Dynamically Characterising Time Series With Relative Moving Moments

Daniel Bloch proposes to dynamically characterize price series and identify trading opportunities by combining moving averages with price action to benefit from both approaches. Practically, the author relates moving moments of the time series with specific asset prices, or statistical measures, used as anchors in the sliding window. The rolling moments of this normalized data (called relative moving moments) represent measures around the rolling distance between the data in the segment and its average. Even though the author could achieve the same results by combining different technical indicators and extracting different pieces of information, following their approach, they have all the necessary information at a glance, so that they can directly perform market analysis and identify the right market conditions. To illustrate their approach, the author generates realistic market prices, computes the relative moving moments and present their results. Finally, Bloch uses these results to discuss trading strategies and risk management. The author extends the RMA approach to Pairs Trading in order to benefit both from price expansion and price contraction and not just price contraction.