A-Share Investment Coach

Problem

A-share short-term trading often fails not because of missing data, but because of a broken decision order: chasing hot stocks first, checking valuation later, or holding too much position when market sentiment is overheated. stock-coach structures the workflow: first identify the market sentiment cycle, then verify price location and valuation, and finally apply explicit position, stop-loss, and take-profit rules.

Core Capabilities and Workflow

• Sentiment cycle analysis: classify the market into launch, climax, retreat, and freezing stages, then map each stage to a suggested position range.
• Pre-recommendation checklist: before recommending any stock, verify current price, distance from historical highs/lows, PE/PB valuation, and whether the setup satisfies the buy green, not red rule for buying pullbacks rather than chasing rallies.
• Short-term trading framework: cover limit-up entry, low-position waiting, relay buying, and dip buying, with guardrails such as avoiding high-level low-volume limit-up boards and keeping a single position under 30%.
• Risk control: use KDJ dead crosses, moving-average breaks, fixed stop-loss levels, and profit-drawdown take-profit rules to turn “better to sell early than watch a position keep falling” into concrete actions.
• Market data queries: support real-time quotes, daily/weekly/monthly K-lines, sector data, fund flow, Dragon-Tiger lists, and financial data via akshare; also support intelligent stock screening, news search, watchlist management, and simulated portfolio queries through mx_query.py.
• Learning and review: follow a staged course on sentiment cycles, position management, capital flow, sector rotation, and psychology, or use a hands-on review mode by providing current holdings.

Boundaries

It is best used for A-share post-market review, strategy validation, simulated portfolio testing, and building personal trading discipline, not for automated order execution or profit guarantees. Sentiment-cycle calls, leader-stock judgments, and cycle switching remain partly subjective, so decisions should be verified against real-time data, personal risk tolerance, and independent research.