PURPOSE OF FUNDS
Tambourine Capital invests in the 6 main currencies: EURUSD - GBPUSD - USDJPY - USDCHF - AUDUSD and USDCAD.
We give investors the flexibility to choose the initial and final currency for which a different return is attached.
Investors can also choose their level of risks (among 5) from VERY LOW to VERY HIGH and their maximum loss.
THE ALGORITHM
The Algorithm was tested using historical data collected between January 1st 2010 and December 31st 2019, as provided by a data provider recognized worldwide. This ten year period includes different crisis for different currencies (Pound and Brexit or the Swiss Franc during the Euro crisis during summer of 2011).
The methodology was developed and tested by an experienced trader and implemented by a data scientist to process 100 million data points through thousands of simulations. With this approach we were able to classify the best returns and Sharpe Ratios with different scenarios of Stop Loss and Take profit levels . We were also able to create a strategy to always improve the results by modifying the scenarios mentioned earlier.
Your Personal Backtesting
Just by providing a few parameters, we can confidentially and very quickly return our assessment, informed by our algorithm.. Our software will calculate the optimal parameters for trading during the first 5 years (2010 - 2014) and apply it to the period you choose (between initial and final date). You can select the reference currency (the one used to calculate daily nominals) and the tradable currencies we can use in the optimization. You also need to select the periodicity of calculation (usually monthly) and can choose levels of maximum loss and profit at which we close the position and restart a new one. We can apply a capital guarantee to the optimization (maximum loss) at which we return the fund to the investor.
Lastly, you will need to provide which parameter you would like to optimize: for example, if you choose the average daily amount loss, we will look at the best parameters to minimize the average daily loss, and return the list of parameters corresponding to the best one.
