Financial data is the foundation of business intelligence, risk assessment, and regulatory compliance. With the adoption of XBRL (Extensible Business Reporting Language), companies are required to file financial reports in a structured format, making financial data extraction more accessible than ever.
This guide provides a step-by-step approach to extracting XBRL data using Python, integrating it into analytics pipelines, and applying machine learning for predictive insights.
Section 1: Setting Up Your Environment
Before diving into data extraction, ensure you have the following installed:
1.1 Install Required Libraries
pip install arelle pandas matplotlib scikit-learn
1.2 Import Necessary Modules
from arelle import Cntlr
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
Section 2: Extracting Financial Data from XBRL
XBRL reports contain structured financial data that can be extracted using Arelle, an open-source XBRL processing tool.
2.1 Load an XBRL Report
# Initialize Arelle Controller
controller = Cntlr.Cntlr()
# Load an XBRL report
report_path = "sample_report.xbrl"
model_xbrl = controller.modelManager.load(report_path)
2.2 Extract and Structure Data
# Extract facts from the report
facts = []
for fact in model_xbrl.facts:
facts.append({"Concept": fact.concept.qname, "Value": fact.value})
# Convert to DataFrame
df = pd.DataFrame(facts)
print(df.head())
2.3 Save Extracted Data for Further Analysis
df.to_csv("xbrl_data.csv", index=False)
Section 3: Integrating XBRL Data into Analytics Pipelines
3.1 Load Extracted Data into a Data Warehouse
For scalable analytics, store the extracted data in a database or cloud storage:
from sqlalchemy import create_engine
# Create a database connection
engine = create_engine("sqlite:///financial_data.db")
# Load data into database
df.to_sql("financial_reports", con=engine, if_exists='replace', index=False)
3.2 Visualizing Financial Data
# Plot financial metrics
plt.bar(df["Concept"], df["Value"], color='blue')
plt.xlabel("Financial Metric")
plt.ylabel("Value (USD)")
plt.title("Company Financial Overview")
plt.xticks(rotation=90)
plt.show()
Section 4: Applying Machine Learning for Financial Insights
4.1 Preparing Data for Machine Learning
# Convert numeric values to float
df["Value"] = pd.to_numeric(df["Value"], errors='coerce')
df.dropna(inplace=True)
# Feature Selection
X = df.index.values.reshape(-1, 1) # Using index as a proxy for time
y = df["Value"]
# Split data into training and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
4.2 Predicting Financial Trends with Linear Regression
# Train the model
model = LinearRegression()
model.fit(X_train, y_train)
# Predict future values
y_pred = model.predict(X_test)
# Plot predictions
plt.scatter(X_test, y_test, color='blue', label="Actual Values")
plt.plot(X_test, y_pred, color='red', linewidth=2, label="Predicted Values")
plt.xlabel("Time")
plt.ylabel("Financial Metric Value")
plt.title("Financial Trend Prediction")
plt.legend()
plt.show()
By following this guide, you now have a fully automated pipeline for:
✅ Extracting financial data from XBRL reports
✅ Storing and integrating data into analytics workflows
✅ Applying machine learning to generate predictive insights
Next Steps
🔹 Explore more XBRL taxonomies to enhance data accuracy
🔹 Integrate real-time financial data into your models
🔹 Automate XBRL data extraction at scale using cloud computing
Ready to take your financial data analytics to the next level? Get in touch with our team of experts and start building your AI-powered financial insights platform today!
