Quantra – Trading Alphas: Mining, Optimisation and System Design

Quantra – Trading Alphas: Mining, Optimisation and System Design

Why should you choose micro alpha models over other trading strategies such as traditional factor models, risk-parity, or trend following? In short, these models, if built well, can provide better performance, stability, and risk management than other trading systems. In this course, you will learn where micro-alphas reside and how to write the most efficient codes to quickly analyse, backtest, optimise and go live with your trading strategy in the least amount of time possible.

  • Backtesting, adding stop-loss and profit-take using vectorised approach
  • Mining micro-alphas using trends, mean-reversion, correlation across assets, and cointegration
  • Metrics for analysing strategy which include total profit, sharpe ratio, sortino ratio, profit factor, drawdown, and profit per trade
  • Parameter optimisation using machine learning techniques such as clustering
  • Building a trading system from scratch
  • Explain software architecture, logging, storage, hardware, testing and version control
  • Brief study on execution models, implement parallel computing and describe different levels of logging

What You’ll Learn In Trading Alphas: Mining, Optimisation and System Design?

Quantra (@GoQuantra) / Twitter

  • Introduction
  • Micro Alphas
  • Market Inefficiencies: Trend
  • Market Inefficiencies: Mean Reversion
  • Trading with Trends and Mean Reversion
  • Market Inefficiencies: Chart Patterns
  • Market Inefficiencies: Correlation, Fundamental and Alternative
  • Market Inefficiencies: Cointegration
  • Time Series Alphas
  • Live Trading on Blueshift
  • Live Trading Template
  • Cross-Sectional Alphas
  • Timing Alphas
  • Combinations of Alpha
  • Finding Micro-Alphas
  • Assessing Results
  • Total Profit
  • Sharpe and Sortino Ratios
  • Profit Factor and Drawdown
  • Profit Per Trade
  • CAGR, Alpha, and Beta
  • Strategy Execution
  • Micro-Alpha Portfolio
  • Portfolio Optimisation
  • Advanced Alpha Mining
  • Machine Learning Alphas
  • Basics of Vectorized Backtest
  • Adding Vectorized Stop-loss and Profit-takes
  • Impact of Profit Take and Stop Loss on Strategy
  • Designing a Trading System
  • Asynchronous Computing
  • Distributed Computing
  • Importance of Logging and Storage
  • Hardware Elements of a Trading System
  • Software Elements of a Trading System
  • Testing and Version Control
  • Implementation of a Trading System
  • Types of Servers
  • Trading Logic
  • Testing and Operation
  • Capstone Project
  • Run Codes Locally on Your Machine
  • Summary

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Quantra – Trading Alphas: Mining, Optimisation and System Design

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