23 Facts About Algorithmic trading


Algorithmic trading is a method of executing orders using automated pre-programmed trading instructions accounting for variables such as time, price, and volume.

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Examples of strategies used in algorithmic trading include systematic trading, market making, inter-market spreading, arbitrage, or pure speculation, such as trend following.

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Many fall into the category of high-frequency Algorithmic trading, which is characterized by high turnover and high order-to-trade ratios.

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Systematic trading includes both high frequency trading and slower types of investment such as systematic trend following.

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Pairs Algorithmic trading or pair Algorithmic trading is a long-short, ideally market-neutral strategy enabling traders to profit from transient discrepancies in relative value of close substitutes.

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In dark pools, Algorithmic trading takes place anonymously, with most orders hidden or "iceberged".

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Many HFT firms are market makers and provide liquidity to the market, which has lowered volatility and helped narrow bid–offer spreads making Algorithmic trading and investing cheaper for other market participants.

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Wide range of statistical arbitrage strategies have been developed whereby Algorithmic trading decisions are made on the basis of deviations from statistically significant relationships.

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Low latency trading refers to the algorithmic trading systems and network routes used by financial institutions connecting to stock exchanges and electronic communication networks to rapidly execute financial transactions.

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Algorithmic trading has been shown to substantially improve market liquidity among other benefits.

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However, improvements in productivity brought by algorithmic trading have been opposed by human brokers and traders facing stiff competition from computers.

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Algorithmic and high-frequency trading were shown to have contributed to volatility during the May 6,2010 Flash Crash, when the Dow Jones Industrial Average plunged about 600 points only to recover those losses within minutes.

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Algorithmic trading's firm provides both a low latency news feed and news analytics for traders.

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In July 2007, Citigroup, which had already developed its own Algorithmic trading algorithms, paid $680 million for Automated Trading Desk, a 19-year-old firm that trades about 200 million shares a day.

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The data is analyzed at the application side, where Algorithmic trading strategies are fed from the user and can be viewed on the GUI.

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The complex event processing engine, which is the heart of decision making in algo-based Algorithmic trading systems, is used for order routing and risk management.

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Automated Algorithmic trading must be operated under automated controls, since manual interventions are too slow or late for real-time Algorithmic trading in the scale of micro- or milli-seconds.

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One of the more ironic findings of academic research on algorithmic trading might be that individual trader introduce algorithms to make communication more simple and predictable, while markets end up more complex and more uncertain.

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Since Algorithmic trading algorithms follow local rules that either respond to programmed instructions or learned patterns, on the micro-level, their automated and reactive behavior makes certain parts of the communication dynamic more predictable.

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Economies of scale in electronic Algorithmic trading have contributed to lowering commissions and trade processing fees, and contributed to international mergers and consolidation of financial exchanges.

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Algorithmic trading has caused a shift in the types of employees working in the financial industry.

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Algorithmic trading has encouraged an increased focus on data and had decreased emphasis on sell-side research.

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Algorithmic trading trades require communicating considerably more parameters than traditional market and limit orders.

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