Prompt generator

Finance AI prompt generator

Describe a loosely defined finance task and get 3 well-structured prompts ready to copy.

Describe what you need

3 variants

Analyze risk Build portfolio Backtest strategy

Example finance prompts

3 examples

Backtest reliability review

Beginner

Act as a trading-strategy reviewer. Explain whether my bot's backtest is credible by checking overfitting, look-ahead bias, data leakage, transaction costs, slippage, sample size, and changing market regimes. Ask for missing inputs, then summarize the main risks in plain language and propose the next validation steps.

Walk-forward validation plan

Medium

Act as a systematic-trading analyst. Build a walk-forward validation plan for my bot with in-sample and out-of-sample windows, robustness checks, parameter sensitivity, realistic costs and slippage, drawdown review, and pass/fail criteria. Return a checklist for deciding whether the strategy is ready for the next test stage.

Quantitative strategy audit

Pro

Act as a quantitative-trading auditor. Review my bot for hidden overfitting, feature leakage, survivorship bias, regime fragility, execution assumptions, liquidity constraints, unstable indicators, and risk-adjusted performance quality. Produce a diagnostic report with severity levels, required evidence, failure modes, and experiments that could invalidate the strategy before capital is deployed.

Similar prompts from the 360-prompt library

Keyword-matched from the existing prompt library.

Build a Trading Backtest in Google Sheets

Medium

Shows how to create a traceable spreadsheet backtest that is simple enough to use without programming and includes basic data-integrity controls.

Technical Analysis Strategy Backtesting

ID 162

Act as a trading-system analyst. Design a Google Sheets backtest with columns for timestamp, signal, entry, stop, exit, size, result in currency and R multiples, fees, slippage, regime, and notes. Provide formulas for P/L, drawdown, and expectancy and rules for partial exits, stopped trades, missed signals, and overlapping positions. Add checks against duplicates, future information, formula errors, and silently missing data.

AI-Assisted Backtesting with Validation Controls

Medium

Explains where AI can assist a backtest and which decisions still require reproducible rules, verified data, code review, and independent validation.

Technical Analysis Strategy Backtesting

ID 166

Act as a quantitative research assistant. Design a safe workflow for using AI in data cleaning, rule specification, test-case generation, code review, documentation, and scenario analysis. Define what must not be delegated without evidence, how to verify generated code and references, how to detect data leakage and hidden assumptions, and how to prevent repeated prompting from becoming unrecorded optimization. End with an audit checklist.

Universal Strategy Backtesting Framework

Medium

Creates a reproducible test with explicit rules, clean data, realistic costs, relevant metrics, and strict separation between development and validation.

Technical Analysis Strategy Backtesting

ID 161

Act as a quantitative trading researcher. I want to test a strategy on the crypto market using manual charts / Google Sheets / Python / AI tools. Ask for missing facts, then define entry, exit and risk rules; data sources, timestamps and cleaning; survivorship and look-ahead controls; transaction costs and execution; metrics and uncertainty; development and out-of-sample periods; and forward testing. Include a reproducibility checklist and predeclared pass/fail criteria.