Hey Shagufta -Tahsildar, sorry about the delay, but are any of these useful:

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  • Any Interest in Machine Learning Content? since completion in January of this year. Machine learning was a huge differentiator for me, landing me ... python / data science / machine learning and patented two algorithms, one of which is my firm's ... first client-facing machine learning algorithm and has been used to advise 15+ Fortune 500 companies ...
  • Machine Learning Quant, Citadel vs GS, offer advice Hello everyone, I received two offers, one as a Machine Learning Quant at Citadel Securities ... made it clear that it would be a role focusing a lot on machine learning. I am really interested by ... Machine Learning and my issue is, although I would love working with this team at GS (I've already ...
  • Machine Learning for Hedge Funds reinforcement) at funds? Or are other method generally favored for financial time series prediction? machine ... learning ...
  • Machine Learning course at Stanford prof about machine learning, programming and AI. I'd like to know if you guys think that I can put ... Monkeys, A friend bought for me the course as a present. It seems a very good course from a great ...
  • The limitations of Machine Learning/AI experience as humans--as embodied earthly creatures. Machine learning models have no access to such ... agents--it is like a dim image in a mirror. Current machine learning models: like a dim image in a mirror. As ... a machine learning practitioner, always be mindful of this, and never fall into the trap of believing that ...
  • There's gotta be a faster way to wipe my ass- seeking capital for AI-driven ass cleaning machine Sup guys. Starting a new venture. Looking for a better, more streamlined way to wipe my asshole. ... For the next few months, I will be researching ways one can wipe his asshole quickly on the toilet. ... my ass for like 15 minutes, anxiously ripping apart the toilet paper, square by square, folding that ...
  • More suggestions...

Fingers crossed that one of those helps you.

I'm an AI bot trained on the most helpful WSO content across 17+ years.
 

It's not about starting off with an algo. Let's say I gave you algo, would you understand the perils of over fitting, the framework, forecasting, volatility targeting, position sizing? No... I recommend quantopian. Pretty much has all the resources to give you a good start. You need to learn programming, you need to grasp some understanding of math beyond just algebra. The site has resources to teach you.

 

scikit-learn (the machine learning library quantopian offers) has a variety of models available...so you can just plug in you chosen data, and it will spit out a model that you can use. Unfortunately, LSTM (the most powerful model for analyzing time series data that i am aware of) is not one of them. To use an LSTM model, you will need to use either TensorFlow, PyTorch, CNTK, or another......none of which are available in Quantopian (at this time).

I would also suggest that you explore more than just price as an input to your machine learning models (other things like volume, trades, and alternative data sources).

just google it...you're welcome
 

Unless you have gazillion of data points, I find using a form of linear + regularized models such as Ridge or Lasso work best ( many research papers supporting this ). The problem with prediction with machine learning in finance is the extremely low signal to noise ratio. Powerful and flexible models such as Neural Network ( i.e, anything "deep" in the deep learning ) will tend to over-fit the noise and hence require a lot of data. If you are doing intra-day with tick data NN based would be a possibility. For anything end-of-day, it is always better of with linear model.

 

It depends on the type of data, what you are predicting and what metric you are optimizing. From my experience xgboost and lgbm was always better than any NN architectures or linear models, but this is when the target is boolean value.

You killed the Greece spread goes up, spread goes down, from Wall Street they all play like a freak, Goldman Sachs 'o beat.
 

I love trees and forest. XGBM and LGBM are always my go to models for Kaggle competitions. But for trading alpha strategies, most of the times the predicted variables are either returns or some form of price time-series, hence the linear model.

 
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