Simple Moving Average (SMA): The Foundation of Trend-Following

The Simple Moving Average is the arithmetic mean of price over N periods. It is the oldest, most widely used indicator in technical analysis — and the foundation every more complex trend indicator is built on.

Section 1: Core Mechanics

The SMA answers one question: what is the average closing price over the last N bars?

Formula

Where is the closing price of bar , and is the period.

Each new bar drops the oldest price and adds the newest — a rolling window across the price series.

Inputs

• Price source: Close price (default). Can apply to Open, High, Low, or HL/2.
• Period (N): Number of bars in the average. Standard values: 9, 20, 50, 100, 200.

Parameters

Output

SMA outputs a single value per bar on the same price scale as the chart. It plots as a smooth curved line overlaid on candlesticks.

Visual Behavior

• Price above SMA → uptrend bias
• Price below SMA → downtrend bias
• Two SMAs on chart → crossover system (fast crosses slow = signal)
• SMA slope rising → trend accelerating; flat → trend stalling; falling → downtrend

Section 2: Interpretation & Signals

Signal Zones

Crossover Signals

The two-SMA crossover is the most common signal system:

• Entry: Next candle close after the crossover — not at the exact cross bar
• Exit: Reverse crossover, or price closes below the faster SMA for two consecutive bars

Dynamic Support and Resistance

In an uptrend, price repeatedly tests and bounces off the SMA. The SMA becomes a dynamic support level — more useful than a fixed horizontal line because it rises with the trend.

False Signals — The Whipsaw Problem

In ranging or choppy markets, price repeatedly crosses above and below the SMA, generating a series of losing crossover signals. This is the most common failure mode.

Section 3: Pass vs. Live — Real-Time Reliability

The SMA value on a live (unclosed) bar updates in real time as price moves. Once the bar closes, the SMA value for that bar is fixed and does not change. There is no repaint risk.

Section 4: Practical Use Cases

Real example: SPY on 2023-01-13 weekly close above SMA(200) at 396.10 after 9 months below — position entry signal for a multi-month long that ran to 480.

Section 5: Pseudo Code

INPUT: close_prices[], period=20

PROCESS:
  Step 1: Require at least `period` bars of data
  Step 2: For each bar i starting from index (period - 1):
            window = close_prices[i - period + 1 : i + 1]
            sma[i] = sum(window) / period
  Step 3: Bars before index (period - 1) = NaN (not enough data)

OUTPUT: sma[] — array of SMA values, same length as close_prices[]
EDGE CASES:
  - If close_prices has fewer than `period` elements: return all NaN
  - NaN in close_prices: propagate NaN for that bar (standard behavior)
  - Period = 1: SMA equals raw close (no smoothing)

Section 6: Parameters & Optimization

Standard Period Conventions

Parameter Impact

Section 7: Synergies & Conflicts

Section 8: Common Mistakes

Section 9: Cheat Sheet