QuanTAlib/lib/core/avgprice/Avgprice.md

AVGPRICE: Average Price

The four-point average distills an entire bar into a single representative price — open, high, low, and close in equal measure.

Property	Value
Category	Core
Inputs	OHLCV bar (TBar)
Parameters	None
Outputs	Single series (AVGPRICE)
Output range	Varies (see docs)
Warmup	1 bars
PineScript	avgprice.pine

• AVGPRICE computes the arithmetic mean of a bar's four canonical prices: Open, High, Low, and Close.
• No configurable parameters; computation is stateless per bar.
• Output range: Varies (see docs).
• Requires 1 bars of warmup before first valid output (IsHot = true).
• Validated against TA-Lib, Skender, and Tulip reference implementations where available.

AVGPRICE computes the arithmetic mean of a bar's four canonical prices: Open, High, Low, and Close. The formula \frac{O + H + L + C}{4} produces a single representative price that weights all four price components equally, unlike Typical Price (which excludes Open) or Weighted Close (which double-weights Close). This equal weighting makes AVGPRICE the least biased single-bar summary statistic, useful as a neutral input to downstream indicators when no particular price component deserves emphasis. The calculation is stateless, requires no warmup, and costs a single FMA instruction per bar.

Historical Context

Average Price is one of the oldest price transforms in technical analysis, predating computer-based charting by decades. Its inclusion in the TA-Lib function set (TA_AVGPRICE) standardized it as a canonical operation alongside MEDPRICE, TYPPRICE, and WCLPRICE. The four-price average gained popularity because it distributes weight across the full intra-bar range: Open captures the session's starting sentiment, High and Low bound the extremes where supply and demand exhausted themselves, and Close reflects the final consensus.

In practice, AVGPRICE and OHLC4 are identical. QuanTAlib exposes both: TBar.OHLC4 as a zero-cost computed property for inline use, and Avgprice as a streaming indicator class supporting bar correction, event chaining, and batch processing. The indicator form exists because downstream consumers (Quantower adapters, chained indicator pipelines) require the ITValuePublisher interface and isNew rollback semantics that a bare struct property cannot provide.

Architecture & Physics

1. Core Formula

\text{AvgPrice}_t = \frac{O_t + H_t + L_t + C_t}{4}

Implemented as FMA to avoid division on the hot path:

\text{AvgPrice}_t = \text{FMA}(O_t + H_t,\; 0.25,\; (L_t + C_t) \times 0.25)

2. State Management

No rolling window, no lookback buffer. The indicator is stateless per bar. State exists only for:

• Last-valid substitution: If any OHLC component is NaN/Infinity, the last finite value for that component is used.
• Bar correction: isNew=false rolls back to previous state, enabling same-timestamp rewrites.

3. Complexity

O(1) per bar. Two additions, one FMA. No memory allocation. Always hot after the first bar.

Mathematical Foundation

Parameters

Parameter	Description	Default	Constraint
(none)	No user-configurable parameters

Relationship to TBar Properties

Transform	Formula	TBar Property	Indicator Class
Average Price	(O+H+L+C) \times 0.25	OHLC4	Avgprice
Median Price	(H+L) \times 0.5	HL2	Medprice
Typical Price	(H+L+C) \times \frac{1}{3}	HLC3	Typprice
Weighted Close	(H+L+2C) \times 0.25	HLCC4	Wclprice

Output Interpretation

Context	Meaning
AVGPRICE > Close	Intra-bar action skewed higher than settlement
AVGPRICE < Close	Close settled above the bar's center of mass
AVGPRICE \approx Close	Symmetric bar (doji-like)

Performance Profile

Operation Count (Streaming Mode)

Operation	Count	Cost (cycles)	Subtotal
ADD (O+H)	1	1	1
ADD (L+C)	1	1	1
MUL ((L+C) × 0.25)	1	3	3
FMA ((O+H) × 0.25 + prev)	1	4	4
Total (hot)	4		~9 cycles

Batch Mode (SIMD Analysis)

Aspect	Assessment
SIMD vectorizable	Yes: element-wise arithmetic, no inter-bar dependency
Optimal strategy	Vector<double> over OHLC spans; 4-wide on AVX2, 8-wide on AVX-512
Memory	O(1) streaming; O(n) batch output span
Throughput	Near memory-bandwidth bound for large series

Resources

• TA-Lib TA_AVGPRICE function reference.
• Murphy, J.J. Technical Analysis of the Financial Markets. New York Institute of Finance, 1999.