Udemy - Algorithmic Trading - Mathematical and Strategic Theories
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Last checked: Jun. 24th '25
Date uploaded: Jun. 24th '25
Seeders: 7
Leechers: 4
INFO HASH: 4E5211C45B923D9DEBF8CD67E783EFA9D72A02E3
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.
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.