Hacker News Board Hacker News, browsed like a classic message board
Live data from Hacker News

Python For Finance: Algorithmic Trading

medium.com

Post reply on HN
Print view View original

51–60 of 164 posts

Re: Python For Finance: Algorithmic Trading

#51

Post by princeb »

SirLJ wrote: post #12
gricardo99 wrote: post #11

Earlier quoted context omitted.

I'll third this comment. Some more details on what you trade, where you trade, how long you've been doing it, performance metrics, etc... would be fascinating.

As you can see I am paranoid, because the more people trading the same strategy, the less effective it becomes, not to mention, if someone knows your trading strategy it can trade against you... it’s a zero sum game after all... All that said, here are the 2 most important advises I wish someone told me when I started years ago: Have an iron clad risk management strategy for your portfolio, e.g. if you are willing to…

> more people trading the same strategy, the less effective it becomes

you should pick a more scalable strategy, the kind of strategy that becomes more profitable the more you talk your own book.

Re: Python For Finance: Algorithmic Trading

#52

Post by rl3 »

jaredbroad wrote: post #31

QuantConnect recently announced full python library support; and we have launched https://www.quantconnect.com/tutorials to help people write quantiative strategies in Python. QuantConnect & LEAN gives you ability to do tick->daily resolutions; for equity, morning-star, future, option, forex and cfd trading - all with a fully open source project which includes samples of data to get you started. The grunt work is sti…

Hey Jared, two questions:

1. Any chance of a Robinhood integration à la Quantopian?

2. How are the architectural revisions[0] coming along?

Also some totally unsolicited feedback:

If I'm being completely honest, I found it difficult to get going with QC. The documentation is decent, but there's not enough to avoid having to review LEAN source right off the bat. The examples also tend to mix helper classes with lower-level functionality, and that can create confusion.

The framework itself feels a bit over-reliant on OOP. Some aspects feel too tightly coupled, others too little. Obviously LEAN has been around for many years now, so architectural baggage is perfectly understandable.

A total rewrite I'm sure isn't feasible, though I'd suggest the following ethos in any case:

a) Design primitive user-accessible data structures with virtually no inbuilt functionality.

b) Build low-level components that operate using those data structures.

c) Build high-level components that compose low-level components.

d) Allow users to author their own components, and to compose components of any type however they see fit.

Pretty sure you're already on that track in a sense, so it's good to see things headed in the right direction. What keeps me from writing a custom framework is the data, the fact QC does a ton of grunt work, and that it's well-tested.

tl;dr Please break apart the monolithic QCAlgorithm class as much as you can! :)

[0] https://www.quantconnect.com/forum/discussion/1816/qc-algori...

Re: Python For Finance: Algorithmic Trading

#53

Post by dsacco »

SirLJ wrote: post #10
kevstev wrote: post #9

Earlier quoted context omitted.

Because they do so much work upfront for you. I worked in algorithmic trading for a bit over 10 years but left in 2014. I spent a few months in my spare time building out a trading and backtesting platform, pulling data off of yahoo finance, connecting to Interactive Brokers via their C++ API, etc. It was all very slow and tedious, and during my googling one day, I come across quantopian. I felt like an idiot- they a…

My experience is unfortunately different, I found the data for some delisted stocks like HTZ missing which will massively screw the past results due to the survivorship bias...

Can you please prove this or stop saying it? Quantopian's data is from Nanex and is free of survivorship bias, as I mentioned to you in another comment.

Re: Python For Finance: Algorithmic Trading

#54

Post by [deleted] »

dsacco wrote: post #49
lowpro wrote: post #45

The main issue I found in algo and financial aspects of programming is that the market is a zero sum game, and my intro knowledge of finance and algorithms, even when I know python, are no match for MIT PHD Quants who does it full time. There's no real way to compete with that, and therefore I would lose money, even if the data showed it might be successful in the future, firms and full time workers on algo trading w…

I agree with most of what you're saying in principle, but it is very possible to identify alpha or acquire an edge that institutional market participants don't have. If you have access to data that most of the market does not, you can effectively trade on it. You can also effectively trade on a novel insight on a combination of data sources. There is a lot of information asymmetry, and an individual is capable of cap…

[deleted]

Re: Python For Finance: Algorithmic Trading

#55

Post by SirLJ »

dsacco wrote: post #44
SirLJ wrote: post #16

Earlier quoted context omitted.

The main problem with Quantopian is that the data missing especially for delisted stocks, here are some examples you can test yourself: AA, CWH, NIHD, PANL, HTZ, DSL - I have a whole list I found very fast, just imagine what other mistakes could be there... And the other problem is that they are my competitor, so I would never give them my trading strategy or ideas I am testing at the moment...

This is false. Quantopian sources their historical and real time data from Nanex, and the data is explicitly free of survivorship bias (i.e. they maintain data for delisted equities). I don't know what you verified, but it directly contradicts my own experience and the FAQ: https://www.quantopian.com/faq#data-sources To your second point, as another commenter said Quantopian doesn't see your algorithm unless you give…

Please go back and check and you'll se the data is missing for the symbols stated above, and I am sur for many more based on my limited test... everyone can see I am right by trying to request the data, nothing false about that...

Re: Python For Finance: Algorithmic Trading

#56

Post by dsacco »

SirLJ wrote: post #46
rl3 wrote: post #36

Earlier quoted context omitted.

> When promoting your company, please say so ... His bio and pronoun usage said as much. > also on QuantConnect one cannot actually see the data, so there is no way to verify how good the data is... Can you not load up the data and test it however you wish? They even say where they get their data from.

No you cannot

First of all, you're criticizing QuantConnect's data when you claim to get your data from ebay.

Second, yes you absolutely can verify QuantConnect's data. You can use it as much as you want within the context of their platform, you just can't download the data en masse from their platform and use it on your own. But if you have tick data (equities) or minute data (options) yourself, you can certainly verify it (which of course, you don't, because you think ebay is a good source for financial data).

I am going to continue griefing you in threads like this where you spread blatant misinformation, because at this point I'm convinced you have an ulterior motive or are in fact selling this data you keep talking about on ebay.

Re: Python For Finance: Algorithmic Trading

#57

Post by SirLJ »

dsacco wrote: post #49
lowpro wrote: post #45

The main issue I found in algo and financial aspects of programming is that the market is a zero sum game, and my intro knowledge of finance and algorithms, even when I know python, are no match for MIT PHD Quants who does it full time. There's no real way to compete with that, and therefore I would lose money, even if the data showed it might be successful in the future, firms and full time workers on algo trading w…

I agree with most of what you're saying in principle, but it is very possible to identify alpha or acquire an edge that institutional market participants don't have. If you have access to data that most of the market does not, you can effectively trade on it. You can also effectively trade on a novel insight on a combination of data sources. There is a lot of information asymmetry, and an individual is capable of cap…

Dsacco, I really like your way of thinking, too bad we cannot agree on the cheap data sources :-) My hope is that one day you can see the world trough my glasses :-)

Re: Python For Finance: Algorithmic Trading

#58

Post by yonkshi »

lowpro wrote: post #45

The main issue I found in algo and financial aspects of programming is that the market is a zero sum game, and my intro knowledge of finance and algorithms, even when I know python, are no match for MIT PHD Quants who does it full time. There's no real way to compete with that, and therefore I would lose money, even if the data showed it might be successful in the future, firms and full time workers on algo trading w…

Even if it's a zero sum game (which it's not), most participants are not quant/algo based. As long as you can beat 50% percentile, you can make a profit.

Sure you may not be as profitable as top quant companies, but do you really mind that much?

Re: Python For Finance: Algorithmic Trading

#59

Post by empath75 »

lowpro wrote: post #45

The main issue I found in algo and financial aspects of programming is that the market is a zero sum game, and my intro knowledge of finance and algorithms, even when I know python, are no match for MIT PHD Quants who does it full time. There's no real way to compete with that, and therefore I would lose money, even if the data showed it might be successful in the future, firms and full time workers on algo trading w…

You can get an advantage if you focus on smaller markets or industries where it's not worth the time for the big quants to play in.

Re: Python For Finance: Algorithmic Trading

#60

Post by SirLJ »

dsacco wrote: post #56
SirLJ wrote: post #46

Earlier quoted context omitted.

No you cannot

First of all, you're criticizing QuantConnect's data when you claim to get your data from ebay. Second, yes you absolutely can verify QuantConnect's data. You can use it as much as you want within the context of their platform, you just can't download the data en masse from their platform and use it on your own. But if you have tick data (equities) or minute data (options) yourself, you can certainly verify it (which…

Unfortunately you cannot display QC tic data to be able to very it against you broker for example. As I said before if you have a good and cheap source, please share it with everyone...
Post reply on HN