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Market Data Utilities

This folder contains utilities for processing, analyzing, and fetching market data for financial applications.

Available Utilities

Market Data (market_data_utils.py)

  • Return calculations (simple and logarithmic)
  • Outlier detection and missing data handling
  • Market sentiment analysis
  • Data validation and quality control
  • Market timing indicators
  • Data smoothing techniques

API (api_utils.py)

  • HTTP request handling with retry logic
  • API key generation and secure storage
  • Error handling and timeout management
  • Response parsing and validation

Usage

# Market data operations
from market_data_utils import calculate_returns, detect_outliers, calculate_market_sentiment
from api_utils import make_api_request, retry_api_request, generate_api_key

# Data analysis
returns = calculate_returns(prices, 'log')
outliers = detect_outliers(data, 'iqr')
sentiment = calculate_market_sentiment(news_data, keywords)

# API operations
response = make_api_request("https://api.example.com/stocks/AAPL")
api_key = generate_api_key(32)

Installation

Requires numpy, scipy, and requests:

pip install numpy scipy requests

Testing

Run each utility directly to see demonstrations:

python market_data_utils.py
python api_utils.py

Common Use Cases

  • Data Processing: Clean and validate market data
  • Technical Analysis: Calculate indicators and signals
  • API Integration: Fetch data from external sources
  • Quality Control: Ensure data accuracy and completeness
  • Sentiment Analysis: Analyze market sentiment from news
  • Algorithm Development: Prepare data for trading strategies