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What type of content would you like to see?

Hey all,

I've been thinking about what type of content might be most interesting to people and I wanted to get your opinion. What type of content would you like to see us produce more? Some options might be:

  • Short form educational videos
  • Videos explaining how to use the platform
  • Long form educational videos
  • Lectures
  • Tutorials
  • Forum posts explaining quant concepts
  • Forum posts with example strategies
  • Contest roundup
  • Strategy reviews

We're open to all ideas, please leave them below. I can also make a google form if that's easier.



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41 responses


From my side, the things that would be of most interest:

  • Forum posts explaining quant concepts

For me, this would be an interesting way to introduce brand new quant concepts to those not particularly experienced in quant trading. I have a sense that a lot of the people here will have joined with a single idea that they wanted to implement, based on a single quant / finance concept, and once they have (successfully) implemented that, they may be lost for ideas of how to improve / broaden their strategy. This is certainly the case for me!

  • Forum posts with example strategies

This would be very helpful for a number of reasons. Firstly, it would help to reinforce the point above (i.e. introduce a strategy and then share an example strategy based on that idea). Secondly, it would offer beginners (and non-beginners alike!) a wider variety of templates to start working from - I have a number of close friends who would like to start work on Quantopian (I work in finance), but they do not have any coding experience and unfortunately do not have the time to invest in order to learn from scratch. I believe this could be a real positive for Quantopian commercially - lowering the barriers to entry for new joiners on the platform. Finally, it would give consistent updates on best practices / new example code where updates are made to certain functionalities (e.g. following the recent change in the slippage model, it took me ages to find the correct code snippet as I don't think it was updated in the user guide yet).

  • Strategy reviews

I don't know exactly how you would plan to implement this, or what exactly it could cover, but I have found Jess Stauth's talks extremely helpful (the ones where she goes through contest entries and picks apart what is good and what is bad).

In addition to the items you have mentioned, I would also like to push for some additional features that are perhaps not so much on the "content" side of things:

  1. Ability to trade non-US equities
  2. Ability to trade Single Stock Futures (albeit, I do not know the real life implications of this request - I just think they could potentially be an area of currently untapped alpha)
  3. Reliable data relating to short interest for any given security - I would have thought this one would be relatively simple to implement and I am very surprised that it has not been so far
  4. Most importantly: simple stop-loss / trailing stop-loss / other types of limit orders via the Optimize API. This is something that has been consistently requested by numerous users, and something that would be exceedingly useful! Ideally, users should also be able to "white-list" and "black-list" certain securities (dynamically) for a pre-determined period

Apologies if these latter requests are off-track. Feel free to suggest I create a new forum post for these if you think it would be more appropriate!



Hi Delaney,

Personally, I think there is great merit in introducing more of the following concepts:

Data cleaning, normalization and labelling - Although some datasets are good enough by itself, there are a lot of opportunities to gain an edge through preprocessing existing datasets in several ways.

Feature selection - This is extremely important since it determines the building blocks of the any algorithm and how the data sets will interact with other features. It can be quite confusing when attempting to combine features with different data frequency (quarterly versus daily).

Parameter Optimization - Although this can be bad if done incorrectly, parameter optimization would be extremely beneficial for enhancing statistical significance. Sometimes, it can be hard to determine the length of the lookback period, the frequency of learning, and also the number of stocks to train on. All of these inputs can lead to different results.

Model Selection - After opening up scikit, I realized there are numerous techniques and models available. It’s quite hard for beginners to pick the right model to use and learn about the pros and cons for each of them. This is especially difficult in with financial time series data.

Feature Combination - Combining multiple features can be done in so many ways. No single model, signal or strategy can hope to last forever. Smart ways of combining alphas can lead to a longer edge with less decay.

I'm not sure what is the best way to go about doing this, but I imagine lectures, tutorials and forum posts would work wonders


I'd like to see the following you alluded to:

Example Strategies
I'm the type of person/student who always preferred learning by doing. In school, I couldn't sit through lectures but I would "happily" do the homework and learn through trial and error... and reviewing the example problems. I like teaching myself, and I learn and comprehend a heck of a lot more that way than through "traditional" practices.

I think this approach would be especially helpful if you also put together a collection of strategies. So you may want to post individual strategies that do particular elements well (risk management, machine learning, different factors etc.) but also have a post or lecture that provides an executive summary almost of the different elements and links to them. I had trouble navigating the various forms of content and the shear amount of it. A detailed list may be helpful with links. Another point here was that I learned more from the forum due to the searchable nature of it.

Contest Roundup / Strategy Reviews
I assume this means a review of algorithms entered in the contest and a discussion on what was good and what about the strategy would concern the Q investment team? Strategy reviews would be similar but may include more information on the algorithm?

I find this part of the Quantopian development process significantly lacking. You have a lot of content for folks to learn and develop their strategies. But now that many of us have done this, I'd think the next logical step for all parties would be to refine those strategies and improve them. It's very difficult to improve something when we don't get any feedback. I (Bright Orange Cheetah) recently won contest 33 with a strategy I think meets what you're looking for and I've been told that I'm on a list of strategies thats being considered for allocation... but I still haven't heard anything definitive. I wasn't and aren't currently expecting Q to accept that strategy or any on purely the face value and out-of-sample performance. I'd expect there to be some refining that would come from a conversation between Q and the developer... but when do we have that conversation? I can't imagine I'm the only one in this position either.

This new contest may help this process but I still want to see more examples of Q's team evaluting strategies and saying what they liked and what they didn't like. The only lecture/webinar I've watched was the one by Jess where she went through a few examples of strategies. I thought that was gold and would want to see more of those to gain an understanding of what you're looking for and where my strategies either meet your needs or where they may need more work. But ultimately I'd love to have even a 30 minute conversation with someone who gives me some indication of that.

The goal is to actually give out allocations I imagine and then use those strategies to make institutional investors money. I look at the contest results and there are only about 80 community members with algorithms that even pass the filters. So even with the vast amount of content, resources and community members you're down to 80. I'd like to see a bit more closing the deal with these 80 to help them refine the strategies into something your team could invest in. The content you already have will continue to generate new opportunities and developers for you, but at some point you'll need to do some more one-on-one work.

Hi Delaney,

Machine learning is a hot topic right now.

How about creating video(s) that illustrate a problem and how to solve it using various machine learning methodologies (random forest, support vector machines etc.) within the Quantopian platform. Things I would like to see in the video are
a) Problem statement - including dataset
b) Machine learning algorithm used and code
c) Discussion of the solution in some detail as to what the machine learning inference is that is not intuitive to the human mind.

Ideally would like to see a set of machine learning videos and then a summary video comparing them (what each ML algorithm is good in etc.)


Hi Delaney -

Thanks for this opportunity. Some feedback:

Functional block diagrams - A good example was provided by your former Chief Investment Officer, Jonathan Larkin, in his blog post, A Professional Quant Equity Workflow. This type of high-level architectural diagram is effective in putting detailed efforts into context. For example, in his diagram, it is immediately clear that Alpha Combination is a separate function from Portfolio Construction, what inputs and outputs are required for each function, and generally what role each plays in the whole system. Under the heading of "functional block diagram" I would include all forms of informal and formal diagrams, depending on the context. I'd recommend considering how you could support diagrams and more generally, editable graphical representations of information (e.g. a "virtual white board"). This would include your research notebooks, algos, and the forum.

Math - There is a tendency to jump too quickly into commented Python code, rather than fleshing out details in the more approachable universal language of mathematics. Be sure to cover the math thoroughly first (with some diagrams) before showing the specific implementation in Python and the Quantopian API (in fact, skip these, if they don't add to the discussion). If code is used, it should tie directly and transparently to the math.

Simple, readable code - You have a bunch of Pythonistas, but it can lead to all manner of fanciness in your examples. There is just too much of a mental load and time-consuming effort in unraveling "elegant" Pythonic code that I'm sure is world-class, but is not helpful for non-experts. Generally, this is a huge barrier, I think, for your success. You require a rare bird--some background and interest in trading, math and statistics expertise (possibly mixed with data science and machine learning), Python coding expertise, and a good dose of patience and persistence, plus spare time. So, you just need to be sure to lighten the load wherever possible, even if the code examples are less-than-Pythonic. There also seem to be multiple styles/frameworks within Quantopian, which adds to the mental load. If you are publishing code, and the intent is to educate, I'd make sure that it is reviewed and edited for read-ability and meets some sort of consistent style that a beginner/advanced beginner can digest.

Industry veterans - I think it would go a long way if you could get an experienced quant trader to be on the front lines of support and training. Frankly, Quantopian from the inception has felt like the blind leading the blind. Jonathan Larkin was a good addition and he did interact to some extent with "the crowd" but unfortunately he departed, and has not been replaced. Having one or more people who've actually worked at hedge funds and can provide specific, real-world advice would be great (of course, it would take a special kind of patience to field questions from inexperienced knuckleheads like me, but that's your business, right?). Along these same lines, as mentioned above, hearing directly from folks like Jess Stauth, who (as I understand...you guys are very secretive) leads the algo selection team for the 1337 Street Fund would be nice. Also, you have other folks on your leadership team who I'm sure have a wealth of knowledge that could be shared, beyond the nitty-gritty of researching and writing algos. For example, a Q&A on the legal/regulatory issues of setting up and running a hedge fund would be fascinating.

Base algo templates - You need to get serious about publishing and maintaining standard algo templates that conform to the workflow. This is very simple, so don't over-think it. Develop and release a modular multi-factor/feature template on Github, and maintain it regularly. Open it up to users, so that they can provide feature requests and report bugs. The algo should run on the QTradableStocksUS back to at least 2005 without glitches and conform to the contest rules and fund needs. A user should be able to research a handful of Pipeline factors, plug them in, and be able to submit the algo to the contest--presto!. One nice option would be to select a basic ML algo for the alpha combination step, without having to do any coding--just grab the module and plug-and-play.

Forum style - For moderated threads, it would be very helpful if the moderator/presenter kept a running Q&A at the top of the thread, versus doing lots of individual @user replies scattered about. I wouldn't necessarily change the forum format (e.g. something akin to Slack, which almost has too many features), but if you are trying to educate on a specific set of topics, capturing them at the top would be very helpful.

Specific, actionable feedback - I would echo Stephen's comments above that one would think that getting specific feedback on an algo on its potential suitability for the 1337 Street Fund would be a standard part of your process. I have algos in the contest, and I'm doing o.k. (can buy more than a sandwich with my winnings, which was my initial goal), but I have no clue what I should be doing to bring a given algo to the point where I would definitely get an allocation. What I'd like is some definite measure of the probability of getting an allocation for a given algo, and specific guidance for improving that probability.

Public issue tracker - Simple. Don't over-think it. Just do it. Zipline and other open-source projects don't count. I'm talking about your entire platform.

Open-source code - Another no-brainer. For example, you've held off publishing the Optimize API code. And my understanding is that the risk model and QTradableStocksUS code will not be published. Not really sensible, in my opinion, as I understand your business, but maybe I'm missing something.

Business model & culture - I (and I suspect other users) would be glad to provide guidance on how you've set up the business, the culture, and other fundamental issues that impact your efforts in attracting, training, and engaging quants. I realize that this is far afield from your request, but I do believe you've missed the mark as a truly crowd-sourced effort, and I have a sense why (I have been advised that you are not likely to change, but I'm still holding out hope). Fawce and the management team could do an open Q & A, for example. Not in their DNA, I suspect, but I thought I'd throw it out there for consideration.

The question is, how can we serve you however the examples look like: What can we -- present to you, generous of course but I think the actual goal is: How can we all coordinate more toward success for all, I have to broaden it that way to not risk being sort of off-topic with this.

I would set up a special page where we can post wishes and concerns and others can vote on them.
Searchable, sortable, limited characters (or using [more ....]), and one optional image as thumbnail clickable to view full size.

Hello Delaney, thank you for taking feedback from the community.

In the future, I would personally like to see more emphasis on the following topics/sections:

Lectures: This section is in my opinion probably the most underrated, yet most important sections of the website. Over time, it's been of great help, and I'm sure others can attest to this as well. I agree with Grant Kiehne to add more math, it really helps to understand it in the long run.

In addition, this is just extra things that I would like, but haven't find the right time to post:

Collaboration in Research: Often times, I have a notebook I want to work on with a friend or colleague, but am unable to do so unless I save, send the file over, and wait for the code to run again in the notebook. This is hardly efficient, and I would prefer a fully functional collaboration system, kind of like the one Google has built here: https://colab.research.google.com - I understand there's probably some security concerns with making a system like that, but I would love to have that feature.

Thank you once again for seeking feedback from the community.
EDIT: Formatting

White papers & peer-reviewed published research papers - Content in the form of edited white papers and peer-reviewed published papers would be helpful, on a variety of topics (looking forward to the forthcoming white paper on the risk model), with bibliographies. Case studies of actual money-making strategies from yesteryear would be particularly interesting, illustrating the entire Q workflow. Analysis of Q meta-data, such as All that Glitters Is Not Gold: Comparing Backtest and Out-of-Sample Performance on a Large Cohort of Trading Algorithms, would be good. Of course, publishing information about the 1337 Street Fund would be cool, too (but maybe not feasible from a regulatory standpoint or your business practices).

Zipline schematics - It would be nice to have a set of "schematics" for zipline, to understand how all of the pieces fit together, and how the backtester works. Perhaps you have these and could just publish them? Alternatively, a lecture with a slide pack would be of interest, by one of your subject-matter experts.

@ Delaney

Thank you for asking. I'd respectfully flip the question back to ask what is Quantopian seeking, other than for a trading savant to miraculously emerge? Assume for the moment an above average intelligence individual stumbles upon the Q website and ultimately is an allocation candidate. In Q's due diligence, what are the essential learnings this person must have for Q to grant an allocation? After all, a lucky streak can happen to anyone, and a few can even present a rational justification for their lucky outcome. What steps would you like to hear that person to have taken to achieve this positive outcome?

An even better question is what is the essence to these learnings, and what is the most efficient order to acquire these learnings? Of the above postings, I believe Cheng Peng may come closest to an answer.

Library/bibliography - Presumably you are not aiming to provide general education, but rather have a goal of your audience writing algos that will conform to your present need for diversified long-short market neutral, using the QTradableStocksUS universe. So, there should be a body of literature germane to the endeavor--academic papers, case studies, theses, books, lectures, videos, blogs, etc. My understanding is that long-short equity is as old as the hills as far as strategies go, so there should be a wealth of specific information out there. One particularly useful approach would be for you to do a thorough literature review and then publish the result in an edited paper, with a bibliography. Another less formal approach would be to go through readily available information on the Q site and select particularly relevant references, explaining why they would be worthwhile reading (e.g. Trading Strategy Ideas thread). There are also various posts that could be reviewed and commented upon by subject matter experts as to their relevance. It would basically mean building on existing content such as The 101 Alphas Project, Enhancing Short-Term Mean-Reversion Strategies, Machine Learning on Quantopian Part 3: Building an Algorithm, and Alpha Vertex PreCog Dataset (the last one, by the way, might be a great case study of the perils of over-fitting in ML). In the case of the ML thread, I think it would be an opportunity to pick it up and do the re-factoring, etc. discussed in the thread, to illustrate various alpha combination techniques. I would note that you already have a library of sorts: Q Algorithm and Research Idea Library. It was last updated by the author on Feb. 2, 2017 (a request for an update was posted on Sept. 27, 2017, and there was no response).

Guarding against bad/missing data - This is a special topic, but I think it deserves coverage. Despite your best intentions, there are problems with Q data, and so various guards need to be in place. It would be great if y'all put your collective heads together and put out a definitive guide on the topic. I find myself kinda hacking this in my code, with my fingers crossed--there should be a better way, supported by the Q documentation/API. A related topic is systematically dealing with outliers (you could leverage sci-kit learn Preprocessing data, however, note that last I checked, robust_scale is not yet supported on Q).

Black-box risk and the 1337 Street Fund - Presumably, over-fitting risk is significant for the 1337 Street Fund, since it is composed of more-or-less black-box algos. So, specific guidance on what authors need to do to overcome this barrier to entry would be welcome, since the alternative is longer out-of-sample periods to compensate for the black-box risk. There is vague guidance on
Getting Capital Allocations from Quantopian in terms of "Strategic Intent" which presumably is meant to mitigate the black-box risk. It is not clear what is required to fulfill this "Stategic Intent" requirement, and so some educational content would be helpful. Also, presumably, it would be optional for authors to share their code, eliminating the black-box risk. So, examples of what you'd like to see in the code, in terms of style, comments, etc. would be of interest.

Hi Delaney -

Your list includes "Forum posts with example strategies." I'd flesh it out a bit, since a strategy is kinda meaningless if it can't be traded with real money:

Forum posts with example strategies - As an example strategy, it would be interesting to see an actual strategy, with it eventually trading in your 1337 Street Fund (or perhaps separately, if necessary). You could write something that would be a kind of basic bread-and-butter long-short equity strategy that you'd be willing to fund (but wouldn't accept from the crowd, since in this case, you'd have to pay out a share of the profits). For example, I assume that for your various style risk factors, there must be some alpha; they aren't just noise, or what would be the point in mitigating them? So, if this logic holds, you could construct a strategy using your style risk factors (which wouldn't conflict with crowd-based algos, since they are style-risk constrained). Something with a low-ish Sharpe ratio, e.g. 0.5-1.0. The idea would be for you to work through your entire process in the public domain, including running the algo for 6 months out-of-sample, and subsequently putting capital on it. You could then track its real-money performance, and use it as a teaching vehicle over the span of a couple years, with research & development, in-sample/out-of-sample backtesting, paper trading and deployment, and eventually real-money trading at-scale.

Algo Templates.

As someone new to coding, but experienced in trading, I have spent many hours searching the forums for code snippets that help me implement fairly simple strategies.

That is not a complaint by any means--I'm constantly grateful to find that members have shared their code and answered questions over the years. It would be great, however, to have more "best practices" type templates using Q features and datasets to help get new users up to speed.

For example, using intraday bars in algos and traditional "hold until target, stop or time period reached" strategies.

Following up on Doug's inquiry above, it would be interesting to hear how it is envisioned Q would work in the long run. How do you end up with engaged, committed part-time/full-time professionals, each with 10,000+ hours of relevant experience under his belt? How would the various forms of content support that goal, over the next 10 years, assuming folks coming to this field cold and can only dedicate a little time each day/week? How would the content give a boost to individuals with "transferable skills" or even help professional traders, who perhaps worked at comparable hedge funds? How many crowd-sourced quants are required, in the end? And how do you measure the success of the content and training efforts, to get some sense for the pay back?

Contest algo reviews

Quantopian could offer a monetary incentive to authors who did not qualify for the allocation to share their strategy and algorithm. Attached video walkthrough would be a plus.

Thanks everybody for all the feedback and suggestions. I'm going to step through and read it as soon as I have a chance.

Hi Delaney -

I have to say that you seem to be coming at this in an odd way as a data scientist type. I would think that Q has lots of data to mine germane to your question. You could formulate some hypotheses and then get the data and analyze it. It seems you are trying to get at what works for attracting and retaining the kind of users your business needs. You've been at this since 2011 and presumably have lots of data. Have you analyzed it? What does it tell us?

One way to flip this around would be for you to provide guidance on what data sets you have, and then we could propose hypothesis tests, and you could run studies, and report back.

One specific curiosity would be analyzing the potential impact of your decision to drop broker integration and real-money retail trading. You should have a nice picture of the impact by now.

You say "We're open to all ideas..." so I figure applying science to the problem should be in-scope.

Heuristics/rules-of-thumb - As an alternative to the deep-dive data science approach, it would be interesting to hear about practical heuristics that work reasonably well, or even better than the fancy stuff. I gather than some heuristics may have been used to construct your risk model, so that might be a starting point.

Retail trading/speculation with limited capital - I realize Q has gotten out of the retail trading business (although zipline-live was spun out, and perhaps Q will be involved in fostering its adoption). My hunch is that your user base is still keenly interested in trading their own money (and perhaps parlaying contest winnings or Q fund allocation earnings). So, a series on this topic would be of interest. If your goal is to educate on the basics of risk-reward, the mechanics of trading, applied statistics, machine learning, etc., using examples that are accessible to users with their own capital would be potentially a way of engaging a broader audience. It may seem counter-intuitive not to stick to the the $10M long-short market-neutral equity path, but if your goal is to engage an audience and educate, you might consider the low capital retail side of this world. Then, you could show how the same basic principles germane to retail trading apply to institutional-grade algos.

Many of the content produced is no longer compatible with the current platform.
It would be nice first to have a set of algorithm, notebook that is actively maintained, so when something
changes in the platform they are adapted.

I'd like to see a list of threads I am listening to and a search feature only for these threads. If I clone a notebook or algorithm from a thread, it would be nice too if I can see the exact thread and message I cloned it from.

Hi Delaney -

I'm following up. Any useful feedback above? Perhaps you could provide a summary of your findings with an edit to your original post?

Hi Delaney, thanks for posing this question. My answer is this: the only content I would like to see is a return of the ability to live-trade algorithms from your site to brokerages.



Practical advice on combining signals other than the basic zscore/rank and add with even weighting. Cheng's recent webinar highlights that rank loses information in the tails, but zscore may fit the "model" (a normal distribution) to outliers. I'm sure those are only two extremes, and there's plenty of middle ground.

Hi Delaney -

It would be nice to get your perspective on the feedback above. Is any of it useful? Do you need more?

I have to say the training material and content here is awesome and very professionally done!

I do hope you'll continue with the weekly webinars, and produce video lectures to all tutorials and lectures that don't already have videos. I'd also be interested in learning more about how to make use of CustomFactor, efficient/smart coding do's and don'ts, as well as different ways of combining factors and what the pros and cons are of the different ways.

Hey all,

First of all, apologies for taking so long to respond to this thread. I left it out with the hope that it would accumulate a bunch of good suggestions and it certainly did, it also helped me understand what people currently feel is lacking or have trouble doing. I'd like to figure out ways we can get this kind of feedback much more regularly from the community.

I want to go through and respond to each post, but that would take forever. This thread is going to be permanently bookmarked for me over the coming months as we decide what content to produce and when. I'll also leave thoughts if I think I have anything useful to say, but honestly I'd rather just gather as much unbiased feedback as I can and not let my intervention ruin my data collection. Please keep posting here if you have further ideas or thoughts, I'll keep checking in and watching. As of right now this is a lot so it will definitely take us time to start producing content that addresses things people mentioned.

Thanks again to everybody who proposed something. If you'd like to start any offshoot threads with specific questions about specific issues, feel free to do that and I can try to watch for those and comment there too.

I'd like to figure out ways we can get this kind of feedback much more regularly from the community.

Dedicated page, User Suggestion Box, sortable by upvotes or date.
Right/left arrows to be able to run through them quickly/easily.

Is it possible for the top performing members of a contest, share their ideas and approaches that helped them win. Something one the lines of Kaggle interviews .
Not asking for the entire code but bits and pieces about strategy formulation to help the newbies think differently.


@ Delaney - Thanks for checking in. Lots of potential for improvements. I'd encourage you (and the Q team) to consider the best way to respond. Blue Seahawk offered up some suggestions; I would add that perhaps you could simply leverage your presence on https://github.com/quantopian. You may not realize it, but a lot of opportunities for good feedback and potential for user engagement end up dying. A recent example is https://www.quantopian.com/posts/short-selling-in-backtester-time-for-improvement-1. Is accounting properly for short selling in your backtester an issue? Maybe you have some subject matter experts who could chime in to provide some sense of whether it is relevant or not to your hedge fund efforts? Back-of-the-envelope, what might be the magnitude of the problem? Is there literature on the topic? Can your prime broker provide guidance? Just scope it out, and then park it on a user-visible list, with a priority, rather than basically ignoring interest in the topic. It is a missed "teachable moment" for users.

@ Quant Quotient - I'd be willing to share the algo template I'm using for the contest, but it would be really nice of Delaney (or someone at Q) would collaborate with me on improving the framework. As I suggested above, a base algo template that is revision-controlled, maintained, and evolved would be really useful. One thing I'm particularly interested in is a framework that would allow various alpha combination techniques to be tried, including the ML example from Thomas W. I'd like to be able to compare a simple alpha combination technique (e.g. sum of z-scores), to more sophisticated approaches (e.g. ML), in a modular fashion.

Tools for managing content - There is a lot of content. It would be helpful to have a kind of personal library to keep track of it. As part of the library, one simple thing would be to provide users with a list of all of the forum discussions to which they are "listening" (to "unlisten" to ones no longer of interest, for future reference, and for follow up).

Library of factors - There are a lot of Pipeline factors scattered all over the place. For example, just today we got a fix to one that I hadn't considered (see https://www.quantopian.com/posts/pipeline-custom-factor-for-downside-volatilty). If it could be captured in a Quantopian-curated Github library (even just for copy-and-paste into Quantopian), then it wouldn't just get lost in the wind, and maybe folks would try it and modify it.

Power distance in finance culture - I get the impression that finance culture, generally, is high power distance (this may also extend to venture capital and sources of investment capital, as well--money is power, and it can lead to unproductive hierarchies). It would be interesting to hear how Quantopian is approaching this, given the open, crowd-sourced hedge fund model (and now the SaaS model, with the FactSet partnership). What sort of power distance do you want, relative to your competition, and might it be advantageous to be radically different? And supposing that you want a different culture, how might it make dealing with high power distance cultures challenging (e.g. I read Black Edge: Inside Information, Dirty Money, and the Quest to Bring Down the Most Wanted Man on Wall Street and got the distinct impression that one of your funding sources is a high power distance culture)?

Hi Delaney,

A tutorial series on how to use 'fetcher' and My Data once it's ready for all to use, to load our own datasources in research and the IDE would be great. Ideally a start-to-end walkthrough from getting the data (EDGAR or Google Trends might be a good start) to data-cleaning and mapping symbols/sids and finally to using the data in research or IDE.

Hey Joakim,

You're in luck, we're getting ready to release a new feature that allows you to upload a custom dataset. In the new feature you can upload a cross sectional dataset, compared to the 1 dimensional format fetcher would accept. It's currently in closed beta, you can check it out here. https://www.quantopian.com/posts/alpha-testers-wanted-upload-your-own-data-to-pipeline

This feature should allow people to do some really cool things. For example, to test it we downloaded patent litigation data from the US Patent Office and constructed a signal telling us how many cases each company was involved in at each date. We're planning on releasing this example as part of a tutorial on the new feature, so please stay tuned for that.

Awesome! I'm looking forward to it.

Code snippet archive - It would be handy if code snippets could be captured in a convenient way. We have https://github.com/quantopian/research_public/tree/master/code_snippets, which is a start. I came across this:


The Hurst exponent code looks like it would be worth capturing, and I'll submit it as a snippet, but I'm wondering if the process could be stream-lined. I don't have a solution (and don't recommend jumping to one); I just get the sense that having users log into Github and submit requests every time a snippet needs to be captured is probably not the way to go.

Opportunities for technical synergy/collaboration between Quantopian Community users and Quantopian Enterprise users - Potentially, there could be a lot of possibilities for Quantopian Community users (and vice versa). It would be interesting to hear about what is envisioned, and how the platform might support technical cross-pollination.

Both theoretical and practical content on reducing slippage and fees.

Hi Delaney
Being a newbie to this platform I muddled along and am still muddling along, and if it was not for the generous and kind support I got from this forum I would have moved on. Some thoughts from this experience:
1. When someone first signs up (or before) give them a list of prerequisites and recommended links - it took me a few weeks to find a Python course that was directly related to just what I needed to know (in particular Pandas), without needing to become an expert in interesting but unrelated minutae. In my case a course that tests your knowledge as you go was more helpful than watching videos without adding muscle memory.
2. A must keep in mind cookbook that you can go to - for example make sure you set your slippage carefully or use the default. Or if you use order target percent then you do not need to worry about early close (in my case at least - may not apply to all strategies). I am writing code to get fills on orders. A canned method would have been nice. Perhaps one exists but I have not found it. If I do a search on the API docs for fills I get 11 entries that tell me nothing about how to get fills. If I do a search on filled I get somewhere but only to learn that I need to get the order id first. So as you can see muddling along. The API documentation is good but not very descriptive for a newbie. As Grant mentions in his sections on math and functional block diagrams more context would be helpful.
3. Let people know that posting a question on the forum is more effective than sending an email (better still remove the send us an email option). I realize you guys are busy so to save everyone time push them to the forum instead of creating an expectation that someone will respond in a timely manner to an email.
4. Provide the option to sign up to see posts that have the seeking help tag - I am happy to help as well if I know the answer but have not yet found out how people that have been helping me have seen my requests for help.
5. Someone mentioned this but giving the community a vote on your priority list would be good.
The rest has been covered by comments from everyone else.
Hope that helps - looking forward to seeing some of the feedback implemented.
Have a nice weekend

Guidance on how to manage factors with differing optimal holding periods in a multi-factor algo

Hi @Delaney,

Just 'bumping' this thread by asking if there was anything specifically (or generally) from above posts that you are (or will be) taking onboard? The Short videos and (quarterly?) strategy review webinars I've noticed, both which are great and I find very helpful!

Log chart option on backtests