Chapter 1.1: Understanding the Volatility-Adjusted Moving Average

Moving averages are essential tools for confirming trends and determining entry and exit points. Their simplicity and reliability make them one of the most utilized technical indicators. They aid in identifying support and resistance levels, setting stop-loss orders, and discerning the market's underlying trend. This adaptability renders them indispensable in trading.

# Function to add columns to an array

def adder(Data, times):

for i in range(1, times + 1):

new_col = np.zeros((len(Data), 1), dtype=float)

Data = np.append(Data, new_col, axis=1)

return Data

# Function to remove columns from an index

def deleter(Data, index, times):

for i in range(1, times + 1):

Data = np.delete(Data, index, axis=1)

return Data

# Function to skip a number of rows from the beginning

def jump(Data, jump):

Data = Data[jump:, ]

return Data

The Volatility-Adjusted Moving Average (VAMA) was developed by Tushar S. Chande and is also referred to as the Variable Index Moving Average. It accounts for recent volatility by utilizing two standard deviation periods, distinguishing it from other indicators.

To compute VAMA, the initial step involves determining alpha, which is calculated using the ratio of short-term to long-term volatility, then multiplied by 0.20. The first VAMA value corresponds to the closing price, and subsequent values are computed using the following formula.

Chapter 1.2: The SuperTrend Indicator

Before delving into the SuperTrend indicator, it’s crucial to grasp the concept of volatility, often measured with the Average True Range (ATR). Although ATR is a lagging indicator, it provides insights into current and historical volatility.

The true range is determined as the maximum of the following three price differences:

• High - Low
• High - Previous Close
• Previous Close - Low

Once the true ranges are calculated, averaging them over a specified period yields the ATR. During volatile periods, the ATR tends to rise, whereas it generally decreases during stable trends.

# Function to calculate ATR

def atr(data, lookback, high, low, close, where):

data = adder(data, 2)

for i in range(len(data)):

try:

data[i, where] = max(data[i, high] - data[i, low],

abs(data[i, high] - data[i - 1, close]),

abs(data[i, low] - data[i - 1, close]))

except ValueError:

pass

data[0, where] = 0

data = ema(data, 2, (lookback * 2) - 1, where, where + 1)

data = deleter(data, where, 1)

data = jump(data, lookback)

return data

The SuperTrend indicator serves as a guide for entry and exit points for trend traders. It operates similarly to a moving average or MACD but offers unique advantages due to its intuitive nature. Key parameters include the ATR lookback period and a multiplier, typically set to 2 or 3.

To calculate the SuperTrend, we first compute the average price, then derive the basic upper and lower bands by adding or subtracting the product of the multiplier and ATR from this average.