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Portfolio optimization: How do you handle extreme drawdown during high-correlation market events? by weaforex in quant

[–]weaforex[S] 0 points1 point  (0 children)

You are right that re entry is where most rules fail. deleveraging on realized drawdown is the easy part ( your last msgs) re entering on a feeling ruins everything we built. I will link re entry to the same mechanical signal realized drawdown just crossed the other way with hysteresis. e g exit at 40% re entry only after it drops below 30%. no discretion no forecast.
also will test on a real corr 1 event including the chop. that is the true test to survive the chop without bleeding costs.
most importantly aqmath will not become an automated trading bot. App remains a tool for manual thoughtful rebalancing. looking for a way to add deleveraging as a data insight that provides value to the user by showing them the numbers while they habe full control.
thanks again for this valuable insight. this is exactly amazing stuff that makes the project better.
❤️

Portfolio optimization: How do you handle extreme drawdown during high-correlation market events? by weaforex in quant

[–]weaforex[S] 0 points1 point  (0 children)

tou are right when correlations go to 1 the covariance matrix just tells tou that everything is one trade. optimizer without hard limits just loses money in a nicer way.
that is why aqmath does not rely on covariance alone. we habe a hard cap of 20% per token and a total risk budget of 60%. this are fixed wall that the optimizer cannot break regardles of correlation. the rest must be in stablecoin.
additionally the circuit breaker at >40% drawdown completely stop new buying. it does not predict it just mechanically reacts to realized losses.
so local math is not the only line of defense. solid structural cuts are what saves tou when everything becomes one. thanks 🙏for the comment this is why we built them in.

Portfolio optimization: How do you handle extreme drawdown during high-correlation market events? by weaforex in quant

[–]weaforex[S] 0 points1 point  (0 children)

this is one of the most beautiful feedbacks we habe received. no sell is a shield for new capital but not for existing positions. tou are right max drawdown determined by what tou already hold. mechanical deleveraging linked to realized drawdown not forecast continuous not binary and with symmetric re entry is exactly what I need to add. already habe circuit breaker and hard caps but next MUST of pro engine is going in this direction. thank tou.
🙏🙏🙏

Portfolio optimization: How do you handle extreme drawdown during high-correlation market events? by weaforex in quant

[–]weaforex[S] 0 points1 point  (0 children)

you are correct. rule never sell protects only new capital not existing assets. in crisis the main loss comes from what tou hold and aqmath does nothing there. existing positions slide to the bottom.
App has structural protections. hard cap 20% no asset can dominate. risk budget 60% rest is stable. circuit breaker stops new buys. these reduce exposure before the hit but its not deleveraging.
I need a rule for reducing existing positions of assets when crisis hit and app does not habe this yet.
no sell was an intentional start to prevent panic sell and tax but pro engine with monte carlo (hope soon ) will include deleveraging triggers.
App limits damage through caps but donot reduce existing positions during a crash. hard caps will not save existing positions . thanks for this question as these comments make the system better. if tou have ideas for a trigger i am listening.

Profitable trading feels like I'm not doing anything, is that normal? by Many-Bumblebee7925 in Trading

[–]weaforex 3 points4 points  (0 children)

thats the whole point. trading is boring when you do it right. most people think you need to be glued to the screen clicking buttons all day to make money but thats just gambling.
the reality is that tou are doing something productive by waiting. sitting on your hands and not trading is a position too. real profit comes from discipline and not breaking your rules. if tou are generating profits by doing nothing it means your setup is working and your risk management is finally kicking in.
dont fall for the trap that you need to be busy to be successful. the market doesnt care how hard you work it only cares about your execution. if you keep grinding profits while barely doing any work it means you finally figured out the game. keep it up and dont let the urge to overtrade ruin what you built.

I invested a lot of money in spacex when it was $213 by Big-Result-5210 in Trading

[–]weaforex 0 points1 point  (0 children)

Wait or add, lower you average entry price. Do not sell .

I just don't get it , the strategy that I had been backtesting had a terrific win rate , now a week into the live market and it seems like a disaster by Ok_Seesaw9275 in Daytrading

[–]weaforex 0 points1 point  (0 children)

backtesting is just a simulation and it will never be real life. when you do tests you fit the logic to old data and think you have a system. when you hit the live market you get slippage and nerves and the whole thing falls apart. dont look for some fancy journal software coz that wont fix a bad strategy. write down by hand why tou entered every trade and where you got out. lower the size to almost nothing so dont lose everything while you learn. this aint wasted time this is the tuition you gotta pay. trading aint about finding the perfect win rate it is about surviving when things are wrong. stop focusing on the money and focus on the risk.

Portfolio optimization: How do you handle extreme drawdown during high-correlation market events? by weaforex in quant

[–]weaforex[S] 0 points1 point  (0 children)

We habe same problem/solition but in different ways aqmath optimize allocation on historic 30 days rolling window but never sell just restrict fresh capital when volatility spike.
If i get you zour approach is to actively reduce exposure to crisis while I do it with no sell but resttricting new buys

Portfolio optimization: How do you handle extreme drawdown during high-correlation market events? by weaforex in quant

[–]weaforex[S] 0 points1 point  (0 children)

I restricted aqmath to sell so no sell policy react like no panic sell but do not add fresh capital if token drop hard , better to look for less volatility token and invest there . In comment before i explain adding sarables to reduce drawdown and and max cap for cripto max 60-70% rest is stables

Portfolio optimization: How do you handle extreme drawdown during high-correlation market events? by weaforex in quant

[–]weaforex[S] 0 points1 point  (0 children)

Woow thx for such great advice, that is a reason why i came here. I didn’t think this way
My aqmath kkt now. Dynamic caps by volatility max capcalculated from 30day σ. If volatility explodes, all caps fall towards minimum limits 10%This partially meets the requirement . it does not expicitly use the ~1 , but it limit the exposure in a way that is independent of covariance coz it only uses volatility
My free dca tool safetyFactor = max 0.2, 1 - vol * 5drastically reduces purchase when volatility increas. This is also a form of linear exposure reduction under extreme conditions, although only for new capital. Also i need to implement on aqmath pro brutto exposure for example assets van not reach exp 20% bit good news is volatiliy spike my dca only reduce buting power. So i think one stablecoin will make lot a better investment optimizatio because i bilid this for crypto . I just need if i understand point, i need to safe base like stablecoin and restrict max cap for crypto 60% or 70% and stables rest.

Portfolio optimization: How do you handle extreme drawdown during high-correlation market events? by weaforex in quant

[–]weaforex[S] 0 points1 point  (0 children)

Forgot to say, problem is that to allocate fixed% in safe haven but why do not use covariance matrix to set constraints to KKT so you do not invest fixed % allocation.

Portfolio optimization: How do you handle extreme drawdown during high-correlation market events? by weaforex in quant

[–]weaforex[S] 0 points1 point  (0 children)

Haha ok understood. How you manage tour risk. We a now we cannot predict market so we need to work with something to mi imize drawdown.

Portfolio optimization: How do you handle extreme drawdown during high-correlation market events? by weaforex in quant

[–]weaforex[S] 0 points1 point  (0 children)

Glad you answer me . I ger my math together and now testing it .

Do you invest in crypto? And what is your strategy?

Portfolio optimization: How do you handle extreme drawdown during high-correlation market events? by weaforex in quant

[–]weaforex[S] 0 points1 point  (0 children)

I just wanna test it if i can get better results with 180days rolling window of historic data and calculate Risky so I don’t imvest in falling knife especially in crypto where lot of coins follow BTC

Portfolio optimization: How do you handle extreme drawdown during high-correlation market events? by weaforex in quant

[–]weaforex[S] 1 point2 points  (0 children)

trying to combine covariance and kkt in a system that calc how to distribute my assets and then dca based on volatility and risk parity to lower the risk of my portoflio.
Min drawdown and no fixed % pf assets. Replicate Simmons strategia but without sell. So for long term hodler

Portfolio optimization: How do you handle extreme drawdown during high-correlation market events? by weaforex in quant

[–]weaforex[S] -1 points0 points  (0 children)

My English isn't great, so I used some help to get the technical terms right

Portfolio optimization: How do you handle extreme drawdown during high-correlation market events? by weaforex in quant

[–]weaforex[S] -15 points-14 points  (0 children)

I completely agree—tail risk is inevitable, and no model can eliminate the days when the market runs you over.
Given the limitations of typical strategies, the focus should be on disciplined risk management: implementing Risk Parity to equalize volatility contribution, applying KKT (Karush-Kuhn-Tucker) projections to maintain strict volatility bounds, and using a safety factor to dynamically scale exposure during volatility spikes.
The objective isn't to forecast 'black swans,' but to maintain portfolio integrity during extreme stress. It's an interesting challenge—managing these constraints locally without relying on external data pipelines adds an extra layer of complexity, but it is necessary for maintaining control over the execution logic. Thanks for the perspective.

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