EQma - Adaptive Smoothing Based On Optimal Markets DetectionIntroduction
"You don’t put sunscreen when there is no sun, you don’t use an umbrella when there is no rain, you don’t use a kite when there is no wind, so why would you use a trend following strategy when there is no trend ?"
This is how i start my 4th paper "A New Technical Indicator For Optimal Markets Detection" where i present two new technical indicators. We talked about the first one, running equity, which aim to detect the best moment to enter trades, based on this new metric i made an adaptive moving average.
You can see the full paper here figshare.com
The Indicator
The moving average is based on exponential averaging and use a smoothing variable alpha based on the running equity metric, in order to calculate alpha the running equity is divided by the optimal equity which show the best returns possible for the conditions used. Basically the indicator work as follow :
When the running equity is close to the optimal equity it means that the price need no/little filtering since it does not contain information that need to be filtered, therefore alpha is high, however when the running equity is far from the optimal equity this mean that the price posses malign information that need to be removed.
This is why the indicator will be closer to the price when length is high :
See the full paper for an explanation on how this work.
I added various options for the indicator, one will reduce the lag by squaring alpha, thus giving for length = 14 :
The efficient option will make use of recursion to provide a more efficient indicator :
In green the efficient version, note how this option can allow a better fit with the price.
Conclusion
This is an indicator but at its core its rather a framework, if you have read the paper you'll see that the conditions are just 1 and -1 that changes with time, basically its like making a strategy with :
Condition = if buy then 1 else if sell then -1 else Precedent value of condition.
So those two indicators allow to give useful and usable information about your strategy. I hope it can be of use for anyone here, if so don't hesitate to send me what you made using the proposed indicator (and with all my indicators in general). If you are writing a paper and you think this indicator could fit in your work then let me know so i can be aware of it :)
Thanks for reading !
Acknowledgement
My papers are quite ridiculous but they still manage to get some views, some researchers don't even reach those number in so little time which is quite unfortunate but also really motivating for me, so thanks to those who take time to read them and give me some feedback :)
Trendstrength
Running Equity - A New Indicator For Optimal Markets DetectionIntroduction
Winning trades and gaining profits in trading is not impossible, however having gross profits superior to gross losses is what make trading challenging, it is logical to think that it is better to open a position when the probability of winning the trade is high, such probability can’t be measured with accuracy but a lot of metrics have been proposed in order to help determining when to open positions, technical analysis support the fact that a trending market is the best market condition for opening a position, which is logical when using a trend following strategy, therefore a long-term positive auto-correlated market is optimal for trading, this is why this paper present a new method for detecting optimal markets conditions in order to open a position.
The Indicator
The proposed indicator is based on the assumption that positive returns using a trend following strategy are a strong indication of trend strength, the proposed indicator is built from the conditions of a simple SMA cross trend following strategy, which are to go long when price > SMA and to go short when price < SMA. Then the equity from those conditions is built, in order to provide a more flexible indicator, length control the period of the sum.
When the indicator is positive it means that the market allow for potential returns, it can thus be considered being trending. Else a negative value of the indicator indicate a ranging market that won't allow for returns.
Filtering Bad Trades
The indicator can be used to filter bad trades entries, in this example a Bollinger band breakout strategy is used, without any changes the strategy return the following equity on EURUSD
The proposed indicator is then applied with the following conditions : buy and sell only if Req > 0
With an indicator period = 100 we filtered unprofitable trades.
Conclusion
I presented a new indicator for the detection of optimal markets based on a running equity. I hope both indicators may find applications in technical analysis and help investors get pertinent outputs from them.
it would mean a lot if you could read the original paper : figshare.com
Relative Vigor IndexHere we are looking at a trend strength indicator based on the Relative Vigor Index(RVI). The RVI measures trend strength by comparing the open-close and high-low ranges for the current and three most recent periods. As a zero-centered oscillator, the RVI oscillates above and below zero to signal the strength of the trend.
As there are different ways to interpret the RVI, we have included 3 different modes for traders to choose from in the input option menu:
1. Zero-Crossing:
The RVI Histogram will turn green when it crosses above zero and red when it crosses below. Therefore, a green RVI means the trend is bullish and red means bearish. This mode is better for longer-term swing trading in comparison to the other 2 modes.
2. Increasing / Decreasing:
The RVI histogram will turn green when it is increasing(rvi >= rvi ) and red when it is decreasing. A green RVI is viewed as a bullish signal and red means bearish. This mode is a good middle-ground between the Zero-Crossing and Signal Comparison modes.
3. Signal Comparison:
Here, the RVI is compared to its signal line. If the RVI is greater than its signal line, the histogram is green, indicating a bullish trend, while red means bearish. This mode is preferred for scalping.
Hope everyone finds this one useful!
You can check out our other invite only studies/strategies at our website: profitprogrammers.com
Surface Roughness EstimatorIntroduction
Roughness of a signal is often non desired since smooth signals are easier to analyse, its logical to say that anything interacting with rough price is subject to decrease in accuracy/efficiency and can induce non desired effects such as whipsaws. Being able to measure it can give useful information and potentially avoid errors in an analysis.
It is said that roughness appear when a signal have high-frequencies (short wavelengths) components with considerable amplitudes, so its not wrong to say that "estimating roughness" can be derived into "estimating complexity".
Measuring Roughness
There are a lot of way to estimate roughness in a signal, the most well know method being the estimation of fractal dimensions. Here i will use a first order autocorrelation function.
Auto-correlation is defined by the linear relationship between a signal and a delayed version of itself, for exemple if the price goes on the same direction than the price i bars back then the auto-correlation will increase, else decrease. So what this have to do with roughness ? Well when the auto-correlation decrease it means that the dominant frequency is high, and therefore that the signal is rough.
Interpretation Of The Indicator
When the indicator is high it means that price is rough, when its low it indicate that price is smooth. Originally its the inverse way but i found that it was more convenient to do it this way. We can interpret low values of the indicator as a trending market but its not totally true, for example high values dont always indicate that the market is ranging.
Here the comparison with the indicator applied to price (orange) and a moving average (purple)
The average measurement applied to a moving average is way lower than the one using the price, this is because a moving average is smoother than price.
Its also interesting to see that some trend strength estimator like efficiency ratio can treat huge volatility signals as trend as shown below.
Here the efficiency ratio treat this volatile movement as a trending market, our indicator instead indicate that this movement is rough, such indication can avoid situation where price is followed by another huge volatile movement in the opposite direction.
Its important to make the distinction between volatility and trend strength, the trend is defined by low frequencies components of a signal, therefore measuring trend strength can be resumed as measuring the amplitude of such frequencies, but roughness estimation can do a great job as well.
Conclusion
I have showed how to estimate roughness in price and compared how our indicator behaved in comparison with a classic trend strength measurement tool. Filters or any other indicator can be way more efficient if they know how to filter according to a situation, more commonly smoothing more when price is rough and smoothing less when price is smooth. Its good to have a wider view of how market is behaving and not sticking with the binary view of "Trending" and "Ranging" .
I hope you find a use to this script :)
Best Regards
