Fetching and Visualizing Forex Data in Python with yfinance • Bin Li

Photo by Ibrahim Boran on Unsplash

I am preparing for a Forex-related interview, so after some research, I’ve summarized how to get Forex data in Python. In this blog, we will use yfinance and seaborn to fetch hourly exchange rates for EUR/USD, check the data quality, and finally, plot the closing prices in a line plot.

Install Packages

To get started, we need the following packages:

yfinance: to fetch Forex data
matplotlib: for plotting
seaborn: for enhanced plotting with better visualization

pip install yfinance matplotlib seaborn

Fetch the Data

import yfinance as yf
import seaborn as sns
import matplotlib.pyplot as plt

sns.set_style("whitegrid")

# Define the forex pair and date range
pair = "EURUSD=X"
# Define the start date and end date
start_date = "2024-01-01"
end_date = "2024-12-31"
# Define the interval
interval = '1h'

# Fetch forex data
forex_data = yf.Ticker(pair)


# Fetch historical forex data
data = forex_data.history(start=start_date, end=end_date, interval=interval)

# Data overview
data.info()

"""Output
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 6178 entries, 2024-01-01 18:00:00+00:00 to 2024-12-30 23:00:00+00:00
Data columns (total 7 columns):
 #   Column        Non-Null Count  Dtype
---  ------        --------------  -----
 0   Open          6178 non-null   float64
 1   High          6178 non-null   float64
 2   Low           6178 non-null   float64
 3   Close         6178 non-null   float64
 4   Volume        6178 non-null   int64
 5   Dividends     6178 non-null   float64
 6   Stock Splits  6178 non-null   float64
dtypes: float64(6), int64(1)
"""

Data Quality Check

Missing values

We can check the missing values by

data.isna().sum()

""" Output
Open            0
High            0
Low             0
Close           0
Volume          0
Dividends       0
Stock Splits    0
dtype: int64
"""

No missing values, nice :)

Removing Irrelevant Columns

Upon closer inspection, we notice that the Volume, Dividends, and Stock Splits columns have all values equal to 0. These columns are not useful for our analysis, so let’s drop them.

cols = ['Volume', 'Dividends','Stock Splits']
(data[cols] == 0).all()

""" Output
Volume          True
Dividends       True
Stock Splits    True
dtype: bool
"""

data.drop(columns= cols, inplace = True)

After this, we have a cleaned DataFrame.

data.info()

""" Output
DatetimeIndex: 6178 entries, 2024-01-01 18:00:00+00:00 to 2024-12-30 23:00:00+00:00
Data columns (total 7 columns):
 #   Column        Non-Null Count  Dtype
---  ------        --------------  -----
 0   Open          6178 non-null   float64
 1   High          6178 non-null   float64
 2   Low           6178 non-null   float64
 3   Close         6178 non-null   float64
 4   Volume        6178 non-null   int64
 5   Dividends     6178 non-null   float64
 6   Stock Splits  6178 non-null   float64
dtypes: float64(6), int64(1)
"""

Data Visulazation

We will now use seaborn to plot the hourly closing prices.

import seaborn as sns
import matplotlib.pyplot as plt

# Set up the plot style
sns.set_style("whitegrid")
plt.figure(figsize=(12, 6))

# Plot the hourly closing prices
sns.lineplot(x=data.index, y=data["Close"])

# Formatting the plot
plt.xlabel("Date")
plt.ylabel("EUR/USD Hourly Closing Price")
plt.title("EUR/USD Hourly Closing Price (2024)")
plt.xticks(rotation=45)
plt.show()

This will generate a line plot showing the hourly closing prices for the EUR/USD pair in 2024.

Conclusion

In this blog, we learned how to fetch Forex data using the yfinance library, cleaned the dataset by removing irrelevant columns, and visualized the EUR/USD hourly closing prices with seaborn. This simple step can serve as the foundation for more complex Forex analysis and hedging strategies.