I used to work for a brokerage API geared at algorithmic traders and in my experience anecdotal experience many strategies seem to work well when back-tested on paper but for various reasons can end up flopping when actually executed in the real market. Even testing a strategy in real time paper trading can end up differently than testing on the actual market where other parties are also viewing your trades and making their own responses. The post did list some potential disadvantages of backtesting, so they clearly aren't totally in the dark on it.

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.

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chroma205 15 hours ago | parent | next [-]

>but for various reasons can end up flopping when actually executed in the real market.

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

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dubcanada 5 hours ago | parent | next [-]

There is a big difference between back testing scalping and back testing buy 100 NVIDA at $103 and sell at $110.

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derrida 14 hours ago | parent | prev | next [-]

Dear 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?

	▲	tim333 3 hours ago \| parent \| next [-]
		Retail speculator here. Re 2 it's often quite easy to demo on thinly traded markets - I'm more familiar with crypto. Say the spread is 81.00 buy, 81.03 sell. Put in a limit buy at 81.00 and watch someone/something immediately outbid you ate 81.01. In the short term that kind of thing is done by algorithms but there are humans behind it and doing it too. There's quite a lot of other game playing going on also.
	▲	gosub100 24 minutes ago \| parent \| prev \| next [-]
		Even a 1 lot order could be the deciding factor for some algorithm that's calculating averages or other statistics. Especially for options books.
	▲	ainiriand 13 hours ago \| parent \| prev [-]
		Sometimes the spread is really tight.

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this_user 5 hours ago | parent | prev | next [-]

If you actually were in the industry, you would know that most retail traders don't fail, because they lose a tick here or there on execution, they fail, because their strategies have no edge in the first place.

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chroma205 4 hours ago | parent [-]

> If you actually were in the industry, you would know that most retail traders don't fail, because they lose a tick here or there on execution

Where did I say “retail trader”?

Because “institutional” low-latency market makers trade 1 lot all the time.

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this_user 3 hours ago | parent [-]

The context from parent was obviously that. Instis don't trade on Alpaca.

> Because “institutional” low-latency market makers trade 1 lot all the time.

That sentence alone tells me that you're a LARPer.

	▲	chroma205 an hour ago \| parent [-]
		> That sentence alone tells me that you're a LARPer 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.

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Maxatar 3 hours ago | parent | prev [-]

>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.

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.

	▲	chroma205 12 minutes ago \| parent [-]
		> There are no exchanges where market makers get any kind of priority to bypass the FIFO queue. 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”.

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acrooks an hour ago | parent | prev | next [-]

A really important part of this is the emotional component. When real money is involved, then you will sometimes face actual losses. It’s hard for a human to completely trust the machine in real world trading

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ddtaylor 15 hours ago | parent | prev | next [-]

Alpaca?

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lisbbb 16 hours ago | parent | prev | next [-]

This. This all day. I used to paper trade using ThinkOrSwim and I was doubling and tripling my money effortlessly. Then I decided to move my strategy to the real deal and it didn't do very well at all. It was all bs.

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bmitc 16 hours ago | parent | prev [-]

I've honestly never understood what backtesting even does because of the things you mention like time it takes to request and close trades (if they even do!), responses to your trades, the continuous and dynamic input of the market into your model, etc.

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.

	▲	tim333 3 hours ago \| parent \| next [-]
		I'm not sure about deep technicalities but backtesting is a useful thing to see how some strategy would have performed at some times in the past but there are quite a lot of limitations to it. Two of the big ones are the market reacting to you and maybe more so a kind of hindsight bias where you devise some strategy that would have worked great on past markets but the real time ones do something different. 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.
	▲	Maxatar 3 hours ago \| parent \| prev [-]
		We use back testing at my firm for two primary reasons, one as a way to verify correctness and two as a way to assess risk. We do not use it as a way to determine profitability.