SuperTrend

Warmup Window

Minimum bars needed: atr_length bars (default params: 10 bars (atr_length=10))

The ATR component needs atr_length bars before the first SuperTrend value is computed. After 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

Trend flips and buy/sell signals lag behind the actual price reversal.

Event	Lag	Detail
Trend flips bullish ↔ bearish	≈ atr_length / 2 bars	ATR smoothing is the primary lag source
Buy / sell signal fires	≈ atr_length / 2 bars	Signal fires on the bar the trend flips

Formula for custom params: lag ≈ atr_length / 2

The SuperTrend indicator uses a fixed ATR multiplier factor to create a trend-following trailing stop. When the price is above the SuperTrend line the trend is bullish; when below, bearish. Trend changes generate buy/sell signals.

def supertrend(
    data: Union[PdDataFrame, PlDataFrame],
    atr_length: int = 10,
    factor: float = 3.0
) -> Union[PdDataFrame, PlDataFrame]:

Returns the following columns:

• supertrend: The SuperTrend trailing stop value
• supertrend_trend: Current trend (1=bullish, 0=bearish)
• supertrend_upper: Upper band
• supertrend_lower: Lower band
• supertrend_signal: 1=buy signal, -1=sell signal, 0=no signal

Example

from investing_algorithm_framework import download

from pyindicators import supertrend

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 SuperTrend
pd_df = supertrend(pd_df, atr_length=10, factor=3.0)
pd_df.tail(10)

Chart Parameters

The image above uses the following parameters:

Parameter	Value
atr_length	10
factor	3.0