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Rather than posting a new topic every time, may as well just post papers and links here. Please keep it to concrete strategy ideas, the more explicit the better, and preferably those that could be implemented in Quantopian!
Quite a few papers in Turnkey's alpha DB: And do you mean the Academic Alpha section? Yes I did, my mistake. It's a free sign-in. They are one half of the guys who wrote Quantitative Value the other half is Empiritrage , and they regularly write little papers about the sort of exploitable opportunities people here might be interested in.
I am not affiliated with them. This blog post on Limited Attention and the Earnings Announcement looks interesting login not required. I also have some code that I can share. What are the top 3 you'd recommend reading through carefully, that could be coded in Quantopian without a heroic effort? Hi Grant, I think good old trend following is always fun. In case you haven't checked it out, I noticed that Claus Herther has a great starting point. I'd like to add in measurement of the slope of a trend, momentum, and williams to help add some "trend anticipation" into a standard trend following system.
Just stumbled upon this goldmine of hundreds of papers, most with pdf links, on a variety of topics: Note he doesn't actually give the formula for this indicator, so one would have to do some work to try and figure out what he's talking about Hi everyone, is ist possible to program the black litterman approach with Quantopian? Tips are highly welcom. Thanks in advance for your help.
It should be possible, someone wrote a minimum variance portfolio re-balancing algorithm a few months ago. You'd need to use fetcher to get your index weights for your prior, make sure to fetch them "as-of" the date you are at in the back-test.
It would be an excellent demonstration and example, perhaps you can get the quantopian folks to code it up! Thank Simon for your comment. I wrote my last thesis about BL so I have the theoretical background. But to be honest with you, I am not quite good in programming.
Nevertheless I will try and let the community know. Thomas Wiecki posted the article first on https: I just copied the link here. If you have comments on the article, I suggest posting them to Thomas' thread. Mebane Faber has a few interesting papers at Cambria Investments' website http: If you search for Mebane you should find them. I don't know if they still work in the backtester.
This will, of course, add a strong long-term long-biased mean reversion factor to the system. We analysed one year of front page banner headlines of three financial newspapers, the Wall Street Journal, Financial Times, and Il Sole24ore to examine the influence of bad news both on stock market volatility and dynamic correlation. Our results show that the press and markets influenced each other in generating market volatility and in particular, that the Wall Street Journal had a crucial effect both on the volatility and correlation between the US and foreign markets.
We also found significant differences between newspapers in their interpretation of the crisis, with the Financial Times being significantly pessimistic even in phases of low market volatility. Our results confirm the reflexive nature of stock markets. When the situation is uncertain and unpredictable, market behaviour may even reflect qualitative, big picture, and subjective information such as streamers in a newspaper, whose economic and informative value is questionable.
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The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances. There is a clear and consistent dropoff in return as years progress from toward , and I'm curious to see if this trend has continued in the three years since. I've noticed that the many cryptocurrency exchanges out there have a significant spread. The spread between Mt.
Gox has a surge. Personally I'm fascinated by it. I found this overview of quant investing by Max Dama http: At page 16 he very briefly explains a possible trading idea through the exploitation of the "first day of the month concept". Its probably the most important trading day of the month, as inflows come in from k plans, IRAs, etc. For instance, one time I was visiting Victors office on the first day of a month and one of his traders showed me a system and said, If you show this to anyone we will have to kill you.
Basically, the system was: If the last half of the last day of the month was negative and the first half of the first day of the next month was negative, buy at 11a. This is an ATM machine the trader told me.
I leave it to the reader to test this system. I tried this using excel and intraday data I got from a russian website giving away free historical prices for the 40 most traded stocks in the US, but obviously quantopia is a much better way of trying this simple strategy.
I didn't calculate the sharpe ratio, but my thinking is that if the sharpe ratio is high and you do this 12 mths a year and use a healthy amount of leverage you can make a nice stat arb payoff. I'm a novice to coding so I haven't made an attempt yet at coding this, so if any of u guys who are fast at this feel free to try it and post a backtest.
This one looks particularly easy to implement in Quantopian, since it's basically just technical analysis. We study whether exchange traded funds ETFs —an asset of increasing importance—impact the volatility of their underlying stocks. Using identification strategies based on the mechanical variation in ETF ownership, we present evidence that stocks owned by ETFs exhibit significantly higher intraday and daily volatility.
The driving channel appears to be arbitrage activity between ETFs and the underlying stocks. Consistent with this view, the effects are stronger for stocks with lower bid-ask spread and lending fees. Finally, the evidence that ETF ownership increases stock turnover suggests that ETF arbitrage adds a new layer of trading to the underlying securities. I found this pretty interesting, seems relevant. Optimal Trading Stops and Algorithmic Trading. I don't know how this page hasn't made up here yet, unless I missed it.
Looks promising, and simple for someone to implement! Man is it ever hard to find this thread every time, searching doesn't work well. Is there anything in this thread that would be particularly interesting to code in Quantopian and backtest? I would check out quantpapers.
Grant, I think that's really a personal question, what sort of trading strategy does someone want to deploy, and how does it fit in with their existing trading strategies? For purely academic interest, I am not sure I would be doing quant trading: Well, let me put the question another way. If so, what has been the result? Can't speak for others. Something with a good win loss ratio would be ideal. I would appreciate it.
Anyone know if you can import Futures data? Sam, I don't know about getting the data from volatility made simple, but you can use Quandl to import the data, or get it directly from CBOE. I believe they have the historical holdings as well. This link is for VXX, the others are available as well though. Their concept of "Dual Momemtum" is very intriguing. As well, extending it in the manner which is described here:.
Campbell Harvey's website is also a useful site for financial glossaries and papers on risk. It's not clear if this is a mean-reversion strategy on this cointegrated basket, or whether it's a static investment portfolio somehow optimized for low variance. Simon - have you looked through the "premium" offerings on Quantpedia at all?
Am curious whether they are worth the fee or not. I haven't, no, I was just planning on going through their free stuff to see what anomalies and papers look interesting and suitable. Not sure where else to put this.
Useful for HF algo development. Videos and PDFs are available. Statistical Arbitrage and Algorithmic Trading: Matthieu, that looks like a great resource indeed. The link seems to have changed, here is an updated one: Folks, whilst all these seem to be great resources, they need a certain amount of knowledge in Statistics.