A Brief Review of Reinforcement Learning

Reinforcement Learning is a mathematical framework for experience-driven autonomous learning. An RL agent interacts with its environment and, upon observing the consequences of its actions, can learn to alter its own behaviour in response to the rewards received. The goal of the agent is to learn a policy ππ that maximizes the expected return (cumulative, discounted reward).

Machine Learning for Intraday Stock Price Prediction 2: Neural Networks

This is the second of a series of posts on the task of applying machine learning for intraday stock price/return prediction. Price prediction is extremely crucial to most trading firms. People have been using various prediction techniques for many years. We will explore those techniques as well as recently popular algorithms like neural networks. In this post, we will focus on applying neural networks on the features derived from market data.

Machine Learning for Intraday Stock Price Prediction 1: Linear Models

This is the first of a series of posts on the task of applying machine learning for intraday stock price/return prediction. Price prediction is extremely crucial to most trading firms. People have been using various prediction techniques for many years. We will explore those techniques as well as recently popular algorithms like neural networks. In this post, we will focus on applying linear models on the features derived from market data.

[Paper Summary] StarSpace: Embed All The Things!

This paper describes a way to generate embeddings for various tasks. The algorithm is general enough which enables it to achieve strong results in very diverse tasks.

[Paper Summary] Deep Neural Networks for Youtube Recommendations

Youtube switched their recommender system from matrix factorization to neural networks few years ago. This paper describes the neural network models as well as the overall system around it, including the data processing and deployment aspects.

[Paper Summary] Deep learning networks for stock market analysis and prediction

In this paper, Deep learning techniques are applied to the financial market data directly rather than using any text/alternative data sources. This has been a relatively tricky dataset for any non-linear machine learning technique because of the extremely high noise-to-signal ratio. The authors use a relatively high-frequency dataset sampled at every 5 minutes. They consider 38 stocks from Korea KOSPI.

[Paper Summary] Deep Learning for Event-Driven Stock Prediction

In this post, I attempt to summarize this paper by Ding et al. This paper proposes a way to use the convolutional neural network on the news events for stock direction prediction. The data and the prediction granularity is 1-day.

Practical Text Classification for Production Systems

This post is about using a relatively simple yet powerful text classification model for a production text classificaiton system. Other topics like deployment, testing for out-of-sample texts are also discussed - they are often not the sexiest aspects, but it makes sense to discuss them in this post.

SELU vs RELU activation in simple NLP models

RELU activation function has become the de facto choice in neural networks these days. Few weeks ago, some researchers proposed Scaled Exponential Linear Unit (SELU) activation function. They show a far better convergence using SELU. In this post, I am posting a simple comparison of SELU against RELU using a simple BoW model on SNLI dataset.

Prophet - Time series prediction

Predicting daily (and intraday) volume is a classic time series problem in finance. We try to use the Prophet library for this task.