Learning Track: Advanced Algorithmic Trading Strategies

Some familiarity with t-statistics and autoregressive model is useful but not mandatory

The track covers knowledge of Python for trading, which will help you to replicate the models in your trading

You should understand the basic terminology around financial markets such as sell, buy, margin, entry, exit positions, etc

Apply position sizing and capital allocation using Kelly Criterion, Modern Portfolio Theory, and Risk Parity, and evaluate portfolio performance using Sharpe ratio, maximum drawdown, gain to pain ratio and Grit ratio

Build a screener to monitor different stocks and identify stocks for short selling, taught by expert Laurent Bernut

Create and backtest time series momentum and cross sectional momentum strategy using price and fundamental data

Implement academic research and seasonal trading strategies, taught by Quantpedia researchers

Learn about mean reversion trading strategies taught by Dr. Ernest Chan, who employed these techniques in his own hedge fund and trading experience

One click integration of quantitative models into live trading platform to analyze strategies in live trading environment