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Roy Yang

Senior data scientist

Roy Yang

Roy Yang

Engineering AI for financial systems that drive value.

Bridging 15+ years of financial domain expertise with frontier AI engineering. On my own time I build the system that explores financial values.

In banking and insurance
15+ yrs
In banking and insurance
In analytics and AI
9+ yrs
In analytics and AI
Quant and applied AI research
Point-in-time
Quant and applied AI research
View projectsRésuméContact

Financial domain

15+ years in banks and insurers

Treasury · liquidity · risk · corporate banking

AI engineering

ML and LLM agents

Forecasting · agent systems · cloud pipelines

problem solved

VALUE

  • 90% → 40% forecast error (sMAPE) across 18 Asia-Pacific markets
  • −90% manual liquidity reporting; monthly became daily
  • >20% less risk-weighted assets, with double-digit profit growth

The pattern behind my work

Models

Prices, macro, filings, news

Market data

15-minute bars for 75 symbols since 2010, FRED macro series with their revision history, SEC filings, news and prediction markets. Ten collectors write to PostgreSQL, and every fetch is logged with what it got and what failed.

  • PostgreSQL
  • IBKR API
  • FRED
See the project
The pattern behind the projects below: data in, models and agents propose, deterministic code decides. Select a stage to see where it's built.

Services

How I can help your business

The data you already have, turned into results like these.

Your data

  • cash balances
  • inflows and outflows
  • deposits and loans
  • income by product
  • Bloomberg prices
  • client profiles
  • policy and payments
  • risk-weighted assets

  1. Forecasting models

    Build predictive models that drive direct profitability.

    40%

    forecast error (sMAPE), down from 90%, across 18 markets

    AIA · Group Treasury

  2. Data analytics

    Turn financial and business data into smarter, faster decisions.

    >90%

    of senior managers adopted my executive dashboard

    Bank of China (HK)

  3. AI integration

    Implement AI solutions to cut costs and save time.

    1,317

    strategy ideas my LLM agent system proposed and tested

    Strategy Orchestra

  4. Process automation

    Streamline business operations to stay ahead of the competition.

    −90%

    manual reporting effort; monthly became daily

    AIA · Liquidity

  5. Custom AI ideas

    Have a unique project or concept in mind? Let's explore it together.

    10

    projects of my own, from idea to running system

    On my own time

Explore my experience, projects and skills to see how we can collaborate. Let's connect for a brief chat to discuss how I can help.

Let's talkSee projects

01Selected work

Systems I've built

Personal research and engineering projects, from point-in-time research to AI agents and the engine that runs strategies. Each one lists the skills it shows.

Recorded run, step through it

AI trading swarm

A committee of AI analysts that reads point-in-time market data, cites every claim and hands its ideas to Strategy Orchestra to test, with a human approving each idea and every number reproduced before a strategy counts.

Watch one real idea go through the swarm: five analysts and a Critic on TQQQ, the thesis I approved, and the campaign that tested it, from 9,826 trades stopped at a gate to the Judge's 2.4 out of 100. Every line comes from the run's own records.

  • Python
  • PostgreSQL
  • TradingAgents
  • LangGraph
  • LM Studio
  • FastAPI
  • React
Read the write-up
royyang.ai/demos/ai-trading-swarm
RECORDED RUN · TQQQ
  1. Data
  2. Analysts
  3. Critic
  4. Thesis
  5. Owner
  6. Orchestra
  7. Judge
  1. DATAas of 24 Sep 2026 · 7 of 10 sources ready, 3 stated missing

Beats the baseline5 / 5

Horizons where the volume forecast beats seasonal naive

01Forecasting and ML

TSBench: multi-series forecasting benchmark

A benchmark harness that runs forecasting models from several libraries over 16 years of 15-minute bars for 75 US tickers, scores them against finance-specific baselines, and picks winners with a Model Confidence Set.

  • Python
  • pandas
  • statsforecast
  • LightGBM
  • arch

Low correlation10 of 23

ETFs kept for low correlation: mean ρ 0.12, against 0.42

02Reinforcement learning

Reinforcement-learning trading strategy

Two agents, PPO and A2C, choose which of 10 low-correlation ETFs to trade every 15 minutes, and how much. The pipeline runs end to end. The evaluation doesn't hold up yet, and this page shows why.

  • Python
  • stable-baselines3
  • Gymnasium
  • PyTorch
  • TA-Lib
See all projects

02What I work on

From research notebook to running system

The problems I spend my time on, inside banks and on my own.

03Skills and tooling

The stack I reach for

Select a skill to see the projects that use it.

Quant research and ML

Skills, with the projects that show them

Tools

Python, DuckDB, turbovec, Jupyter

AI agents and LLMs

Skills, with the projects that show them

Tools

Claude Code, Ollama, LM Studio, Docker

Systems and execution

Skills, with the projects that show them

Tools

asyncio, SQLite (WAL), Interactive Brokers API, YAML config

04About me

Inside the banks, and on my own

Roy Yang

I am a data scientist with over ten years in banking and insurance, the last several centered on analytics and machine learning. I have led research and quantitative strategy for global corporate banking, built comprehensive executive dashboards for corporate banking strategy, and developed machine learning forecasting models, liquidity analytic models, and risk analyses. My models have leveraged cashflow forecast results to drive consistent direct profit growth.

My path wasn't a straight line. I started on the front line of banking, advising clients, then moved into middle office for project and global corporate banking strategy, and along the way earned a master's in financial computing. That mix of client-facing experience, collaboration with both internal and external stakeholders, finance business knowledge, and quantitative and qualitative training shapes how I work: start from the decision someone actually needs to make, then build the model that informs it for practical outcome with direct measurable impact.

any data analytics related roles in financial services.

More about meGet in touch