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Smart Willpower

New City Trader — Fri, 05 Jan 2024 23:57:00 +0000

Smart Willpower…

In the current lesson we continue with the theme of willpower. Since, according to research, we have willpower we build it outside of trading, in everyday life, and then transfer it to market actions.

Training of Inner Strength

New City Trader — Wed, 03 Jan 2024 23:55:00 +0000

Training of Inner Strength

As I promised a couple of days ago, today I’m giving another piece about building the discipline you need in trading.

This time we discuss willpower as a central ingredient for success.

Willpower is

The ability to overcome one’s internal resistances.
The ability to resist impulses, thoughts and desires.

Let’s recall the most important conclusions of the research:

The most important conclusion of modern research: discipline depends on willpower, willpower is an acquired trait, just like muscle strength, and it can be trained. There are no different types of discipline, a different one in life and a different one in trading, so trained in one field works for all.

Further materials in the series

The 4th pillar of success in trading: Inner strength

New City Trader — Mon, 01 Jan 2024 15:55:09 +0000

Inner strength – the ingredient of every success

In whatever way you define your success – satisfying money, a good job, a happy relationship, financial security, the freedom to do what you feel like doing, we will always mention inner strength and discipline as factors that contribute to “someone succeeding.”

What is inner strength in trading?

We will define it by listing several situations and qualities that are then necessary. Then we will deal (in the following lessons) with the education of the listed qualities one by one. To begin with, I will only point out that at the root of each of them we have discipline and willpower. You will read about what they are in a moment.

And since the material is very rich, I made a special PDF for you.

Further materials in the series

Stress In Trading: Manage The State Of Your Mind

Darius Swierk, Chief Editor — Tue, 23 May 2023 07:00:00 +0000

Good preparation, good comprehensive knowledge of the system and practicing it in a multitude of equal situations is the best recipe for good results and low stress. Because you will be very, very, very well prepared.

Stress, losses and their consequences

In this lesson we will begin the subject of stress and its impact on the trading results – in the short and the long term. In addition, you will get the first tasks/exercises, which will lead to long-term reduction in stress level.

In the short term – high level of emotions and stress cause reduction in your intellectual ability. Lower intellectual ability, in other words, is less efficient thinking. This is the reason that we, in general, do worse at exams, e.g. we forget the simplest and most obviously things.

In the long term – constant stress may lead you to irritability, headaches, migraines, cardiac arrhythmias, gastric ulcers, and more! Some believe that biochemical reactions, that cause stress in our body, can cause heart attacks, strokes and may be one of the reason for the emerging and development of cancer.

Attempts to discharge stressful situations with alcohol can lead straight to alcoholism. Only few people know that William Griffith Wilson, founder of Anonymous Alcoholics, was a professional broker on Wall Street and even succeed some time ago.

Trading-related stress is not just a loss on the market! It has a broader impact on your life. I know traders who, because of high level of stress, are not able to perform their profession – they had to stop trading. So, there is a serious matter. All the active traders I know, have their own solutions of dealing with stress and its effects.

Because our goal in this course is long term profitability – we will deal with all sources of stress one after another. Are you ready? 😉 Let’s start!

The causes of stress in trading

Loss of money on the market can be a source of stress. Especially if it is a signification sum for you. For each one this sum can be different, but it exists for everyone. It is the so-called glass ceiling that we will deal with in future lessons.

Interestingly, profit can also be a reason for a strong stress response. The biggest profit, the higher probability of stress. I know traders whose large profits have cased traumatic reactions (psychological shock). In one extreme case, a trader, who in one day earned a huge sum on options, experienced such a strong psychological shock that for a year (!) he was unable to appear in his office.

Just being on the market can cause stress reaction. At some point it is associated in the psyche of the trader in such a way that later the stress response appears constantly. Soon you are going to learn how this happens and how to change that to positive reactions.

Being out of the market may be stressful, especially if you wait for the signal for a long time. Another case is a situation when you exit from the market, e.g. under the influence of emotions, and the market continued a strong move in your direction so, if you were in position you would earn very good money. Another example of stressful situation is a lost opportunity, a clear signal, which you missed.

Uncertainty is a next source of stress, indicated by practically all researchers. Trading is essentially the ability to manage uncertainty and risk – you enter the market with money not knowing “how will it end”. You should get used to this situation. Experienced traders practically do not pay attention to “uncertainty” – they know that the result of the single order is unknown but nevertheless, at the end of the month, after a series of orders, they will be on the profit.

Two basic sources of stress: external stimulants and imagination

Let’s overtake some material and point out one important thing – note that we have 2 types of sources of stress. The first is the situation in which we are, for example: with open position on the market. The second source is our thoughts and imagination which we create.

Several times I mentioned the fact that our mind does not quite distinguish reality from imagination. This means that the memory of stressful situation can generate the same reaction as an event.

Example:

Stop reading for a moment and call some stressful situation to your mind: exam, accident, difficult interview e.g. for a job… You may soon experience the onset of stress in your body.

That’s how worrying and uncontrolled imagining negative scenarios works – it causes stress and robs you of strength.

Note: The other thing is psychological preparation depicting black scenarios with the intention of preparing their own psyche, sometimes inventing reactions and lines of conduct in a given case. Such an active approach to the topic, builds mental strength and makes you have a sense of control.

It is a very important issue, so I will talk about this in details in the future lessons.

Stress as the sum of the stress from different sources

At the end of this brief introduction I will say more about another important issue: if you have other stressful situations, besides investing and trading, your stress level will usually be the sum of these stresses.

That means that other stressful life events e.g. quarrel, accident, illness, theft, stress in traffic on the street, jet-lag… … … can also have negative influence on your market results.

It is good to know that the source of stress can also be strong noise or crowd of people. The US stock market, where traders shout offers one after another and where is a crowd and noise, become an icon of a stressful environment.

Each of us reacts differently to potentially stressful event

There are people who, in the majority of these situations, will not experience the stress reaction. As researchers say, their high resistance to stress may be associated with the absence of several factors that contribute to stress: internal sabotage, internal conflicts, fear of success, impact of results on self-assessment.

In the next lesson you will learn another exercise that builds strong, positive self-assessment, to remove one of the tricky and powerful stress factors.

Our goal, through all the lessons devoted to stress, will be to understand how stress affects you, how it can negatively affect your results, and of course, what you can do to prevent such a situation, or how to reverse it.

Will not be here without specific techniques, not only directly affecting your level of stress in trading and in life but also improving your life prospects, building optimism, strength and internal resistance. These techniques are designed to help you become a more relaxed and happy person, pursuing your goals by trading on the markets with as few levels of stress and emotion as possible.

High and low sensibility to stress and results on different time frames (TF)

Stress develops as a result of our response to external events, we can also generate it ourselves – just by thinking… People who are worrying have increased level of stress.

In extreme cases, people who worry too much can induce a constant feeling of high stress. Often, these people perceive as stressful practically every event.

In this case, they will have problems in trading. Not only because of dealing with new stressful factors, but that the overall stress level can break quickly out of control. A high level of stress is: worse perception of the market, worse quality of analysis, poor decisions, losses.

On the second side, we have people, who can go through a lot of stressful situations without special problems.

Looking from a perspective and slightly generalizing we can tell that people with higher stress level can achieve better results at a higher timeframes (weekly, daily, 4-hours) and worse results with speculation on lower TFs:
1-hour, 15 minutes, 5 minutes, one minute.

Tip: 2 phases of „making a pile” on the market for people with high stress level

I understand the situation of many investors who want to take off from small capital and “get rich quick”. This usually requires multiple transactions in the short term in order to build capital.

If you are a person less resistant to stress – consider two phases of your game.

- First – when you accept a higher level of stress and actively apply exercises and tips that I will give you in the next lessons, making sure not to exceed your level of stress.
- Second – when you consciously move on to a higher TF, where the burden on your psyche will be lower.

The point is, do not over estimate the level of your mental burden too much, because many traders have had to leave the market for that reason and never come back because there was no one who trouble them out.

Another important thing: if you have high level of stress in your life, think about how to change it before you deal with a serious investment or trading. It’s very important, because you can’t withstand it all mentally.

The level of stress decreases with increasing experience and skills.

Of course, it is also possible that the level of emotion and stress over time is reduced. Remember, how it is after driving course and how it is after a while of driving – is no longer stressful and often becomes a pleasure. The trading can be the same. The greater will be your competence, familiarity with the system and market – the less stress you will get.

Of course, there is still a question of the glass ceiling when stress suddenly builds up, but this topic will be addressed in future lessons.

How to identify stress reaction (response)

Read the following description very carefully because on this basis you will learn how to deal with stress in short and long-lasting term.

When something happens suddenly, you are under attack on the street, a car is about to hit you, etc. – your body responds automatically. It is called the “fight or flight” so “fight or run”. And these action occur automatically:

- Muscle tense in wait for an action
- Breath accelerates and deepens. It may take a moment to hold your breath in the immediate danger situation
- The frequency of heart beats, the volume of blood being thrown at each contraction, the blood pressure rises
- Glands inject hormones to the blood, which accelerate the heart rate, respiratory rate, muscle activity, nutrient distribution needed by the muscles
- The pulmonary ducts dilate, blood vessels expand in the muscle and brain.
- The pupils dilate and the auditory sensitivity increases.

The body prepares for action: fighting, defending or escaping. This is a reaction inherited from ancestors, helping a person at a time of danger.

The basic action to prevent the stress response we will use, will be to control muscle tension and breathing in stressful situations.

The first method I will describe in today’s task at the end of the lesson. There is a whole package of such methods: basic will always work, another will trigger similar effects a little bit faster.

Learn more slowly to achieve success faster

At the beginning we have a lot of things to analyze ON THE MARKET, but over time it becomes easy and then often intuitive.

Simulation and “very slow trading” initially give our brain time to learn the realities of the market and the system. It will be very different to learn to undergo stress by trading on live account (which usually ends in a series of losses). In this case slower really means faster.

Before you risk your money on the market, be very well prepared. If you know yourself and the market you have a chance to win. Always get ready to fight.

Meditate on the classic quote:

„Battles are won before they start”
– Sun Tzu “The Art of War”

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How to stop price declines? How to trigger increases?

Darius Swierk, Chief Editor — Tue, 16 May 2023 07:00:00 +0000

The FTX scandal caused a real storm around the world and strong declines on BTC. Recently, the markets have returned to the upside, however, after a close look, the upside is not as strong as the previous declines. The question of whether this is a real reversal or just a temporary upward correction remains open.

All the more reason to look at the macro factors that cause price changes on BTC and the factors that may lead to further declines. Because, unfortunately, the latter are quite numerous.

For now, I don’t have any good news, we have before us potentially three groups of problems threaten the price of Bitcoin. In the following article, I will first discuss the four most dangerous problems I see, and then show how to solve them (or at least in what direction to look for a solution) so that price increases return. I believe that the overall goal of action taken should be to bring BTC price increases to levels of 500,000+, a rallying target that is very ambitious and requires the activation of many strong mechanisms.

Problems #1 and #2: Negative news tsunami multiplied by two

The events surrounding FTX have exposed many of the weaknesses and pathologies that exist in the crypto industry. Many companies that held their money in FTX are at risk, some of them likely to go bankrupt. Each of these developments is reflected immediately in Bitcoin price declines.

The keywords that will appear are: fraud, embezzlement, theft of billions of dollars, buying residences with customers’ money. This kind of news usually scares retail investors, there is an outflow of small capital from the market.

Currently there have been increases, but… ahead is the trial of Elon Musk, the richest man in the world. The Musk – Johnson trial over the value of Doge will attract media attention from around the world. Many journalists will carefully read the two lawsuits (there are already two, a basic one and an extended one) and find dozens of quotes from various influential figures. All of them are negative.

Well-known investors, heads of large funds, directors of central banks, there is even a Nobel laureate in economics. They will find many negative keywords: financial pyramid, Bitcoin and cryptocurrencies are worthless, scam, fraud, Ponzi scheme, speculative bubble about to burst. Such content will poison news cycles for weeks.

Again.

In this wave of negative information, opponents of cryptocurrencies will become active again. Particularly dangerous are politicians, heads of central banks, for whom bitcoin and crypto are competitors because they are beyond their control. As a result, this will be a good breeding ground for the most dangerous phenomenon for the entire industry: negative regulations, limiting or completely banning Bitcoin and others.

Bitcoin’s ban is currently most feasible in the US and Canada

Problem #3: Possible outright ban on BTC in the US, Canada and the EU.

It has long been known that digging and transactions are energy intensive. The annual energy consumption of all crypto is often compared to those of medium-sized countries: Australia, Argentina. About 80% of this is consumed by bitcoin.

Currently, crypto in the US consumes between 0.9 and 1.7% of annual energy consumption. For the US government, greenhouse gas emissions are very important. One BTC transaction is equal to 1,712,000 Visa transactions. Or the energy consumption of an average household for 47.49 days. That’s a huge amount of energy. With rising energy prices, this topic is becoming political in both the US and Europe. In September of this year, a White House report on the climate and energy implications of cryptocurrency mining (Climate and Energy Implications of Crypto-Assets in the United States) appeared, listing the five most serious problems posed by cryptocurrency mining:

– energy consumption (the data quoted above is from the report),
– excessive load on power grids,
– high greenhouse gas emissions,
– contributing to rising energy prices,
– deterioration of the financial and energy situation of the most vulnerable communities.

As a result, we have an increasingly tense political situation around cryptocurrencies, especially BTC. The solution that is realistically being considered is a ban on proof-of-work cryptocurrencies (such as BTC). No matter how much the BTC/crypto community outrages and protests against aggressive policy solutions, the U.S. government may simply cut off the electricity. This means a panic sell-off, for many (perhaps even most) miners – the end of profitability and the prospect of bankruptcy.

The same will happen in Europe. I live in the UK, electricity costs have more than doubled. If there are negative regulations in the US, practically within a month we will have similar solutions throughout Europe. For BTC, this will be a disaster. This is the prospect of a panic sell-off, a panicked flight from the markets. To put it in one sentence: if the US imposes a ban, almost certainly Europe will do the same.

There will be a domino effect – blocking mining and transactions. Disaster. We will be lucky if BTC stops somewhere around $2000. But if panic begins, it could go even lower. Those who have $1 million in BTC today will have $100,000. Or less.

Problem number 4: Wall Street and London City

As if all this wasn’t enough, we have a third factor. BTC has become too risky for big City and Wall Street investors, and there is an outflow of capital from the industry. Let’s start with a very simple truth in investing: capital inflows cause prices to rise (e.g., stocks or BTC), and outflows cause declines. Different types of institutional investors have the most capital: investment banks, hedge funds, family offices. Big players read the analyses of major investment banks and take their recommendations into account when making decisions.

Let’s look at one such analysis, issued by Goldman Sachs: Insights: “Beauty is not in the eye of the beholder.” Note that they do not distinguish between BTC and Altcoins, calling them all cryptocurrencies.

“We do not consider cryptocurrencies to be a strategic asset that adds value to our clients’ portfolios,” he says.

“After analyzing six different approaches, we believe it is virtually impossible to build a convincing framework for valuing cryptocurrencies.”

“We believe that an asset whose increase in value depends mainly on whether someone else is willing to pay a higher price for it is not a suitable investment for our clients.

On valuation: “Using the same valuation method that the market uses to value other credit card payment systems, the price of Bitcoin is estimated to be around $22.” My comment: this is not a mistake, they value BTC not at $22,000, but at $22 (twenty-two dollars).

Without going into a deeper analysis of whether these calculations are right or wrong (easy to dispute in my opinion), let’s consider how such documents affect mainstream investors. Investors first read that BTC has no value, there is no valuation model, Bitcoin builds its price solely on psychological effect, politicians think about banning BTC, and then they see valuations at: $22. How do you think, after reading these analyses, will anyone feel like investing, for example, half a billion dollars in Bitcoin? If you answered no – you’re right.

Of course, major investors also read the press and government reports and know about the possibility of a ban. It’s a risk factor they certainly don’t overlook, and perhaps even take advantage of – playing for declines. To sum up, we have not one reason for the crash, but three (or even four), one worse than the other, which unhappily coincide at the same time.

The solution: it's time to get organized

Given the intensity and scope of the problems, there should be some kind of institutional solution. That is, some kind of organization, preferably a foundation, which will solve problems, counteract negative information, try to stop negative regulations. And on top of that, it will launch value-building projects for BTC

Traditional currencies have the state apparatus behind them, and BTC has no one. This must change, in my opinion, there should be such an organization that realistically looks after the interests of the industry. And to make sure that those who today have assets worth millions of dollars in BTC do not wake up in a few months with assets worth…. 1/10 of what they are today.

Today, you have to gather arguments, people, prepare strategies for action, then execute them and repeat as necessary. This is a job for a dozen, maybe a few dozen people. I think the whole industry is facing a broad discussion about the future and gathering reasonable ideas about what to do next.

Of course, major investors from Wall Street and London City should be invited to the discussion. Many very interesting ideas can come from their side. But above all, the hodlers themselves should be asked for their opinions. Below I will make some comments on what can and should be done about each of the problems described above.

Tsunami of negative news

What is needed is a group of people who will actively oppose the argument against BTC (and also Doge). Now and in the future.

The industry needs people who will be tasked every day with following, commenting, presenting counter-arguments where necessary: in the press, on the radio, on television. Television and the mainstream media are currently controlled by those with traditional views, and it’s time to change that.

It’s time to provide arguments contrary to the views of W. Buffet, Krugman, Goldman Sachs analysts, central bank heads and their ilk. This is not easy, it is a huge task, but it is possible. A list of counter-arguments is in the pipeline.

Similarly, with the written media and mainstream journalists, you need to reach out to those who will comment on the process and provide them with counter-arguments against the opponents of BTC. I have prepared a list of more than 50 contacts, producers and journalists of major programs in the US, this is definitely something to start with.

In addition to this, consider publishing articles about prospects, opportunities on major news networks to maintain a positive narrative on the subject.

In this area, it’s worth spending some time trying to take over the narrative and weave more and more positive content about BTC, since we’re already going to stop the bad things, it’s worth simultaneously promoting the good things with interest. I have a list of about 60 counter-arguments (and positive news), which I will develop.

Ban

This is the most dangerous issue. If there is a ban and a domino effect, it will be a matter of saving BTC from the 2000 – 3000 level, if we are lucky. To avoid such a situation, several things need to be done.

Review all the technical, political and social arguments that support a BTC ban. Prepare strategies and solutions for each of them. Why on all of them? If we address the energy consumption argument alone and the effects of greenhouse gas emissions, and, for example, prepare a program to transition to renewables, we may suddenly find that the grid load and social arguments become paramount. In politics, it often happens that the arguments presented are just a facade behind which the real decision is hidden.

I fear that this may also be the case in our case. And the decision to ban could benefit many big players who have shorts on BTC and cryptocurrencies. The ban will cause panic and allow them to come out with huge, multi-billion dollar profits. Too late to investigate whether such a situation exists, it is safer to assume that it does. And make every effort to ensure that politicians have no way to “reasonably justify” the decision to ban. That is, take every argument very seriously. In addition, closely follow the mainstream media and react to comments calling for a ban or treating it as a reasonable solution.

Aggregation of values

BTC is to grow to 100k, 200k and more, I see only one way. We have to figure out a way to do it together.

The main principle is this: only an influx of capital (and a lot of it) into the industry will increase the price of BTC and cryptocurrencies in general. I wrote about this above: it is necessary to create such a way for BTC to “aggregate” value, so that the new pricing model will be accepted by the largest investors. I have several proposals, but I think it is better to generate a large discussion and listen to the most diverse ideas, rather than limiting ourselves to one or two solutions. If we are successful, there should be a way to “add” and build value for BTC to the level of $500k-$1 million.

This should be our premise in this challenge. Ideally, the entire BTC/crypto community should start thinking about this. In addition to these challenges, it is worth adding a few others that will accelerate the development of BTC and cryptocurrencies in general, I have put them in two additional projects.

Project 1: Accelerating business adoption

To accelerate business adoption, we have several initiatives to choose from: information and training (on business development and BTC use) for companies that are considering BTC adoption, creating ready-made programs for universities to help introduce topics related to the modern economy and BTC into university curricula, exploring ways in which the BTC/crypto environment can positively influence corporations to introduce payments and develop access for their customers. In each area, it is worth creating a knowledge base, best practices to accelerate and facilitate adoption in traditional business.

Project 2: Facilitate BTC adoption by countries

Active networking strategies for countries looking to introduce BTC more widely, databases of best practices (what has worked and what hasn’t and why), Expand initiatives to use renewable energy sources for mining,

Discussion of geopolitical risks (in the U.S., they are concerned that BTC could threaten the position of the dollar, reduce the influence of the U.S. in the global economy. Such opinions are published in several foreign policy journals, so they should be taken seriously in order to gain as much favor for BTC and cryptocurrencies as possible).

Conclusion

We have entered a difficult period for BTC and cryptocurrencies. A tsunami of negative news, capital flight, possible banning – this is very bad news. It’s time to think about creating an institution that will protect the interests of the BTC industry, deal with stopping the ban, falling prices, deal with building an investment value for BTC that will be acceptable to mainstream investors and attract large capital. If you want to avoid being left with 10 cents of every BTC/crypto dollar, now is the time to act. Creating a foundation should be the first step.

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[PL] Analiza: EURO Może Odrobić

New City Trader — Mon, 15 May 2023 09:36:39 +0000

Gregor Horvat
– CEO i analityk w wavetraders.com

EURO może SPORO odrobić. Cykl podwyżek stóp procentowych Banku Centralnego US może zbliżać się do końca. W ciągu ostatnich kilku lat EUR doświadczyło silnego spadku w stosunku do USD, co było niedźwiedzim trendem, który przyspieszył przez pierwszą część 2021 roku, a kurs EUR/USD spadł poniżej parytetu i w pewnym momencie osiągnął nawet poziom 0,96.

Powodem tak silnego spadku w ubiegłym roku była jastrzębia polityka FOMC, który zazwyczaj jest pierwszym bankiem dokonującym dużych zmian w polityce. Nie jest zaskoczeniem, że inwestorzy i spekulanci stali się byczo nastawieni do USD, gdy Fed szybko podniósł stopy z 0 do 5%.

Podczas gdy inne banki centralne pozostawały w tyle, w końcu zaczęły działać z opóźnieniem. FED zasugerował jednak, że jastrzębia polityka może zbliżać się do pauzy, i to właśnie wtedy wiele walut, w tym EUR, zawróciło znacząco do 1,1 na początku 2023 r.

Widzimy to ożywienie jako wzór odwrócenia, ponieważ jest to ostry i silny ruch, zwany impulsem w terminologii fal Elliotta. Taka akcja cenowa zazwyczaj wskazuje, że rynek jest na dnie i że czasy się zmieniają.

Z tej perspektywy interesujące może być wznowienie tego nowego ożywienia po korekcie – retracement A-B-C. Porównując dane w czasie rzeczywistym z “podstawową strukturą” widzimy, że najprawdopodobniej para EUR/USD znajduje się obecnie w fazie korekcyjnej. W związku z tym uważamy, że odzyskiwanie może zostać wznowione po zakończeniu tego pullbacku.

Co to jest fala impulsowa? Formacja pięciofalowa, która porusza się w tym samym kierunku co trend wyższej skali.

Zasady:

– Fala 2 nie powinna przełamać się poniżej początku Fali 1.

– Fala 3 nie powinna być najkrótszą falą wśród fal 1, 3 i 5.

– Fala 4 nie powinna pokrywać się z Falą

EURUSD wykres tygodniowy – formacja bycza fali elliottowskiej

Pomimo tej byczej struktury musimy wziąć pod uwagę to, co dzieje się na innych rynkach oraz fundamenty i poglądy makro, które mogą wspierać ideę silniejszego EUR w najbliższych miesiącach lub latach.

Jednym z najważniejszych czynników napędzających parę EUR/USD są stopy procentowe, w szczególności dyferencjał stóp. Istotne są również kontrakty terminowe na EURODOLARA, ponieważ reprezentują one dolary amerykańskie zdeponowane na zagranicznych rachunkach bankowych. To zawsze poruszało się w cyklach, a zainteresowanie trzymaniem USD w zagranicznych bankach jest mniejsze, jeśli Fed zamierza zatrzymać cykl podwyżek.

Analiza cyklu EURODOLAR, na tle notowań USA i pary EUR/USD

Zauważmy, że kontrakty terminowe na EURODOLAR zbliżają się do końca cyklu wieloletniego i dekadowego. Może to być znaczący okres dla nowej zmiany trendu, biorąc pod uwagę, że naszym zdaniem FED naprawdę spowolni cykl podwyżek ze względu na ryzyko recesji po ostatnim kryzysie bankowym.

Widzimy, że 10-letnia rentowność w USA również handluje przy horyzontalnym oporze, testując poprzednie minima, co wydaje się być tym samym wzorcem w porównaniu z 1989 i 2006 r.

Dlatego też, jeśli Fed zakończy swoje podwyżki lub nawet je ograniczy do końca 2023 r., niższe 10-letnie rentowności USA będą miały wpływ na kontrakty terminowe na EURODOLARY, a wtedy właśnie różnica stóp UE-USA może wysłać parę EUR/USD znacznie wyżej, tak jak miało to miejsce po 1989 i 2006 r. Za każdym razem po tym wczesnym dnie para EUR/USD odzyskiwała następnie około 30%. Mając to na uwadze, uważamy, że może istnieć przestrzeń dla EUR/USD do powrotu w rejon 1,3-1,4.

Drodzy czytelnicy, traderzy i inwestorzy, jestem szczęśliwy i zaszczycony, że zostałem zaproszony do udziału w tym niesamowitym magazynie. Mam nadzieję, że podobała Wam się ta lektura. Jeśli jesteś aktywnym inwestorem i interesujesz się analizą cyklu, sentymentu i fal Elliotta, sprawdź nasze usługi na www.WaveTraders.com

Gregor Horvat. CEO i analityk w wavetraders.com. Zwycięzca FXStreet.com za Najlepszą Analizę Forex w 2016 roku, plus Najlepszą Analizę Buy-Side w 2018 roku. Lubi uczyć i pomagać innym klientom oraz dzielić się swoją opinią na temat zasady fal Elliotta i psychologii tłumu w czasie rzeczywistym. Elliott wave jest jego pasją!

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An Interview With ChatGPT About Trading

Darius Swierk, Chief Editor — Sat, 29 Apr 2023 11:23:29 +0000

The ChatGPT-4 interview about trading, for simplicity’s sake, takes the form of a conversation with a live person. The development of AI will go through several stages, the first two of which are: treating AI as a tool and treating AI as a colleague. The latter we understand – treating as an assistant or colleague at work. This is the spirit in which the conversation is conducted, this is what awaits us in the near future in virtually every field of work and personal life. During the interview I repeatedly ask questions about any terms I am interested in. It is important to take into account that sometimes the answers may not be true (it happens) and in order to use the code (there is an example of the code he gave) it is not enough to copy it. To make it work you need to know a lot about the architecture of programs and programming itself.

And now the conversation…

How can ChatGPT-4 be used in preparing or making investment decisions in stock and currency markets?

ChatGPT-4, which is an advanced language model based on the GPT-4 architecture, can be used in various aspects of preparing and making investment decisions in the stock and currency markets.

Sentiment analysis: ChatGPT-4 can analyze news stories, articles and social media posts to determine overall market sentiment toward specific companies, sectors or currencies, which can help investors choose the right instruments to invest in.
Price forecasting: The model can be used to analyze historical price data, indicating possible trends and price directions for the future, which can be useful to investors when making decisions about short- and long-term investments.
Fundamental analysis: ChatGPT-4 can help investors analyze companies’ financial reports, pulling out key indicators and information that can affect the value of a company’s stock.
Identification of market events: The model can monitor news to detect significant market events, such as mergers and acquisitions, changes in interest rates or reports of geopolitical conflicts that may affect stock and currency markets.
Technical analysis: ChatGPT-4 can help investors identify important support and resistance levels on price charts, which can be crucial in making investment decisions based on technical analysis.
Portfolio optimization: The model can be used to evaluate the diversification of an investment portfolio, suggesting changes that can reduce risk or increase expected returns.
Investment strategy suggestion: ChatGPT-4 can present various investment strategies, such as value, growth, or long-term investments, tailored to the investor’s individual preferences.
Investment idea generation: The model can generate investment ideas by analyzing market data, company performance, the macroeconomic situation and many other factors.
Risk evaluation: ChatGPT-4 can help the investor

Elaborate on the first point in 5 paragraphs. How can ChatGTP-4 be used?

The first point is about using ChatGPT-4 to analyze sentiment in stock and currency markets. Let’s consider this issue more broadly, presenting five aspects in which the model can be useful.

identifying sources of information: Using ChatGPT-4 to analyze sentiment in stock and currency markets can begin with identifying relevant sources of information. The model can search websites, discussion forums, blogs, and social media to identify where investors and analysts are posting their opinions on companies, sectors or currencies. In this way, an investor can access a rich database of diverse and up-to-date information on financial markets.
Data extraction: Next, ChatGPT-4 can be used to extract data from collected sources. The model can analyze texts, extracting key information from them, such as positive or negative opinions on companies or currencies, comments on the macroeconomic situation, or price forecasts. In addition, ChatGPT-4 can recognize emotional language in analyzed texts, which can further provide information on market sentiment.
Sentiment classification: After data extraction, ChatGPT-4 can be used to classify sentiment. The model can assign sentiment ratings to analyzed information based on the language used, determining whether the opinion is positive, negative, or neutral. In this way, the investor gets a consistent picture of market sentiment that can be used to make investment decisions.
Aggregation and visualization of results: Once sentiment is classified, ChatGPT-4 can help aggregate results, creating synthetic sentiment indicators for individual companies, sectors or currencies. The model can present these indicators in the form of easy-to-read charts, tables or reports, making it easier for investors to interpret the results of sentiment analysis.
Use of results in investment strategies: Based on the sentiment analysis conducted by ChatGPT-4, investors can adjust their investment strategies. For example, an investor may decide to buy shares of a company with a high positive sentiment, hoping for a continuation of the upward trend. On the other hand, an investor may avoid companies with negative sentiment, which may be vulnerable to declines in value. In the case of currency markets, sentiment analysis can help investors identify currencies with strong upward prospects, as well as signal potential risks associated with currencies with negative sentiment. In this way, sentiment analysis supported by ChatGPT-4 can become an important component of both short- and long-term investment strategies.

Are you able to analyze Twitter sentiment around Apple, for example?

As an AI language model, I cannot directly search the Internet or social media in real time. My abilities are based on knowledge accumulated during training, which was completed in September 2021. Therefore, I am unable to provide up-to-date information on the sentiment around Apple on Twitter.

To analyze the sentiment around Apple on Twitter, I suggest using analytical tools dedicated to social media sentiment analysis. You can also program your own tool, using Twitter’s API and a language model such as ChatGPT-4 to analyze and classify tweets. This kind of analysis can provide up-to-date information on the sentiment around a company and help you make investment decisions.

What would it take for you to analyze sentiment around Apple on Twitter in real time?

As an AI language model, I am not able to directly perform real-time analysis. However, to enable sentiment analysis around Apple on Twitter, you can take the following steps:

access Twitter’s API: To retrieve data from Twitter, you will need to register as a developer and access the service’s API. Twitter’s API will allow you to retrieve tweets related to Apple, based on keywords, hashtags or mentions of the company.
Gather data: Using Twitter’s API, retrieve relevant tweets related to Apple. You can limit your search results using time, language or geolocation filters to get the most relevant data.
Prepare the data: Process the collected tweets, clearing them of unnecessary elements such as links, special characters or emoticons. Make sure the data is in a suitable format that can be processed by the AI language model.
Use the AI language model: Integrate ChatGPT-4 or another AI language model with the acquired data. Use the model to analyze tweets, identifying positive, negative or neutral opinions about Apple.
Analyze and visualize the results: Once the AI language model has processed the data, analyze the results, aggregating sentiment at a general level and in selected categories (e.g., products, services or Apple-related events). Visualize the results in the form of charts or tables to facilitate interpretation and conclusions.

It is worth noting that real-time data analysis will require continuous monitoring and updating of input data. As a result, you may need to use appropriate streaming data processing techniques and maintain an infrastructure to support real-time analysis.

Regarding point 3, are you able to clean this data yourself?

Yes, as an AI language model, I can help clean up text data. You can use me to remove unnecessary elements, such as links, special characters or emoticons, and convert the text into a suitable format that can be processed by the AI language model during sentiment analysis. Here’s an example of how I would clean up the data:

Input: “Check out the new iPhone 13! 📱😍 https://apple.example.com #Apple #iPhone13”

Result: “Check the new iPhone 13”

However, it’s worth remembering that as an AI language model, I operate based on the text data you provide me. I am not able to retrieve tweets from Twitter or interactively process the data myself. Therefore, you will have to collect the data from Twitter yourself and then provide it to me as text so that I can help clean up the data.

Are you able to briefly explain what kind of results we can get and what valuable information about Apple we can get from them?

Sentiment analysis around Apple on Twitter can provide valuable information that can help you make investment decisions or understand the general perception of the company and its products. Here are some examples of the results we can get from such an analysis:

general sentiment: The analysis can show whether the overall sentiment toward Apple is positive, negative or neutral. This can help assess the strength of the brand and understand whether investors and customers have positive expectations of the company.
Product and service sentiment: The analysis can identify positive and negative opinions about specific Apple products or services, such as the iPhone, iPad or iCloud services. This can help identify the strengths and weaknesses of the company’s offerings and forecast demand for specific products.
Reactions to events and announcements: Sentiment analysis can reveal reactions to specific events, such as product launches, conferences or financial results. This allows you to assess how markets and customers perceive this information and how it may affect the company’s value.
Comparison with competitors: Sentiment analysis can provide information on how Apple is doing compared to competitors such as Samsung, Google and Microsoft. Sentiment comparisons can assess Apple’s position in the market and identify possible threats.
Trend detection: Systematic sentiment analysis can detect changes in Apple’s perception, both positive and negative. Detecting trends allows you to react early to changing market conditions and adjust your investment strategies.

The results of sentiment analysis, combined with other market and financial data, can provide valuable information to help investors make decisions, and allow companies to better understand the perception of their operations, products and services

Let’s move on to the second point: “Price forecasting: The model can be used to analyze historical price data, indicating possible trends and price directions for the future, …” How can Chat do this?

ChatGPT-4 can be used to forecast stock and currency prices, but it is worth noting that, as a language model, its main purpose is to analyze and generate text, not numerical or statistical analysis. Nevertheless, ChatGPT-4 can be used to analyze historical price data, indicating possible trends and price directions for the future. Here’s how Chat can do this:

processing historical data: The first step is to provide the model with relevant historical data on stock or currency prices. This data should include information on opening, closing, highest, lowest prices and trading volumes.
Initial data analysis: ChatGPT-4 can be used to identify significant events that have affected stock or currency prices in the past. The model can search its knowledge of economic, political or technological events that may have affected prices during the period under review.
Identify patterns and trends: ChatGPT-4 can be used to analyze price data to identify patterns and trends, such as ups, downs, consolidations or reversals. The model can also suggest how these patterns have affected prices in the past and how they may affect them in the future.
Analytics-based forecasting: Based on analysis of historical data, ChatGPT-4 can attempt to forecast future price directions. However, it is worth remembering that these forecasts will be based on the knowledge accumulated by the model during training, and not on actual statistical or econometric calculations. Therefore, these forecasts should be treated as preliminary and complementary to more advanced methods of financial analysis.
Verify and update forecasts: As new data becomes available, ChatGPT-4 can be used to update forecasts, taking into account new information. The model can also help you assess how accurate your forecasts were, identifying any errors and adjusting your forecasts for the future.

While ChatGPT-4 can be used to forecast stock prices and currencies, it is worth noting that there are more advanced methods of financial analysis, such as econometric models, technical analysis or learning algorithms you have.

And how do you physically do this? I could, for example, provide you with data on the last two hundred OHLC values for the eurodollar and based on that you can say something?

Although ChatGPT-4 can be used to analyze textual data, it is not optimized for analyzing numerical data such as OHLC data. Physically performing such analysis with ChatGPT-4 would be limited, and the results may be inaccurate. Instead, statistical models and machine learning algorithms developed specifically for financial analysis are more suitable. However, if you wanted to use ChatGPT-4 to analyze OHLC data for the eurodollar, we could try an approach based on a textual representation of the data. Here’s what that might look like.

convert numerical data to text format: to enable analysis by ChatGPT-4, OHLC data must be converted to text. This can be done by presenting the data as a text table or by describing each data point with a sentence.
Example: “Date: 2023-04-07, Opening: 1.1000, Highest: 1.1100, Lowest: 1.0900, Closing: 1.1050.”
Provide data to the model: Then, enter the transformed text data into ChatGPT-4, giving clear instructions on OHLC data analysis and expected conclusions.
Data analysis by the model: ChatGPT-4 will perform a limited analysis of the text data, identifying potential trends and patterns based on its prior knowledge of the currency markets. Note, however, that the results of such analysis may not be precise or reliable, as the model is not optimized for analyzing numerical data.

Alternatively, I would recommend using dedicated financial analysis tools, such as statistical models, technical analysis or machine learning algorithms, to get more precise predictions. For example, you can use linear regression, ARIMA, LSTM, or XGBoost models, which are better suited for time series analysis and price forecasting.

It is worth remembering that ChatGPT-4 can be useful in the context of analyzing currency markets as a support in identifying potential economic, political or technological events that may affect the Eurodollar exchange rate. This information can be useful in conjunction with more advanced financial analysis methods.

If so, are you able to create and run such an advanced model: it will give you market data (OHLC plus volume) and a list of events (based on the news of the period), What will you be able to build from such groups of information?

As an AI language model, ChatGPT-4 is not able to directly create or run advanced financial analysis or machine learning models. My main task is to analyze and generate texts, not to create and run numerical models. Nevertheless, I can help you develop a strategy for analyzing the data you provide, and point out methods that can be used to analyze this data by specialists or dedicated tools. Based on the market data provided (OHLC plus volume) and a list of events (news) from the period, here’s what you can do:

conduct a technical analysis: Based on the OHLC data and volume, specialists can apply various technical analysis methods such as indicators (e.g., RSI, MACD, moving averages) and chart formation analysis (e.g., triangles, head and shoulders) to forecast future price movements.
Conduct fundamental analysis: A list of events and news stories can be used for fundamental analysis to assess the impact of the news on the value of a stock or currency. Fundamental analysis can include an assessment of economic, political and technological conditions and their impact on the value of the instrument being analyzed.
Use of statistical models and machine learning: Based on market data and event information, various statistical and machine learning models such as linear regression, ARIMA models, neural networks (e.g., LSTM) or XGBoost can be used to forecast future price movements. News list events can be included as explanatory variables in these models.
Integrate different analysis methods: Technical analysis, fundamental analysis, and statistical and machine learning models can be combined for more accurate forecasts. Such integrated analysis can take into account both historical information from market data and the impact of economic, political or technological events on the value of a financial instrument.
It is worth remembering, however, that as ChatGPT-4, I cannot directly create or run such models. My role is to provide knowledge and guidance on how these methods can be applied to data analysis. In fact, you will need financial analysis specialists.

But if I wanted you to write me code in Python to build such a model are you able to do it?

Yes, I can help you write basic code in Python that can serve as a starting point for further development and customization. Here is an example of a simple predictive model based on linear regression that can be used to analyze OHLC data:

python
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error

# Loading the data (assuming the data is stored in a CSV file)
data = pd.read_csv("your_data.csv")

# Pre-processing the data
data["Date"] = pd.to_datetime(data["Date"])
data.set_index("Date", inplace=True)

# Create features (features) and target variable (target)
X = data.drop("Close", axis=1) # Remove the "Close" column to make the other columns serve as features
y = data["Close"] # Use the "Close" column as the target variable

# Divide the data into training and test sets

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Create and teach a linear regression model
model = LinearRegression()
model.fit(X_train, y_train)

# Predicting on the test set
y_pred = model.predict(X_test)

# Calculation of the root mean square error (RMSE).
rmse = np.sqrt(mean_squared_error(y_test, y_pred))
print("RMSE:", rmse)

The code above reads data from a CSV file, processes it, creates features and a target variable, and then divides the data into training and test sets. It then creates and teaches a linear regression model, predicts values on the test set, and calculates the root mean square error (RMSE) as a measure of model quality.

It is worth noting that this code serves as an example of a simple model and may require further customization and optimization. For more advanced models, such as neural networks (LSTM) or XGBoost, the code will be much more complex. In such cases, I recommend consulting data analysis or machine learning specialists for a more advanced solution.

This is the ending of part one. You can read the whole thing in the magazine. Just subscribe below!

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For Funds: Comments on the Risk of Losing Investors

Darius Swierk, Chief Editor — Thu, 02 Mar 2023 09:07:36 +0000

Surprising events one afternoon brought me insight into the existence of a risk that few people talk about, although it raises serious concerns in virtually every fund.

It is the risk of large shareholders or investors leaving. The costs and consequences of leaving are large. It costs a lot of effort and money to acquire new clients too. There are many reasons for leaving but one of them is just plain foolish.

It all began innocuously enough. I wanted to gain a better understanding of market dynamics, including trends, innovative ideas and strategies employed by traders in different funds. My goal was to stay informed about what’s currently hot.

There is no better way to do this than to read letters and reports written by funds. Of course, no one will write the best details, but one will write some good stuff, another a little too, and all in all, after reading a certain amount, one has a pretty good picture of what is going on in the world.

Some traders do this routinely, get new ideas to increase profits, reduce risks, see new directions. I got this idea from a conversation with one such very experienced person.

This time, however, something completely surprising happened. But first a short introduction.

A couple years ago I entered the publishing industry. So, out of necessity, I had to figure out the basics of editing to know what to require from people. Simply put, the publication must look well, it must make a good impression, not contain basic errors and, of course, I must be able to catch a large part of them.’

Back to our main topic: I have collected about 400 reports and letters to shareholders.

One afternoon over Italian coffee I got down to reading. After the first ten, I had a feeling that something wasn’t right. The content was OK, but something was wrong. After the next 20 I knew for sure. After reading about 100, I noticed that many (actually most of the funds) had a serious problem.

Most reports are very interesting and enjoyable to read. Great knowledge presented by professionals often with decades of experience.

But also most of them contain fundamental editorial errors. Before, I didn’t pay attention to fonts, margins, spacing, the way charts and photos are placed.

Suddenly I began to see how columns are misaligned, spacing and margins are a real nightmare (most documents cannot be printed because of this, the content will be outside the margins), paragraphs of text are badly transferred between pages. In some the font is so small as if someone specifically wanted to make it impossible to read. A mess. This is a serious communication problem.

And these were not exceptions. Such, unfortunately, was the majority! I’ll admit that I spent the rest of the evening in complete bewilderment as to how it is possible for so many funds to release such lax documents.

A small part looked like a text written in a simple word processor and converted to pdf. No graphics, no fund logo, all badly formatted. As a result, the whole thing made a bad impression, probably the worst of all possible – cheapness.

Why do I think this is a serious issue?

Let’s look at it through the eyes of an investor. Let’s think he brought $30 million into the fund. Every few months he gets a report or document that looks like it was done by a 12-year-old child. Careless. Done poorly. What kind of message does the investor get from this?

At the very least, it is a show of disrespect.

Of course, everyone will say that what matters most is results. That’s obvious. The result will always be king, no doubt.

But let’s think of a situation when a fund has a weaker quarter or even a weaker year. And let’s think about the question whether these poor documents will help to keep the client or not?

When everything is OK and results are fine then a poorly prepared document is unlikely to be a problem. But in the case of a weak quarter, such things may begin to sting.

And the effort put in, the time, money, dinners and gifts can go to waste by having a weaker result presented in a weak, messy, sloppy document.

The second, equally important issue is new investors. Potential investors or their representatives are bound to read maybe dozens of old reports back to back. I think everyone does that.

What will they find in them? What image of the company will emerge from the content and the form? Will the company be seen as professionals or as amateurs?

It’s a really sad situation when the next document in a row contains basic communication errors, has a font that is too small, charts that are too small (a large proportion of people today read on tablets and even phones).

Those who print reports for themselves the old-fashioned way usually find the margins and top and sides cut off. Such a printout is unreadable and only fit for the round archive.

Some of the reports and letters give a bad impression. Carelessness. Lack of attention to form. Cheapness.

Over the past year I have read maybe 30 Goldman-Sachs reports. They are flawless, even exemplary. Someone there is certainly making sure the client doesn’t get anything lousy. This is a good example, worth following.

What does a Bentley or Maserati catalog look like? Is it superbly prepared graphically and editorially, or does it look like a collection of text and photos thrown into a word processor and turned into a pdf? Fund investors are used to the Bentley level so the gap is huge.

A few conclusions at the end

The advice I have is simple – it’s best to leave it to the professionals.

The investment industry is not a cheap one, clients here are used to good style, class and luxury. The documents that a company sends to them should reflect this.

They should be carefully written, crafted, and should take into account how the client will read them: whether on a PC, laptop or a tablet.

You need to communicate uniqueness, luxury and security. Fund customers are used to this.

Having read many reports, I must honestly say that there is top-notch content in them.

It is really worth spending some amount of cash to make the editorial level similar, so that the two create harmony and show both a high level of the company and respect for the investor.

I’m not saying this to take an editor from Vogue right away (although it certainly wouldn’t hurt) but about a simple matter: to have the text, graphics and editing looked at by a professional.

For help with your documents, please check Alpha Lupi Media – the professionals.

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[Full] Artificial Intelligence: The lowest-hanging fruit. Part I

Darius Swierk, Chief Editor — Wed, 01 Sep 2021 07:00:16 +0000

I recently spoke with an elderly gentleman – a trader and fund owner. This conversation inspired me to write an article about Artificial Intelligence tools that are used in trading today. His fund employs over a dozen traders investing in various markets, and he is a veteran of oil trading. His trading style is conservative – after finding a signal, he opens a trade holding a single position, sometimes for several weeks.

I want to show you where and how you can use the most modern solutions in this example. The largest funds use such solutions daily.
As we know, a trader’s performance depends on his decision-making process, signal quality, available liquidity and what happens during position management. New Artificial Intelligence tools will improve each stage of this process.

The solutions and ideas can be applied to any discrete trading system.

Alternative data means more accurate price forecasts and better, less risky signals

Many years ago, to find out the financial results of a chain of stores, one had to wait for their earnings announcement. Today, the analysis of satellite images of parking lots makes it possible to estimate whether there are more or fewer customers and, on this basis, to forecast the results.

Data from credit card transactions also allow estimating whether there will be an increase or a decrease in sales in the chain.
By analyzing card transactions and various data from the company’s environment, we can estimate the financial results that are yet to be published by this company. If we have access to relevant data, we can make such estimates ongoing and make investment decisions.

For the oil market, some companies forecast price movements based on inventories and demand. Today, you can count the amount of oil in tanks in remote areas of China by measuring the length of the shadow they cast (using satellite images). To this estimate, the amount of oil stored in tankers and on railway sidings is added. Thanks to this, we know the size of stocks in virtually every country “right now”.

Today’s “forecasting” goes to the real-time estimation of the selected quantity. It is one of the strong trends changing investing. As a result, traders and investors have a better picture of the current market situation. Consequently, entries are more precise and less risky. And funds that build this kind of competency at home are winning in the markets.

Supertrend: Moving from forecasting to nowcasting.

Traders who traded moves after the news are now seeing that these moves are getting weaker. On the other hand, traders in the largest mutual funds are rarely surprised when publishing data because they can estimate it in advance. It is also one of the reasons for the disappearance of some systems.

I read an article a few months ago by an economics professor arguing that alternative data is unnecessary because large funds react to earnings releases anyway. Of course, there is some point in this. However, suppose someone can assess the data before publication. In that case, he has a chance to enter the market faster – even before the others. Or take advantage of the entry of other large investors to exit the market with profits at the right time.

Market behaviour is changing because major investors have changed their behaviour. Those who fail to recognize this in time will be left with old signals and many stocks that are no longer as attractive as before.

Entry and exit - minimizing costs and adverse moves

One of the main problems that most institutional traders complain about today is the difficulty in finding liquidity.

The automated tool can analyze the available liquidity with different brokers, plan and execute market entry. Their use is to take advantage of available liquidity with minimal cost and minimal adverse move.

Virtually every large fund has this type of tool. For example, quants analyze order structures using Machine Learning methods and, on this basis, program various entry strategies. As a result, the program will execute the entry faster and better than a large team of traders.

Automation gives the trader more time for other tasks – more important than position building.

Trend: Entry automation: The program will execute an entry faster and better than a large trading team. The trader has more time for other tasks – more important than position building.

Entry when the signal is very good

Low risk attracts significant capital. Moreover, new signals are getting better and less risky, so the automated trading solutions get new tasks: increasing positions to the maximum for the best signals.

This change makes it difficult for other traders to find liquidity. As a result, they cannot enter the market as before, and the adverse movement is more substantial than previously.

However, this is not a problem for smaller traders. Some consider the lack of liquidity to confirm high signal quality. To quote:

“Lack of liquidity in good companies means that the biggest ones are already there.”

This situation is a problem for large traders who do not use the latest tools. The signals are still good and followed by the expected moves. However, there are no longer opportunities to build large positions.

The struggle for liquidity, especially in the best market situations, will only increase. Therefore, the focus will be on getting a good signal as early as possible to enter with significant capital quickly. It is with alternative data that this becomes possible.

The next step will be to exploit weak signals (I will describe what they are and how to exploit them in a future article).

Better signals and lower risk attract more capital. Automation of entries and exits becomes necessary; the program can utilize the available liquidity to the maximum. The competitive battle focuses on having a good signal as soon as possible.

Better assessment (view) of the situation while taking positions

While taking positions, a better view of the situation comes with new tools. However, this improvement does not end with market entry. Social media sentiment analysis and automated news analysis gives you a picture of market interest in a particular instrument.

Example:

One of the funds uses the mean reversion system. If the price of a company after good news has moved too far away from the average, then the program places a sell order.

The basis of this system is pure statistics. The program counts the main moving average the deviation from it. The system monitors the price, and if the deviation is “too high”, it places a sell order back to the average.

However, when good news keeps coming, the market often continues moving up.

To cope with this situation, they added a module for analyzing news and sentiment in social media. Suppose the information about the company is still good (or there are many positive posts on social media). In that case, the sell position is automatically closed.

Profit increase achieved.

Discretionary traders can use the same scheme. However, while it is still possible to follow the news for a few positions, it is difficult to follow and create any meaningful picture of social media sentiment. The Machine Learning tools for analyzing Twitter, Reddit, Finwiz, FB or any media where potential traders can undoubtedly help here.

Knowing what others think about a stock or commodity can help you manage your position better. If the sentiment gets bad, we can exit the market earlier, and a good social mood can make us hold the position.

Of course, these tools should be approached with caution – the market is dominated by the most significant players, and they do not publicly announce their intentions. But they have these tools, and with their aid, the smaller players can see what the big ones see and thus better understand their intentions and decisions.

Today, you can shorten or lengthen a position with news and social moods analysis because you have a better picture of what others are seeing, reading, and thinking.

Availability of the new solutions

The tools described here are available to the largest funds. However, their development is expensive and requires teams of good specialists (machine learning, social media analysis, liquidity analysis, microstructure of markets and its evolution over time).

As time goes on, these solutions will become more widespread, available from larger brokers or offered as stand-alone solutions.

Conclusion:

Thanks to alternative data, we see a shift from forecasting to nowcasting. We also have better forecasts of supply, demand and price behaviour. As a result, trading signals are better and less risky.

Current tools allow us to make better use of available liquidity, minimize entry costs and get a better average price. Over time, these two goals will be joined by a third: an emphasis on exploiting the best signals to the maximum.

Less risk will attract more capital, and liquidity will be depleted in many excellent current oves over time. The struggle for liquidity will intensify further. As a result, many sound systems will continue to yield high-quality signals. However, there will be no way to build a bigger position. Systems will still be good, but they will no longer be as profitable as before.

There will be a struggle to improve signal quality in the long run, mainly to make the signal appear faster.

These changes may, in some time, hit the best traders, like my interlocutor – a trader with enormous market experience. Why exactly them? People in his group have the most experience and the best signals. These are the signals that the biggest traders/funds will hunt for and try to improve.

This evolution does not mean that new tools are readily available or cheap. On the contrary, creating them requires a team of good Machine Learning specialists in finance. It also entails gathering much knowledge about the microstructure of markets.

In the next issue:

– The fastest path to cash in a fund – what to support with new tools first – some practical tips.
– AI and entry and exit hiding.
– Evolution of tools towards dynamic MM.
– Position and environment analysis.
– New and improved efficiency analysis of market paths.
– Automatic portfolio optimization (including new optimization models).
– New models for managing the size of positions on the horizon.

Full version of the article is available for you in New City Trader magazine (now free of charge):

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Stress as the sum of the stress from different sources

Each of us reacts differently to potentially stressful event

High and low sensibility to stress and results on different time frames (TF)

Tip: 2 phases of „making a pile” on the market for people with high stress level

How to identify stress reaction (response)

Learn more slowly to achieve success faster

Invitation

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How to stop price declines? How to trigger increases?

Problems #1 and #2: Negative news tsunami multiplied by two

Problem #3: Possible outright ban on BTC in the US, Canada and the EU.

Problem number 4: Wall Street and London City

The solution: it's time to get organized

Tsunami of negative news

Ban

Aggregation of values

Conclusion

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[PL] Analiza: EURO Może Odrobić

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An Interview With ChatGPT About Trading

And now the conversation…

This is the ending of part one. You can read the whole thing in the magazine. Just subscribe below!

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New Issue On The Way - Join Us

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For Funds: Comments on the Risk of Losing Investors

Why do I think this is a serious issue?

A few conclusions at the end

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[Full] Artificial Intelligence: The lowest-hanging fruit. Part I

Alternative data means more accurate price forecasts and better, less risky signals

Entry and exit - minimizing costs and adverse moves

Entry when the signal is very good

Better assessment (view) of the situation while taking positions

Availability of the new solutions

Conclusion:

In the next issue:

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