Name: Full-Stack Trading Platform Development
Price: 7999 INR
Availability: InStock

Course Overview

Every serious algorithmic trader eventually hits the same ceiling: existing platforms can't do exactly what you need. The risk management logic is fixed. The dashboard doesn't show your custom metrics. The alert system can't combine the signals you care about. The backtester doesn't support your strategy's execution model. The only way past this ceiling is to build your own platform — and that's exactly what this course teaches.

The Full-Stack Trading Platform Development program is Vianmax Academy's most ambitious course. Over 14 weeks, you will design and build a complete, production-grade trading system from a blank repository: a FastAPI backend that handles market data ingestion, order management, and strategy execution; a PostgreSQL + Redis database layer optimised for time-series financial data; a React + TypeScript frontend with real-time WebSocket-driven dashboards; full Zerodha Kite API integration for live order routing; and a Docker + AWS deployment pipeline that keeps your system running 24/5.

This is not a survey course. Every component you build connects to every other component — by Week 14, you will have a single, unified system: a live web application that ingests real market data, runs your strategies, displays positions and P&L in real-time, and routes orders to a broker. The architecture is professional-grade: the same patterns used in institutional trading systems, scaled down to something a single developer can build and operate.

What You Will Have Built by Week 14

Real-time market data dashboard — live OHLCV charts, depth, and tick feed via WebSocket

Strategy execution engine — plug in any rule-based strategy, run it live or in paper mode

Order management system — place, modify, cancel, and track orders across broker accounts

Risk management module — per-strategy limits, daily drawdown stops, and position monitors

Backtesting engine — run any strategy against historical data stored in your own database

Multi-user auth — JWT-based login with role permissions (admin, trader, viewer)

Production deployment — Docker containers on AWS EC2 behind Nginx with HTTPS and CI/CD

Alert engine — multi-condition alerts routed to Telegram with configurable escalation rules

Prerequisites are significant: this course requires solid Python skills (equivalent to completing the Algorithmic Trading with Python course), a working knowledge of REST APIs and HTTP, and basic familiarity with React or any frontend JavaScript. Students who arrive with this foundation will find the course challenging but achievable. Students below this baseline will struggle from Week 1.

What You Will Learn

Design a professional trading system architecture — services, data flows, API contracts, and database schema

Build a production FastAPI backend with async endpoints, dependency injection, background tasks, and WebSocket support

Design and query a PostgreSQL time-series schema optimised for OHLCV data, trades, and positions

Build a real-time React trading dashboard with live price charts, order book, and position P&L updates via WebSocket

Integrate the Zerodha Kite Connect API — live data subscriptions, order routing, and account management

Implement a pluggable strategy engine — deploy any Python strategy class into the live execution loop

Containerise the entire application with Docker Compose and deploy to AWS EC2 behind Nginx with Let's Encrypt HTTPS

Set up a CI/CD pipeline with GitHub Actions — automated testing, Docker build, and deployment on every push to main

Who This Course Is For

Algorithmic traders who have outgrown existing platforms and want a system they fully own, control, and can extend without limitation
Python developers with trading knowledge who want to build a serious financial product — either for personal use or as a client project
Students who have completed the Algorithmic Trading with Python course and want to bring their strategies into a proper, production-grade architecture
Software engineers who want to specialise in fintech and learn the full stack of a trading system from data ingestion to live execution
Entrepreneurs building a trading-related SaaS or tool who want to understand the technical architecture before hiring a team

Prerequisites

This is the most technically demanding course Vianmax Academy offers. You must be comfortable writing Python at the level taught in the Algorithmic Trading with Python course — functions, classes, async/await, and working with APIs. Some prior experience with REST APIs, SQL, and JavaScript is strongly recommended. Students who arrive without this foundation should expect significant difficulty from Week 1.

Strong Python skills — classes, decorators, async functions, and working with third-party libraries
Basic SQL knowledge — SELECT, INSERT, JOIN, and GROUP BY (full PostgreSQL taught in the course)
Familiarity with REST APIs — making HTTP requests with requests or httpx, reading JSON responses
Basic JavaScript / TypeScript familiarity — the React frontend modules are heavily scaffolded but require some JS literacy
Git version control — branching, committing, and pushing to GitHub (all CI/CD pipelines are Git-based)
Completion of Algorithmic Trading with Python (Vianmax) or equivalent experience with broker APIs and trading data is strongly recommended

Curriculum

14 Modules 82 Lessons 14 weeks duration 1 production platform built FastAPI · React · PostgreSQL · AWS

Course Phases at a Glance

1

Phase 1 — Foundation & Backend (Weeks 1–5)

Architecture design, FastAPI backend development, PostgreSQL schema, market data pipeline, and Kite API integration.

Modules 1–5 · 30 Lessons

2

Phase 2 — Trading Engine (Weeks 6–9)

Order management system, strategy execution engine, risk management module, and backtesting engine.

Modules 6–9 · 24 Lessons

3

Phase 3 — Frontend & Real-Time (Weeks 10–12)

React trading dashboard, WebSocket real-time updates, interactive charts, and alert notification engine.

Modules 10–12 · 18 Lessons

4

Phase 4 — Security, Deployment & Capstone (Weeks 13–14)

Authentication, Docker Compose, AWS EC2 deployment, CI/CD with GitHub Actions, and full platform capstone demo.

Modules 13–14 · 10 Lessons

01

Platform Architecture & Development Environment

Week 1 · 6 Lessons

6 lessons

1.1 Trading Platform Architecture — System Design Fundamentals

Drawing the full system architecture: data ingestion layer, market data store, strategy engine, order management service, risk monitor, frontend dashboard, and notification system. How these components communicate (REST, WebSocket, message queues). Monolith vs. microservices: why we start with a well-structured monolith and the natural seams for later separation. Architecture decision record (ADR) template.
1.2 Development Environment — Python, Node, Docker, PostgreSQL

Setting up the complete development environment: Python 3.11+ with uv package manager, Node.js 20+ for the React frontend, Docker Desktop for local service orchestration, and PostgreSQL running in a Docker container. VS Code configuration: extensions, Python interpreter, and debugging setup. The docker-compose.dev.yml that launches the full dev stack with a single command.
1.3 Repository Structure & Project Conventions

Monorepo layout: /backend (FastAPI), /frontend (React), /infra (Docker + Terraform), /scripts (data tools). Python project structure: /app/api, /app/services, /app/models, /app/core. Configuration management with Pydantic Settings and .env files. Git branching strategy: main (production), develop (integration), feature/* branches. Pre-commit hooks for linting (ruff) and formatting (black).
1.4 Database Design — PostgreSQL Schema for Trading Data

Designing the core database schema: instruments table (symbol, exchange, lot_size, tick_size), ohlcv_1min table (partitioned by date for performance), ticks table, orders table, positions table, trades table, strategies table, and alerts table. Time-series optimisation: using TimescaleDB extension for automatic hypertable partitioning on the OHLCV tables. Entity-relationship diagram for the full schema.
1.5 SQLAlchemy ORM & Alembic Migrations

Defining SQLAlchemy ORM models for all database tables. Async SQLAlchemy with asyncpg for non-blocking database access. Alembic for database migrations: creating the initial migration, running it against the local database, and the workflow for schema changes in a production system. Why migrations matter for a financial system and how to handle them safely with rollback plans.
1.6 FastAPI Quickstart — Your First API Endpoint

Building the initial FastAPI application: app factory, router setup, dependency injection for the database session. Writing the first endpoint: GET /api/instruments that queries all instruments from the database and returns a Pydantic response model. Swagger UI — the auto-generated documentation that FastAPI provides for free. Running the backend and testing the endpoint from the browser.

Key Takeaway: Your development environment is fully operational, your PostgreSQL schema is created and version-controlled with Alembic migrations, and your FastAPI app serves its first endpoint — the foundation everything else builds on.

02

FastAPI Backend — REST API Design & Service Layer

Week 1–2 · 6 Lessons

6 lessons

# FastAPI endpoint with Pydantic response model — Module 2.2
@router.get("/instruments/{symbol}", response_model=InstrumentOut)
async def get_instrument(
    symbol: str,
    db: AsyncSession = Depends(get_db),
):
    instrument = await InstrumentService(db).get_by_symbol(symbol)
    if not instrument:
        raise HTTPException(status_code=404)
    return instrument

2.1 Pydantic Models — Request Validation & Response Schemas

Using Pydantic v2 for request body validation and response serialisation. Separate schemas for creation (OrderCreate), update (OrderUpdate), and response (OrderOut) to control what data flows in and out. Validators, field aliases, and custom serialisers for financial data types (Decimal for prices, datetime with timezone). Why strong typing at the API boundary prevents an entire class of bugs in financial systems.
2.2 Service Layer Pattern — Separating Business Logic from Routes

The service layer: a class that holds all business logic for a domain (e.g., OrderService, InstrumentService, PositionService) and depends on the database session. Why routes should be thin (validate input, call service, return response) and services should be fat (all logic). Building InstrumentService and PositionService with full CRUD operations and domain-specific methods.
2.3 Background Tasks & Async Workers with FastAPI

Using FastAPI's BackgroundTasks for non-blocking post-request work (e.g., send alert after order placed). Celery + Redis for heavier background jobs: market data downloads, end-of-day P&L calculations, and strategy parameter updates. Setting up a Celery worker and beat scheduler. Why background task architecture is critical for a trading system that must respond to HTTP requests in <100ms while running heavy data operations.
2.4 WebSocket Endpoints — Real-Time Data Streaming

Building FastAPI WebSocket endpoints for real-time data push. A ConnectionManager class that tracks active connections and broadcasts messages to all subscribers. Endpoints: /ws/ticks/{symbol} for live tick feed, /ws/positions for live P&L updates, /ws/orders for order status notifications. Message protocol design: typed JSON messages with event types (TICK, POSITION_UPDATE, ORDER_STATUS) for clean frontend handling.
2.5 Error Handling, Logging & Observability

Global exception handlers in FastAPI: catching validation errors, database errors, and broker API errors and returning consistent error response JSON. Structured logging with structlog: every log entry includes timestamp, service, request_id, and context. Request ID middleware for tracing a single user request across all service calls. Why financial systems must log every order attempt, every API response, and every exception — with enough context to reconstruct what happened.
2.6 API Testing — pytest, Fixtures & Test Client

Writing async tests for FastAPI endpoints with httpx AsyncClient and pytest-asyncio. Database fixtures: a test database that resets between tests. Testing the full request-response cycle: valid inputs, invalid inputs, 404 cases, and database side effects. Test coverage reporting with pytest-cov. Why an untested trading system API is a liability and the minimum test suite every endpoint should have.

Key Takeaway: Your backend has a clean service-layer architecture, typed Pydantic contracts, real-time WebSocket endpoints, structured logging, and a test suite — a professional-grade API foundation for all the trading logic built on top of it.

03

Market Data Pipeline — Ingestion, Storage & Distribution

Week 2–3 · 6 Lessons

6 lessons

3.1 Historical Data Ingestion — NSE OHLCV into PostgreSQL

Building a data ingestion pipeline: fetch 5-year OHLCV history for Nifty 50 stocks using yfinance or NSEpy, transform to a normalised DataFrame, and bulk-insert into the ohlcv_1min TimescaleDB hypertable. Handling duplicates with INSERT ... ON CONFLICT DO NOTHING. Logging ingestion metadata (symbol, start date, end date, rows inserted) for auditability. A scheduled Celery task that runs nightly to keep the database current.
3.2 Live Tick Data — Kite WebSocket to Redis Pub/Sub

Connecting the KiteTicker WebSocket to receive live tick data during market hours. Publishing each tick to a Redis Pub/Sub channel keyed by symbol. Why Redis is the right intermediary: it decouples the market data producer (KiteTicker) from all consumers (strategy engine, frontend WebSocket, alerting) and allows each to consume at its own pace. Building the tick publisher and verifying messages in the Redis CLI.
3.3 Tick Aggregation — Building Real-Time OHLCV Candles

A Celery worker that subscribes to the Redis tick channel and aggregates ticks into 1-minute OHLCV candles in memory. On candle close, the completed candle is written to PostgreSQL and published to another Redis channel for frontend consumption. Handling partial candles for the current open bar. Why this two-stage pipeline (live ticks → Redis → aggregated candles → PostgreSQL) is the industry standard for low-latency market data systems.
3.4 Market Data API Endpoints

Building the market data REST endpoints: GET /api/ohlcv/{symbol}?timeframe=1min&from=2025-01-01&to=2025-03-31 with efficient TimescaleDB time_bucket() queries. GET /api/ticks/{symbol}/latest for the most recent tick from Redis cache. GET /api/instruments/search for symbol lookup. Pagination for large date ranges. Response time benchmarks — targeting sub-50ms for any historical query on the local stack.
3.5 Data Quality & Corporate Actions

Detecting data quality issues: missing bars (gaps in the time series), extreme price spikes (outlier ticks), and zero-volume anomalies. A data validation job that runs nightly and logs issues to a quality_issues table. Corporate actions: how stock splits and dividends create discontinuities in price data. Implementing backward-adjusted close prices using a Pandas-based adjustment pipeline. Why unadjusted data produces misleading backtest results.
3.6 Market Data WebSocket — Broadcasting Live Candles to Frontend

Connecting the backend's market data pipeline to the frontend WebSocket endpoint. The FastAPI WebSocket endpoint subscribes to the Redis channel for a given symbol and forwards each new candle and tick to all connected browser clients. Building the subscription management: clients send a SUBSCRIBE message with symbol and timeframe; the server tracks subscriptions and routes messages accordingly. Testing with wscat and verifying live candle flow.

Key Takeaway: You have a complete, production-quality market data pipeline — historical data in PostgreSQL, live ticks flowing through Redis, real-time candle aggregation, and a WebSocket API pushing live data to clients.

04

Broker API Integration — Kite Connect & Order Routing

Week 3–4 · 6 Lessons

6 lessons

4.1 Broker Abstraction Layer — Designing for Multiple Brokers

Before writing a single line of Kite code, define a BrokerInterface abstract base class: place_order(), cancel_order(), get_positions(), get_orders(), get_account_balance(). This abstraction means your trading engine calls BrokerInterface methods — not Kite-specific code. Swap to Upstox, Angel One, or a paper trading mock by changing one config value. Building the KiteConnectBroker concrete implementation that inherits from BrokerInterface.
4.2 Kite Connect Authentication & Token Management

Implementing the Kite OAuth flow in FastAPI: the /api/broker/auth endpoint redirects to Kite login, the callback endpoint exchanges the request_token for an access_token, stores it encrypted in PostgreSQL, and makes it available to all broker service calls. Automated daily token refresh with a Celery Beat scheduled task at 8:00am. Token expiry handling: detecting expired tokens at runtime and triggering re-authentication flow gracefully.
4.3 Order Placement, Modification & Cancellation

Implementing the full order lifecycle through the BrokerInterface: MARKET, LIMIT, SL, and SL-M orders. Translating internal order objects to Kite's kite.place_order() parameters and back. Handling Kite error codes: order rejection (invalid price, insufficient margin), network errors (retry with exponential back-off), and partial fills. Storing every broker response in the orders table — including the raw API response JSON for debugging.
4.4 Position & Portfolio Reconciliation

Fetching live positions from Kite and reconciling with the internal position store. Why the platform's internal position state can diverge from the broker's (manual trades, partial fills, broker-side GTT triggers) and how to detect and resolve discrepancies. A reconciliation job that runs every 5 minutes during market hours: compare broker positions to internal positions, log mismatches, and optionally auto-correct. The reconciliation log as a critical audit trail.
4.5 Paper Trading Mode — Full Simulation Without a Broker

Building a PaperTradingBroker that implements BrokerInterface using only the market data in your database. order fill simulation: MARKET orders fill at the next bar's open price; LIMIT orders fill when the bar's low/high crosses the limit price. Slippage simulation: add a configurable spread to fill prices. PaperTradingBroker stores all state in PostgreSQL so paper trading sessions persist across restarts and are fully queryable.
4.6 Broker API Endpoints & Account Dashboard Data

Building the broker management REST endpoints: GET /api/broker/positions, GET /api/broker/orders, GET /api/broker/account, POST /api/broker/orders (place order), DELETE /api/broker/orders/{order_id} (cancel). An account summary endpoint that aggregates realised P&L, unrealised P&L, margin used, and available margin into a single response for the dashboard. All endpoints working in both live Kite mode and paper trading mode without code changes.

Key Takeaway: Your platform can place, track, and reconcile orders through Kite Connect in live mode — and run identically in paper trading mode by switching a single config flag. The broker abstraction means adding a second broker later is a single new class.

05

Strategy Engine — Pluggable Execution Framework

Week 4–5 · 6 Lessons

6 lessons

5.1 Strategy Interface — Designing the Plugin Architecture

Defining the BaseStrategy abstract class: on_bar(bar: OHLCVBar) → Signal | None, on_tick(tick: Tick) → Signal | None, on_position_update(position: Position) → None. Any Python class that inherits BaseStrategy and implements these methods can be plugged into the engine. Strategy configuration stored in the strategies table: class name, parameters (JSON), symbols, and enabled/disabled status. Dynamic class loading: instantiating a strategy from its class name string at runtime.
5.2 The Execution Loop — From Signal to Order

The core execution loop: for each new bar, call on_bar() for all enabled strategies. If a Signal is returned, pass it to the Risk Manager for pre-trade checks. If the Risk Manager approves, send the order to the BrokerInterface. Log the signal, risk decision, and order to the database. The execution loop runs as a Celery task triggered by the market data pipeline on each new completed candle. Why bar-level execution (vs. tick-level) is safer for a first production deployment.
5.3 Plugging In Strategies — Live EMA Crossover Deployment

Writing a production EMAcrossoverStrategy that inherits from BaseStrategy. Registering it in the database via a POST /api/strategies endpoint. Watching it execute in paper trading mode on live Nifty data — signals appearing in the signals log, paper orders appearing in the orders table, paper positions updating. The full loop working for the first time: market data → candle aggregation → strategy signal → risk check → paper order → position update.
5.4 Risk Management Module — Pre-Trade & Portfolio-Level Controls

Building the RiskManager service with configurable controls: max position size per symbol (in ₹ value), max open positions simultaneously, daily P&L stop (pause all strategies if day's loss exceeds X%), per-strategy drawdown limit, and margin utilisation cap. Each control evaluated synchronously before every order. Risk rejections logged with reason codes. A dashboard endpoint showing current risk utilisation across all controls.
5.5 Strategy Management API

Full CRUD API for strategy management: POST /api/strategies (register), GET /api/strategies (list all with status), PATCH /api/strategies/{id}/enable, PATCH /api/strategies/{id}/disable, PUT /api/strategies/{id}/parameters (update config without restart). A strategy instance registry that reflects enable/disable changes within one candle cycle without restarting the execution loop. Signal history endpoint: GET /api/strategies/{id}/signals with filtering by date and outcome.
5.6 Backtesting Engine — Running Strategies on Historical Data

Building a BacktestRunner that instantiates any BaseStrategy subclass, iterates through historical OHLCV bars from PostgreSQL, and simulates the full execution loop using PaperTradingBroker. The backtest runs in a Celery task so it doesn't block the API. Results stored in a backtest_runs table: configuration, equity curve (daily), trade log, and performance metrics. GET /api/backtests/{id}/results returns the full report JSON. Comparing two strategy configurations side-by-side via the results API.

Key Takeaway: Your platform has a fully operational strategy execution engine — any Python strategy class can be plugged in, run live on paper, backtested on historical data, and controlled through the REST API with no code changes to the engine itself.

6–9

Modules 6–9 — Order Management, P&L, Alerts & Performance Analytics

Week 5–9 · 24 Lessons

24 lessons

Module 6 — Order Management System (6 Lessons)

Full order lifecycle management: order states (PENDING → OPEN → FILLED / CANCELLED / REJECTED), partial fill handling, order book reconstruction, GTT order management, and a complete order audit trail. REST endpoints for manual order placement from the dashboard. Order event streaming via WebSocket for real-time UI updates.
Module 7 — Real-Time P&L Engine (6 Lessons)

Computing unrealised P&L in real-time from live ticks: position quantity × (current price − average entry price). Computing realised P&L from fill history. FIFO cost basis accounting. Daily, weekly, and cumulative P&L aggregation. P&L attribution by strategy. A P&L WebSocket channel that pushes updates on every tick for open positions — the data source for the live dashboard.
Module 8 — Alerts & Notification Engine (6 Lessons)

A flexible alert engine: price alerts (trigger at level), indicator alerts (RSI below 30), P&L alerts (position down more than 2%), and system alerts (execution loop failure). Alert routing to Telegram (bot API), Discord (webhook), and email (SMTP). Alert history and acknowledgement. Rate limiting to prevent alert floods during volatile markets. Integration with the strategy engine: strategy-generated signals can trigger alerts.
Module 9 — Performance Analytics API (6 Lessons)

Computing all core trading metrics from the trades table: CAGR, Sharpe ratio, Sortino ratio, max drawdown, win rate, profit factor, average holding period, and best/worst individual trades. Equity curve generation: a daily equity series from realised + unrealised P&L. Strategy comparison endpoint: compare two strategies across any time period. Analytics API designed to power the dashboard's performance section.

Key Takeaway: Phase 2 completes the platform's core trading infrastructure — orders are fully tracked, P&L updates in real-time, alerts fire on any condition, and the analytics API powers a complete performance view across all strategies.

10–12

Modules 10–12 — React Frontend & Real-Time Dashboard

Week 10–12 · 18 Lessons

18 lessons

Module 10 — React + TypeScript Foundation (6 Lessons)

Project setup with Vite + React + TypeScript. Folder structure for a trading dashboard: /pages, /components, /hooks, /services (API client), /store (Zustand). React Query for data fetching and server state management. The API client layer: a typed wrapper around fetch() that maps backend endpoints to typed TypeScript functions. Authentication flow: login page, JWT storage, and protected routes.
Module 11 — Live Charts & Dashboard Components (6 Lessons)

Building the trading dashboard layout: sidebar navigation, header with account summary, and main content area. Interactive OHLCV candlestick charts with Lightweight Charts (TradingView's open-source library) — loading historical data and appending live candles via WebSocket. Position cards with live P&L colouring. Order book table. Strategy status panel. The portfolio overview page: equity curve chart, performance metrics table, and daily P&L bar chart.
Module 12 — WebSocket Integration & Real-Time State (6 Lessons)

Building a useWebSocket React hook that manages the WebSocket connection, handles reconnection with exponential back-off, and dispatches incoming messages into Zustand state slices. Live candle updates appending to the chart without re-rendering the entire component. Position P&L cards updating every tick. Order status toast notifications. Handling the "stale data" problem: when the WebSocket reconnects, fetch the latest REST snapshot before resuming the stream.

Key Takeaway: The full platform now has a professional React frontend — live candlestick charts, real-time P&L updates, position monitoring, and order management — all connected to your own backend via REST and WebSocket.

13–14

Modules 13–14 — Security, AWS Deployment & Capstone

Week 13–14 · 10 Lessons

10 lessons

Module 13 — Auth, Docker & AWS Production Deployment (6 Lessons)

JWT authentication with FastAPI: login endpoint, access token generation, refresh tokens, and route protection with a CurrentUser dependency. Production docker-compose.yml: services for backend, frontend (Nginx-served static build), PostgreSQL, Redis, Celery worker, and Celery Beat — all networked together with health checks. AWS EC2 deployment: provisioning a t3.small instance, SSH setup, Docker installation, and running the compose stack. Nginx reverse proxy with Let's Encrypt HTTPS. Deploying to a real domain name with SSL.
Module 14 — CI/CD Pipeline & Capstone Platform Demo (4 Lessons)

GitHub Actions workflow: on every push to main, run the test suite, build the Docker images, push to Amazon ECR, and SSH into the EC2 instance to pull and restart the containers. Zero-downtime deployment with docker compose pull + up -d --no-deps. The capstone: a live demonstration of the complete platform running in production — historical charts loading, a paper trade strategy executing on live data, real-time P&L updating in the browser, and the deployment pipeline running a change end-to-end in front of the batch.

Key Takeaway: Your complete trading platform is live on AWS at your own domain, secured with HTTPS and JWT authentication, deployed via a fully automated CI/CD pipeline. Every push to GitHub automatically updates production — the same workflow used by professional engineering teams.

AlphaSync

Included with this Course

Practice Live on AlphaSync

AlphaSync is your live reference architecture throughout this course. Its production FastAPI backend, React dashboard, real-time WebSocket feeds, and broker API integrations represent the exact system you're building. Use it as a benchmark — and a developer sandbox for testing your own platform.

Module 1–2

Architecture Reference Study

Open AlphaSync at demo.alphasync.app. Using browser DevTools (Network tab), observe the WebSocket connections, REST API calls, and data structures. Map what you see to the architecture diagrams from Module 1.

Open AlphaSync

Module 3–5

Market Data Pipeline Benchmarking

While building your FastAPI data pipeline, use AlphaSync as your reference — compare your tick data latency, OHLCV aggregation logic, and WebSocket streaming against what AlphaSync delivers. Close the gap.

Open AlphaSync

Module 6–9

Parallel Paper Trading for Testing

As you build your order management and P&L engine, run the same trades on AlphaSync paper trading simultaneously. Compare your system's P&L calculations against AlphaSync's outputs to validate your engine's accuracy.

Open AlphaSync

Module 10–14 Capstone

Developer Sandbox & Final Benchmark

Before submitting your capstone, run your production platform and AlphaSync side-by-side for one full trading day. Document where your platform matches or exceeds AlphaSync's capabilities. Include this benchmark in your project demo.

Open AlphaSync

Learning Format

Recorded Sessions

HD video with full screen code walkthroughs. Every line of code written live — no pre-written scripts.

GitHub Repository

A complete starter repo with scaffolding. Every module corresponds to a tagged commit checkpoint.

Live Review Sessions

Bi-weekly live code reviews where students share their implementations and receive feedback.

4 Integration Milestones

At the end of each phase, a milestone submission verifies the whole system is working end-to-end.

WhatsApp & Discord

Dedicated channel for architecture questions, debugging help, and peer code review.

Instructor Code Review

Personal instructor review of capstone platform — architecture, code quality, and security assessment.

What You Will Have Built

Unlike courses where you build isolated exercises, every component in this program connects into a single, unified production platform. By the end of Week 14, your deliverable is one complete system:

Production FastAPI Backend

A fully tested, async Python backend with 50+ REST endpoints covering instruments, market data, orders, positions, strategies, alerts, and analytics. Service-layer architecture, Pydantic schemas, structured logging, and 80%+ test coverage. Running in Docker on AWS EC2 with Nginx and HTTPS.

TimescaleDB Market Data Store

A PostgreSQL + TimescaleDB database containing 5 years of OHLCV history for Nifty 50 stocks, live-updated with intraday candles during market hours via the data pipeline. All orders, positions, strategies, signals, and backtest results stored with full audit trails and version-controlled Alembic migrations.

Strategy Execution Engine + Backtesting

A pluggable strategy engine that runs any BaseStrategy subclass in live paper mode or historical backtest mode using identical code paths. Pre-trade risk controls, daily drawdown stops, signal logging, and a complete backtest results API with equity curve, trade log, and all performance metrics.

React Trading Dashboard

A React + TypeScript single-page application with real-time candlestick charts (Lightweight Charts), live P&L position cards updating on every tick, order management, strategy controls, backtest results viewer, and portfolio analytics — all connected to your backend via REST and WebSocket.

Full AWS Production Deployment with CI/CD

The complete platform running at your own domain, secured with Let's Encrypt HTTPS, deployed via Docker Compose on AWS EC2, with a GitHub Actions CI/CD pipeline that runs tests, builds images, and deploys automatically on every push to main. Zero-downtime deployments. Your trading platform, live on the internet, owned completely by you.

Course Materials

Complete starter GitHub repository

Docker Compose dev & prod configs

GitHub Actions CI/CD workflow templates

System architecture diagram (Excalidraw)

Database schema ERD (with annotations)

API contract reference (OpenAPI spec)

AWS EC2 deployment runbook

Production security checklist

BaseStrategy interface + 3 example strategies

Performance metrics calculation reference

Time Commitment

14

Weeks total duration

12–15h

Per week (study + coding)

1

Complete live platform built

This is not a casual course. 12–15 hours per week for 14 weeks is a serious commitment — roughly equivalent to a part-time degree module. The integration milestones at the end of each phase are mandatory: they verify that all components built in the phase are connected and working before moving forward. Students who fall behind on milestones find it increasingly difficult to reconnect the pieces later.

The most common failure mode in this course is starting strong and losing momentum in Weeks 7–10 (the trading engine phase), which is the most abstract and technically demanding section. Students who have the best outcomes are those who write code every day, even if it's just 30 minutes, and who ask questions early in the WhatsApp group rather than spending days stuck on a single problem alone.

Certificate of Completion

Vianmax Academy — Certificate of Completion

Students who complete all 14 modules, pass all four integration milestone reviews, and successfully demo their live production platform earn the Vianmax Academy Certificate of Completion — Full-Stack Trading Platform Development. This is the most substantive certificate Vianmax Academy offers: it requires demonstrating a fully operational, cloud-deployed trading system of professional architectural quality. Issued digitally with a unique verification code and a link to the student's GitHub repository — making it independently verifiable by any technical hiring manager or client.

Instructor Information

Vianmax Academy Lead Faculty

Head of Full-Stack Engineering & Trading Systems Architecture

11+ years full-stack dev

8+ years fintech systems

FastAPI · React · AWS

Production trading systems

Our full-stack instructor has spent over a decade building production financial software — from a retail trading platform serving 15,000 users to institutional-grade order management systems with sub-millisecond latency requirements. The curriculum for this course was built by reverse-engineering what such systems actually look like in production and then making each component teachable in a 14-week programme. The architecture taught here — service layer, broker abstraction, pluggable strategy interface, event-driven data pipeline — is not academic; it is drawn directly from systems the instructor has shipped and maintained in live markets. Every design decision has a war story behind it, and those stories are woven into the lessons.

Frequently Asked Questions

When does this course launch, and how do I get notified?

We are targeting a Q3 2026 launch for the first batch. The course is currently in final curriculum development and infrastructure testing. Join the waitlist via the "Join Waitlist" button on this page — you will be the first to know when Enrollment opens, you will receive the early bird pricing (₹7,999 vs. the full ₹11,999), and you will get access to the starter architecture kit and a pre-course Q&A session with the instructor before the first lesson. Waitlist members' pricing is locked even if we raise the fee before their batch begins.

I haven't done the Algo Trading with Python course. Can I still join?

This course requires strong Python skills — equivalent to the Algorithmic Trading with Python program or 1+ year of Python development experience. If you are comfortable with Python classes, async functions, working with APIs, and Pandas, you have enough foundation. The specific Kite API knowledge from the Algo Trading course is covered again in Module 4 from scratch. However, if your Python is weak or you have never worked with REST APIs, this course will be overwhelming from Week 1. We recommend completing the Algo Trading course first — it specifically teaches the Python patterns and API concepts that underpin this program's backend development.

How much will AWS and other services cost per month?

Running costs for the platform during the course are modest. An AWS t3.small EC2 instance costs approximately ₹800–₹1,200/month. You can reduce this further using AWS Free Tier (t2.micro) for the first 12 months if you are a new AWS customer — the platform runs on this tier during development. Kite Connect API access (for Zerodha integration) costs ₹2,000/month but is only required if you want to test with a live broker; all exercises before deployment work with the paper trading mode and do not require Kite. PostgreSQL and Redis run inside your EC2 instance, so there are no additional database service fees.

Do I need prior React or TypeScript knowledge for the frontend modules?

Module 10 starts from the basics of React and TypeScript — you do not need prior React experience. However, you do need basic JavaScript literacy: variables, functions, arrays, objects, and the concept of asynchronous code (Promises / async-await). Students with zero JavaScript background will need to supplement with a JavaScript fundamentals resource before Module 10. That said, the frontend modules are the most heavily scaffolded part of the course — a significant portion of the React components are provided as starting points, and students extend them rather than writing everything from scratch, which reduces the raw coding burden compared to the backend modules.

Can I use this platform for someone else as a freelance project or sell it as a SaaS?

Absolutely — the platform you build is 100% yours. The code is your intellectual property, and you are free to use it commercially, deploy it for clients, or build a SaaS product on top of it. Several previous cohort students (in preview and beta versions of this course) have gone on to use the architecture for client trading dashboards and personal fund management tools. We do ask that you do not re-sell the course content or materials themselves, but the platform code you write is entirely yours to commercialise as you see fit.

Is this relevant for Forex/CFD trading, or only Indian equities?

The architecture is broker-agnostic by design — the BrokerInterface abstraction layer is explicitly built so you can swap Kite Connect for any other broker API (Alpaca for US equities, Interactive Brokers for global markets, or a MetaTrader-connected REST bridge for Forex). The course uses Kite Connect as the primary implementation because most of our students trade Indian markets, but the interface pattern means a new broker is a new class — not a new platform. If you trade Forex, you would implement the BrokerInterface for your broker's API. The market data pipeline, strategy engine, risk manager, frontend, and deployment architecture are entirely instrument-agnostic.

Full-Stack
Trading Platform
Development

Waitlist benefits

Course Overview

What You Will Have Built by Week 14

What You Will Learn

Who This Course Is For

Prerequisites

Curriculum

Course Phases at a Glance

Practice Live on AlphaSync

Architecture Reference Study

Market Data Pipeline Benchmarking

Parallel Paper Trading for Testing

Developer Sandbox & Final Benchmark

Learning Format

Recorded Sessions

GitHub Repository

Live Review Sessions

4 Integration Milestones

WhatsApp & Discord

Instructor Code Review

What You Will Have Built

Production FastAPI Backend

TimescaleDB Market Data Store

Strategy Execution Engine + Backtesting

React Trading Dashboard

Full AWS Production Deployment with CI/CD

Course Materials

Time Commitment

Certificate of Completion

Vianmax Academy — Certificate of Completion

Instructor Information

Vianmax Academy Lead Faculty

Frequently Asked Questions

Build the platform other traders wish they had.

Waitlist benefits

Recommended Before This Course

Full-StackTrading PlatformDevelopment

Waitlist benefits

Course Overview

What You Will Have Built by Week 14

What You Will Learn

Who This Course Is For

Prerequisites

Curriculum

Course Phases at a Glance

Practice Live on AlphaSync

Architecture Reference Study

Market Data Pipeline Benchmarking

Parallel Paper Trading for Testing

Developer Sandbox & Final Benchmark

Learning Format

Recorded Sessions

GitHub Repository

Live Review Sessions

4 Integration Milestones

WhatsApp & Discord

Instructor Code Review

What You Will Have Built

Production FastAPI Backend

TimescaleDB Market Data Store

Strategy Execution Engine + Backtesting

React Trading Dashboard

Full AWS Production Deployment with CI/CD

Course Materials

Time Commitment

Certificate of Completion

Vianmax Academy — Certificate of Completion

Instructor Information

Vianmax Academy Lead Faculty

Frequently Asked Questions

Build the platform other traders wish they had.

Waitlist benefits

Recommended Before This Course

Full-Stack
Trading Platform
Development