Do not forget about the last few slots for the Quant Elite membership. This means you get immediate 10+ years archive plus 3 another years on what I do in the future. See the many benefits here as deep reinforcement learning.
Some may start doubting machine learning techniques like
Deep Learning or Deep Reinforcement Learning. If you consider Google’s Deep
Mind losing $572 million a year, many hope for the future of Artificial Intelligence
will falter like this.
Ten years from now we will conclude that deep reinforcement learning was
overrated in the late 2010s, and that many other important research avenues
were neglected. Every dollar invested in reinforcement learning is a dollar not
invested somewhere else, at a time when, for example, insights from the human
cognitive sciences might yield valuable clues. Researchers in machine learning
now often ask, “How can machines optimize complex problems using massive
amounts of data?” We might also ask, “How do children acquire language and come
to understand the world, using less power and data than current AI systems do?”
If we spent more time, money, and energy on the latter question than the
former, we might get to artificial general intelligence a lot sooner.
Could you link the relationship VIX futures with negative stock performance?
The tools for winners I have spoken about for month.
This is all you need to know:
But when it comes to APIs, all roads lead to Python – the clear API of choice for deep learning tool developers. Given these trends, and announcements around TensorFlow 2.0 that bring it parity with PyTorch, expect more tools spinning off from TensorFlow with Python APIs.
There are some very good tips on ensuring to use clear and established indutry data. It seems you also need to apply business definitions to the your models. You think this could be applied to enterprise wide strategy with these types? I think large High Frequency Trading shops like Sigma Two implement across their own enterprise. This is the definitely the future of trading outfits.
It seems that deep learning has been questioned when it comes to financial modelling. A decent article came out from a WorldQuant. This Medium article covers how to improve your forecasting using both in and out samples. It also covers:
1. Return Prediction comparing ARIMA, VAR, Deep Regression, Convoluted Neural Networks, and Long Short Term Memory.
2. Portfolio Construction with deep learning and deep index.
Overview of backtrader with Python and GUI project
I also tried to implement a GUI Python project that sat on top of backtrader. It seems I did not get anywhere after an hour. Sorry for the spoiler alert but you can learn how to install backtrader by watching the first part of the video.
A deep learning framework for financial time series using stacked autoencoders and long-short term memory
Most accurate machine deep learning model type?
This appears to use LSAM and SAE (long short term memory and stacked auto encoders) which appears to be more accurate than recurrent neural network (RNN). Do I sound like a machine learning experience here? Haha. I am way off it. This does show that this technique could be most accurate when it comes to forecasting financial time series.