An environment for evaluating algorithm-driven model portfolios under controlled, transparent simulated conditions, with six comparing model portfolios of different selection methods and rebalance rules.
We simulate the behavior of different model portfolio algorithms using historical and daily market data.
Not investment advice: Results are illustrative, subject to data limitations and theoretical assumptions.
The primary goal of this website is to demonstrate how identified model portfolio algorithms perform under simulated conditions. The focus is on understanding algorithm behavior, not on predicting or recommending investments.
Model portfolios here are tools for study and comparison, not actionable trading signals.
We have observed differences in results when downloading price data from different data sources. Small discrepancies in historical prices can lead to variations in reported performance.
Our simulation assumes that portfolios are calculated at the market close and rebalanced at the same time. This is theoretically convenient but not achievable in real-world trading.
An algorithm aims to increase the likelihood of selecting performing stocks, but the daily gain of a model portfolio remains influenced by random factors. Short-term performance can be dominated by noise.
For this reason, model performance should not be evaluated over very short durations. The simulation is more informative over longer horizons and across multiple market conditions.
Historical and daily price data are retrieved from external data providers. Differences between providers can lead to small variations in simulated performance.
Each model applies a defined rule: machine learning signals, momentum ranking, or random selection from a defined stock universe.
Portfolios are recalculated at scheduled intervals (biweekly or monthly), using end-of-day prices, and rebalanced at the theoretical market close.
This website does not provide investment advice. The simulations are for educational and illustrative purposes only. All performance figures are hypothetical, based on historical data and theoretical assumptions.
Actual investment results may differ materially due to execution costs, slippage, taxes, liquidity constraints, and behavioral factors.