The Camouflage Reversal Pattern
Coding the TD Camouflage Pattern and Analyzing its Predictability
Pattern recognition is the search and identification of recurring patterns with approximately similar outcomes. This means that when we manage to find a pattern, we have an expected outcome that we want to see and act on through our trading. For example, a head and shoulders pattern is a classic technical pattern that signals an imminent trend reversal. The literature differs on the predictive ability of this famous configuration. In this article, we will discuss the TD Camouflage another timing and reversal pattern presented by Tom Demark.
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Coding the TD Camouflage Pattern
Tom Demark, the renowned famous technical analyst has created many indicators and patterns. Among his many discoveries, he came up with a few short-term patterns, among them is a pattern called Camouflage.
Tom has developed these patterns to find short-term tops and bottoms. Any trader or even a receiver of such information would be excited knowing about this, but with hindsight and confirmation bias, the person seeing this, will test the patterns on the chart and focus on the ones that work while disregarding the ones that do not. The only way to remedy this problem is by developing a systematic trading strategy based on these patterns, and that is precisely what we are going to do.
Requirements for a TD Camouflage Buy entry:
The close of the current price bar must be below the close of the previous price bar.
The close of the current price bar must be above the open of the current price bar.
The low of the current price bar must be lower than the true low two price bars earlier.
Requirements for a TD Camouflage Sell short entry:
The close of the current price bar must be above the close of the previous price bar.
The close of the current price bar must be below the open of the current price bar.
The high of the current price bar high must be above the true high two price bars earlier.
To code this pattern, we need to define the primal manipulation functions first:
def adder(data, times):
for i in range(1, times + 1):
new = np.zeros((len(data), 1), dtype = float)
data = np.append(data, new, axis = 1) return datadef 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
Assuming we have an OHLC array, we can define and use the scanner below that will look for the pattern and input 1 in case of a bullish Camouflage or input -1 in case of a bearish Camouflage.
def td_camouflage(Data):
# Adding columns
Data = adder(Data, 20)
# True Low Calculation
for i in range(len(Data)):
Data[i, 5] = min(Data[i, 2], Data[i - 2, 2])
# True High Calculation
for i in range(len(Data)):
Data[i, 6] = max(Data[i, 1], Data[i - 2, 1])
# Bullish signal
for i in range(len(Data)):
if Data[i, 3] < Data[i - 1, 3] and Data[i, 3] > Data[i, 0] and Data[i, 2] < Data[i - 2, 5]:
Data[i, 7] = 1
# Bearish signal
for i in range(len(Data)):
if Data[i, 3] > Data[i - 1, 3] and Data[i, 3] < Data[i, 0] and Data[i, 1] > Data[i - 2, 6]:
Data[i, 8] = -1
# Cleaning
Data = deleter(Data, 5, 1)
return Data
Back-testing the Pattern With no Risk Management
The pattern will be back-tested in three ways. The first which is covered in this section, does not use any risk management. It opens and closes positions on closing prices and upon the next signal. The second way is to use the optimal 2.0 risk-reward ratio which is simply setting the stop at half the distance from the entry compared to the distance of the profit level from the entry. Finally, the third way will use a suboptimal risk-reward ratio of 0.20. This means that we will target one fifth the stop we set.
The below are the conditions of the back-test:
The time frame is hourly since 2010 using a 0.2 pip spread in order to simulate an institutional algorithm.
Opening of the position is based on a signal and its exit is based on finding another signal whether the same or a contrarian one.
It looks like a free fall for any algorithm that uses this pattern. This is music to the ears of brokers who are looking to trade against you.
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Back-testing the Pattern With 2:1 Risk-Reward Ratio
Intuitively, we need to risk less than we expect to gain and therefore by setting a theoretical 2.0 risk-reward ratio, we are respecting this risk management technique. The way we theoretically set this ratio is by using an indicator called the Average True Range which will be covered in a later section below.
The below are the conditions of the back-test:
The time frame is hourly since 2010 using a 0.2 pip spread in order to simulate an institutional algorithm.
Opening of the position is based on a signal and its exit is based on finding another signal whether the same or a contrarian one.
Stops are triggered when the current quote hits 1x the value of the Average True Range at entry.
Profit orders are triggered when the current quote hits 2x the value of the Average True Range at entry.
The strategy is still overall extremely painful to look at.
Remember to always do your back-tests. You should always believe that other people are wrong. My indicators and style of trading may work for me but maybe not for you.
I am a firm believer of not spoon-feeding. I have learnt by doing and not by copying. You should get the idea, the function, the intuition, the conditions of the strategy, and then elaborate (an even better) one yourself so that you back-test and improve it before deciding to take it live or to eliminate it. My choice of not providing specific Back-testing results should lead the reader to explore more herself the strategy and work on it more.
One Last Word
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