Algorithmic Trading Advance Trading Strategies

Description

It is an ability where finance, trading, maths and computer sciences are combined. The computer algorithms are designed based on the arts performance of the strategy back-tested against historical information.

The objective is to introduce students with comprehensive aspects of Algorithmic Trading and execution. It is designed for both buy and sell-side industry. This course involves a top-down approach to Algorithmic Trading, in which we will equip students with the identification of target markets and trading styles for Algorithmic Trading, identified the factors driving the growth of electronic trading with an overview of Electronic and Algorithmic Trading, designing trading strategies and their algorithmic components.

Students also get opportunity to work on trading terminals, learn infrastructure requirements, techniques of risk management and portfolio improvement and business strategies to protect the combative provide in Algorithmic Trading.

Students/Corporate cover courses in finance, financial economics, financial econometrics, financial mathematics, stochastic calculus, numerical methods, monte carlo simulation, derivatives modelling, portfolio theory, portfolio optimization, performance analysis and financial risk analysis. You instruct to write financial applications and derivative modelling programs in VBA / C++, C# Sharp, Easy language making use of the theories and methods you have learned. You get hands-on learning session on the commercial Financial Software applications from Thomson Reuters. You also acquire to do assets financial modelling using the advanced features of Excel.

Brief Course Content

Module A: R software based back testing and Visual basic applications based trading (this module would be covered throughout the course)

• R software based backtesting Visual basic applications based coding and trading
  • Probability and statistics using R. regressions and model fitting.
  • Time series analysis using R: Spectral analysis, ARIMA, moving averages and differencing models. Forecasting from model fitting and residual diagnostics.
  • Pairs trading using R. Downloading S&P500 historical data, making pairs and simulating pairs trading.
  • Options, futures and derivatives pricing using R. Portfolio optimization and performing finance managerial tasks using R.
  • Backtesting trading strategies using systematic investor toolbox in R. Strategies based on:
  • • Probabilistic momentum, seven twelve portfolio strategies, volatility regimen based trading etc.
    • One month reversal, seasonality, calendar strategies, based on dates on expiration of options etc.
• Visual basic applications based coding and trading
  • Introduction to coding in VBA
  • Real time trading simulation and implementation of technical analysis indicators using VBA

Module B: Financial mathematics, random number theory, probability and statistics, stock data analysis.

• Financial mathematics
  • Concepts of interest, internal rate of return and present value of money. Bind pricing.
  • Theoretical pricing of bonds and equities and decision making
  • Present values in annuities. Annuities payable. Deferred annuities. Advanced topics in annuities.
  • Prevailing instruments in stock market. Debentures ,shares and futures. Cost of carry models. Option and option strategies.
  • Diversification and portfolio management. Concept of risk and return for collection of financial instruments.
• Financial data summarising., dispersion, correlation and regression
  • Types of data, individual , discrete and continuous distribution
  • Mean, median and Mode
  • Measures of dispersion, variance and standard deviation. Symmetry and skewness of data
  • Correlation and covariance. Regression analysis
  • Least squares model and maximum likelihood estimation
• Probability, confidence interval and hypothesis testing
  • Confidence interval and hypothesis testing for means and variance in one and two sample case
  • Sets and subset. Sample space, union and intersection of set
  • Additive and multiplicative rule of probability. Conditional probability
• Random theory, discrete and continuous probability distributions
  • Uniform, bernoulli, binomial, poisson, geometric and negative binomial distribution
  • Chi square, gamma, exponential and normal distribution. T distribution and f distribution
  • Central limit theorem and Normal distribution using central limit theorem
  • Types of random variable. Summarising random variables. Calculating probability distributions.
• Trading 
  • Participants in Trading
  • Stocks, Options, Bonds, Mutual Funds, ETFs and Forex Trading
  • Current Scenario of Trading & Future Prospective
  • Introduction to Algorithmic and High Frequency Trading
• Time series analysis
  • Time Series Analysis: Autocorrelation, White noise, Stationarity, Autoregressive models. ARIMA models. Spectral Analysis, Fourier Transformation
  • Analysis of time series stock data
  • Estimation of volatility using various models
  • Pairs Trading: Correlation, Distance, Co integration

 Module C: Finance, economics, options and futures

• Basics of Finance
  • Financial Planning
  • Various Types of Costs & Risks, Overview of Taxation
  • Financial Institutions & Markets
  • Topic 4:  Capital Markets & Commodity Markets Operations
•  Options and future
  • Options Theory
    • Options terminology, Options Payoffs, Options payoff profile and strategies.
    • European and American Options. Asian options, greeks
    • Pricing of Options. Binomial tree models, black scholes equations, monte carlo random walks models for options pricing.
  • Futures and Forwards Markets
  • Swap Contracts & Swap Markets

 Module D: Technical analysis; Indicators and candlestick patterns; Real trading

• Technical Analysis
• Tradestation I: Getting started with Tradestation. Beginner level coding.
  •  Trend Analysis, Oscillators, Moving Averages, momentum indicators
  •  Technical Theory & Technical Analysis Indicators
  •  Inter market Technical Analysis
• Tradestation II: Medium level coding and sound knowledge of Technical Analysis (some of the practicals are listed below)
  
  • Money management: stop loss, percent trailing loss, profit target etc.
  • Implement a function that invests at Kelly’s fraction.
  • Code for a Indicator based on maximas and minimas
  • Code for various patterns candlestick patterns.
    • Bearish engulfing, shooting star, Hanging man, piercing pattern,Doji star, etc
  • Implement a strategy based on candlestick patterns and stochastic crossovers.
  • Implement filters, such as a) stochastic, b) CCI, c) Trends or d) Day of week at which trading has to happen.
• Tradestation III: Higher level coding and sound knowledge of trading. (some of the practicals are listed below)
  
  • Breakout strategy. I.e., how to capitalize on the rally up of the stocks
  • Write the code to pause for certain number of days if consecutive loose trades happen.
  • Trading based on Fibonacci
  • Trading based on bar pattern
  • Trading based on Bollinger bands and keltner bands
  • Meander indicator and meander strategy (it’s a scalping strategy)
  • Intraday strategy: Bar Reversal Long/Short Entry Strategy.
  • Stochastic trap: To get the optimum buy and sell