Bollinger Bands Overshoot

Warmup Window

Minimum bars needed: period bars (default params: 20 bars (period=20))

Built on Bollinger Bands — same warmup as BB. After warmup, the overshoot value updates in real-time.

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

Lagging Indicator

Inherits the same lag as Bollinger Bands.

Event	Lag	Detail
Overshoot value reacts to price move	≈ `period / 2` bars	Inherits SMA + std dev lag from BB
Overshoot crosses zero	≈ `period / 2` bars	Same underlying Bollinger Bands smoothing

Formula for custom params: lag ≈ period / 2

Bollinger Bands Overshoot measures how far the price has exceeded the upper or lower Bollinger Band, expressed as a percentage of the half-band width (distance from middle to upper/lower band). This indicator helps identify extreme price movements and potential mean reversion opportunities.

Calculation:

When price > upper band (bullish overshoot): ((Price - Upper Band) / (Upper Band - Middle Band)) × 100
When price < lower band (bearish overshoot): ((Price - Lower Band) / (Middle Band - Lower Band)) × 100
When price is within bands: 0%

Interpretation:

Positive values indicate overbought conditions (price above upper band)
Negative values indicate oversold conditions (price below lower band)
High overshoots (e.g., 40%) indicate increased risk of mean reversion

def bollinger_overshoot(
    data: Union[PdDataFrame, PlDataFrame],
    source_column='Close',
    period=20,
    std_dev=2,
    result_column='bollinger_overshoot'
) -> Union[PdDataFrame, PlDataFrame]:

Example

from investing_algorithm_framework import download

from pyindicators import bollinger_overshoot

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 Bollinger Bands Overshoot for Polars DataFrame
pl_df = bollinger_overshoot(pl_df, source_column="Close")
pl_df.show(10)

# Calculate Bollinger Bands Overshoot for Pandas DataFrame
pd_df = bollinger_overshoot(pd_df, source_column="Close")
pd_df.tail(10)

BOLLINGER_OVERSHOOT

Chart Parameters

The image above uses the following parameters:

Parameter	Value
`source_column`	`Close`
`period`	`20`
`std_dev`	`2`