Quantitative Study Of Noise Volatility Relationship in Price Action | Real-World Trading Approaches

[@RightBackInRehab]

Looking forward to the volatility series!

[@ctrodrums]

Demolishing again entire walls of misinformed concepts on trading. Looking forward to the next episode!

[@seanjohnson7145]

Thanks Martyn! hands down, the best resource I've found for algo trading

[@kylemaharaj9563]

Very grateful for your content 🙏

[@mohamadalikolahdouzmohamad5877]

These materials are as precious as gold. Thank you sir. Although I haven't been able to create profitable algorithm but I strongly believe that the best and maybe the only way of having an edge over the market is to be as objective and scientific as this episode and the whole series you are sharing are.

[@jack_sparrow1049]

This series was amazing. Thanks a lot

[@YairElmaliah]

Hi Martyn , as usual very informative video ! , would be amazing if you consider doing a series about data (especially on Forex data), whether we need tick data , m1 data , 1m ohlc, fixed spread vs recorded one , differences between slippage and spread (& how to take them into account in our back-test), and basically the importance of an accurate data and how we can achieve optimal results when dealing with historical data correctly .. Just Consider it :) Thanks again you are by far the best teacher in this field on YT !

[@albertocastellucchio3865]

So if i understand correctly we could exploit those moments of correlation (both noise and volatility growing) with a mean reverting strategy until the market regime shifts. And maybe a trend strategy would better fit a high volatility low noise setup. Did i miss something?

[@chibuikemezepue3569]

Amazing content. I definitely cannot wait!

[@saurabhlaloraya]

thanks for this explanation

[@mattlewis-UK]

Immense ! What a study of work and series of YT episodes. Brilliant and thanks for all you have shared. Question: Could you use Noise as an alternative for the ADX when it comes to saying something is trending or range bound? Or would they compliment each other and thus utilising them as a pair would be better?

[@Darwinexchange]

No. ADX helps with market regime. Noise tends to be more of a nuisance (especially for trend following systems) and tends to get in the way of identifying the real signal.

[@matevzokrslar1038]

Hi Martin. Thank very much for the great content you are delivering... A quick question... you were mentioning upcoming volatility series... is it still planned? Thanks.

[@amaldevasia4143]

Why did you use price density which is of less useful than effitiency ratio to find noise ?

[@laughinginthe90s]

Are those instantaneous correlations related to news?

[@maluzfilm1136]

When you stated - via the yellow lines - that on smaller timeframes you could actually have 7-8 candles exhibiting high correlation, did you actually verify these as coming in succession? / Thanks again for interesting and high quality information shared.

[@khalidbinreaz8039]

Darwinex as a trader provide any core volume indicator,,,,or ask and bid volume,,,, institution level???

[@Alexter6277]

Great explanation, very clear and by adding this to the previous episodes it gives a great variety of ideas for advanced trade filtering in strategies mixing both. In your opinion, would it be beneficial to look at ATR and efficiency metrics in a relative Percentile way to quantify relative volatility and efficiency instead of absolute. (Eg, absolute would be using ATR% or classic ATR with X value as threshold. While relative would be to calculate the relative Percentile rank of ATR value vs the previous say 200 and that way have a relative figure if ATR from 0-100 to measure volatility. Similar concept can be applied to efficiency and could be used for filtering.) This would be separate than for risk management since ATR% or classic ATR with multipliers are (in my experience) more useful for dynamic stops and risk allocation.

[@mjay9283]

Good episode. I've already taken a look at the KAMA from within algo-trading series. It seems to reduce whip saws from observation, however I still find that it lags much for my own liking. Perhaps I'll find valuable clues on next episode... :-) can't wait!

[@madlarch]

@Mjay - have you tried playing around with the ER parameter in KAMA. You can, for example, use 5 instead of the standard 10 in order to reduce lag. You can also get useful information from longer periods such as 20 or even 30. Best of luck!