Technical_Analysis_and_Feature_Engineering
Unverified ML strategy on Multi by jo-cho. BotFinder score 18 out of 100.
Feature Engineering and Feature Importance in Machine Learning for Financial Markets
Source: github
BotFinder analysis pending.
Technical_Analysis_and_Feature_Engineering
Feature Engineering and Feature Importance in Machine Learning for Financial Markets Background knowledge for Feature Analysis in Finance Technical Indicators - I studied over 80 technical indicators. - 1. Technical Indicators - Volume.ipynb - 2. Technical Indicators - Volatility.ipynb - 3. Technical Indicators - Trend.ipynb - 4. Technical Indicators - Momentum.ipynb - 5. Using technical indicators in Meta-labeling.ipynb - Technical Indicators Binary Matrix - for input Old ones - T.I. Analysis (old version) - TI Analysis - Is TA better than simple market data? - TI vs. Simple Feature Importance - Which one is important? with MDI - Correlation with the PC (principle component) of highest MDI Feature Engineering (.. in progress) - Deep Autoencoder - CNN architecture - FinEmbedding Data - High Frequency Cryptos Prices - Daily Stock Prices Other example - ML trading rule - Simple trading strategies using technical indicators - References - De Prado, M. L. (2018). Advances in financial machine learning. John Wiley & Sons. - Chapter 8 Feature Importance - Dixon, M. F., Halperin, I., & Bilo
⚠ No verified equity curve — no track-record source connected.
Drawdown profile
Data unavailable — contact the owner.
Verification ledger
How the score has moved
Recalculated at each data collection. Transparency means showing the bad weeks too.
No score history is stored yet — only the current score is shown.
Reviews & comments
No reviews collected from the source yet.
⚠ No live verification account connected — ask for proof before buying.
Alerts on changes: coming soon
Prop-firm compatibility not provided.
Open-source maintainer on GitHub.
Data-completeness & trust index (not a profitability rating)