How to calculate an EMA Death Cross with Python 3, MetaTrader 5, and Pandas
Automated Trading Bot with MetaTrader5 and Python
There are a ton of signals to analyze when using quantitative analysis for stock/crypto/futures/FOREX trading!
About This Series
This series demonstrates the automated analysis of 8 different market signals.
Using Python 3, Python Pandas, and MetaTrader5, I’ll show you how to calculate 8 common signals.
All code for this tutorial can be found on my GitHub, and I’ve included working code samples throughout (use at your own risk, give me a shout-out if you do).
James Hinton
What You Need
Requirements and assumed knowledge as follows:
- Already connected to MetaTrader 5. This article shows you how, I’m using the variant from IC Markets.
- Windows 10 or above. For reasons known only to MetaTrader, the Python API only works on Windows 😊
- Python 3. This series was built with Python 3.10
The Death Cross
Introduction to the Cross of Death
The ‘Cross of Death’ (aka Death Cross) is a stock market chart pattern reflecting recent price weakness. In generic terms, it occurs when a shorter-term moving average drops below a longer-term moving average.
For EMA stock calculations, these time periods are frequently the 15-day moving average and 50-day moving average. I.e. a death cross occurs when the 15-day moving average drops below the 50-day moving average. Visually this might look like this:
How to Calculate
A Death Cross occurs mathematically when the previous short-term moving average is above the previous long-term moving average and the current short-term moving average is below the current long-term moving average.
Simplified into pseudo-code:
- Determine the previous 15-day moving average
prev_15 - Determine the previous 50-day moving average
prev_50 - Determine the current 15-day moving average
curr_15 - Determine the current 50-day moving average
curr_50 - If
prev_15 > prev_50ANDcurr_15 < curr_50:cross_of_death = True
This can be calculated using the Simple Moving Average (SMA) and Exponential Moving Average (EMA).
How to Code
Before demonstrating the code to calculate an EMA Death Cross, I’ll outline a couple of considerations:
- Precision. Because the EMA is a weighted average, any imprecision in the price will continue to replicate, effectively forever. The only way to eliminate any imprecision would be to calculate the EMA using every available data point for a given timeframe.
- Compute Time. Depending on your system, the compute time required to calculate an EMA can be huge. For example, in my testing, I calculated a single EMA across 50,000 1-minute candles, taking approximately 15 minutes. Clearly, that is unworkable. There are techniques such as multiprocessing to speed this up, however, I’ve chosen to limit the calculation for this series.
As a result, you’ll need to figure out what level of precision you’re comfortable with. I’ve chosen to use a candle limit of 1000 rows for this series, on my system this completes in < 1 second.
Generic Death Cross Function
Let’s start with a generic EMA function that returns an intact Pandas Dataframe. Here’s the code:
# Define function to calculate an arbitrary EMA and return Dataframe
def calc_generic_ema_with_dataframe(symbol, timeframe, ema_size, num_rows):
raw_data = mt5_interface.query_historic_data(symbol=symbol, timeframe=timeframe, number_of_candles=num_rows)
# Convert into Dataframe
dataframe = pandas.DataFrame(raw_data)
# Create column string
ema_name = "ema_" + str(ema_size)
# Create the multiplier
multiplier = 2/(ema_size + 1)
# Calculate the initial value (SMA)
# pandas.set_option('display.max_columns', None) # <- use this to show all columns
# pandas.set_option('display.max_rows', None) # <- use this to show all the rows
initial_mean = dataframe['close'].head(ema_size).mean()
# Iterate through Dataframe
for i in range(len(dataframe)):
if i == ema_size:
dataframe.loc[i, ema_name] = initial_mean
elif i > ema_size:
ema_value = dataframe.loc[i, 'close'] * multiplier + dataframe.loc[i-1, ema_close]*(1-multiplier)
dataframe.loc[i, ema_name] = ema_value
else:
dataframe.loc[i, ema_name] = 0.00
# print(dataframe) # <- use this to print the dataframe if you want to inspect
return dataframeThe previous function returns a list of all EMA’s for the last 1000 candles (required for a weighted moving average). Therefore, to determine if a Cross of Death event has occurred, extract the close values for the last 2 candles and compare them.
Here’s the code:
import generic_ema
import pandas
# Function to calculate a generic Cross of Death
def generic_ema_death_cross(symbol, timeframe):
# Retrieve the 15 candle EMA
ema_15 = generic_ema.calc_generic_ema_with_dataframe(symbol=symbol, timeframe=timeframe, ema_size=15, num_rows=1000)
# Retrieve the 50 candle EMA
ema_50 = generic_ema.calc_generic_ema_with_dataframe(symbol=symbol, timeframe=timeframe, ema_size=50, num_rows=1000)
# Extract the previous values. Number_of_rows - 2 for both EMA 15 and EMA 50
prev_15 = ema_15.loc[998, 'close']
prev_50 = ema_50.loc[998, 'close']
# Extract the current values. Number_of_rows - 1 for EMA 15 and EMA 50
curr_15 = ema_15.loc[999, 'close']
curr_50 = ema_50.loc[999, 'close']
# Compare
if prev_15 > prev_50 and curr_15 < curr_50:
return True
return FalseEMA Death Cross Function
Finally, the function to calculate a ‘classic’ death cross is as follows:
# Calculate a 'classic' cross of death event
def ema_death_cross(symbol):
return generic_ema_death_cross(symbol=symbol, timeframe="D1")Wrapping Up
Nice. Calculating an EMA Death Cross can really help your trading algorithm make smarter decisions. Let me know how you’re planning to use it in the comments below!
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