QuanTAlib/lib/cycles/ebsw/ebsw.md

EBSW: Ehlers Even Better Sinewave

Overview and Purpose

The Ehlers Even Better Sinewave (EBSW) indicator, developed by John Ehlers, is an advanced cycle analysis tool. This implementation is based on a common interpretation that uses a cascade of filters: first, a High-Pass Filter (HPF) to detrend price data, followed by a Super Smoother Filter (SSF) to isolate the dominant cycle. The resulting filtered wave is then normalized using an Automatic Gain Control (AGC) mechanism, producing a bounded oscillator that fluctuates between approximately +1 and -1. It aims to provide a clear and responsive measure of market cycles.

Core Concepts

• Detrending (High-Pass Filter): A 1-pole High-Pass Filter removes the longer-term trend component from the price data, allowing the indicator to focus on cyclical movements.
• Cycle Smoothing (Super Smoother Filter): Ehlers' Super Smoother Filter is applied to the detrended data to further refine the cycle component, offering effective smoothing with relatively low lag.
• Wave Generation: The output of the SSF is averaged over a short period (typically 3 bars) to create the primary "wave".
• Automatic Gain Control (AGC): The wave's amplitude is normalized by dividing it by the square root of its recent power (average of squared values). This keeps the oscillator bounded and responsive to changes in volatility.
• Normalized Oscillator: The final output is a single sinewave-like oscillator.

Common Settings and Parameters

Parameter	Default	Function	When to Adjust
Source	close	Price data used for calculation.	Typically close, but hlc3 or ohlc4 can be used for a more comprehensive price representation.
HP Length	40	Lookback period for the 1-pole High-Pass Filter used for detrending.	Shorter periods make the filter more responsive to shorter cycles; longer periods focus on longer-term cycles. Adjust based on observed cycle characteristics.
SSF Length	10	Lookback period for the Super Smoother Filter used for smoothing the detrended cycle component.	Shorter periods result in a more responsive (but potentially noisier) wave; longer periods provide more smoothing.

Pro Tip: The HP Length and SSF Length parameters should be tuned based on the typical cycle lengths observed in the market and the desired responsiveness of the indicator.

Calculation and Mathematical Foundation

Simplified explanation:

1. Remove the trend from the price data using a 1-pole High-Pass Filter.
2. Smooth the detrended data using a Super Smoother Filter to get a clean cycle component.
3. Average the output of the Super Smoother Filter over the last 3 bars to create a "Wave".
4. Calculate the average "Power" of the Super Smoother Filter output over the last 3 bars.
5. Normalize the "Wave" by dividing it by the square root of the "Power" to get the final EBSW value.

Technical formula (conceptual):

1. High-Pass Filter (HPF - 1-pole): angle_hp = 2 * PI / hpLength alpha1_hp = (1 - sin(angle_hp)) / cos(angle_hp) HP = (0.5 * (1 + alpha1_hp) * (src - src[1])) + alpha1_hp * HP[1]
2. Super Smoother Filter (SSF): angle_ssf = sqrt(2) * PI / ssfLength alpha2_ssf = exp(-angle_ssf) beta_ssf = 2 * alpha2_ssf * cos(angle_ssf) c2 = beta_ssf c3 = -alpha2_ssf^2 c1 = 1 - c2 - c3 Filt = c1 * (HP + HP[1])/2 + c2*Filt[1] + c3*Filt[2]
3. Wave Generation: WaveVal = (Filt + Filt[1] + Filt[2]) / 3
4. Power & Automatic Gain Control (AGC): Pwr = (Filt^2 + Filt[1]^2 + Filt[2]^2) / 3 EBSW_SineWave = WaveVal / sqrt(Pwr) (with check for Pwr == 0)

🔍 Technical Note: The combination of HPF and SSF creates a form of band-pass filter. The AGC mechanism ensures the output remains scaled, typically between -1 and +1, making it behave like a normalized oscillator.

Interpretation Details

• Cycle Identification: The EBSW wave shows the current phase and strength of the dominant market cycle as filtered by the indicator. Peaks suggest cycle tops, and troughs suggest cycle bottoms.
• Trend Reversals/Momentum Shifts: When the EBSW wave crosses the zero line, it can indicate a potential shift in the short-term cyclical momentum.
  • Crossing up through zero: Potential start of a bullish cyclical phase.
  • Crossing down through zero: Potential start of a bearish cyclical phase.
• Overbought/Oversold Levels: While normalized, traders often establish subjective or statistically derived overbought/oversold levels (e.g., +0.85 and -0.85, or other values like +0.7, +0.9).
  • Reaching above the overbought level and turning down may signal a potential cyclical peak.
  • Falling below the oversold level and turning up may signal a potential cyclical trough.

Limitations and Considerations

• Parameter Sensitivity: The indicator's performance depends on tuning hpLength and ssfLength to prevailing market conditions.
• Non-Stationary Markets: In strongly trending markets with weak cyclical components, or in very choppy non-cyclical conditions, the EBSW may produce less reliable signals.
• Lag: All filtering introduces some lag. The Super Smoother Filter is designed to minimize this for its degree of smoothing, but lag is still present.
• Whipsaws: Rapid oscillations around the zero line can occur in volatile or directionless markets.
• Requires Confirmation: Signals from EBSW are often best confirmed with other forms of technical analysis (e.g., price action, volume, other non-correlated indicators).

Performance Profile

Operation Count (Streaming Mode, per Bar)

Operation	Count	Cost (cycles)	Subtotal
ADD/SUB	9	1	9
MUL	11	3	33
DIV	1	15	15
SQRT	1	15	15
Total	22	—	~72 cycles

Breakdown:

• High-Pass Filter (1-pole): 2 MUL + 2 ADD = 8 cycles
• Super Smoother Filter: 4 MUL + 3 ADD = 15 cycles
• Wave averaging (3 bars): 2 ADD + 1 DIV = 4 cycles
• Power calculation: 3 MUL + 2 ADD = 11 cycles
• AGC normalization: 1 SQRT + 1 DIV = 30 cycles

Complexity Analysis

Mode	Complexity	Notes
Streaming	O(1)	IIR filters + fixed 3-bar window
Batch	O(n)	Linear scan, no lookback iteration

Memory: ~48 bytes (filter states + 3-bar history for power)

SIMD Analysis

Optimization	Applicable	Notes
AVX2 vectorization	❌	IIR recursion prevents cross-bar parallelism
FMA	✅	HPF: α × (src - src[1]) + α × prev
Batch parallelism	❌	Sequential dependency on filter states

FMA Optimization: Both HPF and SSF recursions benefit from FMA. SSF inner loop: c1×avg + c2×prev1 + c3×prev2 reduces to 2 FMA + 1 MUL.

Quality Metrics

Metric	Score	Notes
Accuracy	8/10	Band-pass isolates dominant cycle
Timeliness	8/10	Super Smoother minimizes lag
Overshoot	7/10	AGC can amplify noise at low power
Smoothness	8/10	Normalized output is well-bounded

References

• Ehlers, J. F. (2002). Rocket Science for Traders: Digital Signal Processing Applications. John Wiley & Sons.
• Ehlers, J. F. (2013). Cycle Analytics for Traders: Advanced Technical Trading Concepts. John Wiley & Sons.