Understanding the SuperTrend Indicator

To effectively utilize the SuperTrend indicator, one must first grasp the concept of volatility, often measured through the Average True Range (ATR). While the ATR is a lagging indicator, it provides valuable insights into current and past volatility levels.

The True Range is calculated as the maximum of three values:

1. High - Low
2. High - Previous Close
3. Previous Close - Low

The ATR is simply the average of the True Range over a specific period. Typically, during periods of market distress, the ATR tends to rise, whereas it decreases during stable trends.

Keep in mind that the ATR is a lagging indicator and should be used judiciously. Below is a Python function for calculating the ATR, assuming you have an OHLC data 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

def deleter(Data, index, times):

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

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

return Data

def jump(Data, jump):

Data = Data[jump:, ]

return Data

After understanding ATR, we can move on to the SuperTrend indicator, which provides entry and exit signals for traders. It functions similarly to a moving average or MACD, but its unique calculation method sets it apart.

The two primary variables involved in the SuperTrend are the ATR lookback period and the multiplier, usually set to integers like 2 or 3. To begin, average the high and low prices of a bar, then adjust this average by adding or subtracting the product of the multiplier and ATR. This yields two arrays: the basic upper and lower bands, essential for constructing the SuperTrend.

The following code illustrates how to compute the SuperTrend:

def supertrend(Data, multiplier, atr_col, close, high, low, where):

Data = adder(Data, 6)

for i in range(len(Data)):

Data[i, where] = (Data[i, high] + Data[i, low]) / 2

Data[i, where + 1] = Data[i, where] + (multiplier * Data[i, atr_col])

Data[i, where + 2] = Data[i, where] - (multiplier * Data[i, atr_col])

for i in range(len(Data)):

if i == 0:

Data[i, where + 3] = 0

else:

if (Data[i, where + 1] < Data[i - 1, where + 3]) or (Data[i - 1, close] > Data[i - 1, where + 3]):

Data[i, where + 3] = Data[i, where + 1]

else:

Data[i, where + 3] = Data[i - 1, where + 3]

for i in range(len(Data)):

if i == 0:

Data[i, where + 4] = 0

else:

if (Data[i, where + 2] > Data[i - 1, where + 4]) or (Data[i - 1, close] < Data[i - 1, where + 4]):

Data[i, where + 4] = Data[i, where + 2]

else:

Data[i, where + 4] = Data[i - 1, where + 4]

for i in range(len(Data)):

if i == 0:

Data[i, where + 5] = 0

elif (Data[i - 1, where + 5] == Data[i - 1, where + 3]) and (Data[i, close] <= Data[i, where + 3]):

Data[i, where + 5] = Data[i, where + 3]

elif (Data[i - 1, where + 5] == Data[i - 1, where + 3]) and (Data[i, close] > Data[i, where + 3]):

Data[i, where + 5] = Data[i, where + 4]

elif (Data[i - 1, where + 5] == Data[i - 1, where + 4]) and (Data[i, close] >= Data[i, where + 4]):

Data[i, where + 5] = Data[i, where + 4]

elif (Data[i - 1, where + 5] == Data[i - 1, where + 4]) and (Data[i, close] < Data[i, where + 4]):

Data[i, where + 5] = Data[i, where + 3]

Data = deleter(Data, where, 5)

return Data

The above code calculates the SuperTrend for EUR/USD hourly values.

When interpreting the SuperTrend indicator, consider that if it appears above the market price, it suggests a potential short position, while if it is below, it indicates a possible long position. This indicator serves as a vital tool for capturing trends and exiting when they conclude.

Integrating the Parabolic SAR

The Parabolic Stop-and-Reverse (SAR) is another effective trend-following indicator developed by Wilder Wilder, also known for the RSI. It's primarily used as a trailing stop that follows the trend but can be utilized as a standalone trading strategy. While it performs well during steady trends, it may struggle in ranging markets.

To calculate the Parabolic SAR, you can leverage the talib library in Python. Below is a modified implementation of the SAR function:

def sar(s, af=0.02, amax=0.2):

high, low = s.high, s.low

sig0, xpt0, af0 = True, high[0], af

sar = [low[0] - (high - low).std()]

for i in range(1, len(s)):

sig1, xpt1, af1 = sig0, xpt0, af0

lmin = min(low[i - 1], low[i])

lmax = max(high[i - 1], high[i])

if sig1:

sig0 = low[i] > sar[-1]

xpt0 = max(lmax, xpt1)

else:

sig0 = high[i] >= sar[-1]

xpt0 = min(lmin, xpt1)

if sig0 == sig1:

sari = sar[-1] + (xpt1 - sar[-1]) * af1

af0 = min(amax, af1 + af)

if sig0:

af0 = af0 if xpt0 > xpt1 else af1

sari = min(sari, lmin)

else:

af0 = af0 if xpt0 < xpt1 else af1

sari = max(sari, lmax)

else:

af0 = af

sari = xpt0

sar.append(sari)

return sar

The primary idea is that when the Parabolic SAR (represented as dots around market prices) lies below the current price, the outlook is bullish, and when it is above, the outlook is bearish.

Creating the Trading Strategy

The strategy hinges on the confluence of signals from both indicators. The trading rules are as follows:

• A long (buy) signal is generated when the market price rises above the SuperTrend while also being above the Parabolic SAR.
• A short (sell) signal is triggered when the market price falls below the SuperTrend while also being below the Parabolic SAR.

The following code implements this strategy:

def signal(Data, close, psar_col, super_trend_col, buy, sell):

Data = adder(Data, 10)

for i in range(len(Data)):

if Data[i, close] > Data[i, psar_col] and

Data[i, close] > Data[i, super_trend_col] and

Data[i - 1, close] < Data[i - 1, super_trend_col]:

Data[i, buy] = 1

if Data[i, close] < Data[i, psar_col] and

Data[i, close] < Data[i, super_trend_col] and

Data[i - 1, close] > Data[i - 1, super_trend_col]:

Data[i, sell] = -1

return Data

This approach utilizes the SuperTrend as the primary trigger while the Parabolic SAR serves as a filter, improving signal quality.

When deploying this strategy, it is advisable to focus on trending markets, which can be assessed using indicators like the ADX or moving averages. You may also consider reversing the roles of the filter and trigger for alternative trading rules.