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Python For Finance: Algorithmic Trading

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Re: Python For Finance: Algorithmic Trading

#41

Post by wtvanhest »

dsacco wrote: post #38
wtvanhest wrote: post #35

Earlier quoted context omitted.

The problem with learning finance that it takes a lot og effort. A good place to start would be taking the 3 CFA exams. After about 1000 hours of study, you will have a basic grasp of finance.

This is utterly the wrong way to learn finance for quantitative trading. That would be an unproductive use of time. Read Options, Futures and Other Derivatives and Algorithmic Trading and DMA and you basically know everything you'll get from public sources that could be meaningful for trading. If he knows how to code and is looking for "mathematics of finance" he should start with those, not the CFA.

That is your opinion, but if you look at the resumes of scientific active equity fund managers and quants, they all have it. Just understanding the quantitative side leads to vast underperformance over the course of market cycles...

Re: Python For Finance: Algorithmic Trading

#42

Post by dsacco »

nickthemagicman wrote: post #25

How is algorithmic trading not equivalent to astrology? Nothing can be predicted because there's way too many confounding factors.

I have a mean reversion strategy based on the comparing the results of several types of sentiment analysis in real time. It earns between 3 - 15% in options trading every few days with a win rate of around 70% and an average holding time of a few hours. Stop losses are at -70% or so.

Algorithmic trading is very difficult, but it is empirical. Information asymmetry exists in the market, and if you can capture it you profit. I don't know how capacity constrained my strategy is (I think it would be difficult to work this strategy profitably with over $10M depending on how much effort you put into execution; my execution is unsophisticated and I'm working with two orders of magnitude less than that), but it's working.

Re: Python For Finance: Algorithmic Trading

#43

Post by SirLJ »

jaredbroad wrote: post #37
SirLJ wrote: post #33

Earlier quoted context omitted.

When promoting your company, please say so, also on QuantConnect one cannot actually see the data, so there is no way to verify how good the data is...

Sorry SirL! I edited it within 10 sec to be explicit but you must've refreshed before I'd updated it =) We provide FX/CFD data for free download; the other data is restricted by the exchanges sadly so we can't make it available. Instead we put the tools we used to make it into LEAN format into LEAN (/Toolbox) so you can purchase it and convert it yourself.

Thanks jaredbroad, just out of curiosity, what are the average yearly returns on your top 3 users, how much are you investing with them and how do you split the profits?

Re: Python For Finance: Algorithmic Trading

#44

Post by dsacco »

SirLJ wrote: post #16
TACIXAT wrote: post #14

Earlier quoted context omitted.

My understanding is that Quantopian has no interest in being a platform for HFT. It's there to democratize trading strategies and test them in a sound way. They do a lot to have a good backtesting platform and clean the data that is available in the platform. If I wanted to automate a strategy, I'd have to figure out the Robinhood API and basically recreate what they have. Instead, I can use their platform, their bac…

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 them permission, as outlined in the terms of service (which are very readable, by the way).

NB: I am not unaffiliated with Quantopian and don't use the platform for my own trading, but I have tried it out before.

Re: Python For Finance: Algorithmic Trading

#45

Post by lowpro »

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 would simply be faster, more focused, have more funding, and be able to quickly and constantly adapt at the scale an individual could not.

So despite the fact that the subject is interesting, I'd consider it a waste of time to try and gain anything but a basic understanding of the industry and how algo trading works.

Re: Python For Finance: Algorithmic Trading

#46

Post by SirLJ »

rl3 wrote: post #36
SirLJ wrote: post #33

Earlier quoted context omitted.

When promoting your company, please say so, also on QuantConnect one cannot actually see the data, so there is no way to verify how good the data is...

> 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

Re: Python For Finance: Algorithmic Trading

#47

Post by SirLJ »

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…

Sorry, but this is like saying those CalTech PHD in Computers Science are the best, so no point for me to go into that field... This is like everything in life, not easy, but if you work on it it'll pay out in the end

Re: Python For Finance: Algorithmic Trading

#48

Post by etep »

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…

If the market is a zero sum game, then for every winning trade, there must be a losing trade. Obviously some teams must be winning consistently, which implies that some are losing consistently. This says that the consistent losers go out of business. So who are the winners going to trade with? By contradiction, it is not zero sum.

Re: Python For Finance: Algorithmic Trading

#49

Post by dsacco »

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 capturing that without requiring a PhD or the resources of a large firm. That's not to say it's easy per se, but it's not hopeless. It requires special expertise or an unconventional approach.

Otherwise I agree that most people probably shouldn't attempt it (for risk tolerance reasons).

Re: Python For Finance: Algorithmic Trading

#50

Post by dsacco »

wtvanhest wrote: post #41
dsacco wrote: post #38

Earlier quoted context omitted.

This is utterly the wrong way to learn finance for quantitative trading. That would be an unproductive use of time. Read Options, Futures and Other Derivatives and Algorithmic Trading and DMA and you basically know everything you'll get from public sources that could be meaningful for trading. If he knows how to code and is looking for "mathematics of finance" he should start with those, not the CFA.

That is your opinion, but if you look at the resumes of scientific active equity fund managers and quants, they all have it. Just understanding the quantitative side leads to vast underperformance over the course of market cycles...

What resumes? Can you show me these vast numbers of resumes with CFAs? Are you talking about fund managers or quants? If your goal is develop quantitative trading strategies, you shouldn't be getting a CFA because it's superfluous.

http://news.efinancialcareers.com/us-en/150266/why-the-cfa-w...

On another note, even if what you were saying were true (and it isn't), it wouldn't demonstrate that it's useful to have the CFA. Certifications are usually helpful for HR or for regulation, not for actual knowledge. And everything you need to know about finance can be learned in far less than 1000 hours for trading if you're in a research or development role.

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