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Forward testing the algorithm is the next stage and involves running the algorithm through an out of sample data set to ensure the algorithm performs within backtested expectations. Live testing is the final stage of development and requires the developer to compare actual live trades with both the backtested and forward tested models. In a non-ergodic system, the success of a strategy depends on its ability to anticipate market evolutions. Metrics compared include percent profitable, profit factor, maximum drawdown and average gain per trade. In fact, empirical evidence shows that returns are neither independent nor normally distributed, making forecasting more complex. In modern algorithmic trading, financial markets are considered non-ergodic, meaning they do not follow stationary and predictable dynamics.

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Unlike in the case of classic arbitrage, in case of pairs trading, the law of one price cannot guarantee convergence of prices. In practice, execution risk, persistent and large divergences, as well as a decline in volatility can make this strategy unprofitable for long periods of time (e.g. 2004-2007). It belongs to wider categories of statistical arbitrage, convergence trading, and relative value strategies. This is especially true when the strategy is applied to individual stocks – these imperfect substitutes can in fact diverge indefinitely. In finance, delta-neutral describes a portfolio of related financial securities, in which the portfolio value remains unchanged due to small changes in the value of the underlying security. In theory, the long-short nature of the strategy should make it work regardless of the stock market direction.

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Orders built using FIXatdl can then be transmitted from traders’ systems via the FIX Protocol. However, improvements in productivity brought by algorithmic trading have been opposed by human brokers and traders facing stiff competition from computers. Technological advances in finance, particularly those relating to algorithmic trading, has increased financial speed, connectivity, reach, and complexity while simultaneously reducing its humanity. More complex methods such as Markov chain Monte Carlo have been used to create these models.

Many fall into the category of high-frequency trading (HFT), which is characterized by high turnover and high order-to-trade ratios. As a result, in February 2013, the Commodity Futures Trading Commission (CFTC) formed a special working group that included academics and industry experts to advise the CFTC on how best to define HFT. HFT strategies utilize computers that make elaborate decisions to initiate orders based on information that is received electronically, before human traders are capable of processing the information they observe.