القائمة
Global stock index data — S&P 500, DJI, NASDAQ, DAX, FTSE, Nikkei 225, Hang Seng, KOSPI, and more. Free via yfinance.
يُحدَّد Prompt الذي يراجع المصدر أولًا بشكل افتراضي. يمكنك التبديل إلى أمر مباشر أو تنزيل نسخة محلية.
اقرأ SKILL.md وأي ملفات مرافقة يعرضها SkillsMP قبل أن تقرر التثبيت.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
يتجاوز الأمر المباشر Prompt المخصّص للمراجعة. افحص المصدر قبل تشغيله.
npx skills add https://github.com/SZWzz/AStockPursue --skill global-indicesيبقى الأمر في سطر واحد. مرّر أفقيًا لمراجعته كاملًا قبل النسخ.
تفضّل نسخة محلية؟ نزّل الملفات المتاحة حاليًا لدى SkillsMP.
Professional finance research toolkit — backtesting (7 engines + benchmark comparison panel), factor analysis, Alpha Zoo (452 pre-built alphas across qlib158/alpha101/gtja191/academic), options pricing, 75 finance skills, 29 multi-agent swarm teams, Trade Journal analyzer, and Shadow Account (extract → backtest → render) across 6 data sources (tushare, yfinance, okx, akshare, ccxt, futu).
Harmonic Patterns signal engine. Identifies XABCD five-point structures such as Gartley/Bat/Butterfly/Crab based on Fibonacci geometry, and generates trading signals in the PRZ (Potential Reversal Zone).
Write signal_engine.py for portfolios spanning multiple markets (A-shares + crypto, equity + forex, etc.)
استنادا إلى تصنيف SOC المهني
عرض SKILL.md
| name | global-indices |
| description | Global stock index data — S&P 500, DJI, NASDAQ, DAX, FTSE, Nikkei 225, Hang Seng, KOSPI, and more. Free via yfinance. |
| category | data-source |
Provides OHLCV data and real-time snapshots for 15+ major global stock indices via yfinance. Completely free, no API key required.
The project has a built-in GlobalIndices DataLoader (backtest/loaders/global_indices.py). For backtesting, use source: "global_indices".
| Code | Name | Ticker |
|---|---|---|
| SPX | S&P 500 | ^GSPC |
| DJI | Dow Jones Industrial | ^DJI |
| IXIC | NASDAQ Composite | ^IXIC |
| NDX | NASDAQ 100 | ^NDX |
| RUT | Russell 2000 | ^RUT |
| Code | Name | Ticker |
|---|---|---|
| DAX | DAX 40 | ^GDAXI |
| FTSE | FTSE 100 | ^FTSE |
| CAC | CAC 40 | ^FCHI |
| STOXX | Euro STOXX 50 | ^STOXX50E |
| Code | Name | Ticker |
|---|---|---|
| N225 | Nikkei 225 | ^N225 |
| HSI | Hang Seng Index | ^HSI |
| KOSPI | KOSPI | ^KS11 |
| ASX | ASX 200 | ^AXJO |
| SENSEX | BSE SENSEX | ^BSESN |
| Code | Name | Ticker |
|---|---|---|
| CSI300 | CSI 300 ETF | 510300.SS |
| SZ50 | SSE 50 ETF | 510050.SS |
from backtest.loaders.global_indices import DataLoader, list_indices, get_index_info
# List all indices
all_idx = list_indices()
# List by region
us_idx = list_indices(region="US")
# Fetch historical data
loader = DataLoader()
data = loader.fetch(
codes=["SPX", "DJI", "IXIC"],
start_date="2025-01-01",
end_date="2025-12-31",
interval="1D",
)
# Latest snapshot
latest = loader.fetch_latest() # All indices
us_latest = loader.fetch_latest(codes=["SPX", "DJI", "NDX"])
Use the market_sentiment tool with indicator="global_indices" to get the latest index snapshot.
Returned OHLCV DataFrame follows the standard schema:
trade_date (index), open, high, low, close, volume