- Forex algorithmic trading: Understanding the basics
- The leader in alpha generation technology for financial institutions
- Securities.io
- Algorithmic Trading Strategies Explained
- What is Algorithmic Trading in Forex?
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Forex algorithmic trading: Understanding the basics
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Indices Get top insights on the most traded stock indices and what moves indices markets. Cryptocurrencies Find out more about top cryptocurrencies to trade and how to get started. Fed Harker Speech. Balance of Trade MAR. Fed Kaplan Speech. Company Authors Contact. Long Short. Today, technological advancements have transformed the forex market. Trades can be made quickly over your computer, allowing retail traders to enter the market, while real-time streaming prices have led to greater transparency , and the distinction between dealers and their most sophisticated customers has been minimized.
Another significant change is the introduction of algorithmic trading , which may have lead to improvements to the functioning of forex trading, but also poses risks. In this article, we'll identify some advantages algorithmic trading has brought to currency trading by looking at the basics of the forex market and algorithmic trading while also pointing out some of its inherent risks.
In forex markets, currency pairs are traded in varying volumes according to quoted prices. A base currency is given a price in terms of a quote currency. Forex is considered to be world's largest and most liquid financial market, trading 24 hours a day, five days a week. The bulk of this trading is conducted in U. Activity in the forex market affects real exchange rates and can therefore profoundly influence the output, employment, inflation and capital flows of any particular nation.
For this reason, policymakers, the public and the media all have a vested interest in the forex market. An algorithm is essentially a set of specific rules designed to complete a defined task.
The leader in alpha generation technology for financial institutions
In financial market trading, computers carry out user-defined algorithms characterized by a set of rules such as timing, price or quantity that determine trades. There exist four basic types of algorithmic trading within financial markets:. One of the subcategories of algorithmic trading is high frequency trading, which is characterized by the extremely high rate and speed of trade order executions.
High-frequency trading can give significant advantages to traders, including the ability to make trades within milliseconds of incremental price changes , but also carry certain risks when trading in a volatile forex market. Much of the growth in algorithmic trading in forex markets over the past years has been due to algorithms automating certain processes and reducing the hours needed to conduct foreign exchange transactions.
The efficiency created by automation leads to lower costs in carrying out these processes , such as the execution of trade orders. Automating the trading process with an algorithm that trades based on predetermined criteria, such as executing orders over a specified period of time or at a specific price, is significantly more efficient than manual execution. Banks have also taken advantage of algorithms that are programmed to update prices of currency pairs on electronic trading platforms. These algorithms increase the speed at which banks can quote market prices while simultaneously reducing the number of manual working hours it takes to quote prices.
Some banks program algorithms to reduce their risk exposure. This allows the bank to maintain a pre-specified level of risk exposure for holding that currency. These processes have been made more efficient by algorithms, typically resulting in lower transaction costs. Yet, these are not the only factors that have been driving the growth in forex algorithmic trading. Algorithms have increasingly been used for speculative trading, as the combination of high frequency and the ability to quickly interpret data and execute orders has allowed traders to exploit arbitrage opportunities arising from small price deviations between currency pairs.
Within the forex market, the primary methods of hedging trades are through spot contracts and currency options. Spot contracts are the purchase or sale of a foreign currency with immediate delivery.
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The forex spot market has grown significantly from the early s due to the influx of algorithmic platforms. In particular, the rapid proliferation of information, as reflected in market prices, allows arbitrage opportunities to arise. Triangular arbitrage , as it is known in the forex market, is the process of converting one currency back into itself through multiple different currencies. Algorithmic and high frequency traders can only identify these opportunities by way of automated programs. As a derivative , forex options operate in a similar fashion as an option on other types of securities.
The foreign currency options give the purchaser the right to buy or sell the currency pair at a particular exchange rate at some point in the future. However as the trading frequency of the strategy increases, the technological aspects become much more relevant. All quantitative trading processes begin with an initial period of research.
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You will need to factor in your own capital requirements if running the strategy as a "retail" trader and how any transaction costs will affect the strategy. Contrary to popular belief it is actually quite straightforward to find profitable strategies through various public sources. Academics regularly publish theoretical trading results albeit mostly gross of transaction costs. Quantitative finance blogs will discuss strategies in detail. Trade journals will outline some of the strategies employed by funds. You might question why individuals and firms are keen to discuss their profitable strategies, especially when they know that others "crowding the trade" may stop the strategy from working in the long term.
The reason lies in the fact that they will not often discuss the exact parameters and tuning methods that they have carried out. These optimisations are the key to turning a relatively mediocre strategy into a highly profitable one.
In fact, one of the best ways to create your own unique strategies is to find similar methods and then carry out your own optimisation procedure. A mean-reverting strategy is one that attempts to exploit the fact that a long-term mean on a "price series" such as the spread between two correlated assets exists and that short term deviations from this mean will eventually revert. A momentum strategy attempts to exploit both investor psychology and big fund structure by "hitching a ride" on a market trend, which can gather momentum in one direction, and follow the trend until it reverses.
Another hugely important aspect of quantitative trading is the frequency of the trading strategy. Low frequency trading LFT generally refers to any strategy which holds assets longer than a trading day. Correspondingly, high frequency trading HFT generally refers to a strategy which holds assets intraday. Ultra-high frequency trading UHFT refers to strategies that hold assets on the order of seconds and milliseconds. As a retail practitioner HFT and UHFT are certainly possible, but only with detailed knowledge of the trading "technology stack" and order book dynamics.
We won't discuss these aspects to any great extent in this introductory article.
Algorithmic Trading Strategies Explained
Once a strategy, or set of strategies, has been identified it now needs to be tested for profitability on historical data. That is the domain of backtesting. The goal of backtesting is to provide evidence that the strategy identified via the above process is profitable when applied to both historical and out-of-sample data. This sets the expectation of how the strategy will perform in the "real world". However, backtesting is NOT a guarantee of success, for various reasons.
What is Algorithmic Trading in Forex?
It is perhaps the most subtle area of quantitative trading since it entails numerous biases, which must be carefully considered and eliminated as much as possible. We will discuss the common types of bias including look-ahead bias , survivorship bias and optimisation bias also known as "data-snooping" bias.
Other areas of importance within backtesting include availability and cleanliness of historical data, factoring in realistic transaction costs and deciding upon a robust backtesting platform. We'll discuss transaction costs further in the Execution Systems section below. Once a strategy has been identified, it is necessary to obtain the historical data through which to carry out testing and, perhaps, refinement. There are a significant number of data vendors across all asset classes. Their costs generally scale with the quality, depth and timeliness of the data.