"Pair-trading VXX and XIV based on the StockTwits sentiments of the SPY at market open. The backtest did really well from 2011 to 2014 with 1700-1800% return in 3 years; and flat between 2014 to present-time... would love to see what people come up to reduce the drawdown's and improve the performance from 2014-2016".
The reason I share my algorithms is trading is one of the hardest ways to make easy money. Making money due to slippage, regime change and overfitting bias is difficult, a poor investment (most hedge funds don't beat index funds) but sharing and learning about statistics, machine-learning and big data is a better investment in self.
Also philosophically, most strategies have limited shelf-life, so it is better to learn how to fish than to hold onto the fish you've got.
Hi, a shameless plug: I went to the Quantopian (the company that is behind Zipline and essentially uses Zipline as the core backend to their cloud platform) algo-trading hackathon two weekends ago and came up with this algo:
Pair-trading VXX and XIV based on the StockTwits sentiments of the SPY at market open. The backtest did really well from 2011 to 2014 with 1700-1800% return in 3 years; and flat between 2014 to present-time,
I'd really love it if people can improve upon the algo and see what people when they clone the algo and come up with ways to mitigate the drawdown's and improve the performance!
http://changefol.io is a site that I use to set up micro-donations that's based off my daily spending, e.g., donate $0.05 for every $1 I spend at the gas pump to Sierra Club; $0.10 for every $1 I spend at the grocery store to WWF (World Wild Life Fund).
Uhh, I really hate INTJ personalities, most are so uptight centered on being judgmental. The "worker bee" personality that I see at IT/software places.
INFP ftw, feeling up and perceiving people over thinking and judging people!
What is your impression of them as a pernicious/positive force? Are they essentially market making?
Open for debate. Depends on what you mean as a pernicious/positive force. Good for retail investors, institutional investors, stability of the market, or the sell-side? All of these are conflicting sides. It is generally SEC's mission to protect the small individual investors' fair access to the market, while trying to walk the fine line of not disrupting the big institutional investors/sell-side brokers' way of doing business (and their political lobbying groups).
Pro HFT argument: HFT are virtual market makers that through the use of technology and arbitraging through multiple ECNs, are decreasing the bid/ask spread of the traditional market makers and providing more liquidity to the market. They serve as stabilizing force during irrational exuberances.
Con HFT argument: HFT are bad predators who through technology, jump ahead of institutional investors' block orders and in term pass on higher priced liquidity to retail investors that no one needs. They don't serve as stabilizing force, as they stop trading as soon as they stop making money and in fact may fan the fire by employing high frequency short selling in a flash crash.
You are right that for a individual investor focused on the long term, a algo-order probably wouldn't make a difference in comparison to a market order with your E-Trade/Schwab account (most likely, your order-flow won't go directly to the market anyways; it is either crossed internally, or re-routed to a broker/dealer that's paying retail brokers for the order flow such as Timberhill).
However, I respectfully disagree that humans are better at making trading decisions than computers. The world of algo trading can be divided into two sides, a) high frequency traders, who through fast cancel-and-replace limit orders and colo with the market centers, try to act as virtual market-makers (or scalpers, depending on your perspective), b) buy-side institutional fund managers who want to complete their orders, without HFT predators and negative market pressure. Large block orders are spliced into small lots (i.e., VWAP) and sent to the market using intentional limit price over time to hide the movement of a huge buy or sell order on the market.
Human beings might be better than machines at picking single stocks for long term investing (although most people are probably still better off investing in an ETF). But that's not how sell-side traders make money in the first place. Guys like GS/MS/Timberhill make money by having the unfair advantage of faster execution speed, more capital and specialized trading algo's against the small retail investors.
"Yes it takes practice to become a good singer, but any number of hours of practicing poor technique won't do much good."
Yes, this is true and oft-repeated mantra on YC News. But it begs the question, how does one practice the "proper" technique (I mean after all, doesn't everyone want to follow the correct form of shooting hoops/strumming a guitar chord, but most people don't have a dedicated shooting coach/guitar teacher to watch their every move or they do know the right technique by heart in theory but can't carry it out in practice for various reasons).
The best protip I've received in learning an instrument (and actually doing anything) is when you are stuck at a particular exercise, move onto the next hardest exercise anyways. When you stumble upon the simplest guitar lick on the first page of a guitar book,you might say "are you crazy? I can't even play the first exercise." But if you play the next hardest lick and stumble upon it for a couple of days, then try to play a third lick even harder than the second lick for a couple of days, and then go back to the very first lick. You might find that you can now play it pretty well, or suck much less than before.
Yes, practicing is all about hard work and all that, but it is also about momentum and keeping things fresh and new. If it's not fun, you are doing something wrong.
Yea, write a simple Python/Perl script that interfaces with TOR and force-reset your exit nodes each time you send out a new request and randomize the time interval. There are plenty of TOR exit nodes in the world to swing the vote your way.
I think your analysis is correct despite of all the self-congratulations that goes on here on Hacker News.
A couple caveats though; I think your definition of technology is too narrow, Web 2.0 is only one segment. The smartest people have moved on from IT which has already matured, and are working on green tech and genetic therapy where supposedly the next break through will come.
Also, even in software since money seems to be the biggest concern to you, consumer-centric software isn't where the money is made - it's just what appeals to be the most sexy to the young & naive. High frequency trading, e-discovery, medical informatics, Sarbones-Oxley compliance modules - there are tons of enterprise software companies in those fields make revenue more than most of the Web 2.0 startup's you listed.
Finally, I don't think what motivates programmers aren't necessarily the same that motivates a business-person. The best way I can describe this is by the musician and A&R example; there are some musician's who want to make it big and appeal to as many people as they can (i.e., Linkin Park), there are those complementary record labels who can help popular musicians by handling the business side and tailor their records to the focus groups. But there are also those musician's who care more about pushing than envelope than selling records, and if they are successful, like the early black blues & jazz musician's in the 40's and 30's, their influence will get heard in the 60's and 70's (ripped off by white musicians). Most people in IT operates on this sliding scale between craftsmen and A&R as well.
After you have learned the basic open chords, scales and the fretboard notes, and "Wonderwall" - and are bored with the strings. You could go always back to silicon and build
a) an Arduino interface to an software/hardware mixer such as Ableton Live for live mixing and improvisation
b) an machine-learning program that takes GuitarPro tabs of the entire catalog of your favorite band and permute MIDI compositions/new guitar tabs based on the band's style.
c) Program your own synth in Max/MSP.
d) Develop a real-time music note recognition software that tab/annotate the chord changes, drum tabs, make music sheets for live online music jamming.
Also try to play the sitar/banjo/mandolin, there's already lots of guitar players.
Thank you for your $0.02; this might be really obvious to you, but could you be kind and enlighten the oblivious engineering crowd on this forum and be more specific as to what kind of value-add does sales and marketing add to a early-stage hardware startup or any consumer-oriented tech startup in general?
Maybe someone as a musician can enlighten me, but I always have so much trouble doing rote scale at every guitar fret position, arpeggio, memorize-these-chords exercises.
I always hear two sides of the argument that a) a la Mr.Sivers, oh you need to learn how to play fast, memorize all of these scale/chord patterns, and every single guitar-player style from Blues to Jazz to Folk to Pop, or b) you shouldn't worry too much about playing or learning too fast, but concentrate on the music; enjoy yourself, find your own style and get in the zone and slowly you'll understand how to improvise, compose and tab by immersion rather than memorizing patterns. (Because if you try to learn or play too fast, you get frustrated quickly that you aren't doing well and concentrate too much on the mechanics of note perfection that it affects your performance). Some books I read recommend daily practice sessions of only 20 minutes per day, but consistent daily practice.
I'm sure I'm painting a totally false dilemma but curious as to how some of the pro musicians out there takes are.
EDIT: Since we are on a programming forum and I assume that the advice is geared towards developing for startups, IMO, the best way to learn how to program is to learn by working on your own project and ruthlessly plagiarizing off of other people's open-source code base. Because you are motivated to finish the project because it's something that motivates you and you are forced to look deep down into the stacks because you almost always have to customize/hack 3rd party code to do something your way.
Rote memorization is a problem in programming, too. Not to rag on PHP or Visual Basic, but people could get by with just memorizing certain keywords - or not solve hard problems by using a third-party plugin or googling for code snippets.
I hated my Automata and Functional Programming Language classes because I was forced to write out formal proofs to prove really obvious programs to be correct, or to demonstrate something arcane as the Turing machine. But it forced me to think about programming in a different light and find my own way to find the solution.
This reminds me to always to look under the hood of all of the web frameworks, data ORM's that I'm using and to improve my 3rd party libraries, in programming. Also in music, not to blindly play the scales or the tabs of popular music, but learn the different patterns, what makes a song tick, and composition. Also in sports, not just play or practice according to drills, but to analyze post-game what went right, what went wrong, and to apply it in future matches.
Affirmative action doesn't bother me. So what if the SAT-tutored and AP/IB class-taking kids from suburbia couldn't get into Harvard, Princeton or Yale? Boohoo! It's the end of the world when they go to the Duke, CMU, or Cornell where upon graduation, you have finally (phew!) saved the family face after all by going to medical school, law school or becoming that engineer - so that your Asian or Jewish mother has something to good to say about her son at her friends' dinner party.
Without the elite ivy league schools offering 100% financial aid (vs. middle-tier state schools that try to amp up their U.S News & Report Ranking by offering full-rides to high SAT scorers from the 'burbs, and hence could not accommodate 100% to need-based financial aid), a lot of urban kids wouldn't be able to afford college at all, not even state school. Also, schools that could afford to let down their average SAT scores often pick students based on their "narrative". A technical forum may sneer at the qualitative over quantitative, but it means that schools consider their applicants' background, what odds they had to overcome in their environment vs. say, how much money someone's parents spent for their child's Princeton Review classes.
However, I agree with you in that affirmative action have a lot of inefficiencies. For instance, a lot of under-represented minorities from the 'burbs and prep school game the system by offering a offer that colleges can't refuse: high SAT score and diversity, but haven't overcome any serious odds as an urban student would. A lot of colleges game the system by claiming diversity on their admissions broshure, when recruiting a lot of black/hispanic students to their freshman pool - but do not do a proper job of trying to graduate their minority students at all.
When I'm sixty five, I'll be playing WoW and listening to the nostalgic popular records of my day, from fine artists such as Soulja Boy Tell 'Em and T-Pain.
"Don't care if the cat is a black cat or a white cat, as long as it catches the mouse, it's a good cat."
Basically for those who are interested in the background of the quote, Deng arose to power at the end of the Cultural Revolution, at the height of Chinese communism ideology when the government was so communist that they broke away from the Soviet's and accused of USSR as "revisionist"; destroyed all of the Buddhist temples in China as "backwards and Confucius," imprisoned the sons and daughters of former capitalists (because there were no current one's left) for being "Western corrupution," and stripped intellectual's of their post in Universities and forced them to "country-side re-education camps" for being "radical leftists against communism." Before he rose to power, Deng himself was denounced and stripped of his political post, and sent down for "re-education."
In little as four or five years, in that kind of environment, Deng has reformed the former Chinese market-command economy to become more capitalistic. Liberalized the political and free-speech in China, that allowed for media/film/journalism criticize the Chinese Communist party which eventually led to the Tienanmen Incident. Deng, confronted tremendous criticism and resistance from the hardliners of the Community party when he tried enact his reform. He used this quote about "black cat" or "white cat" in a speech he delivered to the political cadres to persuade them to be a bit more pragmatic and less ideological about their communism, after 43 million people have died from Great Leap Forward and the Culture Revolution. It was also Deng who eventually issued the order to strike down the student protesters in Tienanmen.
This ambivalent character of Deng, of balancing liberalization versus stability has become a hallmark quality of the Chinese government. See how the Chinese government encourages citizens towards private asset ownership and entrepreneurship, but not towards democratic representation. See how the Chinese government agrees in principle to a Korea/Iran nuke disarmament, but they are more so wary of the stability of Korea/Iran region in the event of a international escalation - that they block the UN Security Council sanctions. Whether the current Chinese/Russian model of free markets but central strong political oligarchy without an ideological emphasis, versus the American model of free markets and free democracy with a strong ideology (leader of the free world), the viability of either models remains to be seem.
I'll share my algorithm here:
https://www.quantopian.com/posts/xiv-slash-vxx-pair-trade-1
"Pair-trading VXX and XIV based on the StockTwits sentiments of the SPY at market open. The backtest did really well from 2011 to 2014 with 1700-1800% return in 3 years; and flat between 2014 to present-time... would love to see what people come up to reduce the drawdown's and improve the performance from 2014-2016".
The reason I share my algorithms is trading is one of the hardest ways to make easy money. Making money due to slippage, regime change and overfitting bias is difficult, a poor investment (most hedge funds don't beat index funds) but sharing and learning about statistics, machine-learning and big data is a better investment in self.
Also philosophically, most strategies have limited shelf-life, so it is better to learn how to fish than to hold onto the fish you've got.