The death cross is a chart pattern that tells us when a short-term moving average (e.g. 50 SMA) crosses below a long-term moving average (e.g. 200 SMA).

It is the exact opposite to the golden cross and is often known as a trigger point for a trend reversal.

Here is how to compute it

# How to compute the golden cross in Python

# In order to get Apple stock price data from yahoo
import yfinance as yf

# Basic data manipulation libraries
import numpy as np
import pandas as pd

# Data visualisation library
import matplotlib.pyplot as plt

# We download the stock price from start date until end date
df = yf.download("AAPL", start="2018-01-01", end="2021-06-01")

# We compute our simple moving averages
df["50_sma"] = df["Adj Close"].rolling(50).mean()
df["200_sma"] = df["Adj Close"].rolling(200).mean()

# This is important so that we have both SMA starting a the same time.
df = df.dropna() 

# We compute our death cross
df["death_cross"] = df.apply(lambda row: 1 if row[f"50_sma"] < row[f"200_sma"]  else 0, axis=1)

# To store when our death crosses are happening
list_death_cross_ts = []
first_death_cross = False

# We take the date where the first 50 SMA < 200 SMA appears
for idx, each in df["death_cross"].iteritems():
    if each == 1:
        # If its the first death cross we see we add the timestamp
        if first_death_cross:
            list_death_cross_ts.append(idx)
            first_death_cross = False
    else:
        first_death_cross = True

# We plot our prices / SMAs and Death Cross dates
fig, axes = plt.subplots(1,1, figsize=(8,4))
df[["200_sma","50_sma","Adj Close"]].plot(figsize=(8,4), grid=True, title="SMA 50 vs SMA 200 on AAPL prices", ax=axes)

for each in list_death_cross_ts:
    axes.axvline(x=each, label="Death Cross", c="black")
    
axes.legend()
fig.tight_layout()
plt.savefig("death_cross_python.png")

Here is how to compute the death cross with Pandas in Python