading, from which I derive my trading edge. In the previous articles I have briefly introduced HFT and program trading and I showed how computerized algorithms are very widespread and, in some cases, they even regulate and ‘run’ markets. If these statements look extreme then I invite you to read my previous articles linked here below:

‘In the last few weeks I have introduced the topic of HFT and Program trading to my newsletter subscribers. HFT is so important going forward because it really affects markets health and their normal functioning. A trader with decades of experience I value and respect, Joe DiNapoli, studied profoundly the effects of HFT and lack of liquidity on the markets. I urge you read his article ‘Illiquidity and the danger it fosters for World Equity markets©‘ and that you request all the other links to free stuff he distributes on his website. Even if I don’t use DiNapoli levels and his method because I think the trading method of measured moves better explains price structure and, moreover, I don’t believe in the use of indicators (however you blend and displace them, they should have a very small part in a trading method, in my humble opinion). However, I am firmly convinced Joe DiNapoli has really got a deep understanding on how the market works and what’s going on the markets with regards to the effects of HFT.

Joe DiNapoli was there in 1987 when we had the firts market crash after 1929 and, actually, he predicted the crash (500 points lost in the Dow Jones index, or -20% in just one day) by observing lack of liquidity in the pit. In his very interesting article DiNapoli reports on several other market crashes he has witnessed, including the recent Flash Crash happened in 2010. That was not a surprise to those who understand markets and its mechanics from the bottom up, i.e. the bid, the ask, and the size. All of these crashes have one common cause: illiquidity. In a crash HFT computerized programs front-run bids and actually steal business to professional traders who intend to establish long positions in financial instruments now at much lower prices. Phantom bids appear and disappear (induced by HFT) in the process and a professional trader never has the possibility to participate. So either traders ends up owning a position when he/she are wrong (i.e. the market continues lower) or they never have the possibility to participate (they cannot establish a long position in a flash crash). For instance a trader bids $20 for a stock and HFT programs input a sub-penny bid at $20.001, so the trader does not get filled only to witness that bid disappear few seconds later. This process steals liquidity from the markets because in a such rigged market traders decide to reduce risk, i.e. position size, or not participate at all. The overall effect is even lower liquidity, and increase in trading costs, because of wider bid-ask spreads. With  decreased participation prices take on an non-natural shape where there are fewer or no retracements.

When there are less or no retracements in market structure, the overall market is less efficient. Those who are wrong have less opportunity to exit their positions and the market becomes over-punishing. Traders must be very precise in their entry and take partial profits frequently. Entering the market is more difficult too and it is very common to not get filled when you are right about market direction.
Among other reasons Joe DiNapoli brings to our attention that exasperate this tendency are the significant reduction in the number of traders (both professional and private) due to the economic crisis, sub-penny pricing and fraudulent denial of service trading techniques of HFT, computerized trading taking near riskless profits out of markets by exploiting time differentials in micro-second intervals in various markets, fraudulent naked short selling and less freedom in the markets that are no longer free to trade normally because of government and central banks policies (e.g. QE3).

What regulators are saying about HFT? While traders like DiNapoli believe regulators are well behind the curve and do not understand market structure and how market works, I would agree that the effect of HFT on the markets should not be studied only by academic personnel or government bodies. Real traders who understand the markets as auctions know what is really going on and, of course, know very well the psychology reactions of the group they belong to. Moreover we cannot exclude the existence of conflicts of interest as suggested by the lack of success regulators have showed in handling the financial crisis now 4 years old. Therefore the impression is that conditions are not in place (just yet) to tackle the HFT phenomenon in the right way. The approach is still reactive and not proactive: circuit breakers and forensic software helpful only ex-post (and not ex-ante), i.e. after the fact, in limiting the outcomes and analyzing issues, are the solutions proposed at the moment.

In particular, the adoption of software for forensic analysis of extreme market events, includes synchronized timestamps that could allow regulators in different countries to detect the sequence of trades. In fact some researchers conclude that regulating the algorithms used by computer traders would be too costly and cumbersome. Costly? Do we know how much banks running computer algorithms are making out of this bad practice? It could be in the millions every day. Other researchers found that high-frequency trading is not  spurring broad increases in volatility even as it sometimes creates “instability” that may lead to crashes. I think instability could lead to a crash like in 1987. By today’s definitions that would simply be a form of ‘instability’. As you can see I tend to stigmatize the blatant inaction of regulatory body with regards to the effects of HFT on markets’ health because I trust more direct experience of traders. However to get the full picture I will take a look at what researchers have found so far on HFT and what their reccomendations are.

In conclusion, it is key to keep monitoring the effects of HFT and the steps of regulators because HFT can affect market liquidity, the very functioning of the markets we love and know how to trade. For this reason it is also good to stick to futures or forex markets having the highest liquidity like the EUR/USD and the S&P500, as well as, to stocks with high volume.’

The above article appeared on my free Newsletter sent out last Sunday, November the 4th along with other information typically including: a weekly review for the Euro-Dollar cross and other Forex, indices or commodities futures, articles on my trading method, market commentaries and HFT/Program Trading articles like the one you just read. Please, register here to receive the free weekly newsletter.

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EDUCATION – How HFT and program trading are relevant to my trading method and can be in yours – Part 2

You should know by now that HFT and program trading is everywhere. In the first part of this series of articles on HTF and program trading I explained what HFT and program trading are and how they can affect the market. They however offer an excellent trading edge, as well, which can be used in our trading. The decisions taken through the application of rules identified by observing the effect of program trading on price are often in contrast or well in advance of the observations and signals offered by classical Technical Analysis (TA). Typically these decisions will go against what the crowd is thinking too and this fact alone is important because it is suggests that following program trading is the right way to approach the markets (or at least it is right for me).

Computer programs, indeed, do not have an emotional component. They continuously repeat their instructions and follow rules always the same way at each price setup and target or at each setup failure’s event and in every timeframe. This can be seen from the type of participation that can be witnessed on the tape (for instance when trading the S&P500) when price approaches specific levels. Programs also take profits regularly at some specific levels and, after a trend failure, fake out moves in the same direction of the failed trend often stop in known area where profit taking and participation in the opposite direction starts with a precision, violence and coordination that can only be explained with the action of computerized algorithms.  When programs are present and active — and there are days of the week and times of the day in which they are not — price behavior is largely based on cause and effect and can be modified, on lower timeframes (like 15min and 4-hour charts), only by the short-term effect of news breaking in.

Rules followed by computer programs are always the same and decisions are based on price levels even if news bring in a temporary modification in market structure and, let me repeat, only on the smaller timeframes.  Contrary to what a lot of participants and professional do believe, news only complete the technical patterns and measured moves already in place. Only a strong participation from central banks can break the technical patterns in place and that, of course, cannot and does not happen often. Computer programs and traders group psychology are responsible for trends and price direction: when the next setup in a sequence of a measured moves fails , that is signalling a new trend in the opposite direction. In order to establish when a trend fails and a new opposite trend is born it is important to know the rules used by programs to the accuracy required. Programs will trade a trend until it comes to an end, and then they will start trading the new trend in the opposite direction. Programs exist on different timeframes from the weekly timeframe down to the hourly, 15min, 5min, 1m and even tick chart. The interaction between trends and measured moves taking place in the different timeframes generates price behavior that can often be foretold, once rules used by program trading are identified.

Trading rules were born and improved upon from the analysis and observation of price subdued to the actions provoked by human emotions and that have the effects of dynamically altering the balance between demand and offer. Such rules became very efficient with time and, with continuous rising of computer-based trading in the stocks and derivative markets and increased volumes, they bring about price behavior that materializes in a truly self-fulfilling prophecy. This is what happens on the markets today and is somehow similar to what happens when price approaches some important moving average (like the 50-day or the 200-day moving averages), there is always some sort of reaction. When, for instance , measured moves are applied backwards to price data going back to the last 100 years (for instance using Dow Jones end of day data) it is disconcerting how the related trade setups work and offer valid entry areas. This is a very significant fact because it shows that the logic of programs, which was initially derived from the study of the psychological response of traders group to price dynamics, evolved in a direction that correctly managed money, risks and profits but taking such important psychological aspect into account, as well. Moreover it shows that the basic rules implemented by computer algorithms worked before computer technology was even invented.

The effect of the ever-increasing presence of program trading in the markets, the high participation and increasing volume in Forex and commodities, an ever-increasing number of professional and retail traders, which can exploit the different ways now available to trade (for instance,Forex crosses can now be traded with mini-futures, micro-futures, variable forex accounts and trade size, cash, etc.) have the combined effect to increase volume more and more. In fact, results of technical analysis, trades signals and setups derived from the observation of the effects of program trading ,work better when the market participation is high, i.e. when volume is high. In such conditions, some markets show a much more technical and predictable behavior and patterns, than they ever had in the past. With ‘technical behavior’ I mean that price responds very well to measured moves and program trading rules. This is especially true for some hedging instruments trading very high volume, like the Euro-Dollar cross and the US indices, and on the most traded Forex crosses, as well. From this viewpoint there was never a better opportunity and today we, retails traders, have the possibility for profiting in these markets as the big boys do. Will you walk the traditional path of learning to use classical TA, or do you want to discover how learning the rules used by Program Trading can make a difference in your trading? Your choice.

This article appeared on my free Newsletter sent out last Sunday, October the 28th along with other information typically including: a weekly review for the Euro-Dollar cross and other Forex, indices or commodities futures, articles on my trading method, market commentaries and HFT/Program Trading articles like the one you just read. Please, register here to receive the free weekly newsletter.

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EDUCATION – How HFT and program trading are relevant to my trading method and can be in yours – Part 1

Some newletter subscribers and people I interact with on the social networks, as well as, in real life frequently ask me how High Frequency Trading (HFT) and computer algorithms trading the markets (program trading) can be exploited in trading. First of all what is HFT all about? A simple definition for High Frequency Trading is a practice that attempts to gain an advantage on the smallest differences in price which can be exploited with high speed trading execution — in the order of milliseconds — using computer hardware infrastructure directly linked to the markets (e.g. AMEX, NYSE, NASDAQ, CME, ect.). To get an idea of what these programs can do, and their effects, please read my article “HFT and program trading is everywhere“.

However there are also programs and algorithms which are less disruptive, yet not less important, as they keep a firm grasp on some markets, not to say that they actually ‘run’ those markets. Seems incredible or not realistic? Well, think again, and observe price: proof is in its behavior. Indeed, on certain markets these programs run on all timeframes, from weekly down to the TICK chart, and their presence is so pervasive that they often play one another on different and even the same timeframes, following exactly the same rules. Why would that be?

Probably some of the reasons are:

  1. the future is unknown and trading against the current, established trend could mean being in a great position should a change in trend take place;
  2. the creators of such programs devised a way to trade the markets, constantly taking partial profits out while keeping runners in case of continuation into a larger move in the targeted timeframe, as well as, in the larger timeframe just above it;
  3. programs take profits while respecting and leveraging on group psychology of market traders (if you want to get an idea of such psychology read my article The Bandwagon Theory, which applies equally to intraday, swing trading and investing);
  4. by removing emotions, programs also gather an additional edge that only highly disciplined discretionary traders have.

I personally have no doubts of the presence of program trading acting on financial instruments used for hedging like the forex futures, the indices e-minis, gold, 30-year bonds and a few other instruments, and this presence and the effects of it form the basis of my trading method.

These algorithms base their logic on the common psychology of traders, who often buy the long green or white candlesticks, when they should be selling them, and sell the long red or black candlesticks, when they should be buying them. But you don’t have to take my words for granted and I invite you to verify the information. Data reported by studies in the financial markets industry says that an amount ranging from 60% to 90% of trade volume (some analysts mention higher percentages, up to 94%!)  is generated by transactions on futures contracts and most US stocks executed through computerized algorithms.

While these numbers are certainly controversial and are continuously subject to research and discussion, the actual numbers are not very important because the proof that automated algorithms exist and participate at specific price levels, well identifiable in advance on several timeframes, is in the behavior of price itself.

Those who follow my market’s video reviews know that I regularly identify in advance valid support and resistance levels, along with current trend failure threshold levels, only analyzing price with the rules observed by studying the effects of program trading. I cannot say my rules are exactly the same of program trading, but they ‘explain’ price very well and that should be enough for any trader; and for sure it is for me.

Every single day, by those rules, it is possible to observe price reacting and bouncing off specific levels that can be identified with hours and, often, days in advance. Moreover, after reacting to these levels, price often gets to specific target levels where profits are systematically taken out of the table. These target levels too can be identified in advance and are strictly related to the same areas where price initially got participation and reversed. Professional traders and automated computer algorithms (program trading) regularly take profits off the market at these levels. Again, proof is indeed in price behavior.

As a certified technical analyst, but especially as a student of the market and trader myself I am sure of the existence of algorithms working on some futures contract, including the Euro-dollar  FX currency futures contract that I follow closely. The rules I use also work on several US and European stocks and, generally, work well on highly exchanged stocks: clearly the key is participation and high volume.

The trading rules were identified based on observations of price patterns that repeat on different timeframes, thanks to the painstakingly action of program trading and repeated behavior of traders’ groups, sharing the same psychology. Understanding and learning such rules allowed me to create a reference framework in which market moves make sense. The decisions taken through the application of these rule, along with the related anticipated changes in price direction, often not foreseen (in time) by classical Technical Analysis (TA), go against what the crowd is thinking of doing, and then commonly does.  This fact comforts me that the approach I use is the right way to approach the markets (or at least it is right for me).

Will you walk the traditional path of learning to use classical TA, or do you want to discover how learning the rules used by Program Trading can make a difference in your trading? Your choice.

This article appeared on my free Newsletter sent out last Sunday, October the 21st along with other information: typically including a weekly review for the Euro-Dollar cross and other forex, indices or commodities futures, articles on my trading method, market commentaries and HFT/Program Trading articles like the one you just read. Please, register here to receive the free weekly newsletter, to read the following parts of this article.

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