Wind MCP

A Model Context Protocol server that gives AI assistants direct access to Wind Financial Terminal data.

Wind MCP bridges the Wind Financial Terminal (万得) with AI assistants via the Model Context Protocol. It exposes 23 tools + 1 resource covering market data, fundamentals, macro economics, screening, estimates, portfolio management, and date utilities — all accessible through natural language.

Platform: Windows only (WindPy is a Windows COM-based library).

Companion project to Bloomberg-MCP. Same architecture, same philosophy — but for Wind (万得).

You: "拉一下贵州茅台过去20天的量价数据"

Claude: runs wind_historical with codes=600519.SH, fields=close,volume
        → Returns structured price/volume time series

You: "Find me A-share stocks with PE < 15 and ROE > 20%"

Claude: runs wind_dynamic_screen with universe=A-shares, filters, rank
        → Returns filtered and ranked stock list

Architecture

graph TB
    subgraph Clients
        CC[Claude Code]
        WC[Web Client]
        CA[Custom App]
    end

    subgraph "Wind MCP Server"
        direction TB
        MCP["FastMCP Server<br/><i>23 tools + 1 resource</i>"]

        subgraph Handlers["Handler Layer"]
            direction LR
            SNAP[Snapshot & Historical]
            INTRA[Minute Bars & Ticks]
            MACRO[Macro & Sector]
            SCREEN[Screening]
            EST[Estimates & Holders]
            CAL[Calendar & Dates]
        end

        subgraph Core["Core Layer"]
            direction LR
            SESSION["WindSession<br/><i>Singleton + atexit</i>"]
            CACHE["WindCache<br/><i>TTL-based</i>"]
            PARSER["Parser<br/><i>12 parsers</i>"]
            CONV["Converter<br/><i>BBG → Wind</i>"]
        end
    end

    WIND["Wind Terminal<br/><i>WindPy (w.start)</i>"]

    CC -- stdio --> MCP
    WC -- HTTP/SSE --> MCP
    CA -- HTTP/SSE --> MCP
    MCP --> Handlers
    Handlers --> Core
    SESSION <--> WIND

    style MCP fill:#1a73e8,stroke:#1557b0,color:#fff
    style WIND fill:#e65100,stroke:#bf360c,color:#fff
    style SESSION fill:#2e7d32,stroke:#1b5e20,color:#fff
    style CACHE fill:#2e7d32,stroke:#1b5e20,color:#fff

Tools Overview (23 tools + 1 resource)

graph LR
    subgraph "Market Data (5)"
        T1["wind_snapshot<br/><i>WSS current data</i>"]
        T2["wind_historical<br/><i>WSD time series</i>"]
        T3["wind_minute_bars<br/><i>WSI candles</i>"]
        T4["wind_ticks<br/><i>WST raw ticks</i>"]
        T5["wind_realtime<br/><i>WSQ live quotes</i>"]
    end

    subgraph "Fundamentals & Screening (5)"
        T6["wind_dataset<br/><i>WSET tables</i>"]
        T7["wind_screen<br/><i>WEQS screens</i>"]
        T8["wind_dynamic_screen<br/><i>Custom filter + rank</i>"]
        T9["wind_estimates<br/><i>Consensus</i>"]
        T10["wind_holders<br/><i>Shareholding</i>"]
    end

    subgraph "Macro & Sector (3)"
        T11["wind_macro<br/><i>EDB series</i>"]
        T12["wind_sector_series<br/><i>WSES time series</i>"]
        T13["wind_sector_snapshot<br/><i>WSEE snapshot</i>"]
    end

    subgraph "Connect & Calendar (2)"
        T14["wind_stock_connect<br/><i>Northbound flows</i>"]
        T15["wind_event_calendar<br/><i>IPO, earnings...</i>"]
    end

    subgraph "Composite & Portfolio (4)"
        T16["wind_company_profile<br/><i>One-stop overview</i>"]
        T17["wind_portfolio_report<br/><i>WPF reports</i>"]
        T18["wind_portfolio_snapshot<br/><i>WPS holdings</i>"]
        T19["wind_portfolio_series<br/><i>WPD time series</i>"]
    end

    subgraph "Date Utilities (3)"
        T20["wind_trading_days<br/><i>TDAYS</i>"]
        T21["wind_date_offset<br/><i>TDAYSOFFSET</i>"]
        T22["wind_days_count<br/><i>TDAYSCOUNT</i>"]
    end

    subgraph "Observability (1 tool + 1 resource)"
        T23["wind_metrics<br/><i>Server stats</i>"]
        R1["wind://health<br/><i>Health check</i>"]
    end

    style T6 fill:#1a73e8,stroke:#1557b0,color:#fff
    style T7 fill:#1a73e8,stroke:#1557b0,color:#fff
    style T8 fill:#1a73e8,stroke:#1557b0,color:#fff
    style T11 fill:#7b1fa2,stroke:#6a1b9a,color:#fff
    style T12 fill:#7b1fa2,stroke:#6a1b9a,color:#fff
    style T13 fill:#7b1fa2,stroke:#6a1b9a,color:#fff
    style T16 fill:#00695c,stroke:#004d40,color:#fff
    style T17 fill:#00695c,stroke:#004d40,color:#fff
    style T18 fill:#00695c,stroke:#004d40,color:#fff
    style T19 fill:#00695c,stroke:#004d40,color:#fff
    style R1 fill:#546e7a,stroke:#37474f,color:#fff

Key Features

Bloomberg → Wind Ticker Converter — Accepts Bloomberg-style identifiers (AAPL US Equity, 700 HK Equity, 601012 CH Equity) and auto-converts to Wind codes (AAPL.O, 00700.HK, 601012.SH). Covers US, HK, JP, LN, CH equities, indices, commodities, and currencies.
Modular architecture — server.py is a thin entry point. Handlers, models, formatters, parsers, and converters cleanly separated.
16 FieldSet shortcuts — Pre-defined field collections (PRICE, VALUATION, PROFITABILITY, etc.) that expand to Wind field mnemonics.
Cache layer — TTL-based in-memory cache with data-type-aware expiration (30s for realtime, 24h for static).
12 WindPy parsers — Handle WSS, WSD, WSI, WST, WSQ, WSET, EDB, WSES, WSEE, TDAYS, TDAYSOFFSET, TDAYSCOUNT with NaN→None cleanup and column-to-row transposition.
Dynamic Screening — Custom filtering with operators (gt, lt, between, in, eq) + ranking + slicing, without knowing Wind mnemonics.

Data Flow

sequenceDiagram
    participant Client as AI Assistant
    participant MCP as MCP Server
    participant Conv as Ticker Converter
    participant Expand as Field Expander
    participant Cache as Cache Layer
    participant Session as WindSession
    participant Wind as Wind Terminal

    Client->>MCP: Tool call (JSON)
    MCP->>Conv: Convert BBG → Wind codes
    Conv-->>MCP: Wind codes

    alt FieldSet shortcuts used
        MCP->>Expand: Expand FieldSet shortcuts
        Expand-->>MCP: Resolved field list
    end

    MCP->>Cache: Check cache
    alt Cache hit
        Cache-->>MCP: Cached result
    else Cache miss
        MCP->>Session: Call WindPy API
        Session->>Wind: w.wsd() / w.wss() / ...
        Wind-->>Session: WindData response
        Session-->>MCP: Parsed dicts
        MCP->>Cache: Store with TTL
    end

    alt Markdown format
        MCP-->>Client: Formatted table
    else JSON format
        MCP-->>Client: Structured JSON
    end

Tool Reference

Market Data

Tool	WindPy API	Description	Key Parameters
`wind_snapshot`	WSS	Current field values for any security	`codes`, `fields`, `trade_date`
`wind_historical`	WSD	Time series with configurable periodicity	`codes`, `fields`, `begin_date`, `end_date`
`wind_minute_bars`	WSI	OHLCV intraday candles (1/3/5/15/30/60 min)	`codes`, `fields`, `begin_time`, `end_time`, `bar_size`
`wind_ticks`	WST	Raw tick-level trade data	`codes`, `fields`, `begin_time`, `end_time`
`wind_realtime`	WSQ	Live streaming quotes	`codes`, `fields`

Fundamentals & Screening

Tool	WindPy API	Description	Key Parameters
`wind_dataset`	WSET	Structured datasets (index members, IPOs, etc.)	`table_name`, `options`
`wind_screen`	WEQS	Execute saved Wind stock screens	`screen_name`, `options`
`wind_dynamic_screen`	WSS	Custom filter + rank + slice	`universe`, `fields`, `filters`, `rank_by`, `top_n`
`wind_estimates`	WSS	Consensus estimates and target prices	`codes`, `metrics`, `year`
`wind_holders`	WSS/WSET	Top holders, institutional, fund holdings	`codes`, `holder_type`, `date`

Macro & Sector

Tool	WindPy API	Description	Key Parameters
`wind_macro`	EDB	Macroeconomic indicator time series	`codes`, `begin_date`, `end_date`
`wind_sector_series`	WSES	Sector-level time series	`codes`, `fields`, `begin_date`, `end_date`
`wind_sector_snapshot`	WSEE	Sector-level current snapshot	`codes`, `fields`, `trade_date`

Stock Connect & Calendar

Tool	WindPy API	Description	Key Parameters
`wind_stock_connect`	WSET/WSS	Northbound/Southbound flow data	`codes`, `direction`, `date`
`wind_event_calendar`	WSET	IPO, earnings, dividend calendars	`event_type`, `begin_date`, `end_date`

Composite & Portfolio

Tool	WindPy API	Description	Key Parameters
`wind_company_profile`	WSS+WSD	One-stop company overview (snapshot + estimates + price history)	`codes`
`wind_portfolio_report`	WPF	Portfolio performance/attribution/risk reports	`product_name`, `table_name`, `options`
`wind_portfolio_snapshot`	WPS	Portfolio NAV, holdings, weights, PnL	`portfolio_name`, `fields`, `options`
`wind_portfolio_series`	WPD	Portfolio performance time series	`portfolio_name`, `fields`, `begin_date`

Observability

Tool	Type	Description
`wind_metrics`	Tool	Server metrics: tool call counts, latency, cache hit rate, uptime
`wind://health`	Resource	Wind connection and cache health status

Date Utilities

Tool	WindPy API	Description	Key Parameters
`wind_trading_days`	TDAYS	List trading days in a range	`begin_date`, `end_date`, `calendar`
`wind_date_offset`	TDAYSOFFSET	Offset a date by N trading days	`date`, `offset`, `calendar`
`wind_days_count`	TDAYSCOUNT	Count trading days between two dates	`begin_date`, `end_date`, `calendar`

All tools support response_format: "markdown" (default) or "json".

FieldSet Shortcuts

Instead of remembering Wind field mnemonics, use shorthand names that expand to multiple fields.

FieldSet	Fields	Key Wind Mnemonics
`PRICE`	5	close, open, high, low, pct_chg
`MOMENTUM`	4	pct_chg, pct_chg_5d, pct_chg_1m, pct_chg_ytd
`VOLUME_PROFILE`	4	volume, amt, turn, free_turn
`VALUATION`	5	pe_ttm, pb_lf, ps_ttm, pcf_ocf_ttm, ev2_to_ebitda
`VALUATION_EXTENDED`	9	+ pe_est, dividend_yield, total_mkt_cap, ev
`PROFITABILITY`	6	roe_ttm, roa_ttm, grossprofitmargin, netprofitmargin, operatingprofitmargin, roic
`GROWTH`	4	yoyprofit, yoyrevenue, yoyocf, qfa_yoygr
`BALANCE_SHEET`	6	debttoassets, current_ratio, quick_ratio, cashflow_to_debt, longdebttodebt, equity_ratio
`CASH_FLOW`	5	ocfps, fcf, cf_from_ops, capex, dividendps
`TECHNICAL`	5	rsi, macd, vol_20d, beta_100w, atr_14d
`ANALYST`	4	rating_avg, est_target_price, est_eps_fy1, est_net_profit_fy1
`ESTIMATE_REVISIONS`	4	est_eps_chg_4w, est_eps_chg_13w, est_num_up, est_num_down
`SECTOR`	2	industry_sw, industry_sw_lv2
`NORTHBOUND`	4	sh_hk_share_pct, sh_hk_share_chg, sh_hk_share_amt, sh_hk_share_rank
`MARGIN`	3	margin_buy_bal, margin_sell_bal, margin_net_bal
`RISK`	5	beta_100w, volatility_20d, volatility_60d, sharpe_20d, max_drawdown_1y
`SCREENING_FULL`	50+	All of the above combined

Bloomberg → Wind Ticker Conversion

Wind MCP automatically converts Bloomberg-style identifiers to Wind codes:

Bloomberg Format	Wind Code	Market
`AAPL US Equity`	`AAPL.O`	US (NASDAQ)
`JPM US Equity`	`JPM.N`	US (NYSE)
`700 HK Equity`	`00700.HK`	Hong Kong
`7203 JP Equity`	`7203.T`	Japan
`VOD LN Equity`	`VOD.L`	London
`601012 CH Equity`	`601012.SH`	A-share (Shanghai)
`000001 CH Equity`	`000001.SZ`	A-share (Shenzhen)
`300750 CH Equity`	`300750.SZ`	A-share (ChiNext)
`SPX Index`	`SPX.GI`	Index
`HSI Index`	`HSI.HI`	Index
`CL1 Comdty`	`CL.NYM`	Commodity
`EURUSD Curncy`	`EURUSD.FX`	Currency
`600519.SH`	`600519.SH`	Passthrough (already Wind)

Dynamic Screening

Build custom screens with pre-validated field sets, filters, and ranking — no need to know Wind field mnemonics.

flowchart LR
    A["Universe<br/><i>index, sector,<br/>or ticker list</i>"] --> B["Field Expansion<br/><i>FieldSet shortcuts<br/>→ Wind fields</i>"]
    B --> C["Wind API<br/><i>WSS request</i>"]
    C --> D["Filter<br/><i>gt, lt, between,<br/>in, eq, ...</i>"]
    D --> E["Rank & Slice<br/><i>rank_by + top_n</i>"]
    E --> F["Response<br/><i>Markdown table<br/>or JSON</i>"]

    style A fill:#e8f5e9,stroke:#2e7d32
    style C fill:#fff3e0,stroke:#ff6f00
    style F fill:#e3f2fd,stroke:#1a73e8

Filter Operators

Operator	Description	Example
`gt` / `gte`	Greater than (or equal)	`{"field": "roe_ttm", "op": "gt", "value": 20}`
`lt` / `lte`	Less than (or equal)	`{"field": "pe_ttm", "op": "lt", "value": 15}`
`eq` / `neq`	Equals / not equals	`{"field": "industry_sw", "op": "eq", "value": "电子"}`
`between`	Range (inclusive)	`{"field": "pe_ttm", "op": "between", "value": [10, 25]}`
`in`	Value in list	`{"field": "industry_sw", "op": "in", "value": ["电子", "计算机"]}`

Example: Find Undervalued High-ROE A-shares

{
  "universe": "sector:全部A股",
  "fields": ["PRICE", "VALUATION", "PROFITABILITY"],
  "filters": [
    {"field": "pe_ttm", "op": "lt", "value": 15},
    {"field": "roe_ttm", "op": "gt", "value": 20}
  ],
  "rank_by": "roe_ttm",
  "rank_descending": true,
  "top_n": 20
}

Cache Layer

Built-in cache reduces Wind API load with data-type-aware TTLs:

Data Type	Default TTL	Rationale
Realtime quotes (WSQ)	30 seconds	Near real-time
Snapshot (WSS)	5 minutes	Moderate refresh
Minute bars (WSI)	5 minutes	Intraday refresh
Ticks (WST)	1 minute	High-frequency
Portfolio (WPF/WPS/WPD)	5 minutes	Moderate refresh
Screening (WEQS)	10 minutes	Moderate refresh
Estimates	4 hours	Consensus updates infrequently
Historical (WSD)	12 hours	End-of-day data stable
Macro data (EDB)	24 hours	Periodic updates
Dataset (WSET)	24 hours	Reference data
Sector (WSES/WSEE)	24 hours	Rarely changes
Holders / Dates	24 hours	Rarely changes

All TTLs are configurable via wind_mcp.toml or environment variables. See Configuration.

Configuration

Server behavior can be customized via wind_mcp.toml in the project root. All values can also be overridden via environment variables with the prefix WIND_MCP_ (e.g., WIND_MCP_CACHE_MAXSIZE=5000).

[session]
connect_timeout = 30        # Seconds to wait for Wind connection
reconnect_retries = 3       # Auto-reconnect attempts
reconnect_backoff = 1.0     # Backoff multiplier between retries

[cache]
maxsize = 2000              # Max cached entries (LRU eviction)
ttl_realtime = 30           # WSQ — near real-time
ttl_snapshot = 300          # WSS — 5 min
ttl_historical = 43200     # WSD — 12 hours
ttl_dataset = 86400        # WSET — 24 hours
ttl_macro = 86400          # EDB — 24 hours
ttl_portfolio = 300        # WPF/WPS/WPD — 5 min

[api]
timeout = 30.0              # Per-call timeout (seconds)
retries = 2                 # Retry count on transient errors
backoff = 1.0               # Backoff multiplier

[log]
format = "text"             # "json" or "text"
level = "INFO"

Project Structure

wind-mcp/
├── pyproject.toml
├── README.md
├── LICENSE
├── wind_mcp.toml                  # Server configuration (cache TTL, timeouts, retries)
├── run_server.bat / run_server.ps1
├── src/wind_mcp/
│   ├── __init__.py
│   ├── __main__.py
│   ├── server.py                  # FastMCP entry point, 23 tools + 1 resource
│   ├── formatters.py              # Markdown table & JSON output
│   ├── core/
│   │   ├── session.py             # WindSession singleton + reconnect
│   │   ├── cache.py               # TTL cache with LRU eviction
│   │   ├── parser.py              # 12 WindData parsers
│   │   ├── converter.py           # Bloomberg → Wind ticker converter
│   │   ├── executor.py            # Single-thread executor for Wind API calls
│   │   ├── validators.py          # Input validation (codes, dates, fields)
│   │   ├── resilience.py          # Timeout, retry, stale-cache fallback
│   │   ├── config.py              # Centralized config (env > toml > defaults)
│   │   ├── metrics.py             # Counters + histograms for observability
│   │   ├── universe.py            # Universe resolution (index/sector → security list)
│   │   └── filters.py             # In-memory data filtering (gt/lt/between/in/...)
│   ├── models/
│   │   ├── enums.py               # ResponseFormat, Periodicity, etc.
│   │   └── inputs.py              # Pydantic input models
│   ├── tools/
│   │   ├── fieldsets.py           # 16 FieldSet definitions
│   │   └── field_expander.py      # FieldSet → field list resolver
│   ├── handlers/                  # 16 handler modules
│   │   ├── snapshot.py            # WSS
│   │   ├── historical.py          # WSD
│   │   ├── minute_bars.py         # WSI
│   │   ├── ticks.py               # WST
│   │   ├── realtime.py            # WSQ
│   │   ├── dataset.py             # WSET
│   │   ├── macro.py               # EDB
│   │   ├── sector.py              # WSES / WSEE
│   │   ├── screening.py           # WEQS + dynamic screen
│   │   ├── estimates.py           # Consensus estimates
│   │   ├── holders.py             # Holder analysis
│   │   ├── stock_connect.py       # Northbound/Southbound
│   │   ├── calendar.py            # Event calendar
│   │   ├── dates.py               # TDAYS / TDAYSOFFSET / TDAYSCOUNT
│   │   ├── composite.py           # Company profile (multi-source)
│   │   └── portfolio.py           # WPF / WPS / WPD portfolio tools
│   └── data/                      # Static mapping files
│       ├── us_exchange_map.json   # ~200 US tickers → exchange
│       ├── index_map.json         # BBG → Wind index mapping
│       ├── commodity_map.json     # BBG → Wind commodity mapping
│       └── currency_map.json      # BBG → Wind currency mapping
├── tests/                         # 101 unit tests (no Wind needed)
│   ├── test_parser.py
│   ├── test_fieldsets.py
│   ├── test_cache.py
│   ├── test_config.py
│   ├── test_converter.py
│   ├── test_filters.py
│   ├── test_formatters.py
│   ├── test_inputs.py
│   ├── test_metrics.py
│   ├── test_resilience.py
│   └── test_validators.py
└── examples/
    ├── basic_usage.py
    └── screening_example.py

Installation

Prerequisites

Windows (WindPy is a Windows COM-based library)
Python 3.10+ (must match Wind Terminal bitness — typically 64-bit)
Wind Financial Terminal (万得) running and logged in (iWind or WFT)
WindPy — installed via Wind Terminal, NOT via pip. Open Wind Terminal → click "Repair" or install the WindPy plugin from the Tools menu.

Verify WindPy

Before installing Wind MCP, verify that WindPy works in your Python environment:

python -c "from WindPy import w; w.start(); print(w.isconnected())"
# Should print: True

If this returns False or errors, fix your WindPy installation first. Common issues:

Wind Terminal not running / not logged in
Python bitness mismatch (e.g., 32-bit Python with 64-bit Wind)
WindPy not installed (repair via Wind Terminal)

Install

# From source
git clone https://github.com/QmQsun/Wind-MCP.git
cd Wind-MCP
pip install .            # standard install
# or: pip install -e .   # editable mode (for development)

# Or from PyPI
pip install wind-mcp

Configure Claude Code

Add a .mcp.json file to register the MCP server. Two options:

Global (all projects can use Wind MCP) — place at ~/.mcp.json (i.e., C:\Users\<you>\.mcp.json):

{
  "mcpServers": {
    "wind-mcp": {
      "command": "wind-mcp",
      "args": []
    }
  }
}

Project-level (only one project) — place .mcp.json in the project root directory.

The wind-mcp command is the console entry point installed by pip. Alternatively, use "command": "python", "args": ["-m", "wind_mcp.server"].

Quick Start

As an MCP Server

# stdio (default — for Claude Code)
python -m wind_mcp.server

# HTTP transport (for web clients)
python -m wind_mcp.server --http --port=8080

# SSE transport (for streaming clients)
python -m wind_mcp.server --sse --port=8080

Windows

# Command Prompt
run_server.bat

# PowerShell
.\run_server.ps1

Wind API Coverage

graph LR
    subgraph "Wind Terminal (WindPy)"
        WSS["WSS<br/><i>Snapshot</i>"]
        WSD["WSD<br/><i>Historical</i>"]
        WSI["WSI<br/><i>Minute bars</i>"]
        WST["WST<br/><i>Tick data</i>"]
        WSQ["WSQ<br/><i>Realtime</i>"]
        WSET["WSET<br/><i>Datasets</i>"]
        EDB["EDB<br/><i>Macro</i>"]
        WSES["WSES<br/><i>Sector series</i>"]
        WSEE["WSEE<br/><i>Sector snapshot</i>"]
        WEQS["WEQS<br/><i>Stock screener</i>"]
        TDAYS["TDAYS<br/><i>Trading days</i>"]
    end

    WSS --- T1[Snapshot + Estimates + Holders + Screen]
    WSD --- T2[Historical Time Series]
    WSI --- T3[Intraday Bars]
    WST --- T4[Tick Data]
    WSQ --- T5[Realtime Quotes]
    WSET --- T6[Datasets + Connect + Calendar]
    EDB --- T7[Macro Indicators]
    WSES --- T8[Sector Time Series]
    WSEE --- T9[Sector Snapshot]
    WEQS --- T10[Saved Screens]
    TDAYS --- T11[Date Utilities]

    style WSS fill:#1a73e8,stroke:#1557b0,color:#fff
    style WSD fill:#1a73e8,stroke:#1557b0,color:#fff
    style EDB fill:#7b1fa2,stroke:#6a1b9a,color:#fff
    style WEQS fill:#e65100,stroke:#bf360c,color:#fff

Contributing

Contributions welcome! Please open an issue or submit a pull request.

pip install -e ".[dev]"
pytest                    # Unit tests (no Wind Terminal needed)
black src/ tests/
ruff check src/ tests/

Related Projects

Bloomberg-MCP — Same architecture for Bloomberg Terminal

License

MIT — see LICENSE for details.

Name		Name	Last commit message	Last commit date
Latest commit History 4 Commits
examples		examples
src/wind_mcp		src/wind_mcp
tests		tests
.gitignore		.gitignore
LICENSE		LICENSE
README.md		README.md
pyproject.toml		pyproject.toml
run_server.bat		run_server.bat
run_server.ps1		run_server.ps1
wind_mcp.toml		wind_mcp.toml

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Wind MCP

Architecture

Tools Overview (23 tools + 1 resource)

Key Features

Data Flow

Tool Reference

Market Data

Fundamentals & Screening

Macro & Sector

Stock Connect & Calendar

Composite & Portfolio

Observability

Date Utilities

FieldSet Shortcuts

Bloomberg → Wind Ticker Conversion

Dynamic Screening

Filter Operators

Example: Find Undervalued High-ROE A-shares

Cache Layer

Configuration

Project Structure

Installation

Prerequisites

Verify WindPy

Install

Configure Claude Code

Quick Start

As an MCP Server

Windows

Wind API Coverage

Contributing

Related Projects

License

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