Algorithmic Trading with Python Course

Overview

A project-based course that takes traders from no Python experience to a documented, back-tested and paper-traded strategy. Learners code indicators from scratch, back-test rule-based strategies with realistic costs, analyse performance, connect to market data through a broker API in paper-trading mode, build risk controls in code and deploy to the cloud. The course ends at paper trading: no real-money trading is part of the course.

Part of Algo Developer (core skills) and Trading Professional (advanced capability).

Who this course is for

Graduates of the Foundation and Technical Analysis courses
Software developers who trade
Manual traders who want to systematise their process
Finance professionals and analysts
Students interested in quantitative finance and fintech

Who this course is not designed for

People looking for tips, calls or recommendations on what to trade
Anyone seeking personalised investment advice
Anyone expecting a course to produce profits or income
Anyone who wants to trade real money during the course — it ends at paper trading

Prerequisites

Trading knowledge is required; Python experience is not
Completion of the Foundation and Technical Analysis courses, or equivalent intermediate trading knowledge
Understanding of at least 2–3 technical indicators and how to identify trade setups
Basic risk management knowledge — stop-loss and position sizing
A laptop or desktop (Windows, Mac or Linux) with at least 8 GB RAM
API access for market data in Modules 5–7 — the course compares the options

What you will be able to do

Write Python scripts for financial data manipulation using Pandas and NumPy
Code technical indicators (RSI, MACD, ATR, Bollinger Bands, EMA) from scratch
Design rule-based trading strategies in Python and run rigorous back-tests
Evaluate back-test results with standard performance and drawdown metrics
Work with a broker API for market data and simulated (paper) order flow
Deploy an automated paper-trading strategy on a cloud server
Build a risk management module — automated stop-loss, daily loss limits and kill-switch logic
Complete a portfolio of five documented projects

Curriculum

8 modules · 55 lessons

Module 01 · Python for Traders — Zero to Data-ReadyWeek 1–2 · 8 lessons

1.1Setting Up Your Trading Dev Environment
1.2Python Fundamentals — Variables, Data Types & Control Flow
1.3Functions, Modules & Writing Clean Code
1.4Pandas — The Trader's Most Important Tool
1.5Working with Date & Time in Financial Data
1.6Fetching Live & Historical Market Data
1.7Visualising Market Data with Matplotlib & Plotly
1.8Project 1 — Market Data Dashboard

Module 02 · Building Technical Indicators in PythonWeek 2–3 · 7 lessons

2.1Why Coding Indicators from Scratch Matters
2.2Moving Averages — SMA, EMA and WMA
2.3RSI — Relative Strength Index from First Principles
2.4MACD — Signal Line, Histogram & Crossovers
2.5Bollinger Bands & ATR — Volatility Measurement
2.6Supertrend & ADX — Trend Filters
2.7Building a Reusable Indicator Library

Module 03 · Strategy Design & Back-Testing with PandasWeek 3–4 · 7 lessons

3.1Strategy Design Framework — Rules to Code
3.2Vectorised Back-Testing in Pandas
3.3Back-Testing Pitfalls — Lookahead Bias & Curve Fitting
3.4Strategy 1 — EMA Crossover on Nifty 50 Stocks
3.5Strategy 2 — RSI Mean Reversion with ATR Stop
3.6Backtrader Framework — Event-Driven Back-Testing
3.7Slippage, Commission & Realistic Simulation

Module 04 · Performance Analytics & Strategy OptimisationWeek 4–5 · 7 lessons

4.1Calculating Core Performance Metrics
4.2Equity Curve Analysis & Drawdown Visualisation
4.3Walk-Forward Analysis — Out-of-Sample Testing
4.4Parameter Optimisation — Grid Search & Heatmaps
4.5Monte Carlo Simulation for Strategy Robustness
4.6Multi-Asset & Portfolio Back-Testing
4.7Project 3 — Strategy Analytics Report Generator

Module 05 · Broker API Integration & Paper TradingWeek 5–7 · 8 lessons

Broker APIs are taught as technology: authentication, data, order types and monitoring, used in paper-trading mode with simulated fills.

5.1Overview of Indian Broker APIs
5.2Broker API Setup — API Keys, Authentication & Sessions
5.3Fetching Live & Historical Data via a Broker API
5.4Order Types in Code — Market, Limit, SL and GTT
5.5Position & Portfolio Monitoring
5.6Building a Paper Trading Simulator
5.7Error Handling, Logging & Alerts
5.8Project 4 — Paper Trading System

Module 06 · Risk Management Systems in CodeWeek 7–8 · 6 lessons

6.1Automated Position Sizing — Fixed Fraction & Volatility-Adjusted
6.2Automated Stop-Loss & Trailing Stop Management
6.3Max Daily Loss Limit & Kill Switch
6.4Concurrency Control — Preventing Duplicate Orders
6.5Portfolio Heat & Drawdown Guards
6.6Audit Trails — Trade Logs & Daily Reports

Module 07 · Deployment & System MonitoringWeek 8–9 · 7 lessons

7.1Cloud Deployment on PythonAnywhere & AWS EC2
7.2Automating Daily Token Refresh
7.3System Health Monitoring & Uptime Alerts
7.4Paper vs Live — Risks and a Checklist
7.5Performance Monitoring Dashboard
7.6SEBI & Exchange Regulations for Algo Trading
7.7Strategy Versioning & Git for Traders

Module 08 · Capstone — Strategy Portfolio & GraduationWeek 9–10 · 5 lessons

8.1Portfolio Design — Combining Uncorrelated Strategies
8.2Student's Choice Capstone Strategy
8.3GitHub Portfolio — Publishing Your Work
8.4Capstone Presentations — Live Code Walk-Through
8.5What's Next — Continuing in Algorithmic Trading

Projects

Project 1Intermediate
Market Data Dashboard
Fetch historical market data for an NSE stock and present it in an interactive chart with volume, moving averages and Bollinger Bands.
Skills
Python · Pandas · Data visualisation
Technology
Python · Pandas · Plotly
Deliverable
A working Python script and an interactive chart dashboard.
Project 2Intermediate
Strategy Backtester
A vectorised back-test of a 20/50 EMA crossover rule across several years of data for a sub-portfolio of Nifty 50 stocks, with a full performance report.
Skills
Back-testing · Pandas · Strategy design
Technology
Python · Pandas
Deliverable
A documented back-test with equity curve, drawdown chart and trade list.
Project 3Intermediate
Strategy Analytics Report
A reusable Python module that turns any back-test trade log into a complete HTML performance report.
Skills
Performance analytics · Python · Reporting
Technology
Python · HTML reports
Deliverable
A reusable report generator and a sample report.
Project 4Intermediate
Paper Trading System with Alerts
An automated paper-trading engine: live data feed, indicator calculation, signal generation and simulated order placement, with alerts on every simulated trade.
Skills
APIs · Event handling · Risk controls
Technology
Python · WebSocket · Telegram
Deliverable
A paper-trading system run for at least two weeks with logs and alerts.
Project 5Intermediate
Capstone Strategy
Your own strategy taken end to end: designed, coded, back-tested, optimised, paper-traded, deployed and monitored, with full documentation.
Skills
Strategy design · Deployment · Documentation
Technology
Python · Cloud deployment
Deliverable
A documented capstone repository and a live code walk-through.

Capstone

Your Original Strategy, End to End

Design, code, back-test, optimise and paper-trade your own strategy, deploy it to a cloud server and monitor it through a dashboard. Present a live code walk-through, back-test results, the paper-trading period and the deployment architecture for instructor and batch feedback. Paper trading only — no real orders are placed.

Practice on AlphaSync

Coming Soon

Practice exercises in AlphaSync's simulated environment are planned for this course. Until AlphaSync is available, practice uses paper trading and the tools named in the curriculum.

Instructors

Instructor profiles are published once each instructor has approved their profile and credentials have been verified.

Assessment

Five project submissions with source code, a minimum two-week paper-trading period and a capstone presentation reviewed by the instructor.

Certification

Certification details available soon.

Frequently asked questions

I have never coded before. Can I learn Python and algorithmic trading in 10 weeks?

Yes, if you commit to it. Module 1 is a Python bootcamp tailored for traders — we teach exactly the Python you need to build trading tools, not computer science theory. By Week 2 you will be fetching market data and drawing charts; by Week 4 you will have a complete back-test running. The key is consistency: around 45 minutes of coding every weekday.

Will I be deploying real money in this course?

No — the course ends at paper trading (simulated trading with real market data but no real orders). Going live with actual capital is a decision you make after the course, once you have seen your strategy perform in paper trading for a satisfactory period. We will never pressure or encourage you to go live with real money during the course.

What laptop or computer do I need?

Any Windows, Mac or Linux machine with at least 8 GB RAM and a reasonably modern processor will work; 16 GB is more comfortable but not required. Python, Pandas and Jupyter run fine on modest hardware. For cloud deployment the course uses free-tier services, and a stable internet connection is important for the live-data modules.

Is this relevant for index options or only equities?

The course primarily uses NSE equity (cash segment) for examples and projects. The Python skills, back-testing framework, API integration and deployment methodology apply equally to F&O. To design options strategy logic itself you would need the options knowledge from Futures & Options Mastery.

Fees and enrolment

Ask about fees, batch dates, format or whether the course fits your experience. The Academy will reply by email.