#course_trial@hunt4quant

Dear all!

One of my clients, a reputed financial institution, purchased an introductory course on ML models application to financial markets “All basic models used by banks and investment firms in 100 slides with no maths”



⚠️This is a brand new course ⚠️

I was given a permission to test the course with my students and I welcome you and any of your peers to join the webinar on May 13th at 18:00 Moscow time (in Russian).

🇬🇧
In fact my lecture is a translation from English to Russian (my client doesn’t speak English 😅) so I would be happy to give you the original lecture if we have enough listeners. If you prefer English version say it in comments, please. We will arrange a webinar.

⚠️ please bear in mind that this is 0% math lecture, feel free to bring those of your friends who are not familiar with formulas.


*Models I plan to consider today (in Russian!)*

1. Classification models:
1.1 Logit model
1.2.SVM
1.3 Decision trees: Random Forest, XGBoost
1.4 Neural Networks(MLP)

2. Regression
2.1 Linear Regression
2.2 Ridge
2.3 Lasso
2.4 kNN-regression
2.5 Decision trees
2.6 MLP

3. Time series analysis
3.1. ARIMA/SARIMA
3.2. ETS (Holt–Winters)
3.3 RNN (LSTM/GRU)
3.4 TCN
3.5 Seq2Seq + Attention

4. Anomalies détection
4.1 Isolation Forest
4.2 Autoencoder
4.3 Local Outlier Factor (LOF)
4.4 K-means / MiniBatch K-means
4.5 DBSCAN
4.6 Hierarchical Clustering

Hedging strategies:

1. Futures and options contracts - Buying put options for a long position
2. Futures and options contracts - Selling call options for coverage (covered call)
3. Correlation instruments - Covering risks through correlated assets (stocks ↔ ETF, currency)
4. Pair trading strategies - Long-Short by spread deviation

Diversification strategies:

1. Selecting assets with low/negative correlation
2. Balancing different classes (stocks, bonds, commodity futures)
3. Mathematical models
3.1 Markowitz: optimization through the covariance matrix
3.2 Risk Parity: equal risk for each position
4. Selection algorithms
4.1 Genetic algorithms for weight selection
4.2 CVaR optimization for "tail" risk