.

[sidhujag]

u will never get time right unless ur name is gann.. just wait for price to show ob or os and buy sell accordingly...

The bollinger bands you seem to be plotting is probably lagging and sometimes it makes sense to take swings but when the breakout occurs you will see an extremely ob aituation where most will sell but it will change the band direction and continue upwards or downwards whichever the case...

My point is the best thing to do is to work on identifying range vs breakout trend markets and switch stategies accordingly... Because anything else is just random guessing.

Even trying to detect a range may be random but if you can do a backtest using stats show some causal relationship then you can try to forward test to see if it holds.. Most of the time achieving even a 1% edge is enough to make it long term but its easier said than done.. Hope u know what im talkin about.

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[sidhujag]

looks like it either way any averaging is obviously lagging the same rules apply...

Hey ive worked alot with bands im just giving insight based on experience..

ie what happens if price touches the lower line would you buy because statistically u think it should come back to average? If you incorrectly identify range vs breakout you will be left holding the
bag probably trying to add more trades to average you entry price lower which in itself is a losing strategy long term. If you do work to identify and then say statistically its probably a swing then u take it and let stat edge help u.

[sidhujag]

looks like it either way any averaging is obviously lagging the same rules apply...

Hey ive worked alot with bands im just giving insight based on experience..

Have you worked with Markov chains?

It is the output of many montecarlo trials. Actually. What is shown are the quantiles of the distribution of those trials, not an average, anywhere.

I can look at it and identify its working as an average easy to tell just look at the bottom green line curving up because price is above the middle and the red line is yet to curve up yet.

[sidhujag]

How are you constructing your markov chains? What inputs are you using?

[sidhujag]

How are you constructing your markov chains? What inputs are you using?

I have the raw input data redimensioned into 'bins' of similar amounts of BTC volume. You might think of these bins kind of like candles, with a high and low price, a volume weighted average, etc. The markov is constructed in part from discrete samples the percent change of the VWA from bin to bin going back in the history by a certain number of samples. But I selectively invert these samples depending on if they are a member of either an uptrending or downtrending price regime as identified by what I have been calling a "reversal indicator" which is really troublesome to describe.

Here'e why I am doing this. The reversal indicator shows pretty clearly when we change between uptrending and downtrending price regimes, but only after the fact. It is pretty easy to look at an indicator after the fact to see what it is showing, that's no big whoop. So what I am trying to do is to see if I can project this indicator to see if I can tell when it is about to turn. That's what this is all about.  

I see so are you trying to detect a VWA candle in real-time by sampling the chain to detect if its a reversal? How much data do you train it with? I would assume all of the daily candles (market is not that old yet to train fully?) Maybe do a weekly or monthly to create a multi-time frame analysis to improve lower time-frame confidence.

Why can't you use yesterdays daily candle to determine if it was a reversal or not? That is not after the fact?

What about defining the signature of a reversal? Sometimes a sequence of candles provide a higher likely hood that it is a reversal situation. Would this model be able to sample based on a set of related inputs?

How are you defining the up/down trend price regimes in order to invert some samples?

[sidhujag]

How are you constructing your markov chains? What inputs are you using?

I have the raw input data redimensioned into 'bins' of similar amounts of BTC volume. You might think of these bins kind of like candles, with a high and low price, a volume weighted average, etc. The markov is constructed in part from discrete samples the percent change of the VWA from bin to bin going back in the history by a certain number of samples. But I selectively invert these samples depending on if they are a member of either an uptrending or downtrending price regime as identified by what I have been calling a "reversal indicator" which is really troublesome to describe.

Here'e why I am doing this. The reversal indicator shows pretty clearly when we change between uptrending and downtrending price regimes, but only after the fact. It is pretty easy to look at an indicator after the fact to see what it is showing, that's no big whoop. So what I am trying to do is to see if I can project this indicator to see if I can tell when it is about to turn. That's what this is all about.  

I see so are you trying to detect a VWA candle in real-time by sampling the chain to detect if its a reversal? How much data do you train it with? I would assume all of the daily candles (market is not that old yet to train fully?) Maybe do a weekly or monthly to create a multi-time frame analysis to improve lower time-frame confidence.

Why can't you use yesterdays daily candle to determine if it was a reversal or not? That is not after the fact?

What about defining the signature of a reversal? Sometimes a sequence of candles provide a higher likely hood that it is a reversal situation. Would this model be able to sample based on a set of related inputs?

How are you defining the up/down trend price regimes in order to invert some samples?

I actually base the tuning on a Bayesian posterior analysis. The calibrations in question are a minimum sample size for the first step in the chain, and the sample size grows at a certain calibrated rate with each subsequent step. I don't care to share exactly what those calibrations are. But I really want to emphasize that time is not used at all as an independent variable, I am only considering the passage of trade volume. Many tests have demonstrated to me that this is key to reducing variance.

As far as the up/down goes, that is based on the reversal indicator. What this does basically is to compare the difference between how BTCOBV and $OBV change in relation to price. The response of this indicator is much sharper at transitions of trend regimes than can be seen just by looking at price. My working theory is that it shows when either BTC or $ at play during the last regime become nearly exhasted, and the trend reverses.

If you're talking about using volume over time which is fair you've really opened a can of worms lol Volume based analysis is a study on its own... THis is why I asked about the signature of a reversal because this can be either:

1) High volume Squat or Doji with stopping volume
2) No Demand bars (end up uptrend) with low volume
3) Accumulation bars with high volume (end of downtrend)
4) Upthrust (end of downtrend or continuation of uptrend by pushing through supply)

I would suggest reading Mastering the markets which focuses on using Volume to determine price action:
http://vsa.pipbuilders.com/mtmv3.pdf

Jag

[sidhujag]

How are you constructing your markov chains? What inputs are you using?

I have the raw input data redimensioned into 'bins' of similar amounts of BTC volume. You might think of these bins kind of like candles, with a high and low price, a volume weighted average, etc. The markov is constructed in part from discrete samples the percent change of the VWA from bin to bin going back in the history by a certain number of samples. But I selectively invert these samples depending on if they are a member of either an uptrending or downtrending price regime as identified by what I have been calling a "reversal indicator" which is really troublesome to describe.

Here'e why I am doing this. The reversal indicator shows pretty clearly when we change between uptrending and downtrending price regimes, but only after the fact. It is pretty easy to look at an indicator after the fact to see what it is showing, that's no big whoop. So what I am trying to do is to see if I can project this indicator to see if I can tell when it is about to turn. That's what this is all about.  

I see so are you trying to detect a VWA candle in real-time by sampling the chain to detect if its a reversal? How much data do you train it with? I would assume all of the daily candles (market is not that old yet to train fully?) Maybe do a weekly or monthly to create a multi-time frame analysis to improve lower time-frame confidence.

Why can't you use yesterdays daily candle to determine if it was a reversal or not? That is not after the fact?

What about defining the signature of a reversal? Sometimes a sequence of candles provide a higher likely hood that it is a reversal situation. Would this model be able to sample based on a set of related inputs?

How are you defining the up/down trend price regimes in order to invert some samples?

I actually base the tuning on a Bayesian posterior analysis. The calibrations in question are a minimum sample size for the first step in the chain, and the sample size grows at a certain calibrated rate with each subsequent step. I don't care to share exactly what those calibrations are. But I really want to emphasize that time is not used at all as an independent variable, I am only considering the passage of trade volume. Many tests have demonstrated to me that this is key to reducing variance.

As far as the up/down goes, that is based on the reversal indicator. What this does basically is to compare the difference between how BTCOBV and $OBV change in relation to price. The response of this indicator is much sharper at transitions of trend regimes than can be seen just by looking at price. My working theory is that it shows when either BTC or $ at play during the last regime become nearly exhasted, and the trend reverses.

If you're talking about using volume over time which is fair you've really opened a can of worms lol Volume based analysis is a study on its own... THis is why I asked about the signature of a reversal because this can be either:

1) High volume Squat or Doji with stopping volume
2) No Demand bars (end up uptrend) with low volume
3) Accumulation bars with high volume (end of downtrend)
4) Upthrust (end of downtrend or continuation of uptrend by pushing through supply)

I would suggest reading Mastering the markets which focuses on using Volume to determine price action:
http://vsa.pipbuilders.com/mtmv3.pdf

Jag

It is usually winds up being higher volume at the bottoms and lower volume at the tops.

Yup but not necessarily true, sometimes you see high volume tops... as the market matures you will see more of the different types for which you will have to factor in. These are tried, tested and true forms of volume analysis leading up to deterministic price action. But I think the model works well for what we have right now all you can really do is play the percentages, when you get errors you go back to the drawing board.

[sidhujag]

I see, well cool goodluck, I have the thread on my watchlist to see new updates improvements that you come up with!

[TERA]

chodpapa, do you still see this drop to 80-110 that your indicator predicted occurring or do you think we might break out of this ~140 pennant now and have a run instead? If this continues for 1 more day we will touch upper bolly and cross MACD.

[TERA]

chodpapa, do you still see this drop to 80-110 that your indicator predicted occurring or do you think we might break out of this ~140 pennant now and have a run instead? If this continues for 1 more day we will touch upper bolly and cross MACD.

In my best estimation that might describe a lower bound on an intermediate scale. But right now I am considering that we won't be trending strongly enough on an intermediate scale to rely on trend reversals the way I have been looking at them. My working thesis at the moment is that we can look forward to the 'slow grind'.

But I have to ask, MACD using what data? There are some who would say that Gox data should simply be dismissed.

Right, its only imminent on gox.  Bitstamp might take a few more days, and probably needs to break this 127 that keeps popping up. But if the mtgox run continues, it might cause that to happen on bitstamp by proxy.

There sure seems to be a lot of buying pressure on gox. It's like  while(true) { market buy 150btc; wait 10 minutes;}

[fallinglantern]

chodpaba, what's the X axis on that latest set of charts?

[maxll]

So, another market reversal ahead? 

[SheHadMANHands]

So, another market reversal ahead?  

On an intermediate time scale (monthly) I think we will be trending less strongly than we have in the price swings since the $266 peak. To set us up the bomb would be a long, slow grind like we experienced in the latter half of 2012. Which we most likely wont see if we have a sizeable runup too early.

As long as they are not too deep, some dips along the way will actually increase the potential for a higher peak at the end of the grind, because they build belief in price strength.

That's pretty interesting.