Simple Moving Average (SMA)

Warmup Window

Minimum bars needed: period bars (default params: 200 bars (period=200))

The first valid SMA value appears once period bars of close data are available. After the warmup, the indicator updates in real-time on every new bar.

✅ After the warmup window is filled, this indicator produces a new value on every incoming bar in real-time.

Lagging Indicator

The SMA line moves with a delay behind price.

Event	Lag	Detail
Line reacts to a price reversal	≈ (period − 1) / 2 bars	Uniform rolling window; center of gravity sits at the midpoint of the window
Line crosses price (trend confirmation)	≈ (period − 1) / 2 bars	Crossover inherits the same smoothing delay

Formula for custom params: lag ≈ (period − 1) / 2

A Simple Moving Average (SMA) is the average of the last N data points, recalculated as new data comes in. Unlike the Weighted Moving Average (WMA), SMA treats all values equally, giving them the same weight.

def sma(
    data: Union[PdDataFrame, PlDataFrame],
    source_column: str,
    period: int,
    result_column: str = None,
) -> Union[PdDataFrame, PlDataFrame]:

Example

from investing_algorithm_framework import download

from pyindicators import sma

pl_df = download(
    symbol="btc/eur",
    market="binance",
    time_frame="1d",
    start_date="2023-12-01",
    end_date="2023-12-25",
    save=True,
    storage_path="./data"
)
pd_df = download(
    symbol="btc/eur",
    market="binance",
    time_frame="1d",
    start_date="2023-12-01",
    end_date="2023-12-25",
    pandas=True,
    save=True,
    storage_path="./data"
)

# Calculate SMA for Polars DataFrame
pl_df = sma(pl_df, source_column="Close", period=200, result_column="SMA_200")
pl_df.show(10)

# Calculate SMA for Pandas DataFrame
pd_df = sma(pd_df, source_column="Close", period=200, result_column="SMA_200")
pd_df.tail(10)

Chart Parameters

The image above uses the following parameters:

Parameter	Value
source_column	Close
period	200