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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?

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    $\begingroup$ I've just called the first one "broker algos" and the second one either "strategies" or "models". $\endgroup$
    – chrisaycock
    Commented Mar 5, 2013 at 22:19
  • $\begingroup$ I'm assuming that you're asking about a non-retail transaction. Unless something special is taking place, there's no need for a human to be involved in the trade. The trade is already "mostly noise", so allowing a human to "decide" something raises the noise level, making things worse. $\endgroup$
    – bill_080
    Commented Mar 6, 2013 at 0:59
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    $\begingroup$ the whole devaluation of the capacity of human intuition is quite disappointing.. $\endgroup$
    – cdcaveman
    Commented Mar 6, 2013 at 5:28
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    $\begingroup$ I don't think it's a devaluation of human intuition. It's simply noise. "Intuition" of noise is just more noise (no different than flipping a coin). Are you willing to pay someone to sit around and flip a coin? $\endgroup$
    – bill_080
    Commented Mar 6, 2013 at 14:15
  • $\begingroup$ A human can filter an algorithm's trade suggestions based on information the algorithm might not know, such as news event. For example, an algorithm might select a Cyprus bank stock to buy based on price, but a human knows better. $\endgroup$
    – Chloe
    Commented Mar 25, 2013 at 19:33

3 Answers 3

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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.

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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.

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  • $\begingroup$ So you call them "trading algorithms" and "quantitative strategies algorithms", respectively? Follow-up question: what is a "trading engine"? $\endgroup$
    – lodhb
    Commented Mar 7, 2013 at 15:19
  • $\begingroup$ Yes, well maybe "execution algorithms" and "quantitative strategies". A quantitative strategy per se includes an algorithm of thinking or a computer algorithm that send you signals. A trading engine system allows you to build your own trading strategies, in the same code you can build the signals using spreads, mid-prices, last prices, high prices, supply, demand, volatility, reversion, momentum, etc. And at the same code you can execute optimally your trades to try to minimize transaction costs or to get the "best" price (buy the lowest), (sell the highest). $\endgroup$
    – AlgoQuant
    Commented Mar 7, 2013 at 16:09
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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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