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# https://matplotlib.org/stable/gallery/lines_bars_and_markers/scatter_demo2.html
from io import StringIO
import matplotlib.cbook as cbook
import matplotlib.pyplot as plt
import numpy as np
# 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.
price_data = cbook.get_sample_data("goog.npz", np_load=True)["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]
fig, ax = plt.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()
buffer = StringIO()
plt.savefig(buffer, format="svg")
print(buffer.getvalue())
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