53módulos
1470lecciones
13 GBde contenido
Todoslos niveles
Temario del curso
53 módulos · 1470 clases · 81 h 2 min de vídeo
Módulo 1 · Automated Trading with IBridgePy using Interactive Brokers Platform11 clases · 37 min
- Introduction to IBridgePy · Quantra Features and Guidance
- Introduction to IBridgePy · Introduction to IBridgePy
- Installation Steps · Python Environment
- Code Structure · Code Structure
- Fetch Data · How to Fetch Real-Time Data？
- Fetch Data · How to Fetch Historical Data？
- Orders Management · How to Place and Cancel Order？
- Orders Management · How to Retrieve Open Orders？
- Portfolio Management · Positions Tracking
- Trading Strategy Implementation · Simple Moving Average Crossover
- Run Codes Locally on Your Machine · Python Installation Overview
Módulo 2 · Introduction to Data Science13 clases · 34 min
- Introduction · Introduction to Data Science
- Introduction · Concepts Covered in This Course
- Introduction · Quantra Features and Guidance
- Problem Statement · Asking right questions for Problem Statement
- Data Collection · Primary and Secondary Data Collection Methods
- Data Quality and Remediation · Factors Causing Data Quality Issues
- Data Quality and Remediation · Data Quality Issues and Remediation
- Data Analysis · What is Data Analysis#
- Exploratory Data Analysis in Python · How to Use Jupyter Notebook？
- Data Modelling · What is Data Modelling#
- Communicating Results · Communicating Results
- Run Codes Locally on Your Machine · Python Installation Overview
- Summary · Summary
Módulo 3 · Investing in Global Markets10 clases · 37 min
- Why Invest in Global Markets · Why Invest in Global Markets
- How to Invest in Global Markets · How to Invest in Global Markets
- How to Invest in Global Markets · Direct Investment & Mutual Funds
- How to Invest in Global Markets · Investment Advisors & ETF
- How to Invest in Global Markets · Popular ETFs
- How to Invest in Global Markets · Comparison of Investment Options
- Key Points to Note Before Investing · Points to Note Before Investing
- Key Points to Note Before Investing · Regulations & Cost
- Key Points to Note Before Investing · Taxes
- Summary · Summary
Módulo 4 · Quant Interview Questions Preparation7 clases · 45 min
- Introduction · Introduction
- Introduction · Resume Tips
- Aptitude · Podcast： Gaurav
- Python Basic · Podcast： Rob
- Time Series · Podcast： Rajib
- Machine Learning · Podcast： Sameer
- Machine Learning · Podcast： Dr. Ernest P Chan
Módulo 5 · Stock Market Basics21 clases · 1 h 9 min
- Financial Markets · Welcome To The Course!
- Financial Markets · Financial Markets
- Financial Markets · Need For Financial Markets
- Financial Instruments · Introduction To Financial Instruments
- Financial Instruments · Introduction To Financial Instruments
- Financial Instruments · Stocks
- Primary And Secondary Markets · Primary And Secondary Markets
- Financial Intermediaries · Introduction To Various Intermediaries
- Financial Intermediaries · Brokers
- Financial Intermediaries · Central Securities Depository
- Financial Intermediaries · Stock Exchange
- Financial Intermediaries · Clearinghouses
- Financial Intermediaries · Financial Regulators
- Tracking Markets · Market Indices
- Market Participants · Introduction
- Market Participants · Speculators
- Market Participants · Hedgers
- Market Participants · Arbitrageurs
- Types Of Markets · Various Financial Markets
- Market Terminology · Glossary
- Summary · Summary
Módulo 6 · Getting Started with Algorithmic Trading!28 clases · 1 h 14 min
- Introduction · Introduction
- Introduction · Certification Process
- Introduction · Course Structure
- Introduction · Quantra Features and Guidance
- What is Algorithmic Trading？ · What is Algorithmic Trading？
- What is Algorithmic Trading？ · Direct Market Access
- What is Algorithmic Trading？ · What is High-frequency trading？
- Why Algorithmic Trading？ · Why Go Algo (part 1)
- Why Algorithmic Trading？ · Why Go Algo (part 2)
- Why Algorithmic Trading？ · Why Go Algo (part 3)
- Why Algorithmic Trading？ · How to Start Algorithmic Trading
- Available Platforms & Languages · Available Platforms & Languages
- Strategy Paradigms · Types of Algorithmic Trading Strategies
- Strategy Paradigms · Market Making Strategy
- Strategy Paradigms · Statistical Arbitrage
- Strategy Paradigms · Momentum Based Strategies
- Strategy Paradigms · Machine Readable News & Ml
- Introduction to Python · Need for Python
- Introduction to Python · How to Use Jupyter Notebook？
- Introduction to Python · Pandas Dataframe
- Financial Market Data and Visualisation · Importing Data
- Moving Average Crossover Strategy · Moving Average Crossover Strategy
- Live Trading on Blueshift · Section Overview
- Live Trading on Blueshift · Live Trading Overview
- Live Trading on Blueshift · Blueshift Code Structure
- Live Trading on Blueshift · Backtest and Live Trade on Blueshift
- Run Codes Locally on Your Machine · Python Installation Overview
- Summary · Summary
Módulo 7 · Python for Trading： Basic25 clases · 1 h 22 min
- Welcome · Introduction
- Welcome · If Algo, Then How Does Python Contribute？
- Welcome · Quantra Features and Guidance
- Hello Python · Python Environment
- Hello Python · Variables, Object References and Operators
- Hello Python · How to Use Jupyter Notebook？
- Hello Python · Learn Modules, Packages & Libraries
- Expressions · Introduction to Time Value of Money
- Expressions · Learn Compounding in Time Value of Money
- Python Data Structures · What Are Lists？
- Python Data Structures · What Are Dictionaries？
- Python Data Structures · What Are Tuples and Sets？
- Importing Data and Data Visualisation · What is Time Series Data？
- Importing Data and Data Visualisation · How to Import Time series data？
- Importing Data and Data Visualisation · How to Plot Market Data？
- Importing Data and Data Visualisation · What are Candlesticks？
- Functions · What Are Functions？
- Numpy · What Are NumPy Arrays？
- Pandas · Pandas and Data Manipulation
- Conditional Statements and Loops · What Are Conditional Statements and Loops？
- Live Trading on Blueshift · Section Overview
- Live Trading on Blueshift · Live Trading Overview
- Live Trading on Blueshift · Blueshift Code Structure
- Live Trading on Blueshift · Backtest and Live Trade on Blueshift
- Run Codes Locally on Your Machine · Python Installation Overview
Módulo 8 · Getting Market Data： Stocks, Crypto, News & Fundamental3 clases · 7 min
- Course Introduction · Introduction to the Course
- Course Introduction · Course Structure
- Run Codes Locally on Your Machine · Python Installation Overview
Módulo 9 · Day Trading Strategies for Beginners32 clases · 1 h 29 min
- Introduction to the Course · Prologue
- Introduction to the Course · Course Overview
- Introduction to the Course · Course Structure
- Introduction to the Course · Quantra Features and Guidance
- Introduction to Python · Need for Python
- Introduction to Python · How to Use Jupyter Notebook？
- Introduction to Python · Pandas Dataframe
- Financial Market Data and Visualisation · Importing Data
- Basics of Financial Markets · Precap of Financial Markets
- Basics of Financial Markets · Introduction to Financial Markets
- Basics of Financial Markets · Introduction to Financial Instruments
- Basics of Financial Markets · Financial Market Jargon
- Intraday Trading · Overview of Intraday Trading
- Intraday Trading · How to Create a Stock Universe
- Momentum Trading Strategies · Momentum Trading Strategies
- Momentum Trading Strategies · Gap-Up and Gap-Down
- Live Trading on Blueshift · Section Overview
- Live Trading on Blueshift · Live Trading Overview
- Live Trading on Blueshift · Blueshift Code Structure
- Live Trading on Blueshift · Backtest and Live Trade on Blueshift
- Post Earnings Announcement Drift · Post Earnings Announcement Drift
- Scalping · Introduction to Scalping
- Scalping · ATR Scalping Strategy
- Scalping · Exit Optimisation
- High Frequency Trading Strategy · Exchange Order Types
- High Frequency Trading Strategy · Order Book Parameters
- High Frequency Trading Strategy · Order Book
- High Frequency Trading Strategy · Ticking Strategy
- Risk Management · Risk Management
- Risk Management · Podcast： Brian Blandin
- Run Codes Locally on Your Machine · Python Installation Overview
- Course Summary · Course Summary
Módulo 10 · Volatility Trading Strategies for Beginners37 clases · 2 h 28 min
- Introduction · Introduction
- Introduction · Quantra Features and Guidance
- Entry Signals · Section Overview
- Entry Signals · Moving Average Crossover
- Entry Signals · How to Use Jupyter Notebook？
- ATR · Measuring Volatility using ATR
- SL & TP using ATR · ATR to Determine Exits
- Live Trading on Blueshift · Section Overview
- Live Trading on Blueshift · Live Trading Overview
- Live Trading on Blueshift · Blueshift Code Structure
- Live Trading on Blueshift · Backtest and Live Trade on Blueshift
- Measuring Volatility Using Standard Deviation · Standard Deviation
- Measuring Volatility Using Standard Deviation · Calculation of Standard Deviation
- Applications of Standard Deviation In Trading · How to Use Standard Deviation In Trading？
- Bollinger Bands · Bollinger Bands Calculation
- Bollinger Bands · Interpretation of Bollinger Bands
- Bollinger Bands Phases · Interpretation of Bollinger Bands Phases
- Breakout Strategy · Breakout Strategy Using Bollinger Phases
- VIX · Introduction to VIX
- VIX · Types of Volatility
- VIX · Interpretation of VIX
- VIX · Types of VIX
- VIX · Webinar Snippet - Introduction to Volatility
- VIX · Webinar Snippet - Why Is VIX Called the Fear Index？
- More on VIX · Calculation of VIX
- More on VIX · Properties of VIX
- More on VIX · VIX Derivatives
- Hedging Using VIX · Hedging With VIX ETF
- Selective Long on VIX · Selective Long VIX Strategy
- VIX Spread · VIX Spread Concept
- Understanding Beta · Beta and Its Interpretation
- Calculating Beta · How to Calculate Beta
- Betting Against Beta · Application of Beta
- Betting Against Beta · Betting Against Beta
- Research on BAB · Research on BAB
- Run Codes Locally on Your Machine · Python Installation Overview
- Course Summary · Summary
Módulo 11 · Backtesting Trading Strategies37 clases · 2 h 9 min
- Introduction · Introduction
- Introduction · Quantra Features and Guidance
- Backtesting · What is Backtesting？
- Backtesting · Backtesting vs Simulation
- Backtesting · Backtesting Process
- Financial Data · Financial Data
- Financial Data · Financial Data Storage
- Financial Data · How to Use Jupyter Notebook？
- Financial Data · Limitations of Financial Data
- Data Pre-Processing · Data Pre-Processing
- Data Pre-Processing · Survivorship Bias
- Trading Rules · Developing Trading Rules
- Trading Rules · Entry and Exit Rules
- Trade Level Analytics · Trade Level Analytics I
- Trade Level Analytics · Trade Level Analytics II
- Performance Metrics · Equity Curve and CAGR
- Performance Metrics · Sharpe Ratio
- Performance Metrics · Maximum Drawdown
- Risk Management · Stop-Loss and Take-Profit
- Risk Management · Guidelines For Setting Stop-Loss and Take-Profit
- Transaction Costs and Slippage · Transaction Costs and Slippage
- Paper Trading · Introduction to Paper Trading
- Paper Trading · Things to Keep in Mind While Paper Trading
- Live Trading on Blueshift · Section Overview
- Live Trading on Blueshift · Live Trading Overview
- Live Trading on Blueshift · Blueshift Code Structure
- Live Trading on Blueshift · Backtest and Live Trade on Blueshift
- Common Pitfalls in Backtesting · Biases to Avoid
- Common Pitfalls in Backtesting · Common Mistakes Done With Trading Volume
- Common Pitfalls in Backtesting · Data Snooping
- Common Pitfalls in Backtesting · Over Reliance on Backtesting
- FAQs · Ideal Time Period for Backtesting
- FAQs · Number of Assets to Backtest On
- FAQs · Risk Metrics and Sharpe Ratio
- FAQs · Paper and Live Trading
- Run Codes Locally on Your Machine · Python Installation Overview
- Course Summary · Summary
Módulo 12 · Event Driven Trading Strategies29 clases · 1 h 26 min
- Introduction to the Course · Prologue
- Introduction to the Course · Course Structure
- Introduction to the Course · Quantra Features and Guidance
- Introduction to Event Trading Strategies · Seasonal Event-driven Trading Strategies
- Introduction to Event Trading Strategies · Theory Behind Event-driven Trading Strategies
- Turn of Month Effect in Equities · Precap of Calendar Anomalies in Equities
- Turn of Month Effect in Equities · Turn of Months Effect
- Turn of Month Effect in Equities Code · How to Use Jupyter Notebook？
- Live Trading on Blueshift · Section Overview
- Live Trading on Blueshift · Live Trading Overview
- Live Trading on Blueshift · Blueshift Code Structure
- Live Trading on Blueshift · Backtest and Live Trade on Blueshift
- Payday Effect in Equities · Payday Effect
- FED Day Effect in Equities · FED Day Effect
- Options Expiration Effect in Equities · Options Expiration Effect
- Auction Trading Effect in Fixed Income · Auction Trading Effect
- End of the Month Effect in Fixed Income · End of the Month Effect
- Calendar Effect in Volatility Market · VIX Futures Expiration Effect
- Calendar Effect in Volatility Market · VIX Futures Expiration Enhancement
- December Effect in Volatility Market · December Seasonality Effect
- Composite Strategy · Introduction to Composite Seasonal Strategy
- Composite Strategy · Composite Strategy - Equal Weighted
- Composite Strategy · Composite Strategy - Volatility Weighted
- Composite Strategy · Composite Strategy - Enhanced Volatility
- Composite Strategy Enhancement · Effect of Trading Cost
- Composite Strategy Enhancement · Composite Strategy Improvement
- Effect of COVID-19 · Effect of COVID-19
- Run Codes Locally on Your Machine · Python Installation Overview
- Course Summary · Course Summary
Módulo 13 · Financial Time Series Analysis for Trading50 clases · 2 h 31 min
- Introduction · Course Introduction
- Introduction · Course Structure
- Introduction · Quantra Features and Guidance
- What is Time Series？ · Introduction to Time Series
- What is Time Series？ · Why is Time Series Analysis Required？
- What is Time Series？ · When Is Time Series Analysis Not Required？
- Simple and Cumulative Returns · Introduction to Returns
- Simple and Cumulative Returns · Cumulative Returns
- Simple and Cumulative Returns · How to Use Jupyter Notebook？
- Log Returns · Log Prices
- Log Returns · Log Returns
- Components of Time Series · Components of Time Series
- Components of Time Series · Trending Time Series
- Components of Time Series · Mean Reverting Time Series
- Components of Time Series · Cyclical Time Series
- Components of Time Series · Seasonal Time Series
- Linear Regression · Linear Regression Fundamentals
- Types of Errors · Types of Error Calculations
- Goodness of Fit · Introduction to Goodness of Fit
- Multivariate Linear Regression · Multivariate Linear Regression
- Multivariate Linear Regression · Limitations and Advantages of Linear Regression
- Correlation Analysis · Correlation and Covariance
- Autocorrelation and Partial Autocorrelation · What is Autocorrelation？
- Autocorrelation and Partial Autocorrelation · What is Partial Autocorrelation？
- Noise · Noise
- Autoregressive Model · Overview of Part II
- Autoregressive Model · Autoregressive Model - I
- Autoregressive Model · Autoregressive Model - II
- Moving Average Model · Moving Average Model
- ARMA · ARMA Model
- Stationarity · Stationarity
- ARIMA Model · ARIMA Model
- Live Trading on Blueshift · Section Overview
- Live Trading on Blueshift · Live Trading Overview
- Live Trading on Blueshift · Blueshift Code Structure
- Live Trading on Blueshift · Backtest and Live Trade on Blueshift
- Introduction to Volatility · Fundamentals of Volatility
- Introduction to Volatility · Importance of Volatility
- Stylised Facts and Importance of Volatility · Stylised Facts of Volatility
- Stylised Facts and Importance of Volatility · Applications of Volatility
- ARCH · Need for the ARCH and GARCH model
- ARCH · Introduction to the ARCH Model
- ARCH · Equation of the ARCH Model
- ARCH · Performance Analysis of the ARCH Model
- GARCH · Implementation of the GARCH Model
- GARCH · Performance Analysis of the GARCH Model
- Limitations · Limitations of Time Series Analysis
- Future Enhancements · Future Enhancements
- Run Codes Locally on Your Machine · Python Installation Overview
- Course Summary · Course Summary
Módulo 14 · Statistical Arbitrage Trading22 clases · 1 h 9 min
- Definition and Background · Introduction of the Course
- Definition and Background · Certification Process
- Definition and Background · Quantra Features and Guidance
- Definition and Background · Arbitrage Strategies in Commodities
- Definition and Background · What is Statistical Arbitrage？
- Statistical Concepts in Pairs Trading · Mean Reversion and Z-Score Overview
- Statistical Concepts in Pairs Trading · What is Cointegration？
- Statistical Concepts in Pairs Trading · How to Use Jupyter Notebook？
- Statistical Concepts in Pairs Trading · How to Select Pairs？
- Pairs Trading Strategy in Excel · Check for Cointegration of Pairs
- Pairs Trading Strategy in Excel · Generating Buy⧸Sell Signals： I
- Pairs Trading Strategy in Excel · Generating Buy⧸Sell Signals： II
- Pairs Trading Strategy in Python · Import Libraries and Initialise Variables
- Pairs Trading Strategy in Python · Define Functions
- Pairs Trading Strategy in Python · Execute Pairs Trading Strategy
- Live Trading on Blueshift · Section Overview
- Live Trading on Blueshift · Live Trading Overview
- Live Trading on Blueshift · Blueshift Code Structure
- Live Trading on Blueshift · Backtest and Live Trade on Blueshift
- Managing Risks in Stat Arb · Risks in Statistical Arbitrage
- Managing Risks in Stat Arb · Course Summary
- Run Codes Locally on Your Machine · Python Installation Overview
Módulo 15 · Candlestick Patterns based Automated Trading28 clases · 1 h 23 min
- Introduction · Introduction
- Introduction · Quantra Features and Guidance
- Candlestick Patterns · All About Candlesticks
- Candlestick Patterns · What are Candlestick Patterns？
- Bullish Marubozu Pattern · Bullish Marubozu
- Bullish Marubozu Pattern · Marubozu in Action
- Bullish Marubozu Pattern · How to Use Jupyter Notebook？
- Live Trading on Blueshift · Section Overview
- Live Trading on Blueshift · Live Trading Overview
- Live Trading on Blueshift · Blueshift Code Structure
- Live Trading on Blueshift · Backtest and Live Trade on Blueshift
- Bearish Marubozu Pattern · Bearish Marubozu
- Hammer Candlestick Pattern · Hammer Patterns
- Hammer Candlestick Pattern · Hammer Pattern in Action
- Hanging Man Candlestick Pattern · Hanging Man
- Hanging Man Candlestick Pattern · Enhanced Hanging Man
- Shooting Star Candlestick Pattern · Shooting Star
- Doji Candlestick Pattern · Doji Pattern
- Doji Candlestick Pattern · Doji in Action
- Engulfing Patterns · Bullish Engulfing Pattern
- Engulfing Patterns · Bearish Engulfing Pattern
- Piercing Pattern · Piercing Pattern
- Piercing Pattern · Piercing Pattern in Action
- Dark Cloud Cover · Dark Cloud Cover
- Combining Candlestick Patterns · Combining Candlestick Patterns
- Limitations of Candlestick Patterns · Limitations of Candlestick Patterns
- Run Codes Locally on Your Machine · Python Installation Overview
- Course Summary · Summary
Módulo 16 · Swing Trading Strategies35 clases · 1 h 40 min
- Introduction · Course Introduction
- Introduction · Course Structure
- Introduction · Quantra Features and Guidance
- Swing Trading Overview · What is Swing Trading？
- Swing Trading Overview · Properties of Swing Trading
- Swing Trading Style · Swing Trading Styles
- Introduction to Python · Need for Python
- Introduction to Python · How to Use Jupyter Notebook？
- Introduction to Python · Pandas Dataframe
- Financial Market Data and Visualisation · Importing Data
- Strategic Plan · Strategic Plan
- Technical Analysis in Trading · Technical Analysis in Trading
- Technical Analysis in Trading · Types of Technical Indicators
- MACD · MACD Entry Points
- MACD · MACD Line & Signal Line
- MACD · MACD Histogram
- Exit Strategy · Need of Exit Rules
- Exit Strategy · Exit Rules
- Introduction to Backtesting · What is Backtesting？
- Introduction to Backtesting · How to do Backtesting？
- Different Performance Measures · Different Performance Measures
- Different Performance Measures · Measuring Risk
- Live Trading on Blueshift · Section Overview
- Live Trading on Blueshift · Live Trading Overview
- Live Trading on Blueshift · Blueshift Code Structure
- Live Trading on Blueshift · Backtest and Live Trade on Blueshift
- Optimum Number of Indicators · Optimum Number of Indicators
- Williams Fractals · Williams Fractals
- Stock Screener · Need for Screening Stocks
- Stock Screener · Identify Trending Stocks
- Stock Screener · Identifying Direction of Trend
- Risk Management · Risk Management
- Risk Management · Position Sizing
- Run Codes Locally on Your Machine · Python Installation Overview
- Course Summary · Course Summary
Módulo 17 · Technical Indicators Strategies in Python42 clases · 2 h 34 min
- Introduction · Introduction to the Course
- Introduction · Quantra Features and Guidance
- Principles of Technical Analysis · Principle of Technical Analysis
- Principles of Technical Analysis · Why Technical Analysis Gets a Bad Reputation？
- Trend is your Friend · Trend is your Friend
- Moving Average · Simple Moving Average
- Moving Average · How to Use Jupyter Notebook？
- Transaction Costs and Slippage · Transaction Costs and Slippage
- Live Trading on Blueshift · Section Overview
- Live Trading on Blueshift · Live Trading Overview
- Live Trading on Blueshift · Blueshift Code Structure
- Live Trading on Blueshift · Backtest and Live Trade on Blueshift
- Weighted Moving Average · Weighted Moving Average
- Exponential Moving Average · Exponential Moving Average
- Multiple Moving Averages · Moving Average Crossovers
- MACD · MACD Entry Points
- MACD · MACD Line & Signal Line
- MACD · MACD Histogram
- ROC · ROC
- Intuition and Interpretation of RSI · Intuition of RSI Indicator
- Intuition and Interpretation of RSI · Interpret Values of RSI
- Properties and Practical Application of RSI · Properties of RSI Indicator
- Properties and Practical Application of RSI · RSI in Action
- Volume · Spikes in Volume
- Volume · On-Balance Volume
- Chaikin A⧸D · Chaikin A⧸D
- Limitations of Chaikin A⧸D and On-Balance Volume · Limitations of OBV and Chaikin A⧸D
- Putting It All Together · Combing Indicators
- Putting It All Together · Chaikin Oscillator with ROC
- Multiple Timeframes · Multiple Timeframes
- ATR · ATR
- Risk Management · ATR-Based SL & TP
- Market Breadth Analysis · Need of Market Breadth Analysis
- McClellan Indicator · McClellan Indicator
- Application of McClellan Indicator · Application of McClellan Indicator
- Application of McClellan Indicator · Can McClellan Indicator Predict a Market Crash？
- Calculation of TRIN Indicator · TRIN Indicator
- TRIN Indicator Based Strategy · TRIN Strategy
- Creation of a Screener Using Technical Indicators · Creation of a Screener Using Technical Indicators
- Five Secrets of Successful Traders · Five Secrets of Successful Traders
- Run Codes Locally on Your Machine · Python Installation Overview
- Summary · Summary of the Course
Módulo 18 · Price Action Trading Strategies Using Python33 clases · 1 h 52 min
- Introduction · Introduction
- Introduction · Quantra Features and Guidance
- Basics of Price Action Trading · Price Action Trading
- Supply and Demand Analysis · Supply and Demand Analysis
- Head and Shoulders Pattern · Head and Shoulders Pattern
- Head and Shoulders Pattern · Trading Head and Shoulders Pattern
- FAQs on Head and Shoulders Pattern · Variations in Shoulders
- FAQs on Head and Shoulders Pattern · Commonly Asked Questions
- Detecting Head and Shoulders Pattern · How to Use Jupyter Notebook？
- Transaction Costs and Slippage · Transaction Costs and Slippage
- Inverse Head and Shoulders Pattern · Inverse Head and Shoulders Pattern
- Double Top Pattern · Double Top Pattern
- Continuation Patterns · Continuation Patterns
- Triangle Pattern · Triangle Pattern
- Flag Pattern · Flag Pattern
- Pennant Pattern · Pennant Pattern
- Support and Resistance · Support and Resistance
- Strategy Using Support and Resistance · Support and Resistance in Action
- Live Trading on Blueshift · Section Overview
- Live Trading on Blueshift · Live Trading Overview
- Live Trading on Blueshift · Blueshift Code Structure
- Live Trading on Blueshift · Backtest and Live Trade on Blueshift
- Pivot Points · Pivot Points
- Types of Pivot Points · Types of Pivot Points
- Woodie's Range Trading · Range Trading using Woodie's Pivots
- Woodie's Trend Trading · Woodie's Trend Trading
- Camarilla Trend Trading · Trend Based Strategy Using Camarilla Pivots
- Fibonacci Ratios · Fibonacci Ratios
- Fibonacci in Action · Fibonacci in Action
- Fibonacci Retracement Strategy · Fibonacci Retracement Strategy
- Fibonacci Retracement Strategy · FAQs on Fibonacci Strategy
- Short-Selling with Fibonacci Retracements · Short Selling with Fibonacci
- Run Codes Locally on Your Machine · Python Installation Overview
Módulo 19 · Python For Trading!23 clases · 1 h 20 min
- Introduction to Course · Welcome to the World of Python
- Introduction to Course · Introduction to the Course
- Introduction to Course · Certification Process
- Introduction to Course · Quantra Features and Guidance
- Introduction to Python · Python Basics
- Introduction to Python · How to Use Jupyter Notebook？
- Functions, Variables and Objects · What Are Objects？
- Functions, Variables and Objects · What Are Containers and Namespaces？
- Functions, Variables and Objects · Introduction of Classes
- Data Structures： Lists and Dict · What Are Lists？
- Data Structures： Lists and Dict · What Are Dictionaries？
- Data Structures： Lists and Dict · What Are Tuples and Sets？
- Data Structures： Series and Dataframe · What Are Data Structures in Pandas？
- Financial Market Data · Importing Data
- Dealing With Financial Data · What is Data Processing in Trading？
- Data Visualisation · What are Candlesticks？
- Backtesting · What is Backtesting？
- Performance Metrics · What is Performance Metrics？
- Live Trading on Blueshift · Section Overview
- Live Trading on Blueshift · Live Trading Overview
- Live Trading on Blueshift · Blueshift Code Structure
- Live Trading on Blueshift · Backtest and Live Trade on Blueshift
- Run Codes Locally on Your Machine · Python Installation Overview
Módulo 20 · Quantitative Trading Strategies and Models20 clases · 1 h 28 min
- Introduction to Quantitative Trading · Introduction to Quantitative Trading
- Introduction to Quantitative Trading · Quantra Features and Guidance
- Introduction to Quantitative Trading · Definition of Quantitative Trading
- Technical Trading Strategies · Volume Reversals and Fibonacci Retracements
- Technical Trading Strategies · How to Use Jupyter Notebook？
- Technical Trading Strategies · Introduction to Trend and Volatility
- Technical Trading Strategies · Analysing Price Breakouts with Bollinger Band
- Live Trading on Blueshift · Section Overview
- Live Trading on Blueshift · Live Trading Overview
- Live Trading on Blueshift · Blueshift Code Structure
- Live Trading on Blueshift · Backtest and Live Trade on Blueshift
- Econometric Models · Introduction to Heteroskedasticity & Autocorr
- Econometric Models · Time Series & Autoregressive Model
- Econometric Models · Understanding the ARIMA Model
- Econometric Models · Predicting volatility using GARCH Model
- Quantitative Trading Strategies for Options · Introduction to Options Greeks
- Quantitative Trading Strategies for Options · Building a Delta Neutral portfolio with Gamma
- Quantitative Trading Strategies for Options · Using Gamma Scalping to Solve Negative Theta
- Run Codes Locally on Your Machine · Python Installation Overview
- Summary · Course Recap
Módulo 21 · Short Selling in Trading29 clases · 2 h 6 min
- Introduction · About the Author
- Introduction · About the Course
- Introduction · Quantra Features and Guidance
- Introduction · Introduction to Short Selling
- Short Selling · Short Selling
- Short Selling · Long-Short Strategy
- Relative Series · Relative Series
- Relative Series · Calculating Relative Series
- Relative Series · How to Use Jupyter Notebook？
- Regime Definition · Regime Definition
- Regime Change Detection · Breakout⧸ Breakdown Model
- Regime Change Detection · Crossover Model
- Floor and Ceiling · Floor and Ceiling
- Floor and Ceiling · Swings
- Regime Methods · Compare Regime Methods
- Stock Classification · Classify Stocks to Bulls or Bears
- Strategy Creation · Overview of Strategy Creation
- Strategy Creation · Strategy
- Strategy Creation · Strategy Logic
- Live Trading on Blueshift · Section Overview
- Live Trading on Blueshift · Live Trading Overview
- Live Trading on Blueshift · Blueshift Code Structure
- Live Trading on Blueshift · Backtest and Live Trade on Blueshift
- Stop Loss and Position Sizing · Performance Metrics
- Stop Loss and Position Sizing · Optimisation
- Stop Loss and Position Sizing · Stop Loss and Position Sizing
- Run Codes Locally on Your Machine · Python Installation Overview
- Course Summary · Course Summary
- Course Summary · Short Selling in Current Market
Módulo 22 · Options Trading Strategies In Python： Basic13 clases · 46 min
- Know Your Options! · Introduction
- Know Your Options! · Quantra Features and Guidance
- Know Your Options! · Options Terminology
- Know Your Options! · Put Options
- Know Your Options! · How to Use Jupyter Notebook？
- Know Your Options! · Call Options
- Options Nomenclature · Moneyness
- Options Nomenclature · Put-Call Parity
- Types of Volatility · Volatility
- Options Trading Strategies · Delta Trading Strategies
- Options Trading Strategies · Hedging With Options
- Run Codes Locally on Your Machine · Python Installation Overview
- Wrapping Up! · Summary
Módulo 23 · Futures Trading： Concepts & Strategies54 clases · 2 h 56 min
- Introduction · Futures Trading Introduction
- Introduction · Introduction by Andreas Clenow
- Introduction · Course Structure
- Introduction · Quantra Features and Guidance
- Futures Contract · What Makes Futures Unique？
- Standardisation & Clearing · Standardisation
- Standardisation & Clearing · Clearing
- Futures Specific Properties · Futures Specific Properties I
- Futures Specific Properties · Futures Specific Properties II
- Futures Profit and Loss · Futures PnL Calculation
- Futures Profit and Loss · How to Use Jupyter Notebook？
- Futures Profit and Loss · Futures and Currency Exposure
- Futures Market · Futures Sectors： Overview
- Futures Market · Futures in the Commodity Sector
- Futures Dataset · Futures Data
- Futures Dataset · The Issue of Limited Life Span
- Futures Dataset · Price Difference in Futures Contracts
- Futures Continuations · Default Futures Continuations
- Futures Continuations · Other Methods of Futures Continuation
- Analysing Tradable Assets · Trade What You Analyse
- Analysing Tradable Assets · Futures Trading Concept
- Trend Following Introduction · Trend Following Background
- Trend Following Introduction · Principles of Trend Following
- Trend Following Entries · Trend Following Entries
- Risk Management · Financial Risk Primer
- Risk Management · Measuring Financial Risk Using Volatility
- Risk Management · Position Allocation
- Trend Following Exits · Trend Following Exits
- Trend Following Exits · Setting the Stop Distance
- Trend Following Analysis on Single Markets · Trend Following Rules
- Trend Following Analysis on Single Markets · Trend Following on Single Markets
- Diversification in Trend Following · The Power of Diversification
- Strategy Analysis · Strategy Analysis
- Strategy Analysis · Trend Following Trades
- Strategy Analysis · Limitations of Trend Following Trades
- Counter Trend Models · Counter Trend Models
- Counter Trend Entries · Counter Trend Entries
- Counter Trend Exits · Counter Trend Exits
- Counter Trend Strategy Analysis · Counter Trend Strategy Analysis
- Term Structure · Futures Price and Delivery Dates
- Term Structure · Introduction to Term Structure
- Term Structure · Quantifying Term Structure
- Term Structure · Term Structure Concept
- Term Structure Trading · Contract Selection
- Term Structure Trading · Calendar Spread Strategy
- Term Structure Trading · Trading Term Structure
- Term Structure Trading · Term Structure Strategy Analysis
- Term Structure Trading · Term Structure Strategies
- Pushing Diversification Further · Pushing Diversification Further
- Run Codes Locally on Your Machine · Python Installation Overview
- Live Trading on IBridgePy · Section Overview
- Live Trading on IBridgePy · Live Trading Overview
- Live Trading on IBridgePy · Code Structure
- Course Summary · Conclusion
Módulo 24 · Options Trading Strategies In Python： Intermediate30 clases · 1 h 32 min
- Options Pricing Models · Course Introduction
- Options Pricing Models · Course Structure
- Options Pricing Models · Quantra Features and Guidance
- Options Pricing Models · Analogy to Pricing a Call Option： Dice Game
- Options Pricing Models · Intuitive Explanation of Bsm Model
- Options Pricing Models · How to Use Jupyter Notebook？
- Options Pricing Models · Recap
- Evolved Options Pricing Model · Derman-Kani Model and Heston Model
- Options Greeks： Delta · Delta
- Options Greeks： Delta · Delta With Respect to Underlying Price
- Options Greeks： Delta · Delta With Respect to Time to Expiry
- Options Greeks： Delta · Delta With Respect to Volatility
- Option Greeks： Gamma · Gamma
- Option Greeks： Gamma · Gamma Sensitivity
- Option Greeks： Vega · Vega
- Option Greeks： Vega · Vega With Respect to Time to Expiry and Vol
- Option Greeks： Theta and Rho · Theta
- Option Greeks： Theta and Rho · Recap
- Options Trading Strategies · Arbitrage Strategy
- Options Trading Strategies · Box Trading
- Options Trading Strategies · Recap
- Run Codes Locally on Your Machine · Python Installation Overview
- Volatility Trading Strategies · Forward Volatility
- Volatility Trading Strategies · Volatility Smile
- Volatility Skew · Predicting Market Movement： Volatility Skew
- Volatility Skew · Recap
- Live Trading on IBridgePy · Section Overview
- Live Trading on IBridgePy · Live Trading Overview
- Live Trading on IBridgePy · Code Structure
- Wrapping Up! · Summary
Módulo 25 · Systematic Options Trading37 clases · 2 h 39 min
- Introduction · Introduction
- Introduction · Quantra Features and Guidance
- Introduction · Backtesting and Automation Overview
- Systematic Trading Process · Systematic Trading Process
- Options Data · Data Structure
- Options Data · Data Storage
- Data Pre-Processing · Data Pre-Processing
- Data Pre-Processing · How to Use Jupyter Notebook？
- Creation of an Options Screener · Creation of an Options Screener
- Butterfly Strategy for Options Trading · Strategy Selection
- Butterfly Strategy for Options Trading · Butterfly Set-Up
- Butterfly Strategy Payoff · Payoff Calculation
- Probability of Profit · Need of Probability of Profit
- Probability of Profit · Intuition of Probability of Profit
- Lognormal Distribution · Use of Lognormal Distribution to Calculate Probability
- Probability of Profit Using Lognormal Distribution · Probability of Profit Using Lognormal Distribution
- Probability of Profit Using Empirical Distribution · Probability of Profit Using Empirical Distribution
- Expected Profit · Expected Profit
- Types of Volatility · Volatility
- Implied Volatility · Implied Volatility
- Implied Volatility Percentile · Implied Volatility Percentile
- Butterfly Strategy Backtest · Backtesting Short Butterfly
- Risk Management · Risk Management
- Risk Management · Position Sizing
- Trade Level Analytics · Trade Level Analytics I
- Trade Level Analytics · Trade Level Analytics II
- Cost of Setting Up Options Strategy · Cost of Setting Up Butterfly Spread
- Strategy Analysis · Sharpe Ratio and Tail Risk
- Iron Condor · Iron Condor
- Spread Trading · Spread Trading
- Do's and Don'ts · The 4 Do's
- Do's and Don'ts · The 4 Don'ts
- Live Trading on IBridgePy · Section Overview
- Live Trading on IBridgePy · Live Trading Overview
- Live Trading on IBridgePy · Code Structure
- Run Codes Locally on Your Machine · Python Installation Overview
- Summary · Course Summary
Módulo 26 · Trading using Options Sentiment Indicators17 clases · 1 h 18 min
- Introduction to Sentiment Trading · What is Fear and Greed in Markets？
- Introduction to Sentiment Trading · Course Overview
- Introduction to Sentiment Trading · Quantra Features and Guidance
- Breadth Measure · TRIN： Indicator & Interpretation
- Breadth Measure · Code the TRIN Trading Strategy： I
- Breadth Measure · Code the TRIN Trading Strategy： II
- Option Trading Measures · Put Call Ratio： Indicator and Interpretation
- Option Trading Measures · Code the PCR Trading Strategy
- Volatility Measures · What is the Volatility Index (VIX)？
- Volatility Measures · Interpretation of VIX
- Volatility Measures · Code the VIX Trading Strategy
- Risks in Trading · What are the risks involved in Trading？
- Run Codes Locally on Your Machine · Python Installation Overview
- Live Trading on IBridgePy · Section Overview
- Live Trading on IBridgePy · Live Trading Overview
- Live Trading on IBridgePy · Code Structure
- Conclusion and Downloadable Resources · Course Summary
Módulo 27 · Options Trading Strategies In Python： Advanced21 clases · 1 h 21 min
- Mathematical Models for Options Trading · Introduction
- Mathematical Models for Options Trading · Quantra Features and Guidance
- Mathematical Models for Options Trading · Binomial Trees
- Dispersion Trading · Dispersion Trading
- Dispersion Trading · How to Use Jupyter Notebook？
- Machine Learning · Machine Learning： Classification
- Exotic Options · Exotic Options Part A
- Exotic Options · Exotic Options Part B
- Exotic Options · Compound Options
- Exotic Options · VaR and ES
- Exotic Options · Recap
- Risk Management · Delta Neutral Portfolio
- Risk Management · Gamma Scalping
- Risk Management · Recap
- Scenario Analysis · Scenario Analysis
- Scenario Analysis · Recap
- Run Codes Locally on Your Machine · Python Installation Overview
- Live Trading on IBridgePy · Section Overview
- Live Trading on IBridgePy · Live Trading Overview
- Live Trading on IBridgePy · Code Structure
- Summary · Course Recap
Módulo 28 · Options Volatility Trading： Concepts and Strategies47 clases · 2 h 50 min
- Introduction · Course Introduction
- Introduction · Course Structure
- Introduction · Quantra Features and Guidance
- Introduction · Options Trading： Key Takeaways
- Edge and Risk · Making Money： Edge
- Edge and Risk · Edge and Risk
- Edge and Risk · Trading Process
- American vs European Options · American vs European Options
- Put and Call Parity · Put Call Parity Principle
- Pricing Models · Why Trade Options？
- Pricing Models · Options Pricing
- Pricing Models · How to Use an Options Pricing Model？
- Pricing Models · Black-Scholes-Merton Model
- Options Valuation · BSM for European Options
- Greeks： Price & Time · Keeping Money： Risk Management
- Greeks： Price & Time · Delta
- Greeks： Price & Time · Gamma
- Greeks： Price & Time · Theta
- Greeks： Volatility and Interest Rates · Vega
- Greeks： Volatility and Interest Rates · Rho
- Volatility · What is Volatility？
- Volatility · Volatility
- Volatility · Volatility Characteristics and Considerations
- Implied Volatility · Implied Volatility
- Implied Volatility · Volatility Skew
- Implied Volatility · IV Term Structure and Forward Volatility
- Close-to-Close Estimator · Close-to-Close Estimator
- Close-to-Close Estimator · Limitation of Close-to-Close Estimator
- Parkinson Estimator · Parkinson Estimator
- Garman-Klass Estimator · Garman-Klass Estimator
- Volatility Estimators · Volatility Estimators： Comparison
- Volatility Forecasting · Searching for Edge
- Volatility Forecasting · Introduction to Volatility Prediction
- Volatility Forecasting · GARCH Model for Volatility Prediction
- How to Trade Variance Premium · Variance Premium
- How to Trade Variance Premium · Reason for Existence of Variance Premium
- P⧸L Distribution of Options Strategies · Need To Study The P⧸L Distribution Of Options Strategies
- P⧸L Distribution of Options Strategies · Section Overview： P⧸L Distribution of Options Strategies
- Monte Carlo Simulation · Need for Monte Carlo Simulations
- Monte Carlo Simulation · Monte Carlo Simulation in Trading
- Practical Hedging · Practicalities of Hedging
- Live Trading on IBridgePy · Section Overview
- Live Trading on IBridgePy · Live Trading Overview
- Live Trading on IBridgePy · Code Structure
- Run Codes Locally on Your Machine · Python Installation Overview
- Summary · Conclusion and Q⧸A
- Summary · Course Summary
Módulo 29 · Introduction to Machine Learning for Trading19 clases · 48 min
- Introduction · Welcome to Machine Learning!
- Introduction · Course Structure
- Introduction · Course Learning Outcomes
- Introduction · Quantra Features and Guidance
- Machine Learning · Terminologies of Machine Learning
- Types of Machine Learning · Types of Machine Learning - Part 1
- Types of Machine Learning · Types of Machine Learning - Part 2
- Supervised Learning · How to Use Jupyter Notebook？
- Reinforcement Learning · Reinforcement Learning Example- Part 1
- Reinforcement Learning · Reinforcement Learning Example- Part 2
- Predict Trend Using Classification · Stock Market Prediction
- Predict Trend Using Classification · Classification Algorithm
- Live Trading on Blueshift · Section Overview
- Live Trading on Blueshift · Live Trading Overview
- Live Trading on Blueshift · Blueshift Code Structure
- Live Trading on Blueshift · Backtest and Live Trade on Blueshift
- Data & Feature Engineering · Data & Feature Engineering
- Run Codes Locally on Your Machine · Python Installation Overview
- Summary · Summary
Módulo 30 · Trading with Machine Learning： Regression29 clases · 1 h 22 min
- Problem Statement · Getting Started
- Problem Statement · Quantra Features and Guidance
- Introduction to Data Generation · Importing Data
- Introduction to Data Generation · Input Parameters
- Introduction to Data Generation · Recap
- Data Preprocessing · Creating X and Y Datasets
- Data Preprocessing · Hyperparameters
- Data Preprocessing · Cross Validation, Test and Train
- Data Preprocessing · Grid Search and Randomized Search
- Data Preprocessing · Recap of Data Pre-Processing
- Regression · Introduction to Linear Regression
- Regression · Errors and Residuals
- Regression · Cost Function and Gradient Descent
- Regression · Multivariate Linear Regression
- Regression · Recap of Regression
- Bias and Variance · Bias and Variance
- Bias and Variance · Overfitting and Underfitting
- Bias and Variance · Concept of Regularization
- Bias and Variance · Recap Video
- Applying the Prediction · Prediction and Model Assessment
- Applying the Prediction · Recap
- Creating the Algorithm · Trading Strategy
- Creating the Algorithm · How to Use Jupyter Notebook？
- Creating the Algorithm · Recap
- Live Trading on Blueshift · Section Overview
- Live Trading on Blueshift · Live Trading Overview
- Live Trading on Blueshift · Blueshift Code Structure
- Live Trading on Blueshift · Backtest and Live Trade on Blueshift
- Run Codes Locally on Your Machine · Python Installation Overview
Módulo 31 · Python for Machine Learning in Finance22 clases · 1 h 6 min
- Introduction · Course Introduction
- Introduction · Quantra Features and Guidance
- Machine Learning Overview · What is Machine Learning？
- Machine Learning Overview · Application of Machine Learning in Finance
- Introduction to Python · Need for Python
- Introduction to Python · How to Use Jupyter Notebook？
- Introduction to Python · Pandas Dataframe
- Financial Market Data and Visualisation · Importing Data
- Machine Learning Tasks · Machine Learning Tasks
- Target Variable and Features · Target Variable
- Target Variable and Features · Features
- Metrics to Evaluate Classifier · Evaluating Classifier Model Effectiveness
- Metrics to Evaluate Classifier · Beyond Accuracy
- Introduction to Backtesting · What is Backtesting？
- Introduction to Backtesting · How to do Backtesting？
- Live Trading on Blueshift · Section Overview
- Live Trading on Blueshift · Live Trading Overview
- Live Trading on Blueshift · Blueshift Code Structure
- Live Trading on Blueshift · Backtest and Live Trade on Blueshift
- Metrics to Evaluate a Regressor · Goodness of Fit
- Run Codes Locally on Your Machine · Python Installation Overview
- Course Summary · Course Summary
Módulo 32 · Data & Feature Engineering for Trading33 clases · 1 h 25 min
- Introduction to the Course · Introduction by Dr. Ernest Chan
- Introduction to the Course · Course Overview
- Introduction to the Course · Quantra Features and Guidance
- Challenges in Financial Data Engineering · Challenges in Financial Data Engineering
- Exploratory Data Analysis in Finance · Closer Look At the Data
- Exploratory Data Analysis in Finance · How to Use Jupyter Notebook？
- Exploratory Data Analysis in Finance · Irregularities
- Survivorship Bias for Stock Data · Survivorship Bias
- Redundant Stocks Data · Dealing With Redundant Stocks
- Multiple Stock Classes： One or All？ · Dealing With Multiple Stock Classes
- Outliers： How to Identify and Deal With Them？ · Dealing With Outliers
- News Data： Numerical Features · Overview of the News Data
- News Data： Numerical Features · Numerical Features
- News Data： Numerical Features · Combine Numerical Features
- News Data： Categorical Features · Categorical Features
- News Data： Categorical Features · Aggregating Categorical Attributes
- News Data： Categorical Features · Recap
- Structural Breaks in Financial Data · Structural Breaks
- Fundamental Data： Merge Them Correctly · Precap of Fundamental Data
- Fundamental Data： Merge Them Correctly · Sources of Fundamental Data
- Fundamental Data： Merge Them Correctly · Examining the Data
- Fundamental Data： Merge Them Correctly · Challenges in Merging Dataset
- Look-ahead Bias： Deceptive Returns · Look-ahead Bias in Futures
- Look-ahead Bias： Deceptive Returns · Look-ahead Bias in CS Strategy
- Types of Bars： Features Extraction · Tick and Time Bars
- Types of Bars： Features Extraction · Volume Bars
- Types of Bars： Features Extraction · Dollar Bars
- Information Bars： Market Order Imbalances · Information Bars
- Data Labelling for Better Outcomes · Fixed-Time Horizon
- Data Labelling for Better Outcomes · Triple Barrier Method
- Why Stationary Features？ · Dealing With Features Selection
- Run Codes Locally on Your Machine · Python Installation Overview
- Summary · Summary
Módulo 33 · Decision Trees in Trading27 clases · 1 h 27 min
- Introduction To Decision Trees · Introduction
- Introduction To Decision Trees · Quantra Features and Guidance
- Introduction To Decision Trees · Introduction To Decision Trees
- Introduction To Decision Trees · Decision Tree Inducers
- Splitting, Stopping and Pruning Methods · Splitting Measures
- Splitting, Stopping and Pruning Methods · Stopping Criteria And Pruning
- Classification Model · Classification Decision Tree Model - Part 1
- Classification Model · Classification Decision Tree Model - Part 2
- Classification Model · How to Use Jupyter Notebook？
- Classification Model · Recap
- Live Trading on Blueshift · Section Overview
- Live Trading on Blueshift · Live Trading Overview
- Live Trading on Blueshift · Blueshift Code Structure
- Live Trading on Blueshift · Backtest and Live Trade on Blueshift
- Regression Trees · A Trading Model Using： Regression Trees
- Regression Trees · Precap To Next Section
- Parallel Ensemble Methods · Bagging
- Parallel Ensemble Methods · Random Subspace And Random Forest
- Sequential Ensemble Methods · Boosting
- Cross Validation and Hyperparameter Tuning · Cross Validation
- Cross Validation and Hyperparameter Tuning · Hyperparameters
- Cross Validation and Hyperparameter Tuning · Hyperparameters Tuning
- Challenges in Live Trading · Challenges in Saving Model And Data
- Challenges in Live Trading · Challenges In Retraining The Model
- Challenges in Live Trading · How To Perform Simulation
- Challenges in Live Trading · Course Summary
- Run Codes Locally on Your Machine · Python Installation Overview
Módulo 34 · Trading with Machine Learning： Classification and SVM27 clases · 1 h 25 min
- Introduction · Getting Started
- Introduction · Quantra Features and Guidance
- Introduction · What is Classification？
- Introduction · Technical Indicators - Part A
- Introduction · Technical Indicators - Part B
- Introduction · Recap
- Binary Classification · What is Binary Classification？
- Binary Classification · The Math in Classification
- Binary Classification · Applying Classification
- Binary Classification · Recap
- Multiclass Classification · What is Multiclass Classification？
- Multiclass Classification · What is the One Vs All Algorithm？
- Multiclass Classification · Recap
- Support Vector Machine · What is Support Vector Machine？
- Support Vector Machine · The Parameters in SVM
- Support Vector Machine · Recap
- Prediction and Strategy · How to Use Jupyter Notebook？
- Prediction and Strategy · How to Prepare the Data？
- Prediction and Strategy · Recap
- Live Trading on Blueshift · Section Overview
- Live Trading on Blueshift · Live Trading Overview
- Live Trading on Blueshift · Blueshift Code Structure
- Live Trading on Blueshift · Backtest and Live Trade on Blueshift
- Live Trading on IBridgePy · Section Overview
- Live Trading on IBridgePy · Live Trading Overview
- Live Trading on IBridgePy · Code Structure
- Run Codes Locally on Your Machine · Python Installation Overview
Módulo 35 · Natural Language Processing in Trading20 clases · 45 min
- Introduction to the Course · Introduction by Dr. Terry Benzschawel
- Introduction to the Course · Course Introduction
- Introduction to the Course · Course Structure
- Introduction to the Course · Quantra Features and Guidance
- Applications of Natural Language Processing · Applications of NLP
- Sources of News Headline Data · News Headline Data
- Sources of News Headline Data · How to Use Jupyter Notebook？
- Sentiment Score and Strategy Logic · Calculate Daily Sentiment Score
- Sentiment Strategy on Bonds · Predict Bond Returns
- Introduction to Word Embeddings · Word Embedding Approaches
- Bag of Words · Bag of Words
- Predicting Sentiment Score Using XGBoost · Predict Sentiment Score Using XGBoost Model
- TF-IDF · TF-IDF
- WordVec · Word2Vec
- WordVec · Limitations of Word2Vec Method
- Run Codes Locally on Your Machine · Python Installation Overview
- Live Trading on IBridgePy · Section Overview
- Live Trading on IBridgePy · Live Trading Overview
- Live Trading on IBridgePy · Code Structure
- Course Summary · Summary
Módulo 36 · Unsupervised Learning in Trading29 clases · 1 h 35 min
- Introduction to the Course · Course Introduction
- Introduction to the Course · Course Structure
- Introduction to the Course · Quantra Features and Guidance
- Introduction to Unsupervised Learning · Introduction to Unsupervised Learning
- Introduction to Unsupervised Learning · An Application of Unsupervised Learning
- Clustering · Clustering
- Clustering · Properties of a Cluster
- Clustering · Classification Vs Clustering
- K-Means Clustering · What is K-Means Clustering？
- K-Means Clustering · Mathematics behind K-Means Clustering
- K-Means for Financial Data · K-Means on Financial Data
- K-Means for Financial Data · Using Jupyter Notebook
- Scaling the Data · Scaling the Data
- Feature Selection · Feature Selection
- Selecting Clusters for K-Means · Choosing the Number of Clusters
- Analysing Clusters： Hit Ratio · Cluster Analysis with Hit Ratio
- Analysing Clusters： Skewness · Cluster Analysis with Skewness
- Putting It All Together · Strategising with K-Means
- Curse of Dimensionality · Impact of Adding Features to Clustering
- Curse of Dimensionality · Overcoming Curse of Dimensionality
- Introduction to Principal Component Analysis · Principal Component Analysis
- Introduction to Principal Component Analysis · Mathematical Explanation of PCA
- Maths Behind Principal Component Analysis · Basics of Matrices and Eigenvalues
- Maths Behind Principal Component Analysis · Eigenvectors
- Principal Component Analysis · Choosing Number of Principal Components
- Application of Unsupervised Learning for Pairs Trading · Creating Clusters
- DBSCAN · Intuition of Density-Based Clustering
- Run Codes Locally on Your Machine · Python Installation Overview
- Course Summary · Course Summary
Módulo 37 · Neural Networks in Trading24 clases · 1 h 21 min
- Neural Networks · Introduction
- Neural Networks · Quantra Features and Guidance
- Neural Networks · Neural Networks Intuition
- Neural Networks · Backpropagation
- Neural Networks · Implement a MLPClassifier
- Neural Networks · How to Use Jupyter Notebook？
- Live Trading on Blueshift · Section Overview
- Live Trading on Blueshift · Live Trading Overview
- Live Trading on Blueshift · Blueshift Code Structure
- Live Trading on Blueshift · Backtest and Live Trade on Blueshift
- Deep Learning in Trading · Introduction to Deep Learning
- Deep Learning in Trading · DNN Model Training
- Deep Learning in Trading · DNN Trading Strategy Birdeye
- Recurrent Neural Networks · RNNs in Time Series Analysis
- Long Short Term Memory Unit (LSTMs) · Vanishing and Exploding Gradients
- Long Short Term Memory Unit (LSTMs) · Long Short Term Memory Unit
- Long Short Term Memory Unit (LSTMs) · LSTM based trading strategy
- Cross Validation in Keras · Hyper Parameter Tuning Using Cross Validation
- Cross Validation in Keras · Hyperparameters in a DNN model
- Challenges in Live Trading · Challenges in Saving Model and Data
- Challenges in Live Trading · Challenges in Retraining the Model
- Challenges in Live Trading · How to perform Simulation
- Challenges in Live Trading · Course Summary
- Run Codes Locally on Your Machine · Python Installation Overview
Módulo 38 · Deep Reinforcement Learning in Trading41 clases · 2 h 46 min
- Introduction · Course Introduction
- Introduction · Application of RL in Trading
- Introduction · Live Trading Overview
- Introduction · Course Structure
- Introduction · Quantra Features and Guidance
- Need for Reinforcement Learning · Introduction to Reinforcement Learning
- Need for Reinforcement Learning · Delayed Gratification
- State, Actions and Rewards · States, Actions and Rewards
- State, Actions and Rewards · Reward Function Design
- Q Learning · Creating the Q Table
- Q Learning · DQN and Experience Replay
- State Construction · State Construction
- State Construction · Input Features
- Policies in Reinforcement Learning · Policies in Reinforcement Learning
- Challenges in Reinforcement Learning · Difficulties in RL
- Challenges in Reinforcement Learning · Reinforcement Learning Concept
- Initialise Game Class · Introduction to Part II
- Initialise Game Class · How to Use Jupyter Notebook？
- Positions and Rewards · Positions and Rewards
- Input Features · Input Features
- Input Features · Candlestick Input Features
- Construct and Assemble State · Construct and Assemble State
- Experience Replay · Memory and Saving
- Experience Replay · Q Value Update
- Artificial Neural Network Concepts · Artificial Neural Network
- Artificial Neural Network Concepts · Gradient Descent and Loss Function
- Artificial Neural Network Concepts · Overfitting
- Artificial Neural Network Implementation · Agent Implementation
- Backtesting Logic · Combining Elements of RL Model
- Backtesting Logic · Process of Backtesting
- Performance Analysis： Synthetic Data · Generating Patterns for RL Model
- Performance Analysis： Synthetic Data · Performance on Synthetic Data
- Performance Analysis： Synthetic Data · Configuration Parameters
- Performance Analysis： Real World Price Data · RL Model on Real World Price Data
- Performance Analysis： Real World Price Data · Reinforcement Learning Implementation
- Automated Trading Strategy · Automated Trading Overview
- Automated Trading Strategy · Steps in Live Trading
- Paper and Live Trading · Live Trading FAQs
- Future Enhancements · Future Enhancement
- Run Codes Locally on Your Machine · Python Installation Overview
- Course Summary · Course Summary
Módulo 39 · Machine Learning for Options Trading38 clases · 2 h 13 min
- Introduction · Introduction
- Introduction · Course Structure
- Introduction · Quantra Features and Guidance
- FAQs and Overview · Part 1： Overview
- Features to Predict the Underlying · Features to Predict the Underlying
- Features to Predict the Underlying · How to Use Jupyter Notebook？
- Forecast Direction of Underlying with Decision Tree Classifier · Forecasting Direction of the Underlying with ML
- Metrics to Evaluate a Classifier · Evaluating Classifier Model Effectiveness
- Metrics to Evaluate a Classifier · Beyond Accuracy
- Options Data： Sourcing and Storing · Options Data
- Trade Level Analytics · Trade Level Analytics I
- Trade Level Analytics · Trade Level Analytics II
- Improving the ML Model · Improving Your Decision Tree Strategy
- Hyperparameter Tuning and Cross-Validation Methods · Hyperparameter Tuning
- Hyperparameter Tuning and Cross-Validation Methods · Cross Validation
- Probability Levels For Improving ML Model · Using Probability Levels For Improving Model
- Ensemble Classifiers · Random Forest Classifier Model
- Ensemble Classifiers · Voting Classifier Model
- Blending Models · Blending Machine Learning Models
- Parametric Vs Nonparametric Models · Parametric and Nonparametric Models
- Options Pricing： Feature Engineering · Features for Options Pricing
- ML for Options Pricing · Predicting Options Prices
- Implied Volatility Concepts · Implied Volatility
- Forecasting Implied Volatility · Forecasting Implied Volatility
- Trading Options Using Forecasted IV Values · Backtesting Short Straddle
- Need for ML to Predict Option Strategy to Trade · Need of ML for Strategy Prediction
- Defining the Best Option Strategy to Trade · Creating the Target Variable
- Input Features for Predicting the Best Options Strategy · Selecting The Input Features
- Model Design and Backtesting the Performance · Selecting the ML Model to Deploy
- Challenges in Live Trading · Live Trading Challenges
- Challenges in Live Trading · Challenges In Retraining The Model
- Challenges in Live Trading · How To Perform Simulation
- Run Codes Locally on Your Machine · Python Installation Overview
- Live Trading on IBridgePy · Section Overview
- Live Trading on IBridgePy · Live Trading Overview
- Live Trading on IBridgePy · Code Structure
- Additional Applications of ML for Options Trading · Further Applications of ML for Options Trading
- Summary · Course Summary
Módulo 40 · Forex Trading using Python： Basics11 clases · 36 min
- Introduction to Forex Trading · Quantra Features and Guidance
- Introduction to Forex Trading · Introduction to Forex Trading
- Introduction to Forex Trading · Factors Affecting Forex Trading
- Momentum Trading Strategy · Momentum Trading Strategy Overview
- Momentum Trading Strategy · Code the Momentum Trading Strategy
- Live Trading on Blueshift · Section Overview
- Live Trading on Blueshift · Live Trading Overview
- Live Trading on Blueshift · Blueshift Code Structure
- Live Trading on Blueshift · Backtest and Live Trade on Blueshift
- Risk Management · Risk Management in Forex
- Risk Management · Course Summary
Módulo 41 · Value Strategy in Forex13 clases · 35 min
- Introduction to the Course · Introduction to the Course
- Introduction to the Course · Quantra Features and Guidance
- Introduction to the Course · Macroeconomic Factors Affecting Forex Market
- Valuation of Forex · Purchasing Power Parity (PPP)
- Valuation of Forex · Real Effective Exchange Rate (REER)
- Forex Value Strategy： Logic · Forex Value Strategy
- Forex Value Strategy： Implementation · Code the Forex Value Strategy
- Forex Value Strategy： Implementation · How to Use Jupyter Notebook？
- Forex Value Strategy： Implementation · Course Summary
- Run Codes Locally on Your Machine · Python Installation Overview
- Live Trading on IBridgePy · Section Overview
- Live Trading on IBridgePy · Live Trading Overview
- Live Trading on IBridgePy · Code Structure
Módulo 42 · Crypto Trading Strategies： Intermediate15 clases · 38 min
- Introduction to Cryptocurrencies · Prologue
- Introduction to Cryptocurrencies · Quantra Features and Guidance
- Introduction to Cryptocurrencies · What Are Cryptocurrencies？
- Introduction to Cryptocurrencies · Crypto Wallets
- Introduction to Cryptocurrencies · What Are Trading Bots？
- Trading Using Calendar Anomalies · Calendar Anomalies Strategy Overview
- Trading Using Calendar Anomalies · Strategy Code Overview
- Trading Using Calendar Anomalies · How to Use Jupyter Notebook？
- Ichimoku Cloud · Ichimoku Cloud
- Divergence Strategy · Divergence Strategy
- Run Codes Locally on Your Machine · Python Installation Overview
- Live Trading on IBridgePy · Section Overview
- Live Trading on IBridgePy · Live Trading Overview
- Live Trading on IBridgePy · Code Structure
- Downloadable Resources · Summary
Módulo 43 · Crypto Trading Strategies： Advanced15 clases · 38 min
- Machine Learning in Cryptocurrency Trading · Introduction to the Course
- Machine Learning in Cryptocurrency Trading · Quantra Features and Guidance
- Machine Learning in Cryptocurrency Trading · Understanding K-Means Algorithm
- Machine Learning in Cryptocurrency Trading · Applying K-Means in Python
- Machine Learning in Cryptocurrency Trading · How to Use Jupyter Notebook？
- Pairs Trading Strategy · Pairs Trading
- Code the Pairs Trading Strategy · Strategy
- Hurst Exponent · Hurst Exponent
- Hurst Exponent · Strategy Using Hurst Exponent
- Quant Strategy Framework · Framework Overview
- Code the Long-Only Momentum Strategy · Summary
- Run Codes Locally on Your Machine · Python Installation Overview
- Live Trading on IBridgePy · Section Overview
- Live Trading on IBridgePy · Live Trading Overview
- Live Trading on IBridgePy · Code Structure
Módulo 44 · Quantitative Portfolio Management24 clases · 1 h 5 min
- Introduction · Introduction to the Course
- Introduction · Course Structure
- Introduction · Quantra Features and Guidance
- Basics of Portfolio Construction · Mathematical Terms for Portfolio Construction
- Basics of Portfolio Construction · How to Use Jupyter Notebook？
- Modern Portfolio Theory · Construct Two-Stock Portfolio using MPT
- Kelly Criterion · What is Utility？
- Kelly Criterion · The Kelly Criterion
- Live Trading on Blueshift · Section Overview
- Live Trading on Blueshift · Live Trading Overview
- Live Trading on Blueshift · Blueshift Code Structure
- Live Trading on Blueshift · Backtest and Live Trade on Blueshift
- Risk Parity · Construct Two-Stock Portfolio using Risk Parity
- Risk Parity · Risk Parity vs Traditional Portfolio
- Beta · What is Beta？
- Capital Asset Pricing Model (CAPM) · Introduction to CAPM
- Capital Asset Pricing Model (CAPM) · What is Security Market Line？
- Fama-French Three- Factor Model · Fama-French Three-Factor Model
- Factor Investing · Factor Investing
- Factor Investing · Applications of Factor Investing
- Multi Factor Model · Multi-Factor Model： Momentum Factor
- Multi Factor Model · Multi-Factor Model： Reversal Factor
- Run Codes Locally on Your Machine · Python Installation Overview
- Summary · Summary
Módulo 45 · Position Sizing in Trading40 clases · 1 h 59 min
- Introduction · Course Introduction
- Introduction · Course Structure
- Introduction · Getting Started With Quantra
- What is Position Sizing and Money Management？ · Importance of Position Sizing and Money Management
- What is Position Sizing and Money Management？ · Use of Money Management
- Position Sizing Terms · Performance Measure Terms
- Position Sizing Terms · Risk Management Terms
- Position Sizing Terms · Using Jupyter Notebook
- Trading Strategy · Index Reversal Strategy
- Trading Strategy · Strategy Implementation
- Basic Position Sizing： Fixed Units and Fixed Sum · Fixed Units and Fixed Sum
- Basic Position Sizing： Fixed Units and Fixed Sum · Implementation： Fixed Units
- Basic Position Sizing： Fixed Units and Fixed Sum · Implementation： Fixed Sum
- Basic Position Sizing： Fixed Percentage and Fixed Fraction · Fixed Percentage and Fixed Fraction
- Basic Position Sizing： Fixed Percentage and Fixed Fraction · Implementation： Fixed Percentage
- Volatility Targeting · Introduction to Volatility Targeting
- Volatility Targeting · Different Volatility Models
- Application of Volatility Targeting · Application of Volatility Targeting
- Live Trading on Blueshift · Section Overview
- Live Trading on Blueshift · Live Trading Overview
- Live Trading on Blueshift · Blueshift Code Structure
- Live Trading on Blueshift · Backtest and Live Trade on Blueshift
- Constant Proportion Portfolio Insurance · Introduction to Constant Proportion Portfolio Insurance
- Constant Proportion Portfolio Insurance · Advantages and Disadvantages of CPPI
- Constant Proportion Portfolio Insurance · Application of CPPI
- Kelly Formula · Kelly Formula
- Kelly Formula · Limitations of Kelly Formula
- Optimal F · Optimal F
- Theory Is Grey, but Life Is Green · Inherent Risk in Kelly Criterion and Optimal F
- Theory Is Grey, but Life Is Green · Hidden Risks in Backtesting and Financial Markets
- Theory Is Grey, but Life Is Green · Dealing with Unprofitable Trading Strategy
- Theory Is Grey, but Life Is Green · Martingale Trading Strategy
- Numerical Methods · Bootstrapping
- Numerical Methods · Bootstrapping Results
- Numerical Methods · Monte Carlo
- Conservative Framework for Position Sizing · Testing the Trading Strategy
- Conservative Framework for Position Sizing · Revisiting CPPI and Volatility Targeting
- Conservative Framework for Position Sizing · Result of CPPI and Volatility Targeting
- Run Codes Locally on Your Machine · Python Installation Overview
- Course Summary · Course Summary
Módulo 46 · Factor Investing： Concepts and Strategies45 clases · 2 h 25 min
- Introduction · Course Introduction
- Introduction · Course Structure
- Introduction · Quantra Features and Guidance
- Understanding Smart Beta · Understanding Alpha and Beta
- Understanding Smart Beta · Smart Beta
- All About Factors · Factor Premiums
- All About Factors · Factor Based Advantages
- Factor Approach · Brief History of Factors
- Factor Approach · Types of Factors
- What is Value？ · What is Value？
- Quantifying Value · Quantifying Value
- Identification of Undervalued Stocks · Creation of Value Strategy
- Introduction to Absolute Valuation Method · What is Absolute Valuation
- Introduction to Absolute Valuation Method · How Does Absolute Valuation Work
- Different Approach to Value · Intuition of DCF Valuation Method
- Different Approach to Value · Six Common Myths Of Value Investing
- Momentum Factor · Why Momentum Exists
- Momentum Factor · Characteristics of Momentum
- Time Series Momentum · Types of Momentum
- Time Series Momentum · Time Series Momentum
- Live Trading on Blueshift · Section Overview
- Live Trading on Blueshift · Live Trading Overview
- Live Trading on Blueshift · Blueshift Code Structure
- Live Trading on Blueshift · Backtest and Live Trade on Blueshift
- Cross Sectional Momentum · Cross Sectional Momentum
- Value and Momentum · Combining Value and Momentum
- Size Factor · Size Factor： Definition and Intuition
- Introduction to Quality Factor · Prerequisite for Quality Factor
- Introduction to Quality Factor · Corporate Governance Checks
- Creation of Quality Strategy · How to Identify Quality Stocks
- Factor Timing · Factor Timing
- Factor Timing · Drawbacks of Factor Timing
- Identifying Relevant Factors · Need to Identify Relevant Factors
- Identifying Relevant Factors · Factor Screening Methods
- Capital Allocation To Factors · How to Allocate Capital to Different Factors？
- Ranking Method With Multiple Metrics · Apply Ranking Method With Multiple Metrics
- Ranking Method With Multiple Metrics · Drawback of Ranking Method
- Scoring Method for Capital Allocation · Scoring Method for Capital Allocation
- Compare the Capital Allocation Methods · Compare The Ranking and Scoring Methods
- Exploring Unknown Factors · Exploring Unknown Factors
- Live Trading on IBridgePy · Section Overview
- Live Trading on IBridgePy · Live Trading Overview
- Live Trading on IBridgePy · Code Structure
- Run Codes Locally on Your Machine · Python Installation Overview
- Summary · Course Summary
Módulo 47 · Portfolio Management using Machine Learning： Hierarchical Risk Parity25 clases · 1 h 23 min
- Introduction · Course Introduction
- Introduction · Quantra Features and Guidance
- Portfolio Basics and Stock Screening · Portfolio Diversification
- Inverse Volatility Portfolios · Role of Volatility in Measuring Risk
- Inverse Volatility Portfolios · Allocating Weights Using Volatility
- Implementing Inverse Volatility Portfolios · Inverse Volatility on Three Stocks
- Correlation · Impact of Correlated Stocks on Portfolio’s Performance
- Markowitz Critical Line Algorithm · Intuition of Critical Line Algorithm
- Markowitz Critical Line Algorithm · Selecting the Most Optimal Weights
- Implementing CLA · Limitations of Critical Line Algorithm
- Hierarchical Clustering · What is Hierarchical Clustering？
- Hierarchical Clustering · Hierarchical Clustering On Stocks
- Mathematics Behind Hierarchical Clustering · Euclidean Distance Between Points
- Mathematics Behind Hierarchical Clustering · Implementing Hierarchical Clustering
- Clustering with Dendrograms · Dendrograms
- Scaling Your Data · Importance of Scaling
- Scaling Your Data · Scaling Your Data
- Hierarchical Risk Parity · Weight Allocation
- Live Trading on Blueshift · Section Overview
- Live Trading on Blueshift · Live Trading Overview
- Live Trading on Blueshift · Blueshift Code Structure
- Live Trading on Blueshift · Backtest and Live Trade on Blueshift
- Run Codes Locally on Your Machine · Python Installation Overview
- Course Summary · Course Summary-I
- Course Summary · Course Summary-II
Módulo 48 · AI for Portfolio Management： LSTM Networks25 clases · 1 h 2 min
- Introduction · Introduction to the Course
- Introduction · Quantra Features and Guidance
- Aim of Portfolio Management · Aim of Portfolio Management
- Aim of Portfolio Management · Optimisation of a Portfolio
- Building a Portfolio · Optimisation of Portfolio Using Mean-Variance
- Walk Forward Optimisation · Walk Forward Optimisation
- Walk Forward Optimisation · Walk Forward Optimisation with Fixed Window
- Application of Mean-Variance in Portfolio · How to Use Jupyter Notebook？
- Different Types of Portfolio · Different Types of Portfolio
- Different Types of Portfolio · Summary of Mean-Variance Optimised Portfolio
- AI for Portfolio Optimisation · AI for Portfolio Optimisation
- AI for Portfolio Optimisation · Artificial Intelligence
- Artificial Neural Networks · ANN Architecture
- Artificial Neural Networks · Drawback of ANN
- Long Short-Term Memory For Portfolio Optimisation · Differences Between ANN and LSTM
- Set Up LSTM Network for Portfolio Optimisation · How to Set Up the LSTM Network？
- Live Trading on IBridgePy · Section Overview
- Live Trading on IBridgePy · Live Trading Overview
- Live Trading on IBridgePy · Code Structure
- Hyperparameter Sweep · Hyperparameter Sweep
- Building Long-Short LSTM · LSTM for Long-Short Portfolio： An Overview
- Building Long-Short LSTM · Getting Weights for Long-Short Portfolio
- Building Long-Short LSTM · Modifications to LSTM
- Run Codes Locally on Your Machine · Python Installation Overview
- Summary and Next Steps · Summary and Next Steps
Módulo 49 · News Sentiment Trading Strategies25 clases · 1 h 18 min
- Introduction · Introduction to the Course
- Introduction · Quantra Features and Guidance
- About News-Based Trading · Why Trade the News？
- About News-Based Trading · Sources of News
- Types of News Releases · Planned News
- Types of News Releases · Unexpected News
- Buy the Rumour · Buy the Rumour
- Sell at the Event · Sell at the Event
- Limitations of Buy the Rumour Sell the Event · Limitations of Buy the Rumour and Sell at the Event
- Retrieving and Storing Textual Data · Retrieving and Storing Textual Data
- Qualitative Analysis · Qualitative Analysis
- Using News to Your Advantage · How to Use News to Your Advantage？
- Sentiment Score Using VADER · How Vader Calculates the Sentiment Score
- Sentiment Score Using VADER · How VADER Accounts for Sentiment Intensity
- Limitations of VADER · Limitations of VADER for Sentiment Analysis
- Challenges in Calculating Sentiment of News · Challenges in Calculating Sentiments of News
- Sentiment Analysis Using LLMs · Sentiment Analysis Using LLMs
- Buy the Rumour Sell the Event With Sentiment Analysis · Buy the Rumour Sell the Event Using Sentiment Scores
- Analysis of Buy Rumour Sell Event Using Sentiment Scores Strategy · Analysis of the Buy the Rumour Sell at the Event Using Sentiment Scores Strategy
- Improving the Sentiment Analysis Strategy · Analysing Sentiment Analysis Strategy Performance
- Pitfalls of Trading the News · Common Pitfalls of News Based Trading
- Live Trading on IBridgePy · Section Overview
- Live Trading on IBridgePy · Live Trading Overview
- Live Trading on IBridgePy · Code Structure
- Summary · Course Summary
Módulo 50 · Mean Reversion Strategies In Python31 clases · 1 h 58 min
- Introduction to the Course · Introduction by Dr. Ernest Chan
- Introduction to the Course · Introduction to Mean Reversion Strategy
- Introduction to the Course · Quantra Features and Guidance
- Introduction to the Course · Types of Statistical Arbitrage Strategies
- Stationarity of Time Series · What is Stationarity？
- Augmented Dickey-Fuller Test · What is ADF Test？
- Augmented Dickey-Fuller Test · How to Use Jupyter Notebook？
- Mean Reversion Strategy · Mean Reversion Strategy
- Mean Reversion Strategy · Recap
- Live Trading on Blueshift · Section Overview
- Live Trading on Blueshift · Live Trading Overview
- Live Trading on Blueshift · Blueshift Code Structure
- Live Trading on Blueshift · Backtest and Live Trade on Blueshift
- Cointegration · What is Cointegration？
- Cointegration · What is Hedge Ratio？
- Cointegration · Hedge Ratio Code
- Cointegration · What is CADF Test？
- Pairs Trading · Mean Reversion Strategy on Pairs
- Pairs Trading · Recap
- Pairs Trading · Mean Reversion Strategy
- Triplets · Cointegration Breakdown in the GLD-GDX Pair
- Triplets · Surviving Breakdown of Cointegration
- Triplets · What is Johansen Test？
- Triplets · Mean Reversion on Triplets
- Triplets · Recap
- Half Life · Half Life of Mean Reverting Time Series
- Best Markets to Pair Trade · Best Markets To Pair Trade
- Index Arbitrage · Index Arbitrage Strategy
- Long Short Portfolio · Long-Short portfolio Strategy
- Run Codes Locally on Your Machine · Python Installation Overview
- Summary · Course Summary
Módulo 51 · Momentum Trading Strategies41 clases · 2 h 2 min
- Introduction to the Course · Introduction to Momentum Trading
- Introduction to the Course · Course Structure
- Introduction to the Course · Quantra Features and Guidance
- What is Momentum？ · Introduction to Momentum
- What is Momentum？ · Myths of Momentum
- Why Does Momentum Exist？ · Why Momentum Exists - I
- Why Does Momentum Exist？ · Why Momentum Exists - II
- Why Does Momentum Exist？ · Why Momentum Exists - III
- Introduction to Python · Need for Python
- Introduction to Python · How to Use Jupyter Notebook？
- Introduction to Python · Pandas Dataframe
- Financial Market Data and Visualisation · Importing Data
- Technical Indicators · Role of Technical Indicators
- Technical Indicator Strategy · Technical Indicator Strategy
- Live Trading on Blueshift · Section Overview
- Live Trading on Blueshift · Live Trading Overview
- Live Trading on Blueshift · Blueshift Code Structure
- Live Trading on Blueshift · Backtest and Live Trade on Blueshift
- Types of Momentum · Types of Momentum
- Time Series Momentum · Time Series Momentum
- Time Series Momentum · Time Series Momentum on Multiple Asset
- Hurst Exponent · Hurst Exponent
- Correlation Analysis · Correlation Analysis
- Correlation Analysis · Course Summary - I
- Cross Sectional Momentum · Introduction to Cross Sectional Momentum
- Cross Sectional Momentum · Cross Sectional Momentum Strategy
- Fundamental Momentum · Fundamental Momentum
- Event Driven Strategy · Momentum Due to Unscheduled Events
- Event Driven Strategy · Momentum Due to Scheduled Events
- Treasury Markets · Event Driven Strategy in Treasuries
- Momentum in Futures · Term Structure
- Momentum in Futures · Roll Returns
- Momentum in Futures · Lookahead Bias
- Cross Sectional Momentum Strategy in Futures · Issues With Future Spot Arbitrage
- Cross Sectional Momentum Strategy in Futures · Cross Sectional Momentum in Commodities
- Cross Sectional Momentum Strategy in Futures · P&L Calculation in Continuous Futures
- Momentum Crashes · Momentum Crashes
- Momentum Crashes · Avoiding Momentum Crashes
- Risk Management · Risk Management
- Run Codes Locally on Your Machine · Python Installation Overview
- Course Summary · Course Summary - II
Módulo 52 · Trading Alphas： Mining, Optimisation, and System Design54 clases · 3 h 8 min
- Introduction · Introduction
- Introduction · Quantra Features and Guidance
- Micro Alphas · Micro Alphas
- Micro Alphas · How to Use Jupyter Notebook？
- Market Inefficiencies： Trend · Market Inefficiencies
- Market Inefficiencies： Trend · Trends
- Market Inefficiencies： Mean Reversion · Mean Reversion
- Market Inefficiencies： Chart Patterns · Chart Patterns
- Market Inefficiencies： Correlation, Fundamental and Alternative · Correlation, Fundamental and Alternative
- Market Inefficiencies： Cointegration · Cointegration
- Market Inefficiencies： Cointegration · Types of Market Inefficiencies
- Time Series Alphas · Time Series Alphas
- Live Trading on Blueshift · Section Overview
- Live Trading on Blueshift · Live Trading Overview
- Live Trading on Blueshift · Blueshift Code Structure
- Live Trading on Blueshift · Backtest and Live Trade on Blueshift
- Cross-Sectional Alphas · Cross-Sectional Alpha
- Timing Alphas · Timing Alpha
- Timing Alphas · Most Suitable Alpha
- Combinations of Alpha · Combinations of Alpha
- Combinations of Alpha · Things to Keep In Mind While Combining Strategies
- Finding Micro-Alphas · Finding Micro-Alphas
- Assessing Results · Assessing Results
- Assessing Results · Most Ideal Performance Metric
- Total Profit · Total Profit
- Sharpe and Sortino Ratios · Sharpe and Sortino Ratios
- Profit Factor and Drawdown · Profit Factor and Drawdown
- Profit Per Trade · Profit Per Trade
- CAGR, Alpha, and Beta · CAGR, Alpha and Beta
- Strategy Execution · Strategy Execution
- Strategy Execution · Arrival Price Algorithm
- Micro-Alpha Portfolio · Combining Alphas
- Micro-Alpha Portfolio · Generating Signals
- Portfolio Optimisation · Portfolio Optimisation
- Advanced Alpha Mining · Testing Robustness Across Parameter Space
- Advanced Alpha Mining · Selecting Best Parameter Sets
- Machine Learning Alphas · Machine Learning Alphas
- Basics of Vectorized Backtest · Creating a Basic Backtest
- Adding Vectorized Stop-loss and Profit-takes · Application Of Profit-Take And Stop-Loss Filters
- Impact of Profit Take and Stop Loss on Strategy · Strategy Analysis After Application of Profit Take and Stop Loss
- Designing a Trading System · Software Architecture in Trading Systems
- Designing a Trading System · Parallel Computing
- Asynchronous Computing · Asynchronous Computing
- Distributed Computing · Distributed Computing
- Importance of Logging and Storage · Logging And Storage
- Hardware Elements of a Trading System · Hardware of a Trading System
- Software Elements of a Trading System · Micro-Services and Operating System
- Testing and Version Control · Testing and Version Control
- Implementation of a Trading System · Prerequisites for Implementing a Trading System
- Implementation of a Trading System · Architecture and Start-Up
- Types of Servers · Data Server and Execution Server
- Types of Servers · Trading Server
- Run Codes Locally on Your Machine · Python Installation Overview
- Summary · Summary
Módulo 53 · Trading in Milliseconds： MFT Strategies & Setup43 clases · 2 h 34 min
- Introduction · Course Introduction
- Introduction · Course Structure
- Introduction · Quantra Features and Guidance
- An Introduction to Market Microstructure · Market Microstructure
- Need and Challenges of MFT · Definition of MFT
- Need and Challenges of MFT · Why Should You Focus on MFT and its Challenges
- HFT Gaming · Front-Running
- Ticking Strategy · Ticking Strategy
- Ticking Strategy · Impact of Volatility on Ticking Strategy
- Impact of Ticking Strategy · Impact of Ticking Strategy on MFT
- Impact of Ticking Strategy · Use of Dark Pool Against MFT Traders
- Ratio Trade · Ratio Trade
- Spoofing · Spoofing
- Spoofing · Countering the Spoofing Strategy
- Other Ways HFT Wins · Stop-Hunting
- Other Ways HFT Wins · Hide and Light Orders
- Other Ways HFT Wins · Risk of Hide and Light Orders
- Thin NBBO Liquidity · Thin NBBO Liquidity
- An Overview of Types of Orders · Types of Orders
- Immediate or Cancel Order · Immediate or Cancel Orders
- Immediate or Cancel Order · Toxic Order Flows and Prevention of Adverse Selection
- Intermarket Sweep Order · ISO and Regulation NMS
- Intermarket Sweep Order · Understanding ISO with an Example
- ISO and Flash Crashes · ISO and Flash Crashes
- Day ISO · Day ISO and Hide and Light Orders
- Dark Pools · Use of Dark Pools
- Dark Pools · Abuse of Dark Pools
- Dark Pools · Avoiding Toxic Dark Pool
- Physics of MFT · Physics of MFT
- Backtesting an MFT Strategy · Backtesting an MFT Strategy
- Backtesting an MFT Strategy · Choosing a Backtesting Platform
- Historical Tick Data · Historical Tick Data
- Order Flow Basics · Basics of Order Flow
- Order Flow Basics · Order Flow as an Indicator
- Calculate the Order Flow · The Challenge of Accurate Order Flow Calculation
- Calculate the Order Flow · Computing the Order Flow： Tick Rule
- Quote Rule and Lee-Ready Algorithm for Order Flow · Quote Rule and Lee-Ready Algorithm
- Bulk Volume Classification · Bulk Volume Classification
- Bulk Volume Classification · Bulk Volume Classification with Time Bars
- Bulk Volume Classification · Bulk Volume Classification with Volume Bars
- Trade the Order Flow · Trading the Order Flow
- Run Codes Locally on Your Machine · Python Installation Overview
- Summary · Course Summary
Trading
$19.00$15.00
Master Quantitative & Algorithmic Trading – Quantra – Learn 185+ trading strategies
$19.00
- Formación completa organizada en 53 módulos
- 1470 lecciones en vídeo explicadas paso a paso
- Más de 81 horas de contenido en vídeo
- Acceso inmediato al curso tras la compra
- Acceso de por vida sin pagos recurrentes
- 1077 documentos de apoyo, guías y material descargable
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Lo que aprenderás
- Darás tus primeros pasos en el trading algorítmico
- Programarás en Python para trading con Jupyter Notebook
- Harás backtesting de tus estrategias de trading
- Analizarás series temporales financieras
- Aplicarás estrategias de volatilidad, eventos y arbitraje estadístico
- Automatizarás operaciones con IBridgePy e Interactive Brokers
Contenido incluido
- Fundamentos de bolsa y trading algorítmico
- Python para trading y datos de mercado
- Backtesting y análisis de series temporales
- Estrategias de day trading, volatilidad y eventos
- Preparación de entrevistas quant
- Guías en PDF de instalación y trading en vivo
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