Name: Algorithmic Trading with Python
Price: 5999 INR
Availability: InStock

Course Overview

Algorithmic Trading with Python is Vianmax Academy's most technically ambitious course — and the one our students consistently say changed their careers. In 10 weeks, you'll go from writing your first Python script to having a live, broker-connected trading strategy running on NSE. This is not a theoretical course about what algorithmic trading "could" look like. Every session builds something real: a working script, a live backtest, a deployed strategy.

The Indian retail algo trading landscape has exploded in the last five years. Zerodha's Kite API, Upstox API, and Fyers API have put institutional-grade tools in the hands of individual traders. But access to tools without the knowledge to use them correctly leads to expensive mistakes. This course gives you that knowledge — how to fetch live market data, how to code your technical strategy logic cleanly, how to backtest it with statistical rigour, how to connect to a broker API for paper and live trading, and critically, how to monitor and manage a live strategy so it doesn't silently blow up your account.

Python experience is not required to start. Module 1 is a dedicated Python bootcamp designed specifically for traders — no computer science detours, just exactly what you need to start building trading tools. By Module 3, you'll be calculating RSI, MACD, and Bollinger Bands from raw OHLCV data using Pandas. By Module 6, you'll be connected to a real broker API. By graduation, you'll have a portfolio of 5 complete, functioning trading projects — a body of work that demonstrates real capability to any employer, fund, or proprietary trading desk.

This course is also the formal entry point into trading automation for graduates of the Foundation and Advanced TA programs. The strategies you built conceptually in those courses — you'll automate them here.

What You Will Learn

Write Python scripts for financial data manipulation using Pandas and NumPy — fetching, cleaning, and analysing OHLCV market data

Code technical indicators (RSI, MACD, ATR, Bollinger Bands, EMA) from scratch in Python without relying on TA-Lib black boxes

Design rule-based trading strategies in Python and run rigorous backtests using Backtrader and custom Pandas-based engines

Evaluate backtest results statistically — CAGR, Sharpe ratio, max drawdown, win rate, profit factor, and equity curve analysis

Integrate with the Zerodha Kite Connect API for live data streaming, order placement, and position tracking

Deploy a fully automated paper-trading strategy on a cloud server (PythonAnywhere / AWS EC2) that runs 24/7 without your laptop being open

Build a risk management module in code — automated stop-loss, max daily loss limits, position sizing, and kill-switch logic

Graduate with a portfolio of 5 complete trading projects with source code — demonstrating end-to-end algo trading capability

Who This Course Is For

Graduates of Foundation & Advanced TA Programs — The intended third step in the Vianmax Academy learning path. You understand the markets; now you'll automate your strategies.
Software Developers Who Trade — Engineers and developers who already know Python and want to apply their skills to building real trading systems. You'll move quickly through Module 1 and find the market-specific content highly valuable.
Manual Traders Tired of Screen Time — If you've been watching screens for hours waiting for setups you've identified, this course lets you automate the scanning, alerting, and even execution so your strategy works while you don't.
Finance Professionals & Analysts — Those in banking, asset management, or financial research who want to add Python-based quantitative analysis skills to their professional profile.
Students Targeting Quant / FinTech Careers — Engineering or MBA students who want to break into quantitative finance, proprietary trading, or FinTech development. This course builds the portfolio to make that happen.

Prerequisites

Trading knowledge is required; Python experience is not. You need a solid understanding of technical analysis and market structure before this course — we cannot also teach both trading and Python from zero in 10 weeks. Python, however, we teach from scratch in Module 1. Students with existing Python skills can move through the Python modules quickly and apply their energy to the trading-specific content.

Completion of Vianmax Academy's Foundation + Advanced TA programs, or equivalent intermediate trading knowledge
Understanding of at least 2–3 technical indicators and how to identify trade setups on a chart
Basic risk management knowledge — stop-loss, position sizing
A laptop or desktop (Windows, Mac, or Linux) with at least 8 GB RAM
No Python experience needed — Module 1 starts from absolute zero
A Zerodha account (or any KiteConnect-supported broker) for API integration modules

Course Curriculum

8 Modules 60+ Lessons 10 Weeks 5–6 hrs/week 5 Real Projects

01

Python for Traders — Zero to Data-Ready

Week 1–2 · 8 Lessons

8 lessons

1.1 Setting Up Your Trading Dev Environment

Installing Python 3, VS Code, and Jupyter Notebook. Virtual environments, pip, and the essential trading libraries: Pandas, NumPy, Matplotlib, and yfinance. Every student leaves this lesson with a working setup.
1.2 Python Fundamentals — Variables, Data Types & Control Flow

Variables, integers, floats, strings, booleans, lists, and dictionaries. If/else logic, for loops, and while loops — all taught through trading examples (price lists, trade logs, condition checks).
1.3 Functions, Modules & Writing Clean Code

Writing reusable functions. Organising code into modules. Following PEP 8 naming conventions. Building your first utility — a function that checks whether a candle is bullish or bearish.
1.4 Pandas — The Trader's Most Important Tool

DataFrames and Series. Loading CSV files of OHLCV data, selecting columns, filtering rows, sorting, and basic aggregation. Reading NSE historical data files into a clean Pandas DataFrame.
1.5 Working with Date & Time in Financial Data

Parsing date strings, working with datetime objects, resampling OHLCV data from minute to daily/weekly/monthly using Pandas resample(). Timezone handling for NSE market hours.
1.6 Fetching Live & Historical Market Data

Using yfinance, NSEpy, and the Kite Connect API to download historical OHLCV data for NSE stocks and indices. Building a reusable data fetching function for any symbol and timeframe.
1.7 Visualising Market Data with Matplotlib & Plotly

Plotting candlestick charts, line charts, and volume bars in Python. Creating an interactive chart with Plotly that mirrors what you'd see on TradingView — with price, volume, and indicator overlays.
1.8 Project 1 — Market Data Dashboard

Build a Python script that fetches live OHLCV data for a Nifty 50 stock, cleans it, and generates a Plotly chart with volume. First project deliverable, reviewed in the next live session.

Key Takeaway: You can write Python, work with financial data using Pandas, fetch live market data from NSE, and visualise it in interactive charts — the complete data pipeline every algo trader needs.

02

Building Technical Indicators in Python

Week 2–3 · 7 Lessons

7 lessons

2.1 Why Coding Indicators from Scratch Matters

The problem with TA-Lib black boxes: lookahead bias, incorrect calculation windows, and inability to customise. Why every serious algo trader should be able to code their own indicators.
2.2 Moving Averages — SMA, EMA, and WMA

Coding Simple Moving Average, Exponential Moving Average, and Weighted Moving Average using Pandas. Verifying output against TradingView values. Detecting golden cross and death cross events in code.
2.3 RSI — Relative Strength Index from First Principles

Coding RSI using Wilder's smoothing method (not SMA). Vectorised vs iterative implementation. Detecting overbought/oversold levels and RSI divergence programmatically.
2.4 MACD — Signal Line, Histogram & Crossovers

Building MACD from two EMAs. Generating the signal line and histogram. Writing a function that returns a MACD signal (+1, -1, 0) for a given DataFrame — the exact format a backtest engine needs.
2.5 Bollinger Bands & ATR — Volatility Measurement

Coding Bollinger Bands (standard deviation rolling window). ATR using True Range calculation. Using ATR for dynamic stop-loss placement in a strategy — e.g., stop = entry − 2×ATR.
2.6 Supertrend & ADX — Trend Filters

Coding Supertrend using ATR. ADX to measure trend strength. Using ADX as a trade filter: only trade when ADX > 25 (trending market). The most common filter in professional NSE strategies.
2.7 Building a Reusable Indicator Library

Packaging all indicators into a clean Python module (indicators.py) with consistent function signatures. The indicator library every future project in this course will import from.

# Sample: RSI calculation in Pandas (from Module 2.3)
def compute_rsi(df, period=14):
  delta = df['close'].diff()
  gain = delta.where(delta > 0, 0).ewm(alpha=1/period).mean()
  loss = (-delta.where(delta < 0, 0)).ewm(alpha=1/period).mean()
  return 100 - (100 / (1 + gain / loss))

Key Takeaway: You have a personal indicator library in Python with 8+ indicators you coded yourself — no black boxes, full understanding of every calculation, and a reusable toolkit for every strategy you'll ever build.

03

Strategy Design & Backtesting with Pandas

Week 3–4 · 7 Lessons

7 lessons

3.1 Strategy Design Framework — Rules to Code

Translating a written trading rule into unambiguous Python logic. The strategy specification document: entry conditions, exit conditions, filters, stop-loss, target, and position sizing — before writing a single line of code.
3.2 Vectorised Backtesting in Pandas

The fastest backtest engine: generating signal columns, computing daily returns, and calculating cumulative P&L using NumPy — all in a few lines of Pandas. Understanding when vectorised backtesting is appropriate vs event-driven.
3.3 Backtesting Pitfalls — Lookahead Bias & Curve Fitting

The two mistakes that make a backtest worthless: lookahead bias (using future data in a past decision) and curve fitting (over-optimising to historical noise). How to detect and avoid both in your code.
3.4 Strategy 1 — EMA Crossover on Nifty 50 Stocks

Implementing a 20/50 EMA crossover strategy on 5 years of NSE data. Backtesting across 20 Nifty 50 stocks. Comparing results to a buy-and-hold benchmark. Project 2 deliverable.
3.5 Strategy 2 — RSI Mean Reversion with ATR Stop

Entering on RSI oversold (below 30) with an ATR-based stop-loss and fixed profit target. Adding an ADX trend filter. Full backtest with and without the filter — comparing performance metrics side by side.
3.6 Backtrader Framework — Event-Driven Backtesting

Introduction to the Backtrader library for event-driven, trade-by-trade backtesting. Setting up a strategy class, data feeds, broker commissions, and running the engine. When to use Backtrader vs Pandas vectorised approach.
3.7 Slippage, Commission & Realistic Simulation

Why every backtest must account for brokerage (₹20 per order), STT, exchange charges, and bid-ask slippage. How to configure Backtrader to simulate Indian market transaction costs and what difference it makes to your results.

Key Takeaway: You can design, code, and run a complete backtest of any rule-based strategy on NSE historical data — with realistic transaction costs — and produce results you can trust.

04

Performance Analytics & Strategy Optimisation

Week 4–5 · 7 Lessons

7 lessons

4.1 Calculating Core Performance Metrics

Coding CAGR, annualised Sharpe Ratio, Sortino Ratio, Calmar Ratio, maximum drawdown, win rate, average win/loss ratio, and profit factor from a trade log DataFrame.
4.2 Equity Curve Analysis & Drawdown Visualisation

Plotting the equity curve, underwater curve (drawdown over time), and monthly return heatmap. Building a strategy performance report as a reusable Python function.
4.3 Walk-Forward Analysis — Out-of-Sample Testing

Why in-sample optimisation is meaningless without out-of-sample validation. Implementing a rolling walk-forward test to check whether your strategy's edge persists on data it has never seen.
4.4 Parameter Optimisation — Grid Search & Heatmaps

Using grid search to test parameter combinations (e.g., EMA periods 10–50 × 30–100). Visualising results as a heatmap to find robust parameter zones rather than overfit peaks.
4.5 Monte Carlo Simulation for Strategy Robustness

Running Monte Carlo simulations by randomly shuffling trade outcomes to build a distribution of possible equity paths. Understanding the probability range of your strategy's future performance.
4.6 Multi-Asset & Portfolio Backtesting

Backtesting a strategy across 20+ NSE stocks simultaneously. Capital allocation across positions. Portfolio-level metrics vs individual stock metrics. Correlation of returns across the portfolio.
4.7 Project 3 — Strategy Analytics Report Generator

Build a Python script that takes any backtest result and produces a full HTML performance report: equity curve, drawdown chart, trade list, monthly returns heatmap, and all key metrics.

Key Takeaway: You can rigorously evaluate any trading strategy — not just whether it made money in backtesting, but whether that edge is statistically real, robust across parameters, and likely to persist forward.

05

Broker API Integration & Paper Trading

Week 5–7 · 8 Lessons

8 lessons

5.1 Overview of Indian Broker APIs

Comparing Zerodha Kite Connect, Upstox API, Fyers API, and Angel One SmartAPI — features, costs, rate limits, and supported order types. Why Kite Connect is the industry standard for retail algo traders.
5.2 Kite Connect Setup — API Keys, Authentication & Sessions

Getting API credentials, handling OAuth login flow, generating access tokens, and automating the daily token refresh using Python. Storing credentials securely with environment variables (no hardcoded keys).
5.3 Fetching Live & Historical Data via Kite API

Using kite.historical_data() for OHLCV data. Subscribing to live WebSocket tick feed. Handling connection drops, reconnection logic, and data buffering — essential for any live system.
5.4 Placing Orders — Market, Limit, SL, and GTT

kite.place_order() with all parameters. Handling order rejection errors. Modifying and cancelling orders. GTT (Good Till Triggered) orders for set-and-forget entry/exit management.
5.5 Position & Portfolio Monitoring

Reading live positions, holdings, order book, and trade book via the API. Building a real-time P&L monitor that prints current position status to the terminal every 30 seconds.
5.6 Building a Paper Trading Simulator

Connecting your strategy to live Kite tick data while simulating order fills locally (no real money). Logging every simulated trade and P&L in real time. The safest way to validate a live strategy before going live.
5.7 Error Handling, Logging & Alerts

Python's logging module for structured trade logs. Handling API exceptions gracefully (network errors, rate limits, invalid tokens). Sending trade alerts to your phone via Telegram bot — a must-have for live systems.
5.8 Project 4 — Live Paper Trading System

Build a complete paper trading system: Kite WebSocket data feed → indicator calculation → signal generation → simulated order placement → P&L tracker → Telegram alerts. Run it live for two weeks before Module 6.

Key Takeaway: You have a fully functional paper trading system connected to live NSE data via the Kite API, running your own strategy and sending real-time alerts — the bridge between backtesting and going live.

06

Risk Management Systems in Code

Week 7–8 · 6 Lessons

6 lessons

6.1 Automated Position Sizing — Fixed Fraction & Volatility-Adjusted

Coding a position sizing function that calculates the correct number of shares based on account size, risk %, stop-loss distance, and ATR. The most important function in any trading system.
6.2 Automated Stop-Loss & Trailing Stop Management

Placing bracket orders with calculated stop-loss. Implementing a trailing stop (ATR-based and percentage-based) that automatically updates the stop as the trade moves in your favour.
6.3 Max Daily Loss Limit & Kill Switch

Coding a daily loss monitor that tracks live P&L and automatically cancels all open orders and squares off all positions if a configurable daily loss threshold is breached. The most critical safety mechanism in any live algo.
6.4 Concurrency Control — Preventing Duplicate Orders

Using flags, locks, and state machines to prevent a strategy from placing duplicate orders when signals fire faster than order confirmations arrive. A common and costly bug in poorly written live systems.
6.5 Portfolio Heat & Drawdown Guards

Monitoring total portfolio exposure across all open positions. Reducing position size or pausing new trades when overall portfolio drawdown exceeds a threshold. Dynamic risk scaling based on recent performance.
6.6 Audit Trails — Trade Logs & Daily Reports

Writing all trades to a SQLite database or CSV log with timestamps. Auto-generating a daily P&L summary emailed to yourself every day at market close. Keeping a complete, auditable record of every system action.

Key Takeaway: Your live strategy won't just make trades — it will automatically enforce risk limits, prevent runaway losses, log everything, and alert you the moment anything goes wrong. Risk management in code is not optional.

07

Live Deployment & System Monitoring

Week 8–9 · 7 Lessons

7 lessons

7.1 Cloud Deployment on PythonAnywhere & AWS EC2

Setting up a Linux server, uploading your strategy, scheduling it with cron jobs, and running it 24/7 without your laptop. PythonAnywhere (easiest) and AWS EC2 (most professional) both covered.
7.2 Automating Daily Token Refresh

Kite Connect tokens expire daily. Automating the login and token refresh process using Selenium/Playwright so your strategy can restart itself every morning without manual intervention.
7.3 System Health Monitoring & Uptime Alerts

Setting up watchdog processes, heartbeat checks, and dead-man switch alerts. If your strategy script crashes silently, you need to know within minutes — not discover it after missing a session.
7.4 Live vs Paper — Transitioning Safely

The checklist before going live with real money: minimum paper trading duration, expected vs actual signal frequency comparison, worst-case scenario testing, and starting with reduced position sizes.
7.5 Live Performance Monitoring Dashboard

Building a lightweight web dashboard using Streamlit that shows your live strategy's current P&L, open positions, today's trades, and equity curve — accessible from any browser.
7.6 SEBI & Exchange Regulations for Algo Trading

SEBI's current regulations on algorithmic trading for retail investors (SEBI circular 2022). What is and isn't permitted. API rate limits imposed by exchanges. Running a compliant algo trading operation.
7.7 Strategy Versioning & Git for Traders

Using Git to version-control your strategy code. Branching for experiments, tagging live versions, and maintaining a clean record of every strategy change with the date it went live.

Key Takeaway: Your strategy runs on a cloud server 24/7, refreshes its own token daily, monitors its own health, and reports to a live dashboard — a professional-grade deployment that requires no daily manual intervention.

08

Capstone — 5-Strategy Portfolio & Graduation

Week 9–10 · 5 Lessons

5 lessons

8.1 Portfolio Design — Combining Uncorrelated Strategies

Why running multiple uncorrelated strategies reduces overall drawdown. Analysing the correlation matrix of your 5 strategy equity curves. Capital allocation across strategies based on Sharpe ratio.
8.2 Strategy 5 — Student's Choice Capstone Strategy

Design, backtest, optimise, paper-trade, and document your own original strategy from scratch — applying the complete methodology from Modules 1–7. Full instructor review before inclusion in your portfolio.
8.3 GitHub Portfolio — Publishing Your Work

Setting up a professional GitHub profile. Structuring your 5 strategy repositories with READMEs, performance reports, and backtest results. How to present trading projects to employers and trading desks.
8.4 Capstone Presentations — Live Code Walk-Through

Each student presents their capstone strategy: live code walk-through, backtest results, paper trading period results, and deployment architecture. Instructor and batch provide feedback.
8.5 What's Next — Career Paths in Algo Trading

Proprietary trading desks, quant analyst roles, FinTech development, and independent systematic trading — how to use this course as a career launchpad. Introduction to advanced topics: ML for trading, options pricing models, HFT architecture.

Key Takeaway: You graduate with a GitHub portfolio of 5 complete, documented trading projects — from data dashboard to live deployed strategy — and the ability to build any automated trading system you can conceive.

AlphaSync

Included with this Course

Practice Live on AlphaSync

AlphaSync is your live testing sandbox throughout this course. From market data exploration to paper trading your first Python strategy, every major milestone maps to a parallel AlphaSync exercise — so you always have a live result to compare against your code.

Module 1–3

Live Market Data Exploration

Open AlphaSync's dashboard and observe live NSE tick data for NIFTY 50, RELIANCE, and TCS. Note the bid-ask spread, last traded price, and volume. Compare with the Pandas data you've pulled in Python class.

Open AlphaSync

Module 4–5

Strategy Backtest: MA Crossover

Build a 20/50 EMA crossover strategy in AlphaSync's no-code strategy builder. Backtest it on 5 years of Nifty 50 data. Compare CAGR, max drawdown, and win rate with your Python backtest.

Open AlphaSync

Module 6–7

Paper Trade Your Python Strategy

Deploy your coded strategy in AlphaSync's paper trading mode. Let it run for one full week. Compare the executed trades against your Python simulation — note any discrepancies in fills or slippage.

Open AlphaSync

Module 8 — Capstone

Live Portfolio Performance Week

Run your final capstone strategy on AlphaSync paper trading for 5 trading days. Submit your AlphaSync P&L report (win rate, Sharpe, drawdown) alongside your GitHub portfolio as your graduation deliverable.

Open AlphaSync

Course Format

This is a project-first course. Every module culminates in a real, functioning piece of trading software. Theory is kept tight and immediately applied — you will never watch a concept explanation without building something with it in the same session.

Video Modules

60+ lessons with live coding screencasts. Every lesson is coded in front of you with full explanation — not slides.

Hands-On Coding Labs

Each module has a Jupyter Notebook lab file with exercises. Code along or attempt independently before reviewing the solution.

Weekly Live Code Reviews

Weekend live sessions where instructor reviews student code, fixes bugs, and explains optimisations in real time.

5 Real Projects

Each project is a complete, deployable trading tool. You submit source code and results; instructor provides written feedback.

WhatsApp Dev Group

Dedicated group for code questions, debugging help, and sharing live paper trading results with your batch.

GitHub Portfolio

All 5 projects are published to a GitHub portfolio that you can share with employers, trading desks, and clients.

5 Real Projects You Will Build

1

Market Data Dashboard

A Python script that fetches live and historical OHLCV data for any NSE stock using the Kite API or yfinance, and generates a fully interactive Plotly chart with volume, moving averages, and Bollinger Bands overlaid. The data pipeline foundation every subsequent project builds on. (Module 1)

2

EMA Crossover Backtest — 20 Nifty 50 Stocks

A vectorised Pandas backtest of a 20/50 EMA crossover strategy run across 5 years of historical data for all 20 stocks in a Nifty 50 sub-portfolio. Output includes a full performance report: equity curve, drawdown chart, trade list, monthly returns heatmap, and all key metrics — compared against buy-and-hold benchmark. (Module 3)

3

Strategy Analytics Report Generator

A reusable Python module that takes any backtest result (trade log DataFrame) as input and produces a complete HTML performance report — equity curve, drawdown chart, monthly return heatmap, and all KPIs. Used for every subsequent strategy you build in the course and beyond. (Module 4)

4

Live Paper Trading System

A complete automated paper trading engine: Kite WebSocket live data feed → real-time indicator calculation → signal generation → simulated order placement with position tracking → live P&L monitoring → Telegram alerts on every trade. Runs continuously during market hours and logs all activity. Paper-traded for 2 weeks before graduation. (Module 5)

5

Capstone — Your Original Trading Strategy (Full Stack)

Your own original strategy — designed, coded, backtested (60+ months of data), optimised, paper-traded (2+ weeks), deployed to a cloud server, and monitored via a Streamlit dashboard. Full source code published to GitHub with a professional README, backtest results, and performance report. This is your graduation project and your most important portfolio piece. (Module 8)

Materials & Resources Included

Full source code for all 5 projects

Jupyter Notebook lab files for every module

Backtest result templates & Excel log sheets

10+ PDF reference guides (Kite API, Pandas, Backtrader)

Live deployment checklist (pre-flight before going live)

Risk management module template (kill switch + daily loss guard)

Strategy analytics HTML report template

Session recordings (6-month access)

WhatsApp developer support group

Alumni community & monthly code reviews

Time Commitment

10

Weeks Duration

5–6

Hours per Week

60+

Total Video Hours

This is the most time-intensive course in the Vianmax Academy catalogue, and that's by design. We recommend 5–6 hours per week: 45–60 minutes of video and coding per weekday, plus a 2-hour live session on weekends. The project work — especially paper trading in Modules 5–7 and the capstone in Module 8 — requires additional independent time that's hard to compress.

Students who code along with every lesson (not just watch) and complete the lab notebooks between sessions will make significantly faster progress. If you fall behind, the most important thing is to keep up with the project deadlines — live sessions are structured around reviewing project submissions, and missing a project creates a compounding gap. Contact the instructor early if you're falling behind; we'd rather adjust the pace than have a student disengage.

Certificate of Completion

Vianmax Academy — Certificate of Completion

Students who complete all 8 modules, submit all 5 projects (with source code), paper-trade the live system for a minimum of 2 weeks, and successfully present their capstone strategy earn the Vianmax Academy Certificate of Completion — Algorithmic Trading with Python. Issued digitally with a unique verification code. Designed to be portfolio-worthy: it demonstrates not just course attendance, but the delivery of 5 real, functional trading systems — a claim that can be verified through your public GitHub portfolio.

Instructor Information

Vianmax Academy Lead Faculty

Head of Algorithmic Trading & Systems Development

10+ years algo trading

620+ students taught

4.9★ average rating

Python & NSE API specialist

Our algorithmic trading instructor is a full-time systematic trader with over 10 years of experience building and running automated strategies on NSE and MCX. Having built production algo systems for both personal trading accounts and institutional clients — using the same Kite API and cloud infrastructure taught in this course — the curriculum is grounded in real deployment experience, not academic theory. The 5-project structure was designed because the instructor believes strongly that you cannot learn to build trading systems by watching slides; you must build something real in every single session, even if it's just 10 lines of code. Every concept in this course has been live-traded with real money on Indian markets.

Frequently Asked Questions

I've never coded before. Can I really learn Python and algo trading in 10 weeks?

Yes — if you commit to it. Module 1 is a full Python bootcamp tailored for traders. We don't teach computer science theory; we teach exactly the Python you need to build trading tools. By Week 2 you'll be fetching live NSE data and drawing charts. By Week 4 you'll have a complete backtest running. Many of our strongest graduates had zero prior coding experience. The key is consistency: 45 minutes of coding every weekday, no exceptions. Coding is a skill that compounds quickly with daily practice.

Do I need a paid Zerodha Kite Connect subscription?

Kite Connect API access costs ₹2,000/month from Zerodha, but you only need it for Modules 5–7 (weeks 5–9). For Modules 1–4, we use yfinance and NSEpy which are free. We also show you how to use Upstox API and Angel One SmartAPI as free alternatives if you prefer not to pay for Kite Connect. That said, Kite Connect is the most reliable and best-documented API for Indian markets, and the ₹2,000 investment is worth it for the 2–3 months you'll be using it actively in the course.

Will I be deploying real money in this course?

No — the course ends at paper trading (simulated live trading with real market data but no real orders). Going live with actual capital is a decision you make after the course, once you've seen your strategy perform in paper trading for a satisfactory period. Module 7.4 specifically covers the checklist and criteria we recommend before transitioning from paper to live. We will never pressure or encourage you to go live with real money during the course.

What laptop/computer specs do I need?

Any Windows, Mac, or Linux machine with at least 8 GB RAM and a modern processor (released after 2016) will work. 16 GB RAM is more comfortable but not required. You don't need an expensive machine — Python, Pandas, and Jupyter run fine on a ₹25,000 laptop. For cloud deployment in Module 7, we use free-tier AWS EC2 and PythonAnywhere, so there's no additional hardware cost. A stable internet connection (minimum 10 Mbps) is important for the live data streaming modules.

Is this relevant for trading Nifty/Bank Nifty options or only equities?

This course primarily uses NSE equity (cash segment) for examples and projects. However, all the Python skills, backtesting framework, API integration, and deployment methodology apply equally to equity F&O (options and futures). The Kite API handles F&O orders identically to equity orders. Students who want to automate options strategies after this course have everything they need — they would just need the options pricing knowledge from our F&O Mastery Program to design the strategy logic itself.

Do I get the source code even if I don't complete all the projects?

All Enrollled students receive complete source code for all 5 projects on Day 1 via a private GitHub repository. We share the code upfront because we want you to code alongside it — not because we expect you to copy it. Students who submit their own coded versions of each project for instructor review get substantially more out of the course than those who only study the reference code. The certificate, however, requires your own submitted code, not the reference solution.

Algorithmic Trading with Python

This course includes

Course Overview

What You Will Learn

Who This Course Is For

Prerequisites

Course Curriculum

Practice Live on AlphaSync

Live Market Data Exploration

Strategy Backtest: MA Crossover

Paper Trade Your Python Strategy

Live Portfolio Performance Week

Course Format

Video Modules

Hands-On Coding Labs

Weekly Live Code Reviews

5 Real Projects

WhatsApp Dev Group

GitHub Portfolio

5 Real Projects You Will Build

Market Data Dashboard

EMA Crossover Backtest — 20 Nifty 50 Stocks

Strategy Analytics Report Generator

Live Paper Trading System

Capstone — Your Original Trading Strategy (Full Stack)

Materials & Resources Included

Time Commitment

Certificate of Completion

Vianmax Academy — Certificate of Completion

Instructor Information

Vianmax Academy Lead Faculty

Frequently Asked Questions

Ready to automate your trading edge?

This course includes

Recommended Before This Course

What to Take After