AvgPrice

Average Price (AVGPRICE) — the bar's (open + high + low + close) / 4, a single per-bar price aggregate that folds in the open as well as the high, low and close.

Quick reference

Field	Value
Family	Price Statistics
Input type	Candle (uses open, high, low, close)
Output type	f64
Output range	unbounded (price scale)
Default parameters	none (no parameters)
Warmup period	1
Interpretation	A representative per-bar price that weights the open equally with the high, low and close.

Formula

AVGPRICE = (open + high + low + close) / 4

Where TypicalPrice is (H + L + C) / 3 and WeightedClose is (H + L + 2C) / 4, AvgPrice is the only per-bar aggregate that gives the open a vote. It collapses a full OHLC bar to one number with all four prices weighted equally. The computation is delegated to Candle::avg_price() in crates/wickra-core/src/ohlcv.rs and wrapped as a stateless indicator in crates/wickra-core/src/indicators/avg_price.rs.

Parameters

AvgPrice takes no parameters — AvgPrice::new() in Rust, wickra.AvgPrice() in Python, new ta.AvgPrice() in Node.

Inputs / Outputs

From crates/wickra-core/src/indicators/avg_price.rs:

use wickra::{Indicator, AvgPrice, Candle};
// AvgPrice: Input = Candle, Output = f64
const _: fn(&mut AvgPrice, Candle) -> Option<f64> = <AvgPrice as Indicator>::update;

AvgPrice is a candle-input indicator that reads all four OHLC prices. In Python the streaming update accepts a candle (dict or tuple); the batch helper takes open, high, low, close numpy arrays and returns a 1-D array.array('d') (NaN only never appears here, since warmup is a single bar). Node and WASM expose update(open, high, low, close) and the matching batch.

Warmup

AvgPrice::new().warmup_period() == 1. It is a stateless per-bar transform — it emits a value from the very first candle. The unit test accessors_and_reset pins warmup_period() == 1, and averages_the_four_prices confirms the first candle is immediately ready.

Edge cases

• No warmup. Every candle produces a value immediately; is_ready() flips to true on the first update. The unit test averages_the_four_prices pins this.
• Reset. ap.reset() only clears the is_ready flag; there is no rolling state to discard. The unit test accessors_and_reset pins this.

Examples

Rust

use wickra::{Candle, Indicator, AvgPrice};

fn main() -> Result<(), Box<dyn std::error::Error>> {
    let mut ap = AvgPrice::new();
    let v = ap.update(Candle::new(10.0, 14.0, 6.0, 12.0, 1.0, 0)?);
    println!("{:?}", v);
    Ok(())
}

Output:

Some(10.5)

(10 + 14 + 6 + 12) / 4 = 10.5. This matches the averages_the_four_prices unit test in crates/wickra-core/src/indicators/avg_price.rs.

Python

import numpy as np
import wickra as ta

ap = ta.AVGPRICE()
print(ap.batch(np.array([10.0]), np.array([14.0]), np.array([6.0]), np.array([12.0])))

Output:

[10.5]

Node

const ta = require('wickra');
const ap = new ta.AVGPRICE();
console.log(ap.update(10, 14, 6, 12));

Output:

10.5

Interpretation

Use AvgPrice when you want a single representative price per bar that does not privilege the close. Because it includes the open, it sits closer to the bar's geometric centre than TypicalPrice on bars with a long body — it is slightly more sensitive to where the bar opened. It is a common substitute for the raw close when feeding a moving average or oscillator and you want gaps (open ≠ prior close) to register.

Prefer TypicalPrice when you specifically want the (H + L + C) / 3 series that Cci and Mfi are defined on; prefer MedianPrice when you only care about the bar's (H + L) / 2 midpoint.

Common pitfalls

• Feeding it scalar prices. It needs the full open/high/low/close bar; there is no single-value mode.
• Expecting it to differ from the close on doji-like bars. When a bar opens and closes near its mid with small wicks, AvgPrice ≈ close; the difference only grows with body size and wick asymmetry.

References

The Average Price (O + H + L + C) / 4 is TA-Lib's AVGPRICE function and a standard per-bar price aggregate on most charting platforms.

See also

• Indicator-TypicalPrice — (H + L + C) / 3.
• Indicator-WeightedClose — (H + L + 2C) / 4.
• Indicator-MedianPrice — (H + L) / 2.
• Indicators-Overview — the full taxonomy.