Every Technical Indicator Is Borrowed From Physics. Their Default Parameters Are Almost Always Wrong.

Every indicator on every chart traces its lineage to a problem solved by physicists, electrical engineers, or signal-processing researchers between roughly 1920 and 1970. The mathematics is sound. The defaults are not. They were chosen for the physical system the original engineer was studying — audio waveforms, radar returns, electrocardiograms — and were carried into financial software unchanged. There is no a-priori reason an indicator calibrated for the human voice should work on Brent crude futures.

Indicators Are Filters

A digital filter is a linear operator that maps an input sequence to an output sequence by attenuating some frequencies and passing others. The two basic families are:

• Finite Impulse Response (FIR) — output depends only on a fixed window of past inputs. Simple moving averages, weighted moving averages, and Hull MA are FIR filters.
• Infinite Impulse Response (IIR) — output depends on past inputs and past outputs (feedback). Exponential moving averages, MACD, and Wilder's RSI smoothing are IIR filters.

This is not metaphor. The transfer functions are identical to those used in audio equalisers, radio receivers, and seismograph processing. The same mathematics — convolution, z-transform, frequency response — applies bit for bit.

Where the Standard Indicators Came From

Indicator	Origin	What the default was tuned for
RSI (period 14)	Welles Wilder, New Concepts in Technical Trading Systems (1978), adapted from engineering oscillator design.	Daily commodity bars circa late 1970s — sampling rate ~250/year, instrument volatility ~15%. Period 14 ≈ ½ month of trading days.
MACD (12, 26, 9)	Gerald Appel, 1970s — two EMAs with cutoff frequencies tuned to monthly and bi-monthly cycles on daily bars.	Daily US equity bars. 12 ≈ two trading weeks, 26 ≈ one trading month, 9 ≈ smoothing of the difference.
Bollinger Bands (20, 2σ)	John Bollinger, 1980s — rolling mean ± k·σ, lifted directly from statistical process control charts (Shewhart, 1924).	Daily equity bars where 20 bars ≈ one trading month and 2σ corresponds to ~95% containment under a Gaussian — which equity returns are not.
Stochastic (14, 3, 3)	George Lane, 1950s — min-max normaliser borrowed from servomechanism feedback theory.	Daily futures bars; 14 again chosen as a half-month.
ATR (14)	Wilder, 1978 — exponential smoothing of true range, identical in form to a thermal-noise estimator in receiver design.	Same daily-bar regime as RSI.
Hilbert Transform / MESA	Hilbert (1905), refined for signal processing by Gabor (1946); applied to markets by John Ehlers (1992 onward).	Originally developed for radar pulse analysis and seismic signal decomposition.

Every default in the left column was set for one specific sampling rate (one bar per trading day), one specific instrument class (mid-twentieth-century US equities or commodities), and one specific noise environment (pre-electronic-trading volatility regimes). None of those conditions hold on a 5-minute BTCUSDT chart, a 1-tick ES future, or a daily emerging-market FX cross.

Why the Defaults Mislead

1. Sampling rate mismatch

A FIR filter with N taps has a frequency response whose cutoff scales with 1/N in cycles-per-sample. Period-14 on daily bars cuts at ≈ 1/14 cycles per day. The same period-14 on 1-minute bars cuts at ≈ 1/14 cycles per minute — a completely different physical frequency in the underlying market. The default did not move with the timeframe.

2. Noise-floor mismatch

The optimal Wiener filter length depends on the signal-to-noise ratio of the input. Daily 1978 commodity bars had very different SNR characteristics from modern HFT-saturated minute bars. A length that maximises SNR on one is suboptimal on the other.

3. Cycle-band mismatch

MACD(12, 26, 9) is a band-pass with a centre frequency tuned to monthly cycles on daily bars. On a 1-hour chart, the same constants centre on a 12–26 hour cycle that may have no economic meaning for the instrument being traded. The filter still computes — but it is now band-passing noise.

4. Distributional mismatch

Bollinger's 2σ rule assumes approximate normality. Equity log-returns are leptokurtic; crypto log-returns are extremely so. The default "2σ" containment is closer to 99% on liquid equities and closer to 99.9% on illiquid alts — the bands convey different information at the same constants.

What Signal Processing Actually Requires

The DSP discipline is unambiguous about how to set indicator (filter) parameters: you do not guess, you enumerate. Specifically:

1. Define the signal-space — the full lattice of plausible filter parameters for the indicator family (e.g. RSI period from 2 to ~10,000, source from {Close, HL2, OHLC4}).
2. Define a target property — for trading, this is statistical evidence of predictive power against a null model, not "looks like it works on the chart".
3. Run permutation tests across the entire signal-space — for each parameter combination, compute the realised target under both the actual return series and a permuted (shuffled or block-bootstrapped) series that destroys temporal structure while preserving the marginal distribution.
4. Apply multiple-testing correction — Benjamini-Hochberg FDR, Romano-Wolf, or a similar method. Testing 10,000 parameter combinations without correction makes a Type I error rate of α = 0.05 meaningless; ~500 configurations will appear "significant" by pure chance.
5. Require out-of-sample survival — a configuration that passes permutation testing on the in-sample window must also pass on a strictly later, untouched window. Without this, you have measured fit, not edge.

This procedure is mechanical. It is not optional. It is what "signal processing" means when the signal is embedded in a noisy real-valued series whose generating process is unknown — i.e. always, in finance.

Why the Industry Hides This

Two reasons. First, exhaustive enumeration with proper multiple-testing correction is computationally expensive — a full RSI lattice on a multi-year minute bar series requires millions of independent permutation runs. Second, the honest answer it produces is usually "no configuration in this family survives on this asset, at this timeframe, in this window" — a result that does not sell platform subscriptions or course memberships.

So the industry ships defaults. The defaults look respectable because they were chosen by serious engineers solving serious problems in 1978. They do not look right because the engineer was looking at a different signal.

Summary

Technical indicators are FIR and IIR filters borrowed from physics and signal processing. The mathematics is rigorous. The default parameters that travelled into trading software were calibrated for the sampling rates, noise floors, and cycle bands of mid-twentieth-century physical systems. They have no claim to optimality on modern market data, and frequently no claim to validity either.

Treating an indicator default as anything other than a starting hypothesis is a category error. The DSP-correct workflow is to enumerate the full parameter lattice and let permutation testing with multiple-testing correction surface the configurations — if any — that actually carry signal on the asset and timeframe under study. This is what Student One's enumeration engine does by construction.