A high-performance financial data agent skill that intercepts official tushare API requests and routes them to an optimized StockToday backend gateway for faster and cheaper market data retrieval.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install stocktoday-py
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install stocktoday-py using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is stocktoday-data?
The stocktoday-data skill is a developer-centric data solution designed for AI coding agents to efficiently fetch, clean, and process multi-market financial insights. By leveraging standard tushare syntax, it seamlessly handles data tracking across A-shares, Hong Kong stocks, US stocks, mutual funds, futures, options, bonds, and macroeconomic indicators without requiring proprietary wrappers.
By routing calls through the high-speed StockToday gateway, developers benefit from massive optimization, enhanced connection reliability, and lowered data costs. It dramatically accelerates analytical workflows, converting descriptive requests like reading financial sheets, analyzing sector funds, or tracking money flows into robust, ready-to-use pandas DataFrames.
stocktoday-data Use Cases
- Tracking market trends, historical k-lines, and price actions for specified equity assets.
- Extracting corporate financial indicators, balance sheets, cash flows, and performance forecasts.
- Designing multi-ticker comparison matrixes and programmatic ranking boards based on core metrics like PE and ROE.
- Monitoring sector rotation, industry popularity indexes, and technical ladder progressions.
- Analyzing market sentiment indicators, Northbound fund flows, and institutional transaction details.
How stocktoday-data Works
- The AI agent detects natural language intent regarding financial statistics or trends and parses specific operational parameters.
- The system inserts the skill directory into the runtime path and invokes the backend proxy gateway client configuration.
- Standard python libraries intercept regular API calls, automatically overriding target routing parameters seamlessly.
- Secure HTTP requests directly query the high-speed StockToday server endpoints to return complete payloads with zero client-side token caching.
- The skill transforms data arrays into a standardized multi-layer output structure containing a raw outline summary, analytical boundary markers, tabular metrics, and error constraints.
stocktoday-data Setup
Prerequisites
Ensure your development environment utilizes Python 3.7+ and has the core target dependencies installed.
pip install tushare pandas
Authentication and Environment Configuration
Set your credentials via standard system variables to enable automatic loading by the underlying integration script.
export TUSHARE_TOKEN="your_private_token_here"
Integration Example
import sys
sys.path.insert(0, "<skill_dir>/scripts")
from proxy_demo import get_client
# Automatically reads TUSHARE_TOKEN from environment
pro = get_client()
# Query trading metric histories natively
df = pro.daily(ts_code="600519.SH", start_date="20240101", end_date="20241231")
print(df.head())
stocktoday-data Data Schema & Taxonomy
Tiered API Endpoint Hierarchy
Data sets are structured logically across multiple operational tiers depending on account subscription classifications, mapping over 235 complex interfaces into structured responses:
| Data Category | Core API Tokens / Components | Key Input Variables |
|---|---|---|
| Core Market Data | stock_basic, trade_cal, token_info |
list_status, exchange |
| Trend Aggregators | daily, pro_bar, daily_basic, adj_factor |
ts_code, start_date, end_date, adj |
| Corporate Financials | income, balancesheet, cashflow, fina_indicator |
ts_code, period, ann_date |
| Capital Sentiment Flows | moneyflow, moneyflow_hsgt, top_list, top_inst |
trade_date, ts_code, market_type |
| Indexes & Sectors | index_daily, index_basic, sw_daily, ths_index |
ts_code, market |
| Momentum Tracking | limit_list_d, limit_step, ths_hot, dc_hot |
trade_date, nums |
File Output Convention
When the system outputs tabular logs, files are saved automatically into organized localized paths using strict format criteria:[Interface]_[Asset_ID]_[Start]_[End]_[Generation_Timestamp].csv
stocktoday-data Advanced Features
- Smart Parameter Fallbacks: Financial statements automatically default to the most recent historical quarter whenever specific reporting period parameters are omitted.
- Cross-Market Universal Tickers: Programmatic resolution handles different suffix criteria seamlessly, separating asset classes like futures (.SHF) and Hong Kong equities (.HK).
- Fault-Tolerant Redundant Gateway Routing: Automated structural fallbacks shift traffic to three independent server backup arrays if primary endpoint latency peaks.
- Formatted Context Deliveries: Structural outputs isolate key analytical items and risk bounds over raw DataFrame representations to maximize consumer readability.
- Live Analytical Streams: Access high-frequency sub-minute tick arrays and minute-level candles when running advanced multi-agent automations.
SKILL.md
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