You will get a real-time market data dashboard wired to your broker or data feed API
Rising Talent
Project details
I build the plumbing that gets live market data out of an API and onto a screen you can actually trade from.
Most people who need this already have a data subscription and a rough idea of what they want to see. What they do not
have is a reliable pipeline: something that authenticates correctly, survives a dropped connection, does not silently
stall halfway through a session, and renders fast enough to be useful when the market is moving.
That is the part I do. I have built and run production pipelines against Databento, tastytrade/DXLink, Schwab,
Interactive Brokers, Polygon, Alpaca, Massive.com, ThetaData, just about everything - feeding live analytics platforms that run unattended through every session.
WHAT YOU GET
- A working connection to your data source, authenticated and tested
- A Python backend that ingests, stores, and optionally computes metrics on your stream
- A live web dashboard with the panels you specify
- Deployment and documentation so you can run and restart it yourself
Most people who need this already have a data subscription and a rough idea of what they want to see. What they do not
have is a reliable pipeline: something that authenticates correctly, survives a dropped connection, does not silently
stall halfway through a session, and renders fast enough to be useful when the market is moving.
That is the part I do. I have built and run production pipelines against Databento, tastytrade/DXLink, Schwab,
Interactive Brokers, Polygon, Alpaca, Massive.com, ThetaData, just about everything - feeding live analytics platforms that run unattended through every session.
WHAT YOU GET
- A working connection to your data source, authenticated and tested
- A Python backend that ingests, stores, and optionally computes metrics on your stream
- A live web dashboard with the panels you specify
- Deployment and documentation so you can run and restart it yourself
Programming Languages
JavaScript, PythonCoding Expertise
Cross Browser & Device Compatibility, Performance Optimization, DesignWhat's included
| Service Tiers |
Starter
$550
|
Standard
$1,600
|
Advanced
$3,850
|
|---|---|---|---|
| Delivery Time | 2 days | 3 days | 7 days |
Number of Revisions | 3 | 6 | Unlimited |
Number of Pages | 1 | 3 | 10 |
Design Customization | |||
Content Upload | |||
Responsive Design | - | ||
Source Code | - | - |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$100 - $1,250About Matt
Quant Developer & AI Systems Architect
Los Angeles, United States - 4:29 am local time
Most of my work is live software with real users depending on uptime, not prototypes. Recent examples:
REAL-TIME OPTIONS ANALYTICS PLATFORM (live, in daily use)
Streams market data from Databento, tastytrade/DXLink, Schwab and CBOE data into a Python/FastAPI + Redis backend that computes dealer gamma, charm and delta positioning in real time and broadcasts it to a custom WebSocket charting frontend. Sub-second updates through the cash session, deployed on DigitalOcean behind Cloudflare with systemd supervision, TLS, and alerting.
MULTI-AGENT AI RESEARCH PLATFORM
Eight specialized research agents (equity research, macro strategy, earnings, market intelligence, quantitative research, credit and rates, portfolio risk) orchestrated with LangGraph and the Claude Agent SDK. FastAPI + Celery + Redis workers on the backend, Next.js + Tailwind + shadcn/ui on the frontend, Supabase auth and Postgres, Stripe subscription tiers with credit metering and per-agent entitlements.
COMMERCIAL SAAS, LAUNCH TO REVENUE
A Next.js + Stripe subscription product with email/password auth, single-session enforcement, promo and coupon pricing, an admin console, Discord alerting, and signed-token auth protecting both the REST and WebSocket data plane. I owned all of it: product, code, deploy, payments, and security review.
QUANTITATIVE RESEARCH
I model dealer options positioning (GEX/CEX, charm, vanna, pinning and wall behavior) and test it honestly against null baselines. I have killed more of my own signals than I have shipped, and I will tell a client when a result does not hold up instead of dressing it up.
WHAT I WORK WITH
- Python: FastAPI, Celery, asyncio, pandas, NumPy, WebSockets
- TypeScript / React: Next.js (App Router), Tailwind, shadcn/ui, custom canvas charting
- AI: Claude API and Agent SDK, LangGraph, multi-agent orchestration, tool calling, RAG. I find Claude to be the easiest to work with for complex coding projects.
- Market data and APIs: Databento, DXLink, Schwab, IBKR, Polygon, Finnhub, FMP, options chains and greeks
- Infrastructure: DigitalOcean, Docker, systemd, Nginx, Redis, Postgres/Supabase, Vercel, Cloudflare (DNS, caching, WAF, Turnstile), Sentry
- Payments: Stripe subscriptions, metered and credit-based billing, coupons and promo codes
HOW I WORK
I start by getting clear on what the system actually has to do and who it is for, then build in small shippable pieces so you can see progress instead of waiting on one big reveal. I am direct about scope and tradeoffs, I document what I hand over, and I do not disappear after delivery.
If you are building in market data, options analytics, trading tooling, or AI agents, send me the details and I will tell you straight whether I am the right fit for it.
Steps for completing your project
After purchasing the project, send requirements so Matt can start the project.
Delivery time starts when Matt receives requirements from you.
Matt works on your project following the steps below.
Revisions may occur after the delivery date.
Scoping call
we confirm your data source, symbols, and the panels you need
Feed connection
I authenticate against your API and verify a clean tick stream