turtle_quant_1.strategies.helpers.support_resistance.stnry_fibonacci_retrace

turtle_quant_1.strategies.helpers.support_resistance.stnry_fibonacci_retrace

Stationary Fibonacci retracement support and resistance strategy.

StnryFibonacciRetrace

StnryFibonacciRetrace(
    peak_distance: int = 10,
    peak_prominence_pct: float = 0.02,
    fib_levels: List[float] = [],
)

Bases: BaseSupResStrategy

Stationary Fibonacci retracement support and resistance strategy.

This strategy identifies support and resistance levels by calculating Fibonacci retracement levels between significant swing highs and lows across the entire dataset. It finds the most significant swing points and computes the classic Fibonacci ratios: 23.6%, 38.2%, 50%, 61.8%, and 78.6%.

Initialize the StnryFibonacciRetrace strategy.

Parameters:

Name	Type	Description	Default
peak_distance	int	Minimum distance between peaks (in candlesticks).	10
peak_prominence_pct	float	Minimum prominence for peaks (as fraction of price range).	0.02
fib_levels	List[float]	List of Fibonacci ratios to use. Defaults to standard levels.	[]

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

def __init__(
    self,
    peak_distance: int = 10,  # NOTE: This is a magic number. Depends on resampling.
    peak_prominence_pct: float = 0.02,
    fib_levels: List[float] = [],
):
    """Initialize the StnryFibonacciRetrace strategy.

    Args:
        peak_distance: Minimum distance between peaks (in candlesticks).
        peak_prominence_pct: Minimum prominence for peaks (as fraction of price range).
        fib_levels: List of Fibonacci ratios to use. Defaults to standard levels.
    """
    super().__init__()
    self.peak_distance = peak_distance
    self.peak_prominence_pct = peak_prominence_pct

    # Standard Fibonacci retracement levels
    if not fib_levels:
        self.fib_levels = [0.236, 0.382, 0.500, 0.618, 0.786]
    else:
        self.fib_levels = fib_levels

generate_historical_levels

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

Generate Fibonacci retracement 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 Fibonacci retracement levels.
DataFrame	The columns are: ['datetime', 'level_values'].

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

def generate_historical_levels(
    self, data: pd.DataFrame, symbol: str
) -> pd.DataFrame:
    """Generate Fibonacci retracement 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 Fibonacci retracement levels.
        The columns are: ['datetime', 'level_values'].
    """
    # Ensure we have the required columns
    required_cols = ["High", "Low"]
    if not all(col in data.columns for col in required_cols):
        raise ValueError(f"Data must contain {required_cols} columns")

    # TODO: This is a magic number.
    data_resampled = DataUnitConverter.convert_to_1h_data(symbol, data)
    level_values = self._calc_sup_res_levels(data_resampled)

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

    result = data[["datetime"]].merge(result, on="datetime", how="left")
    result["level_values"] = result["level_values"].bfill().ffill()

    return result