John Ehlers Zero Lag Indicator for ThinkorSwim


New member
2019 Donor
This is not mine and I did not make it, I found it on another thread and wanted to share here with y'all. Also is it possible for someone to make it as close to the original as possible ? I will post a link to John Ehlers original code on his website.

#This is my version, anyone is welcome to play with it and modify it for better results.

input length = 32;
input GainLimit = 22;
input Thresh = 0.75;

def alpha = 2 / (length + 1);

def EMA = alpha * close + (1 - alpha) * EMA[1];

def Value1 = -GainLimit + GainLimit;
def Gain = Value1 / 10;

def EC = alpha * (EMA + Gain * (close - EC[1])) + (1 - alpha) * EC[1];
def Error = close - EC;

def BestGain = Gain;

def LeastError = 1000000;

plot EC1 = alpha * (EMA + BestGain * (close - EC[1])) + (1 - alpha) * EC[1];

plot EMA48 = alpha * close + (1 - alpha) * EMA[1];


#plot EC Crosses Over EMA and 100*LeastError / Close > Thresh Then Buy Next Bar on Open;
#plot EC crosses Under EMA and 100*LeastError / Close > Thresh Then Sell Short Next Bar on Open;


Staff member
Here's the Zero Lag EMA indicator:

# Zero Lag EMA (Using an Error Correcting Filter)
# Mobius
# Ported to TOS 08.31.2016 from Article by John Ehlers
# Using an EMA error term, the current price – EMA[1], the amplitude of the error term is altered by multiplying the error by a gain term. The new filter is an EC or Error Corrected EMA.

# Inputs:
input Length = 20;
input GainLimit = 50;
input LabelsOn = yes;

# Vars:
def alpha;
def BestGain;
def EC1;
def Gain;
def EC;
def Error;
def LeastError;
def EMA;
alpha = 2 / (Length + 1);
EMA = alpha * close + (1 - alpha) * EMA[1];
plot EMAdata = EMA;

Gain = fold g = -GainLimit to GainLimit
       do getValue(g / 10, g);
EC1 = fold V = -GainLimit to GainLimit
      do alpha * (EMA + (fold i = -GainLimit to GainLimit
                         do i / 10) * (Close - EC1[1])) + (1 - alpha) * EC1[1];
Error = AbsValue(Close - EC1);
If Error < 1000000
Then {
LeastError = Error;
BestGain = Gain;
} else {
LeastError = LeastError[1];
BestGain = 0;
EC = alpha * (EMA + BestGain * (Close - EC1)) + (1 - alpha) * EC1;
Plot ECdata = EC;
AddLabel(LabelsOn, "Standard EMA", color.yellow);
AddLabel(LabelsOn, "Error Corrected EMA", color.cyan);
#End Code Error Corrected EMA

Here is the Forward Reverse EMA by Mobius

# Forward / Reverse EMA
# (c) 2017 John F. Ehlers
# Ported to TOS 07.16.2017
# Mobius

declare lower;

# Inputs:
input AA = .1;

# Vars:
def CC;
def RE1;
def RE2;
def RE3;
def RE4;
def RE5;
def RE6;
def RE7;
def RE8;
def EMA;
plot Signal;
plot plot0;

CC = if CC[1] == 0 then .9 else 1 – AA;
EMA = AA * Close + CC * EMA[1];
RE1 = CC * EMA + EMA[1];
RE2 = Power(CC, 2)   * RE1 + RE1[1];
RE3 = Power(CC, 4)   * RE2 + RE2[1];
RE4 = Power(CC, 8)   * RE3 + RE3[1];
RE5 = Power(CC, 16)  * RE4 + RE4[1];
RE6 = Power(CC, 32)  * RE5 + RE5[1];
RE7 = Power(CC, 64)  * RE6 + RE6[1];
RE8 = Power(CC, 128) * RE7 + RE7[1];

Signal = EMA – AA * RE8;
signal.AssignValueColor(if Signal > Signal[1]
Plot0 = if isNaN(close) then double.nan else 0;
addCloud(0, Signal,,;
# End Code Ehlers Forward / Reverse EMA


Well-known member
@Srt4ever Just look in ToS for EhlersRoofingFilter, EhlersDecyclerOscillator or EhlersForwardReverseEMA. All three will give same signals as Ehlers Zero Lag you describe. The study you quoted above should plot as described in the linked article. Just adjust the length as you wish.

The Mobius study mentioned has a less smooth EMA possibly due to high gain. I suggest lowering the gain if you try that one. Why not use one of the lower studies and unclutter your upper chart as they give the same crossover signals. I thought I had a fancier Ehlers one but a quick search did not find it, oh well it gave the same crossovers. I think it colored the candles also. One day I may run across it again.

Closest match I could get with my study is EMA 4 gain 1. Green/Red is study being mentioned by Tenacity. Experiment at your own risk.



Active member
2019 Donor
# Zero Lag EMA (Using an Error Correcting Filter) # Mobius
I just started using this with parameters of 13,3

# Zero Lag Exponential Moving Average
# Mobius
# V01.07.2010
# Maintains the weighting of most recent price but removes the infinite weighting of past price.

nput length = 10;

def K = 2 / (length + 1);
def lag = (length - 1) / 2;
def ZLEMA = K * (2 * close - close[lag]) + (1 - K) * ZLEMA[1];
plot data = ZLEMA;
# End Code Zero Lag EMA


Well-known member
Just wanted to add: as far as I understand and was revealed in comparative testing (and I could be wrong)- The best use of ehlers super smooth filter is to get nice medium-long termish trend (e.g. period 20 or so). Under 10 it does not make much sense to use. On daily 34 period is quite good


New member
2019 Donor
Just wanted to add: as far as I understand and was revealed in comparative testing (and I could be wrong)- The best use of ehlers super smooth filter is to get nice medium-long termish trend (e.g. period 20 or so). Under 10 it does not make much sense to use. On daily 34 period is quite good

His actual study suggest Length= 32 Gain= 22 and Thresh= 0.75

"When the Zero Lag Strategy was applied to five years of 100 shares of MSFT with settings Length = 32, GainLimit = 22, and Thresh = 0.75 we obtained the following hypothetical results:
Net Profit $3,107 # Trades 20 Profit/Trade $155 % Profitable Trades 60% Profit Factor 4.1"

Study can be found here:

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