MR
Montage Resources Corporation Common Stock
stock NYSE

# SUCK MY JONES, MR DOW!

MR. GUH

Hey OP! I'm curious about your findings of filter effectiveness on black swan events.
I read one of Kevin Davey's book - Algo Trading cheat codes and applied a similar research framework from there.
The main concept is to compare backtest results between the baseline strategy and the filtered version to find if there is an overall improvement when adding a certain filter type to a specific strategy archtype. I tested 3 baseline strategies following Mean reversion, Trend following and Breakout logic, and 3 different filter groups, BullBear, Volatility classifiers and Regime filters (market state: TF or MR).
Using the law of large numbers and to prevent the risk of overfitting, I compared backtest results over 26 markets spread across FOREX, Commodities, Index and crypto. I then aggregate the results to find these 3 metrics which are useful in determining the filter's effectiveness, %_of_markets_improved Ret/DD (risk-adjusted returns), profit factor (trade quality), efficiency (defined as Ret/DD / %Exposure). Generally, when a strategy achieves > 50% on all metrics, it signals that the filter complements the strategy archetype.
If you are happy to share your findings, I will be glad to share mine too.

hey, how did you make the move from MR into trading?

First of all thanks for replying without being rude. Much appreciated. The way I scoped this originally, I determined which instruments I wanted that fit my classifiers (MR, trend etc) then set about building around that theory. I decided not to backtest because I'd read about over fitting and came to the conclusion it would probably tell me at best what might not work rather than what could. I am now running a small backtest out of curiosity. I'll report back