Jim Simons Trading Secrets 1.2 SIMULATED Data Generation

Inspired form the book about Jim Simons “The man who solved the market” and how they simulated or created data to perform quantitative analysis we discuss in this video how to create millions of data points for research. This data ranges from Heston model, to Geometric Brownian motion and Monte Carlo models. By doing 1000 simulations on each of these models , we were able create more than 2 million data points starting from just 750 data points during the Global financial crisis years of 2008-2011. Limited amount of data is one of the biggest drawbacks in quantitative trading.These data simulations can help us backtest even more and make sure our strategy works in all these simulations and thus giving us more confidence in deployment of strategy.
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Disclaimer: The contents provided in the channel are purely educational. We do not provide any financial or investment advice. There is a very high degree of risk involved in trading. Past results are not indicative of future returns. quantprogram.com and all individuals affiliated with this site assume no responsibilities for your trading and investment results. The contents, videos, columns, articles and all other features are for educational purposes only and should not be construed as investment advice.

[@gavinhill3164]

I think what your doing is really interesting, thanks

[@quantprogram]

Thanks much mate

[@efrainromero3959]

It's incredible this videos doesn't have hundreds of likes. This is real Smart Money trading strategies!. Then what the traders are learning?

[@quantprogram]

Thanks for watching mate. Happy you liked it

[@haedarajmi7083]

Most people are afraid to take risks because they do not trust their minds🥰

[@teeleo1363]

@MySockKeepMyToesWarmabsolutely correct.. the few who like these videos are those 1% who grow their account..😂

[@8thmarveltraders154]

Hi, I would like to contact you. If you can help me am ready pay.

[@pedrofeliciano2150]

great video. congrats. I just found it strange that the geometric brownian motion with a positive drift is not positively biased.

[@BlackJesus8463]

Do you even trade?

[@ThatonedudeCR12956]

@@BlackJesus8463SPY has positive drift depending on IV. That's pretty well known. Unless IV is incredibly high, SPY is assumed to have a greater than not chance of going up.

[@haedarajmi7083]

Thank you very much for this effort

[@sorte18]

It would be great if you could re-visit this, and create OHLC data in one of the models (say MC, for example).

[@nussbaumerloris]

Great Video, thanks alot!

[@quantprogram]

Thanks for watching mate

[@sakshamsingh1415]

Really good video

[@quantprogram]

Thanks mate

[@osazemeusen1091]

Impressive lecture, thanks for sharing

[@quantprogram]

Glad you liked it!

[@hbw6119]

How many data ( close prices) he uses on those models?

[@LogicRat609]

woah this is amazing !!!

[@kevinli522]

Thank you for your incredible videos. Could you pleas let me know how I can possibly leverage this Synthetic data generation to apply to a panel dataset? Thanks!

[@muhireinnocent2371]

Great content , thanks .Can i apply the same concept to forex trading and when it comes to training lets say a machine learning model how can i combine the simulated data with the real data and the fact that financial market data is a time series data like how would make sure that combine the datasets don't affect my datetime order. Thank you

[@barrychatman]

Depends on if your free hand or scripting your strategy

[@BullBearInsights126]

does this strategy work u=in indian market

[@aaronmugume293]

Ive loved the video ❤

[@quantprogram]

Thanks mate

[@jordiplotnikovpous4844]

why do you take the logarithm of 1+percentage_increase, and not just 1+percentage_increase?

[@quantprogram]

Its important in financial analysis to use log returns. Difficult to explain in 1 comment but its advantage is huge when it comes to normalization of returns and also handling negative values along with analyzing statistical properties. There are lots of info available online including in youtube on the importance of using log returns

[@lteodorescu]

so what's the actual strategy mate ?

[@quantprogram]

The video is about data generation so we can efficiently test strategies

[@icyboy771z]

Even if the actual strategy is given to you the normal ppl like us won't be able to implement it because he has a team of specialized workers and huge database of data.

[@valboolin3538]

What