If your strategies care so much about performance, can you really achieve the same or comparable performance with Cython? At that point, wouldn't those strategies be better suited for a custom "re-implementation"?
I suppose I'm not exactly sure how "high performance" and "low latency" this project is. Are there any latency stats on this project?
We can not see the traders' internal code so anything can happen. I think they look like money-collecting machines. Take care guys.
Edit: Wow, that downvote sounds very interesting, as the first that I pointed is widely known, the so called "Market Makers".
Unregulated exchanges are just a way to bring back the bad old days of "boiler room" penny stock scammers of the 80's. Most of the people making money are running 40 year old scams on 20 somethings who don't know better yet.
I've seen stats from other crypto exchanges that are something like "90% of the retail customers lose 90% of their funds in 90 days or less".
You can right!? You can make beta. Sometimes the beta is negative. Hard to make alpha.
Even did a ~7 year career detour through quant finance "if I like Software Engineering and Mathematics so much, why don't I combine the two?"
Finally realized that the best use of my time was to just to work hard at a career I deeply enjoy (Software Engineering), working on products I actually care about (not valuing arcane derivatives products), and just invest the excess in diversified index funds (with some single stock selections here and there, thanks TSLA and NVDA).
Just as engineers can get "nerd sniped", I feel like "trading" is a somewhat malevolent strong attractor for a lot of folks. Folks do need general financial literacy, but an extra hour spent per day working harder to progress day job / long term career likely has a higher net present value than trading options or crypto.
[1]: https://www.lean.io/
Right now I'm trying to figure out how to consistently make $1/day as a POC exercise. That's it. I did ask the various advanced LLMs the path to earning/clearing $200K/year as an algo-trader.
Fun & sobering responses. I'll give the LLMs this -- no uplift in these conversations.
What's difficult is getting a higher rate of return.
$200k is totally doable with $2M investment capital and a bit of disciplined options trading.
10% probably. Though with the higher-than-market rate, there would also be dispersion in the consistency of the return. I could see some lean years where no money is made. I could also see a decade where you are buying at ever-higher valuations rather than dollar-cost averaging -- thus you could see a lean decade following.
It all depends on how much consistency you want.
Still not easy but it's not impossible.
You can do this with the following strategy:
1. Buy $10,000 of Treasury bonds.
It might be more interesting to target a particular Sharpe ratio rather than an absolute dollar amount since that will be proportional to the amount you invest.
For very small funds, running entirely on IBKR platform (or Alpaca if you can live with their constraints) makes sense. For very large funds, you invariably will have a home-grown system that integrates with all of your expensive vendors. But if you are starting from scratch and want to scale up, using this to bootstrap quickly is most efficient.
Lots of small hedge fund use them as a stop gap until they get bigger, or fail.
It is a very math heavy course, highly technical and requires a great deal of existing math background. But the truth is that you will not be able to do much quantitative finance without that background anyways, so if what you want is a way to gain that math background then just crack open a textbook and get working.
Anything else requires millions of infra spend.
In my opinion: If you trade, trade with a firm.
The problem is the 0.5% of the time, it erases all the gains made on the successful ones.
I'm convinced without information edge or some capital sunk cost edge (for HFT) you are literally just flipping coins when it comes to trading.
What's dangerous is fixation on strategies that form after a period of success.
All in all, I think just buying stock and holding is the best and most successful approach to making money.
Maybe when AI becomes sentient it will know on which days it make sense to buy and sell iron condors with huge ass wings....
Vanguard says I've had a 12% rate of return. I guess I could have done active trading if I'd gone on fewer dates
> 140% gain on your holdings this year
Choose one.
I understand where you’re coming from, but there isn’t a incongruity. Individual stock investments are a relatively small part of my overall portfolio.
Had a period in my college days where I had a neural network running that could successfully trade on patterns of periodicity of non-chaotic windows of the asset. But as soon as the system would go back to being chaotic, and there was no way to identify WHEN the system was chaotic and when it wasn't, the trades would go to shit and I would lose all gains. I was up about 400-450% at an end of a successful cycle, which was 2-4 months, and then it could be a year of decline with gains being eaten up by the option issuers.
Now I only do long-term funds/stocks and have:
a) much less anxiety about losses b) more money.
Index funds work because not everyone buys them, they work because the majority of capital is allocated and constantly being rebalanced in a way that tries to reflect the performance of a corporation. It's only in this circumstance that an index fund, or any kind of passive investment, can be of any utility by leveraging the work of those who are actively trying to assess the genuine value of a corporation.
A trader with experience, working in a firm with deep pockets and terabytes of historical data and FPGAs that can execute his/her strategy and a legal team and an engineering team... can outperform you yes x)
But I should say that any engineer familiar with the AI tech stack could have bought NVDA at any point in the last five years knowing how big their moat is. That same engineer could have sold monthly covered calls, taking 5 minutes out of every month to do so.
And before you say it, no, they wouldn't be full port NVDA.
Extrapolating from past data with N=1 to demonstrate that "any tech person can outperform the index" shows a crass lack of mathematical reasoning, which in my opinion demonstrates why you're unsuited to professional trading.
However, active investors have higher trading fees/management costs, so they are bound to perform at least slightly worse on average. It's just mathematics.
> The Medallion Fund, which has been available exclusively to current and past employees and their families, surged 80% in 2008 despite hefty fees; the Renaissance Institutional Equities Fund (RIEF), owned by outsiders, lost money in both 2008 and 2009; RIEF declined 16% in 2008
A cynic in me would say that Simons used a better strategy for the Medallion Fund than for the RIEF.
REIF just lets them use Medallion as marketing while getting those sweet AUM fees on a massive fund.
More generally, there is no optimal amount of stop loss. It ultimately gets auto-stopped out at 3:30 pm, although by then it could have gone nearly to zero anyway. Either the strategy works or it doesn't, and with Trump manipulating the market on random days with significant news, it increasingly doesn't.
The same applies to your profit target. This means that if your profit mark is $10, and your stop loss is $5 you will lose roughly twice as often as you win, all other things being equal.
What you actually CAN do is use smart money management, (something like the Kelly Criterion) to ensure that you properly capitalize on any slight edge you do manage to find without going broke in the long term.
That of course requires you to find a bet you can win 51% of the time, and that you be made of iron when it comes to sticking to the plan. Most folks can't.
Do you mean this literally or is that an exaggeration for effect?
I'm not sure how you'd do that unless all your trades are like selling a put with a $50 strike price expiring in a month when the stock is trading at $100.
As a fellow options trader, I can confirm that it is spot on what is seen with SPY; it is not an exaggeration at all.
Winning trades made $0.01, losing trades lost $10k.
Covered calls are a good example, you own the stock and sell calls with a strike that is in the money.
You make money on your option as it either expires worthless and you win getting to keep the premium you got paid to write the option or it expires in the money and you sell the shares to the option holder at the strike.
In both cases you can claim you didn't lose money on the option. But you did lose out on potential profit from your share as you were forced to sell them for less than they are worth on the open market and its very easy to get into a case where you would have been better off if you hadn't wrote the option in the first place.
If you manage your own money you can say you didn't lose on the option but you probably did on the underlying stock.
If the stock closes lower you lost money on the stock but make a tiny bit from selling the option.
If the stock closes higher than the strike you wrote you "make" money on the option from the premium but lose out on profit as you have to sell your stock for lower than what you could have in the market.
So again you can say the option didn't lose money but you are worse off than if you hadn't wrote the option, assuming it went up more than the premium you got from writing the option.
So amateur traders can fool themselves into thinking they are geniuses as their option leg doesn't lose money but the overall trade still makes them worse off than if they hadn't of sold the option at all.
This applies to everything in life.
For anyone interested, I can recommend the book "Systematic Trading" by Robert Carver. You don't have to be into algorithmic trading, the sections on risk management and positive vs negative skews are already worth the read.
The only way to make money for 99.999% of traders is to trade with a firm. You should not trade on your own. You will lose in the long term.
> The problem is the 0.5% of the time, it erases all the gains made on the successful ones.
Nassim Taleb calls this "eating like chicken and shitting like elephants"
This is also why people in algo trading that are able to discover or already know working strategies, they are paid big bucks.
1. Nothing we tried with usual strategies worked consistently. Backtesting parameters, ML with smart feature selection, boosting, neural networks - everything failed out of sample. Maybe we were dumb, I don't know.
2. What worked was having a clear edge: - Private exchange programs with rebates for high-volume teams - Pure latency arbitrage - Weird arbitrage trading obscure instruments (e.g. on chain AMMs vs crypto exchange futures).
Both market maker and arbitrage strategies were very sensitive to latency. We built a low-latency trading engine in Java (on top of https://github.com/OpenHFT/Chronicle-Queue). We got 130mqs from market event to order send in a hot loop on 99.99 percentile. It was fun to optimize and benchmark.
- Tail latency matters. You can have 100ms at the 90th percentile and 10-50ms at the 99.9th percentile. For low latency strategies, this is pure loss. - Tail latency matters even more when markets go crazy. Event rates can jump from 10 per second to 1-2k per second. If your trading engine uses trades or bid/ask events, be ready. For OHLC bars it doesn't matter, but nothing based on OHLC worked for us.
---
p.s.
I wouldn't recommend trading to anyone. It's very stressful and exhausting. More importantly, all your hard work disappears like trying to hold sand in your hands. There's very little compounding of your work. Strategies constantly churn. You're always negotiating with managers for fees and API limits. You're always negotiating with managers for fees and API limits. They force you to buy and hold exchange tokens like Binance's BNB just to get slightly better fees, VIP status, or direct API access that bypasses firewalls.
The industry is extremely secretive - it's a zero-sum game with no incentive to share anything. When you meet someone who trades, it's usually hard to have a meaningful conversation. At least that was true for me. All our strategies were small-scale and we couldn't invest all our capital in them. So discussing what we did was basically saying "yes, we print money, here's how you can take it from us".
Overall, I was super lucky. We built a money-printing machine that worked for a few years. But in the end, my co-founder and I decided not to pursue it long-term. One day when yet another strategy stopped working, we just shut down all operations.
Excluding HFT (which is reserved for people with hundreds of millions to invest in infra, fresh Ivy league quant analysts, and a fiber optic cable hooked up directly to the exchange; they likely already have an in-house tool that does what this project does), you're really just left with intraday trading or long term investing. Investing doesn't require algorithmic trading or back testing, so it seems that this projects demographic is aimed toward intraday retail traders.
With intraday trading, your chances of making a successful trading algo are near 0%. I mean, think about it: you have to account for every single variable in the stock market. How are you supposed to account for a truth social post imposing or lifting tariffs? Or a ransomware attack crippling a company? Or if a whale decides to sell all their $BIGCORP shares on the flip of a coin? It's impossible. Your only odds of success with intraday trading is manually doing it. You yourself are an "algo" trader that is capable of changing their strategy on the fly and accounting for unknown variables. A pre-programmed algo can not, no matter how much context you give it.
Furthermore, with back testing, it's impossible to accurately capture the context of the market during that time. Let's say you back test on 180 days of data. Well, do you know exactly what happened on the 71st day of that data? Did you account for that fed meeting, that tariff hike, etc? What about all the other days? Testing on OHLCV alone is not enough; you need the entire context of the market.
While the project itself it neat, I just don't see how algorithmic trading could lead to any long term success.
mapontosevenths•15h ago
More seriously, for anyone else who was curious below is a list of the existing integrations.
https://nautilustrader.io/docs/latest/integrations/
no_wizard•14h ago
Can you actually buy real stocks / etfs / mutual funds with this platform?
TimMurnaghan•14h ago
gosub100•12h ago