The Confirmed Reversal Indicator. Another View on Momentum.
Creating a New Reversal Indicator for Strong Moves.
Combining two of the most powerful indicators in trading can give us quality signals and this is precisely what we will try to do using moving averages and the relative strength index. This article discusses creating the indicator in Python. Let us start first by introducing the main ingredients.
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The Concept of Moving Averages
Moving averages help us confirm and ride the trend. They are the most known technical indicator and this is because of their simplicity and their proven track record of adding value to the analyses. We can use them to find support and resistance levels, stops and targets, and to understand the underlying trend. This versatility makes them an indispensable tool in our trading arsenal.
As the name suggests, this is your plain simple mean that is used everywhere in statistics and basically any other part in our lives. It is simply the total values of the observations divided by the number of observations. Mathematically speaking, it can be written down as:
We can see that the moving average is providing decent dynamic support and resistance levels from where we can place our orders in case the market goes down there. The code for the moving average can be written down as the following:
# The function to add a number of columns inside 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
# The function to delete a number of columns starting from an index def deleter(Data, index, times): for i in range(1, times + 1): Data = np.delete(Data, index, axis = 1) return Data # The function to delete a number of rows from the beginning def jump(Data, jump): Data = Data[jump:, ] return Data
# Example of adding 3 empty columns to an array my_ohlc_array = adder(my_ohlc_array, 3)
# Example of deleting the 2 columns after the column indexed at 3 my_ohlc_array = deleter(my_ohlc_array, 3, 2)
# Example of deleting the first 20 rows my_ohlc_array = jump(my_ohlc_array, 20)
# Remember, OHLC is an abbreviation of Open, High, Low, and Close and it refers to the standard historical data file
The below states that the moving average function will be called on the array named my_data for a lookback period of 200, on the column indexed at 3 (closing prices in an OHLC array). The moving average values will then be put in the column indexed at 4 which is the one we have added using the adder function.
my_data = ma(my_data, 200, 3, 4)
The Relative Strength Index
The RSI is without a doubt the most famous momentum indicator out there, and this is to be expected as it has many strengths especially in ranging markets. It is also bounded between 0 and 100 which makes it easier to interpret. Also, the fact that it is famous, contributes to its potential.
This is because the more traders and portfolio managers look at the RSI, the more people will react based on its signals and this in turn can push market prices. Of course, we cannot prove this idea, but it is intuitive as one of the basis of Technical Analysis is that it is self-fulfilling.
The RSI is calculated using a rather simple way. We first start by taking price differences of one period. This means that we have to subtract every closing price from the one before it. Then, we will calculate the smoothed average of the positive differences and divide it by the smoothed average of the negative differences. The last calculation gives us the Relative Strength which is then used in the RSI formula to be transformed into a measure between 0 and 100.
To calculate the relative strength index, we need an OHLC array (not a data frame). This means that we will be looking at an array of 4 columns. The function for the Relative Strength Index is therefore:
def ema(Data, alpha, lookback, what, where): alpha = alpha / (lookback + 1.0) beta = 1 - alpha # First value is a simple SMA Data = ma(Data, lookback, what, where) # Calculating first EMA Data[lookback + 1, where] = (Data[lookback + 1, what] * alpha) + (Data[lookback, where] * beta) # Calculating the rest of EMA for i in range(lookback + 2, len(Data)): try: Data[i, where] = (Data[i, what] * alpha) + (Data[i - 1, where] * beta) except IndexError: pass return Data
def rsi(Data, lookback, close, where): # Adding a few columns Data = adder(Data, 5) # Calculating Differences for i in range(len(Data)): Data[i, where] = Data[i, close] - Data[i - 1, close] # Calculating the Up and Down absolute values for i in range(len(Data)): if Data[i, where] > 0: Data[i, where + 1] = Data[i, where] elif Data[i, where] < 0: Data[i, where + 2] = abs(Data[i, where]) # Calculating the Smoothed Moving Average on Up and Down absolute values lookback = (lookback * 2) - 1 # From exponential to smoothed Data = ema(Data, 2, lookback, where + 1, where + 3) Data = ema(Data, 2, lookback, where + 2, where + 4)
# Calculating the Relative Strength Data[:, where + 5] = Data[:, where + 3] / Data[:, where + 4] # Calculate the Relative Strength Index Data[:, where + 6] = (100 - (100 / (1 + Data[:, where + 5])))
# Cleaning Data = deleter(Data, where, 6) Data = jump(Data, lookback)
If you are also interested by more technical indicators and strategies, then my book might interest you:
Creating the K’s Confirmed Reversal Indicator
The confirmed reversal indicator — CRI is by default a 13-period RSI applied on the smoothed values of the market using a 20-period simple moving average. Therefore, the steps required to calculate it are as follows (We will see later how to use it):
Calculate a 20-period simple moving average on the market.
Calculate a 13-period RSI on the moving average values.
Set the oversold level at 2 and the overbought level at 98.
lookback_ma = 20 lookback_rsi = 13
my_data = ma(my_data, lookback_ma, 3, 4) my_data = rsi(my_data, lookback_rsi, 4, 5)
The above chart shows the hourly values of the USDCHF pair in the first panel plotted along the 13-period RSI applied on the 20-period simple moving average which is applied on the market price. This is why the caption says CRI(20, 13). The reason why I chose to call it the ‘Confirmed’ reversal indicator is because the signals are generated upon the exit from the extreme levels (Oversold and overbought). Therefore, we can have the following rules:
A long (Buy) signal is generated whenever the CRI(20, 13) surpasses 20 after having been below it.
A short (Sell) signal is generated whenever the CRI(20, 13) breaks 80 after having been above it.
The charts show how the signals are generated after the exit from the extremes. Even though the signal can be slightly lagging, it is worth considering due to the false breakout problem.
def signal(Data, rsi_column, buy_column, sell_column): Data = adder(Data, 10) for i in range(len(Data)): # Bullish Signal if Data[i, rsi_column] > 2 and Data[i - 1, rsi_column] < 2: Data[i, buy_column] = 1 # Bearish Signal elif Data[i, rsi_column] < 98 and Data[i - 1, rsi_column] > 98: Data[i, sell_column] = -1 return Data
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.
To sum up, are the strategies I provide realistic? Yes, but only by optimizing the environment (robust algorithm, low costs, honest broker, proper risk management, and order management). Are the strategies provided only for the sole use of trading? No, it is to stimulate brainstorming and getting more trading ideas as we are all sick of hearing about an oversold RSI as a reason to go short or a resistance being surpassed as a reason to go long. I am trying to introduce a new field called Objective Technical Analysis where we use hard data to judge our techniques rather than rely on outdated classical methods.