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Open ChatGPT, Claude, or Gemini and ask it to build you a low-volatility portfolio. You will get a confident, reasonable-sounding answer. But did it actually check the volatility? Calculate whether the allocation can hit your target? Verify the correlations? Maybe. And maybe it hallucinated the whole thing and told you with complete confidence. You cannot tell which - and if this powers your real portfolio, that is a serious problem.
This is not a bug in any one model. It is how LLMs work: they are good at language, not at being databases or calculators. Real finance questions are multi-step - fetch the data, calculate the metrics, check the correlations, optimize the allocation - and an LLM on its own cannot do them reliably. Unless you give it tools. That is what this book is about.
What you get: four patterns - tool calling, ReAct, CodeAct, and orchestration - that ground an LLM in real data and real calculations, each explained plainly and with working code you run yourself in Google Colab. A finance primer if you want the concepts underneath the metrics. An n8n bonus chapter that builds the same patterns as visual workflows, with no Python. By the end you will have built a working portfolio assistant that fetches real prices, calculates real metrics like volatility, Sharpe ratio, and value at risk, shows its reasoning at each step, and answers follow-ups about your holdings.
It gets you from "LLMs hallucinate" to "I have a working tool I can trust." It covers analysis, not execution, and stops short of production deployment - that is a bigger next step. Helpful but not required: basic Python, some APIs, some finance. Not required: ML expertise or any prior agent experience. If you can read, you have enough to start. The reason it works is not that the model is smarter, but that the architecture is right.
You get the PDF to download and keep, along with the code.
Open ChatGPT, Claude, or Gemini and ask it to build you a low-volatility portfolio. You will get a confident, reasonable-sounding answer. But did it actually check the volatility? Calculate whether the allocation can hit your target? Verify the correlations? Maybe. And maybe it hallucinated the whole thing and told you with complete confidence. You cannot tell which - and if this powers your real portfolio, that is a serious problem.
This is not a bug in any one model. It is how LLMs work: they are good at language, not at being databases or calculators. Real finance questions are multi-step - fetch the data, calculate the metrics, check the correlations, optimize the allocation - and an LLM on its own cannot do them reliably. Unless you give it tools. That is what this book is about.
What you get: four patterns - tool calling, ReAct, CodeAct, and orchestration - that ground an LLM in real data and real calculations, each explained plainly and with working code you run yourself in Google Colab. A finance primer if you want the concepts underneath the metrics. An n8n bonus chapter that builds the same patterns as visual workflows, with no Python. By the end you will have built a working portfolio assistant that fetches real prices, calculates real metrics like volatility, Sharpe ratio, and value at risk, shows its reasoning at each step, and answers follow-ups about your holdings.
It gets you from "LLMs hallucinate" to "I have a working tool I can trust." It covers analysis, not execution, and stops short of production deployment - that is a bigger next step. Helpful but not required: basic Python, some APIs, some finance. Not required: ML expertise or any prior agent experience. If you can read, you have enough to start. The reason it works is not that the model is smarter, but that the architecture is right.
You get the PDF to download and keep, along with the code.