You will get a Custom AI-Powered Web App (Python + React + FastAPI)
Rising Talent
Rising Talent
Project details
I build custom, production-ready AI web apps that solve real business problems—not just demos. You get a clean React/Next.js UI, a secure Python API (Django or FastAPI), and data flows wired to your sources, all documented and deployed to your chosen environment. What sets this apart is clarity and measurability: we define your goal/KPI up front, lock scope into a tier, and ship in reviewed steps so you always know what’s done, what’s next, and when it launches.
Each project includes responsive UI, auth, testing, and handoff assets (README, env templates, API docs). Need more? Choose add-ons like faster delivery, extra integrations, or analytics dashboards to tailor the build without scope creep. This tiered, add-on model keeps pricing transparent and timelines realistic while giving you flexibility.
You’ll also get a smooth kickoff: I collect requirements (users, data, brand, deploy preference) immediately after purchase so work starts fast and stays on track. Expect a dependable process, clear deliverables, and a launch-ready AI app built for maintainability, not just week-one hype.
Each project includes responsive UI, auth, testing, and handoff assets (README, env templates, API docs). Need more? Choose add-ons like faster delivery, extra integrations, or analytics dashboards to tailor the build without scope creep. This tiered, add-on model keeps pricing transparent and timelines realistic while giving you flexibility.
You’ll also get a smooth kickoff: I collect requirements (users, data, brand, deploy preference) immediately after purchase so work starts fast and stays on track. Expect a dependable process, clear deliverables, and a launch-ready AI app built for maintainability, not just week-one hype.
Programming Languages
HTML & CSS, JavaScript, PythonCoding Expertise
Cross Browser & Device Compatibility, Performance Optimization, DesignWhat's included
| Service Tiers |
Starter
$750
|
Standard
$2,000
|
Advanced
$4,500
|
|---|---|---|---|
| Delivery Time | 7 days | 14 days | 25 days |
Number of Revisions | 2 | 3 | 4 |
Number of Pages | 3 | 5 | 8 |
Design Customization | |||
Content Upload | - | ||
Responsive Design | |||
Source Code |
Optional add-ons
You can add these on the next page.
Additional Revision
+$20
Additional Page
(+ 2 Days)
+$100
Content Upload
(+ 1 Day)
+$100
Analytics and Dashboard
(+ 4 Days)
+$350Frequently asked questions
About Muhammad
Quantitative Analyst | Python Backtesting, Factor Research & Financial
Westborough, United States - 8:35 pm local time
My foundation spans computer science and quantitative finance, and my focus is singular: building robust, bias-aware, data-driven systems that translate financial theory into executable research and models.
Every project I work on, whether it is constructing point-in-time financial datasets, designing quantitative signals, or validating strategies through rigorous backtesting, is approached with discipline and intent. I believe meaningful results come not from over-optimization, but from careful modeling, statistical validation, and respect for market structure.
Currently pursuing my MS in Quantitative Finance at Northeastern University (Boston), I bridge theory and execution by combining the statistical rigor of a quantitative researcher with the engineering mindset of a software developer. My work emphasizes correctness, transparency, and repeatability over curve-fitting or surface-level performance.
My primary areas of focus include:
Algorithmic Trading & Quantitative Research
-Designing and evaluating trading signals using time-series analysis, stochastic modeling, and structured backtesting workflows.
Factor Models & Risk Analytics
-Empirical asset pricing, portfolio optimization, systematic risk factors, and performance measurement grounded in statistical finance.
Financial Data Engineering
-Building scalable, point-in-time datasets, handling survivorship and look-ahead bias, and developing clean pipelines for quantitative research.
To me, finance is applied mathematics under uncertainty, and technology is the tool that makes disciplined experimentation possible. Every dataset, model, and result is a step toward understanding how markets behave, not in hindsight, but in real conditions.
If you value precision, statistical integrity, and research-driven execution, we are already aligned.
Core Expertise
Quantitative Research & Analysis:
-Time-series modeling, factor research, signal construction, backtesting frameworks, performance and risk metrics
Programming & Data Science:
-Python (NumPy, pandas, scikit-learn, QuantLib), SQL, Jupyter, statistical computing
Mathematical & Statistical Methods:
-Probability, regression, optimization (LP/QP), Monte Carlo simulation, stochastic processes, Kalman filtering
Financial Engineering:
-Portfolio optimization, derivatives concepts, financial econometrics, empirical asset pricing
Engineering & Tooling:
-Git, HDF5, Bloomberg Terminal, AWS-based data workflows, reproducible research environments
Let’s discuss your problem, define the right analytical approach, and build something.
Steps for completing your project
After purchasing the project, send requirements so Muhammad can start the project.
Delivery time starts when Muhammad receives requirements from you.
Muhammad works on your project following the steps below.
Revisions may occur after the delivery date.
Order & Requirements (48h window).
Client purchases, then submits the required inputs (goal/KPIs, users, data access, brand, deploy choice). If required items aren’t provided in ~48h, the order can auto-cancel.
Kickoff in the Workroom.
We confirm scope, tier, pages, add-ons, timeline, and communication cadence inside the Project Workroom.