Trading Strategy Generation Prompt
Converts a market view into a written, testable strategy with entry rules, invalidation, sizing and explicit failure conditions.
/ quick answer
Use when you have a thesis and need it turned into rules you can follow and evaluate. Converts a market view into a written, testable strategy with entry rules, invalidation, sizing and explicit failure conditions.
Turn the view below into a written, testable strategy. Do not endorse it — stress-test it. VIEW: [ONE PARAGRAPH THESIS] Asset(s): [LIST] Timeframe: [DURATION] Capital at risk: [% OF PORTFOLIO] Risk per trade: [%] Constraints: [venue, chain, liquidity, tax, time available] Produce: 1. Entry rules, precise enough that two people would act identically. 2. Invalidation level and the reasoning behind it. 3. Position sizing derived from the stated risk-per-trade. 4. Exit rules: target logic, partial exits, time-based exit. 5. Assumptions the strategy depends on, ranked by fragility. 6. Market conditions where this strategy loses money repeatedly. 7. Metrics to track to know within 20 trades whether it works. 8. Operational risks: slippage, gas, liquidity, execution latency. No profit projections. No claim that the strategy works.
Entry: buy only after a weekly close above the range high with DEX volume over 2x the 30-day median. Invalidation: weekly close back inside the range — thesis was a breakout, so a re-entry falsifies it. Size: 1% risk / 14% invalidation distance = 7% position. Fragile assumption: liquidity persists at current depth. Loses repeatedly in choppy ranges with false breakouts. Track: win rate, average R, slippage per fill over 20 trades.
What does the Trading Strategy Generation Prompt prompt do?
Use when you have a thesis and need it turned into rules you can follow and evaluate.
Which AI models work with this prompt?
It is model-agnostic: it works with any capable general model. Replace the bracketed variables with your own context before running it.
What output should I expect?
Entry: buy only after a weekly close above the range high with DEX volume over 2x the 30-day median. Invalidation: weekly close back inside the range — thesis was a breakout, so a re-entry falsifies it. Size: 1% risk / 14% invalidation distance = 7% position. Fragile assumption: liquidity persists a.
/ continue exploring
Related concepts
The vocabulary this page depends on.
- →Crypto Risk Management
Risk management in crypto is position sizing plus custody hygiene: deciding what you can lose per trade and what a single compromise can reach.
- →Stop Loss & Take Profit
Stop loss and take profit are pre-committed exit rules that convert a discretionary decision into an executable instruction.
Related workflows
Turn this into a repeatable process.
- →AI Trading Assistant Workflow
Use AI to research, structure and pressure-test a trade plan, keeping approval and execution firmly human.
- →Automated Crypto Alerts
Define the conditions that would change a decision, monitor them automatically, and receive one clean notification instead of watching charts.
- →Crypto Portfolio Monitoring
Aggregate positions across wallets and chains, compute real exposure, and alert on drift instead of checking balances manually.
- →Automated DCA Workflow
Set a recurring buy schedule that runs without your attention, with sizing and frequency chosen so fees stay negligible.
Related tool stacks
The tools that run it in production.
- →AI Trading Stack
Adds an AI analysis and risk-review layer on top of a trading stack, keeping approval and execution human.
- →Crypto Trading Stack
Market data, charting, DEX access and a Web3 wallet — the minimum toolset for deliberate on-chain trade execution.
Related prompts
Reusable prompts for this job.
- →Crypto Risk Analysis Prompt
Runs a pre-mortem on a position or protocol: enumerates failure modes, likelihood, impact and observable early warnings.
- →Position Sizing Prompt
Calculates defensible position size from risk-per-trade, invalidation distance and real exit liquidity.
- →Crypto Market Analysis Prompt
Produces a structured market brief: regime, liquidity conditions, sector rotation, catalysts and what would change the view.
- →Trading Assistant Agent Prompt
System prompt for an assistant that drafts trade plans, argues against them, and never executes.
Related use cases
How people apply it, and what came out.
- →Monitor A Crypto Portfolio
Aggregating six addresses across three chains revealed that a portfolio believed to hold 14 positions actually held one concentrated bet.
- →Automate Trading Alerts
Encoding invalidation levels as automated alerts removed screen-watching and caught two thesis breaks the trader would have slept through.
- →Build An AI Trading Assistant
An assistant that drafts and attacks its own trade plans raised plan completeness to 100% and rejected a fifth of setups on liquidity grounds.
- →Get Token Alerts
Replacing price-only alerts with condition-based rules cut notifications by 80% and caught a liquidity withdrawal before price reflected it.
Comparisons & alternatives
Pick between the options.
- →Manual Trading vs Automated Trading
Manual trading adapts to context; automation enforces consistency. Most durable setups automate monitoring and keep judgement human.
- →CEX vs DEX
Centralised exchanges optimise for liquidity, fiat access and convenience; DEXs optimise for custody, permissionless listing and on-chain transparency.
- →DCA vs Lump Sum
DCA spreads entry over time to reduce timing risk and behavioural error; lump sum maximises exposure time at the cost of concentrated entry risk.
- →DeFi vs Traditional Finance
DeFi offers open access, composability and transparent rules; traditional finance offers legal recourse, insurance and stability.