Algorithmic Trading Masterclass
The quant skill set, taught as engineering: Python for markets, strategies you can backtest honestly, risk management first, then machine learning, no profit promises, real capability.
Syllabus updated August 2026
Recordings are free with a Google sign-in. These recordings show how we teach, not the exact syllabus of this course.
How long this course takes
This course runs 10-12 months (44-52 weeks) at 2 live classes/week + 4-6 hours practice. The range allows for background and pace: a complete beginner uses the full span, and a student with prior knowledge finishes sooner. The syllabus below is planned to the shorter end, with margin kept for revision.
For personalised duration planning, call +91 91233 66161 and we'll map a schedule to your goals.
At a glance
- Who it is for
- Complete beginner (18+); college students, graduates and working professionals. Age: 18+; college students, graduates and working professionals.
- Prerequisites
- None. Everything is taught from the first line
- Format
- Live, interactive classes with a real instructor, never pre-recorded videos. Live classes run about one hour.
- Language
- English or Hindi, depending on the batch.
- Duration
- 10-12 months (44-52 weeks)
- Weekly commitment
- 2 live classes/week + 4-6 hours practice
- Class size
- Group batches of 5 to 10 students, mini batches of 3 to 4, or 1-on-1.
- Certificate
- Certificate from Modern Age Coders, awarded on passing the final exam
- Watch first
- Free recordings of real classes for ages 13 and up, free with a Google sign-in. These recordings show how we teach, not the exact syllabus of this course.
- Live demo
- Optional. Enroll directly on this page, or book a free live demo if you would like to meet a mentor first.
How we teach
Deep understanding. Real projects. Expert guidance.
Learn coding, AI and maths through careful explanations, practical demonstrations, and hands-on problem-solving. Our instructors guide you from the foundations to advanced ideas, helping you understand what happens, why it happens, and how to build it yourself.
You will explore concepts step by step, write and improve code, investigate mistakes, ask questions, and apply your knowledge to meaningful projects. Lessons are designed around active participation, thoughtful feedback, and growing independence.
Expert-led live teaching, with practical participation built into every lesson.
- Students explain their thinking.
- Instructors demonstrate, then guide practice.
- Learners code, solve, or build during lessons.
- Questions and misconceptions get attention.
- Assignments receive useful feedback.
- Progress is checked before advancing.
Ready to Master Algorithmic Trading Course: Strategy, Quant & ML?
Choose your plan and start your journey into the future of technology today.
Rated 4.9 across 547 Google reviews. Enroll directly, or take a free live demo first if you like. No card needed for the demo. Monthly billing, cancel anytime.
International Students (Outside India)
Billed monthly in US dollars, the same price in every country. Contact us with any questions.
Program Overview
Algorithmic trading is a serious quantitative discipline, and this course treats it as one, not as a get-rich pitch. It builds the strategy-and-research half of the field in order: Python for financial data, technical and statistical analysis, then strategy design and honest backtesting (including the traps, lookahead bias, overfitting, survivorship, that make bad strategies look brilliant), then risk management and position sizing taught before anything goes near live markets, and finally machine learning for trading, used with the scepticism the domain demands. Throughout, the message is consistent and repeated: markets carry real risk of loss, backtests do not predict the future, and this is education, not financial advice. A finale month builds a fully backtested strategy system and sits the final exam.
The pace is honest: ten teaching months at two live classes a week plus four to six hours of practice, with two more in hand. Problem sets, monthly mixed reviews, phase exams, and a final exam that decides the certificate with a free retest.
What Makes This Program Different
- Taught as engineering and research, not as a money-making scheme
- Backtesting honesty is a core theme: the biases that fake good results are named and defeated
- Risk management before live markets, not as an afterthought
- Machine learning applied with domain scepticism, not hype
- Honest about risk on every page: education, not financial advice, no profit claims
- Real assessment: weekly problem sets, monthly mixed reviews, phase exams and a final exam with a free retest
Your Learning Journey
Career Progression
Detailed Course Curriculum
Explore the complete week-by-week breakdown of what you'll learn in this comprehensive program.
Topics Covered
- Financial markets overview: stocks, forex, crypto, commodities
- Stock market basics: exchanges, indices, sectors
- Understanding stocks: equity, shares, market cap
- How stock prices move: supply and demand
- Market participants: retail, institutional, market makers
- Trading sessions: pre-market, regular, after-hours
- Order types: market, limit, stop-loss, stop-limit
- Bid-ask spread and liquidity
- Long vs short positions
- Margin trading and leverage
Projects You Build
- Market research on different instruments
- Paper trading practice (manual)
- Order type experiments
- Market analysis report
- Trading journal setup
Practice & Assignments
Paper trade 20 manual trades, document everything
Topics Covered
- Why Python for algorithmic trading?
- Python installation and setup (Anaconda)
- Jupyter notebooks for trading analysis
- Variables and data types
- Lists, tuples, dictionaries for market data
- Control flow: if-else for trading conditions
- Loops for iterating through historical data
- Functions for trading logic
- NumPy for numerical operations
- Arrays for price data manipulation
Projects You Build
- Load and explore stock price data
- Calculate basic statistics (mean, std, returns)
- Price data manipulation exercises
- Simple moving average calculator
- Trading signal generator (basic)
Practice & Assignments
Solve 50 Python problems focused on financial data
Topics Covered
- Data sources: Yahoo Finance, Alpha Vantage, Quandl
- yfinance library for stock data
- pandas_datareader for multiple sources
- OHLCV data: Open, High, Low, Close, Volume
- Data cleaning and preprocessing
- Handling missing data in time series
- Resampling: converting timeframes
- Forward-fill and back-fill methods
- Calculating returns: simple and log returns
- Cumulative returns calculation
Projects You Build
- Multi-asset data downloader
- Returns and volatility analyzer
- Correlation matrix for portfolio
- Interactive price charts
- Market data dashboard
- Historical performance analyzer
Practice & Assignments
Analyze 50+ stocks, create comprehensive reports
Topics Covered
- Technical analysis principles
- Support and resistance levels
- Trend analysis: uptrend, downtrend, sideways
- Trendlines and channels
- Chart patterns: head and shoulders, triangles, flags
- Candlestick patterns: doji, hammer, engulfing
- Moving averages: SMA, EMA, WMA
- Moving average crossovers
- Bollinger Bands: calculation and interpretation
- Relative Strength Index (RSI)
Projects You Build
- Technical indicator library in Python
- SMA/EMA crossover strategy
- RSI-based trading signals
- Bollinger Bands strategy
- Multi-indicator dashboard
- Pattern recognition tool (basic)
- Technical analysis automation
Practice & Assignments
Implement 20+ technical indicators from scratch
Topics Covered
- OOP concepts for trading applications
- Creating trading strategy classes
- Portfolio class design
- Order management system (OMS) structure
- Risk management class
- Data handler class for market data
- Event-driven architecture for trading
- Inheritance for strategy variations
- Encapsulation for strategy logic
- Design patterns for trading systems
Projects You Build
- Trading strategy base class
- Portfolio manager class
- Order execution simulator
- Risk calculator class
- Complete OOP trading framework
- Modular backtesting engine
Practice & Assignments
Refactor all previous code using OOP
Topics Covered
- Descriptive statistics for returns
- Normal distribution and fat tails
- Hypothesis testing for strategies
- P-values and statistical significance
- Correlation and causation
- Cointegration for pairs trading
- Stationarity and unit root tests
- Autocorrelation in time series
- Monte Carlo simulations
- Bootstrap methods for trading
Projects You Build
- Statistical analysis toolkit
- Returns distribution analyzer
- Strategy performance metrics calculator
- Monte Carlo simulator for trading
- Risk metrics dashboard
- Hypothesis testing framework
Practice & Assignments
Statistical analysis of 30 trading strategies
Topics Covered
- Fundamental analysis basics
- Financial statements: P&L, balance sheet, cash flow
- Key ratios: P/E, P/B, ROE, debt-to-equity
- Earnings and revenue analysis
- Economic indicators impact on markets
- News sentiment analysis
- Alternative data sources
- Broker APIs: Interactive Brokers, Zerodha, Alpaca
- Real-time data streaming
- WebSocket connections for live data
Projects You Build
- Fundamental data scraper
- Real-time data streamer
- Market data database setup
- Multi-source data aggregator
- News sentiment analyzer
- PHASE 1 MINI CAPSTONE: Complete Market Analysis Platform
Assessment
Phase 1 exam: a practical build defended live, plus a written paper mixing this phase with everything before it
Topics Covered
- What is backtesting? Importance and limitations
- Backtesting bias: look-ahead, survivorship, overfitting
- Event-driven backtesting architecture
- Vectorized vs event-driven backtesting
- Building custom backtesting engine
- Backtrader library introduction
- Backtrader strategies and indicators
- Position sizing in backtests
- Commission and slippage modeling
- Realistic execution simulation
Projects You Build
- Custom backtesting engine from scratch
- Backtrader strategy implementation
- Slippage and commission simulator
- Walk-forward testing framework
- Multi-timeframe backtester
- Optimization engine
Practice & Assignments
Backtest 20 strategies with different approaches
Topics Covered
- Momentum trading principles
- Relative Strength Momentum
- Moving average crossover strategies
- Dual moving average system
- Triple moving average system
- MACD crossover strategy
- Trend following concepts
- Donchian Channel breakout
- ATR-based trailing stops
- Turtle trading system
Projects You Build
- Moving average crossover bot
- MACD strategy implementation
- Turtle trading system
- Donchian breakout strategy
- Multi-indicator trend follower
- Momentum portfolio strategy
- Optimized trend following system
Practice & Assignments
Develop and backtest 15 momentum/trend strategies
Topics Covered
- Mean reversion theory
- Bollinger Bands mean reversion
- RSI oversold/overbought strategy
- Stochastic mean reversion
- Z-score based strategies
- Pairs trading fundamentals
- Cointegration testing for pairs
- Pairs selection methodology
- Spread calculation and trading
- Statistical arbitrage concepts
Projects You Build
- Bollinger Bands reversion strategy
- RSI mean reversion bot
- Pairs trading system
- Cointegration scanner
- Statistical arbitrage strategy
- Market neutral portfolio
- Kalman filter pairs trader
Practice & Assignments
Build 12 mean reversion strategies
Topics Covered
- Breakout trading concepts
- Support/resistance breakouts
- High/low breakout strategies
- Opening range breakout (ORB)
- Volatility breakout strategy
- ATR-based position sizing
- Volatility expansion trading
- Volatility contraction setups
- Squeeze indicator strategies
- News-based breakout trading
Projects You Build
- Support/resistance breakout bot
- Opening range breakout strategy
- Volatility breakout system
- Squeeze strategy implementation
- Volume breakout trader
- Multi-timeframe breakout system
- False breakout filter
Practice & Assignments
Develop 10 breakout trading strategies
Topics Covered
- Options basics: calls, puts, strikes, expiry
- Option Greeks: Delta, Gamma, Theta, Vega
- Option pricing: Black-Scholes model
- Implied volatility and IV rank
- Covered call strategy
- Cash-secured put strategy
- Bull call spread and bear put spread
- Iron Condor strategy
- Straddle and strangle
- Option selling strategies
Projects You Build
- Options calculator (Greeks, pricing)
- IV rank analyzer
- Covered call screener
- Iron Condor backtester
- Options strategy simulator
- Volatility trading system
Practice & Assignments
Implement 8 options strategies
Topics Covered
- Optimization objectives and pitfalls
- Overfitting vs robust optimization
- Grid search optimization
- Random search methods
- Genetic algorithms for optimization
- Particle swarm optimization
- Bayesian optimization
- Walk-forward optimization
- Sensitivity analysis
- Parameter stability testing
Projects You Build
- Grid search optimizer
- Genetic algorithm optimizer
- Walk-forward optimization framework
- Parameter sensitivity analyzer
- Multi-objective optimizer
- Robust optimization system
- Strategy parameter dashboard
Practice & Assignments
Optimize 20 strategies, compare methods
Topics Covered
- Risk management principles
- Position sizing methods: fixed, percentage, volatility-based
- Kelly Criterion for position sizing
- Risk per trade calculation
- Stop-loss placement strategies
- Trailing stop techniques
- ATR-based stops
- Time-based exits
- Profit target strategies
- Risk-reward ratio analysis
Projects You Build
- Position sizing calculator
- Kelly Criterion implementation
- Stop-loss optimizer
- Risk management system
- Portfolio heat monitor
- Drawdown controller
- Kill switch implementation
Practice & Assignments
Add risk management to all strategies
Topics Covered
- Portfolio construction principles
- Modern Portfolio Theory (MPT)
- Efficient frontier calculation
- Mean-variance optimization
- Risk parity strategies
- Equal weight vs optimal weight
- Tactical asset allocation
- Strategic asset allocation
- Rebalancing strategies
- Correlation-based diversification
Projects You Build
- Portfolio optimizer (Markowitz)
- Risk parity portfolio
- Sector rotation system
- Multi-strategy portfolio manager
- Rebalancing algorithm
- Portfolio backtester
- Asset allocation dashboard
Practice & Assignments
Build 10 portfolio-level strategies
Topics Covered
- Crypto market characteristics
- Round-the-clock trading considerations
- Crypto exchanges: Binance, Coinbase, Kraken
- Crypto data sources and APIs
- Bitcoin and altcoin strategies
- Crypto volatility strategies
- Arbitrage: exchange arbitrage, triangular arbitrage
- DeFi and DEX trading
- Crypto market making basics
- Sentiment analysis for crypto
Projects You Build
- Crypto data collector
- Bitcoin trend following bot
- Crypto arbitrage scanner
- Altcoin momentum strategy
- Crypto mean reversion system
- Exchange arbitrage bot
- Crypto portfolio rebalancer
Practice & Assignments
Develop 12 crypto trading strategies
Assessment
Phase 2 exam: a practical build defended live, plus a written paper mixing this phase with everything before it
Topics Covered
- Machine learning in trading overview
- Supervised vs unsupervised learning
- Feature engineering for trading
- Price-based features
- Technical indicator features
- Sentiment features
- Fundamental features
- Alternative data features
- Label creation: classification vs regression
- Triple-barrier method for labeling
Projects You Build
- Feature engineering pipeline
- Label creation system
- ML data preparation framework
- Cross-validation for time series
- Trading-specific feature library
- Model evaluation toolkit
Practice & Assignments
Create 100+ features for trading
Topics Covered
- Price direction prediction
- Logistic regression for trading signals
- Decision trees for market regimes
- Random Forest for trading
- Feature importance analysis
- Gradient Boosting (XGBoost, LightGBM)
- Support Vector Machines for classification
- Ensemble methods for trading
- Model stacking and blending
- Probability calibration
Projects You Build
- Price direction classifier
- Random Forest trading strategy
- XGBoost market predictor
- Ensemble trading model
- Market regime classifier
- ML signal generator
- Complete ML trading strategy
Practice & Assignments
Build 15 ML classification strategies
Topics Covered
- Neural networks for trading
- LSTM for time series prediction
- GRU networks for trading
- Sequence-to-sequence models
- Attention mechanism for trading
- Transformer models for finance
- CNN for technical pattern recognition
- Autoencoders for feature extraction
- Reinforcement learning for trading (Q-learning)
- Deep Q-Networks (DQN) for trading
Projects You Build
- LSTM price predictor
- CNN pattern recognition strategy
- Autoencoder for anomaly detection
- Reinforcement learning trader
- DQN trading agent
- Transformer-based predictor
- Deep learning ensemble system
Practice & Assignments
Implement 10 deep learning trading models
Topics Covered
- Natural language processing in trading
- News sentiment analysis
- Twitter sentiment for stocks/crypto
- Financial news scraping
- Text preprocessing for finance
- Sentiment scoring methods
- VADER for financial sentiment
- FinBERT for financial text
- Topic modeling for market themes
- Event extraction from news
Projects You Build
- News sentiment analyzer
- Twitter sentiment tracker
- Earnings call sentiment extractor
- Reddit sentiment for stocks/crypto
- Real-time sentiment dashboard
- Sentiment-based trading strategy
- Multi-source sentiment aggregator
Practice & Assignments
Build 8 sentiment-based strategies
Topics Covered
- Alternative data in trading
- Satellite imagery analysis
- Web scraping for trading signals
- Credit card transaction data
- Supply chain data
- Weather data for commodities
- Social media activity metrics
- App download data
- Google Trends for trading
- Data vendor integration
Projects You Build
- Web scraping trading signals
- Google Trends strategy
- Alternative data aggregator
- Factor model implementation
- Multi-factor strategy
- Quantitative research report
- Alpha generation framework
Practice & Assignments
Research and test 10 alternative data sources
Topics Covered
- High-frequency trading overview
- Market microstructure for HFT
- Order book dynamics
- Limit order book analysis
- Market making strategies
- Bid-ask spread capture
- Statistical arbitrage at high frequency
- Latency arbitrage concepts
- Co-location and infrastructure
- Low-latency programming
Projects You Build
- Order book visualizer
- Limit order book simulator
- Simple market making bot
- Order flow analyzer
- Tick data processor
- Microstructure research tools
- HFT backtesting framework
Practice & Assignments
Analyze order book data, simulate HFT strategies
Topics Covered
- Advanced option strategies
- Volatility trading deep dive
- VIX trading strategies
- Volatility arbitrage
- Skew trading strategies
- Calendar spread strategies
- Diagonal spreads
- Ratio spreads
- Butterfly and Condor variations
- Delta hedging automation
Projects You Build
- Volatility trading system
- VIX trading strategy
- Calendar spread optimizer
- Delta hedging bot
- Gamma scalping system
- Options portfolio manager
- Futures spread trader
Practice & Assignments
Implement 12 advanced derivatives strategies
Topics Covered
- Execution algorithms overview
- VWAP (Volume-Weighted Average Price) algorithm
- TWAP (Time-Weighted Average Price) algorithm
- Implementation shortfall
- Adaptive execution algorithms
- Iceberg orders and order slicing
- Smart order routing
- Transaction cost analysis (TCA)
- Slippage measurement and modeling
- Market impact models
Projects You Build
- VWAP execution algorithm
- TWAP execution bot
- Order slicing algorithm
- Transaction cost analyzer
- Slippage tracker
- Execution quality dashboard
- Smart order router (basic)
Practice & Assignments
Implement and analyze 8 execution strategies
Topics Covered
- Advanced portfolio construction
- Black-Litterman model
- Factor-based portfolio construction
- Multi-factor models
- Smart beta strategies
- Long-short portfolio construction
- 130/30 strategies
- Market-neutral portfolio management
- Portfolio rebalancing optimization
- Transaction cost in rebalancing
Projects You Build
- Black-Litterman portfolio optimizer
- Multi-factor portfolio constructor
- Long-short portfolio manager
- Smart beta strategy
- Rebalancing optimizer with costs
- Portfolio analytics dashboard
- Performance attribution system
Practice & Assignments
Build 10 portfolio management systems
Assessment
Phase 3 exam: a practical build defended live, plus a written paper mixing this phase with everything before it
Topics Covered
- Choosing the capstone: a complete, honestly backtested strategy system: research, signals, risk sizing and a defensible evaluation
- A one-page spec: scope, milestones, stack
- Repository and skeleton ready on day one
- Instructor sign-off before the build begins
Projects You Build
- Approved capstone spec plus the running skeleton
Practice & Assignments
Pitch the spec to the batch and absorb one hard question
Topics Covered
- Two focused weeks in vertical slices
- Instructor checkpoints and honest scope cuts
- Using the whole course: Python for markets, strategy design, bias-free backtesting, risk management and ML where it earns its place
- Daily commits and an always-working build
Projects You Build
- The capstone, feature-complete with version control
Practice & Assignments
A working build at the end of every session
Topics Covered
- The finishing pass: edge cases, errors, documentation
- Backtested without self-deception: results that survive out-of-sample and honest cost assumptions
- A README worth reading and a demo worth watching
- A full-course spiral review before the exam
Projects You Build
- The capstone shipped, documented and rehearsed
Practice & Assignments
Two mock demos, each tighter than the last
Topics Covered
- Presenting the capstone: live demo plus the hardest bug story
- The honest map of next roads: quant analyst, quant developer or fintech data roles; trading your own capital is a personal decision, not a placement
- Where our other courses continue the journey
Projects You Build
- Demo day presentation delivered to the batch
Assessment
FINAL EXAM: a practical build plus a written paper with questions mixed from every phase. Passing earns the certificate; falling short earns a focused revision plan and a free retest
Projects You'll Build
Build a professional portfolio with 15+ research and backtesting projects, crowned by a full strategy system real-world projects.
Weekly Learning Structure
Certification & Recognition
Technologies & Skills You'll Master
Comprehensive coverage of the entire modern web development stack.
Support & Resources
Career Outcomes & Opportunities
Transform your career with industry-ready skills and job placement support.
Prerequisites
Who Is This Course For?
Career Paths After Completion
Course Guarantees
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What families say
Straight from the parents and students who learn with us. Rated 4.9 across 547 Google reviews.
“Mivaan enjoys the class. He understands the concepts and completes his tasks with excitement. He started taking interest in coding… truly amazing class.”
“I absolutely love it here! I made new friends and learned important valuable coding skills while having the fun of my life. It's not just coding here, it's outings, bonding and most importantly preparing you for your future. Definitely five stars.”
“What stands out most is how excited my son is before every class. He looks forward to learning, problem-solving, and sharing what he's built. I've noticed a big boost in his confidence!”
“Modern Age Coders has been a game-changer for me! I struggled to grasp IT concepts and coding before joining, but their classes transformed everything. I'm now the topper in my class and can confidently write complex programs with ease.”
“Modern Age Coder have wonderful teachers who teach in a clear, easy and practical way. The teacher boosts students' confidence, keeps them updated with technology, and inspires them to learn without hesitation.”
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“Very good classes. Makes learning very easy and interactive.”
Hear it from our students
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Straight answers about Algorithmic Trading Course: Strategy, Quant & ML
- Who should take this course?
- Complete beginner (18+); college students, graduates and working professionals. Age: 18+; college students, graduates and working professionals. It suits programmers who want the quantitative-finance skill set; students targeting quant analyst and quant developer roles; finance people who want to add real coding and research skill.
- What will they learn and build?
- Algorithmic trading is a serious quantitative discipline, and this course treats it as one, not as a get-rich pitch.
- How deeply are topics covered?
- The course runs 10-12 months (44-52 weeks) at 2 live classes/week + 4-6 hours practice, across 4 phases listed week by week in the syllabus below. A structured, well-paced curriculum taught step by step, with classwork in every session and problem sets after every class.
- Who teaches it?
- Modern Age Coders mentors, who teach the live classes themselves. You can watch them at work in the free class recordings before you decide.
- How do practice, feedback and assessment work?
- Monthly mixed reviews, cumulative phase exams, and a final exam that decides the certificate: real knowledge, real work. Doubt support between classes over WhatsApp, so you are never left stuck. A certificate you earn by passing the final exam, with a free retest after revision if needed.
- What does it cost?
- Three monthly plans: a group batch, a mini batch and 1-on-1. Prices are shown in your currency in the plans section. Monthly billing, cancel any time.
- When can classes take place?
- Live classes are scheduled around your week. Group batches meet at a fixed weekly slot; 1-on-1 students choose their own. International students are scheduled in their own timezone. Ask on WhatsApp for the current slots.
- Can I watch the teaching before deciding?
- Yes. Full recordings of real 13 and up classes are free to watch, free with a Google sign-in. These recordings show how we teach, not the exact syllabus of this course. Some classes are in English and some in Hindi; everyone follows at their own pace. Watch a class end to end, like you are in it. Sessions are interactive and each moment builds on the last.
- Can I enroll without a live demo?
- Yes. Choose a plan on this page and enroll directly; a booking or a demo is not required. A free live demo is optional, for anyone who wants to meet a mentor first. Outside India, our team confirms the plan and completes payment with you over WhatsApp, so allow a little time for that step.
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