report/report.md

# Exec Sum

# Thanks and acknowledgements

# Introduction

# Subject

The first description of my internship project was given to me as:

> The project is about benchmarking a new service we're building related to
> exchange connectivity. It would involve writing a program to generate load on
> the new service, preparing a test environment and analyzing the performance
> results. Time permitting might also involve making performance improvements to
> the services.

To understand this subject, we must start with an explanation of what exchange
connectivity means at IMC: it is the layer in IMC's architecture that ensures
the connection between internal trading services and external exchanges' own
infrastructure and services. It is at this layer that exchange-specific
protocols are normalised into IMC's own protocol messages, and vice versa.

Here is the list of tasks that I am expected to have accomplished during this
internship:

* become familiar with the service,
* write a dummy load generator,
* benchmark the system under the load,
* analyze the measurements.

This kind of project is exactly the reason that I was interested in working in
finance and trading. It is a field that is focused on achieving the highest
performance possible, because being faster is directly tied with making more
trades and results in more profits.

Because I expressed this personal interest for working on high performance
systems and related subjects, I was given this internship project to work on.

# Context of the subject

The exchange connectivity layer must route orders as fast possible, to stay
competitive, reduce transaction costs, and lower latencies which could result in
lost opportunities, therefore less profits.

It must also take on other duties, due to it being closer to the exchange than
the rest of the infrastructure. For example, a trading strategy can register
conditional orders with this service: it must monitor the price of product A and
X, if product A's cost rise over X's, then it must start selling product B at
price Y.

A new exchange connectivity service, called the Execution Gateway, is being
built at IMC, the eventual goal being to migrate all trading strategies to using
this gateway to send orders to exchanges. This will allow it to be scaled more
appropriately. However, care must be taken to maintain the current performance
during the entirety of the migration in order to stay competitive, and the only
way to ensure this is to measure it.

With that context, let's review my expected tasks once more, and expand on each
of them:

* Become familiar with the service: before writing the code for the benchmark
  I must first understand what goes into the process of a trade at IMC, what is
  needed from the gateway and from the clients in order to run them and execute
  orders. There is a lot of code at IMC: having different teams working at the
  same time on different trading service results in a lot of churn. The
  global execution team was created to centralise the work on core services that
  must be provided to the rest of the IMC workforce. The global execution
  gateway is one such project, aiming to consolidate all trading strategies
  under one singular method to send orders to their exchanges.
* Write a dummy load generator: we want to send orders under different
  conditions in order to run multiple scenarios which can model varying cases
  of execution. Having more data for varying corner cases can make us more
  confident of the robustness and efficiency of the service. This is especially
  needed becaue of the various roles that the gateway must fulfill: not only
  must it act as a bridge for the communication between exchanges and traders,
  but also as an order executor. All those cases must be accounted for when
  writing the different scenarios.
* Benchmark the system under the load: once we can run those scenarios smoothly
  we can start taking multiple measurements. The main one that IMC is interested
  in is wall-to-wall latency (abbreviated W2W): the time it takes for a trade to
  go from a trading strategy to an exchange. The lower this time, the more
  occasions there are to make good trades.
  FIXME: probably more context in my notes
* Analyze the measurements: the global execution team has some initial
  expectations of the gateway's performance. A divergence on that part could
  mean that the measurements are flawed in some way, or that the gateway is not
  performing as expected. Further analysis can be done to look at the difference
  between mean execution time and the 99th percentile, and analyse the tail of
  the timing distribution: the smaller it is the better. Consistent timing is
  more important than a lower average, because we must be absolutely confident
  that a trade order is going to be executed smoothly, and introducing
  inconsistent latency can result in bad trades.

# Internship roadmap

# Engineering practices

# Illustrated analysis of acquired skills

# Added value

# Conclusion

# Annex

## About IMC

## Results & Comments