# Unlocking the Aroon Oscillator: Coding and Trading Insights

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## Chapter 1: Introduction to the Aroon Oscillator

The Aroon Oscillator is a significant tool in trend-following strategies, which are essential for traders looking to capitalize on market movements. While many popular indicators are effective in tracking trends, they often exhibit delays due to the necessary confirmation of new trends. Thus, these strategies focus more on being present in the market rather than timing it perfectly. This article delves into the widely used Aroon Oscillator, including coding practices and trading applications.

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## Chapter 2: Creating the Aroon Oscillator

The Aroon Oscillator consists of two components: Aroon Up and Aroon Down. These components evaluate the strength of trends by analyzing recent highs and lows in a unique manner, distinguishing them from traditional indicators.

To begin, the calculations for Aroon Up and Aroon Down can be expressed mathematically. For example, consider an asset that reached a new low in the last period. For a 25-period Aroon Down, the calculation yields a value of 96. This is derived by subtracting 1 from 25, dividing by 25, and then multiplying by 100. In contrast, for a 13-period Aroon Up, if the asset hit a new high one period ago, the Aroon Up value would be 92.30. A higher Aroon value indicates a stronger trend.

Now, let's implement the Aroon calculations in Python:

def aroon(Data, period, close, where):

# Adding Columns

Data = adder(Data, 10)

# Max Highs

for i in range(len(Data)):

try:

Data[i, where] = max(Data[i - period + 1:i + 1, 1])except ValueError:

pass# Max Lows

for i in range(len(Data)):

try:

Data[i, where + 1] = min(Data[i - period + 1:i + 1, 2])except ValueError:

pass# Highs and Lows Comparison

for i in range(len(Data)):

if Data[i, 1] == Data[i, where]:

Data[i, where + 2] = 1for i in range(len(Data)):

if Data[i, 2] == Data[i, where + 1]:

Data[i, where + 3] = 1# Jumping Rows

Data = jump(Data, period)

# Aroon Up Calculation

for i in range(len(Data)):

try:

x = max(Data[i - period:i, 1])

y = np.where(Data[i - period:i, 1] == x)

distance = period - y

Data[i - 1, where + 4] = 100 * ((period - distance) / period)

except (ValueError, IndexError):

pass# Aroon Down Calculation

for i in range(len(Data)):

try:

x = min(Data[i - period:i, 2])

y = np.where(Data[i - period:i, 2] == x)

distance = period - y

Data[i - 1, where + 5] = 100 * ((period - distance) / period)

except (ValueError, IndexError):

pass

# Cleaning

Data = deleter(Data, 5, 4)

return Data

Here, we examine the EURUSD chart showcasing the Aroon Up in blue and Aroon Down in orange. The final step is to compute the Aroon Oscillator, which is the difference between Aroon Up and Aroon Down:

my_data[:, where_aroon_osc] = my_data[:, where_aroon_up] - my_data[:, where_aroon_down]

The Aroon Oscillator, developed by Tushar Chande, is a renowned trend-following indicator, and it's interesting to note that "Aroon" translates to "dawn's early light" in Sanskrit.

The first video titled "Aroon Oscillator Calculation, Programming in Python, and Graphing in Matplotlib" provides a visual guide on implementing this indicator in Python.

## Chapter 3: Trading with the Aroon Oscillator

To effectively trade using the Aroon Oscillator, the flip technique is applied, whereby the oscillator's value transitions from positive to negative or vice versa. Specifically, the trading conditions are as follows:

**Buy (Long position)**: When the Aroon Oscillator exceeds the zero line.**Sell (Short position)**: When the Aroon Oscillator dips below the zero line.

Here's the code for generating trading signals:

def signal(Data, aroon_osc_col, buy, sell):

Data = adder(Data, 10)

for i in range(len(Data)):

if Data[i, aroon_osc_col] > 0 and Data[i - 1, aroon_osc_col] < 0:

Data[i, buy] = 1elif Data[i, aroon_osc_col] < 0 and Data[i - 1, aroon_osc_col] > 0:

Data[i, sell] = -1

return Data

The signal chart illustrates that lag can often be a significant drawback for trend-following strategies. Adjusting the lookback period, refining conditions, adding filters, or altering the strategy can help mitigate this issue.

The second video titled "Python: Programming The Aroon Indicator - Mathematics and stock indicators 17" offers further insights into utilizing the Aroon Indicator effectively.

## Chapter 4: Advanced Trading Strategies

The V strategy is a unique contrarian confirmation technique characterized by a V-shaped pattern. For a bullish (Buy) signal, the Aroon Oscillator must form a V shape, indicating the current reading is above -75, the previous reading is below -75, and the one before that exceeds -75. Conversely, for a bearish (Sell) signal, an inverted V formation is required, where the current reading is below 75, the previous reading is above 75, and the one prior is below 75.

Here's the implementation for the V strategy signals:

def signal(Data, aroon_osc_colr, buy, sell):

Data = adder(Data, 10)

for i in range(len(Data)):

if Data[i, aroon_osc_colr] > -75 and Data[i - 1, aroon_osc_colr] < -75 and Data[i - 2, aroon_osc_colr] > -75:

Data[i, buy] = 1elif Data[i, aroon_osc_colr] < 75 and Data[i - 1, aroon_osc_colr] > 75 and Data[i - 2, aroon_osc_colr] < 75:

Data[i, sell] = -1

return Data

If you're interested in exploring more technical indicators and strategies, my book may be of value to you. Always remember to conduct back-tests and maintain a critical mindset. My approach may work for me but may not suit everyone.

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