Algorithmic

Hi, I want to pick up algorithmic trading on the side. I have a master in economics and have been working as a credit risk modeler for the last two years. I am proficient in python. However I don't really have a background in market finance or trading.

I figured Python for algorithmic trading would be a good place to start. To learn I might want to develop a package in algo trading.

1. Is this book: "Python for algorithmic trading by Yves Hilpisch" a good start?

2. Do you have any other learning suggestions?

I'm Matthew Finney. I'm a Data Scientist and Algorithmic Fairness researcher.

A growing number of experiences in human life are driven by artificially-intelligent machine predictions, impacting everything from the news that you see online to how heavily your neighborhood is policed. The underlying algorithms that drive these decisions are plagued by stealthy, but often preventable, biases. All too often, these biases reinforce existing inequities that disproportionately affect Black people and other marginalized groups.

Examples are easy to find. In September, Twitter users found that the platform's thumbnail cropping model showed a preference for highlighting white faces over black ones. A 2018 study of widely used facial recognition algorithms found that they disproportionately fail at recognizing darker-skinned females. Even the simple code that powers automatic soap dispensers fails to see black people. And despite years of scholarship highlighting racial bias in the algorithm used to prioritize patients for kidney transplants, it remains the clinical standard of care in American medicine today.

That's why I research and speak about algorithmic bias, as well as practical ways to mitigate it in data science. Ask me anything about algorithmic bias, its impact, and the necessary work to end it!

Proof: https://i.redd.it/m0r72meif8061.jpg

You can view Charles Isbell's NeurIPS keynote without registering for the conference here: https://nips.cc/virtual/2020/public/invited_16166.html

This is not your usual keynote: it's "A Christmas Carol" themed exploration of bias in machine learning, with Michael Littman as Ebenezer Scrooge laying out objections to recent concern about bias in learning, and Charles Isbell as the "Ghost of Learning Past/Present/Future".

Abstract: Successful technological fields have a moment when they become pervasive, important, and noticed. They are deployed into the world and, inevitably, something goes wrong. A badly designed interface leads to an aircraft disaster. A buggy controller delivers a lethal dose of radiation to a cancer patient. The field must then choose to mature and take responsibility for avoiding the harms associated with what it is producing. Machine learning has reached this moment. In this talk, I will argue that the community needs to adopt systematic approaches for creating robust artifacts that contribute to larger systems that impact the real human world. I will share perspectives from multiple researchers in machine learning, theory, computer perception, and education; discuss with them approaches that might help us to develop more robust machine-learning systems; and explore scientifically interesting problems that result from moving beyond narrow machine-learning algorithms to complete machine-learning systems.

I am interested in learning programming because I have a desire to build apps that can help people. I have started to learn some of JavaScript before in the past because of how powerful it is. However, I started to do algorithmic questions on sites like codewars and hackerrank but I constantly struggled. I know some of the basics but it seemed extremely difficult for me to solve these questions. I tried to solve the easy questions and that was still extremely hard for me. I just don't know how to break it down into steps for me to put. The biggest problem is that I don't know how to put it into code at all. What is some good advice to offer me? What do you think?

So, seems like the race is on for the most sustainable design to achieve stability via algorithm.

Those are the ones I know of, feel free to add if they are more promising ones.

Which one you think is the best design-wise and why?

Let's make a constructive discussion comparing the advantages and disadvantages of each and learn in the process. There is so much going on in that field right now that is hard (if not impossible) for a single person to keep up and see all the angles.

hi ,

tether is now 21bn mkt cap and there is speculation that it might be behind the 29k bull run

https://www.youtube.com/watch?v=3eFGnsjeNFs

the only legit stable coin is DAI but the hi network fees on ETH have made it unusable

whats needed urgently is a DAI like stable coin on BCH asap , im even comfortable with a ' wrapped ' DAI on BCH

Has any of you implemented a machine learning algorithm for trading? I have some economist friends who think that develioping a succesful model for trading is imposible and cant be done. Is there any plataform to trade using algorithms?

Caveats: A bit meta topic, but I took a casual glance at some of the computer generated translations, and how sometimes algorithms can change the feel or even meaning of a translated sentence. That's not to say that human translators don't also mistranslate. We do. Far more frequently than you might think.

>tl;dr, Manual red-pen fix to algorithm results
> 
> As announced by the company, six talents affiliated with HololiveCN will be graduating from Hololive.
> 
> I would like to express my gratitude once again to all the creators who have been involved in creating HololiveCN and all the fans who have supported us. I am very sorry that this situation has occurred after we had just increased staff and established systems to better support our talents.
> 
> Driven by our vision to "Excite fans all around the world by providing cutting-edge 2D entertainment", we have worked hard to expand not only the overseas audience of our Japanese VTubers, but to operate Vtubers overseas as well. Although our overseas operations are not yet profitable, we have been strengthening our efforts with a desire to spread the VTuber culture from Japan to the world. We believe that VTuber culture can transcend borders and generational divides, bringing together people from all around the world.
> 
> However, crossing borders is no simple task and recent events have made us keenly aware of the importance of considering the feelings of the people in regions where we operate in.
> 
> Nevertheless I continue to believe that Vtuber culture can bring together people across the world, and will continue to persevere in our efforts to expand overseas.
> 
>I hope to have your continued support in this task.

A look at machine translations.

>会社からの発表の通り、ホロライブ中国の所属タレント6名が卒業されることになりました。
 
Deepl: As announced by the company, we are pleased to announce that six talents from HoloLive China will be graduating.
 
Google: As announced by the company, 6 talents belonging to Hololive China will be graduated.
 
Bing: As announced by the company, six talent belonging to Holo Live China will graduate.

*Nowhere in the original text is there any nuance that suggests "we are pleas

Hi! I just released my first song on Wednesday December 30th and it's near 3,000 streams and 1200 listeners. My profile has around 1200 monthly listeners(seeing as that's my first release) and my save rate is around 17% (185 saves). I'm wondering why I haven't been at all pushed by the Spotify algorithm yet? I checked my artist page and the Spotify Algorithm section is at 0%.

Is that something on my end or is my song not performing well enough?

Investing and security analysis relies more and more on access to information and ability to process information efficiently.

From my research there seems to be two areas where’s data/information is used the most for investing and analysis: liquid publicly trades securities (referring to “quantamental” investing) and personal or small business lending.

Liquid publicly traded securities makes sense. There is standardized information in the form of SEC filings and historical pricing, which can be used to evaluate a security.

Personal / small business lending has no public information but banks or alternative lenders (Sofi, Earnest, LendingClub, etc.) have enough data so that they can evaluate credit worthiness.

I view these as two opposite corners of the investing world, large public equities vs small privet debt.

Question: Will there ever be a world where these meet in something like middle / lower middle market direct lending funds? Will direct lending funds (and possibly PE funds) use algorithms to do fundamental analysis and underwrite and invest in businesses?

If there were large amounts of clean financial information (a big ask) which could be used for valuation models and security analysis, there could be expected performance grades similar to something like an Altman z-score. You’d obviously need more info such and the lenders lien type, covenants, etc. but if this becomes possible, I believe that DL funds could take smaller hold sizes (more positions per fund) and create CLO type funds with MM investments.

Please provide your thoughts on 1) if this could be possible 2) what would need to happen to make it possible 3) what are the biggest obstacles that would not make this possible.

Any additional thoughts on use of information/data in private market analysis can be added.

Hello all,

I saw a post the other day asking if people would be interested in sharing strategy ideas or helping each other evolving with algorithmic trading.

I know we are a very divided community skill wise, and as new people tumble into this world you will be very soon to find that:

1. There is very little information available online, it is a very secretive industry, so to simply get into it by yourself is very hard.
2. There are a lot of paid information sources around the internet, but from my understanding very few if any of these are ever worth the money so it might be wise to stay away.
3. It is not very hard to make a strategy that scalps more points than it loses, but it is very hard for someone who is not from within the industry to devise a strategy that scalps more points than it loses including fees.

However, I thought i coud share just some simple tips with you all, i do not claim to be an excellent programmer so rip away into my code all you'd like. It is simply ment to work nothing more ;)

To back test any hypothesis you might have is quite easy with Python, so i'd advise you to use it or learn it. Basically the whole datascience community has, or is moving from R to Python.

In the code i'll link below you will see a very simple example using python to buy and sell on RSI levels in a test enviroment on old data. I tried commenting the code and making it as easy as i possibly could.

For making an actual algo that sends real market orders, i would recommend to start off using IG Markets free open API. You can plug it to a demo account and play around. If you have any interest in how to do this, i could happily show you, just ask.

Anyway here is the code and an image of the graph it plots.
https://github.com/Jaglyser/Algorithmic-Trading/blob/main/RSI
https://imgur.com/a/HvVvfON

Merry christmas,
Jaglyser

In episode 776, Miles and guest host Jamie Loftus are joined by comedian Blake Wexler to discuss conservative TV ratings, the messy Giuliani witness defending herself, Russian media asking Trump to come home, Russians being unimpressed by Covid vaccine, Nick Cage's new Netflix show, Gordon Ramsay's hundred dollar burger, and more!

Song we ride out on: Sunshine by Seneca B

FOOOOTNOTES

In my experience, algorithmic ADCs have faded away.

https://github.com/SamGulinello/TraderPy

Account with the program running the show

After a lot of learning and debugging, I have finally published the first version of my modular trading platform. The way it works is that developers can program individual modules that will perform a technical analysis strategy and predict which stocks to buy. Using the TD Ameritrade API this program will take the predictions from the modules and buy the shares of the stocks. Through testing, I have had success in not only the program working but I have also made some money off the trades. This is still very early in life and I am looking for people to help me either develop code or just give feedback on what is already done.

I am learning programming and I am interested in learning data structures and algorithms. I am not doing algorithmic questions for coding interviews YET. I just wanted to increase my problem solving skills and to get better at algorithmic thinking. I have a couple of questions to ask since I am interested.

1. Is there any best free/low cost sources on the internet where I can learn data structures and algorithms on my own???

2. What is the mathematics required to understand data structures and algorithms?

3. I have tried to solve the easy questions on hackerrank, codingbat, and codewars. I still struggled hard to write out my solution into code. It was difficult. Any tips on how to improve???

This isn't my article, I came across it doing some research, really like it, and thought it was worth sharing here :)

https://epchan.blogspot.com/2019/04/the-most-overlooked-aspect-of.html

The giveaway: it's collaboration. Another algotrader to work hand-in-hand with to share, develop, and implement new ideas, etc..

I've been working on algotrading basically full-time, teaching myself from scratch, for the past 6 months or so. Working by myself for pretty much this whole time, I can definitely attest to a desire for someone I can work with at a much closer level than what we often feel comfortable sharing publicly in forums like this.

Thanks for everything that you all have been willing to share in this sub, it has helped me immeasurably these past months.

As you will see on kotlinlang.org, the next Talk will be Algorithmic Trading with Kotlin.

It will be via crowdcast.io on December 16 at 19:30 GMT+8 (it's intended for Australia/Asia timezones - though others are welcome!).

We will show simply how to develop the code from scratch, develop a simple algo and show it actually trading.

All the code and instructions for building and deploying it will be available on GitHub.

Hello,

I’ve recently been given the choice between choosing the algorithm trading or the model validation team of a large bank.

I was wondering what were the day to day duties and perspectives of each role?

I know how to code but I do not want it to be my sole purpose but I also know that the model validation is away from the money.

What I enjoy as a quant is studying the models & maths behind

I'm a grad student who does AI-related work and I really want to learn more about CS-related ethics as well as algorithmic fairness. My undergrad did not have an ethics class like GT does and I feel this is an important thing missing from my education. Does anyone know if grad classes on these topics are offered at GT so I can look into them? Also happy to hear ways to learn more about these topics outside of GT.

I don’t have anything else to add here, the title really speaks for itself. I’m wondering what are some wonderful papers that algorithmic traders should consider reading?

I've read all the archived threads and I'm still a little troubled by the frequency of trading being a measure of whether I'm passive or active (and therefore a business). Clearly every investment targets profit, so high profits are a ridiculous signal imho.

Here's my question: If I set up an algorithm that takes me 4 hours to code and have it execute all my trades for a year, and it makes 20 trades a day, why does is this considered a business? I have a business that I work at 8 hours a day, and it isn't investing.

Is anyone aware of challenges that have been made or are in the process of being made that address my situation? In a world of automation, I feel that their criteria here are a bit outdated.

View Poll

https://preview.redd.it/x1u6zg03st061.png?width=630&format=png&auto=webp&s=c54770018979f238a989fa5f1ed0f61974a0c166

The first “Bayesian Program Learning” paper used a strange term called “Bayesian Program Merging”. A colleague sent me the link of the paper a few years ago, he had found the paper while he was searching for one of my Heuristic Algorithmic Memory papers that introduce my long-term design for universal machine learning algorithms. Those papers were published at AGI-10, AGI-11, and AGI-14 conferences. I noticed that there was a great deal of similarity between that paper and a paper I submitted and published more than 1.5 years ago before this offending paper was published on the arxiv. At first, I thought that was not a problem, because the paper seemed like a technical report, and not published anywhere. I was wrong.

I had invented Heuristic Algorithmic Memory as a response to a challenge that Ray Solomonoff personally posed to me in 2005. He had said:

>We can use stochastic context-free grammar to guide the search [in a Solomonoff induction approximation]. How are we going to update it if we already have a grammar?

It was Solomonoff that recommended me to play with stochastic context-free grammars, but he did not tell me how to solve the update problem. I solved the problem during 2006-2007, and I also solved a major theoretical problem which nobody else had noticed, and still do not understand, by the way. Solomonoff himself at AGI-10 had recommended using simpler heuristic compression tools like PAQ, PPM, etc., to capture the regularities therein. However, during my brief collaboration with Solomonoff, I had already tested that idea, and seen that it unfortunately did not work. Heuristic Algorithmic Memory was a partial solution to his challenge. It used a very powerful  and fast update logic that captured algorithmic regularities in the solution corpus, and updated the stochastic context free grammar which acts as a guiding probability distribution of programs. Combined with a rigorous Levin search implementation, I obtained a powerful AGI prototype called first gigamachine (sequential version) and then teramachine (parallel version). Gigamachine was tested in early 2010 as part of the research of my government-funded AGI startup, and the results affirmed what Solomonoff was looking for.  We could now solve a long training sequence and measure information transfer between problems.

I submitted to the AGI conferen

Has anyone here ever taken a mod that is related to trading or a financial math mod that has contributed significantly to their trading knowledge? Any suggestions?

So, seems like the race is on for the most sustainable design to achieve stability via algorithm.

Those are the ones I know of, feel free to add if they are more promising ones.

Which one you think is the best design-wise and why?

Let's make a constructive discussion comparing the advantages and disadvantages of each and learn in the process. There is so much going on in that field right now that is hard (if not impossible) for a single person to keep up and see all the angles.

So, seems like the race is on for the most sustainable design to achieve stability via algorithm.

Those are the ones I know of, feel free to add if they are more promising ones.

Which one you think is the best design-wise and why?

Let's make a constructive discussion comparing the advantages and disadvantages of each and learn in the process. There is so much going on in that field right now that is hard (if not impossible) for a single person to keep up and see all the angles.