Algorithmic Trading & Quantitative Finance
Complete Algorithmic Trading Masterclass
From Manual Trading to Fully Automated Profitable Systems
Ready to Master Complete Algorithmic Trading Masterclass - Zero to Profitable Trading Systems?
Choose your plan and start your journey into the future of technology today.
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
- Market research on different instruments
- Paper trading practice (manual)
- Order type experiments
- Market analysis report
- Trading journal setup
💪 Practice
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
- 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
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
- Multi-asset data downloader
- Returns and volatility analyzer
- Correlation matrix for portfolio
- Interactive price charts
- Market data dashboard
- Historical performance analyzer
💪 Practice
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
- 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
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
- Trading strategy base class
- Portfolio manager class
- Order execution simulator
- Risk calculator class
- Complete OOP trading framework
- Modular backtesting engine
💪 Practice
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
- Statistical analysis toolkit
- Returns distribution analyzer
- Strategy performance metrics calculator
- Monte Carlo simulator for trading
- Risk metrics dashboard
- Hypothesis testing framework
💪 Practice
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
- 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
- Custom backtesting engine from scratch
- Backtrader strategy implementation
- Slippage and commission simulator
- Walk-forward testing framework
- Multi-timeframe backtester
- Optimization engine
💪 Practice
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
- 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
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
- Bollinger Bands reversion strategy
- RSI mean reversion bot
- Pairs trading system
- Cointegration scanner
- Statistical arbitrage strategy
- Market neutral portfolio
- Kalman filter pairs trader
💪 Practice
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
- 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
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
- Options calculator (Greeks, pricing)
- IV rank analyzer
- Covered call screener
- Iron Condor backtester
- Options strategy simulator
- Volatility trading system
💪 Practice
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
- Grid search optimizer
- Genetic algorithm optimizer
- Walk-forward optimization framework
- Parameter sensitivity analyzer
- Multi-objective optimizer
- Robust optimization system
- Strategy parameter dashboard
💪 Practice
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
- Position sizing calculator
- Kelly Criterion implementation
- Stop-loss optimizer
- Risk management system
- Portfolio heat monitor
- Drawdown controller
- Kill switch implementation
💪 Practice
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
- Portfolio optimizer (Markowitz)
- Risk parity portfolio
- Sector rotation system
- Multi-strategy portfolio manager
- Rebalancing algorithm
- Portfolio backtester
- Asset allocation dashboard
💪 Practice
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
- Crypto data collector
- Bitcoin trend following bot
- Crypto arbitrage scanner
- Altcoin momentum strategy
- Crypto mean reversion system
- Exchange arbitrage bot
- Crypto portfolio rebalancer
💪 Practice
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
- 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
- Feature engineering pipeline
- Label creation system
- ML data preparation framework
- Cross-validation for time series
- Trading-specific feature library
- Model evaluation toolkit
💪 Practice
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
- Price direction classifier
- Random Forest trading strategy
- XGBoost market predictor
- Ensemble trading model
- Market regime classifier
- ML signal generator
- Complete ML trading strategy
💪 Practice
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
- 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
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
- 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
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
- Web scraping trading signals
- Google Trends strategy
- Alternative data aggregator
- Factor model implementation
- Multi-factor strategy
- Quantitative research report
- Alpha generation framework
💪 Practice
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
- Order book visualizer
- Limit order book simulator
- Simple market making bot
- Order flow analyzer
- Tick data processor
- Microstructure research tools
- HFT backtesting framework
💪 Practice
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
- Volatility trading system
- VIX trading strategy
- Calendar spread optimizer
- Delta hedging bot
- Gamma scalping system
- Options portfolio manager
- Futures spread trader
💪 Practice
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
- VWAP execution algorithm
- TWAP execution bot
- Order slicing algorithm
- Transaction cost analyzer
- Slippage tracker
- Execution quality dashboard
- Smart order router (basic)
💪 Practice
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
- 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
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
- 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
- Broker API integration
- Live trading infrastructure
- Order management system
- Real-time data handler
- Position tracker
- Paper trading framework
- Live trading simulator
💪 Practice
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
- Cloud trading server setup
- Dockerized trading system
- Database integration
- Monitoring dashboard
- Alerting system
- Backup automation
- Production deployment pipeline
💪 Practice
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
- Real-time risk monitor
- Circuit breaker system
- Position limit enforcer
- Drawdown controller
- Emergency shutdown system
- Risk dashboard (live)
- Risk reporting automation
💪 Practice
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
- Live performance tracker
- P&L attribution system
- Trade journal automation
- Performance dashboard
- Automated reporting system
- Strategy health monitor
- Degradation detector
💪 Practice
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
- Live trading checklist
- Capital allocation plan
- Scaling framework
- Trading journal (psychological)
- Tax tracking system
- Business plan for trading
- Improvement tracking system
💪 Practice
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
- Professional trading resume
- Strategy portfolio documentation
- Performance track record
- Research paper writing
- GitHub portfolio
- Trading blog/website
💪 Practice
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
- Interview preparation guide
- Probability problem solutions (100+)
- Coding challenge practice
- Strategy presentation deck
- Mock interview recordings
- Behavioral question prep
💪 Practice
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
- Prop desk business plan
- Infrastructure planning
- Cost-benefit analysis
- Hiring and training plan
- Compliance checklist
- Operations manual
💪 Practice
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
- Fund formation plan
- Investor pitch deck
- Due diligence package
- Fund policies and procedures
- Marketing materials
- Financial projections
- Regulatory compliance checklist
💪 Practice
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
- Continuous learning plan
- Research paper reading list
- Community engagement strategy
- Open source contributions
- Personal research publication
- Mentorship program participation
💪 Practice
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
- Original research project
- Novel strategy development
- Multi-asset system
- Innovation documentation
- Research paper writing
💪 Practice
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
- Cloud-native trading platform
- Scalable infrastructure
- Big data pipeline
- Advanced monitoring system
- Technology roadmap
💪 Practice
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
- Compliance framework
- Audit trail system
- Regulatory reporting automation
- Compliance monitoring dashboard
- Documentation system
💪 Practice
Ensure full regulatory compliance
📚 Topics Covered
- Complete trading business setup
- Live trading deployment
- Performance tracking
- Client/investor reporting
- Continuous optimization
- Career positioning
🚀 Projects
- 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, ~₹5000/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)
Salary Expectations
Competitive industry salaries
Course Guarantees
Money Back
30-day 100% money-back guarantee
Lifetime Access
All content, strategies, updates forever
Data Access
Historical and real-time data for practice
Strategy Library
100+ tested strategies
Community
Lifetime access to trader community
Mentorship
Expert guidance throughout
Job Support
Resume review, interview prep for quant roles
Fund Guidance
Help starting prop desk or fund (for qualified students)
Code Repository
Complete code library for all projects
Paper Trading
Unlimited paper trading practice
Broker Support
Assistance with live account setup