Monte Carlo Simulation for Trading Strategy and Portfolio Risk
Summary
Monte Carlo simulation estimates a range of possible outcomes by repeatedly applying a model to randomly sampled inputs. The document distinguishes fully random sampling, sampling from a normal distribution, and randomizing trade order, and favors fully random simulations as less prone to error. It describes using the technique to assess portfolio allocations, strategy robustness, drawdowns, risk of ruin, and measures such as VaR and CVaR.
The article outlines a workflow of modeling a problem, defining random inputs, running many scenarios, and statistically analyzing the results. It also discusses Python-based implementation and portfolio risk assessment, though much of the detailed material is omitted in the supplied text. Simulation results depend on the model and input distributions; they are estimates rather than predictions, and poor assumptions can mislead. The document also notes computational costs and the need to balance run count against stability.
Key ideas
- Monte Carlo simulation estimates outcome distributions by repeatedly running a model with randomly sampled inputs.
- The article distinguishes fully random sampling, normal-distribution sampling, and random trade-order simulation.
- Trading applications include portfolio allocation, strategy stress testing, drawdown estimates, and risk of ruin analysis.
- Simulation results depend on the assumptions and input distributions, so they are not certainties.
- More iterations can stabilize estimates but require additional computation.
Tags
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.