Algorithmic Trading & Quantitative Finance
Complete Algorithmic Trading Masterclass
From Manual Trading to Fully Automated Profitable Systems
Published October 2025
Flexible course duration
Duration depends on the student's background and pace. Beginners (kids / teens): typically 6 to 9 months. Adults with prior knowledge: often shorter, with an accelerated path.
Standard pace6 to 9 months
AcceleratedAdd class frequency to finish faster
For personalised duration planning, call +91 91233 66161 and we'll map a schedule to your goals.
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International Students (Outside India)
Billed monthly in US dollars, the same price in every country. Contact us with any questions.
Program Overview
This is not just a trading course, it's a complete transformation into a quantitative trader and algorithmic trading professional. Whether you're a complete beginner, manual trader wanting to automate, developer entering finance, or finance professional wanting quantitative skills, this 12-month masterclass will turn you into a skilled algorithmic trader capable of designing, backtesting, optimizing, and deploying profitable automated trading systems.
You'll master algorithmic trading from ground zero to professional level: from financial markets basics to advanced quantitative strategies, from Python programming to machine learning models, from paper trading to live automated execution, from single strategies to portfolio management. By the end, you'll have built 40+ trading strategies, backtested extensively, and deployed live trading systems with proper risk management.
What Makes This Program Different
- Starts from absolute zero - no trading or coding background needed
- Complete 12-month structured curriculum
- Python for trading taught from scratch
- Real market data and live trading experience
- Covers stocks, forex, crypto, commodities, options
- Backtesting on 10+ years of historical data
- Machine learning for trading strategies
- Risk management and portfolio optimization
- Live trading deployment with real money guidance
- Quantitative finance and mathematics
- High-frequency trading concepts
- Build 40+ trading strategies
Your Learning Journey
Phase 1
Foundation (Months 1-3): Markets, Python, Data Analysis, Technical Analysis
Phase 2
Strategy Development (Months 4-6): Trading Strategies, Backtesting, Optimization
Phase 3
Advanced Quant (Months 7-9): ML Trading, Advanced Strategies, Portfolio Management
Phase 4
Professional Trading (Months 10-12): Live Trading, HFT, Fund Management, Career
Career Progression
1
Junior Quant Analyst (after 3 months)
2
Algorithmic Trader (after 6 months)
3
Senior Quant Trader (after 9 months)
4
Quantitative Researcher / Fund Manager (after 12 months)
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 - Markets, Python, Data Analysis, Technical Analysis
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
- 24/7 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
Topics Covered
- Multi-strategy system design
- Complete backtesting pipeline
- Optimization and robustness testing
- Risk management integration
- Performance analysis
- Documentation and reporting
Projects You Build
- PHASE 2 CAPSTONE: Complete Algorithmic Trading System
- Requirements: 5+ strategies, backtested on 5+ years data, optimized, risk-managed, documented
- Option 1: Multi-timeframe trend following system
- Option 2: Market-neutral pairs trading portfolio
- Option 3: Crypto trading bot with multiple strategies
- Option 4: Options selling systematic strategy
Assessment
Phase 2 Exam - Strategy development, backtesting, optimization
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
Topics Covered
- Advanced quantitative system design
- ML model integration
- Portfolio-level strategy
- Advanced risk management
- Performance analysis
- Research documentation
Projects You Build
- PHASE 3 CAPSTONE: Machine Learning Trading Fund
- Requirements: ML models, multiple strategies, portfolio optimization, transaction costs, comprehensive backtesting
- Option 1: Multi-strategy ML-based hedge fund
- Option 2: Market-neutral quant fund with factor models
- Option 3: Crypto quant fund with ML and sentiment
- Option 4: Options trading fund with volatility strategies
Assessment
Phase 3 Exam - ML trading, advanced quant, portfolio management
Topics Covered
- Broker selection and comparison
- Interactive Brokers API (Python)
- Zerodha Kite API (India)
- Alpaca API (US)
- Binance API (Crypto)
- Account setup and API credentials
- Paper trading vs live trading
- Order management system (OMS) development
- Position management system
- Real-time data streaming
Projects You Build
- Broker API integration
- Live trading infrastructure
- Order management system
- Real-time data handler
- Position tracker
- Paper trading framework
- Live trading simulator
Practice & Assignments
Set up live trading for 5 strategies (paper trading)
Topics Covered
- Cloud deployment: AWS, GCP, Azure
- Virtual Private Server (VPS) setup
- Linux for trading systems
- Docker for trading applications
- Continuous deployment for strategies
- Database for trade logging: PostgreSQL, MongoDB
- Time-series database: InfluxDB
- Monitoring and alerting
- Logging best practices
- Strategy health checks
Projects You Build
- Cloud trading server setup
- Dockerized trading system
- Database integration
- Monitoring dashboard
- Alerting system
- Backup automation
- Production deployment pipeline
Practice & Assignments
Deploy 10 strategies to production environment
Topics Covered
- Live trading risk considerations
- Real-time risk monitoring
- Position limits and concentration
- Daily loss limits
- Maximum drawdown controls
- Correlation monitoring
- Portfolio VaR in real-time
- Circuit breakers implementation
- Emergency shutdown procedures
- Broker risk management settings
Projects You Build
- Real-time risk monitor
- Circuit breaker system
- Position limit enforcer
- Drawdown controller
- Emergency shutdown system
- Risk dashboard (live)
- Risk reporting automation
Practice & Assignments
Implement comprehensive risk systems
Topics Covered
- Real-time performance tracking
- P&L calculation and attribution
- Strategy-level performance
- Portfolio-level performance
- Benchmark comparison
- Risk-adjusted returns monitoring
- Sharpe ratio tracking
- Drawdown monitoring
- Win rate and profit factor
- Trade analysis and journaling
Projects You Build
- Live performance tracker
- P&L attribution system
- Trade journal automation
- Performance dashboard
- Automated reporting system
- Strategy health monitor
- Degradation detector
Practice & Assignments
Monitor live strategies, generate reports
Topics Covered
- Transitioning from paper to live
- Starting capital considerations
- Scaling strategy capital
- Psychology of live trading
- Emotional discipline
- Handling losses in live trading
- Position sizing for live trading
- Gradual scale-up approach
- Monitoring and adjustment
- When to stop a strategy
Projects You Build
- Live trading checklist
- Capital allocation plan
- Scaling framework
- Trading journal (psychological)
- Tax tracking system
- Business plan for trading
- Improvement tracking system
Practice & Assignments
Start live trading with small capital (optional)
Topics Covered
- Career options in quant finance
- Quantitative trader roles
- Quantitative researcher positions
- Quantitative developer roles
- Risk manager careers
- Portfolio manager positions
- Prop trading firms
- Hedge funds and asset managers
- Investment banks quant roles
- Starting a quant fund
Projects You Build
- Professional trading resume
- Strategy portfolio documentation
- Performance track record
- Research paper writing
- GitHub portfolio
- Trading blog/website
Practice & Assignments
Build professional presence
Topics Covered
- Quant interview process overview
- Probability and statistics questions
- Brain teasers and logic puzzles
- Coding interviews for quants
- Python algorithmic problems
- Trading strategy design questions
- Market making interview questions
- Risk management scenarios
- Portfolio optimization problems
- Behavioral interview preparation
Projects You Build
- Interview preparation guide
- Probability problem solutions (100+)
- Coding challenge practice
- Strategy presentation deck
- Mock interview recordings
- Behavioral question prep
Practice & Assignments
Solve 200+ quant interview problems
Topics Covered
- Setting up a prop trading desk
- Capital requirements
- Technology infrastructure
- Team structure and hiring
- Strategy development process
- Risk management framework
- Compliance and regulations
- Broker relationships
- Cost structure and profitability
- Scaling trading operations
Projects You Build
- Prop desk business plan
- Infrastructure planning
- Cost-benefit analysis
- Hiring and training plan
- Compliance checklist
- Operations manual
Practice & Assignments
Plan and document prop desk setup
Topics Covered
- Hedge fund structure and setup
- Legal and regulatory requirements
- Fund formation: LLC, LP structures
- Registration with regulators (SEBI, SEC)
- Investor pitch deck creation
- Due diligence preparation
- Fund administration
- Prime brokerage relationships
- Fundraising strategies
- AUM targets and scaling
Projects You Build
- Fund formation plan
- Investor pitch deck
- Due diligence package
- Fund policies and procedures
- Marketing materials
- Financial projections
- Regulatory compliance checklist
Practice & Assignments
Complete hedge fund launch plan
Topics Covered
- Staying current with quant research
- Academic papers and journals
- Quantitative finance books
- Online communities: QuantConnect, Quantopian archives
- Attending quant conferences
- Networking with other quants
- Contributing to open source quant projects
- Publishing research and strategies
- Teaching and mentoring
- Building personal brand in quant finance
Projects You Build
- Continuous learning plan
- Research paper reading list
- Community engagement strategy
- Open source contributions
- Personal research publication
- Mentorship program participation
Practice & Assignments
Develop lifelong learning system
Topics Covered
- Novel strategy research
- Combining multiple approaches
- Regime-adaptive systems
- Machine learning innovations
- Alternative data exploitation
- Cross-asset strategies
- International markets
- Emerging markets trading
- Cryptocurrency innovations
- DeFi trading opportunities
Projects You Build
- Original research project
- Novel strategy development
- Multi-asset system
- Innovation documentation
- Research paper writing
Practice & Assignments
Develop unique trading edge
Topics Covered
- Cloud-native trading systems
- Serverless architecture for trading
- Kubernetes for trading infrastructure
- Real-time data lakes
- Big data for trading
- GPU acceleration for backtesting
- Quantum computing for optimization
- Blockchain for settlement
- Advanced monitoring and observability
- Site reliability engineering for trading
Projects You Build
- Cloud-native trading platform
- Scalable infrastructure
- Big data pipeline
- Advanced monitoring system
- Technology roadmap
Practice & Assignments
Modernize trading infrastructure
Topics Covered
- Global trading regulations
- SEBI regulations (India)
- SEC regulations (USA)
- MiFID II (Europe)
- Algorithmic trading regulations
- Market manipulation rules
- Insider trading compliance
- Best execution requirements
- Audit trails and record keeping
- Compliance monitoring systems
Projects You Build
- Compliance framework
- Audit trail system
- Regulatory reporting automation
- Compliance monitoring dashboard
- Documentation system
Practice & Assignments
Ensure full regulatory compliance
Topics Covered
- Complete trading business setup
- Live trading deployment
- Performance tracking
- Client/investor reporting
- Continuous optimization
- Career positioning
Projects You Build
- FINAL CAPSTONE: Professional Algorithmic Trading Business
- Option 1: Multi-strategy quant fund (simulated AUM)
- Option 2: Proprietary trading desk
- Option 3: Algo trading as a service
- Option 4: Personal trading business
- Requirements: Live deployed, monitored, profitable (or promising), documented, scalable
Assessment
FINAL COMPREHENSIVE EXAM - Complete algorithmic trading mastery
Projects You'll Build
Build a professional portfolio with 100+ trading strategies and systems real-world projects.
Phase 1 25+ foundational projects - data analysis, indicators, technical analysis
Phase 2 30+ strategy projects - backtesting, optimization, various strategies
Phase 3 25+ advanced projects - ML models, HFT, portfolio management
Phase 4 20+ professional projects - live trading, infrastructure, business
Total 100+ trading projects from basics to professional systems
Weekly Learning Structure
Theory Videos
5-7 hours
Coding Practice
8-10 hours
Backtesting
4-6 hours
Research Reading
2-3 hours
Live Trading Practice
2-3 hours
Total Per Week
20-25 hours
Certification & Recognition
Phase Certificates
Certificate after each phase (4 total)
Final Certificate
Certified Algorithmic Trader
Specializations
Options in Crypto/Options/HFT/ML Trading
Track Record
Verified backtesting and live trading results
Portfolio
40+ documented strategies
Digital Badge
LinkedIn shareable credentials
Industry Recognized
Recognized by prop firms and hedge funds
Technologies & Skills You'll Master
Comprehensive coverage of the entire modern web development stack.
Programming
Python (expert), NumPy, Pandas, Matplotlib, OOP for trading
Markets
Stocks, Forex, Crypto, Options, Futures, Commodities
Technical Analysis
50+ indicators, chart patterns, multi-timeframe analysis
Strategy Development
Momentum, mean reversion, breakout, arbitrage, options
Backtesting
Backtrader, custom frameworks, walk-forward, optimization
Machine Learning
Scikit-learn, TensorFlow, PyTorch, feature engineering, NLP
Quantitative Finance
Statistics, portfolio theory, risk management, derivatives
Live Trading
Broker APIs, execution algorithms, real-time systems
Infrastructure
Cloud deployment, databases, monitoring, CI/CD
Risk Management
Position sizing, stop-loss, portfolio heat, VaR
Tools
Jupyter, Git, Docker, PostgreSQL, InfluxDB, Prometheus
Business
Fund management, compliance, investor relations, career development
Support & Resources
Live Sessions
Weekly live trading analysis and strategy sessions
Mentorship
1-on-1 guidance from professional quant traders
Community
Active community of algorithmic traders
Code Review
Expert review of trading strategies and code
Data Access
Historical and real-time data for practice
Cloud Credits
Credits for cloud deployment
Broker Support
Help with broker setup and API integration
Lifetime Access
All content, strategies, updates forever
Strategy Library
Access to 100+ tested strategies
Research Papers
Curated quantitative finance research
Career Outcomes & Opportunities
Transform your career with industry-ready skills and job placement support.
Prerequisites
Education
No formal degree required, basic math helpful
Trading Knowledge
Beginner-friendly, no prior trading experience needed
Programming
No coding experience required (taught from scratch)
Mathematics
High school math (statistics taught in course)
Capital
Not required for learning (paper trading available)
Equipment
Computer, internet, ~₹5,000/month for data and tools
Time Commitment
20-25 hours/week consistently
Age
18+ (due to broker account requirements)
Mindset
Analytical, patient, risk-aware, continuous learner
Who Is This Course For?
Manual Traders
Want to automate their trading strategies
Developers
Software engineers entering finance
Finance Professionals
Want quantitative and algo skills
Data Scientists
Applying ML to trading
Students
Engineering, math, finance backgrounds
Career Switchers
Entering quantitative finance
Entrepreneurs
Want to start trading business/fund
Quants
Enhance systematic trading skills
Investors
Want algorithmic portfolio management
Researchers
Academic interest in quantitative finance
Career Paths After Completion
Quantitative Trader
Algorithmic Trader
Quantitative Researcher
Quantitative Developer
Portfolio Manager (Quantitative)
Risk Manager (Quantitative)
Proprietary Trader
Hedge Fund Manager
Systematic Trading Strategist
High-Frequency Trader
Independent Algo Trader
Trading System Developer
Quant Consultant
Financial Engineer
Research Scientist (Finance)
Course Guarantees
Live Classes
Live, interactive classes with a real instructor, never pre-recorded videos.
Small Batches
Small batches only: group classes are capped at 10 students, with mini-batch (3 to 4 students) and personal 1-on-1 options.
Structured Curriculum
A structured, well-paced curriculum taught step by step, with hands-on practice in every session.
Doubt Support
Doubt support between classes over WhatsApp, so you are never left stuck.
Certificate
A course-completion certificate you can share.
Free Demo
A free demo class before you enrol, so you can decide with no pressure.
📸 Straight from our camera roll
Real students. Real moments. Real joy.
These are our actual student meetups. No stock photos, no filters. Swipe through and meet the community you'll be joining.
💛 Real families, real words
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.”
“The one step solution for my son. Modern Age Coders make learning coding so simple that kids love it.”
“Coding classes here make learning very interesting and conceptual. The teachers teach us in a very easy-to-understand and efficient manner.”
“One of the most wonderful education centres out there. Education is not limited to school syllabus but focuses on skill development. Learning here has been a wonderful journey and still continuing.”
“I highly recommend this computer coding class! The teachers are incredibly knowledgeable and passionate about coding. They make every session engaging and insightful.”
“My child Dhairya is really enjoying the Modern Age Coder IT classes. This is his first online class, and he eagerly looks forward to it.”
“Very good classes. Don't worry about coding—they teach the best, especially Shivam sir.”
“Very good classes. Makes learning very easy and interactive.”
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Hear it from our students
Real parents and students in their own words, on our public YouTube channel.
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