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Python For Finance: Algorithmic Trading

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Re: Python For Finance: Algorithmic Trading

#111

Post by _e »

jlg23 wrote: post #89
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 are oversimplifying and overcomplicating at the same time. If it was an easy/obvious zero-sum game, we'd not have people going into finance at all. MIT does not teach "HFT", those "MIT PHD Quants" are just as untrained in finance as you are. The rest is math, something that requires time to study but not necessarily a certificate. And creativity. I've only seen a single HFT-algo that made me say "wow, that's crea…

1.) Bet what you can afford to lose

2.) The less you know the better

Re: Python For Finance: Algorithmic Trading

#112

Post by chronic940 »

zootam wrote: post #101
SirLJ wrote: post #47

Earlier quoted context omitted.

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

>but if you work on it it'll pay out in the end unless its one of the many things in your life that don't

Like machine learning. Too many on HN are trying to get into it but will be outgunned by the Stanford PhDs et al.

Re: Python For Finance: Algorithmic Trading

#113

Post by thedufer »

SirLJ wrote: post #63
dsacco wrote: post #53

Earlier quoted context omitted.

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.

Yes I can prove it, go and request historical data for symbols like AA, HTZ and you'll see yourself

Last time you listed symbols that were missing dsacco showed that they were not. Why should we believe these?

Re: Python For Finance: Algorithmic Trading

#114

Post by branchless »

yonkshi wrote: post #58
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?

Trading is a zero sum game. You are making nothing. You win, someone else loses.

Re: Python For Finance: Algorithmic Trading

#115

Post by branchless »

etep wrote: post #48
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.

New suckers. Also a zero sum game doesn't mean one side always winning.

Re: Python For Finance: Algorithmic Trading

#116

Post by TACIXAT »

Maven911 wrote: post #22
TACIXAT wrote: post #19

I went to the Quantopian conference for their basic training on algorithmic trading. This blog post was pretty much what they covered, intro to pandas and a simple strategy. There is a lot of educational material on their site too (which is what you ended up getting in the paid training). My biggest thing with the Python for Finance books - I know Python, I want to learn finance. All these books are the inverse of th…

Did you find the conference worth it ? I was thinking of going to it as well or even going to the online version since it's a lot cheaper but ended up not taking part.

I went to the conference 1 or 2 years ago and it was good. Worth the student pricing. I only did the training this year, also on student pricing, and it was pretty disappointing.

Re: Python For Finance: Algorithmic Trading

#117

Post by ndesaulniers »

JabavuAdams wrote: post #91

So, I got semi-seriously interested in this around the end of last year. My takeaway is: it's not about implementing a couple of trading strategies. It's about implementing a pipeline that rapidly allows you to test what-if scenarios. I might have like 10 ideas a day for strategies. How many of those can I rigorously validate per week? What about variations? I.e. tuning various hyper-parameters? Combinations? How qui…

> 5) Decide to ban myself from manual trading as obviously I'm an addict.

> 7) After losing around $20k

Re: Python For Finance: Algorithmic Trading

#118

Post by thedufer »

SirLJ wrote: post #34
jaredbroad wrote: post #30

Earlier quoted context omitted.

Futures have millisecond timestamps (trades/quotes), equity trade ticks are rounded to the nearest second, cfd/forex are millisecond quote bars. For options we have minute resolution data =)

please use a disclaimer if and when you are promoting your own company...

Does the "we" pronoun not make this sufficiently clear?

Re: Python For Finance: Algorithmic Trading

#119

Post by ndesaulniers »

hendzen wrote: post #77

If you want to build a real algorithmic trading strategy.. please pick up a real textbook such as Qian's Quantitative Equity Portfolio Management. You should understand the following concepts at a minimum: - Markowitz portfolio optimization (mean-variance analysis) - Beta-neutral portfolios (i.e. using MSCI BARRA, sector ETFs or PCA factors, etc) - Alpha decay - Time series analysis (autocorrelation, GARCH, ARMA proc…

Any others you (or anyone else) recommend?

Re: Python For Finance: Algorithmic Trading

#120

Post by lz400 »

wtvanhest wrote: post #104
lz400 wrote: post #92

Earlier quoted context omitted.

Quants don't usually have CFAs. A phD in physics is more common to see. CFAs help you working in a real financial environment where you need to be aware of a lot of stuff. If you want to day trade at home, it's close to useless. Please nobody gets a CFA if what you want to do is learning day trading.

If you are just gambling with math at home, dont bother. If you want to understand finance like the op, better start learning. They have phds and cfas...

If you want to understand finance 360, from compliance to settlements, from middle office to government bonds structuring, sure, get a CFA. But somebody learning to trade not only doesn't need one, but very little of what's in it is useful. I've been in finance for ~10 years, trust me, very few traders and quants have CFAs, that's more product management, research analysts, compliance, controllers, risk managers, even IT. We are not talking about learning those jobs.
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