Technical Analysis During Volatility

[TrendSpider_Jake]

One of the biggest mistakes traders make is getting too comfortable with a technical signal that they have been trading for a couple months and they become complacent. Once the status quo changes and that technical signal is no longer "popular" which is what made it self-fulfilling in the first place, the trader gets burned on the trade and has to find a new signal that is coinciding with price moves. This has happened a lot since October...

Has this ever happened to you?

 

[cafeole]

I mentioned in another thread that I have noticed the 1 min charts now look like the 3 and 5 min charts, so just use the 1 min timeframe.

 

[panzerman]

Sort of as if prices randomly changed their state. As if prices were randomly walking around. Maybe one could model such price behavior with geometric Brownian motion (waiting for Mr. Mcginnis to chime in.)

St = St-1* exp((μ-(σ2/2))*t + σWt)
Where,
St is stock price at time t
St-1 is stock price at time t-1
μ is the mean daily returns
σ is the mean daily volatility
t is the time interval of the step
Wt is random normal noise

 

[speedo]

I've basically had the same signals for years. Changing market conditions require time frame and target/stop adjustments. As I use tick charts, tf's are endlessly variable.

 

[tomorton]

I don't accept that signals work because they're popular and fail when they lose their popularity. In reality, this was never a good signal in the first place, it was just generating wins the way a coin toss can generate extended series of wins - even a useless trading strategy like this can still look like its working, but it have nothing to do with the markets changing or losing its popularity.

 

[fordewind]

Sort of as if prices randomly changed their state. As if prices were randomly walking around. Maybe one could model such price behavior with geometric Brownian motion (waiting for Mr. Mcginnis to chime in.)

St = St-1* exp((μ-(σ2/2))*t + σWt)
Where,
St is stock price at time t
St-1 is stock price at time t-1
μ is the mean daily returns
σ is the mean daily volatility
t is the time interval of the step
Wt is random normal noise

Mr. Mcginnis answer should be "averaging"

 

[wrbtrader]

One of the biggest mistakes traders make is getting too comfortable with a technical signal that they have been trading for a couple months and they become complacent. Once the status quo changes and that technical signal is no longer "popular" which is what made it self-fulfilling in the first place, the trader gets burned on the trade and has to find a new signal that is coinciding with price moves. This has happened a lot since October...

Has this ever happened to you?

The issue is the volatility...its not the popularity or lack of such involving the technical signal. Simply, the volatility will still impact technical signals not popular or custom designed (nobody else is using it except for that specific trader).

In October, a season trader would have noticed the increased speed in the price action, increased speed in profit targets being reached, increased speed in stop/loss protection being hit, increased number of no fills, increased number of trade signals, unusual profits, unusual losses and many other unusual things that a season trader should have picked up on.

Also, I think traders using a technical signal only for a couple of months will be more jumpy or less complacent when October showed up in comparison to someone using the exact same technical signal for many years. Thus, the less experience one has with a technical signal...the faster they are going to abandon it and look for something else at the first signs of trouble.

I'm sure months like October to the first few weeks of November taught many valuable lessons...primarily to pay attention to volatility analysis regardless to what type of a technical signal/pattern someone is using...at least it should encourage many to look at their backtest results to see how their technical signal performed in high volatility trading conditions.

The results of looking at the historical performance they can exploit it the next time unusual high volatility conditions show up in the markets or at least stay on the sideliness if their backtest results show poor trading during high volatility conditions.

wrbtrader

 

[Handle123]

Number of years ago, and done every month, identify what is considered "normal" swings for each market that is scalped/day traded. When second to second and minute to minute increased/decreased speed develops, automation for that market is altered by percentages of deviations, thereby protective stops and targets changed, but trading continues up to a point. Where trading is halted is lack of volume by a money increment. i.e. 100 lots per $100, it is too much risk based on back testing of possible risk of ruin for a given day. Systems use max times two based on back testing of worst days covering fifteen years, so as way of not wiping out account, at no time over 5% of account is at risk of general area.

High volatility means to me reduced available lots/shares which dictates to automatic changes of size traded and system parameters must change.

You either learn to adapt or lose opportunities/your account.

 

[EsKiller]

Op is clearly trying to suck some newbies in to his services.

The answer is NO to all of the above. Hasn't happened at all. The way price zig zags chances, but how I get in and out never does. Been working for years. Widen the stops, widen the targets, that's about it.

 

[gaussian]

Sort of as if prices randomly changed their state. As if prices were randomly walking around. Maybe one could model such price behavior with geometric Brownian motion (waiting for Mr. Mcginnis to chime in.)

St = St-1* exp((μ-(σ2/2))*t + σWt)
Where,
St is stock price at time t
St-1 is stock price at time t-1
μ is the mean daily returns
σ is the mean daily volatility
t is the time interval of the step
Wt is random normal noise

I don't know what this post is getting at. Its so low on signal/noise I can't even tell if you're trolling.

The lognormality of stock prices is an assumption that is a little touchy. Under the assumption of GBM (and therefore lognormality) implies a price change series that is approximately normal. If you plot volatility on any instrument, you can see this is not true. Additionally we can see that a key assumption in GBM can be proved false by observing GARCH/ARMA modelling of financial series. Volatility and drift are not constant. In fact regimes can be identified quite easily without any mathematical sophistication proving this. Options models fall apart under these assumptions leaving room for sophisticated arbitrageurs to take riskless profits using better and more accurate models.

Furthermore, prices don't "fluctuate randomly" in the truest statistical sense of the word. This is a stylized fact used by economists and financial engineers to make "good enough" models. Read the above why most models fail. This "random fluctuation" really gets close to the Efficient Market Hypothesis which is quite possibly the greatest piece of garbage ever to infect the industry. You can prove these things at least partly wrong on any series by identifying psychological price barriers. In a true GBM/random process these would not exist with any sort of consistency. More to the point, the fact you can trade on news alone shows that there is more than randomness in a price series in reality. Even more to the point, certain formations of candles have predictive power that is statistically significant.

I don't fault model creators for simplifying reality - I do it all the time in my day job. I do fault people who mindlessly quote financial literature without understand the THOUSANDS of caveats that come with it. "Good enough" models are fantastic when you understand the caveats that come with them. However almost nobody does. The math is hard. This attitude of dismissive yeah but random walk and Geometric Brownian Motion as you look smugly down your nose at the pleb who DARE question that one paragraph from the one book you read, and the thing Joe Rogan said once in a 5min brainless blurb on something else he doesn't understand - it needs to stop. It just makes so much better than you mindlessly quoted the formula without even realizing the constraints on volatility and drift!

One of the biggest mistakes traders make is getting too comfortable with a technical signal that they have been trading for a couple months and they become complacent. Once the status quo changes and that technical signal is no longer "popular" which is what made it self-fulfilling in the first place, the trader gets burned on the trade and has to find a new signal that is coinciding with price moves. This has happened a lot since October...

Has this ever happened to you?

Build volatility into your technical signals? Volatility is mean reverting and therefore predictable. Augmenting your current strategy with it should be fairly easy.