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Walk Forward Analysis was created by Robert E. Pardo in 1992 [1] and expanded in the second edition. [2] Walk Forward Analysis is now widely considered the "gold standard" in trading strategy validation. The trading strategy is optimized with in-sample data for a time window in a data series. The remaining data is reserved for out of sample ...
The zero lag exponential moving average (ZLEMA) is a technical indicator within technical analysis that aims is to eliminate the inherent lag associated to all trend following indicators which average a price over time.
In time series analysis, the Box–Jenkins method, [1] named after the statisticians George Box and Gwilym Jenkins, applies autoregressive moving average (ARMA) or autoregressive integrated moving average (ARIMA) models to find the best fit of a time-series model to past values of a time series.
Basel financial regulations require large financial institutions to backtest certain risk models. For a Value at Risk 1-day at 99% backtested 250 days in a row, the test is considered green (0-95%), orange (95-99.99%) or red (99.99-100%) depending on the following table: [3] backtesting exceptions 1Dx250
The Double Exponential Moving Average (DEMA) indicator was introduced in January 1994 by Patrick G. Mulloy, in an article in the "Technical Analysis of Stocks & Commodities" magazine: "Smoothing Data with Faster Moving Averages" [1] [2] It attempts to remove the inherent lag associated with Moving Averages by placing more weight on recent values.
The trader would then backtest the strategy, using actual data and would evaluate the strategy. The simulator would generate estimated number of trades, the fraction of winning/losing trades, average profit/loss, average holding time, maximum drawdown, and the overall profit/loss. The trader can then experiment and refine the strategy.
In statistics, a moving average (rolling average or running average or moving mean [1] or rolling mean) is a calculation to analyze data points by creating a series of averages of different selections of the full data set. Variations include: simple, cumulative, or weighted forms. Mathematically, a moving average is a type of convolution.
In time series analysis, the moving-average model (MA model), also known as moving-average process, is a common approach for modeling univariate time series. [ 1 ] [ 2 ] The moving-average model specifies that the output variable is cross-correlated with a non-identical to itself random-variable.