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Is there any actual value in doing quantitative finance via TF as opposed to just Pandas? I get that calculating a zillion option prices on a GPU is faster, but

by dvt 6y ago

Is there any actual value in doing quantitative finance via TF as opposed to just Pandas? I get that calculating a zillion option prices on a GPU is faster, but does anyone actually do that? Not to mention that dataframes (both conceptually, and in practice) seem much more mature and easier to grok than tensors.

cliftonk 6y ago
Agreed this is less convenient than pandas for many tasks, however large market makers (citadel securities, Susquehanna, Jane st, Hudson River trading) employ abstractions like this out of necessity.
jabberwcky 6y ago
> but does anyone actually do that? Backtesting a regular single symbol model on CPU is often not cheap, especially at higher frequencies. Now multiply that by 50 correlated instruments, each having say, 1500-3000 strikes+sides+expiries in their respective option markets, and the value of GPU offload should become obvious. Backtesting dispersion trades on the S&P 500 using just 5 strikes per underlying, or any other scenario where the evolution of the volatility surface is important would already potentially involve >25,000 individual option markets All professionals backtest, often numerous times per day
adament 6y ago
If you are pricing and risk-managing complex derivatives books then Pandas is not the right tool for the job. Pandas does not include derivatives pricing models, daycount conventions, schedule generation and algorithmic differentiation which are all basic ingredients in a modern derivatives pricing framework. In general what you are concerned with here is computing the fair value of each instrument usually as an expectation in a simulation or as a solution to a partial differential equation, and the first derivatives and sometimes second-order derivatives of the fair value with respect to the model parameters. Pandas makes a lot of work in empirical finance nicer, data cleaning, time series work, etc. But when pricing and risk-managing a derivatives book as a market maker you are less concerned with inferring the future from the past, and more with interpolating current data in such a way that you do not open yourself to arbitrage. I worked as a model quant for a year a couple of years ago in a bank in the team responsible for developing and implementing the derivatives pricing models. Everything was written in (a lot of) C++, but I suspected and still suspect that you could get the same performance by leveraging one of the off-the-shelf deep learning libraries with a lot less code. Maybe you would have to write a few central components in C++ and assembly to get the exact same performance. I am glad to see someone actually following through on that idea.
mynegation 6y ago
> I get that calculating a zillion option prices on a GPU is faster, but does anyone actually do that? Yes. Counterparty credit risk calculations are done as Monte Carlo simulations far into the future and on multiple scenarios on top of the baseline. It easily grows to 10^8 times of the number of valuations in the baseline scenario at present time (which itself can be millions of positions).
jsinai 6y ago
More likely production systems will be written in C++ than Python.
chewbaxxa 6y ago
They’re completely different tools. Tensorflow is a specialised matrix math library. Pandas is just for sql-like data handling.

by dvt 6y ago

cliftonk 6y ago

Agreed this is less convenient than pandas for many tasks, however large market makers (citadel securities, Susquehanna, Jane st, Hudson River trading) employ abstractions like this out of necessity.

jabberwcky 6y ago

> but does anyone actually do that? Backtesting a regular single symbol model on CPU is often not cheap, especially at higher frequencies. Now multiply that by 50 correlated instruments, each having say, 1500-3000 strikes+sides+expiries in their respective option markets, and the value of GPU offload should become obvious. Backtesting dispersion trades on the S&P 500 using just 5 strikes per underlying, or any other scenario where the evolution of the volatility surface is important would already potentially involve >25,000 individual option markets All professionals backtest, often numerous times per day

adament 6y ago

If you are pricing and risk-managing complex derivatives books then Pandas is not the right tool for the job. Pandas does not include derivatives pricing models, daycount conventions, schedule generation and algorithmic differentiation which are all basic ingredients in a modern derivatives pricing framework. In general what you are concerned with here is computing the fair value of each instrument usually as an expectation in a simulation or as a solution to a partial differential equation, and the first derivatives and sometimes second-order derivatives of the fair value with respect to the model parameters. Pandas makes a lot of work in empirical finance nicer, data cleaning, time series work, etc. But when pricing and risk-managing a derivatives book as a market maker you are less concerned with inferring the future from the past, and more with interpolating current data in such a way that you do not open yourself to arbitrage. I worked as a model quant for a year a couple of years ago in a bank in the team responsible for developing and implementing the derivatives pricing models. Everything was written in (a lot of) C++, but I suspected and still suspect that you could get the same performance by leveraging one of the off-the-shelf deep learning libraries with a lot less code. Maybe you would have to write a few central components in C++ and assembly to get the exact same performance. I am glad to see someone actually following through on that idea.

mynegation 6y ago

> I get that calculating a zillion option prices on a GPU is faster, but does anyone actually do that? Yes. Counterparty credit risk calculations are done as Monte Carlo simulations far into the future and on multiple scenarios on top of the baseline. It easily grows to 10^8 times of the number of valuations in the baseline scenario at present time (which itself can be millions of positions).

jsinai 6y ago

More likely production systems will be written in C++ than Python.

chewbaxxa 6y ago

They’re completely different tools. Tensorflow is a specialised matrix math library. Pandas is just for sql-like data handling.