What is a Market Regime?

Markets typically exhibit either trending or ranging behavior. To maximize profitability, it's crucial to apply the appropriate strategy at the right time. Mean-reversion strategies thrive in sideways markets, while trend-following strategies excel in upward or downward trends.

For example, in a bullish market, the likelihood of upward movement is higher than downward, making trend-following strategies more effective. Conversely, in such a scenario, mean-reversion strategies tend to falter, as they may continuously signal bearish trends while the market rises.

As a reminder, a bullish market indicates an upward trend, whereas a bearish market reflects a downward trend.

The Vertical Horizontal Filter's Role

The Vertical Horizontal Filter (VHF), introduced by Adam White, serves as a tool to gauge the strength of market trends. This indicator helps traders identify which strategies to employ based on market conditions.

To calculate the VHF, follow these steps:

1. Compute the absolute price differences between consecutive periods. For instance, if the current price is $99 and the previous price was $100, the absolute difference is $1.
2. Determine the maximum and minimum values within a specified lookback period. For a lookback period of 5, subtract the highest closing price from the lowest closing price during that timeframe.
3. Divide the result of step 1 by the result of step 2 to obtain the VHF.

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Creating the Vertical Horizontal Filter

Here's a Python implementation of the functions needed to manipulate your data for the VHF:

# Function to add multiple 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 a specific index

def deleter(Data, index, times):

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

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

return Data

# Function to jump a number of rows at the start

def jump(Data, jump):

Data = Data[jump:,]

return Data

For instance, to add three empty columns:

my_ohlc_array = adder(my_ohlc_array, 3)

To eliminate two columns after the third indexed column:

my_ohlc_array = deleter(my_ohlc_array, 3, 2)

And to remove the first twenty rows:

my_ohlc_array = jump(my_ohlc_array, 20)

Note: OHLC stands for Open, High, Low, and Close, representing standard historical data.

Implementing the Vertical Horizontal Filter

Now, let's create the Vertical Horizontal Filter using the following function:

def vertical_horizontal_indicator(Data, lookback, what, where):

Data = adder(Data, 10)

for i in range(len(Data)):

Data[i, where] = Data[i, what] - Data[i - 1, what]

Data = jump(Data, 1)

Data[:, where] = abs(Data[:, where])

for i in range(len(Data)):

Data[i, where + 1] = Data[i - lookback + 1:i + 1, where].sum()

for i in range(len(Data)):

try:

Data[i, where + 2] = max(Data[i - lookback + 1:i + 1, what]) - min(Data[i - lookback + 1:i + 1, what])

except ValueError:

pass

Data = jump(Data, lookback)

Data[:, where + 3] = Data[:, where + 2] / Data[:, where + 1]

Data = deleter(Data, where, 3)

return Data

The challenge lies in utilizing this indicator effectively for trading. The premise is that increasing values signify a trending market (either bullish or bearish), while declining values indicate a ranging market. However, identifying your own thresholds and limits is essential.

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Analyzing the Vertical Horizontal Filter

The chart below illustrates the hourly EUR/USD with a 60-period Vertical Horizontal Filter. Notably, as the indicator rises, it correlates with a trending market, suggesting a potential strategy based on these values.

The following signals can be derived:

• When the indicator is below its moving average, the market regime appears unclear or ranging.
• When the indicator is above its moving average, the market is considered to be trending.

def signal(Data, indicator, indicator_ma, signal):

Data = adder(Data, 10)

for i in range(len(Data)):

if Data[i, indicator] > Data[i, indicator_ma]:

Data[i, signal] = 1

return Data

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