How To Do Out-of-sample Testing With Monte Carlo Simulations

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Did you know you can use StrategyQuant’s Monte Carlo simulator to randomize historical prices and strategy parameters, helping you select robust strategies for live trading? Here’s how!
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⏰ Chapters:
0:00 Introduction
0:35 What are Monte Carlo Simulations?
1:43 Monte Carlo with Randomized Prices
3:42 Confidence Levels
6:40 Randomized Prices Results
7:34 Randomized Strategy Parameters
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[@TradingTact]

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[@TradingTact]

If my content brings value to you, please consider donating $1 to keep the channel going:
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[@mohammadsadeghi1636]

The first person who love your contents..please continue on good strategy development in custome project od STRATEGY QUANT...thanks...

[@TradingTact]

thanks

[@emiliososa7688]

Great video!! 🙌

[@TradingTact]

thanks

[@chenhsuan0401]

There are 9 options in the cross check section in the retester. Obviously there are thousands of ways to perform robustness tests and it is impossible too test everything. What do you think are the mandatory tests strategies have too pass in order to be exported for forward testing and live markets?

[@TradingTact]

Hello, it depends on how you generated your strategy. If you coded it from scratch, I'd say do more OOS testing like on different markets, the MC in this video, or walk forward testing. If you generated it using StrategyQuant's builder with a large OOS data segment, perhaps try the optimization profile or SPP. I like to combine conceptually different tests.

[@chenhsuan0401]

@@TradingTact I am still too new to the algo trading world to understand why different tests work better for different strategies. I'll just try to binge watch all of your videos first.

[@TradingTact]

@@chenhsuan0401 sure, feel free to leave a comment if you have questions.