Meyers

Analytics

Analytics

Advanced Mathematical Trading Strategies & Walk Forward/Out-Of-Sample Analysis

applied to algorithmic trading of stocks, futures & forex

Info: (312) 280-1687 support@meyersanalytics.com

applied to algorithmic trading of stocks, futures & forex

Info: (312) 280-1687 support@meyersanalytics.com

Order Online
Power Walk Forward

Optimizer Walk Forward

Performance Explorer Walk Forward

Metric Explorer Walk Forward

Input Explorer Walk Forward

Surface Explorer Key Daily & Intraday

Trading Strategies Nth Order Fixed Memory

Polynomial Strategy Nth Order Fading Memory

Polynomial Strategy End Point Fast Fourier

Transform Strategy Goertzel DFT

Strategy Five Parameter

Parabolic Strategy Dennis Meyers

Working Papers

Optimizer Walk Forward

Performance Explorer Walk Forward

Metric Explorer Walk Forward

Input Explorer Walk Forward

Surface Explorer Key Daily & Intraday

Trading Strategies Nth Order Fixed Memory

Polynomial Strategy Nth Order Fading Memory

Polynomial Strategy End Point Fast Fourier

Transform Strategy Goertzel DFT

Strategy Five Parameter

Parabolic Strategy Dennis Meyers

Working Papers

Algorithmic Trading Strategies For TradeStation, MultiCharts 64bit & NeuroShell

The Robust Velocity Strategy The Acceleration Strategy The Velocity Strategy

The Next Bar Forecast Strategy The Polychromatic Mtm Strategy The Maximum Likelihood Range

The Noise Channel Breakout I & II The 2-P NextBar Forecast Strategy

Click here for working papers on the Key Daily & Intraday Trading Strategies

The Power Walk Forward Optimizer (PWFO) is a cutting-edge automatic walk forward/out-of-sample analysis program that eliminates the ad hoc curve fitted performance and data mining results produced by combinatorial and genetic(grail) optimization of strategy input values on spurious price movements(noise). Included in the in-sample section output for each set of input parameters are 30 new robust and superior performance metrics. The in-sample and out-of-sample periods are user selectable. The PWFO can generate up to 500 different in-sample and out-of-sample date files in one TS run. Statistically speaking, walk forward out-of-sample (oos) analysis must be performed over many(>30) in-sample/oos sections to be statistically valid.

__Click here to for a User's Comparison Between the TS Walk Forward Optimizer(Grail) and the PWFO__

**Videos On How To Use The PWFO**
__1. How to setup and run the PWFO__
__2. The PWFO output files__

** Available for TradeStation, MultiCharts ***and* NeuroShell Pro

The Walk Forward Metric Explorer (WFME) reads all files generated by the PWFO and searches each PWFO file for performance Metrics that generate statistically best average out-of-sample performance. The Top N Metric filter chooses the PWFO file rows that contain then Top N (N=10 or 29 or etc) values of a PWFO Performance Metric. From the N rows chosen, the WFME chooses the maximum of another PWFO performance metric. This allows two performance metrics in the in-sample section to be used to find the strategy inputs that give the best out-of-sample returns. Experience has shown that the use of only one in-sample performance metric to choose strategy inputs from the in-sample section does not produce good out-of-sample results. The Top N criteria ranges are user selectable generating many filter searches in one run. The WFME is a stand alone exe program that is super fast and automatically displays it's extensive statistical results, equity plots and strategy inputs from each filter in Excel. In addition, using modern "Bootstrap" techniques, the WFME calculates the probability of whether or not each filter's out-of-sample results were due to chance.

**Video: How To Use The Walk Forward, Out-Of-Sample PWFO Metric Explorer**

The Walk Forward/Out-Of-Sample Performance Explorer (WFPE) reads all files generated by the PWFO and searches the in-sample performance metrics in those files for those performance metrics that generate the statistically best average out-of-sample returns. The WFPE eliminates all cases in the PWFO test files that do not meet certain Profit Factor (PF), Losers in a row (LR),and PWFO Performance metrics criteria . The PF, LR and METRIC criteria/search ranges are user selectable. The WFPE is a stand alone exe program that is super fast and automatically displays it's extensive statistical results, equity plots and strategy inputs from each filter in Excel. In addition, using modern "Bootstrap" techniques, the WFPE calculates the probability of whether or not each filter's out-of-sample results were due to chance.

**Video: How To Use The Walk Forward, Out-Of-Sample Performance Explorer **

The Walk Forward/Out-Of-Sample Input Parameter Explorer (WFINP) reads all files generated by the PWFO and searches the performance metrics of profit factor(PF) and losing trades-in-a-row(LR) in those files for those performance metrics that generate the statistically best average out-of-sample returns. The WFINP eliminates the curve fitted results by filtering out from the PWFO file's in-sample sections those strategy inputs that have Profit Factors(PF) greater than x and that have losing trades in a row(LR) of greater than y. The WFINP then determines which strategy filtered inputs generate the statistically best average out-of-sample returns. The PF and LR criteria are user selectable so you can choose which PF and LR values suit you. The WFINP is a stand alone exe program that is super fast and automatically displays it's statistical summary and results in Excel. In addition, using modern "Bootstrap" techniques, the WFINP calculates the probability of whether or not each filter's out-of-sample results were due to chance.

The Walk Forward/Out-Of-Sample Metric Surface Explorer(WFSE) reads each of the files generated by the PWFO and calculates the flattest metric surface plateaus vs the input parameters for each performance metric's (Total Net Profits, Profit Factor, etc) surface. The WFSE can find the flattest surface for up to a 7 dimensional(7D) surface. A 7D surface consists of six input parameter axis vs a performance metric variable. A performance metric flat surface plateaus represent robust input parameters that have a greater chance of producing good out-of-sample results. The WFSE then finds the out-of-sample profits associated with each in-sample section surface's flattest plateau (minimum gradient) for each file. The WFSE sums each file's out-of-sample results for each of the surface's minimum gradients and finds the statistically best out-of-sample returns for each performance metric surface. The WFSE is a stand alone exe program that is super fast and automatically displays it's extensive surface statistical summary and results in Excel.

Using fast advanced mathematical rocket science algorithms, the price series is modeled using an nth order fading memory polynomial of the form: ** price(t) = a**_{0}(t)+a_{1}(t)*t+a_{2}(t)*t^{2}+a_{3}(t)*t^{3}+a_{4}(t)*t^{4}+...+a_{n}(t)*t^{n} The **a**_{n}(t) coefficients are updated *recursively* with each new price bar and then used to give the polynomial's** ***next bar forecast* of price,velocity and acceleration.
As our working papers demonstrate the ** n**^{th} Order Fading Memory Adaptive Polynomial is an effective strategy for trading stocks, futures and currencies.
** Available for TradeStation, MultiCharts ***and* NeuroShell Pro

Using fast advanced mathematical rocket science algorithms that use discrete orthogonal polynomials, the price series is modeled using an n^{th} order polynomial of the form:
** price(t) = a**_{0}+a_{1}*t+a_{2}*t^{2}+a_{3}*t^{3}+a_{4}*t^{4}+...+a_{n}*t^{n}
The **a**_{n} coefficients are recalculated at each new price bar and are then used to give the polynomial's *next bar forecast* of price,velocity and acceleration. As our working papers demonstrate the **n**^{th} Order Fixed Memory Adaptive Polynomial is an effective strategy for trading stocks, futures and currencies.
** Available for TradeStation, MultiCharts ***and* NeuroShell Pro

The Adaptive n Cycle Goertzel Discrete Fourier Transform (nCycleGZ) super fast DLL finds the N(user selectable) cycles(frequencies) with the highest amplitudes at each price bar, and creates a x bars ahead (x is user selectable) noise filtered projected momentum curve. This process gives a more robust noise filtered signal than a single frequency (dominant cycle) procedure. You are no longer constrained to using only a single frequency. The nCycleGZ algorithm is faster and superior to MESA in finding cycles in noisy price series. As our working papers demonstrate the nCycleGZ is an effective strategy for trading stocks, futures and currencies. ** Available for TradeStation, MultiCharts ***and* NeuroShell Pro

The EPFFT super fast DLL takes the FFT at each price bar, filters the noisy price series using a unique noise filter in the frequency domain, and creates a one bar ahead noise filtered projected price. The EPFFT DLL produces an adaptive broadband (many frequencies) noise filtered signal. This process gives a more robust noise filtered signal than a single frequency (dominant cycle) procedure.
As our working papers demonstrate the EPFFT is an effective strategy for trading stocks, futures and currencies.
** Available for TradeStation, MultiCharts **

The five parameter parabolic adds a noise filter and changeable starting stop value that minimizes the whipsaw losses that can occur with the regular parabolic indicator. Here this new system is applied to stock and Futures prices to minimize the noise process.
** Available for TradeStation, MultiCharts**