Time Series with TensorFlow: Downloading & Formatting Historical Bitcoin Data
In this article, we'll start a new time series with TensorFlow project by importing historical Bitcoin data, visualizing it, and preparing it for modeling.
I'm a machine learning engineer, quantitative analyst, and quantum computing enthusiast with a background in SaaS and venture capital.
In this article, we'll start a new time series with TensorFlow project by importing historical Bitcoin data, visualizing it, and preparing it for modeling.
In this article, we'll create a smart contract that is can store a string on the blockchain, is readable by everyone, and is writeable by the person that deployed the smart contract.
In this article, we discuss two different function types available in Solidity: view functions and pure functions. We'll also discuss a special type of function called the constructor.
In this article, we'll discuss a few more fundamental data types in Solidity programming: strings, bytes, and address types.
In this article, we'll review two fundamental data types in Solidity: booleans and integers.
In this article, we're going to look at how to write data to the blockchain after a smart contract is deployed.
In this article we discuss how to create your first smart contract with Solidity with the Remix IDE. In particular, how to configure the compiler, deploy the smart contract, and interact with it in the browser.
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In this section we'll finish our initial deep reinforcement learning trading algorithm by deploying it at a simulated account at Interactive Brokers.
In this section, the objective is to use reinforcement learning to maximize the Sharpe ratio using gradient ascent.
In this section, we're going to add another deep learning model to our trading algorithm and build a convolutional neural network (CNN).
In this guide we build an LSTM for price prediction in our deep reinforcement learning trading algorithm.
In this section we'll start with the imports, model and trading logic inputs, and helper functions that we'll need for this deep reinforcement learning for trading project.
In this project we're going to build a deep reinforcement learning trading agent and deploy it in a simulated trading account at Interactive Brokers.
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In this guide, we introduce the fundamental concepts of blockchain technology including its structure, basic operations, and review the Bitcoin vs. Ethereum blockchain.
Ethereum received a major upgrade to the network today: EIP 1559. In this article, we review key Ethereum on-chain analysis metrics and signals.
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In this article we apply an unsupervised learning technique, K-means clustering, to a group of companies imported from Yahoo Finance.
In this guide, we're going to review an interesting application of AI for trading and investing: machine learning for multiday stock estimates.
In this guide to blockchain analytics, we discuss 14 key terms that every crypto on-chain analyst, trader, and investor should know.
In this article, we review time series analysis with Python, including Pandas for time series data and time series analysis techniques
In this guide, we introduce the fundamentals of Python programming for finance, including two key Python libraries: NumPy and Pandas.
In this guide, we'll discuss exactly what on-chain analysis is and how you can it to improve your crypto trading and investing.
In this guide, we discuss how traders and investors can use AI and machine learning to rank stocks, otherwise known as predictive equity ranking.
In this guide, we discuss variational autoencoders, which combine techniques from deep learning and Bayesian machine learning, specifically variational inference.
In this guide, we discuss how traders and investors can use sentiment analysis and natural language processing (NLP) for SEC filings to speed up their research process.
In this guide, we discuss 8 applications of AI and machine learning for trading and investing. This includes sentiment analysis, return estimates, and more.
In this article, we discuss various applications of classification-based machine learning in finance, including logistic regression for predicting asset returns.
A recurrent neural network (RNN) attempts to model time-based or sequence-based data. An LSTM network is a type of RNN that uses special units as well as standard units.
In this article on natural language processing, we discuss how to use the Naive Bayes formula for the purpose of sentiment analysis.
In this guide, we discuss the application of deep reinforcement learning to the field of algorithmic trading.
In this article, we discuss how to use natural language processing and logistic regression for the purpose of sentiment analysis.
In this article we look at how to build a reinforcement learning trading agent with deep Q-learning using TensorFlow 2.0.
In this article, we discuss two key concepts in portfolio optimization: Markovitz optimization and the Efficient Frontier.
In this article, we'll introduce key concepts of risk and return in portfolio analysis, including Value-at-Risk, Conditional Value-at-Risk, and more.
In this article, we discuss two important topics in reinforcement learning: Q-learning and deep Q-learning.
In this guide, we discuss two types of GANs that allow you to control the output of the model: conditional GANs (cGANs) and controllable generation.