MetaTrader 5 Python User Group - the summary - page 34

New comment
 

Quantitative approach to risk management: Applying VaR model to optimize multi-currency portfolio using Python and MetaTrader 5

Quantitative approach to risk management: Applying VaR model to optimize multi-currency portfolio using Python and MetaTrader 5

Value at Risk (VaR) has become the cornerstone of my research into market risk. Years of practice in Forex have convinced me of the power of this instrument. VaR answers the question that torments every trader: how much can you lose in a day, week or month?

Quantitative approach to risk management: Applying VaR model to optimize multi-currency portfolio using Python and MetaTrader 5
Quantitative approach to risk management: Applying VaR model to optimize multi-currency portfolio using Python and MetaTrader 5
  • www.mql5.com
This article explores the potential of the Value at Risk (VaR) model for multi-currency portfolio optimization. Using the power of Python and the functionality of MetaTrader 5, we demonstrate how to implement VaR analysis for efficient capital allocation and position management. From theoretical foundations to practical implementation, the article covers all aspects of applying one of the most robust risk calculation systems – VaR – in algorithmic trading.
 

Feature Engineering With Python And MQL5 (Part IV): Candlestick Pattern Recognition With UMAP Regression

Feature Engineering With Python And MQL5 (Part IV): Candlestick Pattern Recognition With UMAP Regression

Candlestick patterns are widely used across many different trading strategies and styles by most algorithmic traders in our community. However, our understanding of these patterns is limited to the candlesticks that we have uncovered, while in truth there may be many other profitable candlestick patterns we are simply not aware of yet. Due to the wealth of information covering most modern markets, it is materially challenging for traders to be confident that they are always using the most reliable candlestick patterns available to them in their chosen market.
Feature Engineering With Python And MQL5 (Part IV): Candlestick Pattern Recognition With UMAP Regression
Feature Engineering With Python And MQL5 (Part IV): Candlestick Pattern Recognition With UMAP Regression
  • www.mql5.com
Dimension reduction techniques are widely used to improve the performance of machine learning models. Let us discuss a relatively new technique known as Uniform Manifold Approximation and Projection (UMAP). This new technique has been developed to explicitly overcome the limitations of legacy methods that create artifacts and distortions in the data. UMAP is a powerful dimension reduction technique, and it helps us group similar candle sticks in a novel and effective way that reduces our error rates on out of sample data and improves our trading performance.
1...2728293031323334
New comment