Algorithmic Trading: Market Data

• Algorithmic trading, quantitative trading, and high-frequency trading are discussed.
• Market microstructure is introduced.
• A roadmap of the book is presented.

Market Data

• Market data feeds are explored.
• The order book shows the list of buy and sell orders for a security.
• Top of the book refers to the best bid and offer prices available.
• Last trade indicates the price at which the most recent transaction occurred.
• Historical data is past market information that can be used for analysis.
• Exchange APIs (Application Programming Interfaces) provide real-time market data and order execution capabilities.
• Data vendors supply market data to traders and investors.
• ITCH and OUCH are protocols that facilitate high-speed data transmission.
• FIX (Financial Information Exchange) protocol is a standard for electronic communication in trading.
• Data normalization involves cleaning and organizing market data for consistency.
• Data storage is the process of saving market data for future use.
• Order book simulation is discussed.
• Data-driven simulation uses historical data to simulate market conditions.
• Agent-based modeling simulates the behavior of individual traders and their impact on the market.

Order Placement

• Order types are explained:
  • Market order: An order to buy or sell a security immediately at the best available price.
  • Limit order: An order to buy or sell a security at a specific price or better.
  • Stop order: An order that becomes a market order when a specified price is reached.
  • Good-til-cancel (GTC) order: An order that remains active until it is filled or canceled.
  • Immediate-or-cancel (IOC) order: An order that must be filled immediately, and any unfilled portion is canceled.
  • Fill-or-kill (FOK) order: An order that must be filled completely and immediately, or it is canceled.
  • All-or-none (AON) order: An order that must be filled completely, but not necessarily immediately.
  • Market-on-close (MOC) order: An order to be executed as close as possible to the end of the trading day.
  • Trailing stop order: A stop order with a stop price that adjusts as the market price fluctuates.
  • Hidden order: An order that is not displayed in the order book.
  • Pegged order: An order with a price that is linked to the best bid or offer.
• Order attributes are described.
• Order routing is explored:
  • Smart order router (SOR): An automated system that seeks the best available price and execution venue for an order.
• Order management system (OMS) is a system used to manage and track orders.

Market Impact

• Market impact is defined.
• The market impact of a single order is examined:
  • Temporary impact: The short-term price movement caused by an order.
  • Permanent impact: The lasting effect of an order on the market price.
  • Impact function: A mathematical representation of the relationship between order size and market impact.
  • An example of market impact estimation is provided.
• The market impact of an order flow is discussed:
  • Kyle model: A model that describes the relationship between order flow and price movement.
  • Glosten-Milgrom model: A model that explains how information is incorporated into prices through trading.
• Optimal execution is explored:
  • Slippage: The difference between the expected price of a trade and the actual price at which it is executed.
  • Execution shortfall: The difference between the actual cost of executing a trade and the expected cost based on pre-trade estimates.
  • VWAP (Volume-Weighted Average Price) and TWAP (Time-Weighted Average Price) are execution benchmarks.
  • Implementation shortfall: A measure of the difference between the performance of a trading strategy and the performance achieved by a benchmark.

Transaction Cost Analysis

• Cost components are identified:
  • Explicit costs: Direct costs associated with trading, such as commissions and exchange fees.
  • Implicit costs: Indirect costs associated with trading, such as market impact and opportunity cost.
• Cost estimation is discussed:
  • Pre-trade analysis: Estimating transaction costs before executing a trade.
  • Post-trade analysis: Evaluating actual transaction costs after a trade has been executed.

Execution Tactics

• Market-making is examined:
  • Inventory management: The process of managing a market maker's inventory of securities.
  • Order placement: The specific strategies used by market makers to place orders.
  • Risk management: Techniques used by market makers to manage the risks associated with their activities.
• Order splitting is discussed:
  • Volume-Weighted Average Price (VWAP): An order splitting strategy that aims to execute a trade at the VWAP.
  • Time-Weighted Average Price (TWAP): An order splitting strategy that aims to execute a trade at the TWAP.
  • Percentage of volume (POV): An order splitting strategy that executes a fixed percentage of the market volume.
  • Adaptive shortfall: An order splitting strategy that adapts to changing market conditions to minimize execution shortfall.
• Smart order routing is explored:
  • Routing logic: The criteria used by a smart order router to select the best execution venue.
  • Venue analysis: The process of evaluating different execution venues.

Pairs Trading

• Mean reversion is described.
• Distance methods are explained:
  • Euclidean distance: A measure of the distance between two data points.
  • Correlation: A statistical measure of the relationship between two variables.
  • Cointegration: A statistical property of two or more time series that indicates they have a long-run equilibrium relationship.
• Statistical arbitrage is discussed.

Trading Strategies

• Trend following is examined:
  • Moving average: A technical indicator that smoothes out price data by calculating the average price over a specified period.
  • Moving average crossover: A trading signal that occurs when two moving averages with different periods cross each other.
  • Turtle trading: A trend-following strategy developed by Richard Dennis and William Eckhardt.
• Sentiment analysis is explored:
  • Data sources: Sources of sentiment data, such as news articles, social media, and financial reports.
  • Sentiment indicators: Metrics used to measure market sentiment.
  • Sentiment-based strategies: Trading strategies that use sentiment data to make decisions.

Risk Management

• Volatility is defined:
  • Historical volatility: A measure of past price fluctuations.
  • Implied volatility: A measure of the market's expectation of future price fluctuations, derived from option prices.
• Value at Risk (VaR) is discussed:
  • Historical simulation: A method of calculating VaR based on historical data.
  • Variance-covariance method: A method of calculating VaR based on the statistical properties of asset returns.
  • Monte Carlo simulation: A method of calculating VaR based on simulating possible future scenarios.
• Capital allocation is explored.

Backtesting

• Data preparation is discussed.
• Performance metrics are explained:
  • Profit and Loss (P&L): The difference between the revenue generated by a trading strategy and the costs incurred.
  • Return on Investment (ROI): A measure of the profitability of a trading strategy relative to the amount of capital invested.
  • Sharpe ratio: A measure of risk-adjusted return.
  • Maximum drawdown: The largest peak-to-trough decline during a specified period.
• Implementation of backtesting is discussed.
• Pitfalls in backtesting are identified:
  • Look-ahead bias: Using information that would not have been available at the time of the trading decision.
  • Overfitting: Developing a trading strategy that performs well on historical data but poorly on new data.
  • Transaction costs: Failing to account for the costs of trading when backtesting.