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# Algorithmic Trading ## What is Algorithmic Trading? - Trading based on a set of rules. - Implementable on a computer. Uses - Execution - Trend Following - Arbitrage - Market Making ## Execution - Problem: Execute a large order without moving the market. - Idea: Slice the order into many small orders, and execute each one slowly. - Example: TWAP (Time Weighted Average Price) ### TWAP $$ V_{TWAP} = \frac{\sum_{i=1}^{n}V_i}{n} $$ $V_i$: Volume traded in period i n: Number of periods ## Trend Following - Problem: Detect and profit from trends in the market. - Idea: Use technical indicators to identify trends, and then trade in the direction of the trend. - Example: Moving Average Crossover ### Moving Average Crossover - Compute a short-term and a long-term moving average of the price. - Buy when the short-term moving average crosses above the long-term moving average. - Sell when the short-term moving average crosses below the long-term moving average. ## Arbitrage - Problem: Profit from price differences in different markets. - Idea: Buy in the cheaper market and sell in the more expensive market. - Example: Triangular Arbitrage ### Triangular Arbitrage - Find three assets where the cross-rate between two of them differs from the price of the third asset. - Example: - \$1 → £0.8 - £1 → ¥130 - \$1 → ¥120 - Trade: - Sell \$10,000 → Buy £8,000 - Sell £8,000 → Buy ¥1,040,000 - Sell ¥1,040,000 → Buy \$8,666.67 - Profit: -\$10,000 + \$8,666.67 = -\$1,333.33 *This is not profitable, there is something wrong with the example.* ## Market Making - Problem: Profit from the spread between the bid and ask prices. - Idea: Simultaneously post bid and ask orders for an asset. - Example: Simple Market Making ### Simple Market Making - Post bid and ask orders at a certain distance from the current price. - Collect the spread between the bid and ask prices as profit. ## Risks - Model Risk - Execution Risk - Data Risk - Overfitting - Regulatory Risk

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