Thread downloadable trading books

Thread downloadable trading booksThread: Downloadable trading books

Downloadable trading books

Join Date Jul 2012 Location Asia Posts 279

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Forex demo before you dive in

Forex demo before you dive inForex: Demo Before You Dive In

Because forex (FX) is a decentralized market in which dealers disseminate their own price feeds through proprietary trading platforms, it is crucial to learn the features and idiosyncrasies of each type of trading software before committing real funds to an account. Fortunately, in retail, FX traders can test out each platform using demo dollars. Every reputable dealer in FX allows potential customers to download a free demo version of its software. This is critical if the customer is to become acquainted with the platform's layout and the means of order entry. Here we'll discuss the importance of demo trading and let you know what you should look out for when trying different platforms.

Placing Orders

As a trader, you should always try out dealer demos before trading live - it's the only way to become familiar with the different features of various platforms. For example, some platforms simply use pop-up tickets, while others actually mark the order location on the chart. Typically, to buy a currency pair. you simply click on the offer part of the quote (the ask ), and to sell, you click on the bid part. Some platforms allow you to choose market or limit orders after the quote window pops up, while others force you to make your selection beforehand.

It's a good idea to place at least 20 demo trades on each platform before trading actual money, just so you can master the specifics of order entry on each platform. A trader should never trade live unless he or she can confidently answer all of the following questions:

Backtesting option strategies

Backtesting option strategiesBacktesting Option Strategies

by John A. Sarkett

Looking for a quick way to backtest option strategies? You can use Excel to count occurrences of historical prices outside a price band you set to develop an expectation for future price movement. Heres how.

So much of the option traders life has been enhanced over just the past 10 or so years. We have radically lower commissions — $0.15 to $1.50 per contract, plus various ticket charge plans; we have tighter markets, with high-volume contracts just one penny apart; we have better software with risk curves, what-if scenarios, and charting, with brokers and vendors continue to enhance their offerings; and finally, we have easier access to futures and world markets for intermarket hedging.

Only one aspect of the option trading experience remains, more or less, as it was years ago — option strategy backtesting. If I put on this Spx or Spy or Rut or Etf or Xyz butterfly, condor, calendar, or double diagonal, what are my chances of success based on the recent past? That is the biggest question for every option trader.

For a back-of-the-envelope type calculation, some use deltas as shorthand for the probability of going in-the-money. For example, an option with a 10 delta will have roughly a 10% chance of doing so. Actual probability math is much more complex (see my December 1997 SC article ), but the answers usually come out close enough. Some software calculates “probability of expiration” or “probability of touching” (strike) for you. Thinkorswim does this, for example.

Figure 1: breaking out. Once a bull market run began here in the SPY, it exceeded the daily variance bands (that is, last price was constantly equal to or greater than 7.5% versus price 46 days earlier) nearly every day for two months; there was no regression for those waiting for a pullback — an important consideration for the option trader who must be alert and nimble, and typically does not have one or two years, like a long-term position trader, for a position to come back.

…Continued in the July issue of Technical Analysis of Stocks Commodities

Excerpted from an article originally published in the July 2011 issue of

Technical Analysis of Stocks Commodities magazine. All rights reserved.

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Fxcc metatrader4forex trading platform

Fxcc metatrader4forex trading platformFXCC MetaTrader 4 Forex Trading Platform

MetaTrader 4 is one of the most popular forex trading platforms in the world today. All the necessary trading tools and resources are on hand to allow traders to conduct forex research and technical analysis, enter and exit trades and even use third-party automated forex trading software (Expert Advisors or EA's). Not happy with commercially available EA's? MetaTrader uses its own programming language MQL4, which allows you to program your own automated trading robots.

MetaTrader 4 Broker software offers an impressive array of analytical tools. Nine timeframes are available for each financial instrument. These provide detailed analysis of quote dynamics. A built-in library of more than 50 indicators and tools streamline the analysis process, enabling traders to identify trends, define various market shapes, determine entry and exit points, print charts of any instruments and conduct their own on paper analysis.

MetaTrader 4 includes all the forex trading functions the modern forex trader needs. Market orders, pending and stop orders, trailing stops - all are right there at your fingertips with MT4.

This is the best forex trading platform in a variety of ways, including trading directly from charts. Embedded tick charts are an extremely useful way to determine accurate entry and exit points.

MetaTrader 4 includes trade alerts, a handy tool to help you track the most favourable trading environment and market conditions. With the FXCC MT4 trading arsenal at your disposal, all your energy can be channelled into implementing your forex trading strategies. secure in the knowledge that the suite of tools in MT4 are there to back you up.

As with any financial transactions, the security of the information being transmitted is of fundamental importance. The FXCC MetaTrader 4 Broker software transmits information to and from the markets on 128-bit encrypted connections. This is there to ensure the security of all your trades. In addition to this, FXCC also offers the possibility to use the extended security algorithms of Public Key Cryptography. Information secured in this fashion is virtually impossible to hack in any short amount of time.

MetaTrader 4 includes a whole range of easy-to-understand functions, so it only takes a few minutes to get comfortable with all the different options available to you as a trader. The platform has a built-in Help function so you can get answers to the most common questions directly from within the software itself, so you can concentrate on the important stuff - trading.

In any event, if the MT4 help function can't answer your question, the FXCC support operators can.

MetaQuotes Language4 (MQL4)

MetaTrader 4 trading platform comes with its own built-in language for programming trading strategies. MQL4 allows you to create your own EA (Expert Advisor) and automate your trading based on your own programmed strategy. Using MQL4 you can build-up your own library of custom indicators, scripts and function database. Given the popularity of the Forex MetaTrader 4 Broker platform, a considerable number of forums and online communities have sprung up, where users can interact and exchange forex tips and strategies for getting the most out of the MQL4 programming language and MetaTrader 4 in general.

Expert advisor is a mechanical trade system (MTS) linked up to certain plots. The advisors can not only inform you about a possibility to enter trades, but also make deals on the trade account automatically and direct them right to the trade server. Like most forex trading systems, the MetaTrader 4 Trading terminal supports testing strategies on historical data with graphical displays of trade entry and exit points.

Custom Indicators are the MetaTrader 4 take on technical indicators. Custom Indicators allow the creation of indicators in addition to those already integrated into the MetaTrader 4 terminal. Like the built-in indicators that come pre-loaded on MT4, custom indicators are aimed at forex technical analysis and are not able to open or close trades automatically.

Scripts are programs destined for single execution of some actions. Unlike Forex Expert Advisors, Scripts aren't run tick-wise and have no access to indicator functions.

Libraries are user function databases where frequently used blocks of MQL4 code are stored. When programming a particular strategy or EA in MQL4, users can draw from their code libraries and add these stored functions to their new trading robots.

MetaTrader 4 Mobile

The MT4 Mobile trading platform is your gateway to the financial markets, even when you are on the go, offering you an overview of all the trading features you enjoy using on the award-winning MetaTrader 4 desktop platform.

A wide array of charting tools is now at hand to support your trading activity - 30 of your favourite technical indicators, various time frames and real-time updated market prices. Now it is that easy to power on your trading plan through your iPhone/iPad/iPod Touch or Android.

Gain access to your FXCC trading account in 3 simple steps - download, install and login to the mobile trading platform using your FXCC account credentials.

On the MT4 mobile you can:

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Invest using a state-of-the-art platform.

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7winning strategies for trading forex by grace cheng pdf-free binary signals

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October2014

October2014October 2014

For this months Traders Tips, the focus is mainly Sylvain Vervoorts article from the September 2014 issue, “Exploring Charting Techniques: Creating A Trading Strategy, Part 3.” Here we present the October 2014 Traders Tips code with possible implementations in various software.

Code for NinjaTrader was already provided with Vervoorts article by the author. SC subscribers will find that code at the Subscriber Area of our website here. (Click on “SC Article Code” from the homepage.) Presented here is an overview of some possible implementations for other software as well.

Traders Tips code is provided to help the reader implement a selected technique from an article in this issue or another recent issue. The entries here are contributed by various software developers or programmers for software that is capable of customization.

TRADESTATION: OCTOBER 2014

In “Exploring Charting Techniques: Creating A Trading Strategy, Part 3,” which appeared last month in the September 2014 issue of STOCKS COMMODITIES, author Sylvain Vervoort describes a process for creating indicators and strategies. Through his article series, the author takes us step by step, building up his trading idea with additional criteria along the way.

We are providing EasyLanguage code for both upper (moving averages) and lower (trading signal) indicators, as well as a strategy based on the authors ideas (see Figure 1). As Vervoort points out, advanced chart types can be automated only when the limitations are fully understood.

FIGURE 1: TRADESTATION. Here is an example implementation of the strategy and indicators based on author Sylvain Vervoorts ideas applied to a chart of the SP 500 index using a one-point renko bar.

More on strategy backtesting and automation of TradeStations range, renko, and other advanced chart types can be found in the advance chart type entry within platform help (from the TradeStation platform help menu, select platform help ).

To download the EasyLanguage code for TradeStation, please visit our TradeStation and EasyLanguage support forum. The code from this article can be found here: tradestation/TASC-2014. The ELD filename is “_TASC_OCT2014_CREATINGSTRATEGY. ELD.”

For more information about EasyLanguage in general, please see tradestation/EL-FAQ .

This article is for informational purposes. No type of trading or investment recommendation, advice, or strategy is being made, given, or in any manner provided by TradeStation Securities or its affiliates.

—Doug McCrary

TradeStation Securities, Inc.

eSIGNAL: OCTOBER 2014

For this months Traders Tip, weve provided the formula SVEHaTypeCrossStrategy. efs based on the formula described in Sylvain Vervoorts article in the September 2014 issue of STOCKS COMMODITIES, “Exploring Charting Techniques: Creating a Trading Strategy, Part 3.”

The study contains formula parameters that may be configured through the edit chart window (right-click on the chart and select “edit chart”). A sample chart is shown in Figure 2.

FIGURE 2: eSIGNAL. Here is an example of the SVEHaTypeCrossStrategy. efs shown on a chart of SP 500 emini futures (ES).

To discuss this study or download a complete copy of the formula code, please visit the EFS Library Discussion Board forum under the forums link from the support menu at esignal or visit our EFS KnowledgeBase at esignal/support/kb/efs/. The eSignal formula script (EFS) is also available below for copying pasting or for downloading here .

—Eric Lippert

eSignal, an Interactive Data company

THINKORSWIM: OCTOBER 2014

In part 3 of his series on exploring charting techniques, which appeared in the September 2014 issue of STOCKS COMMODITIES (“Creating A Trading Strategy”), author Sylvain Vervoort takes an in-depth look at defining, testing, and using a trading strategy. At thinkorswim, we have used our proprietary scripting language thinkScript to build a strategy for detecting trends using this method.

We have made the loading process extremely easy: Simply click on the link tos. mx/Hp8IeR and choose Backtest in thinkorswim . then choose to rename your study to “SyVerPart3.” You can adjust the parameters of these within the edit studies window to fine-tune your variables. In the article, Vervoort bases his strategy on a renko chart type. In thinkorswim charts, renko bars can be found under Style → Range for aggregation type . Then you can adjust the range type to “renko” under the style menu as well. A sample chart is shown in Figure 3.

For a detailed description of the strategy itself, see Vervoorts article in the September 2014 issue of SC. Happy swimming!

—thinkorswim, A division of TD Ameritrade, Inc.

WEALTH-LAB: OCTOBER 2014

At first, it may seem that the idea presented in “Exploring Charting Techniques: Creating A Trading Strategy, Part 3” by Sylvain Vervoort, which appeared last month in the September 2014 issue of SC, is trivial. After all, what dont we know about the many variations of a moving average crossover? However, in his article, Vervoort takes the technique a step further, applying moving average crossovers on his modified renko chart with the added twist of heikin-ashi. The premise is to reduce the noise of a typical fixed-time-related chart and to produce fewer losing trades.

First, well build two moving averages: The fast is the simple moving average (SMA) of the renko-based typical price, and the slow is an SMA of heikin-ashi (HA) recalculated prices. For simplicity of our example strategy, we take a standard renko chart and use daily prices. Despite using the same period, the HA-based average always lags due to added smoothing. The rules of the strategy are:

When the eight-period “fast” average crosses above the “slower” counterpart of the same period, a long position is established.

When the eight-period “fast” average crosses below the “slower” average of the same period, the long position is closed.

In Figure 4, the green and red renko bricks are superimposed on the open/high/low/close (OHLC) chart.

FIGURE 4: WEALTH-LAB. This sample Wealth-Lab 6 chart illustrates application of the systems rules on a daily chart of AXP (American Express).

To execute the trading system were providing, Wealth-Lab users can copy paste the strategys C# code, or simply let Wealth-Lab do the job: in the open strategy dialog, simply click download to get the strategy code.

—Eugene, Wealth-Lab team

MS123, LLC

AMIBROKER: OCTOBER 2014

In “Exploring Charting Techniques: Creating A Trading Strategy, Part 3,” which appeared in the September 2014 issue of STOCKS COMMODITIES, author Sylvain Vervoort continued his article series presenting a trading system based on modified renko charts and moving averages.

We are providing a ready-to-use formula for AmiBroker. It is based on the formula that Vervoort presented in his September 2014 article with the addition of trading rules, a colorized background, and moving averages for display in AmiBroker (see Figure 5).

FIGURE 5: AMIBROKER. This modified renko chart of the SP 500 index displays moving average crossovers and sample trading system entry/exit points.

—Tomasz Janeczko, AmiBroker

NEUROSHELL TRADER: OCTOBER 2014

We have recreated the renko bar trading system described by Sylvain Vervoort in his September 2014 article “Exploring Charting Techniques: Creating A Trading Strategy, Part 3,” using NeuroShell Traders point-and-click indicator wizard without the need for programming.

We used the InterChart Tools Renko add-in to NeuroShell and the heikin-ashi close indicator from a previous Traders Tip to quickly set up the eight-period simple moving averages of the typical price and the heikin-ashi average closing price (see Vervoorts article in the September 2014 issue for more details of his technique).

To produce a chart similar to the one we show in Figure 6 of the SP 500 index, you can insert the indicators as follows:

Select “New indicator” from the Insert menu

Choose the averages category and select simple moving average, 8 periods

Substitute the Ict Renko HLC3 (0.10, 1, 1, 10, High, Low, Volume) for the default value to recreate the typical price indicator described in Vervoorts article

Create another average indicator, and this time, substitute the HeikinAshiClose of the corresponding Ict Renko Bars to generate the heikin-ashi average closing price indicator.

FIGURE 6: NEUROSHELL TRADER. This NeuroShell Trader chart displays the crossover of the eight-period SMA of the typical price and heikin-ashi average closing price. This strategy was created using the indicator wizard in NeuroShell Trader, so no programming is needed by the user.

The InterChart Tools Renko bars are virtual bars and perform their calculations using the same methods as traditional renko bars, but once a trading signal is generated by the renko bar, both the trade and fill are correctly displayed on the open of the next bar of the base chart.

The base chart is a 0.10 range bar of the SP 500 index. The value of 0.10 virtual tick size in the Ict Renko Bars corresponds to the size of the base charts range bar. The next two parameters represent the number of ticks used to calculate the up part of the renko bar, followed by the number of ticks used to compute the down part. The “10” represents a multiplier that is applied to the described renko bars up/down ratio to realize its final size. This enables the indicators to use a different number of ticks for the up and down side of the renko bars. Since any bars function is to absorb noise, and rising price jitter is often different from falling price jitter, our renko bars permit an asymmetrical definition to accommodate this.

In the trading system described by Vervoort in his article, the trading signals occur when the average of the typical price crosses above or below the average of the heikin-ashi close of the renko bars. Rather than using a visual system, you could use NeuroShell Traders point-and-click wizard to build the crossover trading rules and allow NeuroShell Traders optimizer to identify the optimal bar size and noise absorption for a given algorithm or equity.

Users of NeuroShell Trader can go to the STOCKS COMMODITIES section of the NeuroShell Trader free technical support website to download a copy of this or any previous Traders Tips.

—Marge Sherald, Ward Systems Group, Inc.

AIQ: OCTOBER 2014

The AIQ code for this month is based on Sylvain Vervoorts article in the September 2014 issue of STOCKS COMMODITIES, “Exploring Charting Techniques: Creating A Trading Strategy, Part 3.”

The code and EDS file can be downloaded from TradersEdgeSystems/traderstips. htm. and is also shown below.

I was not able to code the renko-based charts, so the two moving averages, the simple average of the typical price (typSMA), and the simple average of the heikin-ashi close (haSMA) are based on the closing values from a conventional chart, not the renko chart.

In Figure 7, I show a chart of Netflix (NFLX) with the typSMA and haSMA averages. The chart also shows a trade that was opened on 1/4/2013 and closed on 2/27/2013 for a 90% profit.

FIGURE 7: AIQ. Here is a sample chart of Netflix (NFLX) with the typSMA and haSMA averages plus a sample trade marked with white up and down arrows.

—Richard Denning

for AIQ Systems

TRADERSSTUDIO: OCTOBER 2014

The TradersStudio code I am providing for Sylvain Vervoorts September 2014 article in SC, “Exploring Charting Techniques: Creating A Trading Strategy, Part 3,” can be found at the following two websites:

The following code files are provided in the download:

Function HA_SMA: Returns the heikin-ashi simple moving average of the close based on the “haLen” input

Function TYP_SMA: Returns the typical-price simple moving average of the close based on the typLen input

Indicator plot TYP_HA_SMA: For plotting the two moving averages just described on a chart

System SVE_TYP_HA_SMA_SYS: The system code for trading crossovers on the two moving averages.

I was not able to code the renko-based charts, so the two moving averages, the simple average of the typical price (typSMA), and the simple average of the heikin-ashi close (haSMA) are based on the closing values from a conventional chart, not the renko chart.

In Figure 8, I show a chart of the SP 500 full-size futures contract (SP) using data from Pinnacle Data Corp. (pinnacledata ) with the typSMA and haSMA averages. In addition, this chart shows a sample trade that was opened on 10/14/2013 and closed on 11/11/2013 for a $19,875 profit before commission slippage.

FIGURE 8: TRADERSSTUDIO. Here is an example crossover of the typSMA and haSMA averages on a chart of the SP 500 full-size futures contract (SP). The trade shown here, which was opened on 10/14/2013 and closed on 11/11/2013, would have produced a profit of $19,875 before commission slippage.

—Richard Denning

for TradersStudio

NINJATRADER: OCTOBER 2014

The SveRenkoCross strategy, as introduced by Sylvain Vervoort in the September 2014 STOCKS COMMODITIES article “Exploring Charting Techniques: Creating A Trading Strategy, Part 3,” has been made available for download at ninjatrader/SC/October2014SC. zip .

Once you have it downloaded, from within the NinjaTrader Control Center window, select the menu File → Utilities → Import NinjaScript and select the downloaded file. This file is for NinjaTrader version 7 or greater.

You can review the strategy source code by selecting the menu Tools → Edit NinjaScript → Strategy from within the NinjaTrader Control Center window and selecting the “SveRenkoCross” file. A sample chart implementing the strategy is shown in Figure 9.

FIGURE 9: NINJATRADER. This screenshot shows the strategy applied to a 100-tick SveRenko SP 500 chart in NinjaTrader.

—Raymond Deux Cal Hueber

NinjaTrader, LLC

UPDATA: OCTOBER 2014

Our Traders Tip this month is one that was developed in-house by our Updata team and is for a trading system we named the small-range bars system .

This system is a daily breakout system that identifies when the previous days absolute daily range (high to low) and normalized by the close is at least half a standard deviation below its cumulative period average. The system anticipates the next day will be of larger range. On that day, signals are generated upon breaking of the previous days high or low. All positions are flattened at the close.

The Updata code for this system is in the Updata Library and may be downloaded by clicking the custom menu and indicator library . Those who cannot access the library due to a firewall may paste the code shown below into the Updata custom editor and save it.

FIGURE 10: UPDATA, SMALL-RANGE BARS SYSTEM. This chart shows an example of our small-range bars system as applied to NYMEX-listed WTI crude oil prices.

—Updata support team

MICROSOFT EXCEL: OCTOBER 2014

In “Exploring Charting Techniques: Creating A Trading Strategy, Part 3” by Sylvain Vervoort, which appeared in the September 2014 issue of STOCKS COMMODITIES, the author develops a simple trading strategy around the crossover of two moving averages constructed over the modified renko brick chart that he showed us earlier in his July 2014 SC article of the same series (part 1).

I played around with the renko tick size (700), which translates to a brick size of $700. With the SP 500 as the datastream (which is a big ticket compared to Ford at $17 a share), this allowed more dates on the renko chart for demonstration purposes. Adjust as you like for your trading instrument.

I also made one small change to his signal strategy. Rather than use a set 0.8 signal threshold, my spreadsheet calculates the maximum absolute delta between the moving averages and then takes a user-specified percentage of that delta as the threshold value. This allows the strategy to adapt to tradable instruments with large differences in pricing scales.

This strategy seems to work equally well with tick-level data or end-of-day data.

Unless we get a sharp signal crossover (as we see near 12/18/2013 in Figure 11), the delta between the two moving averages can be less than our signal threshold for one or more bars, and we get white space.

FIGURE 11: EXCEL, CROSSOVER ON RENKO CHART. This shows a renko chart with the position durations per indicator. The crossover spread exceeds the signal threshold.

The chart in Figure 11 has a fixed number of bars to avoid some clutter. The right-most bar is for the same date as the right-most bar on the price chart in Figure 12. Because of the nature of the renko bar construct, the number of renko bars for a given time span will almost never be the same as the number of source data bars for the same time span.

To help get the charts to cover the same time span for the sake of visual comparison, the button to the right of the renko chart will reset the price chart of Figure 12 to the time span shown in the renko chart.

FIGURE 12: EXCEL. Heres a sample price chart for comparison to the renko chart in Figure 11.

Figure 13 details the backtest transactions that were summarized in the blue box at the bottom left of the chart in Figure 12.

FIGURE 13: EXCEL, BACKTEST RESULTS. This shows the log of backtest transactions.

The spreadsheet file can be downloaded here: CreatingATradingStrategy. xlsm. To successfully download it, follow these steps:

Excel and VBA programmer

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Test3

Test3at Georgia State University †

In the context of distribution patterns, the rate of change in retailing around the world appears to be directly related to the _____.

In the context of factors affecting choice of channels, one of the key elements in distribution decisions includes _____.

A. The ability of a camera to take a picture.

choose more standardized promotional efforts.

In most cases, the reason that products which cost relatively little in one country cost more in another is:

The amazing turtletrader trend following story

The amazing turtletrader trend following storyThe Amazing TurtleTrader Trend Following Story

Note . TurtleTrader® is the only location for the complete Turtle story and rulessince 1996. Michael Covels Turtle trading efforts span interviews with the most successful TurtleTrader, Jerry Parker. to his bestselling book The Complete TurtleTrader. Enjoy!

This is the story of how a group of ragtag students, many with no Wall Street experience, were trained to be millionaire traders. Think of Donald Trump’s show The Apprentice, played out in the real world with real money and real hiring and firing. However, these apprentices were thrown into the fire and challenged to make money almost immediately, with millions at stake. They weren’t trying to sell ice cream on the streets of New York City. They were trading stocks, bonds, currencies, oil, and dozens of other markets to make millions. This story blows the roof off the conventional Wall Street success image so carefully crafted in popular culture: prestige, connections, and no place at the table for the little guy to beat the market (and beating the market is no small task). Legendary investor Benjamin Graham always said that analysts and fund managers as a whole could not beat the market because in a significant sense they were the market.

On top of that, the academic community has argued for decades about efficient markets, once again implying there is no way to beat market averages. Yet making big money, beating the market, is doable if you don’t follow the herd, if you think outside the box. People do have a chance to win in the market game, but he or she needs the right rules and attitude to play by. And those right rules and attitude collide head-on with basic human nature. This real-life apprentice story would still be buried had I not randomly picked up the July 1994 issue of Financial World magazine, featuring the article “Wall Street’s Top Players.” On the cover was famed money manager George Soros playing chess.

Soros had made $1.1 billion for the year. The article listed the top one hundred paid players on Wall Street for 1993, where they lived, how much they made, and in general how they made it. Soros was first. Julian Robertson was second, at $500 million. Bruce Kovner was fifth, at $200 million. Henry Kravis of KKR was eleventh at $56 million. Famed traders Louis Bacon and Monroe Trout were on the list, too. The rankings (and earnings) provided a crystal-clear landscape of who was making “Master of the Universe” money. Here were, without a doubt, the top players in the “game.” Unexpectedly, one of them just happened to be living and working outside Richmond, Virginia, two hours from my home. Twenty-fifth on the list was R. Jerry Parker, Jr. of Chesapeake Capital—and he had just made $35 million. Parker was not yet forty years old.

His brief biography described him as a former pupil of Richard Dennis (who?) and noted that he was trained to be a “Turtle” (what?). Parker was described as a then twenty-five-year-old accountant who had attended Dennis’s school in 1983 to learn his “trend-tracking system.” The article also said he was a disciple of Martin Zweig (who?), who just happened to be thirty-third on the highest-paid list that year. At that moment the name “Dennis” was neither more nor less important than “Zweig,” but the implication was that these two men had made Parker extremely rich. I studied that list intently, and Parker appeared to be the only one in the top hundred advertised as having been “trained.” For someone like myself, looking for ways to try and earn that kind of money, his biography was immediate inspiration, even if there were no real specifics. Here was a man who bragged that he was a product of the “Virginia boondocks,” loved country music, and preferred to keep as far away from Wall Street as possible.

This was no typical moneymaking storythat much I knew. The common wisdom that the only way you could find success was by working in eighty-story steel-and-glass towers in New York, London, Hong Kong, or Dubai was clearly dead wrong. Jerry Parker’s office was absolutely in the middle of nowhere, thirty miles outside Richmond in Manakin-Sabot, Virginia. Soon after reading the magazine, I drove down to see his office, noting its lack of pretense, and sat in the parking lot thinking, “You have got to be kidding me. This is where he makes all that money?” Malcolm Gladwell famously said, “There can be as much value in the blink of an eye as in months of rational analysis.” Seeing Parker’s country office was an electrical impulse for me, permanently dispelling the importance of location. But I knew nothing else at the time about Jerry Parker other than what was in that 1994 issue of Financial World. Were there more of these students? How did they become students? What were they taught? And who was this man Dennis who had taught Parker and others?

Richard Dennis was an iconoclast, a wildcatting Chicago trader not affiliated with a major investment bank or Fortune 500 firm. As the “locals” were fond of saying on Chicago trading floors, Dennis “bet his left nut.” In 1983, by the time he was thirty-seven, he’d made hundreds of millions of dollars out of an initial grubstake of a few hundred. Dennis had done it on his own terms in less than fifteen years, with no formal training or guidance from anyone. He took calculated risks leveraging up huge amounts of money.

If he liked a trade, he took all of it he could get. He lived the markets as a “betting” business. Dennis figured out how to profit in the real world from an understanding of behavioral finance decades before Nobel prizes were handed out to professors preaching theory. His competitors could never get a handle on his consistent ability to exploit irrational market behavior throughout all types of markets. His understanding of probabilities and payoffs was freakish. Dennis simply marched to a different drum. He eschewed publicity about his net worth even though the press speculated about it extensively. “I find that kind of gauche,” said Dennis.1 Perhaps he was reticent to focus on his wealth because what he really wanted to prove was that his earning skills were nothing special.

He felt anyone could learn how to trade if taught properly. His partner, William Eckhardt, disagreed, and their debate resulted in an experiment with a group of would-be apprentice traders recruited during 1983 and 1984 for two trading “classes.” That “Turtle” name? It was simply the nickname Dennis used for his students. He had been on a trip to Singapore and visited a turtle-breeding farm. A huge vat of squirming turtles inspired him to say, “We are going to grow traders just like they grow turtles in Singapore.” After Dennis and Eckhardt taught novices like Jerry Parker how to make millions and the “school” closed, the experiment morphed into word-of-mouth legend over the years supported by few hard facts. The National Enquirer version of the story was captured in 1989 by a Wall Street Journal headline, “Can the skills of successful trading be learned? Or are they innate, some sort of sixth sense a lucky few are born with?”

Since the 1980s are long past, many might wonder if the Turtles’ story still has relevance. It has more relevance than ever. The philosophy and rules Dennis taught his students, for example, are similar to the trading strategy employed by numerous billion-dollar-plus hedge funds. True, the typical stock-tip chaser glued daily to CNBC has not heard this story, but the players on Wall Street, the ones who make the real money, know. The inside story has not been told to a wider audience until now because Richard Dennis is not a household name today, and because so much has happened on Wall Street since 1983. After the experiment ended, the characters, both teachers and Turtles, went their separate ways and an important human experiment fell through the cracks, even though what took place is as significant today as then. The effort to get the real story out there started to gain momentum in 2004, when I was invited to visit Legg Mason’s 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 & Poor’s 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 Mason’s 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 book’s publication is a saga in itself. Still, the biggest problem with a story like this is that most people don’t 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 Cramer—a 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 Cramer’s fundamentally driven buy signals and wild prop-smashing antics. In one word: bullshit. That said, a lot of people, many highly educated, believe that Cramer’s 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 investor’s 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 Chicago’s 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 Dennis’s 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, C&D Commodities, budgeted $15,000 for classified ads in the Wall Street Journal, Barron’s, and the International Herald Tribune seeking trainees during late fall 1983 and 1984. Avid job seekers saw this:

Richard J. Dennis of C&D 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: C&D 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 don’t usually expect the road to riches to be in plain sight. The ad invited anyone to join one of Chicago’s 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 C&D 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.

Dennis’s recruitment process took place long before the chain - reaction flow of Craig’s List ads that attract in thousands of re?sume?s 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 can’t be what I think it is” was a common refrain. It was, unbelievably, an invitation to learn at the feet of Chicago’s 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.” Let’s face it, guessing what would make a wealthy, reclusive, and eccentric trader take notice of you in order to get to the next step—a face-to-face interview—had no precedent. This casting of a wide net was all part of Dennis’s 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 Dennis’s book, everything about the markets was teachable, starting with his very first prerequisite: a proper view of money. He didn’t 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 you’re born with trading skills or you’re 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. Let’s 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 didn’t feel the pain when they were losing: “You’re much better off going into the market on a shoestring, feeling that you can’t afford to lose. I’d rather bet on somebody starting out with a few thousand dollars than on somebody who came in with millions.” The ramifications of Dennis and Eckhardt’s intellectual experiment opened a Pandora’s 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 society’s 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 Dennis’s 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 master’s in mathematics from the University of Chicago and four years of doctoral research in mathematical logic. But for the purpose of their nature??versus-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, can’t be overcome.5 That’s bad news for a potentially successful trader or entrepreneur in any field who doesn’t have the so-called pedigree or right IQ score. The irony is that even though Dennis’s experiment proved otherwise over twenty years ago, success in the markets is still perceived by many as a virtual IQ caste system. Skeptics of Dennis’s 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 Street’s 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? It’s hard to accept that fact. It’s too much like a Hollywood script.

It’s a Small World

Richard Dennis wanted a mishmash of personalities, similar to MTV’s 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 Dennis’s 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 Cushman/Wakefield (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 wasn’t 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 don’t want to do the hard work. They don’t want to really take a risk.6 Dennis didn’t 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, it’s 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 Dennis’s experiment. He wondered how Dennis knew, saying, “I would have sided with Bill’s 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 shouldn’t 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 Dennis’s students’ long-term survival. Before getting into what really happened with the Turtles, who the winners and losers were and why, it’s 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 & watching the free DVD .

Islamic principles

Islamic principlesHalal vs. Haram

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.

ACCEPTABLE BUSINESSES

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.

UNACCEPTABLE BUSINESSES

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:

Alcohol

Gambling or gaming activities

Conventional financial services

Pork and pork products

Pornography

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.

HELPFUL RESOURCES

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 “Islamic investing” 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.

SCREENING RESULTS

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.

Buy the higher low and sell the lower high

Buy the higher low and sell the lower highBuy the Higher Low and Sell the Lower High

Article Summary: Trading in the direction of the trend and buying low while selling high are mutually exclusive. Because we recommend you locate the direction of the trend and find a good entry, DailyFX has a new concept for you to consider. Buy the higher low and sell the lower high. This article will provide you with methods to do just that to prevent you from catching a falling knife.

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. What traders can do is recognize that patterns tend to play out and repeat over and over again which can lead to higher probability entries.

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. One trader will be right and the other will be wrong if they entered at the same price with similar stops and limits. 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. However, there are methods that you can use to stay on the likely right side of the big moves. 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 “cant go any lower”, which has been the rallying cry of many losing trades.

Pivot Line s for Support Resistance

Pivot Lines are a leading indicator of sort. In short, Pivot Lines are a famous indicator to help you forecast likely future points of resistance and support to limit risk and find profit targets. Rising Pivot levels overtime can help you find a significant higher low to enter a buy trade or lower high to enter a sell trade on.

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. Combining pivots lines with candlestick analysis is a preferred method of many traders to find strong entries with the trend. A short cut for new traders looking at price action is to fade long wicks (highlighted above) against the trend as they likely are a rejection of a price test and often end up carrying back price in the direction of the trend.

Relative Strength Index (RSI) for Directional Strength

The Relative Strength Index is the utility knife of many traders. When the RSI crosses an extreme level and is making directional moves higher or lower, traders can look for strong entries that favor the RSI bias. One simple way to find a directional bias on RSI is to add a moving average or trendline to the RSI and find bounces off support or breakouts of the RSI for a high probability entry.

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. The value of a trendline or channel is increased every time it is tested. When markets are moving higher a trendline is a form of support that can be used to identify buying opportunities. When markets are moving lower, a trendline is a form of resistance that can be used to identify selling opportunities.

The purpose of this article is to help you understand that buying low and selling high is not a given trading system. You may be buying something thats about to go a lot lower or selling something before it skyrockets. Because price is the ultimate indicator, trendlines or channels can help you pinpoint a higher probability entry as opposed to a cheap entry which could end up costing you a lot if it continues to move against you.

Learn Forex: There Is No Guarantee youll get the Lower High You Want

(Created using FXCMs Marketscope 2.0 charts)

Closing Thoughts

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.

Happy Trading!

---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 provides forex news and technical analysis on the trends that influence the global currency markets.

Learn forex trading with a free practice account and trading charts from FXCM.

Become avip class member

Become avip class memberIntroduction 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.

Forex brokers uk

Forex brokers ukUnited Kingdom

conventional long form: United Kingdom of Great Britain and Northern Ireland

conventional short form: United Kingdom

local long form: Xianggang Tebie Xingzhengqu

abbreviation: UK

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 UK's 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.

Vwap day trading strategy binary options

Vwap day trading strategy binary optionsVwap day trading strategy Binary Options mosesandalice

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. Binary. 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, start_day: 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

Forex brokers with rollover-free(swap-free)accountsForex 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.

Benefits of rollover free trading

For certain currency pairs depending on the market position taken rollover can either be negative (means you have to pay) or positive (means you get paid). With rollover/swap free Forex accounts trader don't pay anything nor they get paid for holding positions past 17:00pm (ask your broker about the Time zone they use) when rollover is calculated.

Forex currency trading Accounts that receive no rollover/no swap can be a good option for traders who employ long term trading strategies, starting from holding trades longer than a day and up to a month or so, and who don't want to pay rollover fees.

Rollover/swap free Forex accounts are perfect for carry trade and hedging strategies where traders look to profit from holding currencies which earn positive rollover (with a broker that applies rollover/swaps) and at the same time look to offset any trading risks by hedging the same currency pair with another broker which applies no rollover fees, but instead charges a small commission.

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.

Benefits of rollover free trading

For certain currency pairs depending on the market position taken rollover can either be negative (means you have to pay) or positive (means you get paid). With rollover/swap free Forex accounts trader don't pay anything nor they get paid for holding positions past 17:00pm (ask your broker about the Time zone they use) when rollover is calculated.

Forex currency trading Accounts that receive no rollover/no swap can be a good option for traders who employ long term trading strategies, starting from holding trades longer than a day and up to a month or so, and who don't want to pay rollover fees.

Rollover/swap free Forex accounts are perfect for carry trade and hedging strategies where traders look to profit from holding currencies which earn positive rollover (with a broker that applies rollover/swaps) and at the same time look to offset any trading risks by hedging the same currency pair with another broker which applies no rollover fees, but instead charges a small commission.

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 scalping strategy with parabolic sar and advanced macd v3indicator

Forex scalping strategy with parabolic sar and advanced macd v3indicatorForex Scalping Strategy With Parabolic SAR and Advanced MACD v3 Indicator

This scalping system consists of the 125 exponential moving average with PSAR dots and the advanced MACD v3 candlestick indicator. This system works best on the 5 minute charts for scalping purposes. You can use it on higher time frames as well for position or 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

Turtle trading rules pdf download

Turtle trading rules pdf downloadThe original turtle trading rules

Article posted June 6th, 2012

By The Original Turtles

Abstract / Introduction:

You probably asked yourself the same questions: “Why 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 Eckhardt?” 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.

Read more

The story of the commodity trading Turtles has become one of the most famous in trading history. Their success has stirred the interest of many new traders and helped Richard Dennis become one of the most famous commodity traders of all time.

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 analysis/articles 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 didn't teach me about trading _____.

It wasn't 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, he's convinced trading can be learned. Over the past 1 1/2 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, Barron's 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. (Here's a pdf in case there are bandwidth issues).

Hang seng index(stock exchange of hong kong limited hsi)

Hang seng index(stock exchange of hong kong limited hsi)Quote Details

Most stock quote data provided by BATS. Market indices are shown in real time, except for the DJIA, which is delayed by two minutes. All times are ET. Disclaimer. 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. All rights reserved. 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 Poor's and SP are registered trademarks of Standard Poor’s Financial Services LLC and Dow Jones is a registered trademark of Dow Jones Trademark Holdings LLC. All content of the Dow Jones branded indices © SP Dow Jones Indices LLC 2015 and/or its affiliates.

Best programming language for algorithmic trading systems

Best programming language for algorithmic trading systemsBest Programming Language for Algorithmic Trading Systems?

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. I/O 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. C/C++ (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 kernal/network interface tuning.

Research Systems

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 C++/C#, 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 GUI/IDE 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 NumPy/pandas 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 NumPy/SciPy 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".

Execution Systems

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++ and/or 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 C++/Java 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 TCP/IP, 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 Considerations

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 I/O, concurrency/parallelism 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 NumPy/SciPy. 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 (disk/network 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 doesn't need to be regenerated upon each loop of the trading algorithm. Such regeneration is likely to be a high CPU or disk I/O 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++ doesn't provide a native garbage collector and so it is necessary to handle all memory allocation/deallocation as part of an object's 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 Amdahl's 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 Nvidia's 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 concurrency/multithreading. 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 7/8, 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 reboots/patching (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 x86/x64 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 prices/volume, sudden rapid drawdowns and account exposure for different sectors/markets 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 won't 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.

Type Systems

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 doesn't 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 NumPy/SciPy 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. There are advantages and disadvantages to both approaches. 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 licensing/maintenance 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 plugins/libraries (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, MySQL/PostgreSQL, 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 dependency/versioning 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.

Batteries Included?

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 system/protocol (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 C++11 spec, including native support for lambda expressions and concurrency.

Python has the high performance NumPy/SciPy/Pandas 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++ (MySQL/C++), JDBC (Java/MatLab), MySQLdb (MySQL/Python) and psychopg2 (PostgreSQL/Python). 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 .

Conclusion

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.

Michael Halls-Moore

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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Powerpoint templates

Powerpoint templatesPowerPoint templates

Using the PowerPoint templates

We have developed a basic set of PowerPoint templates to demonstrate how the NHS identity should be used correctly in a presentation.

There are three examples showing different cover designs and follow-on pages to provide examples of the flexibility of the NHS identity and the NHS colour palette.

Templates 1 and 2 use the primary colour of NHS Blue (Pantone 300) to create a corporate look, while template 3 shows how you can use a combination of colours from the NHS colour palette to create a presentation with visual impact.

Customising the templates

The presentations include instructions on how to customise them. For example, how to insert your own logotype, change the images or change the colours of the presentations to suit your organisation.

To insert your local NHS organisation logotype into these templates we recommend that you use the templates 2 or 3, as these have a white background so this will be easier to edit. The NHS Identity must either be placed on a white background or can be used reversed out in white.

To view the templates you can simply open them on your screen. If you want to edit the templates, please save them to your computer.

Download the basic set of NHS PowerPoint templates

Trading strategies pdf-trading strategies

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Algorithmic Trading: Winning Strategies and Their Rationale (Wiley Trading)

Praise for Algorithmic Trading

Algorithmic Trading is an insightful book on quantitative trading written by a seasoned practitioner. What sets this book apart from many others in the space is the emphasis on real examples as opposed to just theory. Concepts are not only described, they are brought to life with actual trading strategies, which give the reader insight into how and why each strategy was developed, how it was implemented, and even how it was coded. This book is a valuable resource for anyone looking to create their own systematic trading strategies and those involved in manager selection, where the knowledge contained in this book will lead to a more informed and nuanced conversation with managers.

Using an excellent selection of mean reversion and momentum strategies, Ernie explains the rationale behind each one, shows how to test it, how to improve it, and discusses implementation issues. His book is a careful, detailed exposition of the scientific method applied to strategy development. For serious retail traders, I know of no other book that provides this range of examples and level of detail. His discussions of how regime changes affect strategies, and of risk management, are invaluable bonuses.

—Roger Hunter, Mathematician and Algorithmic Trader

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Forex School

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Under certain conditions you may post commercial or promotional posts but ONLY to the commercial forum(s). 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.

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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 and/or 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 topic/thread.

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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 it's 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 don't 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].

Disclaimer

†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.

The information on this site is not directed at residents of the United States and is not intended for distribution to, or use by, any person in any country or jurisdiction where such distribution or use would be contrary to local law or regulation.

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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. By connecting traders from all over the world into a network and sharing their views and trades real-time, investors can use that information to make social rather than fundamental or technical trading decisions.

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: socialtradingguru/#sthash. 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. By connecting traders from all over the world into a network and sharing their views and trades real-time, investors can use that information to make social rather than fundamental or technical trading decisions.

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: socialtradingguru/#sthash. 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. By connecting traders from all over the world into a network and sharing their views and trades real-time, investors can use that information to make social rather than fundamental or technical trading decisions.

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: socialtradingguru/#sthash.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. By connecting traders from all over the world into a network and sharing their views and trades real-time, investors can use that information to make social rather than fundamental or technical trading decisions.

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: socialtradingguru/#sthash.6SAAM80s. dpuf

About mike

About mikeABOUT MIKE

Hi! My name is Michael Halls-Moore and I'm 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 .

QuantStart Begins.

I’m 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 I'd 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 didn't realise I was helping to start something big. Since QuantStart's launch in March 2010, I've 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, I'd like to thank you for using QuantStart!

Your Stories.

The greatest thing about running QuantStart is hearing your stories of quant and trading success. I'm 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 we're 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 I'm making a big difference.

If you're new, again I'm 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!

30day special price backed with90day guarantee

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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.

It’s hard to know what’s for real and what isn’t 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. Learn more here .

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.

I’m 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

Your description goes here

Your description goes hereDownload 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 stock's market data from a certain country you should find the country's page about downloading market data first. Eg. You should visit USA's 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

MSCI Index

MSCI Index

Download help

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 . Chartoasis's 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 product's data download page to "Favourites" or "Bookmarks".

Yahoo! Finance UK . It's 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 product's 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 product's 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?

Chartoasis's 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. Eg. 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. It is recommended to download as much data as possible.

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 . Currency of prices in downloaded data may not be univocal in all cases since some data providers provide data in more currencies. It may happen that you download data of Gazprom from RTS in USD but the dividend is given in RUR on the website of the company. Numbers may be rounded, too.

Errors in data . Data providers do not check all numbers they provide, downloaded data may contain inconsistencies and errors. Having experienced weird things on the chart? We recommend you to check downloaded data because the root cause of problems may lie in the data itself. (Eg. missing data, filled with zeros, daily minium is higher than the maximum and so on. ) Take a look at the example below.

Bond trading strategies pdf-binary option platform

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10rules of technical trading

10rules of technical trading10 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, “Technical Analysis of the Financial Markets,” should be part of every traders library. Murphy wrote “The 10 Important Rules of Technical Trading,” 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 Murphy's 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 “map of the market” 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

How much money(trading capital)do you need to tradeHow 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.

Standard lot

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 saleday trading system-bestselling classic tra

Note this item is not currently being offered for saleday trading system-bestselling classic traNote: this item is not currently being offered for sale.

Day Trading System — 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 2000's. for updated strategies, be sure to see our online course (with newly-recorded video) and my and

Steve Nison's 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 investments review

Fidelity investments reviewFidelity Review 2015: Commissions, Fees, and Minimum Deposit

Fidelity New Account Setup

Fidelity's 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 from/to a bank.

After the account is opened, transferring money from your bank is a simple matter of going to Accounts & Trade -> Transfer Money/Shares 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.

Fidelity's 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 Website/Trading Platform/Tools 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 can't 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 Fidelity's 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.

Fidelity's 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.

Fidelity's 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 Ameritrade's 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

The statistical arbitrage trading strategyThe Statistical Arbitrage Trading Strategy

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 lead/lag, 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.

Bonus250vidsotkiv

Bonus250vidsotkiv«Бонус 250 відсотків»

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Бонус 250 відсотків від депозиту може бути знятий після здійснення угод типу BUY або SELL в сумарному обсязі, рівному X * 3 InstaForex-лотів, де X = сумарний обсяг отриманих бонусів. Прибуток з бонусу можна знімати без обмежень.

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Forex swing trading strategy asimple one

Forex swing trading strategy asimple oneForex Swing Trading Strategy A Simple One

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New moving average strategies

New moving average strategiesNew Moving Average Strategies

What is a moving average. A moving average is the average value of price action data compiled over a specific period of time. Usually the moving average comes in the form of indicators which are used to work out the trend of an asset. In other words, moving averages can be used to check of the price action of a currency pair will move up or down. If you check your MT4 platform on Forex4you. it will be clearly seen that the moving average is classified among the TREND indicators. Moving averages allow the trader some flexibility because it is possible to use them to choose which data points and time periods to construct them with. For instance, you can choose to use the open, high, low, close or midpoint of a trading range and then study that moving average over a time period, ranging from tick data to monthly price data or longer.

There are several types of moving averages. However, the most common types of moving averages listed on retail forex platforms are the simple, exponential, weighted and smooth moving averages. These are the ones that we shall focus on for the purposes of this article.

The snapshot above shows how to plot three moving averages on an hourly chart for EURJPY. The three moving averages are 10-period weighted moving average, a 10-period exponential moving average, and a 10-period simple moving average. These moving averages are represented with the blue, red and gold-coloured lines respectively.

The three moving averages usually carry different weights according to what data they are usually based on. This will have a bearing on what moving averages the trader will use when considering a trade decision. Without going into a long discussion of the math behind these moving averages, The exponential moving average and the weighted moving average tend to put more emphasis on the most recent data while the simple moving average puts equal emphasis on historical and recent data. For trading purposes, we have found that the simple moving average produces the best signals due to its balance in the use of historical and recent data. Many of the strategies we have discussed here have been based on the simple moving averages. The examples to be used in this article will therefore place maximum focus on the 10-period simple moving average.

Most trading signals that are based on the moving average are not based on the use of one single time period. Rather it makes more sense to combine two or even three moving averages of different time periods. This is usually done for filtering and confirmation. With this in mind, let us look at some moving average strategies that make use of multiple time period moving averages.

1. The Dual Moving Average Crossover System

Notice the title, and the use of the words “dual” and “crossover”. This gives us a clue as to the entire essence of the system being discussed. When creating a trading system using moving averages, the use of a dual moving average crossover approach, as depicted in the snapshot below, is usually a good starting point. The system entails the use of two moving averages, usually a shorter and longer period moving average, and then uses the cross of the faster moving, shorter time period moving average either above or below the longer/slower moving average to confirm a trend direction. In this depiction, we use a 5-period simple moving average and a 10-period simple moving average. They are depicted with a thin blue line and a thick black line respectively.

In this example, we see that the 5-period moving average has crossed the 10-period moving average multiple times, but there are key times when the crossover has been accompanied by a rallying trend and a falling trend (at the beginning and end of the time frame in the chart). The periods of crossover are indicated by the red arrows to the downside and green arrows to the upside. These red arrows tell us that the 5-period simple moving average crossed below the 10-period simple moving average, and the green arrow shows that the 5-period simple moving has crossed the 10-period simple moving average to the upside.

Looking at the same chart again but with some emphasis on the circled area, we can see that the signals produced by the dual crossover simple moving average system can create some problems, which in this case is delivering signals when in fact, the market will end up going nowhere because of its range-bound status at that particular moment. So before you see that crossover and think to yourself that a chance has come to make tons of money, think again. Not only do these range-bound situations occur to ruin the party, but you also need to realize that the moving averages have one major drawback: moving averages are lagging indicators. They tend to lag the market, so by the time the moving average has shown a signal, it is probably too late to get in, and you can then find yourself locked in the sideways market or a market where there’s no trend, which is shown in the black circle.

In order to prevent such occurrences from ruining your trade when using a dual simple moving average crossover system, there are some approaches you can deploy. These are as follows:

First, select the currency pair you are interested in trading, as well as the time frame you want to study. This could be for instance, EURUSD on a daily chart.

The second is to select a time period without a trend bias. This is done by ensuring that the time period to be tested contains both a trending section and a non-trending section. Eliminating trend bias helps you get accurate results in your testing.

Conduct an optimization to identify which moving average parameters are the best to use.

Once you have completed these steps, examine a totally different period of time to see if the results you have obtained can be replicated.

You may chose to select a time period from a few years back to confirm that the results you have obtained is something which is evergreen and can be used with confidence in a few months or even years. This step is extremely important. In science, it is not unusual to conduct Double-Blind studies. This is what this step seeks to mimic. In forex can call it Out-of-Sample Data testing. This is the only way to determine if the results of your optimizations can stand the test of time as a viable mechanical trading system. You do not want to get results which will only last a few weeks and then become useless forever.

2. Moving Average Price Channel System

The dual moving average crossover system has a number of drawbacks, chief of which is the propensity of the system to suffer whipsaws. That is why there is a need to come up with a system to counter this. One way to overcome whipsaws or false signals generated with a dual moving average crossover system is by deploying a moving average price channel system. This will involve the use of the moving average indicator set with a periodicity of 20, and separately applied to the high and also to the low of price. The moving average used in this case is a 20-period simple moving average of the price high, which is the higher black line in the snapshot below, as well as the the 20-period simple moving average of the price low, which is the lower black line.

The moving average price channel is the space in between the two black lines, while the blue line is a 5-period simple moving average of the closing price.

The buy and sell signals shown on the snapshot are marked with the corresponding arrows: green arrows for a buy signal and red arrows for a sell signal. A buy signal is seen when the 5-period simple moving average of the close, or the blue line, crosses above the upper boundary line of the moving average price channel. A sell signal is generated when the blue line crosses below the lower line, represented by the 20-period simple moving average of the low.

We can see that the use of a price channel will drastically reduce the number of whipsaws that are seen with the dual moving average crossover system because it creates a much tighter filter for setups that the currency pair(s) must pass through. By creating more significant hurdles for the price action to overcome prior to the generation of a trade signal, fewer signals are generated, but these signals are much more accurate. It is usually better to have more accurate signals which are fewer in number, than to have so many signals generated, but with a large percentage of false signals that would lead to losses on trades.

If a choice is to be made between a dual moving average crossover system and the moving average price channel system, the odds would favor the price channel system because of its superiority in detecting areas of support and resistance, from where trend reversals are expected to occur.

***A note of caution. It is important to remember that currency pairs do not behave in the same manner. Therefore what may work very well for the EURUSD may not necessarily work for the EURJPY. This must be kept in mind when creating a mechanical trading system using any of the moving average setups described above. There are no magic bullets, and it is best to test and optimize settings for each setup on all the currency pairs that you are interested in trading.

3. Combination Strategy: Moving Average Crossover + Moving Average Price Channel

To achieve even more superior results, the trader can decide to use a combination strategy, in which the moving average crossover and moving average price channel techniques are combined. This strategy is demonstrated in the snapshot below:

The price channel system is shown on a one hour chart of AUDJPY. The green arrows identify when the blue line crosses the 20-period moving average of the higher line, which is a 20-period simple moving average of the price high. The red arrows indicate a cross below the 20-period simple moving average of the price low. The diamonds indicate when the 5-period moving average crossed below the 10-period.

The essential element of the strategy is to combine the best of the two moving average systems which have been described above into one. The ultimate purpose is to get the best of the two worlds. What are these?

a) To achieve fewer but more accurate entries and eliminate false trading signals using the moving average price channel system

b) Achieve quick exits so as not to give up large chunks of your unrealized profits back to the market, using the dual moving average crossover system.

The Most Popular Moving Averages

With so many time periods to choose from, which settings are considered the gold standard when using moving averages for forex trading? One of the most popular dual crossover moving average settings used the world over is the combination of the 50-period simple moving average of the close and a 200-period simple moving average of the close. This setting works best on the daily chart because of the length of the time period used in setting the moving averages.

The snapshot above is the daily chart of the USDCAD, and displays an example of when the 200-period moving average provided support, while the 50-period moving average provided resistance (circled on the blue line).

However, the settings work best for stocks and less so for currencies. Since forex trading is our focus here, we will use settings which have been shown to work for currencies, which is the 13-and the 26-period moving averages in tandem, as well as the 100 SMA to be used as the support/resistance filter.

The strategy below shows such a cross over system using a 13-week and a 26-week simple moving average of the close on a currency chart, with the support and resistance for the trades provided by the 100 SMA. How does this work? We will be looking for long entries when price is above the 100 SMA, looking for a bounce on this SMA when the cross of the 13SMA above the 26SMA has occurred. The color-coded MACD can be used as a confirmation for the trade.

We shall be using the daily time frame for this trade. The daily chart shows a single day’s activity in on candlestick. This means that a trader must pay attention to risk management as stops used will be equivalent to the intraday range of some of the currencies (up to 100 pips or more). It therefore means that traders using this strategy should be a bit more patient as the trade will take days to fully play out. Any currency pair can be used to trade this strategy.

Indicators Needed

Color-coded MACD Histogram

13-week simple moving average (13SMA)

26-week simple moving average (26SMA)

100-week simple moving average (100SMA)

Long Entry

The trader should enter long on the asset if:

When the 13SMA crosses above the 26SMA to the upside.

Price is above 100 SMA, or ideally, bounces off it.

The color-coded MACD is blue in color.

The Stop Loss for the long entry should be set to around 10 15 pips below the entry candlestick. In order to take profit for this trade, the profit target can be located under the following conditions:

if the 13 SMA performs a reverse cross from above the 26SMA to the downside.

price breaks below the 100 SMA

2X or 3X the stop loss.

Chart Example:

Look at this chart for the AUDJPY. This is a daily chart which combines the scenarios for long and short order setups.

Short Entry

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. The trader can then take the short position as shown by the red circled area, and take profit as follows:

take profit at the area where the MACD changes colour.

take profit at the area where the price action hits a resistance level, as characterised by new lows made by several candles.

Long Entry

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. Profit targets can be set at the same time that the reverse cross of the 13SMA on the 26SMA occurs, or at the price resistance.

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