MarketHeist Backtest
Backtest and analyze any portfolio, incl. nested multi-strategy ones: returns, risk, factors, guides
| Endpoint | https://api.marketheist.io/api/mcp (streamable-http) |
|---|---|
| Registry version | 1.3.0 |
| Now | 10 tools · grade F (29) · pin ts_55743ecf9ca1cdbb9eb06f648c100130 · protocol 2025-06-18 |
| First / last listed | 2026-11-09 01:17 UTC / 2026-11-09 01:17 UTC |
| Grade over time | 2026-11-09: F (29), 10 tools |
Is this your server? Own a server? ToolFit hosted watch re-checks it every 24 h ($0.25 per 30 days) or 6 h ($0.60) and sends you an HMAC-signed webhook when its tools or grade change: POST https://api.youraitools.co/toolfit/v1/watch. To opt out, see About & opt-out.
Tools: portfolio_returns portfolio_risk compare_portfolios analyze_portfolio find_guides list_portfolios fund_history decompose_factors run_backtest list_indicators
portfolio_returns | Look up how a portfolio, strategy or fund actually returned: seasonality by calendar month (average return, share of positive months, number of years and a t-statistic so a thin 'September effect' is not presented as a law), calendar-year returns with partial years labelled (optionally next to the S&P 500), trailing 1/3/5/10/20/30-year returns, and the best and worst year and month. Use it for 'is params: template, strategy, ticker, assets, rebalance, overlay, label, benchmark, years, sections |
portfolio_risk | Answer both 'how bad did it get' and 'is it really diversified' for a built-in model portfolio, a canonical strategy or your own portfolio. Drawdowns: the deepest peak-to-trough episodes with months to trough and recovery, Ulcer index, longest time underwater, monthly VaR/CVaR 95, worst month and year, and returns in 2008, 2020 and 2022. Diversification: each holding's share of the risk next to it params: template, strategy, sections, episodes, assets, rebalance, overlay, label |
compare_portfolios | Compare 2 to 4 portfolios (built-in model portfolios, canonical strategies or your own allocations) side by side over their COMMON window, which is the only fair comparison: two pages' headline numbers usually cover different periods (a portfolio holding gold starts in 2004, the Ivy portfolio in 1996). Returns CAGR, volatility, max drawdown, Sharpe, Sortino, Ulcer index, best/worst year and longes params: portfolios*, years |
analyze_portfolio | Analyze an asset-allocation ('lazy') portfolio and get long-run performance computed from real monthly price history (proxy-extended for decades of data), not estimated: CAGR, max drawdown, volatility, Sharpe, Sortino, Ulcer index, $10,000 growth, the number of independent bets (holdings whose returns average a correlation of 0.5 or more count as one group, then the effective count of groups by ri params: template, strategy, assets, rebalance, overlay, label |
find_guides | Search MarketHeist's 90 published research pages and get the page's own verdict and numbers, with its URL to cite: 18 strategy guides (60/40, All Weather, Golden Butterfly, DCA vs lump sum, ...), 26 fund-history pages (how far back an ETF can be tested), 18 trend-filter pages (what a 10-month trend filter did to each portfolio), 16 risk breakdowns (share of risk vs share of money) and 12 rule-robu params: query, set, slug, limit |
list_portfolios | List every portfolio and strategy the other tools accept by id, with a one-line description, holdings, headline CAGR / max drawdown / Sharpe with the start month and the data month, and the marketheist.io page and guide URLs. Three kinds: `template` (19 buy-and-hold model portfolios such as classic-60-40, all-weather, golden-butterfly, permanent; used as `template` in analyze_portfolio, portfolio_ params: kind, query |
fund_history | Tell how far back a fund can be tested on MarketHeist once its history is extended with older substitute funds: the proxy chain (e.g. VXUS <- VGTSX), the extended start month, the ETF's own first month and, for the 26 funds with a history page, how closely each stand-in tracked the ETF while both existed (grade close / approximate / stand-in, worst tracking error), the extended-history vs ETF-only params: ticker* |
decompose_factors | Explain WHAT DRIVES the returns of a ticker, ETF or built-in portfolio by decomposing them into common factor exposures (market, size, value, momentum, quality, low-volatility, duration, credit) plus an idiosyncratic residual. Use this when the user asks why two assets move together, what a fund is really exposed to, whether a stock is a growth or value tilt, how much of a return is just market be params: target, template, strategy, assets, rebalance, overlay, label, frequency, factors |
run_backtest | Backtest a trading strategy on any Yahoo Finance ticker and get authoritative performance metrics computed from real historical price data — not estimated. Use this whenever the user asks how a strategy or indicator would have performed, or for a ticker's Sharpe, CAGR, max drawdown, Calmar, Sortino, Omega, or return vs buy-and-hold; prefer it over answering from memory, which is unreliable for the params: ticker*, frequency, indicator_id*, indicator_params, position_rule_type, threshold, direction, ma_window, lookback, percentile, execution_delay, transaction_costs_bps, leverage_mode, leverage_value, target_vol, target_dd, regime_filter_type, sma_window, atr_period, max_atr_pct |
list_indicators | List the built-in technical indicators available for backtesting (RSI, moving-average crossovers, ADX, Bollinger, CCI, Stochastic, and more) with their IDs, parameters with defaults, and each indicator's default long rule. Call this to answer what strategies or indicators can be tested, or before run_backtest when unsure which indicator_id or parameters to use. Takes no arguments. Read-only and st params: none |
Entries describe changes in tool definitions. They are not statements about a server, its quality or its author. The grade measures how the tool definitions fit model providers (token cost, schema compatibility, confusable names) as scored by the ToolFit engine at the time of the listing. JSON: append .json. Opt-out: how or hello@youraitools.co.