Posted on Jul 28

Macro Investments: Accelerating Market Data Decisioning with Bedrock & Q Developer

Why did I choose AWS to grow my trading journey?

We're different. Only a few traders are profitable, so we need to be ① innovative, ② performance-driven, and ③ different from the majority.
And AWS leadership culture, AWS people, and AWS services all have these factors.

AWS x Macro Trader

AWS Leadership Culture = Pure Alpha
AWS People = Innovation & Brave
AWS Services = Absolute Risk Control

This spirit of innovation is something I have never seen elsewhere. Thus, I chose AWS to enhance my trading journey.

AWS Community Builder at AWS Summit Hong Kong

In March 2024, I joined the AWS Community Builder in Machine Learning. Also, I am a macro trader, having worked in the “Sell-Side Macro Trading Desk Execution” and “Prime Brokerage” for nine years.

In August 2025, I became a speaker at the Dev Lounge at AWS Summit Hong Kong. I shared how to use the AWS Cloud AI service Bedrock to research macro market trends and suggest asset allocation portfolios.

My inspiration at the AWS Summit Hong Kong 2025:

Updated on 2025-07-28:

Every successful trader is different, so AWS Bedrock is fragmented, allowing for the creation of an infinite number of workflows.

The following is the latest “macro market trends” AWS Bedrock sell-side research workflow.

Pure alpha x Absolute risk control

Commander ➡️ Asset allocation manager
Risk defense engineer ➡️ Risk manager
Intelligence reconnaissance officer ➡️ Investment analyst
Tactical assault captain ➡️ Trader

Macro hedge traders have three key alpha edges: ① Absolute net value, ② Macro timing, ③ Odds rotation.

Therefore, Bedrock must perfectly align with macro hedge trading to achieve outstanding performance results.

Reference: aws-samples / sample-tech-for-trading / investment_advisor_by_agentic_ai

Marco Investments: Accelerating Market Data Decisioning with Bedrock on AWS [lv200,FSI,AI]

AWS accelerates the flow of global market data, but thousands of market data are volatile and uncertain.
Learn how Frontier Macro Trader processes market data rapidly and boosts trading decision profitability using Amazon Bedrock.
Explore Nova to underpin trading decision engines, build a knowledge base for consistent market insight, and use guardrails for high-quality market data.
Improve trading decision quality by over 75% and reduce market data errors by 80%.

Today, we are going to discover how to "Accelerate Market Data Decisioning with Bedrock & Q Developer on AWS".

I am Kenny Chan, a Macro Trader and AWS Community Builder.
Also, I specialize in GenAI financial infrastructure, tactical portfolio management, and macro trading desk execution.

In this session, you will find how to use Amazon Bedrock and Amazon Q to enhance your market data decisions.
Next 10 minutes, we will go through these 3 steps.

By the end of this session, you will find
① how to improve the quality of trading decision by over seventy-five %
② and also reduce noisy market data by eighty %.
So that, this session can help you transform your business thinking.

Someone may question: Why is Market Data Decisioning important to us?
Yes, because in today's uncertain world, having the right tools and even the right infrastructure can make all the business difference.
The good news is that AWS provides a advanced infrastructure to support market data decisions.

We first focus on step 1 Bedrock Decisioning.
And we will go through how Amazon Bedrock, OpenSearch, and S3 bucket as foundational elements in decision-making.

I believe in "Quality In, Quality Out".
So, I collect lots of Investment Banking Insights and My Historical Trading Journal.

Go to the OpenSearch tab, click the create button, pick the knowledge base with Vector Stores.

Fill in the Knowledge Base name.

Data source name and S3 URL

Meanwhile, choose the both Embedding model and Amazon OpenSearch Serverless.
Here we simply choose Titan Text Embedding.

After that, Pick the knowledge base we just-created.

Click the "Add documents" button to

Upload documents.
Here we just upload Investment Banking Insights and Ours Historical Trading Journal.

Next, choose the Bedrock agent tab, fill in the Bedrock agent name, and

Pick the bedrock agent model.
Here we simply choose Titan Text G1 Premier.

Define the prompt instruction for the bedrock agent.

Assign bedrock agent to knowledge base.

And done. Now, you can ask bedrock agent for market data decisions.

You can even ask bedrock agent for a marco portfolio.

Now, Amazon Q integrates with the AI workflow.
So that, if you forget any steps, Q allows you to quickly find answers.

Here are the examples for global market analysis

and muti-assets allocation.
For me, Amazon Bedrock is so helpful for my trading desk.

When you complete the usage, don't forget to delete the bedrock agent

delete the knowledge base

and delete the OpenSearch Serverless

We next focus on step 3 Q Developer Portfolio P&L

We will go through how Amazon Q Developer and Backtrade, which is a Python trading framework, can be used to backtesting Portfolio P&L.

Here is backtesting P&L. The most amazing thing is we did it in zero-code.

which means that, we just ask the Amazon Q developer and everything is done.

Also, here is the global-market correlation.

The same. we just ask the Amazon Q developer, and done.

Beautiful! You can find my GitHub to get the whole Amazon Q developer source code.

The monthly cost is 400 US dollar. It is a competitive price for both individual traders and Institutional investors.

Finally, we go back to Step 2, Macro Pure-Alpha Architecture.

Because everything in the world is uncertain, so the most important is your business thinking, which means how to build your dynamic architecture.

Here is the golden rule I found in my 10 years of trading experience.
No need always true, but need to know when will fail.

Thank you for joining me today.
① I encourage you to connect with my LinkedIn for further discussions,
② and to stay updated in financial services industry on AWS.

Postscript

I have graduated from the AWS NewVoices 2025 speaking program! LinkedIn

I am a Macro Trader and have never used #AWS Q CLI. Still, I created a backtrader python to #DeltaHedge #Indexing using only two prompts at the AWS Hong Kong for Capital Markets on 10 June 2025. LinkedIn