Moving Average Calculator

Quantitative Analysis & Time Series Smoothing

Moving Averages (MA) are statistical tools used in algorithmic trading, economic forecasting, and signal processing to smooth out high-frequency fluctuations and identify long-term directional trends.

Core Moving Average Equations

• Simple Moving Average (SMA): SMA_N = (P_1 + P_2 + + P_N) / (N)

• EMA Weight Multiplier (K): K = (2) / (N + 1)

• Exponential Moving Average (EMA): EMA_t = P_t · K + EMA_t-1 · (1 - K)

• Weighted Moving Average (WMA): WMA = Σ_i=1^N i · P_i(N) / (2)

• Price Percentage Deviation: Δ\% = ( P_current - SMASMA ) × 100\%

Popular Moving Average Windows in Finance

• 20-Day SMA/EMA: Short-term swing trading trend indicator.

• 50-Day SMA: Medium-term institutional support level.

• 200-Day SMA: Primary bull/bear market baseline boundary line.

• Golden Cross: Occurs when the 50-day SMA crosses above the 200-day SMA (bullish signal).

• Death Cross: Occurs when the 50-day SMA crosses below the 200-day SMA (bearish signal).

Supply Chain & Inventory Demand Forecasting

Logistics managers use 3-month and 6-month moving averages of sales data to smooth out seasonal demand spikes, minimizing stockout risks and optimizing safety stock levels.

How to Use This Calculator

Enter your Data Series (Comma-Separated) (e.g. 10, 12, 15, 18, 20, 22, 25), Window Period (N) (e.g. 5 in periods), and Latest New Value (Current Price) (e.g. 28). Results update instantly as you type — adjust any value to compare scenarios.

The Formula

Formula Reference: Moving Average Calculator

Input variables:

dataPointsString — Data Series (Comma-Separated)

periodLength — Window Period (N)

latestNewValue — Latest New Value (Current Price)

Computed outputs:

simpleMovingAverageSma — Simple Moving Average (SMA)

exponentialMovingAverageEma — Exponential Moving Average (EMA)

smoothingFactorMultiplier — EMA Smoothing Factor (Multiplier K)

percentageDeviationFromSma — Current Price % Deviation from SMA

Mathematical relationships extracted from calculation logic:

windowSlice = combined.slice(-N)

sumSMA = windowSlice.reduce((a, b) => a + b, 0)

K = safeDiv(2, N + 1)

ema = (combined[i] K) + (ema (1 - K))

devPct = safeDiv((currentPrice - sma) * 100, sma)

Standard: NIST / ISO mathematical definitions

Worked Example

Calculating 5-period moving averages for price history [10, 12, 15, 18, 20, 22, 25] with current price 28: Delivers 22.60 SMA, 23.85 EMA, and +23.89% deviation from SMA.

Understanding Your Result

Date and time results depend on the reference timezone — all calculations here use the local system clock. For legal or contractual purposes, "days between" dates should be clarified as calendar days (counting the end date) vs. business days (excluding weekends and public holidays). Age calculations use full completed years — someone born on December 31 is the same age as someone born on January 1 of the same year for most legal purposes until their actual birthday.