Foreign exchange autotrading

Forex autotrading is a trading strategy where buy and sell orders are placed automatically based on an underlying system or program on the foreign exchange market. The buy or sell orders are sent out to be executed in the market when a certain set of criteria is met.

Autotrading systems, or programs to form buy and sell signals, are used typically by active traders who enter and exit positions more frequently than the average investor. The autotrading criteria differ greatly, however they are mostly based on technical analysis.

Contents
1 History
2 Types
2.1 Advantages
2.2 Disadvantages



History

Forex autotrading originates at the emergence of online retail trading, since about 1999 when internet-based companies created retail forex platforms that provide a quick way for individuals to buy and sell on the forex spot market. Nevertheless, larger retail traders could autotrade Forex contracts at the Chicago Mercantile Exchange as early as in the 1970s.

Types

There are two major types of Forex autotrading:
Fully automated or robotic Forex trading: This is very similar to algorithmic trading or black-box trading, where a computer algorithm decides on aspects of the order such as the timing, price or quantity and initiates the order automatically. Users can only interfere by tweaking the technical parameters of the program; all other control is handed over to the program.
Signal-based Forex autotrading: This autotrading mode is based on manually executing orders generated by a trading system. For example a typical approach is to use a service where traders all over the world making their strategies available to anyone interested in the form of signals. Traders may choose to manually execute any of these signals in their own broker accounts.

Advantages

An automated trading environment can generate more trades per market than a human trader can handle and can replicate its actions across multiple markets and timeframes. An automated system is also unaffected by the psychological swings that human traders are prey to. This is particularly relevant when trading with a mechanical model, which is typically developed on the assumption that all the trade entries flagged will actually be taken in real time trading.[2]

Signal Provider based models offer traders the opportunity to follow previously successful signal providers or strategies with the hope that the advice they offer will continue to be accurate and lead to profitable future trades. Traders do not need to have expert knowledge or ability to define their own strategies and instead can select a system based on its performance to date, making Forex trading accessible to a large number of people.

Disadvantages

As a decentralized and relatively unregulated market, it is extremely attractive to a number of Forex scams. Forex autotrading, as it brings Forex trading to the masses makes even more people susceptible to frauds. Bodies such as the National Futures Association and the U.S. Securities and Exchange Commission have issued warnings and rules to avoid fraudulent Forex trading behavior.

Low-latency trading

Low-latency trading

HFT is often confused with low-latency trading that uses computers that execute trades within milliseconds, or "with extremely low latency" in the jargon of the trade. Low-latency trading is highly dependent on ultra-low latency networks. They profit by providing information, such as competing bids and offers, to their algorithms microseconds faster than their competitors.[5] The revolutionary advance in speed has led to the need for firms to have a real-time, colocated trading platform in order to benefit from implementing high-frequency strategies.[5] Strategies are constantly altered to reflect the subtle changes in the market as well as to combat the threat of the strategy being reverse engineered by competitors. There is also a very strong pressure to continuously add features or improvements to a particular algorithm, such as client specific modifications and various performance enhancing changes (regarding benchmark trading performance, cost reduction for the trading firm or a range of other implementations). This is due to the evolutionary nature of algorithmic trading strategies - they must be able to adapt and trade intelligently, regardless of market conditions, which involves being flexible enough to withstand a vast array of market scenarios. As a result, a significant proportion of net revenue from firms is spent on the R&D of these autonomous trading systems.

Strategy Implementation

Most of the algorithmic strategies are implemented using modern programming languages, although some still implement strategies designed in spreadsheets. Basic models can rely on as little as a linear regression, while more complex game-theoretic and pattern recognition or predictive models can also be used to initiate trading. Neural networks and genetic programming have been used to create these models.

Issues and developments

Algorithmic trading has been shown to substantially improve market liquidity among other benefits. However, improvements in productivity brought by algorithmic trading have been opposed by human brokers and traders facing stiff competition from computers.

Concerns

“The downside with these systems is their black box-ness,” Mr. Williams said. “Traders have intuitive senses of how the world works. But with these systems you pour in a bunch of numbers, and something comes out the other end, and it’s not always intuitive or clear why the black box latched onto certain data or relationships.

“The Financial Services Authority has been keeping a watchful eye on the development of black box trading. In its annual report the regulator remarked on the great benefits of efficiency that new technology is bringing to the market. But it also pointed out that ‘greater reliance on sophisticated technology and modelling brings with it a greater risk that systems failure can result in business interruption’.”

UK Treasury minister Lord Myners has warned that companies could become the "playthings" of speculators because of automatic high-frequency trading. Lord Myners said the process risked destroying the relationship between an investor and a company.

Other issues include the technical problem of latency or the delay in getting quotes to traders, security and the possibility of a complete system breakdown leading to a market crash.

"Goldman spends tens of millions of dollars on this stuff. They have more people working in their technology area than people on the trading desk...The nature of the markets has changed dramatically."

Algorithmic and HFT were implicated in the May 6, 2010 Flash Crash,when the Dow Jones Industrial Average plunged about 600 points only to recover those losses within minutes. At the time, it was the second largest point swing, 1,010.14 points, and the biggest one-day point decline, 998.5 points, on an intraday basis in Dow Jones Industrial Average history.

Determinants of exchange rates

The following theories explain the fluctuations in exchange rates in a floating exchange rate regime (In a fixed exchange rate regime, rates are decided by its government):
International parity conditions: Relative Purchasing Power Parity, interest rate parity, Domestic Fisher effect, International Fisher effect. Though to some extent the above theories provide logical explanation for the fluctuations in exchange rates, yet these theories falter as they are based on challengeable assumptions [e.g., free flow of goods, services and capital] which seldom hold true in the real world.
Balance of payments model (see exchange rate): This model, however, focuses largely on tradable goods and services, ignoring the increasing role of global capital flows. It failed to provide any explanation for continuous appreciation of dollar during 1980s and most part of 1990s in face of soaring US current account deficit.
Asset market model (see exchange rate): views currencies as an important asset class for constructing investment portfolios. Assets prices are influenced mostly by people's willingness to hold the existing quantities of assets, which in turn depends on their expectations on the future worth of these assets. The asset market model of exchange rate determination states that “the exchange rate between two currencies represents the price that just balances the relative supplies of, and demand for, assets denominated in those currencies.”

None of the models developed so far succeed to explain exchange rates and volatility in the longer time frames. For shorter time frames (less than a few days) algorithms can be devised to predict prices. It is understood from the above models that many macroeconomic factors affect the exchange rates and in the end currency prices are a result of dual forces of demand and supply. The world's currency markets can be viewed as a huge melting pot: in a large and ever-changing mix of current events, supply and demand factors are constantly shifting, and the price of one currency in relation to another shifts accordingly. No other market encompasses (and distills) as much of what is going on in the world at any given time as foreign exchange.

Supply and demand for any given currency, and thus its value, are not influenced by any single element, but rather by several. These elements generally fall into three categories: economic factors, political conditions and market psychology.