📚

Analytics in Stock Markets Zoomcamp

Created

Sep 1, 2025 10:05 PM

Week 1: Introduction and Data Sources

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Setting the Date

GDP

The next stage is to collect data for Real potential GDP

gdppot = pdr.DataReader("GDPPOT", "fred", start=start)

gdppot['gdppot_us_yoy'] = gdppot.GDPPOT/gdppot.GDPPOT.shift(4)-1
gdppot['gdppot_us_qoq'] = gdppot.GDPPOT/gdppot.GDPPOT.shift(1)-1
gdppot.tail(15)

Objective

To retrieve and analyze U.S. Real Potential Gross Domestic Product (GDPPOT) from the Federal Reserve Economic Data (FRED), and calculate:

Year-over-Year (YoY) growth
Quarter-over-Quarter (QoQ) growth

🧾 Dataset Description

GDPPOT: Real Potential GDP
Unit: Billions of chained 2012 dollars
Frequency: Quarterly
Source: U.S. Congressional Budget Office via FRED
FRED Series Code: GDPPOT

⚙️ Code Breakdown

python
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from pandas_datareader import data as pdr
from datetime import date

# Get current date and create a 70-year date range
end = date.today()
start = date(year=end.year - 70, month=end.month, day=end.day)

end: today’s date
start: same day/month but 70 years earlier
This range captures long-term economic trends.

📥 Download GDPPOT Data from FRED

python
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gdppot = pdr.DataReader("GDPPOT", "fred", start=start)

Retrieves quarterly potential GDP starting from start.
Resulting gdppot DataFrame contains:

Index: Dates (quarterly)
Column: 'GDPPOT'

📊 Calculate Growth Rates

🟢 Year-over-Year (YoY) Growth

python
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gdppot['gdppot_us_yoy'] = gdppot['GDPPOT'] / gdppot['GDPPOT'].shift(4) - 1

Compares each quarter with the same quarter of the previous year
shift(4): Moves data 4 rows (quarters) backward
Output is decimal form (e.g., 0.025 = 2.5%)

🟡 Quarter-over-Quarter (QoQ) Growth

python
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gdppot['gdppot_us_qoq'] = gdppot['GDPPOT'] / gdppot['GDPPOT'].shift(1) - 1

Compares current quarter to the previous quarter
Useful for short-term economic trend analysis

📋 View Recent Data

python
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gdppot.tail(15)

Shows the most recent 15 entries
Helps track recent GDP levels and growth performance

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GDP Analysis Study Notes - Python Implementation

GDP Visualization Study Notes - Dual Y-Axis Plotting

Overview and Concept
Required Libraries
Figure Setup
Primary Axis (GDP Values)
Secondary Axis (Growth Rates)
Formatting and Styling
Complete Implementation
Customization Options

Overview and Concept

Purpose

Create a dual y-axis plot to visualize both:

GDP absolute values (billions of dollars) - Primary axis
GDP growth rates (percentage) - Secondary axis

Why Dual Y-Axis?

Different scales make it impossible to plot both metrics on a single axis effectively:

GDP values: Range from ~$8,000B to $25,000B
Growth rates: Range from -2% to +8%

Reference Source

Based on: cmdlinetips.com dual y-axis tutorial

Required Libraries

import matplotlib.pyplot as plt
import matplotlib.ticker as mtick

Library Components

Library	Module	Purpose
`matplotlib.pyplot`	`plt`	Core plotting functionality
`matplotlib.ticker`	`mtick`	Advanced tick formatting (percentage)

Figure Setup

Basic Figure Creation

fig, ax = plt.subplots(figsize=(20, 6))
plt.grid(True)

Syntax Breakdown

Component	Purpose	Parameters
`plt.subplots()`	Creates figure and axis objects	`figsize=(width, height)` in inches
`figsize=(20, 6)`	Sets plot dimensions	Width=20", Height=6"
`plt.grid(True)`	Adds grid lines	Boolean parameter

Alternative Syntax

# Method 1: Using plt.subplots()
fig, ax = plt.subplots(figsize=(20, 6))

# Method 2: Using plt.figure()
plt.figure(figsize=(20, 6))
ax = plt.gca()  # Get current axis

Primary Axis (GDP Values)

Fill Between Plot

ax.fill_between(gdppot.index, gdppot.GDPPOT,
                color="red", alpha=0.3,
                label="US Potential GDP")

Parameter Details

Parameter	Type	Purpose	Example Value
`gdppot.index`	DatetimeIndex	X-axis values (dates)	Quarterly dates
`gdppot.GDPPOT`	Series	Y-axis values (GDP)	GDP in billions
`color`	string	Fill color	`"red"`, `"#FF0000"`
`alpha`	float	Transparency (0-1)	`0.3` = 30% opacity
`label`	string	Legend label	For legend display

Alternative Fill Options

# Fill between zero and values
ax.fill_between(x, y, color="red", alpha=0.3)

# Fill between two series
ax.fill_between(x, y1, y2, color="blue", alpha=0.2)

# Fill above/below a threshold
ax.fill_between(x, y, 20000, where=(y > 20000),
                color="green", alpha=0.4)

Secondary Axis (Growth Rates)

Creating Secondary Y-Axis

ax2 = ax.twinx()

Syntax Explanation

ax.twinx(): Creates a new axis sharing the same x-axis
ax2: New axis object for right y-axis
Both axes share the same x-coordinates but have independent y-scales

Percentage Formatting

ax2.yaxis.set_major_formatter(mtick.PercentFormatter(1.0))

Formatter Options

Parameter	Meaning	Input Format	Display
`1.0`	Decimal input	`0.05`	`5%`
`100.0`	Percentage input	`5.0`	`5%`

Growth Rate Plot

ax2.plot(gdppot.gdppot_us_yoy, color="blue", marker="o",
         label="US Potential GDP Growth, % Y/Y")

Plot Parameters

Parameter	Purpose	Options	Example
`color`	Line color	Color names, hex codes	`"blue"`, `"#0000FF"`
`marker`	Point markers	`"o"`, `"s"`, `"^"`, `"*"`	`"o"` = circles
`label`	Legend text	String	For legend display

Marker Options

# Common marker styles
markers = {
    "o": "circle",
    "s": "square",
    "^": "triangle up",
    "v": "triangle down",
    "*": "star",
    "+": "plus",
    "x": "x mark"
}

Formatting and Styling

Axis Labels

ax.set_xlabel("Date", fontsize=14)
ax.set_ylabel("US Potential GDP, $b", color="red", fontsize=14)
ax2.set_ylabel("US Potential GDP Growth, % Y/Y", color="blue", fontsize=14)

Label Parameters

Parameter	Purpose	Example Values
`fontsize`	Text size	`12`, `14`, `16`, `'large'`
`color`	Text color	`"red"`, `"blue"`, `"#FF0000"`

Font Size Options

# Numeric sizes
fontsize=10  # Small
fontsize=12  # Medium
fontsize=14  # Large
fontsize=16  # Extra large

# Named sizes
fontsize='small'
fontsize='medium'
fontsize='large'
fontsize='x-large'

Legend Handling

Combining Legends from Both Axes

lines, labels = ax.get_legend_handles_labels()
lines2, labels2 = ax2.get_legend_handles_labels()
ax2.legend(lines + lines2, labels + labels2, loc='upper left')

Step-by-Step Process

Extract from primary axis: ax.get_legend_handles_labels()
Extract from secondary axis: ax2.get_legend_handles_labels()
Combine both: lines + lines2, labels + labels2
Create unified legend: ax2.legend(...)

Legend Locations

Parameter	Position
`'upper left'`	Top left corner
`'upper right'`	Top right corner
`'lower left'`	Bottom left corner
`'lower right'`	Bottom right corner
`'center'`	Center of plot
`'best'`	Automatic best position

Complete Implementation

Full Code Template

import matplotlib.pyplot as plt
import matplotlib.ticker as mtick

# Create figure and primary axis
fig, ax = plt.subplots(figsize=(20, 6))
plt.grid(True)

# Plot GDP values on primary axis (left y-axis)
ax.fill_between(gdppot.index, gdppot.GDPPOT,
                color="red", alpha=0.3,
                label="US Potential GDP")

# Create secondary axis (right y-axis)
ax2 = ax.twinx()

# Format secondary axis for percentages
ax2.yaxis.set_major_formatter(mtick.PercentFormatter(1.0))

# Plot growth rates on secondary axis
ax2.plot(gdppot.gdppot_us_yoy, color="blue", marker="o",
         label="US Potential GDP Growth, % Y/Y")

# Set axis labels
ax.set_xlabel("Date", fontsize=14)
ax.set_ylabel("US Potential GDP, $b", color="red", fontsize=14)
ax2.set_ylabel("US Potential GDP Growth, % Y/Y", color="blue", fontsize=14)

# Combine legends from both axes
lines, labels = ax.get_legend_handles_labels()
lines2, labels2 = ax2.get_legend_handles_labels()
ax2.legend(lines + lines2, labels + labels2, loc='upper left')

# Display the plot
plt.show()

Customization Options

Color Schemes

# Professional color scheme
primary_color = "#2E86AB"    # Blue
secondary_color = "#A23B72"  # Purple
fill_alpha = 0.2

# Economic data color scheme
gdp_color = "#1f77b4"        # Blue
growth_color = "#ff7f0e"     # Orange

Enhanced Styling

# Add title
plt.title("US Potential GDP and Growth Rate Analysis",
          fontsize=16, fontweight='bold', pad=20)

# Customize grid
plt.grid(True, alpha=0.3, linestyle='--')

# Set axis limits
ax.set_ylim(0, gdppot.GDPPOT.max() * 1.1)
ax2.set_ylim(-0.05, gdppot.gdppot_us_yoy.max() * 1.2)

Advanced Formatting

# Rotate x-axis labels
plt.xticks(rotation=45)

# Add annotations
ax2.annotate('Financial Crisis',
             xy=('2008-01-01', -0.02),
             xytext=('2010-01-01', 0.04),
             arrowprops=dict(arrowstyle='->'))

# Tight layout
plt.tight_layout()

Save Options

# Save as high-quality image
plt.savefig('gdp_analysis.png', dpi=300, bbox_inches='tight')
plt.savefig('gdp_analysis.pdf', bbox_inches='tight')

Troubleshooting Common Issues

Issue 1: Legend Not Showing

# Ensure both plots have labels
ax.fill_between(..., label="GDP Values")
ax2.plot(..., label="Growth Rate")

Issue 2: Percentage Not Displaying

# Check formatter parameter
ax2.yaxis.set_major_formatter(mtick.PercentFormatter(1.0))  # For decimal input
# OR
ax2.yaxis.set_major_formatter(mtick.PercentFormatter(100.0))  # For percentage input

Issue 3: Overlapping Text

# Use tight_layout
plt.tight_layout()
# OR adjust figure size
fig, ax = plt.subplots(figsize=(22, 8))

Practice Exercises

Color Variations: Change colors and transparency levels
Different Markers: Experiment with various marker styles
Multiple Series: Add QoQ growth rate to the secondary axis
Time Filtering: Plot only the last 20 years of data
Annotations: Add markers for recession periods
Style Themes: Apply different matplotlib style sheets