Moving Average Convergence Divergence (MACD)

Warmup Window

Minimum bars needed: long_period + signal_period bars (default params: 35 bars (long_period=26, signal_period=9))

The slow EMA needs long_period bars, then the signal line EMA needs signal_period additional bars on top. After warmup, all MACD components update in real-time.

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

Lagging Indicator

MACD line, histogram, and signal line all lag behind price moves.

Event	Lag	Detail
MACD line crosses zero (trend change)	≈ `long_period / 2` bars	Dominated by the slow EMA
MACD histogram changes sign	≈ `long_period / 2` bars	Histogram = MACD − Signal; inherits MACD lag
Signal line crossover (buy/sell trigger)	≈ `long_period / 2 + signal_period / 2` bars	Signal is EMA of MACD; adds extra smoothing on top of MACD lag

Formula for custom params: MACD ≈ long_period / 2; signal ≈ long_period / 2 + signal_period / 2

The Moving Average Convergence Divergence (MACD) is used to identify trend direction, strength, and potential reversals. It is based on the relationship between two Exponential Moving Averages (EMAs) and includes a histogram to visualize momentum.

def macd(
    data: Union[PdDataFrame, PlDataFrame],
    source_column: str,
    short_period: int = 12,
    long_period: int = 26,
    signal_period: int = 9,
    macd_column: str = "macd",
    signal_column: str = "macd_signal",
    histogram_column: str = "macd_histogram"
) -> Union[PdDataFrame, PlDataFrame]:

Example

from investing_algorithm_framework import download

from pyindicators import macd

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 MACD for Polars DataFrame
pl_df = macd(pl_df, source_column="Close", short_period=12, long_period=26, signal_period=9)

# Calculate MACD for Pandas DataFrame
pd_df = macd(pd_df, source_column="Close", short_period=12, long_period=26, signal_period=9)

pl_df.show(10)
pd_df.tail(10)

MACD

Chart Parameters

The image above uses the following parameters:

Parameter	Value
`source_column`	`Close`
`short_period`	`12`
`long_period`	`26`
`signal_period`	`9`