Published 7/2024
Created by Carmen Yu-Richardson, MBA
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 27 Lectures ( 2h 23m ) | Size: 655 MB
Strengthen business communication, team collaboration, navigate workplace challenges, and lead confidently.
What you’ll learn:
Machine Learning for Technical Analysis
Machine Learning Pipeline
Feature Engineering and Selection in Technical Analysis
Labelling Technical Analysis
Create Own Hybrid Indicator
Clustering Equities and Indexes for Trading Upward or Downward Skewness
Decision Tree with technical indicators
Feature Selection in Technical Analysis
Feature Engineering in Technical Analysis
Imbalanced Classes in Technical Analysis
Requirements:
Understand the basics of Technical Analysis
No programming experience needed
Description:
In this course, we move beyond recurring clichés to provide an in-depth, practical exploration of how machine learning (ML) enhances technical analysis (TA). This is particularly valuable for technical analysts, as we focus on adapting AI tools specifically for TA.
You’ll learn how to work with features or predictive variables, adjusting them to avoid common mistakes made by non-practitioners. We’ll also delve into labels, steering clear of unrealistic expectations in an environment with a low signal-to-noise ratio. Our practical examples demonstrate both the power of AI and the potential pitfalls, avoiding the presentation of flawless but impractical scenarios.
You will see how you can easily create your own technical indicators by hybridizing them with dimensionality reduction algorithms, merging other indicators without losing information. For instance, you’ll learn to combine multiple moving averages into one or merge several RSI indicators into a single one.
Additionally, you’ll learn how to cluster stocks, such as those in the S&P 500, using unsupervised machine learning. You’ll analyze each cluster based on its skewness and bullish or bearish bias, establishing buy or sell signals accordingly.
Furthermore, you will master the use of classification algorithms and understand their limitations, always with examples rooted in technical analysis, real data, and actual cases.
This guide will astonish you with how traditional TA can be extraordinarily enhanced by ML. The course includes real, usable case studies and cloud-based Python tools that require no local installation. Upon completing the course, you’ll be equipped to advance with your own models or adjust the ones provided.
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