People want to practice before risking real money. The simulation sells the hope of being competent enough to invest eventually — and the journal analysis layer sells the hope of becoming the kind of person whose judgment improves over time. If simulation doesn't reflect real market mechanics, it feels like a toy and loses credibility. Slippage, transaction costs, and realistic price impact must be simulated. 来源：https://prompts.chat/prompts/cmmxuk7na0009ky04jgx2k8je
Build a paper trading simulation platform called "Paper" — a realistic, risk-free environment for learning to trade and invest. Core features: - Portfolio setup: user starts with $100,000 in virtual cash. Real-time stock and ETF prices via Yahoo Finance or Alpha Vantage API - Trade execution: market and limit orders supported. Simulate 0.1% slippage on market orders. Commission of $1 per trade (realistic friction without being punitive) - Performance dashboard: P&L chart (daily), total return, annualized return, win rate, average gain and loss, Sharpe ratio, and current sector exposure — all updated with each trade. Built with recharts - Trade journal: required field on every position close — "What was my thesis entering this trade? What happened? What will I do differently?" Three fields, each max 200 characters. Cannot close a position without completing the journal - Behavioral analysis: [LLM API] analyzes the last 20 trade journal entries and identifies recurring behavioral patterns — "You consistently exit winning positions early when they approach round-number price levels" — surfaced monthly - Leaderboard: optional, weekly-resetting leaderboard among friend groups — ranked by risk-adjusted return, not raw P&L Stack: React, Yahoo Finance or Alpha Vantage for market data, [LLM API] for behavioral analysis, recharts. Terminal-inspired design — data dense, no decorative elements.
Reviews OpenAPI/API diffs for breaking changes and writes a migration checklist. 来源：https://prompts.chat/prompts/cmuvorj6m0004li067cffi816
Behavioral guidelines to reduce common LLM coding mistakes. Use when writing, reviewing, or refactoring code to avoid overcomplication, make surgical changes, surface assumptions, and define verifiable success criteria. 来源：https://prompts.chat/prompts/cmny4rpjq0003jy08nopcvben
