Causal — Indicators and Strategies

Adaptive Causal Wavelet Trend Filter The Adaptive Causal Wavelet Trend Filter is a technical indicator implementing causal approximations of wavelet transform properties for better trend detection with adaptive volatility response. The Adaptive Causal Wavelet Trend Filter (ACWTF) applies mathematical principles derived from wavelet analysis to financial time series, providing robust trend identification with minimal lag. Unlike conventional moving averages, it preserves significant price movements while filtering market noise through signal processing that i describe below. I was inspired to build this indicator after reading " Wavelet-Based Trend Identification in Financial Time Series " by In, F., & Kim, S. 2013 and reading about Mexican Hat wavelet filters. The ACWTF maintains optimal performance across varying market regimes without requiring parameter adjustments by adapting filter characteristics to current volatility conditions. Mathematical Foundation Inspired by the Mexican Hat wavelet (Ricker wavelet), this indicator implements causal approximations of wavelet filters optimized for real-time financial analysis. The multi-resolution approach identifies features at different scales and the adaptive component dynamically adjusts filtering characteristics based on local volatility measurements. Key mathematical properties include: Non-linear frequency response adaptation Edge-preserving signal extraction Scale-space analysis through dual filter implementation Volatility-dependent coefficient adjustment, which I love Filter Methods Adaptive: Implements a volatility-weighted combination of multiple filter types to optimize the time-frequency resolution trade-off Hull: Provides a causal approximation of wavelet edge detection properties with forward-projection characteristics VWMA: Incorporates volume information into the filtering process for enhanced signal detection EMA Cascade: Creates a multi-pole filter structure that approximates certain wavelet scaling properties Suggestion: try all as they will provide slightly different signals. Try also different time-frames. Practical Applications Trend Direction Identification: Clear visual trend direction with reduced noise and lag Regime Change Detection: Early identification of significant trend reversals Market Condition Analysis: Integrated volatility metrics provide context for current market behavior Multi-timeframe Confirmation: Alignment between primary and secondary filters offers additional confirmation Entry/Exit Timing: Filter crossovers and trend changes provide potential trading signals The comprehensive information panel provides: Current filter method and trend state Trend alignment between timeframes Real-time volatility assessment Price position relative to filter Overall trading bias based on multiple factors Implementation Notes Log returns option provides improved statistical properties for financial time series Primary and secondary filter lengths can be adjusted to optimize for specific instruments and timeframes The indicator performs particularly well during trend transitions and regime changes The indicator reduces the need for using additional indicators to check trend reversion

STD-Filtered, Variety FIR Digital Filters w/ ATR Bands [Loxx]STD-Filtered, Variety FIR Digital Filters w/ ATR Bands is a FIR Digital Filter indicator with ATR bands. This indicator contains 12 different digital filters. Some of these have already been covered by indicators that I've recently posted. The difference here is that this indicator has ATR bands, allows for frequency filtering, adds a frequency multiplier, and attempts show causality by lagging price input by 1/2 the period input during final application of weights. Period is restricted to even numbers. The 3 most important parameters are the frequency cutoff, the filter window type and the "causal" parameter. Included filter types: - Hamming - Hanning - Blackman - Blackman Harris - Blackman Nutall - Nutall - Bartlet Zero End Points - Bartlet Hann - Hann - Sine - Lanczos - Flat Top Frequency cutoff can vary between 0 and 0.5. General rule is that the greater the cutoff is the "faster" the filter is, and the smaller the cutoff is the smoother the filter is. You can read more about discrete-time signal processing and some of the windowing functions in this indicator here: Window function Window Functions and Their Applications in Signal Processing What are FIR Filters? In discrete-time signal processing, windowing is a preliminary signal shaping technique, usually applied to improve the appearance and usefulness of a subsequent Discrete Fourier Transform. Several window functions can be defined, based on a constant (rectangular window), B-splines, other polynomials, sinusoids, cosine-sums, adjustable, hybrid, and other types. The windowing operation consists of multipying the given sampled signal by the window function. For trading purposes, these FIR filters act as advanced weighted moving averages. A finite impulse response (FIR) filter is a filter whose impulse response (or response to any finite length input) is of finite duration, because it settles to zero in finite time. This is in contrast to infinite impulse response (IIR) filters, which may have internal feedback and may continue to respond indefinitely (usually decaying). The impulse response (that is, the output in response to a Kronecker delta input) of an Nth-order discrete-time FIR filter lasts exactly {\displaystyle N+1}N+1 samples (from first nonzero element through last nonzero element) before it then settles to zero. FIR filters can be discrete-time or continuous-time, and digital or analog. A FIR filter is (similar to, or) just a weighted moving average filter, where (unlike a typical equally weighted moving average filter) the weights of each delay tap are not constrained to be identical or even of the same sign. By changing various values in the array of weights (the impulse response, or time shifted and sampled version of the same), the frequency response of a FIR filter can be completely changed. An FIR filter simply CONVOLVES the input time series (price data) with its IMPULSE RESPONSE. The impulse response is just a set of weights (or "coefficients") that multiply each data point. Then you just add up all the products and divide by the sum of the weights and that is it; e.g., for a 10-bar SMA you just add up 10 bars of price data (each multiplied by 1) and divide by 10. For a weighted-MA you add up the product of the price data with triangular-number weights and divide by the total weight. What is a Standard Deviation Filter? If price or output or both don't move more than the (standard deviation) * multiplier then the trend stays the previous bar trend. This will appear on the chart as "stepping" of the moving average line. This works similar to Super Trend or Parabolic SAR but is a more naive technique of filtering. Included Bar coloring Loxx's Expanded Source Types Signals Alerts Related indicators STD/C-Filtered, N-Order Power-of-Cosine FIR Filter STD/C-Filtered, Power-of-Cosine FIR Filter STD/C-Filtered, Truncated Taylor Family FIR Filter STD/Clutter-Filtered, Variety FIR Filters STD/Clutter-Filtered, Kaiser Window FIR Digital Filter

Zero Phase Filtering [Repaint] - Experimental Important ! The indicator is for experimental purpose only, it must not be used as a decisional tool but only as a visual one (like Zig-Zag, Fractal etc). The information this indicator display is uncertain and subject to drastic changes over time. If you have further question feel free to pm me. Introduction Most of the filters you will find are causal, this mean that they depend on present and past input values, this explain the lag they produce. Non causal filters however will use future input values. A well know way to get a zero-phase filter is by using the forward backward method, but this is not possible in pinescript as i recall. So we have to use some kind of function that will display future values, this is possible using the security function in version 2 or the one in version 3 using barmerge.lookahead_on . The Use Of A Repainting Indicator Its always better to filter data in order to have a clearer view of what is happening, this can be useful when doing some forecasting or doing less formal kind of analysis. However since it repaint you cant use it as a signal provider or use signals of other indicators using this filter as source. For example if you want to forecast a smooth indicator, the forecast of this indicator under normal circumstances could still have lag associated with it, so you would have to react before your forecast, this wont happen if you apply this filter as your indicator source. The Filter We smooth with a simple moving average the price provided by the security function twice, length control the smoothing level. Since security depend on the time frame you are in you must select your time frame in the indicator parameter selection window. Filtering using 45 minutes time frame close price in a 5 minutes chart, we fix this by selecting our time frame. Consider the fact that the input of the indicator is just periodic price, so sometimes the lag can sometimes be less or more than 0 and the estimation not centered. The indicator can work on time frames up to 1h, after that the filter have some lag, i tried fixing this and i ended up having data errors. Applying our filter as source for the rsi oscillator. Conclusion It is possible to have a kind of zero-phase filters, but it would be better if pinescript could support backward indexing thus making us able to do forward backward filtering. Since noise can affect our analysis, applying smoothing without having to use offset in plot can be considered useful.