Commodity trading and risk management software house of the year: Ion Allegro

Commodities software firm Allegro Development had a sterling year in 2018, conducting business in 46 countries, executing 31 go-lives and registering a recurring revenue growth of 40%. The company also expanded into the metals and mining markets and enhanced its offerings to the agricultural industry. In April this year, it became part of Ion, the privately held enterprise software group, and rebranded to Ion Allegro.

In addition to impressive revenue growth, one of the biggest standouts of 2018 was its customer retention rates, which averaged 97% across the year.

“The greatest evidence of value that a company can provide is customer satisfaction,” says Frank Brienzi, the former chief executive of Allegro Development and now the CEO of Ion Commodities. “As well as a very high customer retention rate, the bulk of these customers invested further in additional Allegro products,” he says, adding that 91% of existing customers increased their licence sales.

Brienzi attributes a large part of the firm’s success over the past 12 months to its purchase in April 2018 of commodity analytics specialist Financial Engineering Associates.

“FEA provides the gold standard in advanced commodity analytics,” he says. “The integration of FEA and its team of data-analytic PhD scientists strengthened the analytical core of Allegro’s commodity management offering even further.”

The FEA acquisition brought Allegro 110 new customers and enabled the firm to grow its analytics offering in four areas: asset optimisation; option analytics; portfolio analytics; and treasury analytics.

“FEA brought to the table state-of-the-art, well-established quantitative modelling techniques,” Brienzi says. “Its models have been developed over 30 years and implemented in over 30 countries,” he says. The models come with a transparent methodology, PhD quant support and audit-ready documentation, he adds.

The greatest evidence of value that a company can provide is customer satisfaction. As well as a very high customer retention rate, the bulk of these customers invested further in additional Allegro products

Frank Brienzi, Ion Commodities

The first of these tracks – asset optimisation – allows customers to harness massive amounts of data with real options-based decision support, optimisation and valuation tools. The product suite, developed by Allegro and powered by FEA advanced analytics, allows customers to extract maximum value from assets through optimising power generation, transportation and transmission, renewables, liquefied natural gas shipping and storage, including batteries. It works across multiple commodities and complex assets, taking into account commodity price volatility and asset constraints.

The second track – option analytics – allows customers to handle complex optionality, manage risk and mitigate stochastic events, while maximising the asset value output for any commodity. This suite is designed to help customers manage physical and financial derivatives, exchange-traded and over-the-counter derivatives and structured products. It is also intended to help firms derive hedging strategies and decision support using Greeks, as well as helping to manage commodity price volatility.  

The third type of analytics is the portfolio analytics product suite. This has been set up to allow customers to accurately assess the risk of contracts, transactions and assets. Customers can use these analytics to manage value-at-risk including component VAR and earnings-at-risk, as well as to quantify liquidity risks and potential future exposure.

The fourth track is Treasury analytics. These enable the ‘bootstrapping’ of yield curves from traded instruments such as money market rates, Eurodollar futures contracts, and interest rate swaps. They also enable firms to identify their exposure to interest rates and foreign exchange, and to actively manage risks such as basis risk across different currency markets.

Applying these advanced analytics to complex assets and real-world situations can be extremely powerful, Brienzi says. In one case study, the back-testing of a storage model for a natural gas storage facility showed that over the past 10 years the storage model would have extracted 1.1 to 2.71 times the initial intrinsic value of the storage asset, he notes.