MarketHeist Backtest

Reviewed 21h ago.

• tools: 5 tools scanned
• metadata: scanned

No findings.

Tools (5)

• list_indicators

  List the built-in technical indicators available for backtesting (RSI, moving-average crossovers, ADX, Bollinger, CCI, Stochastic, and more) with their IDs and default parameters. Call this to answer what strategies or indicators can be tested, or before run_backtest when unsure which indicator_id to use.

• get_ohlcv

  Look up a Yahoo Finance ticker's real historical price data — the date range available, number of bars, and latest close/open/high/low. Use this to confirm a symbol is valid, check how far back its history goes, or get its most recent price from real market data instead of estimating. No authentication required.

• 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 these figures. Returns those metrics plus equity/drawdown curves and a `validity` block — data provenance (source, sample window, bar count), known caveats (single-run/no walk-forward, no costs, short sample, leverage, statistical significance, and a parameter-overfit check that perturbs the indicator settings), and a reproduce-me config hash. Surface the caveats when reporting results. Always pass execution_delay=1 to avoid lookahead bias. Call list_indicators first if unsure which indicator_id to use.

• 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. Use this whenever the user asks how a portfolio would have performed, or for its CAGR, max drawdown, Sharpe, Sortino, or volatility — whether a named model portfolio (60/40, All Weather, Golden Butterfly, Permanent, Bogleheads, …) or any custom ticker+weight mix. Provide either a `template` id or a custom `assets` allocation. Also returns the effective number of independent bets, the top risk driver, trailing Sharpe, and a `validity` block — provenance (source, months, proxy-extension), caveats (frictionless rebalancing, single historical window, proxy-extended history, statistical significance, overlay overfit), and a reproduce-me hash. Surface the caveats when reporting. Prefer this over answering from memory.

• decompose_factors

  Explain WHAT DRIVES a ticker's or ETF's returns 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 its return is just market beta, or whether it has real alpha. Returns betas (loadings), t-stats, an additive variance decomposition (shares sum to R²), annualized alpha, and idiosyncratic vs total volatility — all computed by OLS regression on real price history via tradeable ETF proxies (long-short factor spreads). This is measured exposure, not a forecast. Prefer it over guessing an asset's style from memory.