$ Pinescript Hull Moving Average (HMA)

What is the Hull Moving Average (HMA)?

Components and Calculation

1. Step 1: Calculate a WMA with period `n/2`: First, a Weighted Moving Average of the input price (typically `close`) is calculated over half the HMA's specified length (`n/2`).
2. Step 2: Calculate a WMA with period `n`: Next, another Weighted Moving Average of the input price is calculated over the full HMA length (`n`).
3. Step 3: Combine and smooth: Subtract the WMA from Step 2 from twice the WMA from Step 1. Then, calculate a WMA of this result, using a period equal to the square root of `n` (`sqrt(n)`). This final WMA is the Hull Moving Average.

Basic HMA Implementation in Pinescript

//@version=5
indicator("My Hull Moving Average", overlay=true)

// Input for HMA length
length = input.int(20, title="HMA Length", minval=1)

// Calculate HMA using the built-in function
hmaValue = ta.hma(close, length)

// Plot the HMA line
plot(hmaValue, title="HMA", color=color.blue, linewidth=2)

Key Advantage

Practical HMA Strategies

1. HMA as a Trend Direction Filter (Color Change)

//@version=5
strategy("HMA Trend Color Strategy", overlay=true)

// Input for HMA length
length = input.int(20, title="HMA Length", minval=1)

// Calculate HMA
hmaValue = ta.hma(close, length)

// Determine HMA color based on its direction
hmaColor = hmaValue > hmaValue[1] ? color.green : color.red

// Plot the HMA line with dynamic color
plot(hmaValue, title="HMA", color=hmaColor, linewidth=2)

// Example entry logic: buy when HMA turns green, sell when HMA turns red
longCondition = hmaValue > hmaValue[1] and hmaValue[1] <= hmaValue[2] // HMA turns up
shortCondition = hmaValue < hmaValue[1] and hmaValue[1] >= hmaValue[2] // HMA turns down

if (longCondition)
    strategy.entry("Long", strategy.long)

if (shortCondition)
    strategy.entry("Short", strategy.short)

2. HMA Crossover Strategy (with Price or another HMA)

//@version=5
strategy("HMA Crossover Strategy", overlay=true)

// Inputs for HMA lengths
fastHmaLength = input.int(10, title="Fast HMA Length", minval=1)
slowHmaLength = input.int(30, title="Slow HMA Length", minval=1)

// Calculate HMAs
fastHma = ta.hma(close, fastHmaLength)
slowHma = ta.hma(close, slowHmaLength)

// Plot the HMAs
plot(fastHma, title="Fast HMA", color=color.blue, linewidth=2)
plot(slowHma, title="Slow HMA", color=color.orange, linewidth=2)

// Crossover conditions (Fast HMA crossing Slow HMA)
longCondition = ta.crossover(fastHma, slowHma)
shortCondition = ta.crossunder(fastHma, slowHma)

// Strategy entries/exits
if (longCondition)
    strategy.entry("Long", strategy.long)

if (shortCondition)
    strategy.entry("Short", strategy.short)

3. HMA for Support and Resistance

//@version=5
indicator("HMA Support/Resistance", overlay=true)

length = input.int(20, title="HMA Length", minval=1)
hmaValue = ta.hma(close, length)

plot(hmaValue, title="HMA", color=color.blue, linewidth=2)

// Highlight potential support/resistance touches (conceptual - adjust thresholds)
isSupportTouch = hmaValue > close and hmaValue[1] < close[1] and close > hmaValue * 0.995 and close < hmaValue * 1.005 // Price nearing HMA from below and bouncing
isResistanceTouch = hmaValue < close and hmaValue[1] > close[1] and close < hmaValue * 1.005 and close > hmaValue * 0.995 // Price nearing HMA from above and bouncing

plotshape(isSupportTouch, title="Potential Support", location=location.belowbar, color=color.new(color.green, 0), style=shape.circle, size=size.tiny)
plotshape(isResistanceTouch, title="Potential Resistance", location=location.abovebar, color=color.new(color.red, 0), style=shape.circle, size=size.tiny)

Optimizing HMA Performance

• ▸ Parameter Tuning: The `length` parameter is the most critical. Experiment with different lengths (e.g., 9, 20, 50, 200) to find what works best for your chosen asset and timeframe. Shorter lengths are more responsive, while longer lengths provide more smoothing for longer-term trends.
• ▸ Multi-Timeframe Analysis: Use HMA on higher timeframes to confirm the overall trend and on lower timeframes for precise entries and exits. For example, a 200-period HMA on a daily chart for long-term bias, and a 20-period HMA on an hourly chart for trade timing.
• ▸ Combine with Other Indicators: While HMA is designed to reduce noise, it's not a standalone indicator. Pair it with volume indicators, oscillators (like RSI for overbought/oversold, or MACD for momentum confirmation), or price action analysis.
• ▸ Avoid Choppy Markets: Despite its smoothing, HMA can still produce false signals in prolonged sideways or non-trending markets. Use a trend strength indicator (e.g., ADX) to confirm a trend is present before relying heavily on HMA signals.

Smoothness vs. Lag

Common HMA Pitfalls

• ▸ Whipsaws in Extreme Choppiness: In extremely flat or highly volatile, non-trending markets, HMA can still generate false signals or become less reliable.
• ▸ Not a Substitute for Price Action: While HMA can show trend direction, it doesn't provide insight into the underlying supply and demand dynamics that candlestick patterns or chart structures reveal.
• ▸ Over-Optimization: Tuning HMA parameters too precisely to historical data can lead to curve-fitting and poor performance in live trading.
• ▸ Lack of Overbought/Oversold Information: HMA is a trend-following indicator and does not inherently provide information on overbought or oversold market conditions, unlike oscillators.

Conclusion