{"product_id":"stcov321pran","title":"Stocks \u0026 Commodities V. 32:1 (16-25): Predictive And Successful Indicators by John F. Ehlers, PhD","description":"Predictive And Successful Indicators by John F. Ehlers, PhD\u003cbr\u003e\u003cbr\u003eDistortions And Noise\u003cbr\u003e\u003cbr\u003eHave you ever thought about how the high-to-low\nprice swings increase as the time interval increases\non a chart? This tends to create more noise and\ndistorts indicators. Here are a couple of filters you\ncan incorporate into your trading system to smooth\ndata and remove indicator distortions.\u003cbr\u003e\u003cbr\u003eIndicators are typically constructed from filters\nof one kind or another. Since the price data basically\nconstitutes a stochastic process, and since\nthe filters can only use historical data and have no\ninsight into future data, there is no such thing as a truly\npredictive indicator. Predictions are usually made by\nother techniques such as extrapolating a trendline,\ncross-correlations such as volume leads price, or in\ncontext with another filter such as a divergence. All\nof these techniques are anecdotal.\u003cbr\u003e\u003cbr\u003eIn this article, I will show you how to carefully craft\nnovel filters to conquer the vagaries of market data,\nand how to combine them into advanced indicators.\nThen I will demonstrate how even advanced indicators\nfail if they are used in the conventional way. Then,\nusing measured probability density functions, I will\nshow how to make the indicators predictive with a\nhigh probability of success.\u003cbr\u003e\u003cbr\u003eNoise\u003cbr\u003e\u003cbr\u003eMarket data is noisy. The systemic noise arises from\nhundreds, if not thousands, of traders placing trades\nnearly simultaneously that each trader, for a variety\nof reasons, thinks will result in profits. In addition, market data is sampled data rather than continuous\ndata; that is, there is only one data point on the close of\neach day when using daily data. Even if you average\nin the high \u0026amp; low prices, there still is only one sample\nper day. Of course, you can change the sample rate\nusing intraday data, but you are still using sampled\ndata. The result of using sampled data is that there is\nsubstantial aliasing noise several octaves below the\nNyquist frequency. If you prefer, you can think of\nthis other kind of noise as autocorrelation noise. For\ndaily data, the period of the Nyquist frequency is a\ntwo-bar cycle. One octave lower is a four-bar cycle,\nand one more octave lower is an eight-bar cycle.\nAliasing noise swamps the signal for these shorter\ncycle periods, and the only thing that can be done is\nto not even try to use cycle periods where the aliasing\nnoise swamps the signal amplitude.\u003cbr\u003e\u003cbr\u003eAliasing noise is also larger than the signal amplitude\nat even longer cycle periods, but the frequency\nseparation enables filters to reduce or nearly remove\nthe effects of aliasing noise. Simple moving averages\n(SMA) or exponential moving averages (EMA) are\noften used to smooth the data in an attempt to reduce\naliasing noise. The problem with using an SMA or\nEMA is that they are not efficient filters. The only\nway to get more smoothing is to increase the length\nof the moving average, which introduces more lag\ninto the filter.","brand":"store-traders-com","offers":[{"title":"Default Title","offer_id":52133937381745,"sku":"V32C01_692EHLE","price":5.95,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0900\/3843\/6209\/files\/unnamed_a6f6254f-af3a-45a1-b877-8e0d4e3e13a2.jpg?v=1739658302","url":"https:\/\/shop.traders.com\/products\/stcov321pran","provider":"Stocks \u0026 Commodities","version":"1.0","type":"link"}