(Part 2) Top products from r/algotrading

( please guys have fun with my answer, obviously a lot of things here are jokes. take home what fits you ).

AFAIK There is actually one OTS online platform for trading crypto, which is very buggy and have a very amateur community ( even veterans on that platform are generally noobs ), the backtest isn't even close to realistic unless you somehow compensate the slippage and some other "real life" problems ( i'm far from having the magic sauce myself, but i can tell when shit smells funny ).

Don't even try to use one of the fast bots available there, they will generally just loose your money quickly before you can actually realise wtf is going on. They have a market place with paid the strategies and they are generally very unreliable, specially because the norm is to hide the source code of the paid strategies and post backtests - not live links -, so basically it could be just a fake strategy doing the other side of the creators trade not to say backtests are generally far from the results you get in the real world, at least the backtests in @ cryptotrader.

The support is very poor and slow, some questions are simply ignored and the UI is lame ( euphemism here ), http://cryptotrader.org

Still it's the only algo trading platform for crypto i manage to have running 24hours to generate some profit while i'm building my own - so far i'm only trading ETH margin on poloniex because of it's volume.

The good side is that the language used for the bots is coffee script which is one of the most pythonish languages out there 😉

I'm a beginner myself but i already have profitable bots earning consistently ( i had to learn how to do my own after only loosing money with the famous bots out there ).

Other relevant comments:

• Quantopian backtest engine is python based and actually open source: https://github.com/quantopian/zipline
  
  
• Even tough quantopian doesn't trade crypto, they have very valuable tutorials which you will surely benefit from: https://www.quantopian.com/tutorials/getting-started their youtube channel is cool: https://www.youtube.com/channel/UC606MUq45P3zFLa4VGKbxsg/videos and also make sure you try out their IPython examples, they are pretty fun: https://www.quantopian.com/research/Tutorials%20and%20Documentation
  
  
• If you like python you probably know you should stay away from .NET and Java and C and whatever other boring languages people might recommend you, python is cool and awesome, you don't want to spend your time typing irrelevant verbosy stuff, you want to react and test stuff quickly - and have cool friends, .NET and Java programmers are notoriously the most boring people around. There is a couple of other nice other languages available, but it's not important to talk about this now.
  
  
• IMHO you are less likely to make good stress-free money with algo trading if you have a very academic approach , you might end up trapping yourself into parts of the business that won't make a big difference in your profits, obviously, studying is always good and you can always learn something from anyone and anything, remember: learning what not to do is actually learning too! not to say you won't probably learning without burning yourself a little bit. The market is 24 hours so unless you know what you doing you will probably end up not sleeping loads of nights.
  
  
• IMHO, HFT is a no go because the rate limit on important API functions is generally ridiculously limiting AND unless you have a lot of crypto to trade ( therefore paying less fees or no fees ) or a very very solid strategy the fees will eat you.
  
  
• If you want to get inspired, watch videos on youtube about algo trading, good keywords on youtube are "Ninja Trader strategy builder", "metatrader expert advisor", etcs.. there is always something to learn from those videos.
  
  
• You will eventually find other options, like this: https://www.youtube.com/watch?v=2at6ZzRIIhc but they generally sux balls big time, or aren't just relevant anymore ( generally both ) or are never released: http://cryptybots.com
  
  
• Leonardo looks great, but it seems unfinished ( i might be wrong ) and i'm not sure to which degree you can create your own strategies in here: https://www.youtube.com/watch?v=hib3xR6Ci1w&feature=youtu.be
  
  
• Since your most important skill will be "making profitable strategies" i reckon you should understand trading to a degree you won't blow your account in a couple of weeks. This book will probably help you out: https://www.amazon.co.uk/Mastering-Trade-Second-Techniques-Profiting/dp/0071775145 ( there are loads of other books you can find for sure that might suit you better ), goes without saying you should do your home work and learn a lot about indicators so you start grasping the basic building blocks and start thinking for yourself instead of being a script kiddie.
  
  
• This market is about money and if someone is rich with algo trading someone probably wouldn't be around giving advices for free, so trust no one other than you, if they are talking too much either they don't make money or they just had a line of coke. Don't even trust me!!
  
  To wrap it up:
  
  
• I'm willing to help and team up with smart people if think that would be a good idea, please get in touch.
  
  
• If You plan to level up your learnings in no time i would be willing to give you a crash course on how to trade ETH on poloniex exchange for a donation, give you some basic ideas and code and then we take it from there, we can then potentially become friends and create stuff together if we see turns out to be interesting for both of us.
  
  
• My email is [email protected] feel free to get in touch if you feel like:
  
  
• You want to become a friend
  
• If you would like to buy some of my time and experience in form of an "easy and fun to learn" masterclass
  
• If you don't know how to code but want to do automated trading and you willing to pay for some basic programming classes
  
  
• If you found any of these information useful, don't feel shy send me a btc tip @ 1DNvZwFpFj9C6YNf9bAhx5bQd66RssXbyp ( pro tip: every time somebody tips me Justin Bieber dies a little more )

Well, the trick is to do one step at a time. Your goal is a very reasonable one, but you'll want to focus on the foundation first. For a non-programmer, I would recommend starting off with Code Academy or Coursera. The advantage of the second link is that it immediately provides you with a sense of direction while learning a language. Code Academy's Python tutorial is really nice in providing interaction with your code. Regardless, you'll want to first gain a sense of syntax on your language of choice.

After you're familiar with at least one language, the next most important thing is to become familiar with data structures and algorithms. This book on Amazon is amazing for giving beginner advice in the area: http://www.amazon.com/gp/product/1468108867

The book is not overly complex and mathematical compared to many other books, and it provides a fairly reasonable foundation for any beginner. If you ever want to practice writing basic algorithms out (optional), visit Codility's lessons to try things out. Once you can comfortably complete some of their lessons with a high grade and understand their topics, you should be ready to dive into the math/finance side. I feel that at this point, the Max Dama paper is a great way to get an overview of the basics. Regardless of the financial instruments you're trading (I've mainly worked with equities), you'll need a sense of portfolio management. Here's two books that may be worth running through:

http://www.amazon.com/Quantitative-Equity-Portfolio-Management-Construction/dp/0071459391

http://www.amazon.com/Expected-Returns-Investors-Harvesting-Rewards/dp/1119990726

They're both equities based (and I could be wrong here about FX), but it's probably a good idea to get a sense of how to measure returns. Regardless of the asset class you're planning to trade, all algorithms should be rigorously backtested and simulated (traded with virtual money) prior to being moved into production, and one of the best ways to improve your outcome is to know how to measure the returns and risks associated in your backtesting/simulations.

Hope this isn't too much information at once, but it should be a start. The first two courses throw-it-out mentioned in Coursera is a great start too.

Edit: I'd also take some time to browse some of the links on the sidebar in this subreddit. Some of those links are immensely helpful (especially the Statistical Learning one). Many of the strategy links are fairly easy reads and are recommended as well.

Do you have an edge? You mention you've been trading for 2 years, so I'll assume you do (but its ok if you haven't nailed it down).

Basically, you just take your edge, write it into python (or whichever language you want) and get it to generate a buy, sell or close signal. Once you have that down, just use an exchange's API to place your orders.

I do have a channel dedicated to this stuff, but at this point I think you're probably a bit more advanced than total beginner, it still might help you out though :)

https://www.youtube.com/watch?v=1nX4YEcTJlc

>what to learn/focus on & recommended resources: math, programming, strategy creation?

For me I've found that, programming wise, its never really that complicated. Sure, if you're going to be using some ML or advanced data analysis or something you might need to sharpen your programming but at least for me, the best resources I found had to do with market psychology and understanding the broader markets and trading in general. Some books I can recommend there are:

Trading in the Zone, By Mark Douglas - https://www.amazon.com/Trading-Zone-Confidence-Discipline-Attitude/dp/0735201447

Fooled by Randomness, by Nassim Taleb - https://www.amazon.com/Fooled-Randomness-Hidden-Markets-Incerto/dp/0812975219/ref=sr_1_1?crid=13LH3VBFX62OH

Skin in the Game, by Nassim Taleb - https://www.amazon.com/Skin-Game-Hidden-Asymmetries-Daily/dp/042528462X/

Algos to Live By, by Brian Christian - https://www.amazon.com/Algorithms-Live-Computer-Science-Decisions/dp/1250118360/

A Short History of Financial Euphoria, by John Galbraith - https://www.amazon.com/History-Financial-Euphoria-Penguin-Business/dp/0140238565/

>process beginning to go live: collect data, write code, test code, start trading?

In simplest terms (this is how I do it), get data via websocket, feed it into your algo, have the algo generate signals, use (write) another program to use those signals to trade. I find splitting up the risk management, buy, sell and close into different parts helps. I would also back and forward test too. Essentially that's all there is to it. 99% of this stuff for me at least is optimizing my algos and trying to run them on multiple markets. The programming behind them isn't that complex, its the math and theory.

Its not terribly impressive but this is what I was able to do with some algos recently:

https://twitter.com/robswc/status/1093328001243189248
https://twitter.com/robswc/status/1082782861869109253

even today I got one in:

https://twitter.com/robswc/status/1121943953564164102

but really, there's ppl out there that can do much better. I'm pretty content with my algos performance. I thought about tweeting every position once upon a time but realized since I'm not shilling some stupid course I don't have to really prove anything other than I'm not pulling stuff out of thin air lol.

I would definitely do forward testing though, whatever you do. Perhaps even add a human element to manage the risk at first. Just get the edge down and go from there, good luck! :)

My most successful algorithmics are based on sound financial ideas, that have been either implemented and well documented by famous fund managers / investors or well researched methodologies documented on white papers by PhDs in the area. The algorithmic simply automates that idea and provides consistency.

Specifically on some models that I have developed, the goal is to have a trading portfolio made of models that are driven by different factors, with low correlation between them. I got models for both US and Canada exchanges. These models rebalance weekly or monthly, so they're not meant for daytrading, but more for swing trading with low to moderate turnover.

For example, one model is focused in income, by seeking quality companies with low volatility. This research paper has the details behind to why it works: https://www.researchaffiliates.com/documents/True%20Grit_The%20Durable%20Low%20Volatility%20Effect%20pdf.pdf
This model makes use of market timing based on economic factors, to switch to other asset classes during times that equities underperform, such as in recession.

Another model is based on growth, exploring inefficiencies from Nasdaq companies, which are the great for growth as they drive innovation and are strategic for mature companies to continue to be competitive. This model relies on both fundamentals and technical analysis, to take advantage of price momentum (and therefore, overvaluation), which wouldn't be possible to capture with a value approach focused on fundamentals only. The technical analysis uses the principal of this book: https://www.amazon.com/How-Make-Money-Stocks-Winning/dp/0071373616

I've recently created a model focused on momentum of fundamentals, basically exploring the inefficiencies of small cap companies with decent fundamentals but with price disconnected from that quality, which are also increasing the rate of which fundamentals keep getting better (while stock price doesn't keep up with the same pace). This is based on this research paper: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2538867
This paper also explains the benefits when combining value, size and momentum, as per this paper: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1720139
The model also makes use of these criteria regarding quality: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2287202

These are some ideas which I have had good success out-of-sample, and these models have been backtested since 1999 using non-survivalship biased data.

I have a website with more details on that, so feel free to PM for more info.

There are always inefficiencies to be explored in the market - any model that looks into mechanism to explore these inefficiencies in a consistent way can provide superior adjusted risk return.

> I have the backtrader and pyfolio modules, but I have ZERO idea where to start with those. I wanna start backtesting and create my own portfolio in Python.

That someone uses my platform (backtrader) is always good for the ego, but the platform is not the starting point (pyfolio isn't either) if you really don't know where to go. From your message it would seem (and my interpretation may be wrong) that you have little or no experience trading or at least not with technical indicators like the moving averages you mention having already created.

There are many factors to take into account to set an objective. For example:

• Timeframe
  
  Are you going to work with minutes, hours, days, weeks? Are you going to base your analysis in multiple timeframes?
  
  
• Technical Analysis (aka Indicators) vs Quantitative Analysis vs Machine Learning
  
  One would gladly master all possible disciplines and do it in less than a week. But we are only humans with some other things to do (day job, studies, you name it ...) and fully concentrating on one is enough. Some will say that Technical analysis is the poor brother of Quantitative Analysis and some others that the only way forward is Machine Learning. Choose what better fits your starting point and your psychology.
  
  I would personally recommend that you use a free charting platform and spend some countless hours looking at many different indicators against different assets and timeframes to try to build up patterns in your brain, which will later allow to model ideas for algorithmic trading. You can of course do the same by looking at countless different statitics for a Quantitative approch
  
  
• Assets
  
  Are you going to trade stocks? And if yes, penny stocks? Futures? (from indices, currencies, commodities?) Forex? Cryptocurrencies? options? (raw options or strategies like butterflies, condors ...?) There are many other things, but that's already enough.
  
  They all have different behaviours and there are two things to look for: that you can find the edge and that it fits your psychology. Even if you are 100% algotrading there will be losses and you need to trust the system and be able to accept the losses with no hesitation. Some assets and how they produce losses will better fit you. Some will let you easily go long and short and some only long.
  
  
• Position sizing. How will you address how much you will be staking with each bet? Linear, kelly, exponential?
  
  My real recommendation would be this book
  
  
• Amazon - Trade Your Way to Financial Freedom
  
  Or the modern version which talks about electronic trading:
  
  
• Amazon - Financial Freedom Through Electronic Day Trading
  
  But I would still recommend the 1st version. It's not going to tell you how to do it. It's going to give you the principles to do it. Some months ago and following a questionhere about the settings for the MACD and based on some of the ideas presented in this book I posted this, you may want to have a look:
  
  
• Backtrader blog - MACD Settings
  
  It shows how a system goes from losing to winning by controlling for example position sizing.
  
  Hope all this helps.

Well, the article is true, in so far as it states what the nature of quantitative analysts might do, but it's also very fair to say that the marketplace really doesn't suffer from a shortage of talent.

What happens is that in roughly 4-5 year cycles, quantitative analysis falls in and out of favor resulting in a bloodletting of talent and staff. My last go around was in roughly 2006-2007 and I left for a less soul-wrenching experience in another similar field - earning substantially less but also with a 20 minute commute and a trivial amount of commuting costs (as opposed to the ludicrous rent or high commuting costs into NYC).

The last gig I had was "interesting" in that it was a kind of "skunk works" small consulting firm, and while they hired some OBSCENELY smart people, they were more than happy to absolutely burn staff out as hard and fast as they could usually in the span of 6-8 months.

Bottom line, quantitative analysis "back in the day" (say the 1970's and 80's) was absolutely and literally being taken over by notable academics mostly from high-energy physics and some aspects of machine-learning/artificial intelligence.

In that way Dr. Emanuel Derman's "My Life as a Quant" gives you a good - if somewhat skewed perspective on the field from way back in the day but WAY before HFT and flash crashes he points out that the market - while very much engaged in an arms-race between major firms, should also heed no small amount of caution on the area of relying too much on computer models and automation which can fall outside of the envelope of how they were designed.

More specifically (if not recently) there has been huge focus put on being the "faster pussycat" in terms of trading in HFT and what have you, but this too has had it's day, Haim Bodek was very deeply involved in this world, Dark Pools (a book into which he contributed) by Patterson covers very adequately, the various major pitfalls and promise of this area.

Chris Steiners' "Automate This" is another good primer for how the advent of serious machine intelligence efforts have absolutely altered markets - likely permanently.

https://www.amazon.com/Probability-Statistics-Finance-Svetlozar-Rachev/dp/0470400935

this books is wildly helpful, it basically goes over all the things you would typically learn in a statistics class but introduces them in ways that are relevant to finance. It would probably be a fine introduction to statistics but I'm using as a guide on how to use statistics and probability for finance after taking a basic stats refresher course. i love this book!

Got a suggestion to read this book that actually shows the real amount of work behind big algo trading companies. https://www.amazon.com/gp/product/B079KLDW21/ref=ppx_yo_dt_b_d_asin_title_o00?ie=UTF8&psc=1

I was also thinking to do on my own, but right now I think it's easier to work towards own indicators(via TradingView pine script) that are good for my trading style and follow them closely, most likely I will automate trading this way.

I would understand:

https://en.wikipedia.org/wiki/KPSS_test


https://en.wikipedia.org/wiki/Augmented_Dickey%E2%80%93Fuller_test


http://www.amazon.com/Introduction-Bootstrap-Monographs-Statistics-Probability/dp/0412042312

The reason I am advocating bootstrap is because you are being freed from strong assumptions about your data.

They're not really "backtesting resources" but Ernie Chan's books all use matlab code examples, and he has all of the full example code on his website (viewable with a password obtained from the book)

• Quantitative Trading: How to Build Your Own Algorithmic Trading Business
  
  
• Algorithmic Trading: Winning Strategies and Their Rationale
  
  
• Machine Trading: Deploying Computer Algorithms to Conquer the Markets
  

thanks for that info. the only reason i wanted to brush up on calc was to maybe then go and study statistics from a more advanced book. those usually have proofs that use calc. i was going to study from this book: https://www.amazon.com/Stats-Models-Richard-D-Veaux/dp/0321986490/ref=sr_1_1?ie=UTF8&qid=1491175118&sr=8-1&keywords=9780321986498

I write strategies for a living. This is the quant bible for portfolio trading.

Active Portfolio Management: A Quantitative Approach for Producing Superior Returns and Controlling Risk https://www.amazon.com/dp/0070248826/ref=cm_sw_r_cp_apa_i_Ze2wDbY25EJWT

You might like the book Blink by Malcolm Gladwell:
https://www.amazon.com/Blink-Power-Thinking-Without/dp/0316010669 . It is all about how we make decisions subconsciously in an instant, without really thinking about it. The Trading Game is an example of that, where we may do much better just using a quick gut reaction, then spending hours doing technical analysis to make the prediction. It talked about how many studies show having more information (P/E ratio, moving average, Bollinger Bands, etc.) in situations like that actually can lead to worse results.

http://www.amazon.com/Trading-Systems-Development-Portfolio-Optimisation/dp/1905641796

and the elements of statistical learning in the sidebar

C++ for dummies

Java for dummies

http://www.amazon.ca/Java-Concurrency-Practice-Brian-Goetz/dp/0321349601

Why not just get the standard options book:

https://www.amazon.com/Options-Futures-Other-Derivatives-10th/dp/013447208X

http://www.amazon.com/Evidence-Based-Technical-Analysis-Scientific-Statistical/dp/0470008741/ref=pd_bxgy_14_img_2?ie=UTF8&refRID=08N24WSYBNRE52ZNDVP4

this book?

Complete beginner about to study Mathematical Finance in college.
Should I purchase Options, Futures and Other Derivates (Hull) first, or the Concepts and Practice of Mathematical Finance (Mark Joshi)?

"Capital Markets for Quantitative Professionals"

https://www.amazon.com/Complete-Quantitative-Professionals-McGraw-Hill-Investment/dp/0071468293

You've probably checked these before but use this for documentation: http://dirk.eddelbuettel.com/code/rcpp/html/index.html

http://dirk.eddelbuettel.com/code/rcpp.html

An example:
http://dirk.eddelbuettel.com/code/rcpp.examples.html

Finally a book:

http://www.amazon.ca/Introduction-Financial-Engineers-Object-Oriented-Approach/dp/0470015381

" Some terrible pirates have placed that book on the internet, please don't search for it with the words pdf"