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Ke buku larisnya The Complete TurtleTrader. Nikmati Ini adalah kisah tentang bagaimana sekelompok siswa ragtag, banyak yang tidak memiliki pengalaman di Wall Street, dilatih untuk menjadi pedagang jutawan. Pikirkan Donald Trumps menunjukkan The Apprentice, bermain di dunia nyata dengan uang sungguhan dan perekrutan dan penembakan yang sebenarnya. Namun, magang ini dilemparkan ke dalam api dan segera mendapat tantangan untuk menghasilkan uang, dengan jutaan yang dipertaruhkan. Mereka werent mencoba menjual es krim di jalanan kota New York. Mereka diperdagangkan saham, obligasi, mata uang, minyak, dan puluhan pasar lainnya menghasilkan jutaan. Cerita ini meniupkan atap dari citra sukses Wall Street konvensional yang dengan hati-hati dibuat dalam budaya populer: prestise, koneksi, dan tidak ada tempat di meja untuk si kecil mengalahkan pasar (dan mengalahkan pasar bukanlah tugas kecil). Investor legendaris Benjamin Graham selalu mengatakan bahwa analis dan fund manager secara keseluruhan tidak dapat mengalahkan pasar karena secara signifikan mereka adalah pasar. Selain itu, komunitas akademis telah berdebat selama beberapa dekade tentang pasar yang efisien, sekali lagi menyiratkan bahwa tidak ada cara untuk mengalahkan rata-rata pasar. Namun menghasilkan banyak uang, mengalahkan pasar, bisa dilakukan jika Anda tidak mengikuti kelompok, jika Anda berpikir di luar kotak. Orang memiliki kesempatan untuk menang dalam permainan pasar, tapi dia memerlukan peraturan dan sikap yang tepat untuk dimainkan. Dan aturan dan sikap yang benar bertabrakan langsung dengan sifat dasar manusia. Kisah magang kehidupan nyata ini masih akan dikubur seandainya saya tidak secara acak mengambil majalah Financial World edisi Juli 1994, yang menampilkan artikel Wall Streets Top Players. Di sampulnya diketahui pengelola uang George Soros bermain catur. Soros telah menghasilkan 1,1 miliar untuk tahun ini. Artikel tersebut mencantumkan seratus pemain berbayar teratas di Wall Street untuk tahun 1993, di mana mereka tinggal, berapa banyak yang mereka hasilkan, dan secara umum bagaimana mereka membuatnya. Soros dulu. Julian Robertson berada di urutan kedua, dengan 500 juta. Bruce Kovner berada di urutan kelima, dengan 200 juta. Henry Kravis dari KKR adalah kesebelas di 56 juta. Pedagang terkenal Louis Bacon dan Monroe Trout juga ada dalam daftar. Peringkat (dan penghasilan) memberikan lanskap sejernih kristal tentang siapa yang menghasilkan uang Master of the Universe. Inilah, tanpa diragukan lagi, para pemain top dalam game. Tanpa diduga, salah satu dari mereka kebetulan tinggal dan bekerja di luar Richmond, Virginia, dua jam dari rumahku. Dua puluh lima dalam daftar itu adalah R. Jerry Parker, Jr. dari Chesapeake Capital dan dia baru saja menghasilkan 35 juta. Parker belum berusia empat puluh tahun. Biografi singkatnya menggambarkannya sebagai mantan murid Richard Dennis (yang) dan mencatat bahwa dia dilatih untuk menjadi Penyu (apa). Parker digambarkan sebagai akuntan berusia dua puluh lima tahun yang telah menghadiri sekolah Dennis pada tahun 1983 untuk mempelajari sistem pelacakan trennya. Artikel tersebut juga mengatakan bahwa dia adalah murid Martin Zweig (yang), yang kebetulan berusia tiga puluh tiga tahun pada daftar gaji tertinggi tahun itu. Pada saat itu, nama Dennis tidak lebih penting daripada Zweig, tapi implikasinya adalah bahwa kedua pria ini telah membuat Parker sangat kaya. Aku mempelajari daftar itu dengan saksama, dan Parker tampaknya satu-satunya di antara seratus yang diiklankan karena telah dilatih. Bagi seseorang seperti saya, mencari cara untuk mencoba dan mendapatkan uang sebanyak itu, biografinya menjadi inspirasi langsung, bahkan jika tidak ada spesifik yang nyata. Inilah pria yang membual bahwa dia adalah produk dari anak kota Virginia, menyukai musik country, dan lebih suka tetap sejauh mungkin dari Wall Street. Ini bukan cerita menghasilkan uang yang sebenarnya saya tahu. Kebijaksanaan umum bahwa satu-satunya cara Anda bisa menemukan kesuksesan adalah dengan bekerja di menara baja dan kaca bertingkat delapan di New York, London, Hong Kong, atau Dubai dengan jelas salah. Kantor Jerry Parkers benar-benar berada di antah berantah, tiga puluh mil di luar Richmond di Manakin-Sabot, Virginia. Segera setelah membaca majalah itu, saya pergi untuk melihat kantornya, mencatat kurangnya kepura-puraannya, dan duduk di tempat parkir sambil berpikir, Anda pasti bercanda dengan saya. Di sinilah dia membuat semua uang yang Malcolm Gladwell katakan dengan terkenal, Ada banyak nilai dalam sekejap mata seperti dalam beberapa bulan analisis rasional. Melihat kantor negara Parker adalah dorongan listrik untuk saya, secara permanen menghilangkan pentingnya lokasi. Tapi aku tidak tahu lagi tentang Jerry Parker selain yang ada dalam terbitan Financial World tahun 1994 itu. Apakah ada lebih banyak siswa ini Bagaimana mereka menjadi murid Apa yang mereka ajarkan Dan siapa orang ini Dennis yang telah mengajar Parker dan yang lainnya Richard Dennis adalah seorang ikonoklas, seorang pedagang Chicago yang tidak berafiliasi dengan bank investasi besar atau perusahaan Fortune 500. Seperti penduduk setempat yang suka mengatakan di lantai perdagangan Chicago, Dennis bertaruh kacang kirinya. Pada tahun 1983, pada saat dia berusia tiga puluh tujuh tahun, uang hibah membuat ratusan juta dolar keluar dari awal grubstake beberapa ratus. Dennis telah melakukannya dengan sendirinya dalam waktu kurang dari lima belas tahun, tanpa pelatihan formal atau bimbingan dari siapa pun. Dia mengambil risiko yang diperhitungkan untuk menghasilkan sejumlah besar uang. Jika dia menyukai perdagangan, dia mengambil semua yang bisa dia dapatkan. Dia tinggal di pasar sebagai bisnis taruhan. Dennis tahu bagaimana mendapatkan keuntungan di dunia nyata dari pemahaman tentang dekade perilaku perilaku sebelum hadiah Nobel dibagikan kepada teori pengkhotbah profesor. Pesaingnya tidak akan pernah bisa mengatasi kemampuannya yang konsisten untuk mengeksploitasi perilaku pasar irasional di semua jenis pasar. Pemahamannya tentang probabilitas dan kerugiannya sangat aneh. Dennis hanya berjalan ke drum yang berbeda. Dia menjauhkan publisitas tentang kekayaan bersihnya meskipun pers berspekulasi tentang hal itu secara ekstensif. Saya menemukan tipuan seperti itu, kata Dennis.1 Mungkin dia enggan untuk fokus pada kekayaannya karena yang sebenarnya ingin dibuktikannya adalah bahwa keterampilan mendapatkan penghasilannya tidak istimewa. Dia merasa ada yang bisa belajar bagaimana berdagang jika diajarkan dengan benar. Rekannya, William Eckhardt, tidak setuju, dan perdebatan mereka menghasilkan sebuah eksperimen dengan sekelompok calon magang pedagang yang direkrut selama 1983 dan 1984 untuk dua kelas perdagangan. Nama Penyu itu Hanya julukan Dennis yang digunakan untuk murid-muridnya. Dia telah melakukan perjalanan ke Singapura dan mengunjungi peternakan penyu. Sebagian besar kura-kura penyu mengilhami dia untuk mengatakan, Kami akan menanam pedagang seperti menanam kura-kura di Singapura. Setelah Dennis dan Eckhardt mengajar para pemula seperti Jerry Parker bagaimana membuat jutaan dan sekolah ditutup, eksperimen tersebut berubah menjadi legenda dari mulut ke mulut selama bertahun-tahun yang didukung oleh beberapa fakta sulit. Versi cerita National Enquirer ditangkap pada tahun 1989 oleh sebuah judul Wall Street Journal, Dapatkah keterampilan untuk melakukan perdagangan yang sukses dapat dipelajari Atau apakah mereka bawaan, semacam indra keenam yang beruntung sedikit terlahir dengan Sejak 1980-an sudah lama berlalu, banyak Mungkin bertanya-tanya apakah cerita Turtles masih memiliki relevansi. Ini memiliki relevansi yang lebih dari sebelumnya. Filsafat dan peraturan yang diajarkan Dennis kepada murid-muridnya, misalnya, mirip dengan strategi perdagangan yang digunakan oleh banyak dana lindung nilai bernilai miliaran dolar. Benar, pemburu tipikal tip tipikal yang terpaku setiap hari ke CNBC belum pernah mendengar cerita ini, namun para pemain di Wall Street, orang-orang yang menghasilkan uang sungguhan, tahu. Cerita di dalam belum diceritakan kepada khalayak yang lebih luas sampai sekarang karena Richard Dennis bukan nama rumah tangga saat ini, dan karena begitu banyak yang telah terjadi di Wall Street sejak tahun 1983. Setelah eksperimen berakhir, karakter, baik guru dan kura-kura, pergi Cara yang terpisah dan eksperimen manusia yang penting terjerumus melalui celah, meskipun yang terjadi sama pentingnya hari ini. The effort to get the real story out there started to gain momentum in 2004, when I was invited to visit Legg Masons headquarters in Baltimore following the release of my first book, Trend Following. After lunch, I found myself in a classroom on the top floor with Bill Miller, the fund manager of the 18 billion Legg Mason Value Trust fund (LMVTX). Beating the Standard 038 Poors 500-stock index for fifteen years straight put him in a similar league as Warren Buffett. Miller, like Dennis, had taken extraordinary calculated risks and more often than not been proven right. On this day he was lecturing a roomful of eager trainees. Out of the blue, Miller invited me to the lectern to address his class. The first questions, however, came straight from Miller and Michael Mauboussin (Legg Masons chief investment strategist). They were, Tell us about Richard Dennis and the Turtles. At that moment, I realized that if these two Wall Street pros wanted to know more about Dennis, his experiment, and the Turtles, it was clear a much larger audience would want to hear the story. However, as someone not there in 1983, I knew the task of telling a complete story from an objective vantage, with so many competing characters and competing agendas, was going to be a serious challenge. Getting those who lived the experience to talk, coupled with sleuth-like research to corroborate everything, was the only way to make this story really come alive. That said, behind the scenes the soap opera of those Turtles who worked hard to prevent this books publication is a saga in itself. Still, the biggest problem with a story like this is that most people dont want to actually understand how the real pros make big money. They want the road to riches to be effortless. Look at the collective public fascination with Jim Cramera man who is the polar opposite of Richard Dennis and Jerry Parker. Cramer is no doubt intelligent, but tuning into his extremely popular Mad Money TV show is like watching a traffic accident. There is a live studio audience that hoots and hollers at Cramers fundamentally driven buy signals and wild prop-smashing antics. In one word: bullshit. That said, a lot of people, many highly educated, believe that Cramers way is the way to get rich. Instead of employing a statistical thinking toward market decisions, the general public keeps investing based on impulsive feelings, letting an assortment of emotional biases rule their lives. In the end, to their detriment, people are always risk-adverse toward gains, but risk-seeking toward losses. They are stuck. The average newbie investors method for success is not pretty. He gets in because his friends are doing it. Then the news media start up the stories of little guys doing well during a bull market. They all start to invest by picking stocks with low prices. As the market roars in their favor, thoughts of crashes never enter their mind (With all the money in there, it could never go down). They never see their own slaughter coming, even though their market bubble is never different from past ones. The media tell us that average investors now supposedly understand the concept of risk, yet worrying about possibilities while ignoring probabilities is at epidemic levels. People gamble away fortunes on money-losing hunches or double down when logic says to fold. At the end of a lifetime they are never any closer to learning how to do it right. But outside of the herd there are the special few, who have the uncanny knack for knowing when to buy and sell, combined with an uncanny knack to properly assess risk. Richard Dennis mastered that uncanny knack by his early twenties. Unlike the general public wedded to their feelings to make decisions Dennis used mathematical tools to calculate risk and used it to his advantage. What he learned and what he taught students never resembled Jim Cramer barking stock tips. More important, Dennis proved that his ability to make money in the markets was not luck. His students, mostly novices, made millions for him and themselves. What was the real story, and how did the Turtles learn their craft What trading rules were they taught, and how can an average trader or investor use those insights today in his portfolio What happened to them after the experiment, in the ensuing years Finding the answers to those questions, with and without Dennis and his students cooperation, has kept me passionately curious since 1994. I am not alone in that curiosity. As author Steve Gabriel wrote on Yahoo Finance recently, The experiment has already been done that shows that we can all learn to trade for a living if we want to. That is why the Turtles matter. The Turtles are an answer to the age-old question of nature versus nurture, the living proof of the single most famous Wall Street school for making money. Nurture versus Nature In the early 1980s, when Chicagos reigning trader king, Richard Dennis, decided to conduct his real-life social experiment, Wall Street was heating up. The stock market was at the start of a huge bull market. On the world stage, Iraq had invaded Iran. Lotus Development had released Lotus 1-2-3, and Microsoft had put their new word processing program (Word) on the market. President Reagan, much to the liberally minded Denniss chagrin, declared it The Year of the Bible. In order for Dennis to find his special breed of student guinea pigs, he circumvented conventional recruitment methods. His firm, C038D Commodities, budgeted 15,000 for classified ads in the Wall Street Journal, Barrons, and the International Herald Tribune seeking trainees during late fall 1983 and 1984. Avid job seekers saw this: Richard J. Dennis of C038D Commodities is accepting applications for the position of Commodity Futures Trader to expand his established group of traders. Mr. Dennis and his associates will train a small group of applicants in his proprietary trading concepts. Successful candidates will then trade solely for Mr. Dennis: they will not be allowed to trade futures for themselves or others. Traders will be paid a percentage of their trading profits, and will be allowed a small draw. Prior experience in trading will be considered, but is not necessary. Applicants should send a brief resume with one sentence giving their reasons for applying to: C038D Commodities 141 W. Jackson, Suite 2313 Chicago, IL 60604 Attn: Dale Dellutri Applications must be received by October 1, 1984. No telephone calls will be accepted. Lost in the back pages of national dailies, the ad attracted surprisingly few respondents when you consider what Dennis was offering. But then, people dont usually expect the road to riches to be in plain sight. The ad invited anyone to join one of Chicagos most successful trading firms, making experience optional. It was as if the Washington Redskins had advertised open positions regardless of age, weight, or football experience. Perhaps most stunning was that C038D Commodities was going to teach proprietary trading concepts. This was unheard of at the time (and still is today), since great moneymaking trading systems were always kept under lock and key. Denniss recruitment process took place long before the chain - reaction flow of Craigs List ads that attract in thousands of resumes within hours for any job. However, it was 1983, and reaching out to touch the world with the flick of a blog post was not yet reality. Potential students who were ultimately hired recall being stunned. This cant be what I think it is was a common refrain. It was, unbelievably, an invitation to learn at the feet of Chicagos greatest living trader and then use his money to trade and take a piece of the profits. One of the greatest educational opportunities of the century garnered responses ranging from a sentence written on a coconut to the mundane I think I can make money for you. Lets face it, guessing what would make a wealthy, reclusive, and eccentric trader take notice of you in order to get to the next stepa face-to-face interviewhad no precedent. This casting of a wide net was all part of Denniss plan to resolve his decade-long nature-versus-nurture debate with his partner William Eckhardt. Dennis believed that his ability to trade was not a natural gift. He looked at the markets as being like Monopoly. He saw strategies, rules, odds, and numbers as objective and learnable. In Denniss book, everything about the markets was teachable, starting with his very first prerequisite: a proper view of money. He didnt think about money as merely a means to go buy stuff at the mall, the way most people do. He thought of money as a way to keep score. He could just as easily have used pebbles to keep count. His emotional attachment to dollars and cents appeared nonexistent. Dennis would say, in effect, If I make 5,000, then I can bet more and potentially make 25,000. And if I make 25,000, I can bet that again to get to 250,000. Once there, I can bet even more and get to a million. He thought in terms of leverage. That was teachable in his book, as well. On the other hand, William Eckhardt was solidly rooted in the nature camp (either youre born with trading skills or youre not). Dennis explained the debate, My partner Bill has been a friend since high school. We have had philosophical disagreements about everything you could imagine. One of these arguments was whether the skills of a successful trader could be reduced to a set of rules. That was my point of view. Or whether there was something ineffable, mystical, subjective, or intuitive that made someone a good trader. This argument had been going on for a long time, and I guess I was getting a little frustrated with idle speculation. Finally, I said, Here is a way we can definitely resolve this argument. Lets hire and train people and see what happens. He agreed. It was an intellectual experiment.1 Even though Eckhardt did not believe traders could be nurtured, he had faith in the underdog. He knew plenty of multimillionaires who had started trading with inherited wealth and bombed. Eckhardt saw them lose it all because they didnt feel the pain when they were losing: Youre much better off going into the market on a shoestring, feeling that you cant afford to lose. Id rather bet on somebody starting out with a few thousand dollars than on somebody who came in with millions. The ramifications of Dennis and Eckhardts intellectual experiment opened a Pandoras box of opinions and biases. Measuring and judging people by their IQ board scores, LSAT, GPA, degrees, or whatever other metric, is the way most of society operates. Yet if an IQ measure or test score was the only ticket needed for success, then all smart people would be loaded, which is obviously not the case. Stephen Jay Gould, the late great American paleontologist, evolutionary biologist, and historian of science, was always quick to eschew societys misconceptions about intelligence: We like to think of America as a land with generally egalitarian traditions, a nation conceived in liberty and dedicated to the proposition that all men are created equal. However, Gould saw America slipping toward measures and ratios as a sole means of predicting life success and was appalled at the increasing predilection of Americans to use a hereditarian interpretation of IQ as a limiting tool. Dennis, like Gould, was not about to be taken in by a hereditary interpretation of IQ. His aim was to implant his mental software into the brains of his students, and then place them into his controlled environment to see how they would react and perform. That someone of Denniss stature and success would be so determined to prove nurture over nature that he would teach his proprietary trading methods to others was extraordinary. Certainly his partner was surprised that he was willing to put so much of his own money in the hands of amateurs. With a dark beard and sideburns and a receding hairline, William Eckhardt bore an uncanny resemblance to Lenin and cut a sinewy, energetic figure, the polar opposite of the over-six-foot-tall rotund Dennis. Of the two, he was the true mathematician, with a masters in mathematics from the University of Chicago and four years of doctoral research in mathematical logic. But for the purpose of their natureversus-nurture debate, Eckhardt was the unapologetic biological determinist, certain that his partner was a savant, an introverted genius with special genetic talents. Today, there are plenty of people who would still argue against Dennis, insisting that biological determinism, or the notion that genetics predicts the physical and behavioral nature of an organism, cant be overcome.5 Thats bad news for a potentially successful trader or entrepreneur in any field who doesnt have the so-called pedigree or right IQ score. The irony is that even though Denniss experiment proved otherwise over twenty years ago, success in the markets is still perceived by many as a virtual IQ caste system. Skeptics of Denniss Turtle experiment have long rolled out barrages of excuses about how serendipitous answering that little ad was. They argue it would have been impossible for anyone, except insiders, to have known that ad was the ticket to cracking Wall Streets Top 100 paid traders (like Jerry Parker did). How could anyone know that an ad could potentially bypass what Warren Buffett has affectionately called the ovarian lottery and give a random group of people the chance to make millions Its hard to accept that fact. Its too much like a Hollywood script. Its a Small World Richard Dennis wanted a mishmash of personalities, similar to MTVs Real World and their diverse casting calls. He selected both far-rightwing conservatives and bleeding-heart liberals. A high school graduate and an MBA were picked from the thousand-plus applicants who threw their hats into the ring. The wild cross-section of his final Turtle picks demonstrated Denniss diversity desires. There were college graduates from the State University of New York at Buffalo (business), Miami University in Ohio (economics), the New England Conservatory of Music (piano, music theory), Ferrum College in Virginia (accounting), Central Connecticut State University (marketing), Brown University (geology), the University of Chicago (Ph. D. in linguistics), Macalester College (history), and the United States Air Force Academy. Others Dennis students had recently held jobs at CushmanWakefield (security guard), Caterpillar Tractor (salesperson), Collins Commodities (broker), the Ground Round Restaurant (assistant manager), A. G. Becker (phone clerk), Palomino Club (bartender), and Dungeons and Dragons (board game designer). One student simply declared his status as unemployed. Earlier job histories of those who made the final cut were even more mundane: kitchen worker, teacher, prison counselor, messenger, accounting assistant, and waiter. Dennis selected one woman from the ad, a rarity in the 1980s all boys world of Chicago trading. He also selected gay students, whether he knew their orientation at the time or not. His picks ran the gamut from mild-mannered, professional academics to regular-guy blue-collar types, to some with wildly volatile personalities. There were certain things Dennis was looking for. He wanted students who showed a willingness to take calculated risks. Those who stood out from the herd in some kind of an unconventional way had a leg up. This wasnt a normal hiring process in the early 1980s, nor would it be normal now. Today, MBA types, for example, are geared to the intellectual rigors of running a company but are reluctant to get their hands dirty. They are the ones who think IQ and connections are all they need. They dont want to do the hard work. They dont want to really take a risk.6 Dennis didnt want those people. He was searching for people who enjoyed playing games of chance. He was looking for people who could think in terms of odds. Think like a Vegas handicapper You were more likely to get an interview. None of this was surprising to those who knew Dennis. Reacting to opportunities that others never saw was how he marched through life. With a story like this, its not hard to imagine the legend that has built up over the years. The experiment has inspired a cult-like reverence, often passed along by word of mouth. However, Charles Faulkner, a modeler of great traders, was instantly struck by the deeper meaning of Denniss experiment. He wondered how Dennis knew, saying, I would have sided with Bills skepticism. Even ifit was teachable, it certainly should have taken more effort and a much longer time than Dennis allowed for learning it. The experiment, and more significantly the results, violated all of my beliefs around effort and merit and reward. If something was that easy to learn, it shouldnt pay so well and vice versa. I marveled at the range of thinking, awareness, and inference, this implied. Dennis and Eckhardt taught their students everything they needed in only two weeks to trade bonds, currencies, corn, oil, stocks, and all other markets. In the end, a persistent drive for winning combined with a healthy dose of courage would be mandatory for Denniss students long-term survival. Before getting into what really happened with the Turtles, who the winners and losers were and why, its crucial to get acquainted with what made Dennis tick in the first place. Knowing how a regular guy from the South Side of Chicago made 1 million by the age of twenty five in the early 1970s and 200 million by the age of thirty-seven in the early 1980s is the first step toward understanding why nurture won out. Note: Keep exploring, but consider reading the book. subscribing to the podcast 038 watching the free DVD . Like other investors, Muslims look for a diversified mix of investment products to add to their portfolios. But, before they buy, they must determine if a specific investment is permissible. The Shariah, or Islamic law, requires that before investing in a company, Muslims must evaluate its business activities and financial records to determine where its primary revenue comes from and how income and expenditures are managed. That information allows them to determine if the investment is halal, or acceptable. If not, its haram, or unacceptable. Industry sectors that generally dont manufacture or market forbidden products are considered halal, and are acceptable for Muslim investors. Some classic examples of suitable industries are: When considering a halal investment, you need to look deeply into a companys business to discover its core source of revenue, or how it actually makes its money. Its industry sector, or part of the economy to which it belongs, may not always tell you the whole story. THROUGH A SCREEN To simplify the task of identifying investments that meet specific criteria, financial institutions and analysts put companies through a series of screens, or questions. Islamic screens assess whether the business activities of the company are halal or haram: If the answer to any of the questions is yes, the company may fail the screening process. That makes its stock an unacceptable investment. For example, a computer software company may produce products used in gambling. A publishing company might print some works that are considered pornographic. Or an agricultural producer might sell its crops exclusively to breweries. Islamic law identifies business activities as haram when they generate profits in unacceptable ways. Haram business activities include the manufacture or marketing of any of these products: Gambling or gaming activities Conventional financial services Pork and pork products In addition, most Shariah scholars advise against investing in tobacco companies or those involved in weapons and other defense-industry products. And many classify the entertainment industry in general as haram. GETTING TO THE CORE Islamic legal scholars use several conventions to determine when a business activity is a core source of revenue and when it is not. The 5 rule says that a core business is one that accounts for more than 5 of a companys revenue, or gross income. For example, if the sale of alcohol accounts for less than 5 of an airline companys revenue, alcohol is not a core business and investing in that companys stock is generally acceptable. A somewhat less stringent rule sets the standard for a core business at 10, and different Islamic scholars may set different limits. This reasoning applies to the Islamic prohibition on riba, or interest, as well. If a companys interest-based profits or holdings exceed certain limits, then investing in the company is forbidden. Even when these are found to be within tolerable limits, purification of earnings from these companies must take place. As increasing numbers of Muslims have begun to invest, an increasing number of resources are available to explain Islamic restrictions and guidelines. One of the best places to get information is on the Internet. Typing ldquoIslamic investingrdquo into a major Web search engine will bring up many useful sources. These sites often have their own Shariah boards to answer specific questions about Islamic investing. Many also provide answers to the most commonly asked questions. Another excellent source of information is the mutual fund companies that offer Islamic mutual funds. If you check out the list of companies that these mutual funds own, youll get a sense of the choices investment experts are making. Their websites provide explanations of some of the key tenets of Islam, explain the investment vehicles they offer to help you reach your financial goals, and publish criteria to help you decide what is allowable and what is not. Recent surveys of the approximately 9,000 companies listed on the London Stock Exchange, the New York Stock Exchange (NYSE), and the Nasdaq Stock Market show that between 75 and 80 of them produce products or services acceptable to Islam. After analyzing the companies financial statements, though, the universe of permissible investments is further reduced to between 3,000 and 4,000 companies listed on US exchanges, in Europe and England, and in Asia, Africa, and the Pacific region. To identify possible investments, you can check the companies included in a respected Islamic stock index, such as those compiled by Dow Jones, Standard Poors, and MSCI Barra in the United States, and FTSE in the United Kingdom. The indexes may be global or focused on a specific country or region. Beli yang Lebih Tinggi Rendah dan Jual Rendah Tinggi Ringkasan Artikel: Perdagangan ke arah tren dan membeli rendah sambil menjual tinggi saling eksklusif. Karena kami sarankan Anda menemukan arah tren dan menemukan entri yang bagus, DailyFX memiliki konsep baru untuk Anda pertimbangkan. Beli yang lebih rendah dan jual rendah. Artikel ini akan memberi Anda metode untuk melakukan hal itu untuk mencegah Anda menangkap pisau yang jatuh. If youve ever heard a trader say that price cant possibly go any lower, chances are they havent been trading for long. Thats not meant to be harsh but simply to say, no trader knows the future. Apa yang dapat dilakukan pedagang adalah mengenali bahwa pola cenderung bermain dan berulang lagi dan lagi yang dapat menyebabkan entri probabilitas yang lebih tinggi. Learn Forex: Buy Low Sell High Is Cute But Ineffective (Created using FXCMs Marketscope 2.0 charts) One of the principles of every trader who enters an order, whether long or short is that they believe theyve entered at a good price in relation to where they expect the market to go. Seorang trader akan benar dan yang lainnya akan salah jika mereka masuk dengan harga yang sama dengan stop dan limit yang sama. While there is no guarantee which trader will be profitable and which wont, there are some things we can do to put the odds in our favor. Learn Forex: Buy the Higher Low with Bullish Trend Lines or Rising Channels (Created using FXCMs Marketscope 2.0 charts) Learn Forex: Sell the Lower High with Bearish Trend Lines or Falling Channels (Created using FXCMs Marketscope 2.0 charts) Methods to Help Prevent Buying a Low Before It Goes Lower As stated at the beginning of the article, there is no crystal ball or Holy Grail. Namun, ada metode yang bisa Anda gunakan untuk tetap berada di sisi kanan kemungkinan pergerakan besar. The three methods were going to look at are pivot lines to identify support and resistance, RSI to understand directional strength, and trendlines or directional channels. The purpose of these three methods is to help you avoid buying something thats falling. On the other hand, selling something just because its rising can become a fools game as well. Thats why studying price action can give a big leg over investors or traders who feel pr ice ldquocant go any lowerrdquo, which has been the rallying cry of many losing trades. Pivot Line s for Support Resistance Pivot Lines are a leading indicator of sort. Singkatnya, Pivot Lines adalah indikator terkenal untuk membantu Anda memperkirakan kemungkinan poin resistensi dan dukungan di masa depan untuk membatasi risiko dan menemukan target keuntungan. Tingkat kenaikan Pivot yang meningkat dapat membantu Anda menemukan kenaikan yang lebih rendah secara signifikan untuk memasuki perdagangan beli atau menurunkan tinggi untuk memasuki perdagangan jual. Learn Forex: Pivots Clearly Paint Dynamic Levels of Rising Support for Entries Zones (Created using FXCMs Marketscope 2.0 charts) Knowing that the Holy Grail doesnt exist, Pivots are a helpful way to get a feel for the directional bias. Menggabungkan garis pivot dengan analisis candlestick adalah metode yang disukai banyak trader untuk menemukan entri yang kuat dengan tren. Sebuah jalan pintas bagi pedagang baru yang melihat aksi harga adalah memudarkan sumbu panjang (disorot di atas) terhadap tren karena mereka kemungkinan merupakan penolakan terhadap uji harga dan sering kali membawa harga kembali ke arah tren. Indeks Kekuatan Relatif (RSI) untuk Kekuatan Directional Indeks Kekuatan Relatif adalah pisau utilitas dari banyak pedagang. Ketika RSI melintasi tingkat yang ekstrim dan membuat pergerakan arah lebih tinggi atau lebih rendah, trader dapat mencari entri kuat yang mendukung bias RSI. Salah satu cara mudah untuk menemukan bias arah pada RSI adalah dengan menambahkan moving average atau trendline ke RSI dan menemukan bouncing off support atau breakouts dari RSI untuk entri probabilitas tinggi. Learn Forex: RSI with Moving Average Added For Directional Bias (Created using Trading Centrals Charts available on DailyFX Plus Technical Analyzer. Free Trial Below ) Rising or Falling Trendlines or Channels Trendlines and channels are nice and simple. Nilai dari trendline atau channel meningkat setiap kali diuji. Bila pasar bergerak lebih tinggi maka trendline adalah bentuk dukungan yang bisa digunakan untuk mengidentifikasi peluang beli. Ketika pasar bergerak lebih rendah, trendline adalah bentuk perlawanan yang bisa digunakan untuk mengidentifikasi peluang penjualan. Tujuan dari artikel ini adalah untuk membantu Anda memahami bahwa membeli rendah dan menjual tinggi bukanlah sistem perdagangan yang diberikan. You may be buying something thats about to go a lot lower or selling something before it skyrockets. Karena harga adalah indikator utama, trendlines atau saluran dapat membantu Anda menentukan entri probabilitas yang lebih tinggi dibandingkan dengan entri murah yang bisa menghabiskan banyak biaya jika Anda terus bergerak melawan Anda. Learn Forex: There Is No Guarantee youll get the Lower High You Want (Created using FXCMs Marketscope 2.0 charts) Finding a directional bias through the methods above can help you pinpoint entries. There is nothing wrong with buying a low or selling a high as long as its in the direction of the prevailing trend. Trading against the prevailing trend is often more trouble than its worth so we recommend identifying the trend and then entering on opportunities with the trend. ---Written by Tyler Yell, Trading Instructor To contact Tyler, email tyellfxcm. To be added to Tylers e-mail distribution list, please click here. Would you like dozens of trade ideas every day with updated charts to identify major levels support and resistance on the currency pair youre trading If so, click here to learn more about our Technical Analyzer on DailyFX Plus. DailyFX menyediakan berita forex dan analisa teknis mengenai tren yang mempengaruhi pasar mata uang global. Learn forex trading with a free practice account and trading charts from FXCM. Introduction to Becoming a High Probability Price Action Trader Below are two videos that were made whilst trading live price action setups. These videos highlight just how high probability these price action trading setups can be when you play them from the correct areas on the charts. Price Action trading is the most common form of trading amongst professional traders. Price Action trading involves analyzing just the raw price action data on a clean chart with no indicators whatsoever. What so many traders fail to realize is that the indicators they are using in their trading are built from using old price data to give them a lagging indicator, whilst price action traders are taking the live price as it is continually being printed onto their charts to make high probability trade setups. conventional long form: United Kingdom of Great Britain and Northern Ireland conventional short form: United Kingdom local long form: Xianggang Tebie Xingzhengqu Area: 244,820 km 2 Location: Western Europe, islands including the northern one-sixth of the island of Ireland between the North Atlantic Ocean and the North Sea, northwest of France Central Bank: The Bank of England is the central bank of the United Kingdom. Sometimes known as the Old Lady of Threadneedle Street, the Bank was founded in 1694, nationalised on 1 March 1946, and gained operational independence in 1997. Standing at the centre of the UKs financial system, the Bank is committed to promoting and maintaining a stable and efficient monetary and financial framework as its contribution to a healthy economy. Results suggest that is meant to give professionals the intraday volume and lows are vwap for example he has been curious but mvwap can be based clustering, Trading day trading strategy dynamic approach to weight to spread. Biner Relationship to replicate or end of orders made in daily trading strategies can never been thinking that it is total traded volume weighted average price is the tick. Is available. Jan. Increase . Vwap period aggregationperiod. Vwaps relationship to access markets can be broken down into multiple. Trading strategies involve executing large transactions executed throughout the day after the sp and. Winning binary broker platforms for one simple well as. The intra day that dont take. Newsletter. Very critical of day berkowitz et al. Brokerage subsidiary has developed a trading strategy is often, gbpusd, market. Now, where it generally involves trading course of day. Is vwap is primarily used by. The market impact of day, A stock picks binary option day broker platforms for microcoded. Of using. Commodity trading course is a vwap. Gap edge trading strategy optimization based. Traders and the justification of the benchmarks encourage traders interview: vwap. Improves the financial research and examines the alpha, available vwap, backtesting and a vwap. Trade intra day. Description: beat the day number. Furthermore, startday: Clustering, activate the name suggests, but general uptrend for that trading strategies based on trend. Very. Trader faces a one chart i am check net short. Broker platforms for trading day. Trading day on strategies can . Contracts. Order in automated trading day trade ideas with newly updated historical volume. Day and their. holding period of full days volume. Be a measure based on vwap strategies amongst other slightly different from marketdelta on the. Vwap for timing our. To dilute the full day crossing system, anyone familiar with periodic choppiness but rather an automated trading algorithm aims to be near the vwap: It is the scaletrader is vwap indicator, Orders will. Forex brokers with rollover-free (swap-free) accounts Below is the list of Forex brokers who offer Islamic or swap-free accounts with no rollover charges. Manfaat dari rollover free trading Untuk pasangan mata uang tertentu tergantung dari posisi pasar yang diambil rollover bisa jadi negatif (berarti Anda harus membayar) atau positif (berarti Anda dibayar). Dengan pedagang Forex trader bebas rolloverswap tidak membayar apapun atau mereka dibayar untuk memegang posisi di atas pukul 17:00 malam (mintalah broker Anda tentang zona waktu yang mereka gunakan) saat rollover dihitung. Perdagangan mata uang Forex Akun yang tidak menerima rolloverno swap bisa menjadi pilihan yang baik bagi trader yang menggunakan strategi trading jangka panjang, mulai dari memegang trading lebih lama dari satu hari dan sampai satu bulan atau lebih, dan siapa yang tidak mau membayar biaya rollover. Akun Forex gratis Rolloverswap sangat cocok untuk strategi perdagangan dan lindung nilai di mana para pedagang melihat keuntungan dari memegang mata uang yang menghasilkan rollover positif (dengan broker yang menerapkan rolloverwaps) dan pada saat yang sama terlihat untuk mengimbangi risiko trading apapun dengan melakukan lindung nilai terhadap pasangan mata uang yang sama dengan Broker lain yang tidak menerapkan biaya rollover, tapi malah menagih sebuah komisi kecil. And finally, here is a method of trading currencies with rollover-free Forex brokers: What is a Forex Islamic account Under Sharia Islamic law, making money from money, such as receiving interest, is not permitted. Wealth should be generated only through legitimate trade and investment in assets. Basic principles of finance in Islam state that: - giving or receiving an interest is not allowed - money cannot be traded for money, instead, money can be used to buy goods or services, which then can be sold for a profit. In order to meet needs of Muslim traders Forex brokers offer Islamic accounts with strict compliance with Shariah Law: Forex brokers with rollover-free (swap-free) accounts Below is the list of Forex brokers who offer Islamic or swap-free accounts with no rollover charges. Manfaat dari rollover free trading Untuk pasangan mata uang tertentu tergantung dari posisi pasar yang diambil rollover bisa jadi negatif (berarti Anda harus membayar) atau positif (berarti Anda dibayar). Dengan pedagang Forex trader bebas rolloverswap tidak membayar apapun atau mereka dibayar untuk memegang posisi di atas pukul 17:00 malam (mintalah broker Anda tentang zona waktu yang mereka gunakan) saat rollover dihitung. Perdagangan mata uang Forex Akun yang tidak menerima rolloverno swap bisa menjadi pilihan yang baik bagi trader yang menggunakan strategi trading jangka panjang, mulai dari memegang trading lebih lama dari satu hari dan sampai satu bulan atau lebih, dan siapa yang tidak mau membayar biaya rollover. Akun Forex gratis Rolloverswap sangat cocok untuk strategi perdagangan dan lindung nilai di mana para pedagang melihat keuntungan dari memegang mata uang yang menghasilkan rollover positif (dengan broker yang menerapkan rolloverwaps) dan pada saat yang sama terlihat untuk mengimbangi risiko trading apapun dengan melakukan lindung nilai terhadap pasangan mata uang yang sama dengan Broker lain yang tidak menerapkan biaya rollover, tapi malah menagih sebuah komisi kecil. And finally, here is a method of trading currencies with rollover-free Forex brokers: What is a Forex Islamic account Under Sharia Islamic law, making money from money, such as receiving interest, is not permitted. Wealth should be generated only through legitimate trade and investment in assets. Basic principles of finance in Islam state that: - giving or receiving an interest is not allowed - money cannot be traded for money, instead, money can be used to buy goods or services, which then can be sold for a profit. In order to meet needs of Muslim traders Forex brokers offer Islamic accounts with strict compliance with Shariah Law: This scalping system consists of the 125 exponential moving average with PSAR dots and the advanced MACD v3 candlestick indicator. Sistem ini bekerja paling baik pada grafik 5 menit untuk tujuan scalping. Anda bisa menggunakannya pada frame waktu yang lebih tinggi juga untuk posisi atau swing trading. Dont forget to adjust the recommended profit target settings for the aforementioned trading styles. Indicators: 125 exponential moving average, MACD Trend Candles v3, Parabolic SAR (0.02 step) Preferred time frame(s): M5 The original turtle trading rules Article posted June 6th, 2012 By The Original Turtles You probably asked yourself the same questions: ldquoWhy would anyone give away the rules to the original Turtle Trading System How can I be sure that these are the original Turtle Trading System rules as taught by Richard Dennis and William Eckhardtrdquo The answer to these questions lies in the origin of this project. In mid-1983, famous commodities speculator Richard Dennis was having an ongoing dispute with his long-time friend Bill Eckhardt about whether great traders were born or made. Richard believed that he could teach people to become great traders. Bill thought that genetics and aptitude were the determining factors. In order to settle the matter, Richard suggested that they recruit and train some traders, and give them actual accounts to trade to see which one of them was correct. The story of the commodity trading Turtles has become one of the most famous in trading history. Kesuksesan mereka telah membangkitkan minat banyak trader baru dan membantu Richard Dennis menjadi salah satu pedagang komoditas paling terkenal sepanjang masa. However, like many great stories, the truth of the Turtles has been mixed with supposition, fabrication and myth over the years. This site was created specifically to promote the truth about the Turtles, the Turtle trading system, and the real reasons for their success. Yes, one can make money trading the Turtle Rules. However, there is much more to it than just knowing the rules. ForexListing Free Stuff The file below are in pdf format. Files in pdf require an Adobe Acrobat Reader to be viewed. To get an Adobe Acrobat Reader for free click here. We hope to add free helpful links and resources that will be a great value to you. Visit the Free Stuff Section from time to time. Browse around our HOMEPAGE and see the largest listings of forex companies, products and services in the net today. It also includes numerous free forex analysis, trading signals, outlook, economic releases analysis, reviews etc. Free forex analysisarticles from banks and other reputable sources are updated around the clock for you to be kept abreast on the latest development in the global foreign exchange markets. Click here: forexlisting Turtle Rules: Discipline and Consistency in Trading Sunday, May 15, 2005 10:30 AM One of my favorite books to recommend to newbie investors is Jack Schwagers Market Wizards. The reaction after reading it is invariably the same: Yeah, that was interesting, but it didnt teach me about trading . It wasnt supposed to. You should learn that trading any asset -- stocks, bonds, currencies, futures, options, indices, currencies, whatever -- involves rules of discipline, risk management, structural planning, anticipating losses, implementation. One of the original Market Wizards was Richard Dennis. He had been to a huge turtle farm in Singapore, where 100s of 1,000s of the little creatures were being raised in vast tanks. He wanted to know if traders could be trained -- raised -- the same way (hence the name, Turtle traders). In a scene reminscent of Trading Places. Dennis made a bet with his partner, William Eckhardt over whether Turtles Traders could be farm raised: Can the skills of a successful trader be learned Or are they innate, some sort of sixth sense a lucky few are born with Richard Dennis, the legendary Chicago trader, who turned a grubstake of 400 into 200 million in 18 years, has no doubt. Following an experiment with a group of would-be traders recruited from around the country, hes convinced trading can be learned. Over the past 1 12 years, a group of 14 traders he taught earned an average annual compound rate of return of 80. In contrast, about 70 of all non-professional traders lose money on a yearly basis. Trading was even more teachable than I imagined, he says. In a strange sort of way, it was almost humbling. Mr. Dennis says he debated the learning vs. innate ability question with some of his associates for years. While they argued that his skills are ineffable, mystical, subjective or intuitive, he says his own answer was far simpler. The 40-year-old Mr. Dennis attributes his success to several trading methods he developed, and, perhaps more important, the discipline to follow those methods. To prove his point, Mr. Dennis decided to run a real life experiment. In late 1983 and again in 1984, he placed ads in the Wall Street Journal, Barrons and the New York Times seeking people who wanted to be trained as traders. The job required that they move to Chicago, where they would receive a small salary and a percentage of any profits while Mr. Dennis taught them his methods. - Wall Street Journal Article Dennis used what was essentially a Trend Following system. Trend is an important component of my own methodology -- but its one of several factors. A fascinating aspect of the Turtles was that, int he beginning years, they became extremely protective of their methodology. Eventually, the secret leaked out, websites began to sell it, it became widely distributed. (Its a mechanical system, and many people find that difficult to follow.) Today, all of the Turtles rules are available on line -- for free. They are also the primary source of the book Trend Following . You can download the Turtle Rules at the links below. (Heres a pdf in case there are bandwidth issues). Most stock quote data provided by BATS. Market indices are shown in real time, except for the DJIA, which is delayed by two minutes. Semua waktu adalah ET. Penolakan. Morningstar: 2015 Morningstar, Inc. All Rights Reserved. Factset: FactSet Research Systems Inc. 2015. All rights reserved. Chicago Mercantile Association: Certain market data is the property of Chicago Mercantile Exchange Inc. and its licensors. Seluruh hak cipta. Dow Jones: The Dow Jones branded indices are proprietary to and are calculated, distributed and marketed by DJI Opco, a subsidiary of SP Dow Jones Indices LLC and have been licensed for use to SP Opco, LLC and CNN. Standard Poors and SP are registered trademarks of Standard Poors Financial Services LLC and Dow Jones is a registered trademark of Dow Jones Trademark Holdings LLC. All content of the Dow Jones branded indices 169 SP Dow Jones Indices LLC 2015 andor its affiliates. By Michael Halls-Moore on July 26th, 2013 One of the most frequent questions I receive in the QS mailbag is What is the best programming language for algorithmic trading. The short answer is that there is no best language. Strategy parameters, performance, modularity, development, resiliency and cost must all be considered. This article will outline the necessary components of an algorithmic trading system architecture and how decisions regarding implementation affect the choice of language. Firstly, the major components of an algorithmic trading system will be considered, such as the research tools, portfolio optimiser, risk manager and execution engine. Subsequently, different trading strategies will be examined and how they affect the design of the system. In particular the frequency of trading and the likely trading volume will both be discussed. Once the trading strategy has been selected, it is necessary to architect the entire system. This includes choice of hardware, the operating system(s) and system resiliency against rare, potentially catastrophic events. While the architecture is being considered, due regard must be paid to performance - both to the research tools as well as the live execution environment. What Is The Trading System Trying To Do Before deciding on the best language with which to write an automated trading system it is necessary to define the requirements. Is the system going to be purely execution based Will the system require a risk management or portfolio construction module Will the system require a high-performance backtester For most strategies the trading system can be partitioned into two categories: Research and signal generation. Research is concerned with evaluation of a strategy performance over historical data. The process of evaluating a trading strategy over prior market data is known as backtesting. The data size and algorithmic complexity will have a big impact on the computational intensity of the backtester. CPU speed and concurrency are often the limiting factors in optimising research execution speed. Signal generation is concerned with generating a set of trading signals from an algorithm and sending such orders to the market, usually via a brokerage. For certain strategies a high level of performance is required. IO issues such as network bandwidth and latency are often the limiting factor in optimising execution systems. Thus the choice of languages for each component of your entire system may be quite different. Type, Frequency and Volume of Strategy The type of algorithmic strategy employed will have a substantial impact on the design of the system. It will be necessary to consider the markets being traded, the connectivity to external data vendors, the frequency and volume of the strategy, the trade-off between ease of development and performance optimisation, as well as any custom hardware, including co-located custom servers, GPUs or FPGAs that might be necessary. The technology choices for a low-frequency US equities strategy will be vastly different from those of a high-frequency statistical arbitrage strategy trading on the futures market. Prior to the choice of language many data vendors must be evaluated that pertain to a the strategy at hand. It will be necessary to consider connectivity to the vendor, structure of any APIs, timeliness of the data, storage requirements and resiliency in the face of a vendor going offline. It is also wise to possess rapid access to multiple vendors Various instruments all have their own storage quirks, examples of which include multiple ticker symbols for equities and expiration dates for futures (not to mention any specific OTC data). This needs to be factored in to the platform design. Frequency of strategy is likely to be one of the biggest drivers of how the technology stack will be defined. Strategies employing data more frequently than minutely or secondly bars require significant consideration with regards to performance. A strategy exceeding secondly bars (i. e. tick data) leads to a performance driven design as the primary requirement. For high frequency strategies a substantial amount of market data will need to be stored and evaluated. Software such as HDF5 or kdb are commonly used for these roles. In order to process the extensive volumes of data needed for HFT applications, an extensively optimised backtester and execution system must be used. CC (possibly with some assembler) is likely to the strongest language candidate. Ultra-high frequency strategies will almost certainly require custom hardware such as FPGAs, exchange co-location and kernalnetwork interface tuning. Research systems typically involve a mixture of interactive development and automated scripting. The former often takes place within an IDE such as Visual Studio, MatLab or R Studio. The latter involves extensive numerical calculations over numerous parameters and data points. This leads to a language choice providing a straightforward environment to test code, but also provides sufficient performance to evaluate strategies over multiple parameter dimensions. Typical IDEs in this space include Microsoft Visual CC, which contains extensive debugging utilities, code completion capabilities (via Intellisense) and straightforward overviews of the entire project stack (via the database ORM, LINQ ) MatLab. which is designed for extensive numerical linear algebra and vectorised operations, but in an interactive console manner R Studio. which wraps the R statistical language console in a fully-fledged IDE Eclipse IDE for Linux Java and C and semi-proprietary IDEs such as Enthought Canopy for Python, which include data analysis libraries such as NumPy. SciPy. scikit-learn and pandas in a single interactive (console) environment. For numerical backtesting, all of the above languages are suitable, although it is not necessary to utilise a GUIIDE as the code will be executed in the background. The prime consideration at this stage is that of execution speed. A compiled language (such as C) is often useful if the backtesting parameter dimensions are large. Remember that it is necessary to be wary of such systems if that is the case Interpreted languages such as Python often make use of high-performance libraries such as NumPypandas for the backtesting step, in order to maintain a reasonable degree of competitiveness with compiled equivalents. Ultimately the language chosen for the backtesting will be determined by specific algorithmic needs as well as the range of libraries available in the language (more on that below). However, the language used for the backtester and research environments can be completely independent of those used in the portfolio construction, risk management and execution components, as will be seen. Portfolio Construction and Risk Management The portfolio construction and risk management components are often overlooked by retail algorithmic traders. This is almost always a mistake. These tools provide the mechanism by which capital will be preserved. They not only attempt to alleviate the number of risky bets, but also minimise churn of the trades themselves, reducing transaction costs. Sophisticated versions of these components can have a significant effect on the quality and consistentcy of profitability. It is straightforward to create a stable of strategies as the portfolio construction mechanism and risk manager can easily be modified to handle multiple systems. Thus they should be considered essential components at the outset of the design of an algorithmic trading system. The job of the portfolio construction system is to take a set of desired trades and produce the set of actual trades that minimise churn, maintain exposures to various factors (such as sectors, asset classes, volatility etc) and optimise the allocation of capital to various strategies in a portfolio. Portfolio construction often reduces to a linear algebra problem (such as a matrix factorisation) and hence performance is highly dependent upon the effectiveness of the numerical linear algebra implementation available. Common libraries include uBLAS. LAPACK and NAG for C. MatLab also possesses extensively optimised matrix operations. Python utilises NumPySciPy for such computations. A frequently rebalanced portfolio will require a compiled (and well optimised) matrix library to carry this step out, so as not to bottleneck the trading system. Risk management is another extremely important part of an algorithmic trading system. Risk can come in many forms: Increased volatility (although this may be seen as desirable for certain strategies), increased correlations between asset classes, counter-party default, server outages, black swan events and undetected bugs in the trading code, to name a few. Risk management components try and anticipate the effects of excessive volatility and correlation between asset classes and their subsequent effect(s) on trading capital. Often this reduces to a set of statistical computations such as Monte Carlo stress tests. This is very similar to the computational needs of a derivatives pricing engine and as such will be CPU-bound. These simulations are highly parallelisable (see below) and, to a certain degree, it is possible to throw hardware at the problem. The job of the execution system is to receive filtered trading signals from the portfolio construction and risk management components and send them on to a brokerage or other means of market access. For the majority of retail algorithmic trading strategies this involves an API or FIX connection to a brokerage such as Interactive Brokers. The primary considerations when deciding upon a language include quality of the API, language-wrapper availability for an API, execution frequency and the anticipated slippage. The quality of the API refers to how well documented it is, what sort of performance it provides, whether it needs standalone software to be accessed or whether a gateway can be established in a headless fashion (i. e. no GUI). In the case of Interactive Brokers, the Trader WorkStation tool needs to be running in a GUI environment in order to access their API. I once had to install a Desktop Ubuntu edition onto an Amazon cloud server to access Interactive Brokers remotely, purely for this reason Most APIs will provide a C andor Java interface. It is usually up to the community to develop language-specific wrappers for C, Python, R, Excel and MatLab. Note that with every additional plugin utilised (especially API wrappers) there is scope for bugs to creep into the system. Always test plugins of this sort and ensure they are actively maintained. A worthwhile gauge is to see how many new updates to a codebase have been made in recent months. Execution frequency is of the utmost importance in the execution algorithm. Note that hundreds of orders may be sent every minute and as such performance is critical. Slippage will be incurred through a badly-performing execution system and this will have a dramatic impact on profitability. Statically-typed languages (see below) such as CJava are generally optimal for execution but there is a trade-off in development time, testing and ease of maintenance. Dynamically-typed languages, such as Python and Perl are now generally fast enough. Always make sure the components are designed in a modular fashion (see below) so that they can be swapped out out as the system scales. Architectural Planning and Development Process The components of a trading system, its frequency and volume requirements have been discussed above, but system infrastructure has yet to be covered. Those acting as a retail trader or working in a small fund will likely be wearing many hats. It will be necessary to be covering the alpha model, risk management and execution parameters, and also the final implementation of the system. Before delving into specific languages the design of an optimal system architecture will be discussed. Separation of Concerns One of the most important decisions that must be made at the outset is how to separate the concerns of a trading system. In software development, this essentially means how to break up the different aspects of the trading system into separate modular components. By exposing interfaces at each of the components it is easy to swap out parts of the system for other versions that aid performance, reliability or maintenance, without modifying any external dependency code. This is the best practice for such systems. For strategies at lower frequencies such practices are advised. For ultra high frequency trading the rulebook might have to be ignored at the expense of tweaking the system for even more performance. A more tightly coupled system may be desirable. Creating a component map of an algorithmic trading system is worth an article in itself. However, an optimal approach is to make sure there are separate components for the historical and real-time market data inputs, data storage, data access API, backtester, strategy parameters, portfolio construction, risk management and automated execution systems. For instance, if the data store being used is currently underperforming, even at significant levels of optimisation, it can be swapped out with minimal rewrites to the data ingestion or data access API. As far the as the backtester and subsequent components are concerned, there is no difference. Another benefit of separated components is that it allows a variety of programming languages to be used in the overall system. There is no need to be restricted to a single language if the communication method of the components is language independent. This will be the case if they are communicating via TCPIP, ZeroMQ or some other language-independent protocol. As a concrete example, consider the case of a backtesting system being written in C for number crunching performance, while the portfolio manager and execution systems are written in Python using SciPy and IBPy . Performance is a significant consideration for most trading strategies. For higher frequency strategies it is the most important factor. Performance covers a wide range of issues, such as algorithmic execution speed, network latency, bandwidth, data IO, concurrencyparallelism and scaling. Each of these areas are individually covered by large textbooks, so this article will only scratch the surface of each topic. Architecture and language choice will now be discussed in terms of their effects on performance. The prevailing wisdom as stated by Donald Knuth. one of the fathers of Computer Science, is that premature optimisation is the root of all evil. This is almost always the case - except when building a high frequency trading algorithm For those who are interested in lower frequency strategies, a common approach is to build a system in the simplest way possible and only optimise as bottlenecks begin to appear. Profiling tools are used to determine where bottlenecks arise. Profiles can be made for all of the factors listed above, either in a MS Windows or Linux environment. There are many operating system and language tools available to do so, as well as third party utilities. Language choice will now be discussed in the context of performance. C, Java, Python, R and MatLab all contain high-performance libraries (either as part of their standard or externally) for basic data structure and algorithmic work. C ships with the Standard Template Library, while Python contains NumPySciPy. Common mathematical tasks are to be found in these libraries and it is rarely beneficial to write a new implementation. One exception is if highly customised hardware architecture is required and an algorithm is making extensive use of proprietary extensions (such as custom caches). However, often reinvention of the wheel wastes time that could be better spent developing and optimising other parts of the trading infrastructure. Development time is extremely precious especially in the context of sole developers. Latency is often an issue of the execution system as the research tools are usually situated on the same machine. For the former, latency can occur at multiple points along the execution path. Databases must be consulted (disknetwork latency), signals must be generated (operating syste, kernal messaging latency), trade signals sent (NIC latency) and orders processed (exchange systems internal latency). For higher frequency operations it is necessary to become intimately familiar with kernal optimisation as well as optimisation of network transmission. This is a deep area and is significantly beyond the scope of the article but if an UHFT algorithm is desired then be aware of the depth of knowledge required Caching is very useful in the toolkit of a quantitative trading developer. Caching refers to the concept of storing frequently accessed data in a manner which allows higher-performance access, at the expense of potential staleness of the data. A common use case occurs in web development when taking data from a disk-backed relational database and putting it into memory. Any subsequent requests for the data do not have to hit the database and so performance gains can be significant. For trading situations caching can be extremely beneficial. For instance, the current state of a strategy portfolio can be stored in a cache until it is rebalanced, such that the list doesnt need to be regenerated upon each loop of the trading algorithm. Such regeneration is likely to be a high CPU or disk IO operation. However, caching is not without its own issues. Regeneration of cache data all at once, due to the volatilie nature of cache storage, can place significant demand on infrastructure. Another issue is dog-piling. where multiple generations of a new cache copy are carried out under extremely high load, which leads to cascade failure. Dynamic memory allocation is an expensive operation in software execution. Thus it is imperative for higher performance trading applications to be well-aware how memory is being allocated and deallocated during program flow. Newer language standards such as Java, C and Python all perform automatic garbage collection. which refers to deallocation of dynamically allocated memory when objects go out of scope . Garbage collection is extremely useful during development as it reduces errors and aids readability. However, it is often sub-optimal for certain high frequency trading strategies. Custom garbage collection is often desired for these cases. In Java, for instance, by tuning the garbage collector and heap configuration, it is possible to obtain high performance for HFT strategies. C doesnt provide a native garbage collector and so it is necessary to handle all memory allocationdeallocation as part of an objects implementation. While potentially error prone (potentially leading to dangling pointers) it is extremely useful to have fine-grained control of how objects appear on the heap for certain applications. When choosing a language make sure to study how the garbage collector works and whether it can be modified to optimise for a particular use case. Many operations in algorithmic trading systems are amenable to parallelisation. This refers to the concept of carrying out multiple programmatic operations at the same time, i. e in parallel. So-called embarassingly parallel algorithms include steps that can be computed fully independently of other steps. Certain statistical operations, such as Monte Carlo simulations, are a good example of embarassingly parallel algorithms as each random draw and subsequent path operation can be computed without knowledge of other paths. Other algorithms are only partially parallelisable. Fluid dynamics simulations are such an example, where the domain of computation can be subdivided, but ultimately these domains must communicate with each other and thus the operations are partially sequential. Parallelisable algorithms are subject to Amdahls Law. which provides a theoretical upper limit to the performance increase of a parallelised algorithm when subject to N separate processes (e. g. on a CPU core or thread ). Parallelisation has become increasingly important as a means of optimisation since processor clock-speeds have stagnated, as newer processors contain many cores with which to perform parallel calculations. The rise of consumer graphics hardware (predominently for video games) has lead to the development of Graphical Processing Units (GPUs), which contain hundreds of cores for highly concurrent operations. Such GPUs are now very affordable. High-level frameworks, such as Nvidias CUDA have lead to widespread adoption in academia and finance. Such GPU hardware is generally only suitable for the research aspect of quantitative finance, whereas other more specialised hardware (including Field-Programmable Gate Arrays - FPGAs) are used for (U)HFT. Nowadays, most modern langauges support a degree of concurrencymultithreading. Thus it is straightforward to optimise a backtester, since all calculations are generally independent of the others. Scaling in software engineering and operations refers to the ability of the system to handle consistently increasing loads in the form of greater requests, higher processor usage and more memory allocation. In algorithmic trading a strategy is able to scale if it can accept larger quantities of capital and still produce consistent returns. The trading technology stack scales if it can endure larger trade volumes and increased latency, without bottlenecking . While systems must be designed to scale, it is often hard to predict beforehand where a bottleneck will occur. Rigourous logging, testing, profiling and monitoring will aid greatly in allowing a system to scale. Languages themselves are often described as unscalable. This is usually the result of misinformation, rather than hard fact. It is the total technology stack that should be ascertained for scalability, not the language. Clearly certain languages have greater performance than others in particular use cases, but one language is never better than another in every sense. One means of managing scale is to separate concerns, as stated above. In order to further introduce the ability to handle spikes in the system (i. e. sudden volatility which triggers a raft of trades), it is useful to create a message queuing architecture. This simply means placing a message queue system between components so that orders are stacked up if a certain component is unable to process many requests. Rather than requests being lost they are simply kept in a stack until the message is handled. This is particularly useful for sending trades to an execution engine. If the engine is suffering under heavy latency then it will back up trades. A queue between the trade signal generator and the execution API will alleviate this issue at the expense of potential trade slippage. A well-respected open source message queue broker is RabbitMQ . Hardware and Operating Systems The hardware running your strategy can have a significant impact on the profitability of your algorithm. This is not an issue restricted to high frequency traders either. A poor choice in hardware and operating system can lead to a machine crash or reboot at the most inopportune moment. Thus it is necessary to consider where your application will reside. The choice is generally between a personal desktop machine, a remote server, a cloud provider or an exchange co-located server. Desktop machines are simple to install and administer, especially with newer user friendly operating systems such as Windows 78, Mac OSX and Ubuntu. Desktop systems do possess some significant drawbacks, however. The foremost is that the versions of operating systems designed for desktop machines are likely to require rebootspatching (and often at the worst of times). They also use up more computational resources by the virtue of requiring a graphical user interface (GUI). Utilising hardware in a home (or local office) environment can lead to internet connectivity and power uptime problems. The main benefit of a desktop system is that significant computational horsepower can be purchased for the fraction of the cost of a remote dedicated server (or cloud based system) of comparable speed. A dedicated server or cloud-based machine, while often more expensive than a desktop option, allows for more significant redundancy infrastructure, such as automated data backups, the ability to more straightforwardly ensure uptime and remote monitoring. They are harder to administer since they require the ability to use remote login capabilities of the operating system. In Windows this is generally via the GUI Remote Desktop Protocol (RDP). In Unix-based systems the command-line Secure SHell (SSH) is used. Unix-based server infrastructure is almost always command-line based which immediately renders GUI-based programming tools (such as MatLab or Excel) to be unusable. A co-located server, as the phrase is used in the capital markets, is simply a dedicated server that resides within an exchange in order to reduce latency of the trading algorithm. This is absolutely necessary for certain high frequency trading strategies, which rely on low latency in order to generate alpha. The final aspect to hardware choice and the choice of programming language is platform-independence. Is there a need for the code to run across multiple different operating systems Is the code designed to be run on a particular type of processor architecture, such as the Intel x86x64 or will it be possible to execute on RISC processors such as those manufactured by ARM These issues will be highly dependent upon the frequency and type of strategy being implemented. Resilience and Testing One of the best ways to lose a lot of money on algorithmic trading is to create a system with no resiliency. This refers to the durability of the sytem when subject to rare events, such as brokerage bankruptcies, sudden excess volatility, region-wide downtime for a cloud server provider or the accidental deletion of an entire trading database. Years of profits can be eliminated within seconds with a poorly-designed architecture. It is absolutely essential to consider issues such as debuggng, testing, logging, backups, high-availability and monitoring as core components of your system. It is likely that in any reasonably complicated custom quantitative trading application at least 50 of development time will be spent on debugging, testing and maintenance. Nearly all programming languages either ship with an associated debugger or possess well-respected third-party alternatives. In essence, a debugger allows execution of a program with insertion of arbitrary break points in the code path, which temporarily halt execution in order to investigate the state of the system. The main benefit of debugging is that it is possible to investigate the behaviour of code prior to a known crash point . Debugging is an essential component in the toolbox for analysing programming errors. However, they are more widely used in compiled languages such as C or Java, as interpreted languages such as Python are often easier to debug due to fewer LOC and less verbose statements. Despite this tendency Python does ship with the pdb. which is a sophisticated debugging tool. The Microsoft Visual C IDE possesses extensive GUI debugging utilities, while for the command line Linux C programmer, the gdb debugger exists. Testing in software development refers to the process of applying known parameters and results to specific functions, methods and objects within a codebase, in order to simulate behaviour and evaluate multiple code-paths, helping to ensure that a system behaves as it should. A more recent paradigm is known as Test Driven Development (TDD), where test code is developed against a specified interface with no implementation. Prior to the completion of the actual codebase all tests will fail. As code is written to fill in the blanks, the tests will eventually all pass, at which point development should cease. TDD requires extensive upfront specification design as well as a healthy degree of discipline in order to carry out successfully. In C, Boost provides a unit testing framework. In Java, the JUnit library exists to fulfill the same purpose. Python also has the unittest module as part of the standard library. Many other languages possess unit testing frameworks and often there are multiple options. In a production environment, sophisticated logging is absolutely essential. Logging refers to the process of outputting messages, with various degrees of severity, regarding execution behaviour of a system to a flat file or database. Logs are a first line of attack when hunting for unexpected program runtime behaviour. Unfortunately the shortcomings of a logging system tend only to be discovered after the fact As with backups discussed below, a logging system should be given due consideration BEFORE a system is designed. Both Microsoft Windows and Linux come with extensive system logging capability and programming languages tend to ship with standard logging libraries that cover most use cases. It is often wise to centralise logging information in order to analyse it at a later date, since it can often lead to ideas about improving performance or error reduction, which will almost certainly have a positive impact on your trading returns. While logging of a system will provide information about what has transpired in the past, monitoring of an application will provide insight into what is happening right now. All aspects of the system should be considered for monitoring. System level metrics such as disk usage, available memory, network bandwidth and CPU usage provide basic load information. Trading metrics such as abnormal pricesvolume, sudden rapid drawdowns and account exposure for different sectorsmarkets should also be continuously monitored. Further, a threshold system should be instigated that provides notification when certain metrics are breached, elevating the notification method (email, SMS, automated phone call) depending upon the severity of the metric. System monitoring is often the domain of the system administrator or operations manager. However, as a sole trading developer, these metrics must be established as part of the larger design. Many solutions for monitoring exist: proprietary, hosted and open source, which allow extensive customisation of metrics for a particular use case. Backups and high availability should be prime concerns of a trading system. Consider the following two questions: 1) If an entire production database of market data and trading history was deleted (without backups) how would the research and execution algorithm be affected 2) If the trading system suffers an outage for an extended period (with open positions) how would account equity and ongoing profitability be affected The answers to both of these questions are often sobering It is imperative to put in place a system for backing up data and also for testing the restoration of such data. Many individuals do not test a restore strategy. If recovery from a crash has not been tested in a safe environment, what guarantees exist that restoration will be available at the worst possible moment Similarly, high availability needs to be baked in from the start. Redundant infrastructure (even at additional expense) must always be considered, as the cost of downtime is likely to far outweigh the ongoing maintenance cost of such systems. I wont delve too deeply into this topic as it is a large area, but make sure it is one of the first considerations given to your trading system. Choosing a Language Considerable detail has now been provided on the various factors that arise when developing a custom high-performance algorithmic trading system. The next stage is to discuss how programming languages are generally categorised. When choosing a language for a trading stack it is necessary to consider the type system. The languages which are of interest for algorithmic trading are either statically - or dynamically-typed. A statically-typed language performs checks of the types (e. g. integers, floats, custom classes etc) during the compilation process. Such languages include C and Java. A dynamically-typed language performs the majority of its type-checking at runtime. Such languages include Python, Perl and JavaScript. For a highly numerical system such as an algorithmic trading engine, type-checking at compile time can be extremely beneficial, as it can eliminate many bugs that would otherwise lead to numerical errors. However, type-checking doesnt catch everything, and this is where exception handling comes in due to the necessity of having to handle unexpected operations. Dynamic languages (i. e. those that are dynamically-typed) can often lead to run-time errors that would otherwise be caught with a compilation-time type-check. For this reason, the concept of TDD (see above) and unit testing arose which, when carried out correctly, often provides more safety than compile-time checking alone. Another benefit of statically-typed languages is that the compiler is able to make many optimisations that are otherwise unavailable to the dynamically - typed language, simply because the type (and thus memory requirements) are known at compile-time. In fact, part of the inefficiency of many dynamically-typed languages stems from the fact that certain objects must be type-inspected at run-time and this carries a performance hit. Libraries for dynamic languages, such as NumPySciPy alleviate this issue due to enforcing a type within arrays. Open Source or Proprietary One of the biggest choices available to an algorithmic trading developer is whether to use proprietary (commercial) or open source technologies. Ada kelebihan dan kekurangan kedua pendekatan tersebut. It is necessary to consider how well a language is supported, the activity of the community surrounding a language, ease of installation and maintenance, quality of the documentation and any licensingmaintenance costs. The Microsoft stack (including Visual C, Visual C) and MathWorks MatLab are two of the larger proprietary choices for developing custom algorithmic trading software. Both tools have had significant battle testing in the financial space, with the former making up the predominant software stack for investment banking trading infrastructure and the latter being heavily used for quantitative trading research within investment funds. Microsoft and MathWorks both provide extensive high quality documentation for their products. Further, the communities surrounding each tool are very large with active web forums for both. The software allows cohesive integration with multiple languages such as C, C and VB, as well as easy linkage to other Microsoft products such as the SQL Server database via LINQ. MatLab also has many pluginslibraries (some free, some commercial) for nearly any quantitative research domain. There are also drawbacks. With either piece of software the costs are not insignificant for a lone trader (although Microsoft does provide entry-level version of Visual Studio for free). Microsoft tools play well with each other, but integrate less well with external code. Visual Studio must also be executed on Microsoft Windows, which is arguably far less performant than an equivalent Linux server which is optimally tuned. MatLab also lacks a few key plugins such as a good wrapper around the Interactive Brokers API, one of the few brokers amenable to high-performance algorithmic trading. The main issue with proprietary products is the lack of availability of the source code. This means that if ultra performance is truly required, both of these tools will be far less attractive. Open source tools have been industry grade for sometime. Much of the alternative asset space makes extensive use of open-source Linux, MySQLPostgreSQL, Python, R, C and Java in high-performance production roles. However, they are far from restricted to this domain. Python and R, in particular, contain a wealth of extensive numerical libraries for performing nearly any type of data analysis imaginable, often at execution speeds comparable to compiled languages, with certain caveats. The main benefit of using interpreted languages is the speed of development time. Python and R require far fewer lines of code (LOC) to achieve similar functionality, principally due to the extensive libraries. Further, they often allow interactive console based development, rapidly reducing the iterative development process. Given that time as a developer is extremely valuable, and execution speed often less so (unless in the HFT space), it is worth giving extensive consideration to an open source technology stack. Python and R possess significant development communities and are extremely well supported, due to their popularity. Documentation is excellent and bugs (at least for core libraries) remain scarce. Open source tools often suffer from a lack of a dedicated commercial support contract and run optimally on systems with less-forgiving user interfaces. A typical Linux server (such as Ubuntu) will often be fully command-line oriented. In addition, Python and R can be slow for certain execution tasks. There are mechanisms for integrating with C in order to improve execution speeds, but it requires some experience in multi-language programming. While proprietary software is not immune from dependencyversioning issues it is far less common to have to deal with incorrect library versions in such environments. Open source operating systems such as Linux can be trickier to administer. I will venture my personal opinion here and state that I build all of my trading tools with open source technologies. In particular I use: Ubuntu, MySQL, Python, C and R. The maturity, community size, ability to dig deep if problems occur and lower total cost ownership (TCO) far outweigh the simplicity of proprietary GUIs and easier installations. Having said that, Microsoft Visual Studio (especially for C) is a fantastic Integrated Development Environment (IDE) which I would also highly recommend. The header of this section refers to the out of the box capabilities of the language - what libraries does it contain and how good are they This is where mature languages have an advantage over newer variants. C, Java and Python all now possess extensive libraries for network programming, HTTP, operating system interaction, GUIs, regular expressions (regex), iteration and basic algorithms. C is famed for its Standard Template Library (STL) which contains a wealth of high performance data structures and algorithms for free. Python is known for being able to communicate with nearly any other type of systemprotocol (especially the web), mostly through its own standard library. R has a wealth of statistical and econometric tools built in, while MatLab is extremely optimised for any numerical linear algebra code (which can be found in portfolio optimisation and derivatives pricing, for instance). Outside of the standard libraries, C makes use of the Boost library, which fills in the missing parts of the standard library. In fact, many parts of Boost made it into the TR1 standard and subsequently are available in the C11 spec, including native support for lambda expressions and concurrency. Python has the high performance NumPySciPyPandas data analysis library combination, which has gained widespread acceptance for algorithmic trading research. Further, high-performance plugins exist for access to the main relational databases, such as MySQL (MySQLC), JDBC (JavaMatLab), MySQLdb (MySQLPython) and psychopg2 (PostgreSQLPython). Python can even communicate with R via the RPy plugin An often overlooked aspect of a trading system while in the initial research and design stage is the connectivity to a broker API. Most APIs natively support C and Java, but some also support C and Python, either directly or with community-provided wrapper code to the C APIs. In particular, Interactive Brokers can be connected to via the IBPy plugin. If high-performance is required, brokerages will support the FIX protocol . As is now evident, the choice of programming language(s) for an algorithmic trading system is not straightforward and requires deep thought. The main considerations are performance, ease of development, resiliency and testing, separation of concerns, familiarity, maintenance, source code availability, licensing costs and maturity of libraries. The benefit of a separated architecture is that it allows languages to be plugged in for different aspects of a trading stack, as and when requirements change. A trading system is an evolving tool and it is likely that any language choices will evolve along with it. Mike is the founder of QuantStart and has been involved in the quantitative finance industry for the last five years, primarily as a quant developer and later as a quant trader consulting for hedge funds. 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Under no circumstances may you post commercial or promotional posts to any non-commercial venue like forums, articles and comments, etc. Please be respectful and do not spam the site with links to your site or products in ways not allowed. It is understood you may believe your products are something everyone should see, and indeed it may be true. However, if everyone was allowed to post commercial content the site would become unusable. The best way to promote is to become a respected and active member of the community and take advantage of the numerous promotional tools available. All Site Locations: You may not use any area of the site including your public social wall and profile to link to resources unrelated to forex simply for the purpose of SEO related link schemes and marketing. You will be quickly banned for this without warning. Social profile and wall: In general, you may update your own profile and social wall as you see fit, however it must be within reason and must be within the guidelines above referring to all site locations. Forum Signatures: Signatures must not be overly large or obnoxious. Commercial forums: commercial forums are forums specifically set aside for commercial-oriented posts, whether that be product promotions or news and analysis from other websites and companies. There are specific rules for this type of content, please read immediately below in the next section. Please note you will definitely be suspended andor banned for repeated abuses, and you may be immediately banned without warning depending on the nature of the offense. No spam in commercial forums Commercial forums are not an open spamming venue. Non-forex related content will be removed and the poster suspended or banned. Forex related commercial posts must adhere to certain criteria as well. They must be meaninful, legitimate, not scams, not redundant, etc. An example of an acceptable commercial post in the commercial forum is a post to let users know about a new forex brokerage promotion. An example of an unacceptable post in a commercial forum is a post advertising a scam product such as one making ridiculous andor untruthful claims. Do not create multiple topics just to increase your exposure. For instance, if you post a daily market analysis article, post everyday to the same topicthread. Anything posted to commercial forums must be well formatted. For instance, copying and pasting something from a Word document that ends up with very poor formatting will be removed. Similarly, posting something with bb tags like . that were meant for use in other forums but do not format correctly in the CBF forum, will be removed. Be Mature, exercise Respect, Tolerance, Equality Abusive, defamatory language is not tolerated. Illegal content: Obviously, no illegal content including infringment. Redundant Posts: Do not post the same question or post in multiple forums. Topic owners: Please consider and respect the wishes of the original topic creator of a forum. MT4 and CashBackForex We take a decidedly different stance regarding drawdown than how it is computed in MT4, insofar as we include open trade drawdown in determining maximal drawdown. Accounting for open trade drawdown is an essential step towards understanding the true drawdown picture. If a trade analysis system ignores it, it is creating a distortion of the actual drawdown. There are far too many systems and traders out there who have had massive open trade drawdowns, and, if they are lucky enough to close these positions in profit, they think they have had no drawdown at all. CBF Calendar is the first global economic calendar to employ its own proprietary http push technology to push data to the user browser immediately after data becomes available. One advantage of employing this technology is that CBF Calendar is able to deliver data faster and more efficiently. CBF Calendar was built with the latency and accuracy sensitive trader in mind so the data is right in front of you, fast and easy to digest. CBF calendar is always streaming so you dont need to refresh it. CBF Economic Impact Analyzer provides traders with a fast and reliable method to research the impact a given economic event had on the market for each historical instance of the event. This allows traders to make more informed trading decisions, such as when to close a position and stay out of the market due to the impending risk of large rate fluctuations, or conversely to get into the market at just the right time using surgical precision to capitalize on the inevitable fluctuations or spikes as they have been so labeled. CBF Economic Impact Charts offers a huge historical database of economic events along with historical pricing giving you research access rivaled only by expensive service subscriptions. You can quickly find the market event you wish to research by selecting the currency associated with the event and then selecting the event name. You may also change the currency pair to research how this event impacted different currency pairs. Each economic event may have occurred at or around the same time of other events and it may be important to consider these as well. Below the price chart you will find a list of all events that occurred at or about the time of the primary event you are researching. When you first register a CBF account, you should setup permissions on your social profile and related assets such as photos based on your desires and objectives. By default, your profile is set to public, meaning your profile, photos etc. are visible to CBF members and general internet users, however only CBF friends can send you private messages. You may edit any of your privacy settings like who can message you, and make it so your profile is private, shared only with CBF friends, or hidden from listings but still visible to anyone. You may also edit further detailed permissions on features such as your wall functions, photo albums, and who can send you messages. To edit your social privacy settings dropdown your My Account tab and select Settings. Spread Betting is currently free from Capital Gains Tax (CGT) and there is no stamp duty. It should be noted that tax treatment depends on your individual circumstances and may be subject to change in the future. Informasi di situs ini tidak ditujukan kepada penduduk Amerika Serikat dan tidak dimaksudkan untuk didistribusikan ke, atau digunakan oleh, setiap orang di negara atau wilayah dimana distribusi atau penggunaan semacam itu bertentangan dengan undang-undang atau peraturan setempat. Apple, the Apple logo, iPod, iPad, iPod touch, and iTunes are trademarks of Apple Inc, registered in the US and other countries. iPhone is a trademark of Apple Inc. App Store is a service mark of Apple Inc. Your capital is at risk. Spread Betting and CFDs are not suitable for all investors and you should ensure that you understand the risks involved and, if necessary, obtain independent financial advice to ensure that these products fit your investment objectives. Bellow is a list of the top Forex Social Trading Networks and Platforms FxPro Supertrader is the social trading network of the FxPro broker. Strategies there go though a strict review and performance test prior to being accepted. It is also backed up by the FxPro Agency Model, No Dealing Desk eliminating any conflicts of interest. Social trading is a new way for retail traders and investors to access the financial markets. It builds on the concept that the collective wisdom of thousands of traders is better than the wisdom of one. Dengan menghubungkan para pedagang dari seluruh dunia ke dalam jaringan dan berbagi pandangan dan perdagangan mereka secara real-time, investor dapat menggunakan informasi tersebut untuk membuat keputusan perdagangan sosial dan bukan fundamental atau teknis. As a trader you can either use this social information to make your own decisions and trades (i. e. self-trade) or decide to automatically follow and copy one or more traders (i. e. auto-trade). - See more at: socialtradinggurusthash. sRHiOqga. dpuf Social trading is a new way for retail traders and investors to access the financial markets. It builds on the concept that the collective wisdom of thousands of traders is better than the wisdom of one. Dengan menghubungkan para pedagang dari seluruh dunia ke dalam jaringan dan berbagi pandangan dan perdagangan mereka secara real-time, investor dapat menggunakan informasi tersebut untuk membuat keputusan perdagangan sosial dan bukan fundamental atau teknis. As a trader you can either use this social information to make your own decisions and trades (i. e. self-trade) or decide to automatically follow and copy one or more traders (i. e. auto-trade). - See more at: socialtradinggurusthash. sRHiOqga. dpuf Social trading is a new way for retail traders and investors to access the financial markets. It builds on the concept that the collective wisdom of thousands of traders is better than the wisdom of one. Dengan menghubungkan para pedagang dari seluruh dunia ke dalam jaringan dan berbagi pandangan dan perdagangan mereka secara real-time, investor dapat menggunakan informasi tersebut untuk membuat keputusan perdagangan sosial dan bukan fundamental atau teknis. As a trader you can either use this social information to make your own decisions and trades (i. e. self-trade) or decide to automatically follow and copy one or more traders (i. e. auto-trade). - See more at: socialtradinggurusthash.6SAAM80s. dpuf Social trading is a new way for retail traders and investors to access the financial markets. It builds on the concept that the collective wisdom of thousands of traders is better than the wisdom of one. Dengan menghubungkan para pedagang dari seluruh dunia ke dalam jaringan dan berbagi pandangan dan perdagangan mereka secara real-time, investor dapat menggunakan informasi tersebut untuk membuat keputusan perdagangan sosial dan bukan fundamental atau teknis. As a trader you can either use this social information to make your own decisions and trades (i. e. self-trade) or decide to automatically follow and copy one or more traders (i. e. auto-trade). - See more at: socialtradinggurusthash.6SAAM80s. dpuf Hi My name is Michael Halls-Moore and Im the guy behind QuantStart. I graduated with a MMath in Mathematics from the University of Warwick, gained a PhD from Imperial College London in Fluid Dynamics, and was working in a hedge fund as a quantitative trading developer for the last few years in Mayfair, London. I now spend time on research, development, backtesting and implementation of intraday algorithmic trading strategies . Im really just a guy who once wanted to get a job as a quantitative analyst in the City of London. As a young postgraduate student, I felt that becoming a quant was the next logical step to make use of my mathematical skills in an exciting, lucrative career. I started studying to be a quant in October 2006, but soon noticed that the information I needed to know was pretty dense and quite difficult to understand without a lot of work. I really needed organisation, diagrams and useful tips from others who knew better. Nothing of that sort really existed for quant job interview preparation on the Internet, so I figured Id do it myself. And thus, QuantStart was born. I organised my notes, read all of the mathematical finance and algorithmic trading textbooks, spoke to a bunch of people and placed all of that information online so I could easily access my notes from anywhere to help me understand. Back then I didnt realise I was helping to start something big. Since QuantStarts launch in March 2010, Ive become a quantitative developer for a London hedge fund and have had more than 500,000 unique visitors use it for help - and more prospective quants and traders keep visiting. Again, Id like to thank you for using QuantStart The greatest thing about running QuantStart is hearing your stories of quant and trading success. Im driven to make the website better by continuing to add more information and providing the world with the best algorithmic trading and quant finance information available, thanks to your kind words and praise. It is a great feeling each time to hear that someone has gained their dream quant job or has finally developed a profitable algorithmic trading model because of this site. Your comments, suggestions and thanks are very much appreciated. I believe that my work with QuantStart is my way of helping the younger generation become employed in these tough economic times were going through. If I can help tens of thousands gain a job in Wall Street or the City of London, and they manage to have a successful quantitative career, then I feel like Im making a big difference. If youre new, again Im here to help you, so if you have any questions or comments (or suggestions) about anything, please feel free to contact me anytime. The best way to reach me is to email mikequantstart. Best of luck to you all Cheers 30 DAY SPECIAL OFFER BACKED WITH 90 GUARANTEE If you are not satisfied with the trading results within the first 90 days of subscribing you can cancel your subscription and receive a pro-rated refund based on time within the system (max 90 days) and the proper refund will be delivered back to you upon cancellation of your AlgoTrades subscription. We provide you with a 100 satisfaction guarantee. After 12 months of using AlgoTrades if you have allocated the recommended of capital required by the system, do not alter any trades, and if your account has not produced a 10 or greater return on your money we will extend your subscription by three months free of charge to help you profit from our automated trading system. Give AlgoTrades a try and let us wow you with its trades to give you an investing experience unlike any other. Its hard to know whats for real and what isnt in the financial industry, and its normal to be apprehensive at first when you find an investing system that looks promising or too good to be true. The good news is that the AlgoTrades algorithmic trading system is TOS certified by a third party investment tracking firm. Pelajari lebih lanjut di sini. The truth is you are here because you are searching for a proven strategy that can make you more money, provide hands-free trading, and reduce stress related to your investing. Im proud to give you with the AlgoTrades System. All you have to do is link our system to your brokerage account and AlgoTrades will take over your trading and risk management for you to make more money in both rising and falling market conditions. Chris Vermeulen, AlgoTrades founder and system developer. Want To Speak Directly With Me Call Toll Free: 1 844-692-5468 Download free market data of stocks, indices, commodities and forex Where to download free market data from Finding stock data by country When looking for a stocks market data from a certain country you should find the countrys page about downloading market data first. Misalnya. You should visit USAs page to see how to download market data of stocks from the USA. Finding forex, index, commodity data Details about the free market data providers Above pages vary not only in the supported equities but in other services too. When looking for free data of a product you should take a look at the table of dataproviders per product or the country index where a free data provider is listed for each equity category. Yahoo Finance. Chartoasiss chart software shows the market price instead of the price adjusted for dividends (whichs is also listed in the downloaded data file). Yahoo Finance stores data of a very long period in some cases (eg. GE - since 1962). Download interval is the longest possible interval by default. Downloading data can be made easier by saving the links of products data download page to Favourites or Bookmarks. Yahoo Finance UK. Its properties are similar to the global Yahoo Finance page. Google Finance. Most data is meant without dividend correction but in most cases data contain correction for splits. Downloading data can be made easier by saving the links of products data download page to Favourites or Bookmarks. RTS. There is no opportunity to save any link to Favourites or Bookmarks. Take care of missing data in the data files. Prices currency may vary. Stooq. Downloading data can be made easier by saving the links of products data download page to Favourites or Bookmarks. In some cases there is an opportunity to download prices with split or dividend correction. Portfolio. hu. It contains the most up to date market data for Hungarian equities. Besides stocks you can download price data of Hungarian bonds, certificates and investment funds. There is no opportunity to save any link to Favourites or Bookmarks. What do you have to take care of when downloading free market data Chartoasiss chart software can draw charts using the downloaded data only. For this reason users must know exactly what kind of data has been downloaded. It depends on many factors how up-to-date the data is: Time of market closure. In many cases market closure happens in the afternoon (according to local time of the market) but if market is continuous (eg. like in the case of commodities) data is summarized at midnight. Updating data. Some providers tell market data before market closure - in this case closing price is the actual price of the equity on the market. It may happen, that closing price is available before market closure but volume data is not accessible. It can also happen that data is updated after market closure with a delay. If market data you want to analyze is available at different providers (see the table of dataproviders per product and the table of alternative data providers for countries ) you have to experience which one suits you better. Daily weekly resolution: chart software awaits daily data. Weekly data must not be downloaded, it is calculated by the chart software itself. Amount of data. using too few data may cause inaccuracy of indicators and functions of market data. When selecting period to download take care of that some methods depend on the amount of data. Misalnya. a SMA with a parameter of 200 on weekly data need at least 200 weeks data to have a valid result at all so it requires more than 3 years data. Dianjurkan untuk mendownload data sebanyak mungkin. Split and dividend. dividends and stock splits can cause jumps in market price that have effects on the indicators and signals read from them. You should take care of if data contains correction for splits and dividends. (Also take care of that the usual percentage of dividends vary from year to year and from country to country. eg. some companies pay dividend 4 times a year (having less effect on the market price) but others pay dividend once a year). Trading volume and prices. there are equities traded on more markets and prices are not exactly the same on each market. (Eg. because there is some natural difference between markets or markets do not close in the same time). Volume may reflect the trading volume of one market only. Trading volume is given as number of traded shares in most cases but in some cases only the turnover (sum value of transactions) is given. For some intervals volume data may be completely missing. You always have to take care of what kind of market data is downloaded. Currencies, numbers. 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Bonds, tells you will learn how to trade filters and risk imqf, we allocations were increased volatility. Using bond matter how elegant your investment authority. Tools and also contains a glossary of knowledgeable contributors provide financial instruments money markets bond portfolios andrea berardi university aug, investment book concept gc: am et st as of risk management industry alongside hour customer satisfaction ratings. Tlo, tlh, fixed income cash markets what is binary options signal indicators binary option methods kites trading santander share price on spanish stock exchange double binary option no risk 10 rules of technical trading By Matthew Bradbard January 13, 2014 There are two major types of analysis normally used to forecast the performance of commodity futures: fundamental and technical. Fundamental analysis examines the supply and demand factors that influence price, while technical analysis is the study of price and price behavior. The world of technical analysis is complex, but with a working knowledge can be applied to virtually any market. There are literally hundreds of different patterns and indicators that technical analysts look at, but you should find early on what works for you and what doesnt. Here we set out to introduce you to 10 important rules of technical trading as first described by technical trading legend John J. Murphy. Anyone who has ever looked into technical analysis will have heard of Murphy a technical analyst with more than 35 years of market experience. His book, ldquoTechnical Analysis of the Financial Markets,rdquo should be part of every traders library. Murphy wrote ldquoThe 10 Important Rules of Technical Trading,rdquo and these principles remain relevant today. We can learn from and build on them. Adding to their appeal, Murphys rules are designed to explain the concept of technical trading to the beginner and to streamline the trading methodology of the more experienced market participant. They are meant to equip traders with a meaningful framework on which to pin their own technical analysis, and assist with answering some of the following questions: Which way is the market moving How far up or down will it go And when will it go the other way These are the basic concerns of the technical analyst. Even if you have read Murphys rules before, it is always a good idea to re-visit them. Below are Murphys 10 Most Important Rules of Technical Trading, followed by my commentary (in italics) on specific aspects of the rules: 1. Map the Trends Study long-term charts. Begin a chart analysis with monthly and weekly charts spanning several years. A larger scale ldquomap of the marketrdquo provides more visibility and a better long-term perspective on a market. Once the long-term has been established, then consult daily and intra-day charts. A short-term market view alone can often be deceptive. Even if you only trade the very short term, you will do better if youre trading in the same direction as the intermediate and longer term trends. Matt Bradbard: When establishing a position trade, look at the monthly, weekly, and then daily chart in that order. My best trades have said the same thing on all three time frames buy or sell. How much money (trading capital) do you need to trade Not everyone is going to have the same amount of money to start with. The amount of money you have the size of your trading capital will determine the position size that you are able to trade with. The position size is essentially the amount of money you put into the market in other words the amount that you trade. The larger the position size, the more money you will make if the trade wins. However, this also means you can lose more money. This is why using the correct position size is so important, because you can keep within the correct limits of money management and protect your capital from losing trades. So how do you determine how much you should risk As you know, you should never risk more than 1-2 of your trading account on any single trade, which is why your trading capital will determine how much money you can trade with. However, the size of your stop loss will also determine the size of your position, because whatever your trading capital is, the larger the stop loss, the more you will have to reduce your position size to make sure that you keep within the correct limits of money management. The different types of position sizes When trading forex. there are three different types of position sizes that are usually available to you: Each one requires a different amount to trade, depending on your stop loss. We will first explain the difference between them using an example of a trade with a fixed 20 pip stop loss. In forex, a standard trading contract equates to 100,000 units of the base currency. This is known as a standard lot. This means that one standard lot has a value of roughly 10 per pip (depending on the currency pair you are trading), so if the market moves 1 pip in your favour, you make 10 if the trade moves against you, then you will lose 10 per pip. If you open a trade and the market moves against you by 10 pips. this equates to 100 . A standard lot equates to 100,000 units of currency. This means that a standard lot has a value of roughly 10 per pip . In order for a trader to be able to trade a standard lot, you would need a large enough account to withstand a losing trade at 10 per pip. If you open a trade that has a 20 pip stop loss this means that a losing trade on a standard lot is 200. In this case, you must have an account of at least 10,000 2 of 10,000 is 200 . Some people do not have a trading capital of 10,000 and so brokers are able to offer a different position size for traders with less capital to start with. They do this by subdividing the standard lot contract into ten this is known as a mini lot. A mini lot equates to 10,000 units of the base currency. This means that a mini lot has a value of roughly 1 per pip . A mini lot is equal to 10,000 units of currency. This means that instead of each trade having a value of 10 per pip, each trade will now have a value of 1 per pip and you can start with less than 10,000. If you open a trade with a 20 pip stop loss this means that a losing trade is 20. In this case, you could trade quite comfortably with an account of 2,000 2 of 2,000 is 40. Some people, however, do not have or do not want to start with a trading capital of 2,000. Brokers have therefore introduced the micro lot that divides the mini lots further by ten. A micro lot equates to 1,000 units of the base currency. This means that a micro lot has a value of roughly 0.10 per pip . This means that each contract traded is 1,000 units of currency and gives each pip the value of 0.10. A trade with a 20 pip stop loss will result in a 2 loss. In this case, someone can start trading with as little as 500 or even 150 2 of 150 is 3 . Determine the maximum position size you want trade with depending on your account Of course, not every trade is going to have a stop loss of 20 pips and so it is important for you to determine the position size for each trade. In order to do so, you can apply the following formula that will tell you how much you can trade depending on the size of your trading account and the size of the stop loss: Position Size in Lots (Account Size X the risk per trade) (Stop Loss in Pips X Loss per Pip per Lot) Lets say that you have a 5,000 trading account and you have a 15 pip stop loss. You only want to risk 2 of your account. Assuming that you are trading with US dollars. where each standard lot traded means that a pip movement is 10, the position size is calculated as follows: Position size (5,000 x 2) (15 x 10) 0.66 You always round the result down. This means that you can trade 0.6 lots, or 6 mini lots for this trade. So, in order to trade comfortably with 6 mini lots, you need an account size of 5,000 to stay within a 2 limit risk. Be careful when using the formula to make sure that the currency of the numerator and denominator are the same if not, convert one into the other at the current market price . So far, you have learned that. the amount you can trade with depends on the amount of trading capital you have and the size of the stop loss on the trade. the different position sizes in the forex market are a standard lot, where each pip moment is worth 10, a mini lot, where each pip movement is worth 1 and a micro lot, where each pip movement is worth 0.1. in order to calculate the exact position size you can use the formula: Position Size in Lots (Account Size X the risk per trade) (Stop Loss in Pips X Loss per Pip per Lot). Note: this item is not currently being offered for sale. Day Trading System 8212 Bestselling Classic Trading DVD Shows REAL Monitors In Action from the Dot-com Days Originally recorded nearly a decade ago, this rough, guerrilla-style home video shows how day trading worked back in the early 2000s. for updated strategies, be sure to see our online course (with newly-recorded video) and my and Steve Nisons 14-DVD Stock Trading Success System instead. This historical video is being made available in newer DVD format (originally it had been VHS format), as a service to those who want to see how trading worked many years ago. NOTE: IN CURRENT MARKETS (2013) WE USE TAPE READING, NOT SOME OF THE THE LEVEL 2 BASED TRADING TECHNIQUES INCLUDED IN THIS VIDEO. WHY BECAUSE AFTER DECIMALIZATION AND HIGH-FREQUENCY TRADING (2001) STARTED, MANY OF THE LEVEL-2 BASED TRADING TACTICS TAUGHT IN THIS VIDEO DO NOT WORK AS WELL AS THEY USED TO. LEARN TAPE READING AS THE PREFERRED ENTRY EXIT TRADE MANAGEMENT TACTIC INSTEAD. IF YOU WANT TO SEE HOW LEVEL 2 AND OTHER HISTORICAL METHODS USED TO WORK, THOUGH, THIS IS A GOOD RESOURCE FOR THAT TRADING STYLE. Fidelity Review 2015: Commissions, Fees, and Minimum Deposit Fidelity New Account Setup Fidelitys account opening process is very well designed and quick - it took only ten minutes to open a regular brokerage (non-IRA) account. The application includes designating a beneficiary, setting up document delivery options (we always choose email) and ACH information to transfer money fromto a bank. After the account is opened, transferring money from your bank is a simple matter of going to Accounts Trade - Transfer MoneyShares in the main menu of the homepage. The main downside is that the ACH deposits you make with Fidelity will generally not be available for trading (or withdrawal) for up to 4-6 business days, which is much longer than with many other brokers. Fidelity Mutual Funds Review Fidelity Investments is one of the largest mutual fund companies in the world, with 430 mutual funds under management, that target dozens of asset classes: index funds, U. S. stocks and international stocks funds, asset allocation funds, sector funds and others. In total, the firm offers over 10,000 mutual funds for clients to invest in. Fidelitys own mutual funds and funds in NTF program are commission free. All other mutual funds can be purchased for 49.95 or 75. Selling mutual funds is free unless client sells within 60 days of purchasing - then Short Term Trading Fee of 49.95 applies (does not apply to Fidelity family funds). There is a Low Balance Fee of 12 per year for each noncore Fidelity fund with balance under 2,000. Fidelity WebsiteTrading PlatformTools Review Fidelity website is now much better designed, less cluttered, and more intuitive than it used to be. But some parts of it are still using old design and parts of it are missing navigation menu. Fidelity cant even list all the fees on the website - instead provides a 6-page PDF document with all the charges. The firm has many of the standard tools that other online brokers offer such as stock screeners and portfolio builders. One of the best things about online trading with Fidelity is the ability to actually trade stocks in 25 countries via various stock exchanges and 16 currencies around the globe with the flexibility to settle in either U. S. dollars or the local currency. Fidelity research tools (such as stock screening, historical trading strategies, daily market commentary, and individual stock research) are impressive. There are research reports from 12 different independent firms covering over 4,500 companies, as well as 36 tax, savings, and retirement calculators. The company also offers two advanced trading platforms: first is Active Trader Pro that requires at least 36 trades in a rolling 12-month period, and the second is Wealth-Lab Pro that requires at least 120 trades during the same period. Both platforms are excellent but most competitors provide their advanced platforms with no conditions to all customers. Fidelity offers applications for iPhone, iPad, Android, and Windows Phone. The interface is intuitive and user friendly. Users can transfer funds between Fidelity and non-Fidelity accounts, make a mobile check deposit, place trades, pay bills, and contact customer service. Fidelity Investments Review: Company Pros No surcharges on penny stocks, large orders, or after-hours trading Great investment research tools with large selection of independent research from 12 firms Huge selection of commission-free mutual funds Free DRIPs (dividend reinvestment plans) No account maintenance or inactivity fees Great customer service Rich selection of investment products Good banking services (checking, savings accounts, and credit cards) 84 commission-free ETFs Fidelity Investments Review: Company Cons Very high commissions - 49.95 (75 for some funds) - to purchase non-Fidelity mutual funds 12 annual Low Balance Fee for each noncore Fidelity mutual fund if balance is under 2,000 High margin rates Foreign currency wires cost up to 3 of principal Advanced trading platform, Active Trader Pro, is available only to clients making 36 trades per year High 2,500 minimum to open an account Fidelity Review Summary Fidelity Investments is one of the largest brokerage companies in the U. S. and as such, provides a wide array of services to their investors. Fidelity offers advice in wealth management, investment guidance, charitable giving, and provides lots of educational content for novice and professional investors alike. The firm is offering over 10,000 mutual funds, life insurance, college savings plans and more. The Fidelitys commission for stocks and ETFs is 7.95 per trade, which is just slightly above the average in the industry. Options trading costs 7.95 plus 0.75 per contract. The company does not charge account maintenance (except 25 year for SIMPLE IRA) or inactivity fees, and does not add surcharges on penny stocks, large orders or extended hours trading. But the minimum initial deposit for most accounts is somewhat high 2,500. Fidelitys mutual fund commission varies from 0 for Fidelity and NTF mutual funds to 75 per transaction for some non-Fidelity funds. Keep in mind that there is a 50 short-term redemption fee for mutual funds sold less than 60 days after being purchased. There is also a 12 annual Low Balance Fee for any non-core Fidelity fund with balance under 2,000. Fidelitys website needs more work, but their independent investment research selection from third parties is among the best in the industry. The firm created powerful, advanced trading platforms but they are only available to clients making at least 36 trades per year. In contrast, the best trading platform on the market, TD Ameritrades ThinkorSwim. is available to all TD Ameritrade clients. We recommend opening an account with Fidelity to someone who wants to invest in Fidelity ETFs and mutual funds for the long run, provided there is enough money to avoid the Low Balance Fee. Open Fidelity Investments Account Fidelity Reviewed by Brokerage-Review on Sep 24, 2015. Reviewed by Editor and 40 Fidelity customers. Rating: 4 The statistical arbitrage trading strategy is an approach to equity trading that uses data mining systems and automated trading, and attempts to make a profit based on a concept similar to pairs trade strategy. Stocks are known to shift up and down over time and so a pairs trade strategy tries to take advantage of these shifts by predicting the next ones based on what has already happened recently. Two stocks that are in the same market might be considered a pair, because you might assume that they will do similarly over a long period of time, very generally speaking. A trader might notice a divergence between two stocks in a pair, for example, stock A goes down and stock B goes up. Using a pairs trade strategy, that trader might purchase some of stock A and short some of stock B with the assumption that their prices will eventually shift towards each other again. The strategy assumes that a stock that has recently gone down is more likely to go back up, and a stock that has recently gone up is more likely to go down soon. Statistical Arbitrage basically takes that concept and applies it to a portfolio of a hundred stocks or more. The portfolio also applies diversification by taking stocks from different industries and regions to eliminate beta exposure and other risks. The construction of this portfolio is automatic and has two steps to its process. The first step is scoring, where each stock in the portfolio is assigned a numeric ranking of desirability based on how well or poorly it has done recently. Continuing out of the pairs trade concept, stocks that have done well recently are given a number that represents low desirability and stocks that have done poorly recently are ranked as being more desirable. This concept is also known as mean reversion, which is that prices will generally always shift back to their historical average. The second step of portfolio construction is risk reduction, where the desired amounts of each stock are computed based on optimizing the risk of the portfolio. There are other types of statistical arbitrage that incorporate different concepts such as leadlag, psychological barriers, corporate movement, and also momentum. One of the risks involved with taking up a statistical arbitrage strategy is the availability of trading time and liquidity. For example, lets say you have an automatic system that accepts 10 quarters at a time and flips the quarters for you, and every time a quarter lands on heads the machine gives you 1. You decided to use this machine because it is known to flip quarters to heads more often than tails. Therefore, you know that overtime that the machine will generate a profit for you. What happens if you run out quarters before that happens That is one of the main basic risks of statistical arbitrage, which is that you could have losses over the short term. Having more available funds means having higher chances of profit. . 250 , . Standard USD, 15 2013. - 250 : I . . . II. . -. AKU AKU AKU . . . 250 . ,. IV. 250 BUY SELL , X 3 InstaForex-, X . . ,,. instaforexuainstaforexbonuses. php . Professional forex strategy profit forex trading strategy, The bladerunner forex strategy is a price action trading so it using tools like candlesticks, pivot points, round numbers and support and resistance levels when. Advanced strategy 10 (trend line trading strategy, A truly great work has been done by myronn, the author of the current trend line trading strategy. support-resistance trading, trend line trading, checking higher. Swing trading forex swing trading strategies swing, Recent comments. mangi madang on london session breakout forex trading strategy dan on london session breakout forex trading strategy doug on how to see and trade. Forex pin bar trading swing-trading-strategies. The pin bar trading strategy great forex trading strategy swing trading. . charts . Trading online swing forex strategy combined. Forex swing analysis based candlestick basic, fibonacci retracement, price action setups trendlines forex strategy metatrader 4 top rated forex. Forex strategies guide day swing traders, Download forex strategies guide ebook. 300 pages forex basics 20 forex strategies profiting 24 hours day forex market.. What is a moving average. A moving average is the average value of price action da ta compiled over a specific period of time. Biasanya rata-rata bergerak masuk dalam bentuk indikator yang digunakan untuk mengetahui tren aset. Dengan kata lain, moving averages dapat digunakan untuk mengecek aksi harga pasangan mata uang akan bergerak naik atau turun. Jika Anda memeriksa platform MT4 di Forex4you. Akan terlihat jelas bahwa rata-rata bergerak diklasifikasikan di antara indikator TREND. Moving averages memungkinkan pedagang memiliki fleksibilitas karena memungkinkan untuk menggunakannya untuk memilih titik data dan periode waktu untuk membangunnya. Misalnya, Anda dapat memilih untuk menggunakan titik terbuka, tinggi, rendah, dekat, atau titik tengah rentang perdagangan dan kemudian mempelajari rata-rata pergerakan selama periode waktu tertentu, mulai dari data kutu hingga data harga bulanan atau lebih lama. Ada beberapa jenis moving averages. Namun, jenis rata-rata bergerak yang paling umum yang tercantum pada platform forex ritel adalah rata-rata bergerak sederhana, eksponensial, tertimbang, dan lancar. Inilah yang akan kita fokuskan untuk tujuan artikel ini. Potret di atas menunjukkan bagaimana merencanakan tiga rata-rata bergerak pada grafik per jam untuk EURJPY. Tiga rata-rata bergerak adalah rata-rata pergerakan tertimbang 10 periode, rata-rata pergerakan eksponensial 10 periode, dan rata-rata pergerakan sederhana 10 periode. Rata-rata bergerak ini ditunjukkan dengan garis biru, merah dan berwarna emas masing-masing. Tiga rata-rata bergerak biasanya membawa bobot yang berbeda sesuai dengan data yang biasanya mereka gunakan. Ini akan berpengaruh pada berapa rata-rata pergerakan yang akan digunakan trader saat mempertimbangkan keputusan perdagangan. Tanpa membahas lebih lanjut tentang matematika di balik rata-rata bergerak ini, Rata-rata pergerakan eksponensial dan rata-rata pergerakan tertimbang cenderung memberi penekanan lebih pada data terbaru sementara rata-rata bergerak sederhana memberi penekanan yang sama pada data historis dan terkini. Untuk tujuan trading, kami telah menemukan bahwa rata-rata bergerak sederhana menghasilkan sinyal terbaik karena keseimbangannya dalam penggunaan data historis dan terkini. Banyak strategi yang telah kita bahas di sini didasarkan pada rata-rata bergerak sederhana. Contoh yang akan digunakan dalam artikel ini akan menempatkan fokus maksimum pada rata-rata pergerakan sederhana 10 periode. Sebagian besar sinyal perdagangan yang didasarkan pada moving average tidak didasarkan pada penggunaan satu periode waktu tertentu. Melainkan lebih masuk akal untuk menggabungkan dua atau bahkan tiga rata-rata bergerak dari periode waktu yang berbeda. Hal ini biasanya dilakukan untuk penyaringan dan konfirmasi. Dengan pemikiran ini, mari kita lihat beberapa strategi rata-rata bergerak yang menggunakan rata-rata moving average beberapa waktu. 1. Dual Moving Average Crossover System Perhatikan judul, dan penggunaan kata dual dan crossover. Ini memberi kita petunjuk tentang keseluruhan esensi sistem yang sedang dibahas. Saat membuat sistem perdagangan menggunakan rata-rata bergerak, penggunaan pendekatan crossover rata-rata bergerak ganda, seperti yang digambarkan dalam cuplikan di bawah, biasanya merupakan titik awal yang baik. Sistem ini memerlukan penggunaan dua moving averages, biasanya moving average yang lebih pendek dan panjang, dan kemudian menggunakan cross moving moving average moving average moving average, di atas atau di bawah moving average longerslower untuk mengkonfirmasi arah tren. Dalam penggambaran ini, kami menggunakan rata-rata pergerakan sederhana 5 periode dan rata-rata pergerakan sederhana 10 periode. Mereka digambarkan dengan garis biru tipis dan garis hitam tebal masing-masing. Dalam contoh ini, kita melihat bahwa rata-rata pergerakan 5 periode telah melewati rata-rata pergerakan 10 periode beberapa kali, namun ada saat-saat kunci ketika crossover disertai oleh tren rally dan tren turun (di awal dan akhir Kerangka waktu dalam grafik). Periode crossover ditunjukkan oleh panah merah ke sisi negatif dan panah hijau ke sisi positif. Tanda panah merah ini memberi tahu kita bahwa rata-rata pergerakan sederhana 5-periode melintasi di bawah rata-rata pergerakan sederhana 10-periode, dan panah hijau menunjukkan bahwa pergerakan sederhana 5 periode telah melewati rata-rata bergerak sederhana selama 10 periode ke sisi atas. Melihat grafik yang sama lagi tapi dengan beberapa penekanan pada area yang dilingkari, kita dapat melihat bahwa sinyal yang dihasilkan oleh sistem rata-rata pergerakan sederhana crossover sederhana dapat menciptakan beberapa masalah, yang dalam hal ini memberikan sinyal padahal sebenarnya pasar akan berakhir. Up ke mana-mana karena status terikat pada saat itu. Jadi sebelum Anda melihat crossover itu dan berpikir kepada diri sendiri bahwa ada peluang menghasilkan banyak uang, pikirkan lagi. Situasi bound-bound tidak hanya terjadi untuk merusak pesta, tapi Anda juga perlu menyadari bahwa rata-rata bergerak memiliki satu kelemahan utama: moving averages adalah indikator lagging. Mereka cenderung ketinggalan pasar, sehingga pada saat rata-rata bergerak menunjukkan sinyal, mungkin sudah terlambat untuk masuk, dan Anda kemudian dapat menemukan diri Anda terkunci di pasar sideways atau pasar dimana tidak ada tren, yang ditunjukkan Di lingkaran hitam. Untuk mencegah kejadian semacam itu merusak bisnis Anda saat menggunakan sistem crossover rata-rata bergerak sederhana, ada beberapa pendekatan yang dapat Anda gunakan. Ini adalah sebagai berikut: Pertama, pilih pasangan mata uang yang Anda minati dalam perdagangan, serta kerangka waktu yang ingin Anda pelajari. Ini bisa misalnya, EURUSD pada daily chart. Yang kedua adalah memilih jangka waktu tanpa bias tren. Hal ini dilakukan dengan memastikan bahwa jangka waktu yang akan diuji mengandung bagian trending dan bagian non-trending. Menghilangkan bias tren membantu Anda mendapatkan hasil yang akurat dalam pengujian Anda. Lakukan optimasi untuk mengidentifikasi parameter rata-rata bergerak mana yang terbaik untuk digunakan. Setelah Anda menyelesaikan langkah-langkah ini, periksa periode waktu yang sama sekali berbeda untuk melihat apakah hasil yang Anda dapatkan dapat direplikasi. Anda dapat memilih untuk memilih periode waktu dari beberapa tahun yang lalu untuk memastikan bahwa hasil yang Anda dapatkan adalah sesuatu yang hijau dan dapat digunakan dengan percaya diri dalam beberapa bulan atau bahkan bertahun-tahun. Langkah ini sangat penting. Dalam sains, tidak biasa melakukan studi Double-Blind. Inilah langkah yang ingin ditiru. Di forex bisa menyebutnya Out-of-Sample Data testing. Inilah satu-satunya cara untuk menentukan apakah hasil pengoptimalan Anda bisa bertahan dalam ujian waktu sebagai sistem perdagangan mekanis yang layak. Anda tidak ingin mendapatkan hasil yang hanya akan bertahan beberapa minggu dan kemudian menjadi sia-sia selamanya. 2. Sistem Saluran Rata-rata Bergerak Rata-rata Sistem crossover rata-rata bergerak ganda memiliki sejumlah kelemahan, yang merupakan kecenderungan sistem untuk menderita whipsaws. Itulah sebabnya ada kebutuhan untuk datang dengan sebuah sistem untuk melawan hal ini. Salah satu cara untuk mengatasi whipsaws atau sinyal palsu yang dihasilkan dengan sistem crossover rata-rata bergerak ganda adalah dengan menerapkan sistem saluran harga rata-rata bergerak. Ini akan melibatkan penggunaan indikator rata-rata bergerak yang ditetapkan dengan periodisitas 20, dan secara terpisah diterapkan pada harga yang tinggi dan juga harga yang rendah. Rata-rata pergerakan yang digunakan dalam kasus ini adalah rata-rata pergerakan sederhana 20 periode dari harga tinggi, yang merupakan garis hitam yang lebih tinggi pada gambar di bawah, serta rata-rata pergerakan sederhana 20 periode dari harga rendah, yaitu Garis hitam lebih rendah Saluran harga rata-rata bergerak adalah ruang di antara dua garis hitam, sedangkan garis biru adalah rata-rata pergerakan sederhana 5 periode dari harga penutupan. Sinyal beli dan jual yang ditunjukkan pada foto ditandai dengan panah yang sesuai: panah hijau untuk sinyal beli dan panah merah untuk sinyal jual. Sinyal beli terlihat ketika rata-rata pergerakan sederhana 5 periode dari penutupan, atau garis biru, melintasi garis batas atas dari kanal harga rata-rata bergerak. Sinyal jual dihasilkan saat garis biru melintasi garis bawah, ditunjukkan oleh rata-rata pergerakan sederhana 20 periode dari rendah. Kita dapat melihat bahwa penggunaan saluran harga secara drastis akan mengurangi jumlah whipsaws yang terlihat dengan sistem crossover rata-rata bergerak ganda karena menciptakan filter yang lebih ketat untuk pemasangan yang harus dilalui pasangan mata uang tersebut. Dengan menciptakan rintangan yang lebih signifikan untuk tindakan harga yang harus diatasi sebelum menghasilkan sinyal perdagangan, semakin sedikit sinyal yang dihasilkan, namun sinyal ini jauh lebih akurat. Biasanya lebih baik memiliki lebih banyak sinyal akurat yang jumlahnya lebih sedikit, daripada memiliki begitu banyak sinyal yang dihasilkan, namun dengan persentase sinyal palsu yang besar yang akan menyebabkan kerugian pada perdagangan. Jika pilihan dibuat antara sistem crossover rata-rata bergerak ganda dan sistem saluran harga rata-rata bergerak, kemungkinannya akan menguntungkan sistem saluran harga karena keunggulannya dalam mendeteksi area dukungan dan hambatan, dari mana kemungkinan pembalikan tren diperkirakan terjadi. . Catatan hati-hati. Penting untuk diingat bahwa pasangan mata uang tidak berperilaku dengan cara yang sama. Oleh karena itu apa yang mungkin bekerja dengan baik untuk EURUSD mungkin tidak harus bekerja untuk EURJPY tersebut. Ini harus selalu diingat saat membuat sistem perdagangan mekanis menggunakan pengaturan rata-rata bergerak yang dijelaskan di atas. Tidak ada peluru ajaib, dan yang terbaik adalah menguji dan mengoptimalkan pengaturan untuk setiap pemasangan pada semua pasangan mata uang yang Anda minati dalam perdagangan. 3. Strategi Kombinasi: Moving Average Crossover Moving Average Price Channel Untuk mencapai hasil yang lebih unggul lagi, trader dapat memutuskan untuk menggunakan strategi kombinasi, di mana rata-rata crossover bergerak dan teknik harga rata-rata bergerak dikombinasikan. Strategi ini ditunjukkan dalam cuplikan di bawah ini: Sistem saluran harga ditunjukkan pada bagan satu jam AUDJPY. Tanda panah hijau mengidentifikasi kapan garis biru melintasi rata-rata bergerak 20 periode dari garis yang lebih tinggi, yang merupakan rata-rata pergerakan sederhana 20 periode dari harga tinggi. Panah merah menunjukkan tanda silang di bawah rata-rata pergerakan sederhana 20 periode harga rendah. Intan menunjukkan kapan rata-rata pergerakan 5 periode di bawah periode 10. Elemen penting dari strategi ini adalah menggabungkan yang terbaik dari dua sistem rata-rata bergerak yang telah diuraikan di atas menjadi satu. Tujuan utamanya adalah untuk mendapatkan yang terbaik dari dua dunia. Apa ini a) Untuk mendapatkan entri yang lebih sedikit namun lebih akurat dan menghilangkan sinyal perdagangan palsu dengan menggunakan sistem saluran harga rata-rata bergerak b) Mencapai jalan keluar yang cepat agar tidak melepaskan potongan besar keuntungan yang belum direalisasi kembali ke pasar, dengan menggunakan pergerakan ganda Sistem crossover rata-rata. Rata-rata Bergerak Paling Populer Dengan begitu banyak periode waktu yang dapat dipilih, pengaturan mana yang dianggap sebagai standar emas saat menggunakan rata-rata bergerak untuk trading forex Salah satu pengaturan rata-rata moving-moving dual crossover terpopuler yang digunakan di seluruh dunia adalah kombinasi dari periode 50 Rata-rata bergerak sederhana dari jarak dekat dan rata-rata bergerak sederhana 200 tahun dari penutupan. Pengaturan ini bekerja paling baik pada daily chart karena panjang periode waktu yang digunakan dalam mengatur moving averages. Potret di atas adalah bagan harian USDCAD, dan menampilkan contoh kapan rata-rata pergerakan 200 periode memberikan dukungan, sementara moving average 50-periode memberikan resistance (dilingkari pada garis biru). Namun, pengaturannya paling sesuai untuk persediaan dan kurang untuk mata uang. Karena trading forex adalah fokus kami di sini, kami akan menggunakan setting yang telah terbukti bekerja untuk mata uang, yaitu rata-rata pergerakan 13 dan 26 periode secara bersamaan, dan juga 100 SMA untuk digunakan sebagai filter supportresistance. Strategi di bawah ini menunjukkan sistem cross over seperti menggunakan rata-rata pergerakan sederhana 13 minggu dan rata-rata 26 minggu dari penutupan pada grafik mata uang, dengan dukungan dan hambatan untuk perdagangan yang diberikan oleh SMA 100. Bagaimana cara kerja ini Kita akan mencari entri yang panjang saat harga di atas SMA 100, mencari pantulan di SMA ini saat cross of 13SMA di atas 26SMA telah terjadi. MACD dengan kode warna dapat digunakan sebagai konfirmasi untuk perdagangan. Kita akan menggunakan kerangka waktu harian untuk perdagangan ini. Bagan harian menunjukkan aktivitas satu hari pada candlestick. Ini berarti trader harus memperhatikan manajemen risiko karena stop yang digunakan akan setara dengan kisaran intraday beberapa mata uang (sampai 100 pips atau lebih). Oleh karena itu, berarti pedagang yang menggunakan strategi ini harus sedikit lebih sabar karena perdagangan akan memakan waktu berhari-hari untuk bermain sepenuhnya. Setiap pasangan mata uang dapat digunakan untuk menukar strategi ini. Color-coded MACD Histogram 13-week simple moving average (13SMA) 26-week simple moving average (26SMA) 100-week simple moving average (100SMA) The trader should enter long on the asset if: When the 13SMA crosses above the 26SMA to the upside. Harga di atas 100 SMA, atau idealnya, memantul dari situ. MACD berkode warna berwarna biru. The Stop Loss for the long entry should be set to around 10 15 pips below the entry candlestick. Untuk mengambil keuntungan dari perdagangan ini, target keuntungan dapat ditemukan di bawah kondisi berikut: jika 13 SMA melakukan reverse cross dari atas 26SMA ke sisi negatifnya. Jeda harga di bawah 100 SMA 2X atau 3X stop loss. Look at this chart for the AUDJPY. Ini adalah bagan harian yang menggabungkan skenario untuk pengaturan pesanan yang panjang dan pendek. A short entry setup is seen there is a corresponding cross of the 13SMA over the 26SMA to the downside at the same time that the MACD histogram is red in color and the price is being resisted off the 100SMA. Pedagang kemudian bisa mengambil posisi short seperti yang ditunjukkan oleh area yang dilingkari merah, dan mengambil keuntungan sebagai berikut: ambil keuntungan di area dimana MACD berubah warna. Ambil keuntungan di area di mana aksi harga menyentuh level resistance, seperti yang ditandai oleh level terendah baru yang dilakukan oleh beberapa lilin. The long entry setup is shown by a cross of the 13SMA over the 26SMA to the upside, at the same time that the MACD histogram has turned blue, and price is bouncing off the 100SMA. Target keuntungan dapat ditetapkan pada saat yang sama dengan cross reverse dari 13SMA pada 26SMA terjadi, atau pada resistansi harga.
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