How to use McGinley Dynamic example

A full example of using McGinley Dynamic bands from the how-to guide.

Full code

import csv
from centaur_technical_indicators import candle_indicators as ci


def load_prices_from_csv(path: str) -> list[float]:
    prices: list[float] = []
    with open(path, "r", newline="") as f:
        # Try to read as DictReader first (with a header)
        sample = f.read(4096)
        f.seek(0)
        has_header = "," in sample and any(ch.isalpha() for ch in sample.splitlines()[0])
        if has_header:
            reader = csv.DictReader(f)
            # Find a 'close' column in a case-insensitive way
            close_key = None
            if reader.fieldnames:
                for k in reader.fieldnames:
                    if k and k.lower() == "close":
                        close_key = k
                        break
            if close_key is None and reader.fieldnames:
                # Fallback to the first column if 'close' not found
                close_key = reader.fieldnames[0]
            for row in reader:
                try:
                    prices.append(float(row[close_key]))
                except (ValueError, TypeError, KeyError):
                    continue
        else:
            # No header: read first column only
            f.seek(0)
            reader = csv.reader(f)
            for row in reader:
                if not row:
                    continue
                try:
                    prices.append(float(row[0]))
                except ValueError:
                    continue
    return prices


# Load prices
prices = load_prices_from_csv("prices.csv")

# Example setup
period = 5
deviation_model = "standard_deviation"
deviation_multiplier = 2.0
previous_mcginley_dynamic = 0.0  # none available on first run

# Calculate McGinley Dynamic bands
bands = ci.bulk.mcginley_dynamic_bands(
    prices,
    deviation_model,
    deviation_multiplier,
    previous_mcginley_dynamic,
    period,
)

print("Loaded", len(prices), "prices")
print("Length of bands", len(bands))

# Use the last value to calculate the next McGinley band
# Next price comes in
new_price = 5689.24
prices.append(new_price)

last_mcginley_dynamic = bands[-1][1]  # previous McGinley Dynamic from bulk result

# Use the last 'period' prices for the single calculation
latest_window = prices[-period:]

next_band = ci.single.mcginley_dynamic_bands(
    latest_window,
    deviation_model,
    deviation_multiplier,
    last_mcginley_dynamic,
)

lower, mid, upper = next_band
print(f"Lower band {lower}, McGinley dynamic {mid}, upper band {upper}")

Run the code

python3 your_file_name.py

Expected output

Loaded 251 prices
Length of bands 247
Lower band 5551.313227907162, McGinley dynamic 5665.614575300795, upper band 5779.9159226944275

Note: Actual values will vary based on your prices.csv data.