import numpy as np
import matplotlib.pyplot as plt


prices = np.full(100, fill_value=np.nan)
prices[[0, 25, 60, -1]] = [80.0, 30.0, 75.0, 50.0]

# Linearly interpolate the missing values and add some noise.
x = np.arange(len(prices))
is_valid = ~np.isnan(prices)
prices = np.interp(x=x, xp=x[is_valid], fp=prices[is_valid])
prices += np.random.randn(len(prices)) * 2


mn = np.argmin(prices)
mx = mn + np.argmax(prices[mn:])
kwargs = {"markersize": 12, "linestyle": ""}

fig, ax = plt.subplots()
ax.plot(prices)
ax.set_title("Price History")
ax.set_xlabel("Time")
ax.set_ylabel("Price")
ax.plot(mn, prices[mn], color="green", **kwargs)
ax.plot(mx, prices[mx], color="red", **kwargs)
plt.show()