LLM in finance
Budget: €30 – €250 EUR
The goal is the creation of a large language model that aids in algorithmic strategy.
I need to ensure that you can use / gather the 2 data sources: pricing data (numerical) and one heterogenous data source. Be sure to include something about correlations.
I want you to help me with aiding this strategy in combining perhaps 1+ non-numerical data source next to the numerical source to emphasize this.
The current model is trained every day.
Ideas:
- LLM filters noise and pings only on material shifts (e.g., |Δcorr|>0.3, new statistically-significant lag)
- If today’s prediction is driven by one unstable factor (like a one-off news spike), the LLM can warn: “Signal comes entirely from short-lived sentiment.” (This is important).
Short insights like this are also great: ‘X stock moves 2 days after Y commodity changes.’
Annotation (the extra layer):
“Strong correlation with Tech basket (0.72, 10d window)”
“Lag of 2 days vs. Semiconductors”
“Prediction is sentiment-driven, low persistence”
The deliverable should include the insights, code, pipeline and preferably include annotations. This is all said for a better clarity on the output of the model.
Basically, more insight.
I need to ensure that you can use / gather the 2 data sources: pricing data (numerical) and one heterogenous data source. Be sure to include something about correlations.
I want you to help me with aiding this strategy in combining perhaps 1+ non-numerical data source next to the numerical source to emphasize this.
The current model is trained every day.
Ideas:
- LLM filters noise and pings only on material shifts (e.g., |Δcorr|>0.3, new statistically-significant lag)
- If today’s prediction is driven by one unstable factor (like a one-off news spike), the LLM can warn: “Signal comes entirely from short-lived sentiment.” (This is important).
Short insights like this are also great: ‘X stock moves 2 days after Y commodity changes.’
Annotation (the extra layer):
“Strong correlation with Tech basket (0.72, 10d window)”
“Lag of 2 days vs. Semiconductors”
“Prediction is sentiment-driven, low persistence”
The deliverable should include the insights, code, pipeline and preferably include annotations. This is all said for a better clarity on the output of the model.
Basically, more insight.