Market volatility has driven greater demand for passive algorithmic strategies in fixed income, Armin Tobaccowala, head of e-FICC sales for the Americas at JP Morgan, tells Trader TV, and buy-side trading desks are increasingly using pre-, post-trade, and real-time data to optimise execution. He says that data is now central, not only in selecting and evaluating algos, but also in monitoring their real-time performance and dynamically adjusting execution based on market conditions. This behavioural shift, he adds, reflects growing trader confidence in adaptive algo tools to achieve spread savings and minimise risk transfer costs, signalling a broader move towards smarter, data-driven execution.
During recent volatility, algo usage rose by 60% according to JP Morgan, with traders favouring passive adaptive algos over aggressive limit-based strategies. Looking ahead, Tobaccowala says the next phase of innovation involves integrating real-time data into execution management systems (EMSs) and order execution management systems (OMSs) for smarter automated routing.
Interview
Jo Gallagher How are buy-side desks using pre-, post-trade and real-time data to inform their trading decisions?
Armin Tobaccowala Let’s talk about post-trade analytics first. Traders are taking the analysis they’ve done on trades that they’ve done, whether it be during volatile times, non-volatile times, and they’re looking at the data to determine whether they should use algos, which algos to use, or should they just do risk transfer? Then we get to pre-trades, right? When they actually have risk and risk to execute. So they will put the details of the trade into the system, and then they can see what’s going on in the markets, whether it’s volatility, spread savings, how much is it going to cost versus risk transfer price? And then if they decide to execute an algo, they can make a decision on which algo to use and then when they kick off the algo, they can use real-time analytics. How is the algo performing? How is it performing against the volume weighted average price? What’s going on in terms of the fills versus the primary market? How is it performing against risk transfer, arrival mid? They can look at all this data and then they can make decisions on what they want to do in real time. They can either be more passive, more aggressive. They can actually do some risk transfer and get some of the risk out and hurry up the algo. Previously, they were just using third-party platforms, which is static data based on historical performance. What we’ve done is we’ve built this GUI execute analytics that has allowed traders to look and based on what’s actually going on in the markets, make decisions in real time. So this allows them to do stuff a little more efficiently.
Jo Gallagher We also had a huge amount of volatility in April. What were some of the trends that you saw in how traders selected and used algos?
Armin Tobaccowala What was interesting that we saw during the bouts of volatility is there was actually an increased usage in our pre-trade analytics tool. We saw a 53% increase, which is surprising because you would think that they just want to do a risk transfer and get out of the risk and move on. But what we saw is that they were actually using the tool to make trading decisions, not only using the tool, but using algos. So we saw a 60% increase in algo usage compared to previous volatile times. Based on that, we saw six of our largest 10 days in algorithmic trading within the JP Morgan franchise. Not only that, they weren’t using aggressive algos, they were using passive algos. Adaptive algos VWAP-type algos versus the very aggressive limit-based algos. So they were trusting the algos to be able to create spread savings for the trader. So we saw a massive change in behavior in that traders were trusting the algos versus having just to do risk transfer or using very aggressive algos that we saw in the past during volatile times.
Jo Gallagher So what’s next? What will be in the next phase of innovation for pre, post, and real-time data analytics?
Armin Tobaccowala We’d like to take this data and integrate it into traders, EMS and OMS systems. So therefore, they could create smart order routing rules based on real-time data. For example, a trade that typically would be, you know, just send it through automated execution, no human touch during volatile times that could kick up to the trader. Based on some of the volatility analysis, some of the real-time market data feeding in there. In order for them to make decisions based on what’s going on in the markets, not on historical static data. So that’s the ultimate goal, and that’s what we wanna see. The problem is technology’s not there yet in terms of the EMS and OMS systems. So to be able to plug an API (application programming interfaces) in directly and these EMSs and OMS systems be able to use it to make the trading decisions, that is something that we’re pushing from our perspective in order to help the trading process and make it more efficient.
Jo Gallagher I’d like to thank Armin for his insight, and of course, you for watching. To catch our other shows, including Trader TV This Week, every Monday at 6.45 am UK time, go to TraderTV.net.
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