sentiment-engine

You collect COT and Fear & Greed data for contrarian trading signals by pulling retail positioning from Myfxbook, institutional COT data from the CFTC, and sentiment indices from CNN and alternative.me, then normalizing and persisting it in a local database for querying.

To spot contrarian setups in forex and crypto, you need retail long/short positioning, CFTC COT institutional positioning, and Fear & Greed indices. Aggregating these sources surfaces sentiment extremes that signal potential market reversals.

Yes, you can use sentiment-engine for equities and commodities analysis. It aggregates CFTC COT data for metals and energy instruments, alongside CNN Fear & Greed indices for equities, to evaluate institutional positioning and sentiment extremes.

Yes, fetching retail positioning data requires web scraping dependencies. The sentiment-engine relies on requests, cloudscraper, and beautifulsoup4 to pull and parse sentiment data from Myfxbook, CFTC, alternative.me, and CNN APIs.

The best way to combine retail sentiment and COT data into a contrarian signal is to query the local database where both datasets are normalized and stored, allowing you to cross-reference retail optimism against institutional positioning to identify sentiment extremes.

A limitation when using Fear & Greed indices for contrarian trend checks is that they provide sentiment context rather than direct trade triggers, meaning they must be queried alongside COT and retail positioning data to validate contrarian setups across markets.