Data Mining Candlestick Patterns With a Genetic Algorithm

neurotrader · Beginner ·🚀 Entrepreneurship & Startups ·3y ago

About this lesson

Finding optimal candlestick patterns for Bitcoin using a evolutionary algorithm. Multiple trading rules from a set of candlestick data and are tested out of sample forming a potential trading strategy. Martin Ratio: https://www.tangotools.com/ui/ui.htm 0:00 Intro 0:29 Pattern Schema 1:10 Random Patterns 1:27 Genetic Algorithm Overview 1:44 Initialization 1:54 Evaluation 2:22 Fitness Functions 2:42 Fitness - Total Return 3:05 Fitness - Profit Factor 3:56 Fitness - Martin Ratio 4:47 Evaluation 5:07 Elitism 5:36 Parent Selection 6:19 Reproduction 7:02 Mutation 7:59 Stopping Criteria 8:21 Genetic Algorithm Parameters 8:35 BTC-USDT Test 9:24 Finding Multiple Patterns 10:16 BTC-USDT Test 10:39 Monte Carlo Test 11:53 Walkforward Analysis 13:12 Conclusions The content covered on this channel is NOT to be considered as any financial or investment advice. Past results are not necessarily indicative of future results. This content is purely for education/entertainment.

Original Description

Finding optimal candlestick patterns for Bitcoin using a evolutionary algorithm. Multiple trading rules from a set of candlestick data and are tested out of sample forming a potential trading strategy. Martin Ratio: https://www.tangotools.com/ui/ui.htm 0:00 Intro 0:29 Pattern Schema 1:10 Random Patterns 1:27 Genetic Algorithm Overview 1:44 Initialization 1:54 Evaluation 2:22 Fitness Functions 2:42 Fitness - Total Return 3:05 Fitness - Profit Factor 3:56 Fitness - Martin Ratio 4:47 Evaluation 5:07 Elitism 5:36 Parent Selection 6:19 Reproduction 7:02 Mutation 7:59 Stopping Criteria 8:21 Genetic Algorithm Parameters 8:35 BTC-USDT Test 9:24 Finding Multiple Patterns 10:16 BTC-USDT Test 10:39 Monte Carlo Test 11:53 Walkforward Analysis 13:12 Conclusions The content covered on this channel is NOT to be considered as any financial or investment advice. Past results are not necessarily indicative of future results. This content is purely for education/entertainment.
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Chapters (23)

Intro
0:29 Pattern Schema
1:10 Random Patterns
1:27 Genetic Algorithm Overview
1:44 Initialization
1:54 Evaluation
2:22 Fitness Functions
2:42 Fitness - Total Return
3:05 Fitness - Profit Factor
3:56 Fitness - Martin Ratio
4:47 Evaluation
5:07 Elitism
5:36 Parent Selection
6:19 Reproduction
7:02 Mutation
7:59 Stopping Criteria
8:21 Genetic Algorithm Parameters
8:35 BTC-USDT Test
9:24 Finding Multiple Patterns
10:16 BTC-USDT Test
10:39 Monte Carlo Test
11:53 Walkforward Analysis
13:12 Conclusions
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