ArcticDB is a powerful tool for efficiently managing and analyzing time-series financial data. When combined with technical analysis techniques, it can streamline backtesting and algorithmic trading strategies. Below, I demonstrate advanced features like deduplication, metadata management, and multi-symbol data analysis for technical indicators.
Why Use ArcticDB for Technical Analysis?
- High Performance: Faster read/write operations for large datasets.
- Versioning: Keep track of data versions for backtesting.
- Metadata Support: Tag datasets with useful information (e.g., source, retrieval time).
- Batch Processing: Efficiently handle multiple stock symbols.
Step 1: Fetch and Store Multiple Stocks with Metadata
import yfinance as yf from arcticdb import Arctic import pandas as pd DB_PATH = "lmdb://./advanced_stock_db" LIBRARY_NAME = "technical_analysis_data" # Connect and create library ac = Arctic(DB_PATH) if LIBRARY_NAME not in ac.list_libraries(): ac.create_library(LIBRARY_NAME) lib = ac[LIBRARY_NAME] symbols = ["AAPL", "MSFT", "GOOG", "TSLA"] for symbol in symbols: ticker = yf.Ticker(symbol) hist_data = ticker.history(period="5y", auto_adjust=False) # Add metadata with data source and timestamp metadata = {"source": "Yahoo Finance", "retrieval_date": pd.Timestamp.now()} if not hist_data.empty: lib.write(symbol, hist_data, metadata=metadata) print(f"{symbol} data stored with metadata.") Step 2: Batch Read and Compute Technical Indicators
import numpy as np # Batch read multiple stock data symbols_data = lib.batch_read(symbols) for symbol_data in symbols_data: df = symbol_data.data symbol = symbol_data.symbol # Compute Technical Indicators: Bollinger Bands and MACD df['SMA_20'] = df['Close'].rolling(window=20).mean() df['Upper_Band'] = df['SMA_20'] + 2 * df['Close'].rolling(window=20).std() df['Lower_Band'] = df['SMA_20'] - 2 * df['Close'].rolling(window=20).std() # MACD calculation df['EMA_12'] = df['Close'].ewm(span=12, adjust=False).mean() df['EMA_26'] = df['Close'].ewm(span=26, adjust=False).mean() df['MACD'] = df['EMA_12'] - df['EMA_26'] df['Signal_Line'] = df['MACD'].ewm(span=9, adjust=False).mean() # Write back updated data lib.write(symbol, df) print(f"{symbol} updated with technical indicators.") Step 3: Deduplication and Versioning
# Fetch and update AAPL data to simulate updates result = lib.read("AAPL") existing_data = result.data # Simulate new overlapping data new_data = existing_data.tail(10) # Fake overlapping update # Combine and deduplicate combined = pd.concat([existing_data, new_data]) deduplicated_data = combined[~combined.index.duplicated(keep='last')] # Write as a new version lib.write("AAPL", deduplicated_data, prune_previous_version=False) print("AAPL data updated without deleting previous version.") Step 4: Metadata and Symbol Statistics
# Fetch metadata result = lib.read("AAPL") print("Metadata for AAPL:", result.metadata) # Get symbol statistics info = lib.get_symbol_info("AAPL") print("Symbol Info for AAPL:", info) Step 5: Visualization and Analysis
import matplotlib.pyplot as plt # Plot Bollinger Bands and Closing Prices df = lib.read("AAPL").data plt.figure(figsize=(12, 6)) plt.plot(df.index, df['Close'], label='Close Price') plt.plot(df.index, df['SMA_20'], label='20-Day SMA') plt.fill_between(df.index, df['Upper_Band'], df['Lower_Band'], color='lightgray', label='Bollinger Bands') plt.title("AAPL Bollinger Bands") plt.legend() plt.show() Summary of Advanced Features
| Feature | Description |
|---|---|
| Metadata Handling | Store additional metadata per dataset |
| Batch Processing | Handle multiple symbols simultaneously |
| Deduplication | Combine and clean overlapping data |
| Versioning | Maintain multiple versions for backtesting |
| Technical Indicators | Compute SMA, Bollinger Bands, and MACD |
This workflow demonstrates how ArcticDB can handle complex financial analysis tasks efficiently. Want more on strategy backtesting or real-time data analysis?
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