Udemy - Algorithmic Trading - Mathematical and Strategic Theories

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Udemy - Algorithmic Trading - Mathematical and Strategic Theories (Size: 2.3 GB)
  1 -Data Acquisition and Preprocessing.mp4 71.4 MB
  1 -Hello and Welcome.mp4 27.1 MB
  1 -Mean Reversion Strategies.mp4 74.4 MB
  1 -Probability and Statistics Review.mp4 95.7 MB
  2 -Backtesting Framework Architecture.mp4 115.8 MB
  2 -Time Series Analysis Essentials.mp4 97.4 MB
  2 -Trend Following Strategies.mp4 111.6 MB
  3 -Execution Algorithms and Order Types.mp4 81.3 MB
  3 -Pairs Trading and Cointegration.mp4 88.5 MB
  3 -Stochastic Processes and Models.mp4 87.5 MB
  4 -Statistical and Risk Arbitrage.mp4 85.8 MB
  4 -Stochastic Calculus Basics.mp4 136.7 MB
  4 -Transaction Costs and Slippage Modeling.mp4 74.8 MB
  5 -Market-Making and Microstructure.mp4 99.8 MB
  5 -Optimization Techniques.mp4 80 MB
  5 -Risk Management and Position Sizing.mp4 107.7 MB
  6 -Factor Models and Alpha Generation.mp4 94.8 MB
  6 -Machine Learning Foundations.mp4 112.4 MB
  6 -Performance Monitoring and Real-Time Analytics.mp4 114.8 MB
  7 -Deployment and Infrastructure Considerations.mp4 97.7 MB
  7 -Machine Learning in Strategy Development.mp4 115.3 MB
  7 -Performance Metrics and Evaluation.mp4 185 MB
  8 -Lesson 2.8 Statistical Testing and Overfitting.mp4 83.7 MB
  8 -Strategy Optimization and Walk-Forward Analysis.mp4 118.4 MB
  Bonus Resources.txt 102.4 B
  Get Bonus Downloads Here.url 204.8 B
  ▲ 26 total files

Description


Algorithmic Trading: Mathematical & Strategic Theories

https://WebToolTip.com

Published 5/2025
Created by Domas Masiulis
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All | Genre: eLearning | Language: English | Duration: 24 Lectures ( 4h 52m ) | Size: 2.3 GB

Master mathematical models, backtesting frameworks, risk controls to design and deploy algorithmic trading strategies.

What you'll learn
Apply probability, time series analysis, and stochastic calculus to model asset price dynamics and forecast market behavior.
Design and implement mean reversion, trend following, pairs trading, and arbitrage strategies using statistical and machine learning methods.
Build event-driven backtesting frameworks, simulate execution costs, and evaluate strategy performance with Sharpe, Sortino, and drawdown metrics.
Implement risk management controls including position sizing, stop-loss, and value-at-risk to protect capital and optimize portfolio stability.

Requirements
Basic programming skills in Python, foundational knowledge of calculus, linear algebra, probability, and statistics, familiarity with trading concepts, and access to Python with numpy, pandas, and scikit-learn.

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