no course link for $3000. all free code and knowledge your channel is a hidden gem
Your video is such a treasure, you explain fondamental details such as Monte Carlo method which very clever ! However you forgot one of the most important detail : the fees. Your broker will apply fees on your positions, funding fees, maker and taker. Your first strategy was good in itself but I am very confident in saying that fees would ruin it, keep going !
Wake up babe. new neurotrader vid just dropped.
YESSSSSSSSSSSSS! i missed these videos SO MUCH! literally binged the entire channel 8 months ago, so happy these are back!
Really good man, this is a very good video, but, only one thing to add, the sharpe ratio takes into account the risk-free rate, for the rest, perfect
Bro got the big words figured out.
No BS just straight to point and so much valuable contribution to the community, thank you
He’s back!
Wow this is great stuff.. i.nwed to rewatch like 5x before i could apply this mainly.bc i dont know py but should be easy enough to learn
In the strategy at 1:00 dont you need to shift the signal by 1 ?
Thanks a lot for this video! I would also love to have more details on your methodology on how to analyze and improve a strategy. I struggle a lot when trying to understand the behavior of the strategies I test 😢
Ever stumble across a video from a random channel you've never seen before and you INSTA-SUBSCRIBE? This.
I'm a bit confused as to how many times and where to optimise the strategy: 1) we optimise the strategy for the 4 years of test canldes (understandable) 2) for each permutation of the test candles, we re-optimise the strategy for that permutation? That seems very computationally expensive. 3) At 15:54 We optimise the strategy even on the validation data? every 30 days? what is going on? Why aren't we just running the previously optimised strategy on validation data and its permutations? If anyone cared to explain i would be very grateful, thanks!
Thanks for all the videos you make, they are very interesting and informative, it would be nice to see new videos more often...
Hi bro do deploy your strategies and trade them yourself?
The stock market is a conglomerate of waves, modulated by the few waves that have a unique impression or impact on the market’s health, as they are the heavyweights of the trend in the market ocean. Those waves are the market’s main normalizers, impingers, or direction makers. A demodulator is needed to expose a winning entry, as it extracts the original information-bearing signal from a modulated carrier wave. It performs the reverse process of a modulator. An FM Demodulator indicator is used for situations where the direction of the stock or option is uncertain for the immediate future, like minutes ahead of the plot. To be fair, your video is a masterclass.
Great video! Keep making more content like this. I think the approach you shared is solid, but I do have a concern. The effectiveness of a strategy on permuted data is closely tied to the optimization technique and the number of degrees of freedom (i.e., the number of tunable parameters). This reinforces the well-known principle of minimizing the number of variables and avoiding optimization with a large parameter range and small step sizes—essentially, minimizing iterations. If you follow these guidelines, I think running this permutation test may become unnecessary. In your testing, have you found that the in-sample permutation test has failed any strategies that seemed promising after optimization on actual market data, especially those developed with a minimal number of parameters and optimized with restraint?
Does anybody know how I can learn to do this and code this myself. I have coded in python before. Know the basic functions, variables, else and if, while and for loops, any help would really be helpful.
Do these testing methods work on a strategy that trades multiple stocks everyday? If so, how do you organize the data to be processed so that these tests can be done?
@cartilo2619