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Articles by Random ForestRegressor
Reimagining Classic Strategies (Part VII) : Forex Markets And Sovereign Debt Analysis on the USDJPY
MetaTrader 5 — Examples | 29 August 2024, 16:42 40 0 Artificial Intelligence holds the potential to create new trading strategies for the modern investor. It is unlikely that any single investor will have enough time to carefully assess each possible strategy before deciding which one to trust with their capital. In this series of articles, we aim to present you with the information you need to arrive at an informed decision as to which strategy best fits your particular investor profile.
Reimagining Classic Strategies (Part VI): Multiple Time-Frame Analysis
MetaTrader 5 — Examples | 20 August 2024, 14:26 19 0 There are potentially infinite ways in which a modern investor can integrate Artificial Intelligence (AI) to enhance their trading decisions. Unfortunately, it is unlikely that you can evaluate all these strategies before deciding which strategy to trust with your hard-earned capital. In this series of articles, we will explore trading strategies to assess whether we can improve the strategy with AI.
Reimagining Classic Strategies (Part V): Multiple Symbol Analysis on USDZAR
MetaTrader 5 — Examples | 16 August 2024, 16:51 55 0 There are innumerable ways we can integrate AI into our trading strategies, but unfortunately, we cannot evaluate each one before we decide which one to trust with our capital. Today, we revisit a popular trading strategy of multiple symbol analysis to determine if we can improve the strategy using AI.
Reimagining Classic Strategies (Part IV): SP500 and US Treasury Notes
MetaTrader 5 — Examples | 13 August 2024, 15:04 368 0 In our previous article, we discussed a potential S&P 500 trading strategy that would rely on us using a selection of stocks that held high weights inside the index. In today's article, we're going to look at an alternative approach of trading the S&P 500 using the Yield of Treasury Notes.
Euro vs Dollar Forecast Application Development
TutorialImplementation of a euro vs dollar forecast application to predict future prices. Euro vs Dollar Forecast Application Predicting currency exchange rates is crucial for economic stability, investment strategies, and international trade. The Euro vs. Dollar forecast using Python can provide insightful predictions for traders and economists.
Random Realizations
What is SHAP? SHAP (SHapley Additive exPlanations) is a conceptual framework for creating explanations of ML model predictions. The term also refers to a set of computational methods for generating these explanations and a python library which implements them. The “SHAP” backronym was introduced in Lundberg and Lee 2017, which I call the SHAP paper, that expanded on several previously existing ideas which we’ll build up in the following sections.
Time Series Forecasting for Appliances Energy Prediction
Time Series Forecasting Original Data Link: Appliances Energy Prediction Dataset Target: Use suitable time series analysis techniques. Apply at least two time-series forecasting methods. Compare the results from all candidate models. Choose the best model. Justify your choice and discuss the results. 1.
Python, ONNX and MetaTrader 5: Creating a RandomForest model with RobustScaler and PolynomialFeatures data preprocessing
MetaTrader 5 — Tester | 27 March 2024, 07:56 115 0 The history of the development of the Random Forest method goes back a long way and is associated with the work of prominent scientists in the field of machine learning and statistics. To better understand the principles and application of this method, let's imagine it as a huge group of people (decision trees) who work together. The random forest method has its roots in decision trees.
Let's Find Out How to Make Engaging Videos?
This article was published as a part of the Data Science Blogathon. Introduction we will be predicting how engaging a video can be at the user level. We have been provided with a dataset that contains that user’s earlier videos engagement score along with their personal information. We will build multiple regression models and see which one performs better.
Machine Learning: Target Feature Label Imbalance Problems and Solutions
The goal of this post is to teach python programmers why they must have balanced data for model training and how to balance those data sets. Often times, in machine learning classification problems, models will not work as well and be incomplete without performing data balancing on train data. This post will serve as an end-to-end guide for solving this problem. Table of contents Before running a model, you need to fit the model on a set of data that is distinct from the data you test it on.
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