Technical Papers  
Monte Carlo Simulation by John Ehlers
This article explains why Monte Carlo Simulation is a better method for analyzing trading system performance.
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Why Traders Lose Money by John Ehlers and Ric Way
This article in Stock & Commodities Magazine (May 2014) describes how to create artificial equity curves in Excel.
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Inferring Trading Strategies from Measured Probability Density Functions by John Ehlers
This paper by was awarded the 2008 Runner-Up Winner of the Market Technician's Association's Charles H. Dow Award.
John Ehlers describes how detrended probability distributions can be used as strategies for effective trading systems.
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Introducing SwamiCharts by John Ehlers and Ric Way
Traditional technical indicators tend to provide a narrow view of the market. SwamiCharts provide a big-picture visualization of market activity that does not require selection of a precise lookback period.
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Fractal Dimension as a Market Mode Sensor by John Ehlers and Ric Way
Article from Stocks & Commodities Magazine V. 28:6 (16-20)
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Empirical Mode Decomposition by John Ehlers and Ric Way
Article from Stocks & Commodities Magazine V. 28:3 (18-24)
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Zero Lag (Well, Almost) by John Ehlers and Ric Way
Article from Stocks & Commodities Magazine V. 28:11 (30-35)
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Using the Fisher Transform by John Ehlers
Many trading systems are designed using the incorrect assumption that the probability distribution of prices have a normal, or Gaussian, probability distribution about the mean. This paper describes how the Fisher Transform converts data to have nearly a normal probability distribution. Download (PDF)
The Inverse Fisher Transform by John Ehlers
The Inverse Fisher Transform can be used to generate an oscillator that switches quickly between oversold and overbought without whipsaws.
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Skinning the Cat by John Ehlers
Another way to measure the spectrum of market cycles. Here, we show how to measure the spectrum using a contiguous bank of bandpass filters. The spectrum is displayed as a heatmap.
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Fourier Transform for Traders by John Ehlers
The problem with Fourier Transform for the measurement of market cycles is that they have a very poor resolution. This paper shows you how to use another nonlinear transform to improve the resolution so that the Fourier Transforms are usable. The measured spectrum is displayed as a heatmap
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Swiss Army Knife Indicator by John Ehlers
Indicators are just transfer responses of input data. By a simple change of constants, this indicator can become an EMA, SMA, 2 Pole Gaussian Low Pass Filter, 2 Pole Butterworth Low Pass Filter, an FIR smoother, a Bandpass filter, or a Bandstop filter.
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Ehlers Filter
An unusual nonlinear FIR filter is described. This filter is among the most responsive to price changes but smoothest in sideways markets.
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System Performance Evaluation
Describes how to build an Excel Spreadsheet to better understand expected performance of a trading strategy. The Profit Factor (gross winnings divided by gross losses) is analogous to the payout factor in gaming. Thus, when the Profit Factor is combined with the percentage winners in a series of random events, instances of how a trading strategy equity growth can be simulated.
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FRAMA by John Ehlers
FRAMA (FRactal Adaptive Moving Average). A nonlinear moving average is derived using the Hurst exponent.
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MAMA by John Ehlers
MAMA is the mother of all adaptive moving averages. Actualy the name is an acronym for MESA Adaptive Moving Average. The nonlinear action of this filter is produced by the flyback of phase every half cycle. When combined with FAMA, a Following Adaptive Moving Average, the crossovers form excellent entry and exit signals that are relative free of whipsaws.
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Time Warp Without Space Travel by John Ehlers
Laguerre Polynomials are used to generate a filter structure similar to a simple moving average with the difference that the time spacing between filter taps is nolinear. The result enables the creation of very short filters having the smoothing characteristics of much longer filters. Shorter filters mean less lag. The advantages of using the Laguerre Polynomials in filters is demonstrated in both indicators and automatic trading systems. The article includes EasyLanguage code.
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What's the Difference? by John Ehlers
A nonlinear moving average is formed by measuring the difference between a Simple Moving Average and a Median Filter.
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The CG Oscillator by John Ehlers
The CG Oscillator is unique because it is an oscillator that is both smoothed and has zero lag. It finds the Center of Gravity (CG) of the price values in an FIR filter. The CG automatically has the smoothing of the FIR filter (similar to a simple moving average) with the position of the CG being exactly in phase with the price movement. EasyLanguage code is included.
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RVI by John Ehlers
The Relative Vigor Index (RVI) is a new indicator formed from old ideas. It is a measure of the average difference between the close and open, normalized to the average daily trading range. It is an excellent oscillator to complement other indicators.
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RSI Smoothing by John Ehlers
There is more to smoothing an RSI than just taking a moving average after the RSI is computed. By applying some advanced filters in the process of computing the RSI you can not only get better smoothing but also enhance the turning points of this proven indicator.
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Gaussian Filters by John Ehlers
Lag is the downfall of smoothing filters. This article shows how lag can be reduced and the highest fidelity smoothing is obtained by reducing the lag of high frequency components in the data. A complete table of Gaussian filter coefficients is provided.
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Hybrid Filters by John Ehlers
Simple Moving Averages are a subset of FIR Filters. Exponential Moving Averages are a subset of IIR filters. Traders are not necessarily limited to the selection of one or the other. This article describes how you can make hybrid filters and realized the best characteristics of both.
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Poles and Zeros by John Ehlers
A description of digital filters in terms of Z Transforms. The ramifications of higher order filters are described. Tables of coefficients for 2 Pole and 2 Pole Butterworth filters are given.
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Zero Lag Data Smoothers by John Ehlers
A comprehensive discussion of removing lag from data smoothing filters and the penalties paid for removing that lag.
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