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Introduction
Introduction
Personal financial analyzer aided by mathematical analysis, relationship or dependency (node-edge) graphs, LLM (Large Language Model) together with math plots and node-edge graph visualization.
Graph Visualization aided Investment Selection
Graph Visualization aided Investment Selection
An investor could select investments that are aided by graph visualization. For example, consider graphs created from Vanguard ETF portfolio holdings (one example of holding data for VOO). These graphs show dependency and non-dependency of ETFs to each other and to stocks and sectors. These graphs presented by the analyzer could aid an investor to make decision on diversification.
Math Plot aided Investment Selection
Math Plot aided Investment Selection
See below.
Future Estimation
Future Estimation
Stock market prediction does not work most of the times. Look at Nvidia above. Could anyone have predicted in say 2020 or before?
But results of experimentation with crude estimation techniques shown below. Estimations were based on analytics in previous period.
Results show that if stock price (adjusted Close) held relatively steady over time, then the estimation was relatively close. But if it is wild (AMZN, NVDA), estimations were way off.
Individual Investment Portfolio Dependency Graph
Individual Investment Portfolio Dependency Graph
Process portfolio statement (CSV) files and generate graphs to see dependency.
Answer question with LLM about investment.
To be added.
AI-LLM
AI-LLM
Guide personal investment via AI-LLM assistant together with methods discussed above.
AI-LLM aided long-term estimation.
AI-LLM aided math plot and graph generation.
Work in progress.
System
System
Experimental.
Developed with Go and Python.
Go: web service, graph generation.
Python: Finance analytics, math plots.
Python3 is invoked from Go.
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