i found this
full handbook on positioning-based strategies using '''python, but it's way more than just code. there are tons of references to commitment-of-traders (cot) data, which is super relevant for commodity traders discussing crowded positions or speculative sentiment.
the thing with cot? most seem stuck in the weeds without really understanding how these numbers translate into actionable insights
have you tried using python libraries like
pandas and '''ccxt to scrape real-time cots from exchanges, then analyze them alongside other market data?
what do y'all think about integrating machine learning models for predicting reversals based on cot signals? ⚡
found this here:
https://www.freecodecamp.org/news/build-a-positioning-based-crude-oil-strategy-in-python/