Let's Talk Indicators



Lately, I’ve been exploring indicators and looking for a new edge to up my trading game. One of the problems I’m constantly dealing with is most indicators are lagging and this just doesn’t gel well with cryptos. So I’ve started exploring what others have done in this area to build a better mouse-trap so to speak.

Then I came across the motherlode of all indicators by this one fella on TradingView named “LazyBear” with more than six pages of indicators he’s produced.

I want to go through LazyBear’s entire collection and start figuring out which ones have the highest potential to raise the game significantly. Anyone want to help me comb through these and find the gems? If nothing else, let’s start talking about indicators and how they help or hinder trading in general for cryptos. I know many of you exclaim “TA doesn’t work on cryptos” and that’s not the discussion I want to have here – I want to have a constructive discussion about what works and doesn’t work on technical merits rather than subjective blanket “drive by” commentary.

In general, there are three things we’re looking for in these indicators:

  1. price action and trend
  2. momentum of movment
  3. volatility

Here’s the motherlode I speak of:


One thing I did not realize when I kicked this thread off was just how many different moving average indicators there were. I knew about Simple Moving Average (SMA), Exponential Moving Average (EMA) and double smoothing those. What I did not know about was others like Hull Moving Average (HMA) and Triple Exponential Moving Average (TEMA) or Weighted Moving Average (WMA). The HMA, TEMA ones aim to smooth while removing lag while WMA aims to help identify areas of congestion (i.e. whipsawing price action).

So, let’s kick this off with a deep dive into moving averages. Here’s a chart of

One thing I’ve discovered in exploring these moving averages is that you really need to know what you’re using them for. I’ve gotten used to EMA and SMA for looking for cross-over points, but watching for cross overs on the non-lagging ones does not work very well.

Simple Moving Average

A simple moving average (SMA )is an arithmetic moving average calculated by adding recent closing prices and then dividing that by the number of time periods in the calculation average. A simple, or arithmetic, moving average that is calculated by adding the closing price of the security for a number of time periods and then dividing this total by that same number of periods. Short-term averages respond quickly to changes in the price of the underlying, while long-term averages are slow to react.1

Exponential Moving Average

An exponential moving average - EMA is a type of moving average that places a greater weight and significance on the most recent data points. The exponential moving average - EMA is also referred to as the exponentially weighted moving average. Exponentially weighted moving averages react more significantly to recent price changes than a simple moving average, which applies an equal weight to all observations in the period.2

Double Exponential Moving Average

Double exponential moving average, or DEMA, is a measure of a security’s trending average price that gives the most weight to recent price data. Like exponential moving average, or EMA, it is more reactive to price fluctuations than a simple moving average, or SMA, thereby bringing more value to short-term traders attempting to pinpoint trend changes. Moving averages are by nature lagging indicators, so the more reactive, the more lead time a trader has to react. Though its name implies that DEMA is simply calculated by doubling the EMA, this is not the case. 3

Triple Exponential Moving Average

The triple exponential moving average, or TEMA, was developed by Patrick Mulloy in 1994 to filter out volatility from conventional moving averages. While the name implies that it’s a triple exponential smoothing, it’s actually a composite of a single exponential moving average, a double exponential moving average, and a triple exponential moving average.4

Weighted Moving Average

Linearly Weighted Moving Average is a method of calculating the momentum of the price of an asset over a given period of time. This method weights recent data more heavily than older data, and is used to analyze market trends. Momentum, one of the most common oscillators used in analyzing market trends, is designed to look at price fluctuations over a period of time. Such calculations can be useful for forecasting future performance and informing investment strategy.5

Hull Moving Average

The Hull Moving Average solves the age old dilemma of making a moving average more responsive to current price activity whilst maintaining curve smoothness. In fact the HMA almost eliminates lag altogether and manages to improve smoothing at the same time.6

Variable Moving Average

Variable Moving Average, often abbreviated as VMA, is an Exponential Moving Average. It was developed by Tushar S. Chande in 1991. VMA automatically adjusts its smoothing constant on the basis of Market Volatility. The Sensitivity of Variable Moving Average keeps growing provided the volatility of data considered is increasing. A major flaw in all forms of moving averages is that, they are unable to function properly and predict future trend during Trending and Non-Trending movements of Stocks occurring one after another. Similarly, when moving averages are determined over a longer period of time, Moving Averages are unable to respond to trend reversals. This may lead to disastrous trade signals. Variable Moving Averages distinguishes itself from other moving averages on the basis of sensitivity. VMA functions far better than other moving averages because it adjusts its smoothing constant according to market conditions like Market Volatility.7

That’s just the tip of the iceberg of what’s out there, but these are by far the most common and popular of the ones I’ve looked at over the last couple days. If you have others you use and favor, definitely share.



Below, I am extracting a few choice paragraphs from John Ehler’s publication explaining what Fisher Transforms are. Inverse Fisher Transforms (IFT) are also a new treatment to various indicators for me and discovered going through Lazy Bear’s 100’s of indicators. Of particular note is the RSI under IFT. Finally, can get a very clear signal on a signal that, for me, can be a tricky one to call correctly. Loving this stuff!

NOTE: This is 100% excerpted from John Ehler’s published article1 and aptly explains Fisher Transforms

In this article I will show you a way to make your oscillator-type indicators make clear black-or-white indication of the time to buy or sell. I will do this by using the Inverse Fisher Transform to alter the Probability Distribution Function (PDF) of your indicators. In the past12 I have noted that the PDF of price and indicators do not have a Gaussian, or Normal, probability distribution. A Gaussian PDF is the familiar bell-shaped curve where the long “tails” mean that wide deviations from the mean occur with relatively low probability. The Fisher Transform can be applied to almost any normalized data set to make the resulting PDF nearly Gaussian, with the result that the turning points are sharply peaked and easy to identify.


Now that you know about the Inverse Fisher Transform, there is no reason to bludgeon the RSI with a blunt instrument like a Stochastic. Instead of picking an observation length that is guaranteed to drive the Stochastic to saturation, you can finesse the indicator PDF using the Inverse Fisher Transform.



Suggested method to use ift indicator:

Suggested by John Ehlers , IFT helps you to determine the exact oversold/overbought points in any oscillator-type indicators.

The 3 IFT based indicators in this chart are:

  • Inverse Fisher on RSI (IFTRSI)
  • Inverse Fisher on MFI (IFTMFI)
  • Inverse Fisher on CyberCycle (IFTCC)

Suggested method to use any IFT indicator is to buy when the indicator crosses over –0.5 or crosses over +0.5 if it has not previously crossed over –0.5 and to sell short when the indicators crosses under +0.5 or crosses under –0.5 if it has not previously crossed under +0.5.

I see you’re on page 2, i’m still on page 1 :slight_smile: Loving this thread @mwlang !


Glad you’re enjoying the thread. Keep 'em coming! I’ve scanned through probably 50% of the 6 pages at this point, but focusing on the simpler ones first. I did notice the one you published and there are many possibilities how to put multiple indicators together for a strong buy/sell signal. Another concept is ribbons, which is same indicator with multiple timeframes at once. There are a few out there for moving averages, but here’s an interesting one in the vein of Fisher Transforms:


Thanks for sharing. I see you enjoy studying the different moving average tools. I have been enjoying using BK’s Ichimoku Cloud on TradingView since a buddy showed it to me about a month or two ago. Basically white line is the moving average with the most days factored into it (longest and “strongest”). Next is orange line. Less days than white factored in, but more than red and green. I think red and green are similar moving average to default MACD. We want green to cross red up, see the price movement doing the same, both pass up through orange, then all break through white. That would be bullish, imo.


Adding BK Ichimoku indicator to my list. I’ve used Ichimoku fairly extensively, so definitely familiar with, but it sounds like BK’s version has a few additions to the standard one I was using the most.

I’ve been doing a lot of trading off the 1 minute time frames lately, so Ichimoku hasn’t been a great indicator for that much noise, but definitely worthwhile at the 4 hour and daily timeframes.

Meanwhile, I was struggling with three lines of IFT indicators in one space, so I averaged them into one line…I’m not entirely sure how to share indicator code on TradingView at the moment, so here’s the code (it’s an adaption of someone else’s script):

study("Inverse Fisher All")

STO = input(false, title="Show INVERSE FISHER TRANSFORM on STOCHASTIC Line?")
RSI = input(false, title="Show INVERSE FISHER TRANSFORM on RSI Line?")
CCI = input(true, title="Show INVERSE FISHER TRANSFORM on CCI Line?")
ccilength=input(5, "CCI Length")
wmalength=input(9, title="Smoothing Length")
v11=0.1*(cci(close, ccilength)/4)
v21=wma(v11, wmalength)

rsilength=input(5, "RSI Length")
v12=0.1*(rsi(close, rsilength)-50)

stochlength=input(5, "STOCH Length")
v1=0.1*(stoch(close, high, low, stochlength)-50)
v2=wma(v1, wmalength)

// plot(STO and INVLine ? INVLine : na, color=blue, linewidth=1, title="STOCH")
// plot(CCI and INV1 ? INV1 : na, color=red, linewidth=3, title="CCIv2")
// plot(RSI and INV2 ? INV2 : na, color=black, linewidth=2, title="RSI")

plot((INVLine + INV1 + INV2)/3, color=red, linewidth=2, title="MASH")

hline(0.5, color=red)
hline(-0.5, color=green)

The red line indicator at bottom of this chart is the result. The original script produces the three colored lines indicator just above.


Hi really like the thread. I’ve also been looking through lazybears scripts :ok_hand:
Could you explain how you are you using your version of the IFT? Are you using it as a trigger entering trades on a divergence or simply when it’s oversold or overbought etc?


I’m just observing right now…but basic potential strategy is when red line is high (above dotted red line), start setting tight stop losses. when it falls below dotted redline, stop/loss triggers (potentially).

When redline is on the floor for an extended period and a recent price drop was large enough it’s is also reflected via the white line (VMA) and that white line is also flat at the time red line crosses above dotted red line, then entry is signaled.

Very alpha strategy at the moment and definitely will change as I follow, backtest, and get more familiar with these new indicators, but so far, looking pretty solid, at least on the 1 minute timeframe.


Thanks @mwlang, I’ll copy the script and have a play around with it. Cheers.


Wow, talk about a regular cycle on the IFT signals…

The red peaks are fairly consistently peaking inside the green half circles at the moment.


This is one of my favorite ones:



Looks interesting. Have you seen a cloned version on Trading View by any chance? This one’s also a bit of a mash up of several indicators into one, which presumably are meant to be used together as a whole unit, but it’s hard for me to deduce much from this one from the source and limited info I found on it. Can you describe it a bit and how to use it?


An explanation is provided here as well as the script:

You’ll need tradingview pro though. The very short gist of it is that you buy/sell when the green band touches the bottom/top of the bollinger and crosses the red band. More details within the post.


Continuing my deep-dive into moving averages and indicators, I came across this really good article talking about MA’s and the quest to remove lag while smoothing out the noise:


The above article covers the following indicators:

  • SMA , the simple moving average, the sum of the last n prices divided by n .
  • EMA , exponential moving average, the current price multiplied with a small factor plus the last EMA multiplied with a large factor. The sum of both factors must be 1 . This is a first order lowpass filter.
  • LowPass , a second order lowpass filter
  • HMA , the Hull Moving Average
  • ZMA , Ehler’s Zero-Lag Moving Average, an EMA with a correction term for removing lag.
  • ALMA , Arnaud Legoux Moving Average, based on a shifted Gaussian distribution
  • Laguerre , a 4-element Laguerre filter:
  • Linear regression , fits a straight line between the data points in a way that the distance between each data point and the line is minimized by the least-squares rule.
  • Smooth , John Ehlers’ “Super Smoother”, a 2-pole Butterworth filter combined with a 2-bar SMA that suppresses the Nyquist frequency:
  • Decycle , another low-lag indicator by John Ehlers. His decycler is simply the difference between the price and its fluctuation, retrieved with a highpass filte.

Comparing Impulse Responses

What is the best of all those indicators? For a first impression here’s a chart showing them reacting on an impulse, a simulated sudden price jump. You can see that some react slow, some very slow, some fast, and some overshoot


Here’s a couple of more callouts on Moving Averages and then I’m moving on to trend analysis indicators.

I have to say, I’m pretty impressed with all the different ways to come up with moving averages, and studying the math behind each and every one of them (as well as implementing them in Ruby – my programming language of choice), it has been quite the rabbit hole of learning something new every day.

Kalman Filter

One novel way to smooth out the price action curve doesn’t rely on periods (or range) of candles at all and that is the Kalman Filter, which has been taking from physics realm for describing physical motions and dynamics and applied to stocks.

The Kalman filter uses a system’s dynamic model (e.g., physical laws of motion), known control inputs to that system, and multiple sequential measurements (such as from sensors) to form an estimate of the system’s varying quantities (its state) that is better than the estimate obtained by using only one measurement alone. As such, it is a common sensor fusion and data fusion algorithm.

Noisy sensor data, approximations in the equations that describe the system evolution, and external factors that are not accounted for all place limits on how well it is possible to determine the system’s state. The Kalman filter deals effectively with the uncertainty due to noisy sensor data and to some extent also with random external factors. The Kalman filter produces an estimate of the state of the system as an average of the system’s predicted state and of the new measurement using a weighted average.1

This is what the Kalman Filter does on a Bitcoin/USD 5 minute chart (light blue/cyan line):

John Ehlers MAMA – The Mother of Adaptive Moving Average

This is a moving average indicator designed with trending in mind. From personal observation and experimentation over the last few days, I have to say this one beats EMAs and cross-over hands down. It’s earlier to detect those trend reversals and mutes the whipsaw issues prevalent in utilizing two indicators against each other to find trend changes via their cross-overs.

The MESA Adaptive Moving Average (MAMA) adapts to price movement in an entirely new and unique way. The adapation is based on the rate change of phase as measured by the Hilbert Transform Discriminator I have previously described. The advantage of this method of adaptation is that it features a fast attack average and a slow decay average so that composite average rapidly ratchets behind price changes and holds the average value until the next ratchet occurs.

An interesting set of indicators result if the MAMA is applied to the first MAMA line to produce a Following Adaptive Moving Average (FAMA). By using an alpha in FAMA that is half the value of the alpha in MAMA, the FAMA has steps in time synchronization with MAMA, but the vertical movement is not as great. As a result, MAMA and FAMA do not cross unless there has been a major change in market direction. This suggests an adaptive moving average crossover system that is virtually free of whipsaw trades.

This is the MAMA/FAMA indicator2 on same chart as Kalman Filter above:

Zero Lag EMA

If you’re a fan of moving averages cross-over trading strategies (buy when fast/short period MA cross slow/long period MA), then give this Zero Lag indicator3 a spin. It’s an extremely simple offset EMA based on the EMA of the period plus it’s difference with the EMA of the EMA of same period.

Kaufman Adaptive Moving Average

Developed by Perry Kaufman, Kaufman’s Adaptive Moving Average (KAMA) is a moving average designed to account for market noise or volatility. KAMA will closely follow prices when the price swings are relatively small and the noise is low. KAMA will adjust when the price swings widen and follow prices from a greater distance. This trend-following indicator can be used to identify the overall trend, time turning points and filter price movements.4

Kaufman’s Adaptive Moving Average (KAMA) is based on the concept that a noisy market requires a slower trend than one with less noise. The basic principle is that the trendline must lag further behind the price in a relatively noisy market to avoid being penetrated by the price. The moving average can speed up when the prices move consistently in one direction. According to Perry Kaufman, who invented the system, KAMA is intended to use the fastest trend possible, based on the smallest calculation period for the existing market conditions.7

What I’m really liking about this filter is how it levels out quickly when the price action starts moving largely sideways. This is similar to properties of the VMA indicator covered earlier.

Fractal Adaptive Moving Average (FAMA)

FRAMA stands for Fractal Adaptive Moving Average and we have classed it as a Log-Normal Adaptive Moving Average (LAMA). Created by John F Ehlers (See his original paper or the article from the 2005 edition from Technical Analysis of Stocks and Commodities – Fractal Adaptive Moving Averages), it utilizes Fractal Geometry in an attempt to dynamically adjust its smoothing period to suit the changing price action over time.5

Below, I have added the FAMA indicator (light blue line) to same chart alongside the Kaufman MA:

  • NOTE: this indicator has a component that leverages Kaufman’s MA above, so it’s somewhat of a “super moving average” – that is one that is composed.


Nice write ups @mwlang - Keep em coming


Thanks, @Entropy – This exercise is really beginning to sharpen my understanding of charting and how to use these indicators effectively. Definitely a worthwhile homework assignment after not quite a year of T/Aing and learning to trade.

There’s something to be said for “second time around” schooling and rebuilding the foundation. The first go around, I was practically overwhelmed with all the choices and so tended to fall back onto the ones most commonly known and documented. But as my trip through the Moving Averages Indicators should show, there’s a wealth of superior works out there for MA’s and they each have their own trade offs for the rewards they bring.

Another thing learned in this exercise: DO NOT blindly apply a strategy that worked with one indicator to a “replacement” indicator. One cannot simply drop-in say, the Zero Lag indicator for the “fast period” EMA in a cross-over strategy and expect “better” interaction with the “slow period” EMA. You’ll more than likely have to adjust the periods of both EMA’s to find a new sweetspot in period lengths that give you a desirable result while eliminating, say, whipsawing if that’s your goal. These newer indicators are far more responsive than standard SMA and EMA so can definitely produce a serious amount of whipsaw in sideways market.

Which leads to another treatise of sorts: Some indicators are better suited in volatile markets while others are better suited in calm markets. The same can be said for long-term HODL strategies on longer-timeframes vs. short-term swinging on the shorter timeframes. So, to up your game, learn to swap out your tooling


As I step back from moving averages and consider the next class of indicators to research, I stumbled across a good summary write up1 of the four classes of indicators that’s worth sharing here (see below).

Four Types of Technical Indicators

There are thousands of different technical indicators developed over the years. While they’re all computed using a security’s price and volume, they can be combined in many different ways to draw many different conclusions about future price movements.

It helps to break down technical indicators into various categories in order to better understand how to use them.

The four major types of indicators include:

  • Trend Indicators – Trend indicators are designed to show the trend or direction of the security. For example, is a stock trending higher, lower or sideways? Examples of trend indicators include moving averages, moving average convergence-divergence (MACD) and average directional index (ADI).

  • Momentum Indicators – Momentum indicators measure the speed at which a security’s price is moving in a given direction. For example, is a stock’s uptrend strong or weak? Examples of momentum indicators include the relative strength index (RSI), stochastics, and the commodity channel index (CCI).

  • Volatility Indicators – Volatility indicators show how volatile a security is at a given point in time. For example, how much might a stock drop over the next week or two? Examples of volatility indicators include Bollinger Bands, envelopes, and average true range (ATR).

  • Volume Indicators – Volume indicators show the volume behind a security’s price movement and serve as a confirmation. For example, is the volume following a breakout strong enough for it to hold key support levels? Examples of volume indicators include on balance volume (OBV), Chaikin Money Flow and the Force Index.