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Tuesday, September 26, 2017
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Trading System Development

Behind Gandalf Project there is the awareness that no Trading System lasts forever. Gandalf Patterns are the results of...

Gandalf Patterns are the results of evolutionary theories applied to machine learning, but the process of validation and monitoring is still human based. 

We believe that most of the time, it is preferable to produce new trading systems instead of trying to adapt old ones to changing market conditions (monitoring, through equity control, is essential)

In the Download Section you can find some of these patterns, provided with Tradestation / Multicharts and VisualTrader Code, and TXT rules (in order to adopt the pattern to the most known chart package).

In the Download section you can find some free examples (with In Sample and Out Sample equity) of Gandalf Patterns on Equities, Forex and Futures Markets, beside the description of some other Patterns that you can buy (according to the price provided below).

Even if each pattern should be controlled with some equity filter logic, these are the results of a group of Patterns we have found with a genetic algorithm, with no filter logic applied, and validated through an out of sample period started from April 2012.

This portfolio is made of 23 long patterns and 3 short patterns, running on these stocks : GE  HD  INTC  MMM  T   WMT  BA  COP  ISRG  LVS  ORCL  UTX  DE  GLD  NKE  AMZN  CAT  MCD  QCOM  X  AMGN  AXP  DIS  MSFT  IWM. 

Since these are equities patterns, we decided to adopt as a benchmark the S&P500 (using the ETF: SPY), in red; the blue curve is the equity of the portfolio of these patterns, whereas the green area is the out of sample period (April 2012-January 2013).

Each position was 10.000 usd worth, so we have decided to use a benchmark, a 300.000 usd worth position on SPY (S&P500).

 

...if we align both equity to the start of the out of sample period, the result is clearly impressive: 

 

Gandalf Pattern Portfolio overperform the S&P500, but what is mostly important is that it accomplishes this result with a lower volatility (as it's clearly visible comparing both equities).

The adoption of an equity control logic on each pattern, would have mostly improved the final results, but our aim was to show how by applying a simply portfolio logic, this could smooth the  equity line and keep volatility under control.

You can check out an equity line in two different ways:

The first one uses a line built with profits on the vertical axis and time on the horizontal axis.

 

The second uses a line with profits on vertical axis and the number of closed transactions on the horizontal axis.

The control of the equity is configured as an insurance policy on the periods of draw down. Like all insurance has a premium that you pay on total profit. It allows you to use the financial resources that otherwise would be bound during the draw down.

Finally, you can use the control on the entire portfolio, alternating a logic of "best in and worst out".

 

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