Deepseek did not sell anything, but did well with holding a lot of tech stocks. I think that can be a bit of a risky strategy with everything in one sector, but it has been a successful one recently so not surprising that it performed well. Seems like they only get to "trade" once per day, near the market close, so it's not really a real time ingesting of data and making decisions based on that.
What would really be interesting is if one of the LLMs switched their strategy to another sector at an appropriate time. Very hard to do but very impressive if done correctly. I didn't see that anywhere but I also didn't look deeply at every single trade.
Is there any reference that explains the deep technicalities of backtesting and how it is supposed to actually influence your model development? It seems to me that one could spend a huge amount of effort on backtesting that would distract from building out models and tooling and that that effort might not even pay off given that the backtesting environment is not the real market environment.
1. Your order can legally be “front run” by the lead or designated market maker who receives priority trade matching, bypassing the normal FIFO queue. Not all exchanges do this.
2. Market impact. Other participants will cancel their order, or increase their order size, based on your new order. And yes, the algos do care about your little 1 lot order.
Also if you improve the price (“fill the gap”), your single 1 qty order can cause 100 other people to follow you. This does not happen in paper trading.
Source: HFT quant
> And yes, the algos do care about your little 1 lot order.
I'm just your usual "corrupted nerd" geek with some mathematics and computer security background interests - 2 questions if I may 1. what's like the most interesting paper you have read recently or unrelated thing you are interested in at the moment? 2. " And yes, the algos do care about your little 1 lot order." How would one see this effect you mentioned - like it seems wildly anomalous, how would go about finding this effect assuming maximum mental venturesomeness, a tiny $100 and too much time?
Where did I say “retail trader”?
Because “institutional” low-latency market makers trade 1 lot all the time.
There's quite a lot of other game playing going on also.
> Because “institutional” low-latency market makers trade 1 lot all the time.
That sentence alone tells me that you're a LARPer.
https://en.wikipedia.org/wiki/Long-Term_Capital_Management was kind of an example of both of those. They based their predictions on past behaviour which proved incorrect. Also if other market participants figure a large player is in trouble and going to have to sell a load of bonds they all drop their bids to take advantage of that.
A lot of deviations from efficient market theory are like that - not deeply technical but about human foolishness.
Unless you're thinking of some obscure exchange in a tiny market, this is just untrue in the U.S., Europe, Canada, and APAC. There are no exchanges where market makers get any kind of priority to bypass the FIFO queue.
We do not use it as a way to determine profitability.
cope.
Equity options are sparse and have 1 order of 1 lot/qty per price. But usually empty. Too many prices and expiration dates.
US treasury bond cash futures (BrokerTec) are almost always 1 lot orders. Multiple orders per level though.
I could go on, but I’m busy as our team of 4’s algos are printing US$500k/hour today.
Nope, several large, active, and liquid markets in the US.
Legally it’s not named “bypass the FIFO queue”. That would be dumb.
In practice, it goes by politically correct names such as “designated market maker fill” or “institutional order prioritization” or “leveling round”.
I am getting the feeling you either are not actually a quant, or you were a quant and just misheard and confused a lot of things together, but one thing is for sure... your claim that market makers get some kind of priority fills is factually incorrect.
By assessing risk is that just checking that it does dump all your money and that you can at least maintain a stable investment cache?
Are you willing to say more about correctness? Is the correctness of the models, of the software, or something else?
Correctness has to do with whether the algorithm performed the intended actions in response to the inputs/events provided to it, nothing more. For the most part correctness of an algorithm can be tested the same way most software is tested, ie. unit tests, but it's also worth testing the algorithm using live data/back testing it since it's not feasible to cover every possible scenario in giant unit tests, but you can get pretty good coverage of a variety of real world scenarios by back testing.