The development of our financial algorithms and their backtesting has been carried out through a methodology that we consider appropriate and share in this section, aimed at investors most interested in quantitative issues.

Financial Algorithms and Backtesting – The Database

To get started, we want to make it accurate that our analytics databases use Nasdaq 100 prices (opening, max, minimum, and closing).

They also consider the closing value of NDX, VXN, S&P 500 and TXN for risk-reward evaluation.

The operating instructions generated by the financial algorithms are then transformed into QQQ units, but all calculations are based on Nasdaq 100.

It is worth clarifying, as the yields of Nasdaq 100 and QQQ are very similar, but not identical.

In addition, QQQ is a real asset and pays dividends, while Nasdaq 100 is an index.

Backtesting and Operations Alerts

As for the prices used to measure results in algorithms, the Nasdaq 100 closing price is used to calculate entry or exit prices.

Notifications are usually made before closing, so there will be differences between the entry and exit prices recorded by our database and those resulting from the exact moment an investor made their transaction that day.

These effects are usually offset in the medium term, as the differences are random.

Performances of Financial Algorithms and their Backtesting

For the calculation of yields, we have neglected the positive effects of interest rates on the Cash %, nor do we consider the negative effects of commission payments (some brokers do not charge you when trading ETFs)

It is also important to note that, over time, we will always measure and publish the results that arise from our algorithms.

Therefore, there may be smaller random differences, in more or less, with the yields that our subscribers actually see in their investment portfolios.

Learn more about financial algorithms and their backtesting, in the following articles:

What is Algorithmic Trading?

Backtesting Yields (Spanish)

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