List of state of the art papers focus on deep learning and resources, code and experiments using deep learning for time series forecasting. Classic methods vs Deep Learning methods.

GPU speedup: XGBoost 7.3x, LightGBM 3.6x (excluding goss results), Catboost 3.3x
"while XGBoost can explore its space of hyper-parameters very fast, it does not always locate the configuration that results in the best score. While it clearly wins in both multi-class ranking tasks (Microsoft, Yahoo), for the Higgs dataset it loses to LightGBM, despite the latter being significantly slower. Furthermore, for the Epsilon dataset XGBoost cannot be used due to memory limitations.
... there are tasks for which LightGBM, albeit slower, can converge to a solution that generalizes better. Furthermore, for datasets with a large number of features, XGBoost cannot run due to memory limitations, and Catboost converges to a good solution in the shortest time. Therefore, while we observe interesting trends, there is still no clear winner in terms of time-to-solution across all datasets and learning tasks. The challenge of building a robust GPU-accelerated GBDT framework that excels in all scenarios is thus very much an open problem."

Lookahead+RAdam vs. Adam on some RL benchmarks

"The Ranger optimizer combines two very new developments (RAdam + Lookahead) into a single optimizer for deep learning. As proof of it’s efficacy, our team used the Ranger optimizer in recently capturing 12 leaderboard records on the FastAI global leaderboards (details here).

Lookahead, one half of the Ranger optimizer, was introduced in a new paper in part by the famed deep learning researcher Geoffrey Hinton (“LookAhead optimizer: k steps forward, 1 step back” July 2019). Lookahead was inspired by the recent advances in the understanding of neural network loss surfaces and presents a whole new way of stabilizing deep learning training and speed of convergence. Building on the breakthrough in variance management for deep learning achieved by RAdam (Rectified Adam), I find that combining RAdam plus LookAhead together (Ranger) produces a dynamic dream team and an even better optimizer than RAdam alone."

light on details

"DeepMind's version of reinforcement learning that uses "temporal value transport" to send a signal from reward backward, to shape actions, does better than alternative forms of neural networks. Here, the "TVT" program is compared to "Long-short-term memory," or LSTM, neural networks, with and without memory, and a basic reconstructive memory agent."

OOP in DS

Millburn’s new equity fund will use machine learning to decipher signals from exchange-traded funds in order to make long bets on the underlying securities such as members of the S&P 500 and MSCI World.

RL algorithms and illustrate the definitions of the reward function, actions and policy functions in details, as well as introducing algorithms that could be applied to FTFs

take a guided tour through features of OpenAI Gym, from utilizing standard libraries to creating your own environments, then discover how to frame reinforcement learning problems so you can research, develop, and deploy RL-based solutions

In finance, the weak form of the Efficient Market Hypothesis asserts that historic stock price and volume data cannot inform predictions of future prices. In this paper we show that, to the contrary, future intra-day stock prices could be predicted effectively until 2009. We demonstrate this using two different profitable machine learning-based trading strategies. However, the effectiveness of both approaches diminish over time, and neither of them are profitable after 2009.

AlphaStock fully exploits the interrelationship among stocks, and
opens a door for solving the “black box” problem of using deep learning models in financial markets. The back-testing and simulation experiments over U.S. and Chinese stock markets showed that

AlphaStock performed much better than other competing strategies. Interestingly, AlphaStock suggests buying stocks with high long-term growth, low volatility, high intrinsic value, and being
undervalued recently.

deep reinforcement learning algorithms to automatically generate consistently profitable, robust, uncorrelated trading signals in any general financial market.

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