(Part 2) Best products from r/algotrading

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! :)

> 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.

It actually does use indicators, and those indicators predict trends.

Mathematical models: I have only studied indicators. In the beginning of my project, I tried to create my own indicators using parametric equations, but it wasn't working. I couldn't get the algorithms to produce results better than random backtests. So I moved from that into real indicators.

Books:
The Ultimate Day Trader
It was the most helpful when I was getting started and learning about indicators. It taught me how trading was done, and it introduced the typical algorithmic trading like MACD crossovers, bullish convergence/divergence. It may be too much for beginners. As a warning, reviewers on Amazon don't think highly of the book.

I had to learn a lot on my own through trial and error and the occasional google search, so I The Ultimate Day Trader is the only book that I fully read.

Building Winning Algorithmic Trading Systems
Gives a lot of good information in getting good backtest results, and the steps an algorithm should have to pass in order to be traded with.

Algorithmic trading: Winning strategies and their rationale.
Currently reading this, and it starts off basic, like most books. It talks about look-ahead biases and that sort of stuff. It also talks about the different backtesting software and programming languages. I'm only on page 40/200, and it looks like it gets more complex.

I also have a few books on options, but those don't have to do with algorithmic trading.

You already have a great foundation. Most who start trading or are even remotely interested in it, don't appreciate the value of statistics in finance, of all professions.

1. The book Forecasting by Makridakis is specifically focussed towards students of business/finance. It is quick and easy to read, and reaches to the meat of the subject (ARIMA stuff) pretty quickly (you can skip or quickly read through the introductory chapters). You can pdf version on scribd. There is another book, Mathematics of Financial Engineering, which provides a more mathematical overview of various financial instruments (eg bonds, options, futures, forwards). Some other books I mention here that's worth reading for trading, but you might know most of it with your background.
   
   
2. I go with Forex, EURUSD specifically simply because: huge volume (trillions worth transaction per day), some specific parameters to follow (interest rate, central bank mandate, gdp, inflation, purchase manager indices), a comparatively easier intuitive assessment (you can get an idea of investor sentiments by looking at the economic indicators/central bank statements). Whether it should be spot FX or futures FX is something to decide: people might prefer futures because there is a central exchange, more transparency.
   
   
3. You can get all the data you need by getting a MT4 platform from your choice of broker. You don't need to pay for it, or even have a live account. Just open a demo account and you will have access to historical data.
   
   
4. MT4 is the most widely used platform. You can write programs for it too (Expert Advisors execute tick-by-tick operations; there are scripts and custom indicators too). The programming language is MQL4, which has a syntax very similar to C/C++, so it's easy to adapt to it.
   
   Best of luck!

Learning all the details is a long journey. I would recommend reading Inside The Black Box as a start it will give you a good overview, how algo trading system is built. Then dive into the some of the books mention on Quanstart on the Systematic Trading section. Chan's books are good to get general ideas and concepts but not concrete strategies, but some of his strategies can be turned into profitable one after improvements. After these, you will find recurring topics and techniques. For building an actual trading system, you have to pick development platform. You can either build your own which is a very long road, and you can easily be lost in details for years, For example, the topic of the time series databases is a big one. Or you can use ready-to-work online systems: Quantopian - their Lectures are a good intro for factor-based equity strategies and Quantconnect are two well established. Reading their forums can give you hints what kind of systems others try to build. I hope this gives you a direction to start.

If you came up with a unique strategy (I don't really want to hear about moving average crossovers or bollinger bands) idea and asked me for feedback on it I'd probably think you were pretty cool.

some books I like. Keep in mind that there aren't very many good "how to books" in this field.

good overviews of auto trading strategies and infrastructure

https://www.amazon.com/Inside-Black-Box-Quantitative-Frequency/dp/1118362411/ref=sr_1_3?s=books&ie=UTF8&qid=1523315492&sr=1-3&keywords=high+frequency+trading




probably the most entertaining and inspirational trading book ever written.
https://www.amazon.com/Education-Speculator-Victor-Niederhoffer/dp/0471249483/ref=sr_1_1?s=books&ie=UTF8&qid=1523315425&sr=1-1&keywords=education+of+a+speculator+by+victor+niederhoffer

Right now I'm reading The Art of R Programming. It seems like it has a lot of good knowledge but also seems really disorganized. The author uses control statements without explanation in the 2nd chapter about vectors to demonstrate their ability, and then doesn't get back to control statements until chapter 7. But being a seasoned programmer I don't think things like that will bother you too much. This is the only R book I've used, so my opinion isn't very broad based. The reviews for R Cookbook seem pretty good and I'm a little sorry I didn't start with that instead.

Hopefully someone else can chime in.

Probably start with something like:

• Mathematical Financial Economics
  
  
  
• Trading and Exchanges: Market Microstructure for practioners
  
  
  
• Building Winning Algorithmic Trading Systems
  
  And then you decide whether you are going to focus on one of the two primary trading strategies (mean reversion or momentum) and focus on that one for a bit.
  
  
• Optimal Mean Reversion Trading
  
• Algorithmic Trading: Winning Strategies and their Rationale
  
  And that's assuming you have the programming knowledge to implement a system that can handle this of course - otherwise I'd go start with C# or C++ until further notice.

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!

i don't want to sound too harsh, but it looks like you don't understand what you are doing, or not giving it a thought or both...

You have some math knowledge, but the way you are applying it makes me question if you understand the basics behind it:

• You select an interval [1, 100]. And your calculation shows that optimum is at 100. If you take a break and give it a little thought, you could arrive to a conclusion that you don't know where even a local optimum is. You just picked a random interval and function optimum is on the border, which means you should keep expanding your interval until optimum lies well in between borders... then you probably found a local extrema
  
  That is just an example of critical thinking. In quant research you should not be making next step, unless your current step and all your previous steps make perfect sense. After each paragraph ask yourself a question: What am I doing? What am I trying to achieve? Why am I choosing certain methods ( do they make sense, are they applicable here, etc..)
  
  >>which parts didn't make sense
  
  Basically nothing past the point where you chose MAs (and I don't even want to touch the SVM part)... You might wish to read John Ehlers' book for example (https://www.amazon.com/gp/product/1118728513/ref=dbs_a_def_rwt_bibl_vppi_i0)
  
  

Maths, statistics, generalised statistical methods - the number theory behind stats.

Finance, calculus etc

There was a book we used to use to give people the idea of what we're looking for:

https://www.amazon.com/Heard-Street-Quantitative-Questions-Interviews/dp/0994103867

It's old old old now, but nothing gives one the out-of-the-box mindset they are after like this.

I'm from the UK and loved my time as a quant. It was a young man's game. There are not many good old quants/mathematicians.

I found this book and going by it, they say it is a classic and a good thing is that it does not start from the Gaussian distribution of returns assumption. I hope it helps you.

https://www.amazon.de/Analysis-Financial-Wiley-Probability-Statistics/dp/0470414359