Visualize Financial Market Data¶
Here, we’ll demonstrate the visualization of financial market data using the capon theme.
In [1]:
# # Install libraries - uncomment if running on a new environment (e.g., Colab)
# !pip install --upgrade themes
In [2]:
import themes
themes.register()
The Data¶
We'll use an out-of-the-box sample data.
In [3]:
from themes import datasets
help(datasets.load_markets)
Help on function load_markets in module themes.datasets:
load_markets()
https://github.com/gialdetti/capon/
In [4]:
markets = (
datasets.load_markets()
.pipe(lambda df: df[df["timestamp"] > "2020"])
.assign(
relative_price=lambda df: df.groupby("symbol")["close"].transform(
lambda g: g / g.iloc[0] - 1
)
)
)
markets
Out[4]:
| timestamp | symbol | currency | volume | open | high | low | close | adjclose | relative_price | |
|---|---|---|---|---|---|---|---|---|---|---|
| 1907 | 2020-01-02 10:30:00-04:00 | ^GSPC | USD | 3458250000 | 3244.669922 | 3258.139893 | 3235.530029 | 3257.850098 | 3257.850098 | 0.000000 |
| 1908 | 2020-01-03 10:30:00-04:00 | ^GSPC | USD | 3461290000 | 3226.360107 | 3246.149902 | 3222.340088 | 3234.850098 | 3234.850098 | -0.007060 |
| 1909 | 2020-01-06 10:30:00-04:00 | ^GSPC | USD | 3674070000 | 3217.550049 | 3246.840088 | 3214.639893 | 3246.280029 | 3246.280029 | -0.003551 |
| 1910 | 2020-01-07 10:30:00-04:00 | ^GSPC | USD | 3420380000 | 3241.860107 | 3244.909912 | 3232.429932 | 3237.179932 | 3237.179932 | -0.006345 |
| 1911 | 2020-01-08 10:30:00-04:00 | ^GSPC | USD | 3720890000 | 3238.590088 | 3267.070068 | 3236.669922 | 3253.050049 | 3253.050049 | -0.001473 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 10059 | 2022-05-25 09:30:00-04:00 | ^RUT | USD | 4322190000 | 1760.420044 | 1807.430054 | 1759.199951 | 1799.160034 | 1799.160034 | 0.079429 |
| 10060 | 2022-05-26 09:30:00-04:00 | ^RUT | USD | 3961940000 | 1808.640015 | 1847.829956 | 1808.640015 | 1838.239990 | 1838.239990 | 0.102876 |
| 10061 | 2022-05-27 09:30:00-04:00 | ^RUT | USD | 3560560000 | 1845.239990 | 1887.900024 | 1845.239990 | 1887.900024 | 1887.900024 | 0.132670 |
| 10062 | 2022-05-31 09:30:00-04:00 | ^RUT | USD | 5192220000 | 1882.000000 | 1883.189941 | 1856.560059 | 1864.040039 | 1864.040039 | 0.118355 |
| 10063 | 2022-06-01 09:30:00-04:00 | ^RUT | USD | 4145710000 | 1871.459961 | 1877.489990 | 1830.199951 | 1854.819946 | 1854.819946 | 0.112823 |
2436 rows × 10 columns
Visualization with Matplotlib¶
In [5]:
import matplotlib.pyplot as plt
%config InlineBackend.figure_format = 'retina'
In [6]:
with plt.style.context("capon"):
plt.figure(figsize=(9, 3), dpi=96)
markets.pivot_table(
index="timestamp", columns="symbol", values="relative_price"
).plot(ax=plt.gca())
plt.xlabel(None)
plt.ylabel("Relative Price")
Visualization with Altair¶
In [7]:
import altair as alt
In [8]:
chart = (
alt.Chart(markets)
.mark_line(interpolate="monotone")
.encode(
x=alt.X("timestamp", title=None, axis=alt.Axis(format="%b %y")),
y=alt.Y("relative_price", title="Relative Price", axis=alt.Axis(format="+%")),
color="symbol",
tooltip=["timestamp", "symbol", alt.Tooltip("relative_price", format="+.2%")],
)
.properties(
title={
"text": f"Market Indexes Change",
"subtitle": f"Relative to {markets['timestamp'].dt.date.min()}",
},
width=600,
height=200,
)
)
display(chart)
In [9]:
with alt.themes.enable("capon"):
display(chart)