Financial-Machine-Learning

Ideas from the book Advances in Financial Machine Learning by Dr. Marcos Lopez de Prado, along with other quantitative finance ideas and techniques I come across. It also includes a few basic trading strategies and code to visualize their execution on real time and historical data, and measuring its performance. Some other ideas include automated intrinsic value estimation of companies, and statistical arbitrage on spreads of cointegrated pairs of stocks.

Contents:

• Financial Data Structures (AFML Chapter 2)

  • Information Driven Bars
    • Imbalance Bars
      • Tick Imbalance Bars
      • Volume/Dollar Imbalance Bars
    • Runs Bars
      • Tick Runs Bars
      • Volume/Dollar Runs Bars

• Trading

  • Basic trading strategies for mean reversion and trend following
  • API class for integration with Interactive Brokers to send orders and get real time market data
  • Visualizing the trading strategies, watch how they run on realtime data

• Backtesting

  • Simulating strategy performance on historical data
  • Variable data sampling rate, i.e. sample the price every 1/4 second.
  • Modeling slippage, commissions, and transaction costs
  • Visualizing the model on historical data, as if it were live (variable playback speed)
  • Testing new strategies