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Pick a topic from the menu above. The Simulator guides cover the three modes; the Quantitative Backtest guides explain each strategy in detail — how it selects stocks and exactly how and when it rebalances.
Simulator
Test how a one-time deposit plus monthly contributions would have grown in real historical prices.
- Single — one stock or index.
- Compare — up to five tickers, normalized to 100.
- Portfolio — a weighted basket vs. the S&P 500.
Quantitative Backtest
Rule-based factor strategies over the entire S&P 500. Each holds the top N equal-weighted; they differ only in how the N are chosen.
- Top-N Market Cap — the largest companies.
- Momentum — the strongest recent performers.
- Low Volatility — the calmest names.
- Value — the cheapest by earnings yield (~2022+).
- Quality — the highest return on equity (~2022+).
All five share one engine — see Rebalance styles for how the weights are handled at each rebalance (Equal-weight, Reset on change, or Delta only), and how to overlay them to compare.
How the engine works, in plain terms
Every tool on this site runs on the same idea: real month-end closing prices, replayed against a plan you define. When you set a starting deposit, the engine buys at the first month's close. Each monthly contribution buys at that month's close, allowing fractional shares. The value line you see is just the running total of those purchases marked to each month's price — no smoothing, no modeled returns, nothing you couldn't recompute by hand from the same price table.
Month-end pricing is a deliberate choice. It keeps every result auditable — one price per ticker per month, from a public source — and it avoids pretending we know intraday execution prices that a backtest can't honestly claim. The cost is resolution: a crash that fully recovered within a month won't show up, and your real-world buy on the 15th would differ a little from our month-end fill. For questions about long-horizon contribution plans, that trade-off is the right one.
The factor backtests add one layer on top: on a schedule you pick, they rank every S&P 500 stock by a single number (size, trailing return, volatility, earnings yield, or ROE), buy the top N equally, and hold until the next rebalance. Two timing details matter and are documented per strategy: Momentum and Low Volatility rank on the prior month's close and trade at the current month's close — a deliberate one-month lag that prevents look-ahead — while Top-N Market Cap ranks and trades at the same close, since a company's size is knowable in real time. The factor data uses today's index membership and share counts applied backward, which flatters momentum in particular; each strategy page spells out how much that matters.
Price history comes from Yahoo Finance data, split-adjusted for single stocks, refreshed weekly. The Value and Quality factors additionally use annual fundamentals applied with a one-year reporting lag, which is why their backtests only begin around 2022.
What the tools include
Historical monthly price changes, date-range filtering, lump-sum and recurring monthly contributions, fractional-share allocation at each monthly close, multi-ticker comparison, weighted portfolios with optional rebalancing, and rule-based factor backtests.
What they do not include
Taxes, dividends, trading fees, slippage, bid/ask spreads, account-specific rules, broker fractional-share limits, or any forward-looking prediction. Everything uses month-end closing prices, which keeps the data auditable and avoids implying intraday execution. Results are historical simulations, not advice.