Take the next step in commodity trading by using quantitative approaches in your trading. Learn and use Python to implement statistical arbitrage strategies in commodities market. Get trained to start your own algorithmic trading.
Pandas: Series and DataFrame, NumPy Code in Python: Moving Average Crossover trading strategy, Relative Strength Index (RSI) trading Strategy
Section 4: Dealing with Financial Data
Duplicate data, Missing values, Incomplete data, Mixed-up data
Section 5: Backtesting
Important things to consider during backtesting: Slippages, transaction costs Code in Python: Momentum trading strategy
Section 6: Performance Metrics
Analyze the performance of the strategy using different performance metrics
Statistical arbitrage trading
Section 1: Definition and Background
An overview of statistical arbitrage and different types of statistical arbitrage strategies. Understand different types of arbitrage strategies in commodities.
Section 2: Statistical Concepts Overview
Understand the concept behind mean reversion and different statistical concepts such as z-score, correlation, stationarity, cointegration, and linear regression. Understand the Augmented Dickey Fuller (ADF) test which checks whether the time series is stationary or not
Section 3: Pairs Trading Strategy in Excel
Check for cointegration in excel and generate the trading signals using the Bollinger bands
Section 4: Pairs Trading Strategy in Python
Fetch the data from quandl, check for cointegration in Python and the code the strategy learned in the previous section in Python.
Section 5: Managing risks
Learn about various risks in a stat arb strategy such as systematic risk, unsystematic risk & execution risk and learn how to overcome the risks.