The Hull Moving Average

Moving averages are essential for confirming and capitalizing on market trends. They are widely recognized due to their simplicity and effectiveness in enhancing analytical efforts. These indicators help identify support and resistance levels, set stops and targets, and understand prevailing trends. Their adaptability renders them crucial tools in any trader's toolkit.

The Hull moving average builds upon the concept of the linear-weighted moving average, where the most recent data point carries the greatest weight, and the significance of earlier points diminishes progressively. This characteristic offers reduced lag compared to traditional moving averages, even though it is less frequently utilized.

To illustrate, consider a dataset of two values [1, 2]. The calculation for the linear weighted average would be as follows: (2 x 2) + (1 x 1) = 5 / 3 = 1.66. Here, the value 2 is the most recent observation. With this foundational understanding, let’s explore the Hull moving average, a robust tool for early trend-following systems.

The Hull moving average employs a weighted moving average as its foundation, calculated through these steps:

1. Select a lookback period (e.g., 20 or 100) and compute the weighted moving average of the closing prices.
2. Divide the first lookback period and calculate the weighted moving average with this new period. If the number is odd, round down (e.g., for a 15 lookback, use 7 or 8).
3. Multiply the second weighted moving average by two and subtract the first weighted moving average from it.
4. Finally, calculate the weighted moving average using the square root of the first lookback (e.g., for a 100 lookback, the third period would be 10) applied to the result obtained in step 3. Ensure this is not calculated on market prices.

Hence, if we choose a lookback of 100, we would calculate for periods of 100, 50, and finally 10.

Now, let’s delve into the Hull moving average strategy.

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Crafting the Strategy

Unlike typical moving average crossover strategies, this approach dictates that we wait for a pullback after the market crosses the moving average to ride the emerging trend. Therefore, we will implement the following conditions in our coding:

• A long (Buy) signal is triggered when the market exceeds the 50-period Hull moving average and subsequently retraces towards the moving average through at least its low.
• A short (Sell) signal is activated when the market falls below the 50-period Hull moving average and then pulls back towards the moving average through at least its high.

Examine the USDCAD chart above. The initial bearish signal occurs after a significant red candle, followed by a pullback to the moving average, resulting in a bearish signal. The subsequent bearish signal showcases similar success. Notice the situation around point 37: the market surpasses the moving average but no bullish signal is generated. Why? The first bullish candle closing above the moving average is not followed by a retracement, invalidating it as a valid bullish signal.

def signal(Data, high_column, low_column, close_column, ma_column, buy_column, sell_column):

Data = adder(Data, 10)

for i in range(len(Data)):

# Bullish Signal

if Data[i, close_column] > Data[i, ma_column] and Data[i - 1, close_column] < Data[i - 1, ma_column]:

for a in range(i + 1, len(Data)):

if Data[a, low_column] <= Data[a, ma_column] and Data[a, close_column] > Data[a, ma_column] and

Data[a - 1, close_column] > Data[a - 1, ma_column]:

Data[a, buy_column] = 1

break

elif Data[a, sell_column] != 0:

break

elif Data[a - 1, sell_column] != 0:

break

else:

continue

# Bearish Signal

elif Data[i, close_column] < Data[i, ma_column] and Data[i - 1, close_column] > Data[i - 1, ma_column]:

for a in range(i + 1, len(Data)):

if Data[a, high_column] >= Data[a, ma_column] and Data[a, close_column] < Data[a, ma_column] and

Data[a - 1, close_column] < Data[a - 1, ma_column]:

Data[a, sell_column] = -1

break

elif Data[a, buy_column] != 0:

break

elif Data[a - 1, buy_column] != 0:

break

else:

continue

return Data

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