akshare-finance_skill

Overview

This skill wraps the AKShare finance data library to provide programmatic access to stocks, futures, options, funds, forex, bonds, indices, and cryptocurrencies. It exposes both realtime and historical market data plus fundamental and derived datasets as pandas DataFrame objects. The wrapper focuses on practical, scriptable calls you can drop into monitoring, analysis, or backtesting workflows.

How this skill works

The skill calls AKShare endpoints and returns standardized pandas DataFrames (symbols, timestamps, OHLCV, indicators, fundamentals). It supports realtime snapshots, K-line/ohlcv series with adjustable periods and forward/backfill adjustments, macroeconomic series, and exchange-specific endpoints (A-share, HK, US, Binance, etc.). You control symbol, period, date ranges, and adjust types; results can be saved to CSV or integrated into downstream pipelines.

When to use it

• Build a portfolio monitor or alerting system for A-share, HK, or US equities.
• Collect historical OHLCV and adjusted prices for backtesting or factor research.
• Fetch macroeconomic series and on-chain/crypto market data for multi-asset analysis.
• Assemble fundamental metrics (PE, PB, earnings) for screening and valuation checks.
• Export exchange snapshots or ranked leaderboards for reporting or dashboards.

Best practices

• Wrap calls with caching or rate-limit handling to avoid repeated network requests and throttling.
• Validate and cross-check critical figures against a second source before making trading decisions.
• Specify explicit start/end dates and timezone-aware timestamps for reproducible datasets.
• Normalize column names and dtypes after retrieval to integrate with scoring or backtesting engines.
• Persist raw responses and cleaned outputs separately so you can reprocess if upstream formats change.

Example use cases

• Intraday portfolio monitor: fetch A-share realtime spot, compute P&L and notify on thresholds.
• Backtest strategy: download adjusted daily K-line series for multiple tickers and run signal evaluation.
• Macro overlay: combine CPI, GDP, and M2 series to inform asset allocation shifts.
• Crypto dataset build: pull Binance symbol lists, historical klines, and normalize into minute/daily tables for research.
• Fundamental screener: retrieve stock valuation and profitability metrics to filter buy candidates.