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Building client wealth SINCE 1998

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Turnkey Trading Systems


Important Information and System Requirements:

  1. All code provided is in Amibroker Formula Language (AFL) for Amibroker v5.0 of higher.
  2. All code is sold 'as is' and a working knowledge of Amibroker is suggested.
  3. Purchase includes Operating Code, Monte Carlo Simulation Code and Instructions.
  4. No refund is available on this product after the code has been provided.
  5. All purchasers will be required to sign a Sale Agreement that conatins a non-disclosure clause.

AUD$660 each

"Just finished my first full year running the Flipper strategy; we had a great year. +38.77% after fees. Thanks a ton!" - JW, Lexington, KY, USA

Bollinger Band Breakout (Original)
This is the exact strategy outlined in Unholy Grails (pg 118) and closely aligned to the strategy that Nick Radge has personally used over the last decade to follow mid-term trends in the stock market. User friendly for daily or weekly timeframes and can be easily adjusted to accomodate most global stock markets. Index Filter and all other attributes discussed in the book come as standard features.

buy now flat

"I purchased the BBO Modified system in June... Here’s a quick snapshot of the results: Return:+18.7% #Trades:34 Winners:76%... I couldn't be happier with the system" - Bob O.; Long Valley, NJ, USA

Bollinger Band Breakout (Modified)
This is a Modified version of the Original strategy and was not highlighted in Unholy Grails. The major difference lies in the Index Filter that, when signals a bearish trend, will immediately exit all existing open positions. This increases trade frequency, lowers holding times and lowers maximum drawdown. Can be used on daily or weekly times frames with easy adjustment to most global stock markets.

buy now flat

20% Flipper
This strategy highlighted on page 109 of Unholy Grails, including Index Filter, volume and turnover filters, price limiters and various position sizing options. Based on the Martin Zwieg 4% Rule it is a unique and original way to trade momentum and trends over extended periods of time. This code provides the unique ability to use an adjustable trailing stop when the trend changes.

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20% Flipper (Modified)
The 20% Flipper (Modified) strategy was not disclosed in Unholy Grails. Like the BBO Modified (above) this strategy will exit all positions immediately when the Index Filter signals a bearish trend. This mechanism was designed to reduce drawdown but will generate a higher level of trade frequency. Because of the nature of the coding this trigger cannot be switched off.

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Weekend Trend Trader
Based on the exact strategy outlined in the globally popular Weekend Trend Trader e-Book. This is a mid-term trend following strategy specifically designed for the US stock market and for weekly timeframes. User friendly interface allows you to adjust various settings to create a more personalised strategy. Robust enough to operate on US and Australian stock markets and should offer opportunities elsewhere (not validated).

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Mean Reversion Strategy
This higher frequency mean reversion strategy is based on the exact strategy outlined in our 'Learn To Earn A Second Income'series of emails. Specifically designed to trade the US stock market on a short term basis, it can be adapted to trade both long and short, or long only. The higher frequency of trading allows strong returns, lower drawdowns and fast recovery from drawdowns. This is the perfect strategy to compliment a trend following strategy.

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100-Day High
This is a weekly version of the strategy highlighted on page 68 of Unholy Grails, including Index Filter, volume and turnover filters, price limiters and various position sizing options. Based on the classic Donchian Channel Breakout with default lookback set to 20-weeks. This can be easily adjusted to suit the user with many input paremeters settings easily adjustable.

buy now flat

Past performance is not indicative of future results. Any results shown are considered to be Hypothetical unless otherwise specified.  Hypothetical performance results have many inherent limitations. Unlike an actual performance record, simulated results do not represent actual trading. Also, since the trades have not actually been executed, the results may have under or over compensated for the impact, if any, of certain market factors, such as lack of liquidity.

This page may contain advice that has been prepared by Reef Capital Coaching (AFSL 288200). Being general advice it does not take account of your objectives, financial situation or needs. Before acting on this general advice you should therefore consider the appropriateness of the advice having regard to your situation. We recommend you obtain financial, legal and taxation advice before making any financial investment decision. This material has been prepared based on information believed to be accurate at the time of publication. Subsequent changes in circumstances may occur at any time and may impact the accuracy of the information.


Survivorship Bias

chart guyLast weeks Double 7's Strategy article proved to be a very popular topic - thanks for all the feedback. In the article I stated, "...we'll use historical constituents to remove survivorship bias" and this has created a few questions, namely what the heck does it mean?

According to Wikipedia, "Survivorship Bias is the logical error of concentrating on the people or things that "survived" some process and inadvertently overlooking those that did not because of their lack of visibility. This can lead to false conclusions..."

When it comes to the stockmarket, investing and systematic trading, Survivorship Bias is not only prolific, but can greatly exaggerate reality going forward. The following chart shows the rise and fall of Regis Resources (RRL), a current member of the ASX-100.

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The Oops! Pattern - In Sample / Out of Sample

Percentage manThe terms In Sample and Out of Sample refer to a technique for backtesting trading strategies where there is some kind of optimisation or data mining taking place, i.e. finding the ideal parameters to trade with. It's common to use two sets of data, however, I was always taught to use three. Here's how it works...

  1. Take a data set, let's use 2000 through 2015.
  2. Divide that set into three, so we'll use 2000-2005, 2005-2010 and 2010-2015
  3. Next, take the middle set 2005-2010, which is known as the In-Sample data, and do all testing, data mining and wanted optimisation on it.
  4. Then take the parameters and rules from that and apply it to the other two sets of data, which are both considered the Out of Sample data.

If the results appear consistent across the entire data series then you may be on to something.

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