Distinguishing Execution Algorithms from Trading Strategies
Summary
The document separates algorithms that decide what and when to trade from those that carry out orders. Execution algorithms break large instructions into smaller trades to manage market impact and may target benchmarks such as volume-weighted average price or time-weighted average price. Strategy algorithms use market data and quantitative rules to generate signals, rebalance portfolios, or identify opportunities; those orders can then be passed to an execution system.
It also surveys other categories, including electronic market making, statistical arbitrage, and liquidity detection, alongside stealth or gaming approaches that seek to infer or exploit other participants’ orders. The discussion is conceptual and cites no performance study or evidence that one category is profitable. The categories can be combined in a trading workflow, and execution methods may use statistical and market microstructure analysis even when they do not choose the investment itself. A short-horizon profitability test is offered as a rough distinction, but it is not a definitive classification method.
Key ideas
- Execution algorithms determine how to fill an existing order while controlling market impact or tracking a benchmark.
- Strategy algorithms generate trading decisions from market data, quantitative rules, or portfolio constraints.
- Strategy signals can be routed to execution algorithms, allowing both algorithm types to operate in sequence.
- Market making and statistical arbitrage are strategy categories, while liquidity detection focuses on inferring resting orders.
- Profitability over a short horizon is only a rough heuristic for distinguishing execution from strategy algorithms.
Tags
Full text
# How to distinguish between different types of algorithmic trading # How to distinguish between different types of algorithmic trading Algorithmic trading involves the use of algorithms to optimally execute trading instructions. Then there are algorithms which initiate trades, based on various quantitative strategies (e.g. pairs trading). I have the impression that "algorithmic trading" (or automated trading) is often used for both types of algorithms, although they're very different. They can be used exclusively (a human executes the trading instruction of an algorithm, or a human manually inputs a trade which the trading algorithm executes) or together sequentially (the latter algorithm submits trades to the former, which executes them). So how do we distinguish these two types of algorithms? ## Answer by lodhb (score 11, accepted) https://quant.stackexchange.com/a/7514 I found this solid overview of different trading algorithms by Deutsche Bank Research: - Trade execution algorithms Designed to minimise the price impact of executing trades of large volumes by ‘shredding’ orders into smaller parcels and slowly releasing these into the market. - Strategy implementation algorithms Designed to read real-time market data and formulate trading signals to be executed by trade execution algorithms. This may involve automatically rebalancing portfolios when certain pre-specified tolerance levels are exceeded, searching for arbitrage opportunities, automatic quoting and hedging in a market maker-type role, and producing trading signals from technical analysis. - Stealth/gaming algorithms Designed to take advantage of the price movement caused when large trades are filled and also to detect and outperform other algorithmic strategies. - Electronic market making Liquidity-providing strategies that mimic the traditional role market makers once played. These strategies involve making a two-sided market aiming at profiting by earning the bid-ask spread. This has evolved into what is known as Passive Rebate Arbitrage. - Statistical arbitrage Traders look to correlate prices between securities in some way and trade off of the imbalances in those correlations. - Liquidity detection Traders look to decipher whether there are large orders existing in a matching engine by sending out small orders (“pinging”) to look for where large orders might be resting. When a small order is filled quickly, there is likely to be a large order behind it. ## Answer by AlgoQuant (score 2) https://quant.stackexchange.com/a/7451 The broker algorithms or the trading algorithms are designed to the optimal execution of large amounts of stocks with different benchmarks (e.g. VWAP, PoV, Implementation Shortfall or Slippage, Price Inline, TWAP, DWAP, etc.). These algorithms sometimes uses statistical methods and market microstructure analysis (to analyse spreads, volume, seasonality, supply/demand). The quantitative strategies are also algorithms, but these algos uses historical data and intradaily data to take decisions of what to invest? and when to invest? These algos send us signals of buy or sell, and we can execute them with our trading algorithms. In my experience I think that the algorithmic trading help us to lose less money when we execute, and the quantitaive strategies algorithms help use to take the "correct" decision of what do we buy or sell and when to execute the order. ## Answer by LazyCat (score 0) https://quant.stackexchange.com/a/7450 The two types you mention are not necessarily mutually exclusive, but you can take a relatively short horizon, and check if the algo consistently makes money. If it does it's the second type, if not, it is more likely to be of the first kind.
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