First section will focus on setting up the environment within which we can run Python functions. Then, we will discuss each metric separately as its own section. Each metric section will follow the same format: 1) present the formula, 2) intuition, 3) example 4) Python implementatio...
$python supervised_learning.py #measure the performance and draw the figures $python performance.pyAbout Using Supervised machine learning methods (Decision Tree, Boosting, KNN, ANN, SVM) to trade stocks Resources Readme Activity Stars 0 stars Watchers 0 watching Forks 0 forks Report repos...
This project is a Stock Trader trained to trade stocks from the S&P 500. It was made using a Deep Q-Learning model and libraries such as TensorFlow, Keras, and OpenAI Gym. It was trained on data from 2006-2016, cross validated on data from 2016-2018, and tested on data from 2018-202...
Place orders for various instruments such as stocks, futures, options, and currencies. Track the status of your orders and your portfolio position in real time. Pros: Simplicity of use: IBridgePy makes it easier for developers and traders with different degrees of Python expertise to connect to...
Trading with Coinbase Pro (GDAX) API in Python Coinbase Pro (formerly known as GDAX) is one of the biggest cryptocurrency exchange, you can trade a large panel of cryptocurrencies against USD, EUR and GBP. I chose to trade on Coinbase Pro because it supports a lot of pairs and the liq...
Statistical Methods are part of the tools for analyzing securities. The following chapter explains the central limit theorem, returns, ranges, boxplots, histograms and other sets of statistical measures for the analysis of securities using Yahoo Finance API....
After creating the building stock dataset, a simulation analysis was developed and executed using Python to evaluate the effects of various urban policies on carbon emissions. From the life-cycle assessment (LCA) standpoint, a building’s age generates several scenarios that affect its emissions. For...
The node ‘Negative capital formation’ corresponds to the use of stocks from previous years that have not been replaced. It is possible for NCF to be positive for some sectors and negative for others. In some diagrams (such as Fig. 1), the node ‘Positive capital formation’ and ‘...
Learn how to apply cutting-edge machine learning techniques to trade options strategies and analyse the performance. Enroll now! File in the download Covered call strategy using Machine learning - Python notebook Login to Download Author: Chainika Thakar (Originally written by Varun Divakar) Note...
They were able to obtain significantly higher returns compared to earlier work. There were numerous exchange-traded funds (ETFs), which attempted to replicate the performance of the S&P 500 by holding the same stocks in the same proportion as the index, and therefore, giving the same percentage...