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The Following is an Excerpt from this Book

Algorithmic trading: An intensive analysis

Algorithmic trading is a process of executing transactions using automated pre-programmed trading directives that account for variables including time, volume, and price. This form of trading has been introduced to leverage the benefits of speed and data processing that computers have over individual traders. Common algos include Pegged, percentage of volume, TWAP, VWAP, target close, implementation shortfall, and so on. Algorithmic trading was gaining popularity for both retail and institutional traders in the curtain-raiser of the twenty-first century. It is commonly applied by pension funds, investment banks, hedge funds and mutual funds that may need to distribute a larger order execution or trading too quickly to respond to human traders. A 2016 report found that more than 85% of FOREX trading was conducted by trading algorithms rather than manual efforts. Almost always, the term algorithmic trading is used synonymously with automated trading which includes trading strategies that depend heavily on complicated mathematical formulas and high-speed computer programming, such as quantitative, or and Quant black-box trading.

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These structures perform techniques such as market creation, inter-market spreading, arbitrage, or mere speculation, such as chasing a trend. A majority of individuals fall into the high-frequency trading (HFT) subset, which has high order-to-trade ratios and high turnover as well. HFT techniques use computers that make complex decisions to execute orders based on the electronically obtained information before human traders can process the data they perceive. Consequently, the Commodity Futures Trading Commission (CFTC) created a specialized working group in February 2012, which involved industry experts and academics for counseling the CFTC to utilize the HFT in a useful manner. Algorithmic trading and HFT have contributed to a massive transformation in the microstructure of the marketing infrastructure, notably in the way liquidity is offered.

Evolution

Computerization of the order flow in money markets goes back to the early 1970s when the designated order turnaround program (DOT) was implemented by the New York Stock Exchange. In 1984 SuperDOT was released as an expanded version of DOT. Both programs allowed the electronic routing of orders to the correct trading post. The opening automated reporting system (OARS) also assisted the professionals in assessing the opening price for market-clearing (SOR; Smart Order Routing). With the emergence of fully digital marketing, it spurred the advent of program trading, which the New York Stock Exchange describes as an offer to buy or sell 16 or more securities worth more than a total of US$ 1.5 million. In action, program trades were pre-harnessed to enter or exit trades automatically based on different factors. In the 1980s, program trading was commonly meant for trading between the S&P 500 stock and futures markets in practice regarded as index arbitrage.

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At approximately the same time, portfolio insurance was formulated to generate a synthetic alternative for the stock portfolio by interactively

Trading the stock index futures as per a computer module based on the Black – Scholes option pricing model. Both approaches, sometimes simply preloaded together as program trading, have been criticized by many surveys (e.g., Brady’s report) for alleviating or even heralding a stock market crash in 1987. However, the influence of computer-driven stock market trading debacles are clouded and widely debated in the academic world.

Growth and fine-tuning

The financial scenery was shifted again with the advent of electronic communication networks (ECNs) in the 1990s, which made it possible to trade stocks and currencies outside conventional exchanges. This enhanced market liquidity prompted to institutional traders splitting orders in tandem with computer algorithms so that orders could be issued at a decent average price. These average price metrics are calculated and measured by computers by enforcing the time-weighted average price or, more precisely, the volume-weighted average price. The trade activities that have been going on for centuries had died its natural death. Today, people experience an electronic market. It is the present and the future too. Other algorithmic trading strategies have been launched, as even more electronic markets have opened up. These methods are more easily replicated by computers, as machines can respond more readily to transitory mispricing and concurrently examine prices from several market variants.

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