4 ms·

This is simply the new normal as too many small-brain (and thus fast) algorithms are crowded into the same strategy space. Periodically, they will all randomly

This is simply the new normal as too many small-brain (and thus fast) algorithms are crowded into the same strategy space. Periodically, they will all randomly converge on the same strategy, liquidity dries up, and the prices jump dramatically. The fix is to add a delay in all public stock markets between order placement and execution. This could be done either through a fixed lower speed limit, through specifying a human-scale atomic time quanta, or adding a tax inversely proportional to the duration between consecutive trades from the same agent -- a tax on simplicity. Increasing the time-scale of the market will increase the sophistication of the algorithms, as they no longer need to fight to be winner-take-all fast. Bigger-brained algorithms mean the strategy space is larger and convergence is less likely. Thus, no more flash crashes.

jfoutz 11y ago
I'd sort of assumed the slower but bigger brained algorithms saw the spike and decided to start selling. They also put a floor on the bottom price (i'd guess).
anigbrowl 11y ago
I wonder where you would go about obtaining high resolution market data? e.g. [ticker_symbol, buyer_id, seller_id, volume, price, time[]] I'd like to find a way to visualize the algorithms, along the lines of http://content.stamen.com/visualizing_a_day_of_financial_transactions_on_nasdaq_part_2 http://content.stamen.com/visualizing_a_day_of_financial_tra... or so. I don't care about the specific securities or trader identities, I just like finding patterns in noisy data.
- dllthomas 11y ago
  As I recall, ICE market data contains the full order book (size and identity of individual orders, not just total size at each level). Anonymized, of course. I have no idea if this is available if you're not actually trading...
mrchicity 11y ago
I don't think that's the case at all. Consider Levine's point about self-trading. Even algorithms developed within the same company have such different views that one believes the price will hold steady or revert and expresses this view by sitting on the offer while another is so confident that it will move that it's willing to lift that offer. Just looking at ultra-low latency algos in liquid products like treasuries you have a lot of diversity: market-makers, yield curve spreaders, cash/future/ETF arbitrageurs, statistical signal trading, along with combinations of all of those. All of these players need to be fast but have vastly different risk profiles and trading style. If you made trading more expensive or slower, traders doing useful things like making quotes or keeping the cash and futures markets in line would be driven out, making the markets less efficient. I'm less sure about the value that high-speed momentum strategies provide. If they're right, they move prices to be more efficient more quickly, but they can also negatively impact liquidity providers and make prices inefficient if they're wrong. I figure the market will sort that out though. Nobody can run a money-losing algo indefinitely.
- msellout 11y ago
  You're correct that without low-latency trading, markets will be less efficient in that bid-ask spreads will increase. However, I think of that efficiency as an architect optimizing a skyscraper's earthquake tolerance. Sure, on a regular day we want to save money, but a prudent person will "waste" on redundant systems and excessive tolerances.
- deleted 11y ago
  [deleted]
lmm 11y ago
> This could be done either through a fixed lower speed limit, through specifying a human-scale atomic time quanta Presumably you mean quantum, and that wouldn't help - as long as there is maker-taker pricing and a huge fixed price increment ($0.01) there is value in being the first to submit your order. > or adding a tax inversely proportional to the duration between consecutive trades from the same agent -- a tax on simplicity. That won't help either. Agents are fungible - indeed as you say the whole reason this happened in the first place was that multiple agents were doing the same things. > Increasing the time-scale of the market will increase the sophistication of the algorithms, as they no longer need to fight to be winner-take-all fast. Bigger-brained algorithms mean the strategy space is larger and convergence is less likely. Thus, no more flash crashes. Very doubtful. If you have an algorithm that can make money on a longer timescale, you would be running it today. The only algorithms where the fact that faster ones exist matters are the simple market-making ones, and there simply aren't many of those - it's not a question of having time to use more parameters, there just aren't any more parameters out there
- msellout 11y ago
  Rather than go point by point, I'll refer you to someone more eloquent and credentialed. Check out some of Mark Buchanan's comments on http://physicsoffinance.blogspot.com http://physicsoffinance.blogspot.com He talks about many things, but if you're curious you'll find several lit review posts about this topic. The heart of good science is solid theory followed by experimental evidence. Simulations such as the El Farol Bar problem demonstrate how systems of prediction agents go through a phase shift from stable to chaotic as the ratio of memory to number of participants decreases. Historical phenomena match the prediction -- as more low-latency algorithms participate, the market exhibits more dramatic movements.

jfoutz 11y ago

I'd sort of assumed the slower but bigger brained algorithms saw the spike and decided to start selling. They also put a floor on the bottom price (i'd guess).

anigbrowl 11y ago

I wonder where you would go about obtaining high resolution market data? e.g. [ticker_symbol, buyer_id, seller_id, volume, price, time[]] I'd like to find a way to visualize the algorithms, along the lines of http://content.stamen.com/visualizing_a_day_of_financial_transactions_on_nasdaq_part_2 http://content.stamen.com/visualizing_a_day_of_financial_tra... or so. I don't care about the specific securities or trader identities, I just like finding patterns in noisy data.

dllthomas 11y ago
As I recall, ICE market data contains the full order book (size and identity of individual orders, not just total size at each level). Anonymized, of course. I have no idea if this is available if you're not actually trading...

mrchicity 11y ago

I don't think that's the case at all. Consider Levine's point about self-trading. Even algorithms developed within the same company have such different views that one believes the price will hold steady or revert and expresses this view by sitting on the offer while another is so confident that it will move that it's willing to lift that offer. Just looking at ultra-low latency algos in liquid products like treasuries you have a lot of diversity: market-makers, yield curve spreaders, cash/future/ETF arbitrageurs, statistical signal trading, along with combinations of all of those. All of these players need to be fast but have vastly different risk profiles and trading style. If you made trading more expensive or slower, traders doing useful things like making quotes or keeping the cash and futures markets in line would be driven out, making the markets less efficient. I'm less sure about the value that high-speed momentum strategies provide. If they're right, they move prices to be more efficient more quickly, but they can also negatively impact liquidity providers and make prices inefficient if they're wrong. I figure the market will sort that out though. Nobody can run a money-losing algo indefinitely.

msellout 11y ago
You're correct that without low-latency trading, markets will be less efficient in that bid-ask spreads will increase. However, I think of that efficiency as an architect optimizing a skyscraper's earthquake tolerance. Sure, on a regular day we want to save money, but a prudent person will "waste" on redundant systems and excessive tolerances.
deleted 11y ago
[deleted]

lmm 11y ago

> This could be done either through a fixed lower speed limit, through specifying a human-scale atomic time quanta Presumably you mean quantum, and that wouldn't help - as long as there is maker-taker pricing and a huge fixed price increment ($0.01) there is value in being the first to submit your order. > or adding a tax inversely proportional to the duration between consecutive trades from the same agent -- a tax on simplicity. That won't help either. Agents are fungible - indeed as you say the whole reason this happened in the first place was that multiple agents were doing the same things. > Increasing the time-scale of the market will increase the sophistication of the algorithms, as they no longer need to fight to be winner-take-all fast. Bigger-brained algorithms mean the strategy space is larger and convergence is less likely. Thus, no more flash crashes. Very doubtful. If you have an algorithm that can make money on a longer timescale, you would be running it today. The only algorithms where the fact that faster ones exist matters are the simple market-making ones, and there simply aren't many of those - it's not a question of having time to use more parameters, there just aren't any more parameters out there

msellout 11y ago
Rather than go point by point, I'll refer you to someone more eloquent and credentialed. Check out some of Mark Buchanan's comments on http://physicsoffinance.blogspot.com http://physicsoffinance.blogspot.com He talks about many things, but if you're curious you'll find several lit review posts about this topic. The heart of good science is solid theory followed by experimental evidence. Simulations such as the El Farol Bar problem demonstrate how systems of prediction agents go through a phase shift from stable to chaotic as the ratio of memory to number of participants decreases. Historical phenomena match the prediction -- as more low-latency algorithms participate, the market exhibits more dramatic movements.