Autoplay
Autocomplete
Previous Lesson
Complete and Continue
Quantitative Finance & Algorithmic Trading Masterclass
Introduction
Introduction (2:48)
Why to use Python (3:27)
Financial models (3:50)
Environment Setup
Installing PyCharm and Python on Windows
Installing PyCharm and Python on Mac
Installing R and RStudio
### QUANTITATIVE FINANCE ###
Quantitative finance section
Stock Market Basics
Present value and future value of money (5:10)
Stocks and shares (8:43)
Commodities (6:10)
Currencies and the FOREX (9:02)
Short and long positions (6:52)
Bonds Theory
What are bonds? (10:28)
Yields and yield to maturity (5:26)
Interest rates and bonds (3:53)
Macaulay duration (4:55)
Risks with bonds (2:48)
Stocks and bonds (1:53)
Bonds Implementation
Bonds pricing implementation I (6:26)
Bonds pricing implementation II (7:32)
Exercise - continuous model for discounting
Solution - continuous model for discounting
Modern Portfolio Theory (Markowitz-Model)
What are mean, variance and correlation? (7:31)
The main idea - diverzification (7:04)
Mathematical formulation (8:28)
Expected return of the portfolio (5:39)
Expected variance (risk) of the portfolio (8:05)
Efficient frontier (6:20)
Sharpe ratio (3:44)
Capital allocation line (3:30)
Markowitz-Model Implementation
Markowitz model implementation I (9:48)
Markowitz model implementation II (12:02)
Markowitz model implementation III (8:24)
Markowitz model implementation IV (11:43)
Markowitz model implementation V (3:34)
Capital Asset Pricing Model (CAPM) Theory
Systematic and unsystematic risk (5:05)
Capital asset pricing model formula (5:05)
The beta value (6:11)
What is linear regression? (8:14)
Capital asset pricing model and linear regression (4:40)
Capital Asset Pricing Model (CAPM) Implementation
Capital asset pricing model implementation I (4:10)
Capital asset pricing model implementation II (12:25)
Capital asset pricing model implementation III (7:18)
Exercise - normal distribution of returns
Solution - normal distribution of returns (4:40)
Derivatives Basics
Introduction to derivatives (7:32)
Forward and future contracts (3:18)
Swaps and interest rate swaps (8:52)
Credit default swap (CDS) (3:42)
Options basics (4:49)
Call option (6:04)
Put option (3:30)
American and european options (1:29)
Random Behavior in Finance
Types of analysis (6:29)
Random behavior of returns (5:56)
Wiener-processes and random walks (10:11)
Wiener-process implementation (7:18)
Stochastic calculus introduction (7:30)
Ito's lemma in higher dimensions (5:04)
Solving the geometric random walk equation (6:07)
Geometric brownian motion implementation (6:19)
Black-Scholes Model
Black-Scholes model introduction - the portfolio (6:44)
Black-Scholes model introduction - dynamic delta hedge (6:09)
Black-Scholes model introduction - no arbitrage principle (4:36)
Solution to Black-Scholes equation (4:07)
The greeks (4:35)
How to make money with Black-Scholes model? (1:56)
Long Term Capital Management (LTCM) (6:03)
Black-Scholes Model Implementation
Black-Scholes model implementation (8:19)
What is Monte-Carlo simulation? (6:18)
Predicting stock prices with Monte-Carlo simulation (10:41)
Black-Scholes model implementation with Monte-Carlo simulation I (4:42)
Black-Scholes model implementation with Monte-Carlo simulation II (8:24)
Black-Scholes model implementation with Monte-Carlo simulation III (2:42)
Value at Risk (VaR)
What is Value-at-Risk? (5:25)
Value-at-Risk introduction (7:37)
Value at risk implementation (11:07)
Value at risk implementation with Monte-Carlo simulation I (12:28)
Value at risk implementation with Monte-Carlo simulation II (3:00)
Collateralized Debt Obligations (CDOs) and the Financial Crisis
What are CDOs? (4:22)
CDOs and diverzification (5:11)
CDO tranches (2:34)
The financial crisis of 2007-2008 (5:48)
Interest Rate Modeling (Vasicek Model)
Why to use interest rate models? (3:00)
The Ornstein-Uhlenbeck process introduction (4:58)
The Ornstein-Uhlenbeck process implementation (5:42)
Vasicek model introduction (4:16)
Vasicek model implementation (5:52)
Pricing Bonds with Vasicek Model
Bond pricing with the Vasicek model I (6:34)
Bond pricing with the Vasicek model II (6:56)
Bond pricing with the Vasicek model III (5:07)
Long-Term Investing
Value investing (4:47)
Efficient market hypothesis (4:42)
### ALGORITHMIC TRADING ###
Algorithmic trading section
TECHNICAL INDICATORS
Technical indicators
Moving Average Indicator
What is the simple moving average (SMA) indicator? (7:15)
Support and resistance levels (3:06)
Simple moving averages (SMA) implementation (10:16)
Exponential weighting (2:43)
Exponential moving average (EMA) implementation (2:57)
Moving Average Crossover Strategy
Moving average crossover strategy I (2:35)
Moving average crossover strategy II (5:52)
Moving average crossover strategy III (4:20)
Moving average crossover strategy IV (8:36)
Moving average crossover strategy V (4:46)
Relative Strength Indicator (RSI)
What is the relative strength indicator (RSI)? (5:27)
Calculating the RSI values (10:48)
Returns and logarithmic returns (5:25)
Relative Strength Indicator (RSI) Strategy
RSI trading strategy I (4:30)
RSI trading strategy II (3:46)
RSI trading strategy III (4:25)
What is Sharpe ratio? (2:42)
Calculating Sharpe ratio of a trading strategy (3:18)
Backtrader Framework
What is backtrader? (4:28)
Backtrader basics - handling data (4:17)
Backtrader basics - using strategies (8:48)
Backtrader basics - using indicators (8:48)
Backtrader basics - results (5:35)
Backtrader basics - broker info and commissions (3:12)
Momentum & SMA Combined Trading Strategy
What strategy will we implement? (5:12)
Average true range (ATR) indicator and position sizing (4:45)
Average true range (ATR) indicator implementation (11:43)
Momentum & SMA Combined Trading Strategy Implementation
Momentum trading strategy implementation I (11:54)
Momentum trading strategy implementation II (6:10)
Momentum trading strategy implementation III (8:22)
Momentum trading strategy implementation IV (5:53)
Momentum trading strategy implementation V (12:59)
Momentum trading strategy implementation VI (3:37)
Momentum trading strategy implementation VII (11:11)
TIME SERIES ANALYSIS
Time series analysis
Time Series Analysis Fundamentals
What are mean, variance and correlation? (7:27)
Downloading the data from Yahoo Finance (6:07)
Calculating useful statistics (7:07)
Stationarity (6:16)
What is serial correlation (autocorrelation)? (11:05)
Correlogram (4:21)
Understanding the correlogram (6:17)
Random Walk Model
White noise introduction (8:56)
White noise process example (3:47)
What is random walk? (8:52)
Random walk example (6:56)
Modeling assets with random walk (6:01)
Autoregressive Model (AR)
Autoregressive model introduction (7:35)
How to select the best model? (AIC and BIC) (7:47)
Autoregressive model example (7:58)
Modeling assets with autoregressive model (10:09)
Moving Average Model (MA)
Moving average model introduction (6:25)
Moving average model example (8:00)
Modeling assets with moving average model (7:42)
Autoregressive Moving Average Model (ARMA)
Autoregressive moving average model introduction (3:41)
What is the Ljung-Box test? (5:15)
Autoregressive moving average model example (6:18)
Autoregressive moving average model example II (10:06)
Modeling assets with ARMA model (8:42)
Autoregressive Integrated Moving Average Model (ARIMA)
ARIMA model introduction (5:05)
ARIMA model example (4:28)
Modeling assets with ARIMA model (5:08)
Autoregressive Conditional Heteroskedastic Model (ARCH)
Heteroskedasticity in finance (6:12)
ARCH model introduction (8:24)
Generalized Autoregressive Heteroskedastic Model (GARCH)
GARCH model introduction (2:30)
GARCH model example (6:51)
Modeling assets with GARCH model (5:27)
FOREX Trading Strategy Implementation
FOREX trading strategy implementation I (2:29)
FOREX trading strategy implementation II (4:48)
FOREX trading strategy implementation III (5:46)
FOREX trading strategy implementation IV (6:23)
FOREX trading strategy implementation V (3:54)
FOREX trading strategy implementation VI (2:53)
MARKET-NEUTRAL STRATEGIES
Two types of risk and CAPM (4:49)
Hedging the market risk (5:02)
Mean Reversion
Ornstein-Uhlenbeck stochastic processes (5:23)
Simulating Ornstein-Uhlenbeck processes (5:51)
What is cointegration? (5:58)
Testing cointegration (12:56)
Bollinger Bands
Bollinger bands introduction (5:32)
Bollinger bands visualization (8:21)
Bollinger Bands Trading Strategy Implementation
Bollinger bands trading strategy implementation I (4:02)
Bollinger bands trading strategy implementation II (9:12)
Bollinger bands trading strategy implementation III (2:56)
Cross-Sectional Mean Reversion
What is the cross-sectional mean reversion strategy? (6:24)
Cross-Sectional Mean Reversion Trading Strategy Implementation
Cross-sectional mean reversion implementation I (8:04)
Cross-sectional mean reversion implementation II (5:04)
MACHINE LEARNING TRADING
Machine learning approaches (5:17)
Logistic Regression
What is linear regression? (8:18)
Optimization techniques (6:51)
Logistic regression introduction (12:11)
Logistic Regression Trading Strategy Implementation
Logistic regression strategy implementation I (11:15)
Logistic regression strategy implementation II (7:18)
Support Vector Machines (SVMs)
Support vector machine introduction - linear case (8:50)
Support vector machine introduction - non-linear case (7:18)
Support vector machine introduction - kernels (4:23)
Support Vector Classifier Trading Strategy Implementation
Support vector machines strategy implementation I (7:31)
Support vector machines strategy implementation II (4:59)
Machine Learning Algorithms and Indicators
SVM with SMA and RSI trading strategy I (5:09)
SVM with SMA and RSI trading strategy II (6:12)
SVM with SMA and RSI trading strategy III (1:44)
Course Materials (DOWNLOADS)
Slides and source code
Support vector machine introduction - non-linear case
Lesson content locked
If you're already enrolled,
you'll need to login
.
Enroll in Course to Unlock