Portfolio Optimization in Python
Build an efficient frontier, compute the Sharpe-optimal portfolio, and visualize portfolio weights using Monte Carlo simulation.
Open analysis →AI Data Analysis Benchmarks for Finance
We defined practical analysis workflows from multiple domains, then ran them with AI Data Analyst using different LLM engines. On this page you can browse each workflow, open full notebook conversations, and compare model quality in shared score tables. The overall results show that modern LLMs perform very well on structured data analysis tasks.
Finance Workflow Examples
Browse reproducible AI data analysis workflows in Finance. Open any example to review prompts, conversation steps, generated code, outputs, and model-level quality scores.
Value at Risk (VaR) Analysis in Python
Compute Value at Risk (VaR), Conditional VaR (CVaR), and maximum drawdown for a stock portfolio using historical simulation.
Open analysis →Model Comparison for Finance
Compare LLM performance across workflows in this category. Open any score chip to jump directly to that model run and inspect the full conversation and notebook output.
Average score (0-10)
glm-5.1
10.00
n=2
gpt-oss:120b
10.00
n=2
gpt-5.4
9.50
n=2
qwen3-coder-next
9.50
n=2
gemma4:31b
7.50
n=2
qwen3.5:397b
6.00
n=2
glm-5.1
Average score: 10.00/10
Scored workflows: 2
gpt-oss:120b
Average score: 10.00/10
Scored workflows: 2
gpt-5.4
Average score: 9.50/10
Scored workflows: 2
qwen3-coder-next
Average score: 9.50/10
Scored workflows: 2
gemma4:31b
Average score: 7.50/10
Scored workflows: 2
qwen3.5:397b
Average score: 6.00/10
Scored workflows: 2
Detailed Workflow Comparison Table for Finance
This table compares model scores for each workflow in Finance. Open any score chip to jump directly to the selected model conversation and review full prompts, code, outputs, and score cards.
What This Benchmark Shows
We tested the same step-by-step data analysis workflows across multiple LLM models and compared results using a shared scoring rubric. In Finance, most models produce strong notebook outputs with high task completion and useful analytical reasoning. Use these examples as a reference for prompt design, model selection, and workflow quality before running similar analyses on your own data in MLJAR Studio.
Start using AI for Finance
MLJAR Studio helps you analyze data with AI, run machine learning workflows, and build reproducible notebook-based results on your own computer.
Runs locally • Supports local LLMs