scatter_demo2.py

# -*- coding: utf-8 -*-
# https://matplotlib.org/3.2.1/gallery/lines_bars_and_markers/scatter_demo2.html

import sys

import matplotlib.cbook as cbook
import numpy as np
from matplotlib.backends.backend_qt5agg import FigureCanvas
from matplotlib.figure import Figure
from PyQt5.QtWidgets import QApplication

app = QApplication(sys.argv)

fig = Figure(figsize=(8, 6))
canvas = FigureCanvas(fig)
canvas.resize(640, 480)
canvas.show()

# Load a numpy record array from yahoo csv data with fields date, open, close,
# volume, adj_close from the mpl-data/example directory. The record array
# stores the date as an np.datetime64 with a day unit ('D') in the date column.
with cbook.get_sample_data("goog.npz") as datafile:
    price_data = np.load(datafile)["price_data"].view(np.recarray)
price_data = price_data[-250:]  # get the most recent 250 trading days

delta1 = np.diff(price_data.adj_close) / price_data.adj_close[:-1]

# Marker size in units of points^2
volume = (15 * price_data.volume[:-2] / price_data.volume[0]) ** 2
close = 0.003 * price_data.close[:-2] / 0.003 * price_data.open[:-2]

ax = fig.subplots()
ax.scatter(delta1[:-1], delta1[1:], c=close, s=volume, alpha=0.5)

ax.set_xlabel(r"$\Delta_i$", fontsize=15)
ax.set_ylabel(r"$\Delta_{i+1}$", fontsize=15)
ax.set_title("Volume and percent change")

ax.grid(True)
fig.tight_layout()

sys.exit(app.exec_())