#!/usr/bin/env python
# coding: utf-8

# # 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)


# 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


# ## Visualization with Matplotlib

# In[5]:


import matplotlib.pyplot as plt


get_ipython().run_line_magic('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)