Using a Gaussian HMM to Classify CSI 300 Market States

Summary

The document presents a market-timing experiment that applies a Gaussian hidden Markov model to daily CSI 300 data. Its feature set combines the one-day log return, five-day log return, and log high-low range. The model is configured with six latent states and diagonal covariance, then used to assign each observation to a state for plotting against the index price.

The post itself reports a data retrieval failure: the data source returned None, so trying to access the close field raised a TypeError. It does not provide a fix or report model results. The proposed state classification is therefore only a code example, not evidence that the timing approach is profitable or robust. Data availability and the consistency of the feature arrays would need checking before model fitting; the sample covers only a short historical period, and no validation procedure is described.

Key ideas

• The example uses daily CSI 300 observations as inputs to a Gaussian hidden Markov model.
• It combines one-day and five-day log returns with the log high-low range.
• The fitted model assigns each observation to one of six latent states for visualization.
• The reported error occurs because the data retrieval result is None when the code expects a table.
• The document offers no resolution, validation, or evidence of trading performance.

Tags

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.