Stockaskill

Smart Stock Selector

Multi-market stock selection with AKShare + SQLite caching. Covers A-shares, HK, US, and funds (ETF/LOF).

Core promise

Given a stock code, market, or investment theme, return actionable signals with scores. All analysis is script-driven and cache-backed. First fetch the pool if cache is cold.

Triggers

Activate on any of these user intents:

Before you start

Refresh all data pools on first run:

python scripts/run.py fetch pool

Workflows

1. Single stock analysis

Normalize code by convention: 6xxxxx/0xxxxx/3xxxxx -> A, xxxx.HK -> HK, plain ticker -> US.

python scripts/run.py analyze 600519 --market A           # terminal output
python scripts/run.py analyze 600519 --market A --format json  # JSON only
python scripts/run.py analyze 600519 --market A --format md     # save MD report

Output: PE/PB/ROE/Dividend, composite factor scores, strategy signal.

2. Deep diagnosis

Comprehensive BUY/SELL/HOLD with risk assessment:

python scripts/run.py diagnose 600519 --market A
python scripts/run.py diagnose 600519 --market A --output-dir ./reports

JSON structure: final_decision.signal, adjusted_score, factors, strategy, technical, sentiment, fundamentals, risks.

Present results:

• BUY -> include stop-loss and take-profit references
• SELL -> explain risk factors
• HOLD -> explain what would need to change

3. Market scan

python scripts/run.py scan A --top 20                    # top 20 A-shares
python scripts/run.py scan HK --top 10 --format json
python scripts/run.py scan FUND --top 20 --output-dir ./reports

If scan returns all zeros, fall back to alpha mode:

python scripts/run.py alpha A --top 10 --candidates 200

For sector-filtered scanning:

python -c "from advisor.scanner import MarketScanner; MarketScanner().scan_by_sector('A', top_n=5)"

4. Alpha momentum scan

Full multi-factor ranking with thread-parallel scoring:

python scripts/run.py alpha A --top 10 --candidates 200

Results include ranked list with scores, signals, F-Score, and BUY summary. Explain which factors drove top rankings (momentum + low-vol + quality ~73% combined).

5. Portfolio construction

python scripts/run.py portfolio --codes 600519,000858,002475 --capital 1000000
python scripts/run.py portfolio-enhanced --capital 1000000

For custom methods (equal weight, risk parity, min variance), see references/python-api.md.

6. Backtest

python scripts/run.py backtest                  # standard Alpha Momentum
python scripts/run.py backtest-enhanced         # enhanced core-satellite

Requires >= 1500 trading days cached. Report CAGR, Sharpe, MaxDD.

7. Fund/ETF screening

python scripts/run.py scan FUND --top 20

For deeper programmatic access:

from data_engine import get_fund_pool, get_fund_nav

8. Data operations

python scripts/run.py fetch pool                # full pool refresh
python scripts/run.py fetch kline 600519        # single stock K-line
python scripts/run.py fetch fundamentals 600519 # single stock fundamentals

Output guidelines

Terminal output (auto-detected):

Use emoji indicators for scannability:

 600519 贵州茅台
   评分 82.3/100 | 信号: BUY (F=8)
   核心驱动: 质量(88.7) + 动量(76.5)
   风险: 低
   参考止损/止盈: 1480.50 / 1980.00

Full diagnosis report format:

1. Decision signal and score
2. Top-3 driving factors
3. Strategy that triggered the signal
4. Sentiment adjustment if >5% impact
5. Risk level and key risks
6. Stop-loss and take-profit reference prices

JSON output (--format json):

All scripts accept --format {json,md,both,none}. JSON for programmatic use, MD for readable reports.

Language:

Use Chinese for A-share content unless the user writes in English. Use English for US/HK.

Gotchas

Key principles

1. Cache first, fetch on miss — SQLite cache before AKShare API. Refresh on TTL expiry.
2. Multi-factor, multi-strategy — Weighted vote across 6 strategies from 7 factor dimensions.
3. Script-driven — Run python scripts/run.py, never reimplement logic.
4. Parallel scoring — Thread pool (8 workers) for alpha scans.
5. Graceful degradation — Cache-only mode on API limit. Partial results over failures.

Reference files

Load only when needed:

• Factor weights & scoring: references/factors.md
• Strategy weights & signals: references/strategies.md
• Sentiment adjustment: references/sentiment.md
• Python API & code: references/python-api.md
• Enhanced Momentum: references/enhanced-momentum.md
• Market sources: references/market-source-playbook.md
• AKShare docs: references/akshare_official_docs.md
• Output style: references/output-style-and-language.md
• Research: references/research-sources.md
• Risk & compliance: references/risk-and-compliance.md
• Dialogue protocol: references/serenity-dialogue-protocol.md

Output file specs

1. Path: ./reports/
2. Naming: {YYYY-MM-DD-HHMM}_{Short Title}.{json,md}
3. Format: Standard Markdown with hierarchy

Risk disclaimer

For investment reference only, not investment advice. Data sources are third-party public platforms with 10-15 minute delay. Past performance does not guarantee future returns.