Opportunities In Online Trading Automation

What is Algorithm Trading?

Algorithmic trading is using computers programmed to follow a clear defined set of instructions for forex online trading in order to generate profits at lighting speed and frequency making it impossible for human traders. The rules are based on timing, price, quantity or any mathematical model. Apart from profit opportunities for the trader, algo-trading makes markets liquid and makes trading more systematic by ruling out emotional human impacts that prevent investors’ behavioural problems in holding losses for a longer time and selling profitable securities too early. It also tests trade ideas on historical data to eliminate poor trading ideas and retain the good ones. These are two major advantages of algo-trading.

Its Importance in the current financial market

Algorithm trading has in fact already infiltrated our daily dealings affecting the financial market. The below incident shows the impacts of algorithm-trading on the markets:  In 2016, slightly past 7 AM on October 7 in Hong Kong, before most traders had started work, the British pound suddenly tanked, losing 6% of its value within minutes. The move caused an avalanche of tradingin a normally quiet period (the handoff from New York to Asia.)

Algorithmic trading is believed to be largely at fault for the acceleration of the sell-off or the initiation of it. The machines have taken over much of the FX market from human traders at the banks, raising concerns about market stability during periods of stress.

In this case, algorithms may have scanned the news and come across French president François Hollande’s remarks that Britain would “pay the price” for Brexit. Technical algorithms started to work when the pound fell below the 31-year low.  The Bank for International Settlements is expected to confirm these suspicions in its initial report.

Algorithmic trading has grown dramatically in popularity over the past decade. In the US, about 70 percent of overall trading volume is generated through algorithmic trading. Moreover, JP Morgan’s Research has estimated that there would be an expected growth of 38% in 2018.

In the recent years, there have been a shift from the traditional form of algorithm trading based on a set of rules to Artificial Intelligence (A.I) & machine learning- based trading in institutional trading. JP Morgan developed a system, LOXM that learns from past-transactions (Analysing how to buy and sell fast at the best prices) and scan every Dow Jones story in real time, looking for textual clues that helps to indicate how investors should feel about a stock. It then sends such information to its algorithmic subscribers, allowing them to parse it further, using the resulting data for their own investing decisions. This has enabled the process of reading the news and drawing insight from it to be automated. The machines are not here just to crunch numbers anymore; they are now making the decisions.

Retail traders’ level algo- trading is widely used and a potentially profitable method. This can be seen in the following short strategies.

  1. Market Making

The bid-ask spread and trade volume can be modelled together to get the liquidity cost curve representing the fee paid by the liquidity taker.  If the liquidity taker only executes orders at the best bid and ask, the fee will be equal to the bid ask spread times the volume. However, when they go beyond, the fee is calculated to be a function of the volume.

It is hard to model trade volume because it relies on the execution strategy of the liquidity takers. The objective should be to find a model for trade volumes that is consistent with price dynamics. Market making models are based on inventory risk and adverse selection.

The former is based on preferred inventory position and prices based on the risk appetite.

While the latter is based on, adverse selection, which distinguishes between, informed and noise trades. Noise trades do not possess any view on the market unlike informed trades.In the short term, profits can be made by tapping upon the statistical edge. Minimizing the transaction cost is the objective in the long term. The long-term strategies and liquidity constraints can be modelled as noise around the short-term execution strategies.

  1. Statistical Arbitrage

Pairs trading is one of the several strategies collectively referred to as Statistical Arbitrage Strategies. In pairs trade strategy, stocks that exhibit historical co-movement in prices are paired using fundamental or market-based similarities. The strategy builds upon the notion that the relative prices in a market are in equilibrium, and that deviations from this equilibrium eventually will be corrected.

When one stock outperforms the other, the outperformer is sold short and the other stock is bought long with the expectation that the short-term diversion will end in convergence. This often hedges market risk from adverse market movements. However, the total market risk of a position depends on the amount of capital invested in each stock and the sensitivity of stocks to such risk.

  1. Momentum

Momentum Strategies seek to profit by taking advantage of market swings.It is counter-intuitive to other well-known strategies. Value investing is generally based on long-term reversion to mean whereas momentum investing is based on the gap in time before mean reversion occurs.

Momentum takes advantages of performance chasers who makes decisions based on emotions .There are usually two explanations given for any strategy that has been proven to work historically, either the strategy is compensated for the extra risk that it takes or there are behavioural factors which exists. Nonetheless, this is easier said than done as trends do not last forever and can exhibit swift reversals when they peak. Momentum trading carries a higher degree of volatility than most other strategies. By using proper risk management techniques, losses can be avoided. Momentum investing requires proper monitoring and appropriate diversification to safeguard against such severe crashes.

  1. Machine Learning based

“Bayesian networks” is a form of machine learning which forecastmarket trends. An AI which includes techniques such as evolutionary computation (which is inspired by genetics) and deep learning might run across hundreds or even thousands of machines. This creates a large and random collection of digital stock traders and test their performance on historical data. It then picks the best performers and uses their style/patterns to create a new of evolved traders, repeating multiple times until it can fully operate on its own.

Business Opportunities

Algorithmic trading needs extensive analytical and technological support. With the advent of algorithmic trading, the old basic trading platforms offered by Brokers and third-party software providers had little use for traders seeking to trade algorithmically.Thus, a completely new business opportunity of providing feature-rich advanced analytics and trading platforms emerged. Many brokers have started offered trading APIs to their clients along with feature-rich platforms. Third-party trading software providers have come up with analytics platforms which can be connected to select brokers for smooth execution of trades.InteractiveBrokers is an example of how traditional trading has merged with technology to give form to smart interactive dashboards.

Unlike discretionary trading, automated trading especially, high-frequency trading and low-latency trading requires order execution within a fraction of seconds. Seeking low latencies in trading demands high-performance computing networks to be quick and efficient. A good system means fast execution with no lag time. As such, the high demand for high-performance computing from HFT firms led to the emergence of specialized firms like Solarflare and Arista etc. which offer solutions like low-latency trading servers, switches, network interface cards and Ethernet solutions.

Additionally, data analysis when combined with python programming skills can create a revenue-generating asset. Subscription based services that publish trading signals and paid apps do the same. Sites that sell fully functional trading bots can be integrated with existing trading platforms.

As can be seen, a number of businesses have emerged around algorithmic trading. This has further led to its adoption among the vast trader community.

 

 


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