One of the most common strategies in commodity trading is inter-commodity futures spread trading. Generally speaking, it is a class of strategies that takes advantage of the price differential between two (or more) related commodities. Here, we illustrate three examples of futures spread trading in three different sectors, namely, Energy…

Empirical mode decomposition and Hilbert spectral analysis — Market observations and empirical studies have shown that asset prices are often driven by multiscale factors, ranging from long-term economic cycles to rapid fluctuations in the short term. This suggests that financial time series are potentially embedded with different timescales. On the other hand, nonstationary and behaviors and nonlinear dynamics…

Real examples, performance summary, and python package — Pairs trading is among the most popular trading strategies in many markets, ranging from equities and ETFs to currencies and futures markets. It involves taking simultaneous positions in two correlated assets. The idea is that while typically it is difficult to accurately capture the price evolution of a single asset…

From portfolio construction to optimal execution — In this new python package called Machine Learning Financial Laboratory (mlfinlab) developed by Hudson & Thames, there is a module that automatically solves for the optimal trading strategies (entry & exit price thresholds) when the underlying assets/portfolios have mean-reverting price dynamics. It covers a few mean-reverting models, including the Ornstein-Uhlenbeck…

Empirical Analysis & Free Book — Leveraged ETFs (LETFs) are typically designed to replicate multiples of the daily returns of some underlying index or benchmark. For example, the ProShares Ultra S&P 500 (SSO) is supposed to approximately generate twice the daily returns of the S&P 500 index. Moreover, investors can take a bearish position on the…

Adaptive complementary ensemble empirical mode decomposition (ACE-EMD) for non-stationary time series — Empirical studies and market observations suggest that asset prices are driven by multiscale factors, ranging from long-term market regimes to rapid fluctuations. Embedded with different timescales, many financial time series often exhibit nonstationary behaviors with trends and time-varying volatilities. …

Futures are standardized bilateral contracts of agreement to buy or sell an asset at a pre-determined price at a pre-specified time in the future. In the futures market, it is commonly observed that the futures prices may deviate from the spot price of the underlying asset. …

The ETF industry now consists of more than 2,000 funds with well over $4 trillion in assets. Within the ETF universe, some funds are designed to replicate a constant multiple (called leverage ratio) of the daily returns of a reference index. …

A drawdown measures the distance (in %) of the portfolio value from its peak, reflecting its downside risk. If a portfolio had previously reached a peak of $100 and subsequently dropped to $90, then the portfolio experienced a 10% drawdown. We examine the drawdown risk sector by sector through a…