turtle_quant_1.strategies.helpers.support_resistance.stnry_local_extrema

turtle_quant_1.strategies.helpers.support_resistance.stnry_local_extrema

Stationary local extrema static support and resistance strategy.

StnryLocalExtrema

StnryLocalExtrema(
    strong_peak_distance: int = 10,
    strong_peak_prominence_pct: float = 0.01,
    peak_distance: int = 5,
    peak_rank_width: float = 2.0,
    res_min_pivot_rank: int = 3,
)

Bases: BaseSupResStrategy

Stationary local extrema static support and resistance strategy.

Refer to https://medium.com/@itay1542/how-to-calculate-support-and-resistance-levels-using-python-a-step-by-step-guide-e94a33c6cbda.

This strategy identifies support and resistance levels by finding local extrema (peaks and troughs) in the price data and ranking them based on their frequency and proximity to other extrema.

Initialize the StnryLocalExtrema strategy.

Parameters:

Name	Type	Description	Default
strong_peak_distance	int	Distance between strong peaks (in days).	10
strong_peak_prominence_pct	float	Prominence for strong peaks (as fraction of price range).	0.01
peak_distance	int	Distance between general peaks (in days).	5
peak_rank_width	float	Width for grouping nearby peaks.	2.0
res_min_pivot_rank	int	Minimum rank for resistance levels.	3

Source code in src/turtle_quant_1/strategies/helpers/support_resistance/stnry_local_extrema.py

def __init__(
    self,
    strong_peak_distance: int = 10,
    strong_peak_prominence_pct: float = 0.01,
    peak_distance: int = 5,
    peak_rank_width: float = 2.0,
    res_min_pivot_rank: int = 3,
):
    """Initialize the StnryLocalExtrema strategy.

    Args:
        strong_peak_distance: Distance between strong peaks (in days).
        strong_peak_prominence_pct: Prominence for strong peaks (as fraction of price range).
        peak_distance: Distance between general peaks (in days).
        peak_rank_width: Width for grouping nearby peaks.
        res_min_pivot_rank: Minimum rank for resistance levels.
    """
    super().__init__()

    self.sup_res_zone_threshold = 0.01  # Within 2% of the level

    self.strong_peak_distance = strong_peak_distance
    self.strong_peak_prominence_pct = strong_peak_prominence_pct
    self.peak_distance = peak_distance
    self.peak_rank_width = peak_rank_width
    self.res_min_pivot_rank = res_min_pivot_rank

generate_historical_levels

generate_historical_levels(
    data: DataFrame, symbol: str
) -> DataFrame

Generate all support and resistance levels for historical data.

Parameters:

Name	Type	Description	Default
data	DataFrame	DataFrame with OHLCV data containing columns: ['datetime', 'Open', 'High', 'Low', 'Close', 'Volume']	required
symbol	str	The symbol being analyzed.	required

Returns:

Type	Description
DataFrame	DataFrame with support and resistance levels.
DataFrame	The columns are: ['datetime', 'level_values'].

Source code in src/turtle_quant_1/strategies/helpers/support_resistance/stnry_local_extrema.py

def generate_historical_levels(
    self, data: pd.DataFrame, symbol: str
) -> pd.DataFrame:
    """Generate all support and resistance levels for historical data.

    Args:
        data: DataFrame with OHLCV data containing columns:
              ['datetime', 'Open', 'High', 'Low', 'Close', 'Volume']
        symbol: The symbol being analyzed.

    Returns:
        DataFrame with support and resistance levels.
        The columns are: ['datetime', 'level_values'].
    """
    # Ensure we have the required columns
    if not all(col in data.columns for col in ["High", "Low"]):
        raise ValueError("Data must contain 'High' and 'Low' columns")

    level_values = self._calc_sup_res_levels(data)

    # Create output DataFrame with 1-to-1 mapping to original data
    result = pd.DataFrame(
        {
            "datetime": pd.to_datetime(data["datetime"]).reset_index(drop=True),
            "level_values": level_values,
        }
    )

    return result