Topic: Monte Carlo Recommendations
Hello,
Am trying the Binance data for Crypto trading. However Monte Carlo keeps failing. I am using the default Monte Carlo options.
What's the best way to work with Monte Carlo ?
Thank you
- Popov
- Lead Developer
- Offline
Re: Monte Carlo Recommendations
> However, Monte Carlo keeps failing. I am using the default Monte Carlo options.
Monte Carlo tests the robustness of the strategy by modifying historical data, market variables, or strategy parameters. If a strategy fails, it means it was overoptimised.
Overoptimisation (or curve-fitting) is the major problem when developing and testing strategies on historical data.
How can we fight that?
- using Monte Carlo - it tests the strategy behaviour in a changed environment
- using Multi Market - it tests the strategy on different data sources (usually compatible symbols). Testing in the same market (symbol/period) but with a different broker also helps.
- Out-of-sample testing - when we backtest the strategy on data not used during the building stage. usually newer data the backteser never seen.
How can you make it easier?
- Use the Reactor to automatically generate strategies and pass through different validation tools
- Use Validator to test already created collections.
Here is an example of the Reactor with Monte Carlo enabled. It generates strategies and automatically validates them. Only the strategies that pass all of the enabled validation tools are pushed to the Collection.
Re: Monte Carlo Recommendations
tobeawinner wrote:
Hello,
Am trying the Binance data for Crypto trading. However Monte Carlo keeps failing. I am using the default Monte Carlo options.
What's the best way to work with Monte Carlo ?
Thank you
Monte Carlo failing is often not just about the settings, but about how the strategy was built before reaching that stage.
I also use the default Monte Carlo settings, but I look at the results in a specific way.
I don’t read the table horizontally, but more vertically.
What I focus on is:
how fast the metrics break down
where stability disappears
whether the strategy degrades gradually or collapses
A strong strategy usually:
loses profit gradually
shows a controlled increase in drawdown
and the SQN drops, but still stays meaningful
A weak strategy usually:
loses profit suddenly
drawdown spikes hard
and SQN collapses quickly
Personally, I focus a lot on the 80–85% confidence levels.
For me, that represents a realistic stress scenario, not just an extreme worst case.
At that level, the strategy should still look healthy in terms of behavior.
Another important point is that I don’t look at Monte Carlo in isolation.
The base strategy still matters.
A strategy with weak metrics but a “good” Monte Carlo result is not something I would trust.
On the other hand, a strong strategy with solid metrics that shows slightly weaker Monte Carlo results, for example around 70%, can still be valid, as long as the behavior remains stable and does not collapse.
I also work more portfolio-based, so I don’t expect every robot to be perfect on its own.
What matters to me is whether it is structurally robust enough and behaves consistently under stress.
Also, from my experience, if a strategy struggles on unseen data before Monte Carlo, it will almost always fail Monte Carlo as well.
So usually I don’t try to “fix” Monte Carlo. I try to improve the process before it.
For me, it’s always about the full picture, not just one test.
Another important point is that Monte Carlo should be realistic.
I make sure the spread has enough room and the slippage is realistic.
For me, the goal is not just to “pass” Monte Carlo, but to make it resemble live trading conditions as much as possible.
That means trying to reflect your broker environment as closely as you can.