Advanced ArcticDB Usage for Technical Analysis with Python

Finance & Maths (4 Part Series)

1 Using ArcticDB for Financial Analysis with Python and yFinance
2 Advanced ArcticDB Usage for Technical Analysis with Python
3 ArcticDB Financial and Historical analysis just way faster.
4 Stock Fundamental Analysis Using Python and Streamlit

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.")

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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.")

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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.")

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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)

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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()

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Summary of Advanced Features

This workflow demonstrates how ArcticDB can handle complex financial analysis tasks efficiently. Want more on strategy backtesting or real-time data analysis?

Finance & Maths (4 Part Series)

1 Using ArcticDB for Financial Analysis with Python and yFinance
2 Advanced ArcticDB Usage for Technical Analysis with Python
3 ArcticDB Financial and Historical analysis just way faster.
4 Stock Fundamental Analysis Using Python and Streamlit

原文链接：Advanced ArcticDB Usage for Technical Analysis with Python