Hello to whoever sees this I would like some advice. I am a 19 year old that started trading forex about a year and a half ago and I’ve learned a lot in that period of time. Ive took some extensive breaks in between this time period as well. I’ve tried trading pure price action, mechanical systems, zone to zone, different indicators and I just can’t seem to figure out what is the best style of trading for me. I feel like I’ve mastered risk management but I just can’t find a strategy that I can be consistent with. I need to get over the hump of becoming a consistently profitable trader. Could anyone provide some advice to help me get to that point? Any help is greatly appreciated.

Hello Fellow Traders!
A few weeks ago my college decided to drop me (M21) out because there was a mistake made by a third party which led to me not being in the school system.
I have been into trading cryptocurrencies for a few years now and a couple of months ago I came in contact with day/swing trading. In these months I got the basics down and began trading forex/indices on a paper trade account and doubled this account within a month (probably some beginners luck haha)
Since I'm out of college I have a ton of time towards myself. I want to make this time useful and teach myself a lot of new skills like trading, marketing and building websites.
Now my goal for trading is to start learning more about it, especially day and swing trading. I want to invest at least 5 hours a day studying the market, learning trading techniques and getting proper risk management in.
My question towards you guys is, how likely/possible is it for me to make a consistent 2/5% profit each month? And turn this into an income of let's say 20k a year (Given that I have created proper risk management, and studying at least 5 hours each day)
Thanks for the read, and if you have any questions just let me know! :)

This is an updated copy of the version on BadHistory. I plan to update it in accordance with the feedback I got.
I'd like to thank two people who will remain anonymous for helping me greatly with this post (you know who you are)
Three years ago a festschrift for Binay Bhushan Chaudhuri was published by Shubhra Chakrabarti, a history teacher at the University of Delhi and Utsa Patnaik, a Marxist economist who taught at JNU until 2010.
One of the essays in the festschirt by Utsa Patnaik was an attempt to quantify the "drain" undergone by India during British Rule. Her conclusion? Britain robbed India of $45 trillion (or £9.2 trillion) during their 200 or so years of rule. This figure was immensely popular, and got republished in several major news outlets (here, here, here, here (they get the number wrong) and more recently here), got a mention from the Minister of External Affairs & returns 29,100 results on Google. There's also plenty of references to it here on Reddit.
Patnaik is not the first to calculate such a figure. Angus Maddison thought it was £100 million, Simon Digby said £1 billion, Javier Estaban said £40 million see Roy (2019). The huge range of figures should set off some alarm bells.
So how did Patnaik calculate this (shockingly large) figure? Well, even though I don't have access to the festschrift, she conveniently has written an article detailing her methodology here. Let's have a look.

How exactly did the British manage to diddle us and drain our wealth’ ? was the question that Basudev Chatterjee (later editor of a volume in the Towards Freedom project) had posed to me 50 years ago when we were fellow-students abroad.

This is begging the question.

After decades of research I find that using India’s commodity export surplus as the measure and applying an interest rate of 5%, the total drain from 1765 to 1938, compounded up to 2016, comes to £9.2 trillion; since $4.86 exchanged for £1 those days, this sum equals about $45 trillion.

This is completely meaningless. To understand why it's meaningless consider India's annual coconut exports. These are almost certainly a surplus but the surplus in trade is countered by the other country buying the product (indeed, by definition, trade surpluses contribute to the GDP of a nation which hardly plays into intuitive conceptualisations of drain).
Furthermore, Dewey (2019) critiques the 5% interest rate.

She [Patnaik] consistently adopts statistical assumptions (such as compound interest at a rate of 5% per annum over centuries) that exaggerate the magnitude of the drain

Moving on:

The exact mechanism of drain, or transfers from India to Britain was quite simple.

Convenient.

Drain theory possessed the political merit of being easily grasped by a nation of peasants. [...] No other idea could arouse people than the thought that they were being taxed so that others in far off lands might live in comfort. [...] It was, therefore, inevitable that the drain theory became the main staple of nationalist political agitation during the Gandhian era.

- Chandra et al. (1989)

The key factor was Britain’s control over our taxation revenues combined with control over India’s financial gold and forex earnings from its booming commodity export surplus with the world. Simply put, Britain used locally raised rupee tax revenues to pay for its net import of goods, a highly abnormal use of budgetary funds not seen in any sovereign country.

The issue with figures like these is they all make certain methodological assumptions that are impossible to prove. From Roy in Frankema et al. (2019):

the "drain theory" of Indian poverty cannot be tested with evidence, for several reasons. First, it rests on the counterfactual that any money saved on account of factor payments abroad would translate into domestic investment, which can never be proved. Second, it rests on "the primitive notion that all payments to foreigners are "drain"", that is, on the assumption that these payments did not contribute to domestic national income to the equivalent extent (Kumar 1985, 384; see also Chaudhuri 1968). Again, this cannot be tested. [...] Fourth, while British officers serving India did receive salaries that were many times that of the average income in India, a paper using cross-country data shows that colonies with better paid officers were governed better (Jones 2013).

Indeed, drain theory rests on some very weak foundations. This, in of itself, should be enough to dismiss any of the other figures that get thrown out. Nonetheless, I felt it would be a useful exercise to continue exploring Patnaik's take on drain theory.

The East India Company from 1765 onwards allocated every year up to one-third of Indian budgetary revenues net of collection costs, to buy a large volume of goods for direct import into Britain, far in excess of that country’s own needs.

So what's going on here? Well Roy (2019) explains it better:

Colonial India ran an export surplus, which, together with foreign investment, was used to pay for services purchased from Britain. These payments included interest on public debt, salaries, and pensions paid to government offcers who had come from Britain, salaries of managers and engineers, guaranteed profts paid to railway companies, and repatriated business profts. How do we know that any of these payments involved paying too much? The answer is we do not.

So what was really happening is the government was paying its workers for services (as well as guaranteeing profits - to promote investment - something the GoI does today Dalal (2019), and promoting business in India), and those workers were remitting some of that money to Britain. This is hardly a drain (unless, of course, Indian diaspora around the world today are "draining" it). In some cases, the remittances would take the form of goods (as described) see Chaudhuri (1983):

It is obvious that these debit items were financed through the export surplus on merchandise account, and later, when railway construction started on a large scale in India, through capital import. Until 1833 the East India Company followed a cumbersome method in remitting the annual home charges. This was to purchase export commodities in India out of revenue, which were then shipped to London and the proceeds from their sale handed over to the home treasury.

While Roy's earlier point argues better paid officers governed better, it is honestly impossible to say what part of the repatriated export surplus was a drain, and what was not. However calling all of it a drain is definitely misguided.
It's worth noting that Patnaik seems to make no attempt to quantify the benefits of the Raj either, Dewey (2019)'s 2nd criticism:

she [Patnaik] consistently ignores research that would tend to cut the economic impact of the drain down to size, such as the work on the sources of investment during the industrial revolution (which shows that industrialisation was financed by the ploughed-back profits of industrialists) or the costs of empire school (which stresses the high price of imperial defence)

Since tropical goods were highly prized in other cold temperate countries which could never produce them, in effect these free goods represented international purchasing power for Britain which kept a part for its own use and re-exported the balance to other countries in Europe and North America against import of food grains, iron and other goods in which it was deficient.

Re-exports necessarily adds value to goods when the goods are processed and when the goods are transported. The country with the largest navy at the time would presumably be in very good stead to do the latter.

The British historians Phyllis Deane and WA Cole presented an incorrect estimate of Britain’s 18th-19th century trade volume, by leaving out re-exports completely. I found that by 1800 Britain’s total trade was 62% higher than their estimate, on applying the correct definition of trade including re-exports, that is used by the United Nations and by all other international organisations.

While interesting, and certainly expected for such an old book, re-exporting necessarily adds value to goods.

When the Crown took over from the Company, from 1861 a clever system was developed under which all of India’s financial gold and forex earnings from its fast-rising commodity export surplus with the world, was intercepted and appropriated by Britain. As before up to a third of India’s rising budgetary revenues was not spent domestically but was set aside as ‘expenditure abroad’.

So, what does this mean? Britain appropriated all of India's earnings, and then spent a third of it aboard? Not exactly. She is describing home charges see Roy (2019) again:

Some of the expenditures on defense and administration were made in sterling and went out of the country. This payment by the government was known as the Home Charges. For example, interest payment on loans raised to finance construction of railways and irrigation works, pensions paid to retired officers, and purchase of stores, were payments in sterling. [...] almost all money that the government paid abroad corresponded to the purchase of a service from abroad. [...] The balance of payments system that emerged after 1800 was based on standard business principles. India bought something and paid for it. State revenues were used to pay for wages of people hired abroad, pay for interest on loans raised abroad, and repatriation of profits on foreign investments coming into India. These were legitimate market transactions.

Indeed, if paying for what you buy is drain, then several billions of us are drained every day.

The Secretary of State for India in Council, based in London, invited foreign importers to deposit with him the payment (in gold, sterling and their own currencies) for their net imports from India, and these gold and forex payments disappeared into the yawning maw of the SoS’s account in the Bank of England.

It should be noted that India having two heads was beneficial, and encouraged investment per Roy (2019):

The fact that the India Office in London managed a part of the monetary system made India creditworthy, stabilized its currency, and encouraged foreign savers to put money into railways and private enterprise in India. Current research on the history of public debt shows that stable and large colonies found it easier to borrow abroad than independent economies because the investors trusted the guarantee of the colonist powers.

Against India’s net foreign earnings he issued bills, termed Council bills (CBs), to an equivalent rupee value. The rate (between gold-linked sterling and silver rupee) at which the bills were issued, was carefully adjusted to the last farthing, so that foreigners would never find it more profitable to ship financial gold as payment directly to Indians, compared to using the CB route. Foreign importers then sent the CBs by post or by telegraph to the export houses in India, that via the exchange banks were paid out of the budgeted provision of sums under ‘expenditure abroad’, and the exporters in turn paid the producers (peasants and artisans) from whom they sourced the goods.

Sunderland (2013) argues CBs had two main roles (and neither were part of a grand plot to keep gold out of India):

Council bills had two roles. They firstly promoted trade by handing the IO some control of the rate of exchange and allowing the exchange banks to remit funds to India and to hedge currency transaction risks. They also enabled the Indian government to transfer cash to England for the payment of its UK commitments.

The United Nations (1962) historical data for 1900 to 1960, show that for three decades up to 1928 (and very likely earlier too) India posted the second highest merchandise export surplus in the world, with USA in the first position. Not only were Indians deprived of every bit of the enormous international purchasing power they had earned over 175 years, even its rupee equivalent was not issued to them since not even the colonial government was credited with any part of India’s net gold and forex earnings against which it could issue rupees. The sleight-of-hand employed, namely ‘paying’ producers out of their own taxes, made India’s export surplus unrequited and constituted a tax-financed drain to the metropolis, as had been correctly pointed out by those highly insightful classical writers, Dadabhai Naoroji and RCDutt.

It doesn't appear that others appreciate their insight Roy (2019):

K. N. Chaudhuri rightly calls such practice ‘confused’ economics ‘coloured by political feelings’.

Surplus budgets to effect such heavy tax-financed transfers had a severe employment–reducing and income-deflating effect: mass consumption was squeezed in order to release export goods. Per capita annual foodgrains absorption in British India declined from 210 kg. during the period 1904-09, to 157 kg. during 1937-41, and to only 137 kg by 1946.

Dewey (1978) points out reliability issues with Indian agriculutural statistics, however this calorie decline persists to this day. Some of it is attributed to less food being consumed at home Smith (2015), a lower infectious disease burden Duh & Spears (2016) and diversified diets Vankatesh et al. (2016).

If even a part of its enormous foreign earnings had been credited to it and not entirely siphoned off, India could have imported modern technology to build up an industrial structure as Japan was doing.

This is, unfortunately, impossible to prove. Had the British not arrived in India, there is no clear indication that India would've united (this is arguably more plausible than the given counterfactual¹). Had the British not arrived in India, there is no clear indication India would not have been nuked in WW2, much like Japan. Had the British not arrived in India, there is no clear indication India would not have been invaded by lizard people, ~~much like Japan.~~ The list continues eternally.
Nevertheless, I will charitably examine the given counterfactual anyway. Did pre-colonial India have industrial potential? The answer is a resounding no.
From Gupta (1980):

This article starts from the premise that while economic categories - the extent of commodity production, wage labour, monetarisation of the economy, etc - should be the basis for any analysis of the production relations of pre-British India, it is the nature of class struggles arising out of particular class alignments that finally gives the decisive twist to social change. Arguing on this premise, and analysing the available evidence, this article concludes that there was little potential for industrial revolution before the British arrived in India because, whatever might have been the character of economic categories of that period, the class relations had not sufficiently matured to develop productive forces and the required class struggle for a 'revolution' to take place.

A view echoed in Raychaudhuri (1983):

Yet all of this did not amount to an economic situation comparable to that of western Europe on the eve of the industrial revolution. Her technology - in agriculture as well as manufacturers - had by and large been stagnant for centuries. [...] The weakness of the Indian economy in the mid-eighteenth century, as compared to pre-industrial Europe was not simply a matter of technology and commercial and industrial organization. No scientific or geographical revolution formed part of the eighteenth-century Indian's historical experience. [...] Spontaneous movement towards industrialisation is unlikely in such a situation.

So now we've established India did not have industrial potential, was India similar to Japan just before the Meiji era? The answer, yet again, unsurprisingly, is no. Japan's economic situation was not comparable to India's, which allowed for Japan to finance its revolution. From Yasuba (1986):

All in all, the Japanese standard of living may not have been much below the English standard of living before industrialization, and both of them may have been considerably higher than the Indian standard of living. We can no longer say that Japan started from a pathetically low economic level and achieved a rapid or even "miraculous" economic growth. Japan's per capita income was almost as high as in Western Europe before industrialization, and it was possible for Japan to produce surplus in the Meiji Period to finance private and public capital formation.

The circumstances that led to Meiji Japan were extremely unique. See Tomlinson (1985):

Most modern comparisons between India and Japan, written by either Indianists or Japanese specialists, stress instead that industrial growth in Meiji Japan was the product of unique features that were not reproducible elsewhere. [...] it is undoubtably true that Japan's progress to industrialization has been unique and unrepeatable

So there you have it. Unsubstantiated statistical assumptions, calling any number you can a drain & assuming a counterfactual for no good reason gets you this $45 trillion number. Hopefully that's enough to bury it in the ground.
^{1. Several authors have affirmed that Indian identity is a colonial artefact. For example see} ^{Rajan 1969}^:

^{Perhaps the single greatest and most enduring impact of British rule over India is that it created an Indian nation, in the modern political sense. After centuries of rule by different dynasties overparts of the Indian sub-continent, and after about 100 years of British rule, Indians ceased to be merely Bengalis, Maharashtrians,or Tamils, linguistically and culturally.}

^{or see} ^{Bryant 2000}^:

^{But then, it would be anachronistic to condemn eighteenth-century Indians, who served the British, as collaborators, when the notion of 'democratic' nationalism or of an Indian 'nation' did not then exist.} ^[...] ^{Indians who fought for them, differed from the Europeans in having a primary attachment to a non-belligerent religion, family and local chief, which was stronger than any identity they might have with a more remote prince or 'nation'.}

Bibliography

Chakrabarti, Shubra & Patnaik, Utsa (2018). Agrarian and other histories: Essays for Binay Bhushan Chaudhuri. Colombia University Press
Hickel, Jason (2018). How the British stole $45 trillion from India. The Guardian
Bhuyan, Aroonim & Sharma, Krishan (2019). The Great Loot: How the British stole $45 trillion from India. Indiapost
Monbiot, George (2020). English Landowners have stolen our rights. It is time to reclaim them. The Guardian
Tsjeng, Zing (2020). How Britain Stole $45 trillion from India with trains | Empires of Dirt. Vice
Chaudhury, Dipanjan (2019). British looted $45 trillion from India in today’s value: Jaishankar. The Economic Times
Roy, Tirthankar (2019). How British rule changed India's economy: The Paradox of the Raj. Palgrave Macmillan
Patnaik, Utsa (2018). How the British impoverished India. Hindustan Times
Tuovila, Alicia (2019). Expenditure method. Investopedia
Dewey, Clive (2019). Changing the guard: The dissolution of the nationalist–Marxist orthodoxy in the agrarian and agricultural history of India. The Indian Economic & Social History Review
Chandra, Bipan et al. (1989). India's Struggle for Independence, 1857-1947. Penguin Books
Frankema, Ewout & Booth, Anne (2019). Fiscal Capacity and the Colonial State in Asia and Africa, c. 1850-1960. Cambridge University Press
Dalal, Sucheta (2019). IL&FS Controversy: Centre is Paying Up on Sovereign Guarantees to ADB, KfW for Group's Loan. TheWire
Chaudhuri, K.N. (1983). X - Foreign Trade and Balance of Payments (1757–1947). Cambridge University Press
Sunderland, David (2013). Financing the Raj: The City of London and Colonial India, 1858-1940. Boydell Press
Dewey, Clive (1978). Patwari and Chaukidar: Subordinate officials and the reliability of India’s agricultural statistics. Athlone Press
Smith, Lisa (2015). The great Indian calorie debate: Explaining rising undernourishment during India’s rapid economic growth. Food Policy
Duh, Josephine & Spears, Dean (2016). Health and Hunger: Disease, Energy Needs, and the Indian Calorie Consumption Puzzle. The Economic Journal
Vankatesh, P. et al. (2016). Relationship between Food Production and Consumption Diversity in India – Empirical Evidences from Cross Section Analysis. Agricultural Economics Research Review
Gupta, Shaibal (1980). Potential of Industrial Revolution in Pre-British India. Economic and Political Weekly
Raychaudhuri, Tapan (1983). I - The mid-eighteenth-century background. Cambridge University Press
Yasuba, Yasukichi (1986). Standard of Living in Japan Before Industrialization: From what Level did Japan Begin? A Comment. The Journal of Economic History
Tomblinson, B.R. (1985). Writing History Sideways: Lessons for Indian Economic Historians from Meiji Japan. Cambridge University Press
Rajan, M.S. (1969). The Impact of British Rule in India. Journal of Contemporary History
Bryant, G.J. (2000). Indigenous Mercenaries in the Service of European Imperialists: The Case of the Sepoys in the Early British Indian Army, 1750-1800. War in History

Factset: How You can Invest in Hedge Funds’ Biggest Investment
Tl;dr FactSet is the most undervalued widespread SaaS/IT solution stock that exists
If any of you have relevant experience or are friends with people in Investment Banking/other high finance, you know that Factset is the lifeblood of their financial analysis toolkit if and when it’s not Bloomberg, which isn’t even publicly traded. Factset has been around since 1978 and it’s considered a staple like Bloomberg in many wealth management firms, and it offers some of the easiest to access and understandable financial data so many newer firms focused less on trading are switching to Factset because it has a lot of the same data Bloomberg offers for half the cost. When it comes to modern financial data, Factset outcompetes Reuters and arguably Bloomberg as well due to their API services which makes Factset much more preferable for quantitative divisions of banks/hedge funds as API integration with Python/R is the most important factor for vast data lakes of financial data, this suggests Factset will be much more prepared for programming making its way into traditional finance fields. According to Factset, their mission for data delivery is to: “Integrate the data you need with your applications, web portals, and statistical packages. Whether you need market, company, or alternative data, FactSet flexible data delivery services give you normalized data through APIs and a direct delivery of local copies of standard data feeds. Our unique symbology links and aggregates a variety of content sources to ensure consistency, transparency, and data integrity across your business. Build financial models and power customized applications with FactSet APIs in our developer portal”. Their technical focus for their data delivery system alone should make it stand out compared to Bloomberg, whose UI is far more outdated and complex on top of not being as technically developed as Factset’s. Factset is the key provider of buy-side portfolio analysis for IBs, Hedge funds, and Private Equity firms, and it’s making its way into non-quantitative hedge funds as well because quantitative portfolio management makes automation of risk management and the application of portfolio theory so much easier, and to top it off, Factset’s scenario analysis and simulation is unique in its class. Factset also is able to automate trades based on individual manager risk tolerance and ML optimization for Forex trading as well. Not only does Factset provide solutions for financial companies, they are branching out to all corporations now and providing quantitative analytics for them in the areas of “corporate development, M&A, strategy, treasury, financial planning and analysis, and investor relations workflows”. Factset will eventually in my opinion reach out to Insurance Risk Management a lot more in the future as that’s a huge industry which has yet to see much automation of risk management yet, and with the field wide open, Factset will be the first to take advantage without a shadow of a doubt. So let’s dig into the company’s financials now:
Their latest 8k filing reported the following:
Revenue increased 2.6%, or $9.6 million, to $374.1 million compared with $364.5 million for the same period in fiscal 2019. The increase is primarily due to higher sales of analytics, content and technology solutions (CTS) and wealth management solutions.
Annual Subscription Value (ASV) plus professional services was $1.52 billion at May 31, 2020, compared with $1.45 billion at May 31, 2019. The organic growth rate, which excludes the effects of acquisitions, dispositions, and foreign currency movements, was 5.0%. The primary contributors to this growth rate were higher sales in FactSet's wealth and research workflow solutions and a price increase in the Company's international region
Adjusted operating margin improved to 35.5% compared with 34.0% in the prior year period primarily as a result of reduced employee-related operating expenses due to the coronavirus pandemic.
Diluted earnings per share (EPS) increased 11.0% to $2.63 compared with $2.37 for the same period in fiscal 2019.
Adjusted diluted EPS rose 9.2% to $2.86 compared with $2.62 in the prior year period primarily driven by an improvement in operating results.
The Company’s effective tax rate for the third quarter decreased to 15.0% compared with 18.6% a year ago, primarily due to an income tax expense in the prior year related to finalizing the Company's tax returns with no similar event for the three months ended May 31, 2020.
FactSet increased its quarterly dividend by $0.05 per share or 7% to $0.77 marking the fifteenth consecutive year the Company has increased dividends, highlighting its continued commitment to returning value to shareholders.
As you can see, there’s not much of a negative sign in sight here.
It makes sense considering how FactSet’s FCF has never slowed down:
https://preview.redd.it/frmtdk8e9hk51.png?width=276&format=png&auto=webp&s=1c0ff12539e0b2f9dbfda13d0565c5ce2b6f8f1a

https://preview.redd.it/6axdb6lh9hk51.png?width=593&format=png&auto=webp&s=9af1673272a5a2d8df28f60f4707e948a00e5ff1
FactSet’s annual subscriptions and professional services have made its way to foreign and developing markets, and many of them are opting for FactSet’s cheaper services to reduce costs and still get copious amounts of data and models to work with.
Here’s what FactSet had to say regarding its competitive position within the market of providing financial data in its last 10k: “Despite competing products and services, we enjoy high barriers to entry and believe it would be difficult for another vendor to quickly replicate the extensive databases we currently offer. Through our in-depth analytics and client service, we believe we can offer clients a more comprehensive solution with one of the broadest sets of functionalities, through a desktop or mobile user interface or through a standardized or bespoke data feed.” And FactSet is confident that their ML services cannot be replaced by anybody else in the industry either: “In addition, our applications, including our client support and service offerings, are entrenched in the workflow of many financial professionals given the downloading functions and portfolio analysis/screening capabilities offered. We are entrusted with significant amounts of our clients' own proprietary data, including portfolio holdings. As a result, our products have become central to our clients’ investment analysis and decision-making.” (https://last10k.com/sec-filings/fds#link_fullReport), if you read the full report and compare it to the most recent 8K, you’ll find that the real expenses this quarter were far lower than expected by the last 10k as there was a lower than expected tax rate and a 3% increase in expected operating margin from the expected figure as well. The company also reports a 90% customer retention rate over 15 years, so you know that they’re not lying when they say the clients need them for all sorts of financial data whether it’s for M&A or wealth management and Equity analysis:
https://www.investopedia.com/terms/f/factset.asp
https://preview.redd.it/yo71y6qj9hk51.png?width=355&format=png&auto=webp&s=a9414bdaa03c06114ca052304a26fae2773c3e45

FactSet also has remarkably good cash conversion considering it’s a subscription based company, a company structure which usually takes on too much leverage. Speaking of leverage, FDS had taken on a lot of leverage in 2015:

https://preview.redd.it/oxaa1wel9hk51.png?width=443&format=png&auto=webp&s=13d60d2518980360c403364f7150392ab83d07d7
So what’s that about? Why were FactSet’s long term debts at 0 and all of a sudden why’d the spike up? Well usually for a company that’s non-cyclical and has a well-established product (like FactSet) leverage can actually be good at amplifying returns, so FDS used this to their advantage and this was able to help the share’s price during 2015. Also, as you can see debt/ebitda is beginning a rapid decline anyway. This only adds to my theory that FactSet is trying to expand into new playing fields. FactSet obviously didn’t need the leverage to cover their normal costs, because they have always had consistently growing margins and revenue so the debt financing was only for the sake of financing growth. And this debt can be considered covered and paid off, considering the net income growth of 32% between 2018 and 2019 alone and the EPS growth of 33%
https://preview.redd.it/e4trju3p9hk51.png?width=387&format=png&auto=webp&s=6f6bee15f836c47e73121054ec60459f147d353e

EBITDA has virtually been exponential for FactSet for a while because of the bang-for-buck for their well-known product, but now as FactSet ventures into algorithmic trading and corporate development the scope for growth is broadly expanded.
https://preview.redd.it/yl7f58tr9hk51.png?width=489&format=png&auto=webp&s=68906b9ecbcf6d886393c4ff40f81bdecab9e9fd

P/E has declined in the past 2 years, making it a great time to buy.

https://preview.redd.it/4mqw3t4t9hk51.png?width=445&format=png&auto=webp&s=e8d719f4913883b044c4150f11b8732e14797b6d
Increasing ROE despite lowering of leverage post 2016
https://preview.redd.it/lt34avzu9hk51.png?width=441&format=png&auto=webp&s=f3742ed87cd1c2ccb7a3d3ee71ae8c7007313b2b

Mountains of cash have been piling up in the coffers increasing chances of increased dividends for shareholders (imo dividend is too low right now, but increasing it will tempt more investors into it), and on top of that in the last 10k a large buyback expansion program was implemented for $210m worth of shares, which shows how confident they are in the company itself.
https://preview.redd.it/fliirmpx9hk51.png?width=370&format=png&auto=webp&s=1216eddeadb4f84c8f4f48692a2f962ba2f1e848

SGA expense/Gross profit has been declining despite expansion of offices
I’m a bit concerned about the skin in the game leadership has in this company, since very few executives/board members have significant holdings in the company, but the CEO himself is a FactSet veteran, and knows his way around the company. On top of that, Bloomberg remains king for trading and the fixed income security market, and Reuters beats out FactSet here as well. If FactSet really wants to increase cash flow sources, the expansion into insurance and corp dev has to be successful.
Summary: FactSet has a lot of growth still left in its industry which is already fast-growing in and of itself, and it only has more potential at its current valuation. Earnings September 24th should be a massive beat due to investment banking demand and growth plus Hedge fund requirements for data and portfolio management hasn’t gone anywhere and has likely increased due to more market opportunities to buy-in.
Calls have shitty greeks, but if you're ballsy October 450s LOL, I'm holding shares
I’d say it’s a great long term investment, and it should at least be on your watchlist.

Hi, may nakikita akong questions about investing/trading and some about trading mentors/gurus societies etc.
This is my opinion depende na sa inyo kung susundin nyo.
ZFT - Zeefreaks tribe,
First, i do respect "Zee" as a trader,his "tribe" teaches or mentors their students using their system to become their own. Generally, Darvas Box, MAs 20 50 100 and RSI are their weapons but mainly its the Price Action and RSI ang parang laman talaga ng System nila and you can only get better through time as with other systems in general. I think they are good, but yun na nga, just good.
Problem:
They charge you with a hefty sum na i don't think na ma jujustify nila, because at the end of the day more or less its you ( along with practice) and your psychology that can help you along the way. Okay, may Trading psych coach daw sila , si Ma'am Celeste (Zee's Gf) pero overtime you will learn about yourself in the process naman.
Zee is justifying the hefty fee because sabi nya before if im not mistaken na the clients are not paying the mentoring alone pero along with it yung "CULTURE" ng ZFT. I say, bullshit. Di nga nya alam na may mga ZFT "mentors" na that are mentoring other people without his knowledge and charging them less but still a very hefty fee. hehe Akala nya wala na pero meron pa, magaling lang talaga magtago.

KIDLAT- hmm , same with ZFT since dun din naman sya nanggaling, Habits you know.

T3 ( The Tattooed Trader)- well, this guy is LEGIT. He trades international markets too not just PSEI. Reasonable Fee. Good guy,prangka din. He doesnt tolerate Bullshit. He wont try to impose his system sayo but instead encourages you to go with the process. That's it.

Gandah Koh ( Trader's Lounge) - He/She provides free content daw. Yes, FREE content pero at the same time sinasabi nya that he/she is just an average trader. Kicks people who patronizes paid mentorship / who belong sa paid mentorship or kahit magtanong ka man lang ng about paid mentorship rage mode na agad tapos kick na agad. Yung mga followers nya ayaw lang talaga gumastos to learn premium content. Biruin nyo? gusto maging free yung investa? lol.
Ironic, why? kasi sabi nya average trader lang daw sya but he/she shuts off people who would want to learn from those who are better than her. To all hehis followers, Eto po tanong ko.

may member ba sa Trader's lounge na consistently profitable na? with rising equity curve?
Do you think the best traders out there did not spend any cent to boost their career to the top?

Simple lang yan. isip isipin nyo. :D

BOH- Superb! yung mga quant models nila ay one of the best if not the best. Very technical and systematic yung BOH and their team ay may credentials to back it up. Yung Fee ay affordable, kayang kaya ng ordinary working people.
Oakbridge (DAVAO)- not much information about them kasi tahimik lang sila ,but what i know is that bigatin yung mentors dun but apart from that i don't know much kaya i can't say anything more.
Bigote (bigote trading financial advocacy) - Eto yung free content na LEGIT. One of the best people i know, he is a caylum trading institute alumni. Eto, you use his system plus master price action.
Open journal by Javi Medina, Matt flores, Ken Arcano - If you dig Elliot wave then they are the guys you want to learn from, the information they provide are all backtested, no guess works just pure juicy contents day in and day out.
Trivia:
They manage funds from various big time clients.
Tomatrader, Jet mojica(from BOH), Joanne (from investa), Bearyo ( from investa) and etc Joined Open Journal.
Javi Medina - ranked 1st the 2020 US investing competition, also he was an investacup champion.
Ken Arcano - top 5 in investacup.
Matt Flores- i dont know much about him though, silent kind of guy.
OJ's system can be used in trading crypto, Forex , commodities, US stocks and other indices.
Caylum Trading Institute - i think di na kailangan e describe pa yung caylum eh. *wink*

So there you go. It's your choice kung how you will take my opinion, you can bash me or what i really don't care. At the end of the day, choice nyo pa rin yon.
Kung ako lang, id go with

Read the trading code by jason cam.
Download any price action videos/books . Ex. Steve nison books
Try out Bigote's framework or enroll with any one of those services , but i would recommend open journal, BOH, T3 or caylum. If you want ZFT or kidlat then go for it.

At the end of the day, stick with one system , be patient, dont shortcut the process, master one setup at a time and improve your trading psychology. I dont want to spread hate, just spitting out my opinion.
You can share this in fb, twitter or any socmed you like or not share this, do whatever you want.
That's all. Stay home to help the frontliners.

Just recently been looking into forex. Learned on baby pips and went on to try my hand at trading gold starting with 100$ using a 1:100 leverage. Is it honestly possible to produce a gnarly income doing this? And if so any advice or strategy info. Day trading is something I’ve always wanted to do and since my campus is closed due to the virus and I’m stuck with my parents again I’ve been trying to develop some standing group without the use of a “Instagram forex guru” charging large amounts for advice you never really know is real. 23y/o.

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I went headlong into the VP trading belief system about a year ago, worked on it full time for about 3 months after consuming all the material and backtesting, etc....and abandoned it after I had an epiphany as to how absolutely absurd it was.
But who know, if I had stuck with it, maybe I would have been consistent and profitable. According to the anonymous VP (who trades with the online "prop firm" Maverick and lives in Vegas), I should have been doing well right now.
Are there any forex traders out there who, like me, started on the NNFX quest for the grail over a year ago but, unlike me, found it? Don't see too much talk of him anymore, although he did stop releasing content around December of last year.

Disclaimer: None of this is financial advice. I have no idea what I'm doing. Please do your own research or you will certainly lose money. I'm not a statistician, data scientist, well-seasoned trader, or anything else that would qualify me to make statements such as the below with any weight behind them. Take them for the incoherent ramblings that they are.
TL;DR at the bottom for those not interested in the details.
This is a bit of a novel, sorry about that. It was mostly for getting my own thoughts organized, but if even one person reads the whole thing I will feel incredibly accomplished.

Background

For those of you not familiar, please see the various threads on this trading system here. I can't take credit for this system, all glory goes to ParallaxFX!
I wanted to see how effective this system was at H1 for a couple of reasons: 1) My current broker is TD Ameritrade - their Forex minimum is a mini lot, and I don't feel comfortable enough yet with the risk to trade mini lots on the higher timeframes(i.e. wider pip swings) that ParallaxFX's system uses, so I wanted to see if I could scale it down. 2) I'm fairly impatient, so I don't like to wait days and days with my capital tied up just to see if a trade is going to win or lose.
This does mean it requires more active attention since you are checking for setups once an hour instead of once a day or every 4-6 hours, but the upside is that you trade more often this way so you end up winning or losing faster and moving onto the next trade. Spread does eat more of the trade this way, but I'll cover this in my data below - it ends up not being a problem.
I looked at data from 6/11 to 7/3 on all pairs with a reasonable spread(pairs listed at bottom above the TL;DR). So this represents about 3-4 weeks' worth of trading. I used mark(mid) price charts. Spreadsheet link is below for anyone that's interested.

System Details

I'm pretty much using ParallaxFX's system textbook, but since there are a few options in his writeups, I'll include all the discretionary points here:

I'm using the stop entry version - so I wait for the price to trade beyond the confirmation candle(in the direction of my trade) before entering. I don't have any data to support this decision, but I've always preferred this method over retracement-limit entries. Maybe I just like the feeling of a higher winrate even though there can be greater R:R using a limit entry. Variety is the spice of life.
I put my stop loss right at the opposite edge of the confirmation candle. NOT at the edge of the 2-candle pattern that makes up the system. I'll get into this more below - not enough trades are saved to justify the wider stops. (Wider stop means less $ per pip won, assuming you still only risk 1%).
All my profit/loss statistics are based on a 1% risk per trade. Because 1 is real easy to multiply.
There are definitely some questionable trades in here, but I tried to make it as mechanical as possible for evaluation purposes. They do fit the definitions of the system, which is why I included them. You could probably improve the winrate by being more discretionary about your trades by looking at support/resistance or other techniques.
I didn't use MBB much for either entering trades, or as support/resistance indicators. Again, trying to be pretty mechanical here just for data collection purposes. Plus, we all make bad trading decisions now and then, so let's call it even.
As stated in the title, this is for H1 only. These results may very well not play out for other time frames - who knows, it may not even work on H1 starting this Monday. Forex is an unpredictable place.
I collected data to show efficacy of taking profit at three different levels: -61.8%, -100% and -161.8% fib levels described in the system using the passive trade management method(set it and forget it). I'll have more below about moving up stops and taking off portions of a position.

And now for the fun. Results!

Total Trades: 241
Raw Winrates:
- TP at -61.8%: 177 out of 241: 73.44%
- TP at -100%: 156 out of 241: 64.73%
- TP at -161.8%: 121 out of 241: 50.20%
Adjusted Proft % (takes spread into account):
- TP at -61.8%: 5.22%
- TP at -100%: 23.55%
- TP at -161.8%: 29.14%

As you can see, a higher target ended up with higher profit despite a much lower winrate. This is partially just how things work out with profit targets in general, but there's an additional point to consider in our case: the spread. Since we are trading on a lower timeframe, there is less overall price movement and thus the spread takes up a much larger percentage of the trade than it would if you were trading H4, Daily or Weekly charts. You can see exactly how much it accounts for each trade in my spreadsheet if you're interested. TDA does not have the best spreads, so you could probably improve these results with another broker.
EDIT: I grabbed typical spreads from other brokers, and turns out while TDA is pretty competitive on majors, their minors/crosses are awful! IG beats them by 20-40% and Oanda beats them 30-60%! Using IG spreads for calculations increased profits considerably (another 5% on top) and Oanda spreads increased profits massively (another 15%!). Definitely going to be considering another broker than TDA for this strategy. Plus that'll allow me to trade micro-lots, so I can be more granular(and thus accurate) with my position sizing and compounding.

A Note on Spread

As you can see in the data, there were scenarios where the spread was 80% of the overall size of the trade(the size of the confirmation candle that you draw your fibonacci retracements over), which would obviously cut heavily into your profits.
Removing any trades where the spread is more than 50% of the trade width improved profits slightly without removing many trades, but this is almost certainly just coincidence on a small sample size. Going below 40% and even down to 30% starts to cut out a lot of trades for the less-common pairs, but doesn't actually change overall profits at all(~1% either way).
However, digging all the way down to 25% starts to really make some movement. Profit at the -161.8% TP level jumps up to 37.94% if you filter out anything with a spread that is more than 25% of the trade width! And this even keeps the sample size fairly large at 187 total trades.
You can get your profits all the way up to 48.43% at the -161.8% TP level if you filter all the way down to only trades where spread is less than 15% of the trade width, however your sample size gets much smaller at that point(108 trades) so I'm not sure I would trust that as being accurate in the long term.
Overall based on this data, I'm going to only take trades where the spread is less than 25% of the trade width. This may bias my trades more towards the majors, which would mean a lot more correlated trades as well(more on correlation below), but I think it is a reasonable precaution regardless.

Time of Day

Time of day had an interesting effect on trades. In a totally predictable fashion, a vast majority of setups occurred during the London and New York sessions: 5am-12pm Eastern. However, there was one outlier where there were many setups on the 11PM bar - and the winrate was about the same as the big hours in the London session. No idea why this hour in particular - anyone have any insight? That's smack in the middle of the Tokyo/Sydney overlap, not at the open or close of either.
On many of the hour slices I have a feeling I'm just dealing with small number statistics here since I didn't have a lot of data when breaking it down by individual hours. But here it is anyway - for all TP levels, these three things showed up(all in Eastern time):

7pm-4am: Fewer setups, but winrate high.
5am-6am: Lots of setups, but but winrate low.
12pm-3pm Medium number of setups, but winrate low.

I don't have any reason to think these timeframes would maintain this behavior over the long term. They're almost certainly meaningless. EDIT: When you de-dup highly correlated trades, the number of trades in these timeframes really drops, so from this data there is no reason to think these timeframes would be any different than any others in terms of winrate.
That being said, these time frames work out for me pretty well because I typically sleep 12am-7am Eastern time. So I automatically avoid the 5am-6am timeframe, and I'm awake for the majority of this system's setups.

Moving stops up to breakeven

This section goes against everything I know and have ever heard about trade management. Please someone find something wrong with my data. I'd love for someone to check my formulas, but I realize that's a pretty insane time commitment to ask of a bunch of strangers.
Anyways. What I found was that for these trades moving stops up...basically at all...actually reduced the overall profitability.
One of the data points I collected while charting was where the price retraced back to after hitting a certain milestone. i.e. once the price hit the -61.8% profit level, how far back did it retrace before hitting the -100% profit level(if at all)? And same goes for the -100% profit level - how far back did it retrace before hitting the -161.8% profit level(if at all)?
Well, some complex excel formulas later and here's what the results appear to be. Emphasis on appears because I honestly don't believe it. I must have done something wrong here, but I've gone over it a hundred times and I can't find anything out of place.

Moving SL up to 0% when the price hits -61.8%, TP at -100%
- Winrate: 46.4%
- Adjusted Proft % (takes spread into account): 5.36%
Taking half position off at -61.8%, moving SL up to 0%, TP remaining half at -100%
- Winrate: 65.97%
- Adjusted Proft % (takes spread into account): -1.01% (yes, a net loss)

Now, you might think exactly what I did when looking at these numbers: oof, the spread killed us there right? Because even when you move your SL to 0%, you still end up paying the spread, so it's not truly "breakeven". And because we are trading on a lower timeframe, the spread can be pretty hefty right?
Well even when I manually modified the data so that the spread wasn't subtracted(i.e. "Breakeven" was truly +/- 0), things don't look a whole lot better, and still way worse than the passive trade management method of leaving your stops in place and letting it run. And that isn't even a realistic scenario because to adjust out the spread you'd have to move your stoploss inside the candle edge by at least the spread amount, meaning it would almost certainly be triggered more often than in the data I collected(which was purely based on the fib levels and mark price). Regardless, here are the numbers for that scenario:

Moving SL up to 0% when the price hits -61.8%, TP at -100%
- Winrate(breakeven doesn't count as a win): 46.4%
- Adjusted Proft % (takes spread into account): 17.97%
Taking half position off at -61.8%, moving SL up to 0%, TP remaining half at -100%
- Winrate(breakeven doesn't count as a win): 65.97%
- Adjusted Proft % (takes spread into account): 11.60%

From a literal standpoint, what I see behind this behavior is that 44 of the 69 breakeven trades(65%!) ended up being profitable to -100% after retracing deeply(but not to the original SL level), which greatly helped offset the purely losing trades better than the partial profit taken at -61.8%. And 36 went all the way back to -161.8% after a deep retracement without hitting the original SL. Anyone have any insight into this? Is this a problem with just not enough data? It seems like enough trades that a pattern should emerge, but again I'm no expert.
I also briefly looked at moving stops to other lower levels (78.6%, 61.8%, 50%, 38.2%, 23.6%), but that didn't improve things any. No hard data to share as I only took a quick look - and I still might have done something wrong overall.
The data is there to infer other strategies if anyone would like to dig in deep(more explanation on the spreadsheet below). I didn't do other combinations because the formulas got pretty complicated and I had already answered all the questions I was looking to answer.

2-Candle vs Confirmation Candle Stops

Another interesting point is that the original system has the SL level(for stop entries) just at the outer edge of the 2-candle pattern that makes up the system. Out of pure laziness, I set up my stops just based on the confirmation candle. And as it turns out, that is much a much better way to go about it.
Of the 60 purely losing trades, only 9 of them(15%) would go on to be winners with stops on the 2-candle formation. Certainly not enough to justify the extra loss and/or reduced profits you are exposing yourself to in every single other trade by setting a wider SL.
Oddly, in every single scenario where the wider stop did save the trade, it ended up going all the way to the -161.8% profit level. Still, not nearly worth it.

Correlated Trades

As I've said many times now, I'm really not qualified to be doing an analysis like this. This section in particular.
Looking at shared currency among the pairs traded, 74 of the trades are correlated. Quite a large group, but it makes sense considering the sort of moves we're looking for with this system.
This means you are opening yourself up to more risk if you were to trade on every signal since you are technically trading with the same underlying sentiment on each different pair. For example, GBP/USD and AUD/USD moving together almost certainly means it's due to USD moving both pairs, rather than GBP and AUD both moving the same size and direction coincidentally at the same time. So if you were to trade both signals, you would very likely win or lose both trades - meaning you are actually risking double what you'd normally risk(unless you halve both positions which can be a good option, and is discussed in ParallaxFX's posts and in various other places that go over pair correlation. I won't go into detail about those strategies here).
Interestingly though, 17 of those apparently correlated trades ended up with different wins/losses.
Also, looking only at trades that were correlated, winrate is 83%/70%/55% (for the three TP levels).
Does this give some indication that the same signal on multiple pairs means the signal is stronger? That there's some strong underlying sentiment driving it? Or is it just a matter of too small a sample size? The winrate isn't really much higher than the overall winrates, so that makes me doubt it is statistically significant.
One more funny tidbit: EUCAD netted the lowest overall winrate: 30% to even the -61.8% TP level on 10 trades. Seems like that is just a coincidence and not enough data, but dang that's a sucky losing streak.
EDIT: WOW I spent some time removing correlated trades manually and it changed the results quite a bit. Some thoughts on this below the results. These numbers also include the other "What I will trade" filters. I added a new worksheet to my data to show what I ended up picking.

Total Trades: 75
Raw Winrates:
- TP at -61.8%: 84.00%
- TP at -100%: 73.33%
- TP at -161.8%: 60.00%
- Moving SL up to 0% when the price hits -61.8%, TP at -100%: 53.33%
- Taking half position off at -61.8%, moving SL up to 0%, TP remaining half at -100%: 53.33% (yes, oddly the exact same winrate. but different trades/profits)
Adjusted Proft % (takes spread into account):
- TP at -61.8%: 18.13%
- TP at -100%: 26.20%
- TP at -161.8%: 34.01%
- Moving SL up to 0% when the price hits -61.8%, TP at -100%: 19.20%
- Taking half position off at -61.8%, moving SL up to 0%, TP remaining half at -100%: 17.29%

To do this, I removed correlated trades - typically by choosing those whose spread had a lower % of the trade width since that's objective and something I can see ahead of time. Obviously I'd like to only keep the winning trades, but I won't know that during the trade. This did reduce the overall sample size down to a level that I wouldn't otherwise consider to be big enough, but since the results are generally consistent with the overall dataset, I'm not going to worry about it too much.
I may also use more discretionary methods(support/resistance, quality of indecision/confirmation candles, news/sentiment for the pairs involved, etc) to filter out correlated trades in the future. But as I've said before I'm going for a pretty mechanical system.
This brought the 3 TP levels and even the breakeven strategies much closer together in overall profit. It muted the profit from the high R:R strategies and boosted the profit from the low R:R strategies. This tells me pair correlation was skewing my data quite a bit, so I'm glad I dug in a little deeper. Fortunately my original conclusion to use the -161.8 TP level with static stops is still the winner by a good bit, so it doesn't end up changing my actions.
There were a few times where MANY (6-8) correlated pairs all came up at the same time, so it'd be a crapshoot to an extent. And the data showed this - often then won/lost together, but sometimes they did not. As an arbitrary rule, the more correlations, the more trades I did end up taking(and thus risking). For example if there were 3-5 correlations, I might take the 2 "best" trades given my criteria above. 5+ setups and I might take the best 3 trades, even if the pairs are somewhat correlated.
I have no true data to back this up, but to illustrate using one example: if AUD/JPY, AUD/USD, CAD/JPY, USD/CAD all set up at the same time (as they did, along with a few other pairs on 6/19/20 9:00 AM), can you really say that those are all the same underlying movement? There are correlations between the different correlations, and trying to filter for that seems rough. Although maybe this is a known thing, I'm still pretty green to Forex - someone please enlighten me if so! I might have to look into this more statistically, but it would be pretty complex to analyze quantitatively, so for now I'm going with my gut and just taking a few of the "best" trades out of the handful.
Overall, I'm really glad I went further on this. The boosting of the B/E strategies makes me trust my calculations on those more since they aren't so far from the passive management like they were with the raw data, and that really had me wondering what I did wrong.

What I will trade

Putting all this together, I am going to attempt to trade the following(demo for a bit to make sure I have the hang of it, then for keeps):

"System Details" I described above.
TP at -161.8%
Static SL at opposite side of confirmation candle - I won't move stops up to breakeven.
Trade only 7am-11am and 4pm-11pm signals.
Nothing where spread is more than 25% of trade width.

Looking at the data for these rules, test results are:

Winrate: 58.19%
Adjusted Proft % (takes spread into account): 47.43%

I'll be sure to let everyone know how it goes!

Other Technical Details

ATR is only slightly elevated in this date range from historical levels, so this should fairly closely represent reality even after the COVID volatility leaves the scalpers sad and alone.
The sample size is much too small for anything really meaningful when you slice by hour or pair. I wasn't particularly looking to test a specific pair here - just the system overall as if you were going to trade it on all pairs with a reasonable spread.

Raw Data

Here's the spreadsheet for anyone that'd like it. (EDIT: Updated some of the setups from the last few days that have fully played out now. I also noticed a few typos, but nothing major that would change the overall outcomes. Regardless, I am currently reviewing every trade to ensure they are accurate.UPDATE: Finally all done. Very few corrections, no change to results.)
I have some explanatory notes below to help everyone else understand the spiraled labyrinth of a mind that put the spreadsheet together.

I'm on the East Coast in the US, so the timestamps are Eastern time.
Time stamp is from the confirmation candle, not the indecision candle. So 7am would mean the indecision candle was 6:00-6:59 and the confirmation candle is 7:00-7:59 and you'd put in your order at 8:00.
I found a couple AM/PM typos as I was reviewing the data, so let me know if a trade doesn't make sense and I'll correct it.

Insanely detailed spreadsheet notes

For you real nerds out there. Here's an explanation of what each column means:

Pair - duh
Date/Time - Eastern time, confirmation candle as stated above
Win to -61.8%? - whether the trade made it to the -61.8% TP level before it hit the original SL.
Win to -100%? - whether the trade made it to the -100% TP level before it hit the original SL.
Win to -161.8%? - whether the trade made it to the -161.8% TP level before it hit the original SL.
Retracement level between -61.8% and -100% - how deep the price retraced after hitting -61.8%, but before hitting -100%. Be careful to look for the negative signs, it's easy to mix them up. Using the fib% levels defined in ParallaxFX's original thread. A plain hyphen "-" means it did not retrace, but rather went straight through -61.8% to -100%. Positive 100 means it hit the original SL.
Retracement level between -100% and -161.8% - how deep the price retraced after hitting -100%, but before hitting -161.8%. Be careful to look for the negative signs, it's easy to mix them up. Using the fib% levels defined in ParallaxFX's original thread. A plain hyphen "-" means it did not retrace, but rather went straight through -100% to -161.8%. Positive 100 means it hit the original SL.
Trade Width(Pips) - the size of the confirmation candle, and thus the "width" of your trade on which to determine position size, draw fib levels, etc.
Loser saved by 2 candle stop? - for all losing trades, whether or not the 2-candle stop loss would have saved the trade and how far it ended up getting if so. "No" means it didn't save it, N/A means it wasn't a losing trade so it's not relevant.
Spread(ThinkorSwim) - these are typical spreads for these pairs on ToS.
Spread % of Width - How big is the spread compared to the trade width? Not used in any calculations, but interesting nonetheless.
True Risk(Trade Width + Spread) - I set my SL at the opposite side of the confirmation candle knowing that I'm actually exposing myself to slightly more risk because of the spread(stop order = market order when submitted, so you pay the spread). So this tells you how many pips you are actually risking despite the Trade Width. I prefer this over setting the stop inside from the edge of the candle because some pairs have a wide spread that would mess with the system overall. But also many, many of these trades retraced very nearly to the edge of the confirmation candle, before ending up nicely profitable. If you keep your risk per trade at 1%, you're talking a true risk of, at most, 1.25% (in worst-case scenarios with the spread being 25% of the trade width as I am going with above).
Win or Loss in %(1% risk) including spread TP -61.8% - not going to go into huge detail, see the spreadsheet for calculations if you want. But, in a nutshell, if the trade was a win to 61.8%, it returns a positive # based on 61.8% of the trade width, minus the spread. Otherwise, it returns the True Risk as a negative. Both normalized to the 1% risk you started with.
Win or Loss in %(1% risk) including spread TP -100% - same as the last, but 100% of Trade Width.
Win or Loss in %(1% risk) including spread TP -161.8% - same as the last, but 161.8% of Trade Width.
Win or Loss in %(1% risk) including spread TP -100%, and move SL to breakeven at 61.8% - uses the retracement level columns to calculate profit/loss the same as the last few columns, but assuming you moved SL to 0% fib level after price hit -61.8%. Then full TP at 100%.
Win or Loss in %(1% risk) including spread take off half of position at -61.8%, move SL to breakeven, TP 100% - uses the retracement level columns to calculate profit/loss the same as the last few columns, but assuming you took of half the position and moved SL to 0% fib level after price hit -61.8%. Then TP the remaining half at 100%.
Overall Growth(-161.8% TP, 1% Risk) - pretty straightforward. Assuming you risked 1% on each trade, what the overall growth level would be chronologically(spreadsheet is sorted by date).

Pairs

AUD/CAD
AUD/CHF
AUD/JPY
AUD/NZD
AUD/USD
CAD/CHF
CAD/JPY
CHF/JPY
EUAUD
EUCAD
EUCHF
EUGBP
EUJPY
EUNZD
EUUSD
GBP/AUD
GBP/CAD
GBP/CHF
GBP/JPY
GBP/NZD
GBP/USD
NZD/CAD
NZD/CHF
NZD/JPY
NZD/USD
USD/CAD
USD/CHF
USD/JPY

TL;DR

Based on the reasonable rules I discovered in this backtest:

Date range: 6/11-7/3
Winrate: 58.19%
Adjusted Proft % (takes spread into account): 47.43%

Demo Trading Results

Since this post, I started demo trading this system assuming a 5k capital base and risking ~1% per trade. I've added the details to my spreadsheet for anyone interested. The results are pretty similar to the backtest when you consider real-life conditions/timing are a bit different. I missed some trades due to life(work, out of the house, etc), so that brought my total # of trades and thus overall profit down, but the winrate is nearly identical. I also closed a few trades early due to various reasons(not liking the price action, seeing support/resistance emerge, etc).
A quick note is that TD's paper trade system fills at the mid price for both stop and limit orders, so I had to subtract the spread from the raw trade values to get the true profit/loss amount for each trade.
I'm heading out of town next week, then after that it'll be time to take this sucker live!

86 Trades
- Date range: 7/9-7/30
- Winrate: 52.32%
- Adjusted Proft % (takes spread into account): 20.73%
  - Starting Balance: $5,000
  - Ending Balance: $6,036.51

Live Trading Results

I started live-trading this system on 8/10, and almost immediately had a string of losses much longer than either my backtest or demo period. Murphy's law huh? Anyways, that has me spooked so I'm doing a longer backtest before I start risking more real money. It's going to take me a little while due to the volume of trades, but I'll likely make a new post once I feel comfortable with that and start live trading again.

Join the club, it's not that exclusive but you can find a lot of people with common characteristics like having suffered losses and not knowing what to do next.
So far I've tried trading stocks, pennies, options, and even tried a bit of forex, but whatever I try and whichever system I use within them I never seem to be able to acquire consistent profits. My biggest profits are two digits and biggest losses are three.
Is there something you discovered that actually worked?

Factset: How You can Invest in Hedge Funds’ Biggest Investment
Tl;dr FactSet is the most undervalued widespread SaaS/IT solution stock that exists
If any of you have relevant experience or are friends with people in Investment Banking/other high finance, you know that Factset is the lifeblood of their financial analysis toolkit if and when it’s not Bloomberg, which isn’t even publicly traded. Factset has been around since 1978 and it’s considered a staple like Bloomberg in many wealth management firms, and it offers some of the easiest to access and understandable financial data so many newer firms focused less on trading are switching to Factset because it has a lot of the same data Bloomberg offers for half the cost. When it comes to modern financial data, Factset outcompetes Reuters and arguably Bloomberg as well due to their API services which makes Factset much more preferable for quantitative divisions of banks/hedge funds as API integration with Python/R is the most important factor for vast data lakes of financial data, this suggests Factset will be much more prepared for programming making its way into traditional finance fields. According to Factset, their mission for data delivery is to: “Integrate the data you need with your applications, web portals, and statistical packages. Whether you need market, company, or alternative data, FactSet flexible data delivery services give you normalized data through APIs and a direct delivery of local copies of standard data feeds. Our unique symbology links and aggregates a variety of content sources to ensure consistency, transparency, and data integrity across your business. Build financial models and power customized applications with FactSet APIs in our developer portal”. Their technical focus for their data delivery system alone should make it stand out compared to Bloomberg, whose UI is far more outdated and complex on top of not being as technically developed as Factset’s. Factset is the key provider of buy-side portfolio analysis for IBs, Hedge funds, and Private Equity firms, and it’s making its way into non-quantitative hedge funds as well because quantitative portfolio management makes automation of risk management and the application of portfolio theory so much easier, and to top it off, Factset’s scenario analysis and simulation is unique in its class. Factset also is able to automate trades based on individual manager risk tolerance and ML optimization for Forex trading as well. Not only does Factset provide solutions for financial companies, they are branching out to all corporations now and providing quantitative analytics for them in the areas of “corporate development, M&A, strategy, treasury, financial planning and analysis, and investor relations workflows”. Factset will eventually in my opinion reach out to Insurance Risk Management a lot more in the future as that’s a huge industry which has yet to see much automation of risk management yet, and with the field wide open, Factset will be the first to take advantage without a shadow of a doubt. So let’s dig into the company’s financials now:
Their latest 8k filing reported the following:
Revenue increased 2.6%, or $9.6 million, to $374.1 million compared with $364.5 million for the same period in fiscal 2019. The increase is primarily due to higher sales of analytics, content and technology solutions (CTS) and wealth management solutions.
Annual Subscription Value (ASV) plus professional services was $1.52 billion at May 31, 2020, compared with $1.45 billion at May 31, 2019. The organic growth rate, which excludes the effects of acquisitions, dispositions, and foreign currency movements, was 5.0%. The primary contributors to this growth rate were higher sales in FactSet's wealth and research workflow solutions and a price increase in the Company's international region
Adjusted operating margin improved to 35.5% compared with 34.0% in the prior year period primarily as a result of reduced employee-related operating expenses due to the coronavirus pandemic.
Diluted earnings per share (EPS) increased 11.0% to $2.63 compared with $2.37 for the same period in fiscal 2019.
Adjusted diluted EPS rose 9.2% to $2.86 compared with $2.62 in the prior year period primarily driven by an improvement in operating results.
The Company’s effective tax rate for the third quarter decreased to 15.0% compared with 18.6% a year ago, primarily due to an income tax expense in the prior year related to finalizing the Company's tax returns with no similar event for the three months ended May 31, 2020.
FactSet increased its quarterly dividend by $0.05 per share or 7% to $0.77 marking the fifteenth consecutive year the Company has increased dividends, highlighting its continued commitment to returning value to shareholders.
As you can see, there’s not much of a negative sign in sight here.
It makes sense considering how FactSet’s FCF has never slowed down
FactSet’s annual subscriptions and professional services have made its way to foreign and developing markets, and many of them are opting for FactSet’s cheaper services to reduce costs and still get copious amounts of data and models to work with.
Here’s what FactSet had to say regarding its competitive position within the market of providing financial data in its last 10k: “Despite competing products and services, we enjoy high barriers to entry and believe it would be difficult for another vendor to quickly replicate the extensive databases we currently offer. Through our in-depth analytics and client service, we believe we can offer clients a more comprehensive solution with one of the broadest sets of functionalities, through a desktop or mobile user interface or through a standardized or bespoke data feed.” And FactSet is confident that their ML services cannot be replaced by anybody else in the industry either: “In addition, our applications, including our client support and service offerings, are entrenched in the workflow of many financial professionals given the downloading functions and portfolio analysis/screening capabilities offered. We are entrusted with significant amounts of our clients' own proprietary data, including portfolio holdings. As a result, our products have become central to our clients’ investment analysis and decision-making.” (https://last10k.com/sec-filings/fds#link_fullReport), if you read the full report and compare it to the most recent 8K, you’ll find that the real expenses this quarter were far lower than expected by the last 10k as there was a lower than expected tax rate and a 3% increase in expected operating margin from the expected figure as well. The company also reports a 90% customer retention rate over 15 years, so you know that they’re not lying when they say the clients need them for all sorts of financial data whether it’s for M&A or wealth management and Equity analysis:
https://www.investopedia.com/terms/f/factset.asp

FactSet also has remarkably good cash conversion considering it’s a subscription based company, a company structure which usually takes on too much leverage. Speaking of leverage, FDS had taken on a lot of leverage in 2015:

So what’s that about? Why were FactSet’s long term debts at 0 and all of a sudden why’d the spike up? Well usually for a company that’s non-cyclical and has a well-established product (like FactSet) leverage can actually be good at amplifying returns, so FDS used this to their advantage and this was able to help the share’s price during 2015. Also, as you can see debt/ebitda is beginning a rapid decline anyway. This only adds to my theory that FactSet is trying to expand into new playing fields. FactSet obviously didn’t need the leverage to cover their normal costs, because they have always had consistently growing margins and revenue so the debt financing was only for the sake of financing growth. And this debt can be considered covered and paid off, considering the net income growth of 32% between 2018 and 2019 alone and the EPS growth of 33%

EBITDA has virtually been exponential for FactSet for a while because of the bang-for-buck for their well-known product, but now as FactSet ventures into algorithmic trading and corporate development the scope for growth is broadly expanded.

P/E has declined in the past 2 years, making it a great time to buy.

Increasing ROE despite lowering of leverage post 2016

Mountains of cash have been piling up in the coffers increasing chances of increased dividends for shareholders (imo dividend is too low right now, but increasing it will tempt more investors into it), and on top of that in the last 10k a large buyback expansion program was implemented for $210m worth of shares, which shows how confident they are in the company itself.

SGA expense/Gross profit has been declining despite expansion of offices
I’m a bit concerned about the skin in the game leadership has in this company, since very few executives/board members have significant holdings in the company, but the CEO himself is a FactSet veteran, and knows his way around the company. On top of that, Bloomberg remains king for trading and the fixed income security market, and Reuters beats out FactSet here as well. If FactSet really wants to increase cash flow sources, the expansion into insurance and corp dev has to be successful.
Summary: FactSet has a lot of growth still left in its industry which is already fast-growing in and of itself, and it only has more potential at its current valuation. Earnings September 24th should be a massive beat due to investment banking demand and growth plus Hedge fund requirements for data and portfolio management hasn’t gone anywhere and has likely increased due to more market opportunities to buy-in.

Factset: How You can Invest in Hedge Funds’ Biggest Investment
Tl;dr FactSet is the most undervalued widespread SaaS/IT solution stock that exists
If any of you have relevant experience or are friends with people in Investment Banking/other high finance, you know that Factset is the lifeblood of their financial analysis toolkit if and when it’s not Bloomberg, which isn’t even publicly traded. Factset has been around since 1978 and it’s considered a staple like Bloomberg in many wealth management firms, and it offers some of the easiest to access and understandable financial data so many newer firms focused less on trading are switching to Factset because it has a lot of the same data Bloomberg offers for half the cost. When it comes to modern financial data, Factset outcompetes Reuters and arguably Bloomberg as well due to their API services which makes Factset much more preferable for quantitative divisions of banks/hedge funds as API integration with Python/R is the most important factor for vast data lakes of financial data, this suggests Factset will be much more prepared for programming making its way into traditional finance fields. According to Factset, their mission for data delivery is to: “Integrate the data you need with your applications, web portals, and statistical packages. Whether you need market, company, or alternative data, FactSet flexible data delivery services give you normalized data through APIs and a direct delivery of local copies of standard data feeds. Our unique symbology links and aggregates a variety of content sources to ensure consistency, transparency, and data integrity across your business. Build financial models and power customized applications with FactSet APIs in our developer portal”. Their technical focus for their data delivery system alone should make it stand out compared to Bloomberg, whose UI is far more outdated and complex on top of not being as technically developed as Factset’s. Factset is the key provider of buy-side portfolio analysis for IBs, Hedge funds, and Private Equity firms, and it’s making its way into non-quantitative hedge funds as well because quantitative portfolio management makes automation of risk management and the application of portfolio theory so much easier, and to top it off, Factset’s scenario analysis and simulation is unique in its class. Factset also is able to automate trades based on individual manager risk tolerance and ML optimization for Forex trading as well. Not only does Factset provide solutions for financial companies, they are branching out to all corporations now and providing quantitative analytics for them in the areas of “corporate development, M&A, strategy, treasury, financial planning and analysis, and investor relations workflows”. Factset will eventually in my opinion reach out to Insurance Risk Management a lot more in the future as that’s a huge industry which has yet to see much automation of risk management yet, and with the field wide open, Factset will be the first to take advantage without a shadow of a doubt. So let’s dig into the company’s financials now:
Their latest 8k filing reported the following:
Revenue increased 2.6%, or $9.6 million, to $374.1 million compared with $364.5 million for the same period in fiscal 2019. The increase is primarily due to higher sales of analytics, content and technology solutions (CTS) and wealth management solutions.
Annual Subscription Value (ASV) plus professional services was $1.52 billion at May 31, 2020, compared with $1.45 billion at May 31, 2019. The organic growth rate, which excludes the effects of acquisitions, dispositions, and foreign currency movements, was 5.0%. The primary contributors to this growth rate were higher sales in FactSet's wealth and research workflow solutions and a price increase in the Company's international region
Adjusted operating margin improved to 35.5% compared with 34.0% in the prior year period primarily as a result of reduced employee-related operating expenses due to the coronavirus pandemic.
Diluted earnings per share (EPS) increased 11.0% to $2.63 compared with $2.37 for the same period in fiscal 2019.
Adjusted diluted EPS rose 9.2% to $2.86 compared with $2.62 in the prior year period primarily driven by an improvement in operating results.
The Company’s effective tax rate for the third quarter decreased to 15.0% compared with 18.6% a year ago, primarily due to an income tax expense in the prior year related to finalizing the Company's tax returns with no similar event for the three months ended May 31, 2020.
FactSet increased its quarterly dividend by $0.05 per share or 7% to $0.77 marking the fifteenth consecutive year the Company has increased dividends, highlighting its continued commitment to returning value to shareholders.
As you can see, there’s not much of a negative sign in sight here.
It makes sense considering how FactSet’s FCF has never slowed down

FactSet’s annual subscriptions and professional services have made its way to foreign and developing markets, and many of them are opting for FactSet’s cheaper services to reduce costs and still get copious amounts of data and models to work with.
Here’s what FactSet had to say regarding its competitive position within the market of providing financial data in its last 10k: “Despite competing products and services, we enjoy high barriers to entry and believe it would be difficult for another vendor to quickly replicate the extensive databases we currently offer. Through our in-depth analytics and client service, we believe we can offer clients a more comprehensive solution with one of the broadest sets of functionalities, through a desktop or mobile user interface or through a standardized or bespoke data feed.” And FactSet is confident that their ML services cannot be replaced by anybody else in the industry either: “In addition, our applications, including our client support and service offerings, are entrenched in the workflow of many financial professionals given the downloading functions and portfolio analysis/screening capabilities offered. We are entrusted with significant amounts of our clients' own proprietary data, including portfolio holdings. As a result, our products have become central to our clients’ investment analysis and decision-making.” (https://last10k.com/sec-filings/fds#link_fullReport), if you read the full report and compare it to the most recent 8K, you’ll find that the real expenses this quarter were far lower than expected by the last 10k as there was a lower than expected tax rate and a 3% increase in expected operating margin from the expected figure as well. The company also reports a 90% customer retention rate over 15 years, so you know that they’re not lying when they say the clients need them for all sorts of financial data whether it’s for M&A or wealth management and Equity analysis:
https://www.investopedia.com/terms/f/factset.asp
FactSet also has remarkably good cash conversion considering it’s a subscription based company, a company structure which usually takes on too much leverage. Speaking of leverage, FDS had taken on a lot of leverage in 2015:
So what’s that about? Why were FactSet’s long term debts at 0 and all of a sudden why’d the spike up? Well usually for a company that’s non-cyclical and has a well-established product (like FactSet) leverage can actually be good at amplifying returns, so FDS used this to their advantage and this was able to help the share’s price during 2015. Also, as you can see debt/ebitda is beginning a rapid decline anyway. This only adds to my theory that FactSet is trying to expand into new playing fields. FactSet obviously didn’t need the leverage to cover their normal costs, because they have always had consistently growing margins and revenue so the debt financing was only for the sake of financing growth. And this debt can be considered covered and paid off, considering the net income growth of 32% between 2018 and 2019 alone and the EPS growth of 33%
EBITDA has virtually been exponential for FactSet for a while because of the bang-for-buck for their well-known product, but now as FactSet ventures into algorithmic trading and corporate development the scope for growth is broadly expanded.
P/E has declined in the past 2 years, making it a great time to buy.
Increasing ROE despite lowering of leverage post 2016
Mountains of cash have been piling up in the coffers increasing chances of increased dividends for shareholders (imo dividend is too low right now, but increasing it will tempt more investors into it), and on top of that in the last 10k a large buyback expansion program was implemented for $210m worth of shares, which shows how confident they are in the company itself.
SGA expense/Gross profit has been declining despite expansion of offices
I’m a bit concerned about the skin in the game leadership has in this company, since very few executives/board members have significant holdings in the company, but the CEO himself is a FactSet veteran, and knows his way around the company. On top of that, Bloomberg remains king for trading and the fixed income security market, and Reuters beats out FactSet here as well. If FactSet really wants to increase cash flow sources, the expansion into insurance and corp dev has to be successful.
Summary: FactSet has a lot of growth still left in its industry which is already fast-growing in and of itself, and it only has more potential at its current valuation. Earnings September 24th should be a massive beat due to investment banking demand and growth plus Hedge fund requirements for data and portfolio management hasn’t gone anywhere and has likely increased due to more market opportunities to buy-in.
Calls have shitty greeks, but if you're ballsy October 450s LOL, I'm holding shares
I’d say it’s a great long term investment, and it should at least be on your watchlist.

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41. Joanna Wiebe – 10x Facebook Ads
42. Ben Adkins – Facebook Ads Backpack Guide Advanced 2019
43. Wholesale Hackers – Facebook Ads for Real Estate
44. Keith Krance – Facebook Ads University Elite 2019
45. Brian Pfeiffer & Ross Minchev – FaceBook Diet Made EZ Video Course
46. iStack Training – Facebook & Ecom Mastery event Barcelona Replay 2019
47. Istack Training – Facebook & Ecom Mastery event Las Vegas Replay 2019 [Download]
48. Manuel Suarez & Ben Cummings – Facebook Masters Course
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54. Parker Walbeck – Course Creator Pro $ (only on request)
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58. Bimber – Viral Magazine, Video, News WordPress Theme (DOWNLOAD)
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63. First Page Videos – Brian Dean
64. Video Series AWE18 Replay
65. Zamurai Video Immersion
66. Ben Adkins – Clients From Video
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76. Anton Kraly - Dropship Lifestyle
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79. Adam Thomas – Dropshipping Accelerator 2018
80. Hayden Bowles – Hacking Shopify Dropshipping
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86. Advanced Dropshipping Class Till Boadella
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93. eBay Powerseller academy: comprehensive in depth study
94. eBay for newbies: learn the basics to start selling on eBay
95. [Download] “The eCominomics Blueprint” – Resell on eBay/Amazon for PENNIES on the dollar
96. eBay: Make Money Flipping Cars On eBay
97. [Download] Ebay’s Quick Cash-Out 2.0
98. [Download] eBay Sellers Ultimate Bootcamp Double Your Profits
99. eBay Partner Network: Create Affiliate Home Business Fast
100. Cold Email Kings – The Exact COLD Email Sequence to Ultimately Partner with Amazon
101. Dan Meadors – The Amazon Wholesale Formula 2019
102. Michelle Barnum Smith – Amazon Messenger
103. Youngjoon Sun – Amazon FBA Mastermind
104. Matthew Gambrell – Amazon Assassin Drop Shipping Course
105. Augustas Kligys – European Amazon Summit
106. Get Seller Tradecraft – Amazon Playbook
107. Andrew Minalto – Amazon Sharks
108. Jordan Kilburn – Amazon Millionaire Mentorship Program
109. Philip A. Covington – The Ultimate Amazon Seller
110. Todd Snively, Chris Keef – Ecomm Elite Wholesale Amazon
111. Kale and Taylor – Nine University 2.0
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112. Justin Welsh – The LinkedIn Playbook
113. Jimmy Coleman – LinkedIn Lead Challenge
114. Mike Cooch – LinkedIn Advertising Bootcamp
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116. GROWING YOUR SMALL BUSINESS WITH LINKEDIN
117. LINKEDIN INCUBATOR – LIAM AUSTIN

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118. Stock Options Day Trading Mindset For Success
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120. ABS – Reese Shapiro – Binary Option Money Making Private Method
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122. TradeSmart College – Bollinger Bands Necessities
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127. FX CARTEL TRADING COURSE
128. INVESTOPEDIA ACADEMY BY DAVID GREEN
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130. ADVANCE STOCK TRADING (SHORT TERM, SWING AND LONG TERM)
131. The Complete Trading Course – Price Patterns, Strategies, Setups, And Execution Tactics By Corey Rosenbloom
132. Cryptocurrency Trading And Ico Investment Masterclass 2018 | Blockchain
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134. The Trading Boss Method 1 And 2
135. RASHAD SMITH – 7 Figures Forex Course
136. The Forex Scalper Mentorship Package(for download)
137. PIPS UNIVERSITY – The Only Forex Course You Will Ever Need(For Download)
138. URBAN FOREX – Mastering Price Action
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140. FOREVER BLUE – FOREX COURSE
141. ANGEL TRADERS FOREX STRATEGY COURSE
142. MAKE MONEY WORK FROM HOME ONLINE TRADE FOREX 4 BEGINNERS
143. JASON STAPLETON – TRADERS WORKSHOP FOREX FULL COURSE
144. FOREX TRADING FOR NEWBIES
145. CRYPTO TRADING MASTERY COURSE

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148. Sarah Chrisp – Ecomm Clubhouse
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151. Vince Wang & Jordan Welch – eCom Accelerators Private Mastermind Replays
152. Gabriel St. Germain – eCom Blueprint 2.0
153. Ricky Hayes – Ecom Lifestyle University
154. Rafael Cintron – 7 Figure Ecommerce Inner Circle
155. iStack Traning – Ecommerce Mastery live Asia Thailand 2019
156. Tai Lopez – ECOM Agency
157. Ecom Titans – Keys To Consistency
158. Gabriel Beltran – The Ecom Millionaire Mastermind, Miami
159. Bill Dalessandro – Ecommerce: Product To Profit
160. Matt Gartner – eCom Lab
161. Arie Scherson – Ecom Inner Circle
162. Matt Gartner – 8 Hour eCommerce Profits
163. Justin Cener – eCommerce Bootcamp Mentor Program
164. Anthony Mastellone – eCom Success Lab
165. Earnest Epps – High Ticket eCom Secrets
166. Jon Mac – Ecommerce Accelerator
167. Seth Smith – Advanced Ecommerce Academy
168. Chris Blair – eCom Vantage

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169. CXL Institute – 10 Courses Marketing Bundle
170. Matt Serwin – Klaviyo Email Marketing Masterclass
171. Million Dollar Marketing Methods – 2020 SEO
172. Fred Joyal – Marketing Course for Dental Marketing
173. Justin Jackson – Marketing For Developers
174. Brian Bewer – Madcam Marketing 2.0
175. Tiz Gambacorta – Amik Affiliate Marketing Intensive Kickstarter
176. Matt Cramer & Shayne Hillier – Real Estate Marketing Student Beta Program v2.0
177. Sean Vosler – 7 Figure Marketing Copy
178. Russ Henneberry – Content Marketing Mastery Course 2019
179. Ted McGrath – Marketing Masters Map
180. Jon Penberthy – Legit Marketing Academy 2019
181. ConversionXL, Dan McGaw – Optimizing Your Marketing Tech Stack
182. Brandon Belcher – CPA Marketing University
183. Jeremy Haynes – Digital Marketing Manuscript 2.0 + DSP
184. Mohamed Ali Aguel – Momentum Marketing Tribe
185. Jordan Steen -The Digital Marketing School
186. James Jason – Mortgage Marketing Mastery
187. Digital Marketing Nanodegree v3
188. Sean Terry – Marketing Mastery X
189. Simon Colhoun – Affiliate Marketing & List Building Video Course
190. Saj P & Jeevan S – Zero Resistance Marketing
191. Simplilearn – Digital Marketing Certification Training
192. Billy Gene’s Gene Pool | billy gene is marketing
193. Stefan James – Affiliate Marketing Mastery
194. Jaiden Gross – 30-Day Affiliate Marketing Challenge Training
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195. Harmon Bothers – Write Ads That Sell
196. Dental Clients – Proven Tested Ads and Funnel 2019
197. Traffic and Funnels – Advertising Workshop
198. Eugene M. Schwartz – Breakthrough Advertising
199. Kody – Advanced Bing Ads Training
200. Mike Harri – Pinterest Ads Masterclass
201. Ross Minchev – Pin Ads Jumpstart
202. Patrick Wind – Ads Accelerator Program
203. Adskills – Search And Destroy Bootcamp
204. Duston McGroarty – Push Notification Ads Masterclass
205. Epic Mail Machine – $100K Deals With No Paid Ads
206. Tristan Broughton – Google Ads Ecom Academy
207. Google Ads Mastery 2019-2020
208. Justin Sardi – Video Ads Masterclass

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209. Josh Braun – Sales DNA
210. Dan Kennedy – Ultimate Sales Letter 2.0
211. Jim Huffman – The ClickMinded Sales Funnel Course
212. Building Sales Funnels for Backend Profits
213. GKIC – The No B.S. Renegade Guide To Putting Together A Highly Effective Sales Team

SMMA courses

214. Joel Kaplan – SMMA 7 Figure Agency
215. Quenten Chad & Jovan Stojanovic – 30 Days SMMA
216. Nick Kenens – Cold Emails for SMMA

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217. Jim Edwards – Copywriting Secrets
218. Kyle – The Process A Draft By Draft Copywriting Walkthrough
219. Kim Krause Schwalm – Ultimate KKS Bundle (Copywriting)
220. Ray Edwards – Copywriting Academy 2
221. Shortcut Copywriting Secrets
222. Paul Hollingshead – AWAI’s Accelerated Program for Six-Figure Copywriting
223. Pam Foster – Direct Response Copywriting Course
224. Writing Tools & Hacks Copywriting/Blogging/Content Writing (Download)
225. AWAI – The Web Copywriter’s Clear Path to Profits

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226. Sarah Titus – Best Blogging Bundle
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229. Blogging to Generate Leads: Business Blogging Essentials
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230. IMQueen – 1 Hour Consulting
231. Alex Becker – Hero Consulting Accelerator
232. Joe Soto – Local Consulting Academy Update last
233. Sabri Suby – Consulting Empire
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278. Unlimited Persuasion Power
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280. [Download] HypnoRitual
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328. IMAN GADZHI – SIX FIGURE SMMA
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330. IMAN GADZHI – KAIZEN CURE
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Introduction
Contrary to what every Forex 'expert' out there would have you believe, it's not easy to learn how to trade Forex at all. Trading Forex is one of the most challenging skills you can ever set out to learn, which is especially daunting if you're a beginner just starting out to learn how to trade Forex. If you're finding it hard to learn how to trade Forex successfully right now, you're probably wondering: "Can a beginner make money in Forex trading?" By the end of this article, you'll know what you can do to make money in Forex trading right now.
Can A Beginner Make Money In Forex Trading?
If you have a look around the many Forex websites, forums, seminars and magazines, it seems like everyone's making millions of dollars trading Forex! The thing is, Forex traders love to talk about their winning trades and make themselves out to be wildly profitable traders, but the reality is that only 5% of Forex traders are consistently making money. Yes, even a beginner can make money in Forex trading, but there's a big difference between making money in Forex and making a full time income, achieving financial freedom, and building wealth through Forex.
What Stops Beginners From Making An Income
So what's stopping beginners from making a consistent, long term income from trading Forex? Well, unlike the professional Forex traders working for the big banks and hedge funds, most beginner traders learning to trade Forex aren't paid a full time salary to immerse themselves in the markets. If you're just starting out in Forex, then you've probably got a full time job that you spend at least 8 hours a day on, and a family and social life outside of that. That means that you have a very real shortage of time to get yourself to the level where you can trade like a pro, and believe me, it takes a lot of time and consistent effort.
It takes years of study, practice and real experience in the markets to learn how to trade Forex successfully, and get to the level where you can consistently make money in Forex trading. Not to mention that you'll be taking on, for all intents and purposes, an unpaid part time job that will chain you to your computer while you are trading. It's something that will alienate you from your social circle, and put considerable strain on your family relationships as well. It's no wonder that most traders wanting to learn how to trade Forex will give up within 3 months, and never make money in Forex trading.
What You Can Do To Make Money In Forex Trading Now
So what can you do to make money in Forex trading right now? The best shortcut I know is to buy a proven Forex trading system to do your trading for you. I'm not going to look you in the eye and tell you that you can just go out there and pick any system and make millions, because that's simply not true. Profitable trading systems are rare, and you need to choose very carefully. That said, if you can find a trading system that works, you can overcome the biggest challenges any trader faces while they learn how to trade Forex. You'll be able to gain valuable Forex market experience, preserve your personal relationships and most importantly make money in Forex trading while you learn how to trade Forex.
When you've built up the capital and income of your Forex systems operation, and have gathered up valuable trading experience, you may decide to try out trading Forex for yourself. Regardless of whether you trade with an automatic Forex system in the short, medium or long term, it's a powerful solution that will enable you to make money in Forex trading even if you're a beginner.

I’ve been reading these posts on an off for quite some time now and it saddened me to see someone had recently posted their “I quit the game” statement. We all walk through fire to stand in the green valley...and the journey has to be made on foot. And alone. And it’s tough.
In response, I wanted to add a list of pointers for people starting out in this insane game and to address what I’ve learned from over a decade of trading Forex. It’s long-ish but it’s based on reality and not a bunch of meaningless retail junk systems and “insider knowledge” by nitwits on YouTube or some 19-year old “whiz kid” who apparently makes ten billion dollars a week with a mystical set-up that’ll only cost you $1,999 to buy!
I became a profitable trader by keeping everything simple. I lost thousands when I started out, but I look back now and realise how easily I could’ve avoided those losses.
Keep Everything Simple.
For the sake of disclosure, I worked for Morgan Stanley for over a decade in fixed income but learned almost everything I know from the forex guys whom I got to know as good friends. They make markets but there’s still a lot to learn from them as a small fry trader. I got into all this as a hobby after annoying the traders with questions, and all these years later it still pays me. There are still occasional nightmare accidents but they’re far rarer to the point where they don’t affect my ROI.
Possibly the most clear statement I could make about Forex trading in the large institutional setting is actually a pretty profound one: Forex traders are not what you think they are: every single forex trader I ever worked with (and who lasted the test of time) had the exact same set of personality traits: 1. NOT ONE of them was a gung-ho high-five loudmouth, 2. Every single one of them analysed their mistakes to the point of obsession, 3. They were bookish and not jocks, 4. They had the humility to admit that many early errors were the result of piss-poor planning. The loudmouths last a year and are gone.
Guys who last 5, 10, 20 years in a major finance house on the trading floor are nothing like the absurd 1980s Hollywood images you see on your tv; they’re the perfect opposite of that stereotype. The absolute best I ever met was a studious Irish-Catholic guy from Boston who was conscientious, helpful, calm, and utterly committed to one thing: learning from every single error of judgement. To quote him: “Losing teaches you far more than winning”.
Enough of that. These points are deliberately broad. Here goes:

Know The Pairs. It amazes me to see countless small account traders speak as though “systems” work across all pairs. They don’t. Trading GBP/CHF is an entirely different beast to trading CHF/JPY. If you don’t know the innate properties of the CHF market or the JPY or the interplay between the AUD and NZD etc then leave them alone until you do. —There’s no rush— Don’t trade pairs until you are clear on what drives ‘commodity currencies’, or what goes on behind currencies which are easily manipulated, or currencies which simply tend to range for months on end instead of having clear trends. Every pair has its own benefits and drawbacks. Google “Tips on trading the JPY” etc etc etc and get to know the personality of these currencies. They’re just products like any other....Would you buy a Honda without knowing a single thing about the brand or its engine or its durability? So why trade a currency you know nothing about?
Indicators are only telling you what you should be able to see in front of you: PRICE AND MARKET STRUCTURE. Take everything off your charts and simply ask one question: What do I see happening right here and right now? What time frame do I see it on? If you can’t spot a simple consolidation, an uptrend, or a downtrend on a quick high-versus-low time frame scan then no indicator on the planet will help you.
Do you know why momentum indicators work on clear trends but are often a complete disaster on ranges? If not, why not? Do you know why such indicators are losing you tons of trades on low TFs? Do you actually understand the simple mathematics of any indicator? If the answer to these questions is “no” then why are you using these things and piling on indicator after indicator after indicator until you have some psychedelic disco on your screen that looks like an intergalactic dogfight in Star Wars? Keep it simple. Know thy indicator.
Risk:Reward Addiction. The greatest profit killer. So you set up your stops and limits at 1:1.5 or whatever and say “That’s me done” only to come back and see that your limit was missed by a soul-crushing 5 pips before reversing trend to cost you $100, $200, $1000. So you say “Ah but the system is fine”. Guys...this isn’t poker; it doesn’t have to be a zero sum game. Get over your 1:1.5 addiction —The Market Does Not Owe You 50 Pips— Which leads to the next point which, frankly, is what has allowed me to make money consistently for my entire trading life...
YOU WILL NEVER GO BROKE TAKING A PROFIT. So you want to take that 50-pip profit in two hours because some analyst says it’ll happen or because your trend lines say it has to happen. You set your 1:1.5 order. “I’ll check where I’m at in an hour” you say. An hour later you see you’re up 18 pips and you feel you’re owed more by now. “If I close this trade now I could be missing out on a stack”. So what?! Here’s an example: I trade in sterling. I was watching GBP climb against it’s post-GDP flop report and once I was up £157 I thought “This is going to start bouncing off resistance all morning and I don’t need the hassle of riding the rollercoaster all day long”. So I closed it, took the £157, went to make breakfast. Came back shortly afterwards and looked at the chart and saw that I could’ve made about £550 if I’d trusted myself. Do I care? Absolutely not...in fact it usually makes me laugh. So I enter another trade, make another quick £40, then another £95. Almost £300 in less than 45 mins and I’m supposed to cry over the £250 I “missed out on”?

£300 in less than an hour for doing nothing more than waiting for some volatility then tapping a keyboard. It’s almost a sin to make money that easily and I don’t “deserve” any of it. Shut off the laptop. Go out for the day.
Does the following sound familiar? “Okay I’m almost at my take-profit...almost!.....almost!....okay it’s bouncing away from me but it’ll come back. Come back, damnit!! Jesus come back to my limit! Ah for F**k’s sakes!! This is complete crap; that trade was almost done! This is rigged! This is worse than poker! This is total BS!!”
So when you were 50% or 75% toward your goal and could see the trade slipping away why wasn’t $100 or $200 enough? You need more than that?...really?!
So point 6:

Tomorrow Is Another Day. Lordy Lordy, you only made $186 all day. What a disaster! Did you lose anything? Nope. Will the market be open again tomorrow? Yep. Does London open in just four hours? Yep. Is the NOK/SGD/EUR whatever still looking shitty? Yep. So let it go- there are endless THOUSANDS of trades you can make in your lifetime and you need to let a small gain be seen for what it is: ANOTHER BEAUTIFUL PROFIT.

Four or five solid but small profits in a day = One Large Profit. I don’t care how I make it, I don’t care if it’s ten lots of £20, I don’t care if I make the lot in a single trade in 30 seconds either. And once I have a nice sum I switch the computer off and leave it the Fk alone. I don’t care if Brexit is due to detonate the pound or if some Fed guy is going to crap all over the USD in his speech; I’ve made my money and I’m out for the day. There will be other speeches, other detonations.
I could get into the entire process by which I trade but it’s aggravatingly basic trend-following mostly based on fundamentals. Losing in this business really does boil down to the same appalling combination of traits that kill most traders: Greed, Impatience, Addiction. Do I trade every day? Absolutely not; if there’s nothing with higher probability trades then I just leave it alone. When I hit my target I’m out for the day- the market doesn’t give a crap about me and I don’t give a crap about the market, if you see my meaning.
I played poker semi-professionally for two years and it’s absolutely soul-destroying to be “cold decked” for a whole week. But every player has to experience it in order to lose the arrogance and the bravado; losing is fine as long as you learn from it. One day you’ll be in a position to fold pocket Kings because you’ll know you’re dead in the water. The currency markets are exactly the same in that one regard: if you learn from the past you’ll know when it’s time to get out of that stupid trade or that stupid “system” that sounded so great when you had a demo account.
Bank a profit. Keep your charts simple. Know the pairs. Be patient. Touch nothing till you understand it inside out.
And if you’re not enjoying the game....STOP PLAYING.
[if people find this helpful I might post a thread on the best books I’ve studied from and why most forex books are utterly repetitious bullshit].
Peace.

fintech #trading #algotrading #quantitative #quant #hft #forex #fx #crypto #gbpusd

The Trading System That Maximizes Our Edge.. | by tensorbox | TensorBox At the current state, cryptocurrency markets provide more than enough opportunities to make a profit (we’ve shown 11.3% ROI last week, without a single losing day; follow our weekly reports on our Facebook page). But to achieve maximum results, one must have a system to consistently track, quantify and exploit market inefficiencies. So let’s have a look at our system:
Studies show (source) that the cost of 10 milliseconds of communication delay is about twice that of an algorithm configured to run on only 1 millisecond (1/1000th of a second or 1/300th of a blink of an eye!) of latency. In turn, 100 milliseconds of latency result in threefold latency cost as compared to that of an algo using 1-millisecond execution latency. That’s why we have “gateways” as close as possible to every exchange that we work with. These are the servers with modified Linux kernels optimized for real-time and low latency performance. .....
Continue reading at: https://medium.com/tensorbox/the-trading-system-that-maximizes-our-edge-a64e95533959

Back in 2016, I dabbled into checking up how to start a digital business career. I scramble on various materials online on how to make money.
Oh shit! I wouldn't like the one that would want me to invest something first as I feared risking in a business I didn't know much about. I can say my risk believe system on online investment was zero.
My first knowledge in online business was in domain parking and flipping yet that will require you buy the domain from godaddy.com , namecheap.com, justdropped and many of those sites. I was looking for one that is free and I can park to earn me small bucks. But none!
I check up other ways like blogging especially I learnt that one can set up a free WordPress, blogger, tumblr and other free to use sites but your domain name will have there suffix in the dotcom. I innocently set up dailycashlife.wordpress.com
I put up some little articles on it which talks about making money online and some of the articles I have read on the niche.
Fast forward to 2017, I had absorbed much learning online especially on running facebook ads for businesses. I summoned a courage to chat a company I found their website online and I shared my experience. I was sincere to let them know that I am just starting out as a digital marketer but with much zeal to help any firm to turn in more sales and double their revenue.
I was given the chance to start up handling all their social media platforms with my little experience in graphics design.
That was when I made my first $20 for that service. Though I never expected anything as I couldn't consider myself to be an expert.
After making this bucks for a constant 3 months, I started trading forex from part of it and I made few profits too. Though I lost also but my capital management strategy worked out for me to turn in $200 from a $75 trade within 2 months.
That was how I started multiplying my investments online taking up more chances and today, I may not be so rich as you may think but I am comfortable with my strategy of earning online consistently doing so for barely 3 years now.
I doubted the article I read in 2016 on how one can start an investment with $0 capital. I think I owe the writer an apology for my doubting act.

I’m relatively new to Forex (I’m sure that’s said a lot around here) but I’ve dabbled in the stock market and currency quite a bit (a year?) in the past. I’ve seen mixed opinions on demo trading since I started. Some people say you’ll learn more using real money since you’ll be more cautious about the trades you’re placing therefore making more realistic analysis and some people say you should only demo trade for atleast a year to ensure you’ve a proper system in place and risk management that is consistently profitable. So which of these is generally the better option? I personally don’t feel like it would take me a year to be profitable. I’ve spent the last week making my own trades with a demo account. I managed a 74% profit ratio. Now that could obviously be a lucky week but I did follow the basics of trend lines, support resistance levels and uses fib for confirmation. Anyways, my question is, if you’re pro demo account route and you seen you were consistently profitable for say.... a month? Would you make real trades with small risk? Or would you stick to the demo account?
I’ve became obsessed and literally spend all day that I’m awake watching videos and reading from others mistakes (14+ hours daily), so far, I’m loving it.

T3 Newsbeat Live is run by Mark Melnick, a 20-year veteran trader from New York. According to him, he made his first million at the age of 19 during the dot-com boom back in the late 90s.
He claims that his trading room is the fastest growing trading room at T3 and also the Wall Street’s #1 trading room. You can see this in the description of his videos on Youtube.
He is a big proponent of reaching the highest win rate possible in trading. He openly shares some of his trading strategies in free videos and claims that some of his strategies are batting over 70% or even 80 %.
He also often says that some of the members enjoy a win rate over 90% using his strategies.
I will let you be the judge of this.
Self-Promotion
He makes a lot of videos to attract new people into his trading room. His daily videos are uploaded on Facebook and Youtube almost daily even on Weekends (mostly excluding Friday evening & Saturdays).
In so many videos you’d hear him talking about how his trading room has an edge over other trading rooms while bashing other trading rooms as a whole.
He often talks about how his trading room bought stocks/options at the near bottom or shorted at the near top using his “algorithmic analysis” which can be applied to all markets (stocks, future, forex, crypto).
Piques your curiosity, right?
In fact, that’s how I got to give his trading room a try.
“Who in the hell wouldn’t want to catch the top & bottom in the markets, right?”
So, you would think people in his room and himself are making a killing using his algorithmic analysis?
Not so fast… (in fact, his algorithmic analysis is just drawing trendlines and identifying the most probable support and resistance)
When it works (of course, nothing works 100% of the time), you are able to catch just few cents off the top and bottom when it works if you follow his trade.
However, you have no idea how long you’d have to hold your position. Mark doesn’t know either.
So, he usually goes for nickels and dimes and rarely holds a position longer than 5 minutes.
Even if he’s good at picking bottoms and tops, you’d often risk more than nickels and dimes just to make nickels and dimes. Make sense, right?
…….
…….
…….
Also, because he gets out of his positions fast, he misses out on riding some potentially big trades.
Oh, how I wish stay in that position a bit longer. He doesn’t say but one can surmise that he often leave too much on the table.
Of course, it’s important to take your profit fast when you scalp but you consistently leave too much on the table like he does, one has to wonder if he has any system for taking profits (otherwise, it’s all discretionary guessing).
This type of bottom/top picking is not his main strategy, though.
The strategy that makes him the most amount of money might surprise you. I will get to this later.
How Mark Trades (Mark’s Trading Setups and Strategies)
Mainly, he scans the market in the morning for earnings reports, analysts’ upgrades/downgrades and other catalysts that have potential to make moves in the market.
He openly shares his mockery or insult of analysts, calling certain analysts “idiots” or “imbeciles”.
He puts on his first trade(s) early in the morning (from 9:30AM to 10:00AM Eastern Standard Time) when the market move is the most volatile.
Some of his strategies use market order during this period of volatile time using options. You can see why this can be very risky and especially on thinly traded options with side spread.
He does point out this but sometimes you hear people in the room stuck in an options position that they can’t get out.
Just like his trades from calling the top/bottom of a stock, he gets in and gets out of a position within minutes if not seconds while going for nickels and dimes while staring at 1minute and 5-minutes charts.
That applies to most, if not all of his strategies. (Yes, sometimes he does catch bigger moves than nickels and dimes.)
When you trade during the most volatile time in the morning, you’re subjected to wild moves in both directions. If you’re overly prudent or inexperienced in trading, your stop (unless very wide), has a very high chance of hitting. A lot of times it might stop you out and go in the direction that you predicted.
So, when you’ve been trading during this time, you’d probably don’t set a stoploss order or a hard stop to avoid getting fleeced.
You do have to be proactive at cutting your loss as quickly as possible. Otherwise you’d find yourself scrambling to get out your position while the bid keeps dropping.
I have to say that Mark is very cautious and he does get out of trades very fast if he has doubt.
A lot of times he lets out exhausting, heavy sighs and even murmurs some swear words when things don’t seem to go the way he wants in a trade. Besides calling certain analysts, “imbeciles” and “idiots”, this is quite unprofessional but no one in the room has the gut to point things out like this.
The irony is that he is the “head of trading psychology” at T3 and it doesn’t seem like that he doesn’t have much control over his trading psychology and let alone his emotion.
People in trading chatrooms, like a herd of sheep, as a whole exhibit herd mentality. Even in an online chatroom, you don’t often see someone ruffling feathers and say what they really want to say.
This is probably because of the certain amount of people believing whatever he says without questioning the validity and quality of his comments.
He has several strategies and according to him all of them have win rate over %70.
However, he also comes up with new strategies as often as every month. He either comes up with new strategy or tweaks his existing strategies.
According to him, the reason is that the market is always evolving and you need to constantly adapt yourself to the ever-changing market environment.
What do you think? Does this sound like someone with an edge?
And for someone who scalps for nickels and dimes, he claims to have the highest Sharpe Ratio that he has ever seen in the industry. I’m NOT making this up. He often utters remarks like “My Sharpe Ratio is one of the highest I’ve seen in my twenty-year trading career.”, “I want to create a of traders with a very high Sharpe Ratio.
How can you achieve a high Sharpe Ratio when you scalp all the time?
And let’s not even talk about commissions generated from frequent scalping.
Who cares about commissions when you can be a scalper with high Sharpe Ratio?
Now, I want to talk about something controversial about his most profitable strategy.
Chatters
According to him, he makes the most amount of money using what he calls “Chatters”. He admits he bets on this kind of trades heavily.
His chatter trades are based on the “newsflow” of big funds making a move in certain stocks and piggybacking on the same trade before others catch on.
No one knows how he exactly gets his “newsflow” and he doesn’t give a straight answer when asked.
Maybe he pays a lot for this kind of information or maybe it’s given to him for free. Who knows?
But it makes sense. The name of the room is Newsbeat Live. Without this the name wouldn’t be the same.
This is probably the only real edge that he has and it’s understandable that he doesn’t want to reveal how he get this kind of newsflow and from where.
By joining his trading room he’ll make a callout on these trades for you to take advantage of.
In order to do this kind of trade, you have to be very quick on your trigger finger.
Almost always the initial move is done within a couple of minutes, if not seconds. If you get in late, you find yourself a sucker buying at or near the top.
Also, because you want to get in as soon as you hear his “chatter” announcements, he advised people to get in within 5 seconds of each chatter announcement and use market order to get in. He said that if he had a small account, he’d bet 100% on this kind of “high-octane” chatter trades and get in and get out fast for “easy” money.
This was how chatter trades were done
…Until one they when many people got burned badly.
Back in September or October of 2019, a lot of people in the room lost a lot money because they market ordered call options contracts on a chatter trade.
The spread on that trade was something like BID: 0.5 ASK: 5.00 few seconds after he announced it.
I didn’t take that trade. No way, I’m going to buy something that has a spread like that.
If you’ve been trading options you know that this kind of spread can happen. Many people that day in the room marketed-in on the trade, taking the offer at ASK.
They found themselves buying at $5.0 per contract when someone probably bought the same contract at $0.40 or $0.50 just few seconds ago.
Someone walked away with decent profits on that trade.
This was the biggest trading chatroom fiasco I’ve ever seen.
People in the room grieving and throwing numbers of how much they had just lost. 10K, 20K, 30K and even $60K.
Could it be also that someone who lost more and didn’t want to talk about it because it’d hurt too much? And how embarrassing to talk about such a loss. I give credit to people who spoke up about it.
People were obviously distressed and what did Mr. Mark Melnick do at this moment?
Initially, he didn’t say much. But what he said he was going to walk away from the trading desk to clear his mind.
It took a while for him to come back and he mentioned that it hurt him a lot that people lost a lot of money and encouraged people not to hesitate to contact him.
I don’t think he ever said anything about that he made a mistake insinuating to load up on chatter trades. No apology since everyone who took the trade did it at their own risk. He advised people to reach out to their broker and do whatever it takes to get their trades annulled because the market makers in that trades were despicable crooks and evil.
But let’s get one thing clear. Perhaps the cold hard truth.
Since Mark is the one who announces chatter trades. he basically frontruns everyone who gets in on these trades after him. There were times when he doesn’t take his own chatter trades and lets the room have it.
But when he does, it’s a guarantee win for him.
He has some sycophantic followers in his trading room and these people are always hungry for chatter plays. I can imagine drooling over the idea of next chatter trades.
It’s human to naturally seek the least path of resistance and this type of trade requires no skill but having fast trigger finger and a platform that allows fast execution.
By taking his chatter trades, you are most likely to make money as long as you act fast to get in and get out.
The thing is, you don’t know when it’s exactly the next chatter trade is going to happen.
If you take a bathroom break, you just miss it. If you take a phone call or answer a door bell, you just missed it.
So, it requires you to be glued to your monitor(s) if you want to make the most of your subscription.
So, we went over Mark’s most profitable strategy. But wait we haven’t yet to talk about his overnight swing trades.
Mark’s Swing Trades
His overnight swing trades jokes. Yes, jokes.
A lot of his overnight trades are done just before earnings announcements when implied volatility is at the highest.
You’ve ever bought a call option just before earnings, predicted the right direction but only to find out that you still lost money next morning? This is because of the implied volatility crush post earnings. A lot of people new to options don’t know this and get taken advantage by veterans this way.
I don’t know if Mark knows or not but I witnessed him buying options this way. I think he understand the concept of implied volatility but why he gets on such trades is a mystery.
I haven’t exactly checked the result of all of his swing trades but I wouldn’t be surprised if people lost more money following his swing trades than anything in the room.
Final Word
Mark offers “free-consultation” on the phone for people who struggle in their trading.
He said that he takes a lot of phone calls but often you’d get the feeling that he is distracted, unable to give an undivided attention for his consultation.
“How would you like to get on a free consultation with a millionaire scalper who can take your trading to the next level?” Appealing isn’t it?
But would you want to get on the phone with someone who is going to give a consultation, even if he or she is distracted?
Oh, it’s a free consultation. Ok, why not? What do I got to lose?
In his videos, you’d hear him saying that he cares for everyone in his trading room and considers them as part of his family. And he runs the trading room out of his good heart and intention more than making money.
Besides he says that he makes more money from his trading than running the room.
My suggestion is that you have a look and you’d be the judge.
He does hold “open house” for his trading room from time to time.
Also, I believe that if you try his trading room for the first time, you try it for a month for about $50. As for me, he’s just another front runner using his trading room to profit with a bad sense of humor and exaggeration that make you cringe.

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Bibliography

THROW YOUR FD's in FDS

Guide to Stock Market (Trading in General) Mentoring/Mentorship Programs in the philippines

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NNFX Traders -- who' started and stuck with VP's secret dream strategy from the beginning? According to him, you should be profitable now

H1 Backtest of ParallaxFX's BBStoch system