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Tikam Singh Alma
Tikam Singh Alma

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Using ArcticDB for Financial Analysis with Python and yFinance

Using ArcticDB for Financial Analysis with Python and yFinance

Efficient financial data storage and analysis are essential for traders, analysts, and data scientists. ArcticDB, a high-performance database optimized for time-series data, combined with yFinance, makes it easy to fetch, store, and analyze financial data at scale.

Why Use ArcticDB for Financial Data?

  • Fast read/write operations for large datasets
  • Supports DataFrame operations like deduplication
  • Versioning for historical data comparisons
  • Optimized for time-series data

Step 1: Install Required Libraries 

pip install arcticdb yfinance pandas
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Step 2: Fetch Stock Data with yFinance 

import yfinance as yf import pandas as pd # Fetch historical data for Apple (AAPL) ticker = yf.Ticker("AAPL") hist_data = ticker.history(period="5y", auto_adjust=False) print(hist_data.head())
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Step 3: Store Data in ArcticDB 

from arcticdb import Arctic DB_PATH = 'lmdb://./stock_db' LIBRARY_NAME = 'financial_data' # Connect to ArcticDB and create a library ac = Arctic(DB_PATH) if LIBRARY_NAME not in ac.list_libraries(): ac.create_library(LIBRARY_NAME) lib = ac[LIBRARY_NAME] # Write data to ArcticDB lib.write("AAPL", hist_data) print("Data written to ArcticDB.")
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Step 4: Read and Analyze Data from ArcticDB 

# Read data from ArcticDB result = lib.read("AAPL") df = result.data print(df.tail()) # Simple moving average (SMA) example df['SMA_50'] = df['Close'].rolling(window=50).mean() # Plot the stock price with SMA df[['Close', 'SMA_50']].plot(title="AAPL Stock Price with 50-Day SMA")
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Step 5: Update and Deduplicate Data 

# New data update new_data = ticker.history(period="1mo", auto_adjust=False) combined = pd.concat([result.data, new_data]) filtered = combined[~combined.index.duplicated(keep='last')] lib.write("AAPL", filtered.sort_index()) print("Data updated.")
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Conclusion

By integrating ArcticDB and yFinance, you can efficiently fetch, store, and analyze financial data for better decision-making. ArcticDB’s versioning and high-performance capabilities make it a game-changer for large-scale financial analysis.

Want to take your financial analysis even further? Explore more with technical indicators and machine learning techniques!

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