One of the parameters you use to understand your trading strategy performance historically before deploying it is called the RoMaD.
Return over Max Drawdown.
It's essentially (annual return % / max DD % )
Let me explain each part of this.
Average return % :
If your system started with $100,000 in 2010, and ended 2020 with $200,000, that's a 7.18% annual return over 10 years.
Maximum Drawdown % :
If portfolio falls from 150k to 120k, that's a 20% drawdown. Amongst all such drawdowns, maximum value is taken.
Return over Max Drawdown will then be
7.18% / 20% = 0.359
The RoMaD value is expressed as a ratio.
*This helps answer the question*:
Am I willing to accept an occasional drawdown of X% in order to generate an average return of Y%?
Generally, it's prudent to be working with a system where the average annual return is at least 2x the maxDD number.
If your maxDD is 10%, you should have average annual return of at least 20% or more.
When comparing across different systems, this is one of the parameters you could use to evaluate the system's worthiness to deploy into trading further.
That said, this ratio alone isn't sufficient - and is usually taken in tandem with other parameters to evaluate a system.
And btw, this was in response to a friend who asked what the ratio was from a backtesting site report.
I don't know how they arrived at a RoMAD ratio of 44. That sure doesn't look right.
Expectancy calculation also seems wrong.
So calculate the important parameters yourself.
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On conducting monte carlo analysis of the system, I understood that the probability of maxDD to be below 5% is only ~3%.
There was about 61% probability of the maxDD to be between 5-10%
and ~2% probability that it could be around 20-40%.
You need to be aware of these.
Once you know the odds of a certain range of maxDD happening, then you can confidently deploy your strategy.
You'd also face drawdowns that lie within your comfortable range instead of being misled by just the historical maxDD as it happened in the series of trades historically.
Systematic Trading, backtesting, etc., may sound like a current generation fad, but they are clearly not.
As far back as 1990s, several big funds, quant funds have used backtesting/exploration, etc., to develop systems to trade.
Only recently it has become viable for retail.
Until late 2010s, we didn't have faster internet speeds, access to better data, low cost brokers, access to ease-of-use programming languages/tools to backtest strategies thoroughly.
MATLAB was complicated. Excel was limited without VBA. VBA was not everyone's cup of tea.
Python going mainstream as a programming language alongside R programming made it possible for many people to start backtesting their ideas.
And proliferation of several helpful tools/libraries in Python has also helped several people move fast in backtesting.