3 ms·

i'd like to hear more on that $10k PC :)

by kayoone 10y ago

dsacco 10y ago
I don't have a ton of time at the moment, but here's the cliff's notes: * i7-6900K CPU * 4x GTX 1080 GPUs * X99-E WS/3.1 Motherboard * 128GB DDR4 2400Mhz RAM (8x 16GB sticks) * 14 TB between SSD and HDD (Plex, datasets and applications on Ubuntu 14) * 14x 120mm fans run at lowest RPM * Custom liquid cooling loop with dual pumps * 4x Radiators (1x 120mm, 1x 240mm, 2x 480mm) It's not just deep learning, a lot of what I like to do with this is research or projects parallelizing things like cryptanalysis. For the rest I do financial modeling (which has a fair overlap with the cryptanalysis from a signal processing perspective anyway). I used to work primarily in application security, but have been slowly moving into cryptography and data analysis; the latter mostly as a supplement for cryptography as a research interest.
- artmageddon 10y ago
  > For the rest I do financial modeling (which has a fair overlap with the cryptanalysis from a signal processing perspective anyway) What kind of modeling are you doing with a beast of a setup like that?
  - dsacco 10y ago
    The approach has a few tiers: 1. Use one stock that I believe I have unique and actionable data on as the input barometer. Take it as a premise that this stock is currently mispriced by the market, and I have an idea of what it should be. We'll call this the primary stock. 2. Find all stocks that have a consistent positive or negative correlation with the input stock, (do they share volatility?), at different significance levels. (This is not particularly hard, it's just getting accurate data with real granularity that is the challenge.) These are the corollary stocks. If there is a calendar event upcoming, I restrict the backtest to the values of other stocks on similar days. 3. For each correlated stock found, calculate an options model with a new terminal price distribution (as opposed to what is currently priced by market consensus). 4. Simulate different options trading strategies and their outcomes across different projected prices for each stock and present the ones with the highest probability of success versus return. At a minimum, the primary and corollary underlyings should have a large enough misprice that trading them can be profitable across the bid/ask spread. So for example, a $1 increase or decrease will not generally be meaningful enough to make the trade, especially if there is a lot of volatility priced in (such as around earnings releases). You'll notice that there is nothing truly sophisticated about any of this. It's just a bunch of computation used to extrapolate the best leverage available using derivatives. The core thesis involves having material data that the rest of the market has not yet priced in, and I don't often have that (or rather, enough of a directional conclusion based on it). When I do, I like to do this because 1) I don't trust most profitability calculators which assume constant volatility and 2) it maximizes potential results by not just looking at the primary stock I know about. tl;dr: Assuming one stock is mispriced, do a bunch of derivative re-pricing and choose an optimal one.
- Dylan16807 10y ago
  While that sounds like a very nice machine, it's worth noting that a more 'normal desktop' machine that hosts those GPUs could be $4k instead.
  - dsacco 10y ago
    Very true. Much of the cost was getting the cooling down to a level that makes it mostly silent under any load.
j_s 10y ago
https://news.ycombinator.com/item?id=13607648 https://news.ycombinator.com/item?id=13607648

i'd like to hear more on that $10k PC :)

by kayoone 10y ago

i'd like to hear more on that $10k PC :)

I don't have a ton of time at the moment, but here's the cliff's notes: * i7-6900K CPU * 4x GTX 1080 GPUs * X99-E WS/3.1 Motherboard * 128GB DDR4 2400Mhz RAM (8x 16GB sticks) * 14 TB between SSD and HDD (Plex, datasets and applications on Ubuntu 14) * 14x 120mm fans run at lowest RPM * Custom liquid cooling loop with dual pumps * 4x Radiators (1x 120mm, 1x 240mm, 2x 480mm) It's not just deep learning, a lot of what I like to do with this is research or projects parallelizing things like cryptanalysis. For the rest I do financial modeling (which has a fair overlap with the cryptanalysis from a signal processing perspective anyway). I used to work primarily in application security, but have been slowly moving into cryptography and data analysis; the latter mostly as a supplement for cryptography as a research interest.

artmageddon 10y ago
> For the rest I do financial modeling (which has a fair overlap with the cryptanalysis from a signal processing perspective anyway) What kind of modeling are you doing with a beast of a setup like that?
- dsacco 10y ago
  The approach has a few tiers: 1. Use one stock that I believe I have unique and actionable data on as the input barometer. Take it as a premise that this stock is currently mispriced by the market, and I have an idea of what it should be. We'll call this the primary stock. 2. Find all stocks that have a consistent positive or negative correlation with the input stock, (do they share volatility?), at different significance levels. (This is not particularly hard, it's just getting accurate data with real granularity that is the challenge.) These are the corollary stocks. If there is a calendar event upcoming, I restrict the backtest to the values of other stocks on similar days. 3. For each correlated stock found, calculate an options model with a new terminal price distribution (as opposed to what is currently priced by market consensus). 4. Simulate different options trading strategies and their outcomes across different projected prices for each stock and present the ones with the highest probability of success versus return. At a minimum, the primary and corollary underlyings should have a large enough misprice that trading them can be profitable across the bid/ask spread. So for example, a $1 increase or decrease will not generally be meaningful enough to make the trade, especially if there is a lot of volatility priced in (such as around earnings releases). You'll notice that there is nothing truly sophisticated about any of this. It's just a bunch of computation used to extrapolate the best leverage available using derivatives. The core thesis involves having material data that the rest of the market has not yet priced in, and I don't often have that (or rather, enough of a directional conclusion based on it). When I do, I like to do this because 1) I don't trust most profitability calculators which assume constant volatility and 2) it maximizes potential results by not just looking at the primary stock I know about. tl;dr: Assuming one stock is mispriced, do a bunch of derivative re-pricing and choose an optimal one.
Dylan16807 10y ago
While that sounds like a very nice machine, it's worth noting that a more 'normal desktop' machine that hosts those GPUs could be $4k instead.
- dsacco 10y ago
  Very true. Much of the cost was getting the cooling down to a level that makes it mostly silent under any load.

j_s 10y ago

https://news.ycombinator.com/item?id=13607648 https://news.ycombinator.com/item?id=13607648