Posts for year 2019
- Informed trading and market microstructure
- Dai Stablecoin
- Overview of backtrader (Intermediate)
- Optimizing parameters of the trading strategy
- Adding indicators to your trading strategy
- Customizing the trading strategy
- Introduction to automated trading with backtrader
- Comparing Python platforms for automated trading.
- Model validation for logistic regression models
- Basel Market Risk notes
- Model validation for classification trees
- Model validation for linear regression models
- Model validation for time series regression models
- Why should we do our own automated trading?
- Hybrid machine learning solution with Google Colab
- How many neurons and layers for a multilayer perceptron (MLP)?
- Bayes' theorem
- Cluster analysis
- kNN vs k-Means
- Kaggle: Credit risk (Model: Gradient Boosting Machine - LightGBM)
- Kaggle: Credit risk (Model: Random Forest)
- Kaggle: Credit risk (Model: Decision Tree)
- Kaggle: Credit risk (Model: Support Vector Machines)
- Kaggle: Credit risk (Model: Logit)
- Kaggle: Credit risk (Feature Engineering: Automated)
- Kaggle: Credit risk (Feature Engineering: Part 3)
- Kaggle: Credit risk (Feature Engineering: Part 2)
- Kaggle: Credit risk (Feature Engineering: Part 1)
- Kaggle: Credit risk (Exploratory Data Analysis)
- Machine learning model peformance metrics
- Adaptive Boosting vs Gradient Boosting
- Bag of words with gensim
- Bagging vs Boosting
- Capital charge modelling for securitized products (SFA)
- Estimating systemic risk on the equities market
- Fitting a volatility model on indices
- Named Entity Recognition (NER)
- NLP Capstone
- NLP: Detecting the occurence of fakenews
- Polyglot
- Portfolio optimization & backtesting
- Regular expressions (regex)
- Term Frequency - Inverse Document Frequency (tf-idf) with gensim
- Tokenization
- Using spaCy
- Dealing with imbalanced datasets
- What are decision trees and CARTs?
- Cost functions, gradient descent, and gradient boost
- Python tips and tricks