Introduction to Financial Data Structures
Understanding financial data structures is essential for anyone working in FinTech. You will frequently encounter data types such as prices, returns, and time series. Prices refer to the value of a financial instrument—like a stock or bond—at a specific point in time. Returns measure how much an investment has gained or lost over a period, often expressed as a percentage change from one price to the next. Time series are sequences of data points, such as daily closing prices, ordered by time. These data types form the backbone of analysis in areas like trading, risk management, and portfolio optimization.
1234567891011# List of daily closing prices for a stock closing_prices = [100.0, 102.5, 101.0, 103.0, 104.5] # Calculate simple returns: (current_price - previous_price) / previous_price simple_returns = [] for i in range(1, len(closing_prices)): ret = (closing_prices[i] - closing_prices[i-1]) / closing_prices[i-1] simple_returns.append(ret) print("Closing prices:", closing_prices) print("Simple returns:", simple_returns)
In Python, you can use lists and dictionaries to represent financial instruments and their attributes. Lists are useful for storing sequences of values, such as a series of prices or returns. Dictionaries allow you to map keys—such as stock symbols—to values like latest prices or other attributes. This makes it easy to organize and access data about multiple instruments, which is common in financial applications.
12345678# Dictionary mapping stock symbols to their latest prices latest_prices = { "AAPL": 192.32, "GOOG": 2845.12, "TSLA": 715.83 } print("Latest stock prices:", latest_prices)
1. Which Python data structure is best suited for mapping stock tickers to their prices?
2. What is a simple return in the context of financial data?
3. Why are time series important in financial analysis?
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Understanding financial data structures is essential for anyone working in FinTech. You will frequently encounter data types such as prices, returns, and time series. Prices refer to the value of a financial instrument—like a stock or bond—at a specific point in time. Returns measure how much an investment has gained or lost over a period, often expressed as a percentage change from one price to the next. Time series are sequences of data points, such as daily closing prices, ordered by time. These data types form the backbone of analysis in areas like trading, risk management, and portfolio optimization.
1234567891011# List of daily closing prices for a stock closing_prices = [100.0, 102.5, 101.0, 103.0, 104.5] # Calculate simple returns: (current_price - previous_price) / previous_price simple_returns = [] for i in range(1, len(closing_prices)): ret = (closing_prices[i] - closing_prices[i-1]) / closing_prices[i-1] simple_returns.append(ret) print("Closing prices:", closing_prices) print("Simple returns:", simple_returns)
In Python, you can use lists and dictionaries to represent financial instruments and their attributes. Lists are useful for storing sequences of values, such as a series of prices or returns. Dictionaries allow you to map keys—such as stock symbols—to values like latest prices or other attributes. This makes it easy to organize and access data about multiple instruments, which is common in financial applications.
12345678# Dictionary mapping stock symbols to their latest prices latest_prices = { "AAPL": 192.32, "GOOG": 2845.12, "TSLA": 715.83 } print("Latest stock prices:", latest_prices)
1. Which Python data structure is best suited for mapping stock tickers to their prices?
2. What is a simple return in the context of financial data?
3. Why are time series important in financial analysis?
Thanks for your feedback!
