Ultimate Algorithmic Trading System

The maximum drawdown of this algorithm was around $30,000. The probability
of this event occurring again is less than 2%. The probability of having a drawdown
greater than $20,000 is less than 12%. These drawdowns only include closed trade
drawdowns—the amount of drawdown after the trade was closed out, not while
it was open. Open trade drawdown will always be greater than or equal to closed
trade drawdown. The loss isn’t realized until the trade is closed out. Again, keep
in mind we are dealing with hypothetical performance metrics with an inherent
hindsight bias. Stating that a drawdown has less than a 2% chance of occurring again
cannot be said with a straight face. However, historic performance is all we have to
hang our hat on.
■ Summary
The purpose of this chapter was to be a guide and explain concepts that on the
cover seem to be beyond the reach of a typical algorithmic system developer/trader.
Genetic optimization is just a very smart way to get to a solution quickly. Walk-
Forward Optimization, Monte Carlo simulation, and Start Trade drawdown are
tools that can provide the necessary evidence that a trading algorithm might have a
chance of performing in the future. Python code was peppered throughout the text
pertaining to Monte Carlo simulation and Start Trade drawdown to demonstrate
how simple the processes are that carry out these particular forms of analysis. None
of these tools will provide the ‘‘Holy Grail,’’ but in this author’s opinion, they will
help the user develop a trading solution that best fits his or her own trading style.
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GENETIC OPTIMIZATION, WALK FORWARD
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