I got extensive experience on algo trading for cash equity, FX, so just wondering any algo strategy popular for vanilla bond trading?

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    $\begingroup$ High-frequency arbitrage still works on some exchanges, due to the illiquidity and latency of market makers, but don't look at the US. Aside from "technical" solutions, one of the standard "quantitative" approaches is three-steps: (1) plot the Z-spread/OAS curve and look for expensive vs. cheap cash (e.g. weird shapes and/or no monotonicity); (2) analyze the time series of both the expensive and the cheap cash to assess whether a historical equilibrium is now temporarily broken; (3) realize how hard is to go short and hedge with some proxy futures instead ;) $\endgroup$ – Lisa Ann Jun 6 '20 at 9:40
  • $\begingroup$ "vanilla bond trading" - govys or corporates? $\endgroup$ – user42108 Oct 28 '20 at 12:42

Most bond market makers have some sort of algo that does some or even most of the boring market-making for some or all of their bonds, which computers can do better than human traders. The two main motivations I can immediately think of are:

  • sometimes bond market makers are compelled (by a trading venue or by regulators) to publish a bid an ask for many bonds in which they're really not interested in. A computer program can publish bid and ask that 1) are not so unrealistic that they won't be accused of mocking the system 2) are, as much as possible, not the best. So the program finds the best bid and ask and publishes its own that are not as good, but also not clearly ridiculous, quickly changing them if the best quotes move. These obligatory quotes are very unlikely to be hit/lifted.

  • sometimes bond market makers use algo to publish indicative bid or ask for seldom-traded bonds, as a first approximation, and adjusts them for changing market conditions. If someone actually expresses interest, then a human trader verifies whether the computer's quotes actually make sense.

The models that generate these bid and ask can be quite interesting - looking, for example, at parsed news, at historical correlations of bonds (especially corporates) with all sorts of other market quotes, and at trade flows (supply and demand).


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