Applying a Trailing Stop to a Moving Average Strategy in Python.

How to Apply Basic Risk Management Rules on Trend Following Strategies.

Trend following strategies require solid risk management rules which are hard to create. The aim of trend following is to squeeze out the most of the trend before it eventually ends. Hence, we need to move our stop in the direction of the trend when the market moves in our favor. This article discusses the creation and application of a trailing stop with moving average strategies.

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Creating the Rules of the Strategy: 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:

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

def ma(data, lookback, close, where): 
    
    data = adder(data, 1)
    
    for i in range(len(data)):
           
            try:
                
                data[i, where] = (data[i - lookback + 1:i + 1, close].mean())
            
            except IndexError:
                
                pass
            
    data = jump(data, lookback)
    
    return data

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)

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Creating the Trailing Stop Indicator

The trailing stop indicator we will be creating is based on the average true range. We have previously discussed this indicator and now we will code a variation of it that transforms it into a trailing stop.

The trailing stop indicator is an overlay moving line that gives us where exactly we must move our stop when following the move. It is based on volatility, thus uses the average true range. The default version uses 13-period ATR and a multiplier of 3. The multiplier can be thought of as an ingredient in determining the position of the new stop.

def atr_trailing_stop(data, atr_column, multiplier, close, where):
    
    # adding columns
    data = adder(data, 1)
    
    # atr trailing stop    
    for i in range(len(data)):
        
        try:
            
            # stop
            stop = multiplier * data[i, atr_column]                        

            if data[i, close] > data[i - 1, where] and data[i - 1, close] > data[i - 1, where]:
                
                data[i, where] = max(data[i - 1, where], data[i, close] - stop)                        

            elif data[i, close] < data[i - 1, where] and data[i - 1, close] < data[i - 1, where]:
                
                data[i, where] = min(data[i - 1, where], data[i, close] + stop)
                
            elif data[i, close] > data[i - 1, where] and data[i - 1, close] < data[i - 1, where]:
                
                data[i, where] = data[i, close] - stop
                
            elif data[i, close] < data[i - 1, where] and data[i - 1, close] > data[i - 1, where]:
            
                data[i, where] = data[i, close] + stop   
                
        except ValueError:
            
            pass
    
    return data

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Coding and Analyzing the Results of the Strategy

The strategy is simple as it relies on the principle of a cross between the market price and its moving average. The full trading conditions can be found as follows:

• Long (Buy) whenever the market surpasses the 200-period moving average and pulls-back to it. The exit will only occur whenever the market breaks the trailing stop indicator.
• Short (Sell) whenever the market breaks the 200-period moving average and pulls-back to it. The exit will only occur whenever the market surpasses the trailing stop indicator.

def signal(data, high_price, low_price, close_price, ma_column, buy_column, sell_column):
    
    data = adder(data, 2)
    
    for i in range(len(data)):
        
       if data[i, close_price] > data[i, ma_column] and data[i - 1, close_price] < data[i - 1, ma_column]:
           
           for a in range(i + 1, len(data)):
               
               if data[a, low_price] <= data[a, ma_column] and \
                  data[a, close_price] > data[a, ma_column]:
                   
                   data[a, buy_column] = 1
                   break
               
               elif data[a, close_price] < data[a, ma_column]:
                    break
                
       elif data[i, close_price] < data[i, ma_column] and data[i - 1, close_price] > data[i - 1, ma_column]:
           
           for a in range(i + 1, len(data)):
               
               if data[a, high_price] >= data[a, ma_column] and \
                  data[a, close_price] < data[a, ma_column]:
                   
                   data[a, sell_column] = -1
                   break

               elif data[a, close_price] > data[a, ma_column]:
                    break
                   
    return data

# With 7 as buy signals columns, 6 as the trailing stop indicator, 9 as the index where to put the buy PnL, 8 as sell signals, and 10 as the index where to put the sell PnL

for i in range(len(my_data)):
    
    if my_data[i, 7] == 1:
        
        for a in range(i + 1, len(my_data)):
            
            if my_data[a, 3] < my_data[a, 6]:
                
                my_data[a, 9] = my_data[a, 3] - my_data[i, 3]
                break
            
            elif my_data[a, 8] == -1:
                
                my_data[a, 9] = my_data[a, 3] - my_data[i, 3]
                
                break

if my_data[i, 8] == -1:
        
        for a in range(i + 1, len(my_data)):
            
            if my_data[a, 3] > my_data[a, 6]:
                
                my_data[a, 10] = my_data[i, 3] - my_data[a, 3]
                break
            
            elif my_data[a, 7] == 1:
                
                my_data[a, 10] = my_data[i, 3] - my_data[a, 3]
                
                break

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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.

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