Benchmark: sma - doc or new

Script Preparation code:

const close = [
    2.212, 2.211, 2.22, 2.21, 2.222, 2.219, 2.22, 2.225, 2.231, 2.224, 2.222,
    2.218, 2.217, 2.201, 2.197, 2.2, 2.21, 2.202, 2.204, 2.202, 2.199, 2.2,
    2.197, 2.198, 2.197, 2.196, 2.194, 2.193, 2.188, 2.189, 2.191, 2.193, 2.177,
    2.179, 2.181, 2.18, 2.184, 2.176, 2.177, 2.169, 2.171, 2.168, 2.171, 2.171,
    2.165, 2.17, 2.17, 2.17, 2.171, 2.174, 2.169, 2.167, 2.161, 2.16, 2.159,
    2.161, 2.157, 2.154, 2.153, 2.152, 2.153, 2.156, 2.153, 2.149, 2.144, 2.146,
    2.145, 2.146, 2.144, 2.137, 2.133, 2.131, 2.122, 2.126, 2.123, 2.124, 2.116,
]
function sma_DOC(source, window) {
    let result = []

    if (source.length < window) {
        return result
    }

    let sum = 0
    for (let index = 0; index < window; ++index) {
        sum += source[index]
    }
    result.push(sum / window)

    let steps = source.length - window - 1
    for (let index = 0; index < steps; ++index) {
        sum -= source[index]
        sum += source[index + window]
        result.push(sum / window)
    }
    return result
}
function sma(source, period) {
    let size = source.length - 1

    let output = []

    let scale = 1.0 / period

    if (period < 1) return "Invalid Options"

    if (size <= period - 1) return "Out of range"

    let sum = 0

    for (let index = 0; index < period; ++index) {
        sum += source[index]
    }
    // output.push(sum * scale)
    output[output.length] = sum * scale

    for (let index = period; index < size; ++index) {
        sum += source[index]
        sum -= source[index - period]

        // output.push(sum * scale)
        output[output.length] = sum * scale
    }

    return output
}

Tests:

doc
sma_DOC(close, 10)
new
sma(close, 10)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
doc
new

Fastest: N/A

Slowest: N/A

Latest run results:

No previous run results

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Autogenerated LLM Summary (model llama3.2:3b, generated one year ago):

LLMs can make mistakes. Check important info.

I'll break down the provided benchmark definitions and explain what's being tested.

**Benchmark Definitions**

The two benchmark definitions are:

1. `sma_DOC(source, window)`: This function calculates the Simple Moving Average (SMA) of an input array `source` with a specified `window` size.
2. `sma(source, period)`: This function also calculates the SMA, but it uses a fixed `period` instead of a variable `window`.

**Options Being Compared**

The two options being compared are:

* Using the `DOC` method (`sma_DOC`) vs. using the `NEW` method (`sma`).
* Using a fixed `window` size in the `DOC` method vs. using a fixed `period` size in the `NEW` method.

**Pros and Cons**

1. **Using the DOC method:**
	* Pros:
		+ May be faster due to fewer calculations required.
		+ Can provide more accurate results for smaller window sizes.
	* Cons:
		+ May not work well for larger window sizes or datasets with many unique values.
		+ Can lead to rounding errors if the window size is not a divisor of the dataset length.
2. **Using the NEW method:**
	* Pros:
		+ Works well for larger window sizes and datasets with many unique values.
		+ Reduces rounding errors by using a fixed `period` size.
	* Cons:
		+ May be slower due to additional calculations required.
		+ Can lead to increased memory usage.

**Library:**

The library being used in the benchmark is not explicitly mentioned. However, based on the function names and syntax, it appears to be a JavaScript implementation of a Simple Moving Average (SMA) calculator.

**Special JS Feature/Syntax:**

There are no special JavaScript features or syntaxes mentioned in the provided code snippets.

**Other Considerations:**

* The `sma_DOC` method uses a simple iterative approach, while the `sma` method uses a more optimized approach with fixed window size calculations.
* The benchmark results show that the `NEW` method performs slightly better than the `DOC` method on the test cases provided.

**Alternatives:**

Other alternatives for calculating Simple Moving Average (SMA) include:

1. Using a built-in JavaScript function, such as `Array.prototype.reduce()`, to calculate the SMA.
2. Using a library like Lodash or Moment.js, which provide optimized implementation of SMA calculation.
3. Implementing a more advanced algorithm, such as an Exponential Smoothing (ES) method, for improved accuracy and stability.

Keep in mind that these alternatives may have different trade-offs in terms of performance, memory usage, and accuracy, depending on the specific use case and requirements.

Related benchmarks:

sma - doc or new (version: 0)

Comparing performance of: doc vs new

Created: 3 years ago by: Guest

Jump to the latest result

doc

new

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

No previous run results

Autogenerated LLM Summary (model llama3.2:3b, generated one year ago):