06. Program the trading strategy

Overview of Programming a Trading Strategy

The focus is on building a trading strategy using the Geometric Brownian Motion (GBM) model. Here’s a simplified breakdown:

Class Structure

• GBM Class: Serves as the foundation of the strategy.
• Initialization (__init__ function): Establishes three primary variables:
  • Mu: Represents momentum or drift.
  • Sigma: Indicates volatility.
  • RNG: A random number generator for simulating model trajectories.

Functions

• Calibrate Function:

  • Takes parameters: trajectory (time series in a numpy array) and dt (time increment).
  • Converts the trajectory to a logarithmic format and computes differences.
  • Calculates average and standard deviation using bootstrapping to increase accuracy.
  • Utilizes these to estimate parameters Mu and Sigma.

• Forecast Function: Mentioned as the next focus to predict future stock prices.

The explanation centers on creating reliable estimates for GBM model parameters, preparing for future predictive tasks.