One of the biggest frustrations in AI-assisted development is not bad logic.
It is bad context.
You ask an AI agent to build a trading system, and the result looks impressive for a moment, until you discover that it invented a method name, guessed the wrong payload structure, or assumed an SDK flow that does not exist.
That is where most of the wasted time begins.
At Nubra, the better approach is simple: before asking Codex to build your trading infrastructure, give it the full Nubra Master Docs first.
This single Markdown file contains the Python SDK documentation in a format that is easy for LLMs to parse, reason about, and use while generating code. Instead of coding from memory or approximation, the model can work directly from the latest SDK surface.
That one shift changes the quality of the output dramatically.
Why Context Matters More Than Prompting Alone#
Prompting matters, but prompting without documentation is still guesswork.
When an AI agent does not have the right SDK context, it tends to fill in the gaps on its own. That often leads to invented classes, incorrect parameters, mismatched import paths, or fragile integration code that looks polished but breaks at runtime.
By attaching the Nubra Master Docs inside Codex, you replace that uncertainty with direct reference material. The model can see the actual authentication flow, data-fetching methods, structures, and order-placement patterns before it writes a single line of code.
This is what makes the workflow powerful. You are not simply asking AI to help you code. You are grounding it in the real interface your system depends on.
Demo Video#
If you want to embed the full walkthrough, place the demo video here:
The video shows downloading the Nubra Master Docs, attaching the file in Codex, pasting the development prompt, and starting the build.
Phase 1: Download the Documentation#
The first step is to download the master reference file from the Nubra documentation portal.
Visit the Nubra Documentation and grab the Master Docs from the Python SDK section.
Start by visiting the official Nubra API landing page and opening the Documentation section from the top navigation. From there, select the Python SDK option from the available APIs. Inside that section, click Download Nubra Master Docs.
This downloads a single .md file, typically something like nubra-api-python-sdk-llm-builder.md. The file is designed specifically for LLM workflows, which makes it much easier to use than manually copying large parts of the docs into a prompt.
Once the download completes, confirm that the file is available in your Downloads folder or wherever your browser saves attachments.
Phase 2: Attach the Docs in Codex#
With the file ready, open your Codex workspace and begin a new prompt as you normally would.
In the message input area, click the + button and choose Add photos & files. Then browse to your Downloads folder and attach the Nubra Master Docs file. Once the document is added, paste your development request into the input box.
At this point, you are no longer starting from an empty prompt. You are starting with a model that already has the SDK reference in context.
That means when you ask Codex to build an EMA crossover engine, a multi-stock scanner, or a capital-management system, it has the technical grounding needed to write code against the actual Nubra SDK rather than an imagined one.
After that, click the Start icon and let the build begin.
What This Unlocks for Developers#
The real advantage of this workflow is not just convenience. It is precision.
Once the master docs are attached, Codex can work with the actual SDK classes, imports, method signatures, and integration patterns. That reduces the amount of back-and-forth needed to fix API errors later and shortens the time between idea and working implementation.
In practical terms, this leads to three major benefits.
First, it reduces guesswork. The model is not forced to infer how the SDK might behave because it already has the documentation in front of it.
Second, it speeds up iteration. Instead of burning time correcting API mismatches, you can spend more time refining strategy logic, architecture, and execution flow.
Third, it improves production readiness. When the AI is grounded in the correct SDK documentation, the generated code is much more likely to align with how Nubra infrastructure actually works.
A Better Way to Build Trading Systems#
This workflow is especially useful when your request goes beyond a basic script.
For example, if you want Codex to build a system for scanning the NIFTY 100, handling automated login, maintaining rolling dataframes, managing capital dynamically, and executing orders through the Nubra SDK, the quality of the output depends heavily on whether the model understands the real SDK interface.
That is why the master docs matter so much.
They give the model a technical map before the build starts. Once that context is in place, your prompt can focus on the system you want to create rather than spending half its length compensating for missing documentation.
The result is a cleaner development loop: attach the docs, describe the system, generate the code, then move directly into validation and refinement.
Final Thoughts#
AI tools are most useful when they are grounded in the right source material.
For trading systems, where authentication flows, market data methods, and execution logic all need to be correct, documentation context is not optional. It is the foundation of reliable code generation.
By downloading the Nubra Master Docs and attaching them directly inside Codex, you make it far easier for the model to generate precise, current, and production-aligned code.
That means less time fixing hallucinated API usage, less time correcting integration mistakes, and more time building strategies that are actually ready to run.
If you are using Codex to accelerate quant development, this is one of the simplest high-leverage steps you can take: give the model the docs first, then let it build.