terça-feira, 27 de agosto de 2013

     Começou ontem, 26.08.2013, o curso gratuito Computational Investing na plataforma do Cousera. Além de abordar conceitos to mercado financeiro, o curso ensinará métodos de manipulação de dados utilizando Phyton e a biblioteca numpy. Para quem sem interessa pelo mercado financeiro e por python, parece valer muito a pena. Eu já estou acompanhando o curso e estou gostando muito.

Segue abaixo o cronograma para as 8 semanas. Você pode se inscrever em Computation Investing I.


Week 1

  • Module 1: Course Overview
    • Video 11*: Learning objectives of for the course [*need to reshoot to emphasize programming difficulty]
      • Who is this course for?
      • Logistics
      • Instructor background
  • Module 2: So you want to be a fund manager?
    • Video 21: Module learning objectives
      • Viewpoint of course
      • Incentives for portfolio managers
      • Two main types of hedge fund
    • Video 22: Common metrics for assessing fund performance
      • Annual return
      • Risk
      • Reward/Risk
    • Video 23: Common metrics for assessing fund performance
      • Sharpe Ratio
    • Video 24: Demo
      • Download historical data
      • Manipulate historical data in Excel
  • Module 3: Market Mechanics
    • Video 31: Module objectives
      • Major order types
      • The order book
      • How market orders drive prices up and down
      • Live example
    • Video 32: Order book recap
      • How orders flow from trader to execution
      • Colocated computing
      • Mechanics of short selling
    • Video 33: How hedge funds exploit market mechanics
      • Order book-based trading
      • Arbitrage
    • Video 34: The computing inside a hedge fund
      • Trading algos
      • Optimizers
      • Forecasters
  • Module 4: Interview with Paul Jiganti
    • Video 310: How your order gets to the market Part 1
    • Video 320: How your order gets to the market Part 2
    • Video 330: What happened with Knight Capital
  • QUIZ: Market Mechanics

Week 2

  • Module 1: What is a Company Worth?
    • Video 41: Intrinsic value: Value of future dividends
    • Video 42: How and why news affects prices (Event Study)
    • Video 43: Fundamental analysis of company value
  • Module 2: Capital Assets Pricing Model
    • Video 71: Capital Assets Pricing Model
    • Video 72: CAPM: What is Beta
    • Video 73: How Hedge Funds use CAPM
  • Module 3: QSTK Software Overview
    • Video 61*: QSTK software overview
    • Video 63: Installing QSTK on a Mac
    • Video 81: Installing QSTK on Windows and testing QSTK on Windows
  • Module 4: Working with Historical Data* [need to add this module. Daily returns, cumulative returns, etc.]
  • Homework 0: Install QSTK

Week 3

  • Module 1: Manipulating Data in Python with Numpy
    • Video 51: Numpy Part 1
    • Video 52: Numpy Part 2
    • Video 53: Numpy Part 3
  • Module 2: Manipulating Data in QSTK
    • Video 171: QSTK Part 1
    • Video 172: QSTK Part 2
    • Video 173*: QSTK Part 3 [*show how to do major steps for HW1, discuss cached data]
  • Module 3: Homework 1: Analyze and Optimize a Portfolio
    • Video 181: Homework 1 Overview
    • Video 182: Homework 1 Excel example
  • Module 4: Interview with Tom Sosnoff
    • Video 340: Sosnoff Part 1
    • Video 350: Sosnoff Part 2
    • Video 360: Sosnoff Part 3
  • Homework 1: Create and analyze a portfolio

Week 4

  • Module 1: Efficient Markets Hypothesis and Event Studies
    • Video 91: Where does information come from? Arbitrage: Difference between real value and market price
    • Video 92: 3 Versions of Efficient Markets Hypothesis. Is EMH True?
    • Video 93: Event Studies
    • Video 94*: Event Studies Code Demo. Homework 2 Defined. (uses old code)
  • Module 2: Portfolio Optimization and the Efficient Frontier
    • Video 111: Module Objectives and Overview
    • Video 112: The Inputs and Outputs of a Portfolio Optimizer
    • Video 113: The Importance of Correlation and Covariance (in daily returns)
    • Video 114: The Efficient Frontier
    • Video 115: How Optimizers Work (In general, not just for portfolios)
Homework 2: Event Studies

Week 5

  • Module 1: Digging Into Data
    • Video 121: Module Objectives and Overview (Review of the "Correct Answers" to the $5 Event Studies, Survivor Bias)
    • Video 122: Actual vs Adjusted Prices (Dividends & Splits)
    • Video 123: Data Scrubbing (Checking for Sanity)
  • Module 2: Overview of Homework 3
    • Video 131: How Next Two Homeworks Fit Together
    • Video 132: Specification for Homework 3
    • Video 133: Suggestions on Implementation of Homework 3
  • Homework 3*: Build a Market Simulator [clean up example data CSVs]

Week 6

  • Module 1: Overview of Homework 4
  • Module 2: The Fundamental Law
  • Module 3: CAPM for Portfolios: Managing Market Risk
    • Video 141: CAPM recap, overview for portfolios
    • Video 142: Example use of CAPM for long/short bet removing market risk
  • Homework 4: Event Study into Simulator

Week 7

  • Module 1: Information Feeds and Technical Analysis
    • Video 191*: Example Information Feeds
    • Video 192*: Intro to Technical Analysis
    • Video 193*: Some Example Technical Indicators
    • Video 194*: Bollinger Bands
  • Homework 5: Implement Bollinger Bands

Week 8

  • Module 1: Making a Better Market Simulator
    • Commissions
    • Market Impact (Slippage)
  • Module 2: Brief Introduction to Machine Learning
    • Parameterized models
    • Instance based models
  • Module 3: Arbitrage
  • Homework 6: Event Study with Bollinger Bands
  • Homework 7: Bollinger Band-based trading

     Abraços e até o próximo post!


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