Indicator
TA Basics: Creating a Fibonacci Weighted Moving AverageIn the previous 2 posts in this series, we played around with simple math concepts to create a zero-lag moving average that can deliver fast response and less lag - that we can use to enable better trend following, or as filter / signal.
here we take a step further - instead of using equal weight for the moving average (as in the Simple moving average) or linear weights (as in the weighted moving average), we get to pick THE MAGIC SEQUENCE, Fibonacci.
we will use the Fibonacci Sequence as weights to produce our moving average - so practically, we create a "Fibonacci Weighted Moving Average" (let's call it FiMA) - and compare the result with other commonly-used moving averages of the same length
in a Fibonacci moving average, the data will be weighted based on the Fibonacci Series starting from 1 (for the furthest data point)
so for example, if we use a length of 10, the weights will be 1, 1, 2, 3, 5, 8, 13, 21, 34, 55 with the 55 being the weight applied to the most recent / current bar's selected value (close, hl2, hc3..etc) and moving backward
before i posted this script, i searched around to see if someone else has already wrote this - i found a couple, but the approach we use here in this code is different - i can't claim it to be more efficient - i honestly don't know - but the resulting code here, IMHO, is more compact and easier to integrate in other studies that you may like to put together to leverage this idea, to create your own indicators and strategies.
the reason the code here is more compact, is that it utilizes a shorter formula to calculate the FIb(n) - i included the source where i found that formula, and i tested it before using it in the code.
i also added an optional "extra smoothing" for the resulting MA, by simply calling the fima() function a second time (so like doing a 2-pass filter), with a smaller length on the result of the 1st pass. keep this smoothing small not to produce too much lag.
i like the outcome when compared to other moving averages - it has a fast response to data/trend change and less overshoot - but honesty i didn't see any real "Fibonacci Magic" :) .. but i'll leave the final judgement to those who use it - this is more of an experimental code in all cases - please feel free to use, change and share feedback.
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Decaying Rate of Change Non Linear FilterThis is a potential solution to dealing with the inherent lag in most filters especially with instruments such as BTC and the effects of long periods of low volatility followed by massive volatility spikes as well as whipsaws/barts etc.
We can try and solve these issues in a number of ways, adaptive lengths, dynamic weighting etc. This filter uses a non linear weighting combined with an exponential decay rate.
With the non linear weighting the filter can become very responsive to sudden volatility spikes. We can use a short length absolute rate of change as a method to improve weighting of relative high volatility.
c1 = abs(close - close ) / close
Which gives us a fairly simple filter :
filter = sum(c1 * close,periods) / sum(c1,periods)
At this point if we want to control the relative magnitude of the ROC coefficients we can do so by raising it to a power.
c2 = pow(c1, x)
Where x approaches zero the coefficient approaches 1 or a linear filter. At x = 1 we have an unmodified coefficient and higher values increase the relative magnitude of the response. As an extreme example with x = 10 we effectively isolate the highest ROC candle within the window (which has some novel support resistance horizontals as those closes are often important). This controls the degree of responsiveness, so we can magnify the responsiveness, but with the trade off of overshoot/persistence.
So now we have the problem whereby that a highly weighted data point from a high volatility event persists within the filter window. And to a possibly extreme degree, if a reversal occurs we get a potentially large "overshoot" and in a way actually induced a large amount of lag for future price action.
This filter compensates for this effect by exponentially decaying the abs(ROC) coefficient over time, so as a high volatility event passes through the filter window it receives exponentially less weighting allowing more recent prices to receive a higher relative weighting than they would have.
c3 = c2 * pow(1 - percent_decay, periods_back)
This is somewhat similar to an EMA, however with an EMA being recursive that event will persist forever (to some degree) in the calculation. Here we are using a fixed window, so once the event is behind the window it's completely removed from the calculation
I've added Ehler's Super Smoother as an optional smoothing function as some highly non linear settings benefit from smoothing. I can't remember where I got the original SS code snippet, so if you recognize it as yours msg me and I'll link you here.
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Multi-TF Avg BBandsMULTI-TF AVERAGE BBANDS - with signals (BETA)
Overall, it shows where the price has support and resistance, when it's breaking through, and when its relatively low/high based on the magic of standard deviation.
created by gamazama. send me a shout if u find this useful, or if you create something cool with it.
%BB: The price's position in the boilinger band is converted to a range from 0-1. The midpoint is at 0.5
Description of parameters
"BB:Window Length" is the standard BB size of 20 candles.
The indicator plots up to 7 different %BB's on different timescales
They are calculated independently of the timescale you are viewing eg 12h, 3d, 30m will be the same output
You can enter 7 timescales, eg. if you want to plot a range of bbands of the 12h up to 3d graphs, enter values between 0.5 and 3 (days) - you can also select 0 to disable and use less timescales, or select hours or minutes
Take note if you eg. double the main multiplier to 40, it is the same as doubling all your timescales
You can turn the transparency of the 7 x %BB's to 100 to hide them, their average is plotted as a thick cyan line
"Variance" is a measure of how much the 7 BB's agree, and changes colour based on the thresholds used for the strategy
---- TO START FROM SCRATCH ----
- set all except one to ZERO (0), set to 0, and everything after to 0.
Turn ON and right click -> move the indicator to a new pane - this will show you the internal workings of the indicator.
Then there is a few standard settings
"Source Smoothing Amount" applies a basic small sma on the price.
It should be turned down when viewing candles with less information, like 1D or more.
Standard BBands use an SMA, there one uses a blend between VWMA or SMA
Volume Weight settings, the same as SMA at 0, and the same as VWMA at 1
BB^2 is a bband drawn around the average %BB. Adjust the to change its window length
The BB^2 changes color when price moves up or down
Now its time to look at the parameters which affect the buy/sell signals
turn on "show signal range" - you see some red lines
buy and sell each have 4 settings
min/max variance will affect the brigtness of the signal range
range adjust will move the range up/down
mix BB^2 blends between a straight line (0) and BB^2's top or bottom (1)
a threshold of "variance" and "h/l points" is available to generate weaker signals.
these thresholds can be increased to show more weak signals
ONCE YOU ARE HAPPY WITH THE SIGNALS being generated, you can turn OFF , and move it back to the price pane
the indicator then draws a bband around the price to maps some info into the chart:
fills a colour between 0.5 & the mid BB^2 and converts relative to the price chart
draws a line in the middle of the midband.
controls how much these lines diverge from the price - adjust it to reduce noise
converts the signal range (red lines) to be relative to the price chart
if you like, you can adjust the sell & buy signals in the tab from and to and to match the picture. It messes with auto-scaling when moving back to though
enjoy, I hope that is easy enough to understand, still trying to make this more user-friendly.
If you want to send me some token of appreciation - btc: 33c2oiCW8Fnsy41Y8z2jAPzY8trnqr5cFu
I promise it will put a fat smile on my face
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