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 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.

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.