I have 20 year history dataset of stock prices with below columns.
open: Stock opening price
high: Stock highest price of the day
low: Stock lowest price of the day
close: Stock closing price
volume: Total shares traded that day
symbol: The stock ticker symbol (e.g., AAPL, TSLA)
date: The date of the trading day
Roughly we will have 4000 ontology objects per stock (200 trading days per year * 20 years = 4k objects)
I ask below questions.
- When was the last time stock dipped more than 10 percent?
- How many times stock gained more than 5 percent in last 10 years?
Calculator tool is going through each and every object and reaching rate limit.
I was able to get desired output with create_pandas_dataframe_agent from LangChain. Basically it treats input as dataframe and uses pandas.
I can certainly write a function that uses LangChain and call that one in AIP Logic. But, I have to provide a different set of LLM API Keys.
I guess there should be a way to achieve this in AIP itself.