European intraday power and gas markets are experiencing impactful traction in automated trading. Almost all commodities have seen significant growth in automated trading, according to a CFTC report released last year. However, using machine learning and artificial intelligence to really select when and what to trade goes beyond simple trading automation. The popularity of the software market's solutions is a sign that this is also on the rise, which can also be a threat to the physical commodity broker.
The hiring costs for the new breed of trader/programmers appear to be lower, and the playing field has been somewhat levelled. Perhaps knowing a lot of other traders in the market is less important than having proficiency in Python and other languages? It goes without saying that there must be specialists on the underlying commodities, instruments, and markets, but in the end, they may function more as trader analysts who collaborate with programmers and traders to create clever robots and keep up with the competition.
Power + gas, several agricultural commodities (wheat, livestock, corn, and soybeans), and various metals are the key commodity futures that analysts see being traded algorithmically. There is an increase in automation in these markets with trading techniques that are more short-term and basic.
In light of this, analysts predict a decline in the "conventional trader" inside various asset classes as a number of machine learning, AI, and bot technologies automate some or all of the trading process. Such projects are led by a team that combines competent IT with Quant Analysts who create trading methods.
Within these ventures, the "conventional trader" position can change to become a "product owner." Naturally, some of the "best of the best" traders who hold strong positions in their particular markets may continue to be successful, but they will need to adopt technology and change over time to stay ahead of the curve. Many of the better "bulge bracket" institutions have succeeded in switching from traders to programmers.
As a result, the job of the trader will alter, headcount will decline over time, and the combination of traders with assisted or automated trading will be beneficial for profitability while also enhancing the trader's role and reducing part of the trustworthiness of the trader's gut instinct.
Commodity markets have quickly shifted to automated trading, with many exchanges reporting sharp growth in this kind of trade. For example, markets for coffee, sugar, and cocoa, to mention a few commodities affected, have been criticised for being "disconnected" from fundamentals as a result of automated trading, leading to volatility and unforeseen price changes. Many well-known hedge fund managers who are leaving the industry have even gone so far as to partially attribute their departure to automated trading.???????
The effect on systems may also be interesting in light of this. The monolithic CTRM solution is likely to be replaced in the future by a more flexible and dynamic set of applications and APIs known as the trading ecosystem due to automation and faster automated trading.
Some of these applications might be created and distributed by businesses, while others might be created in-house. Looking at the marketplaces of today, we can already see manufacturers like Generation10 going in the direction of an application and API ecosystem model. Smaller vendors on more contemporary platforms that focus on particular functionalities do appear to be performing better, and platform vendors appear to be well-positioned to profit from the trend toward rapid development, deployment, and modification.
