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Fetches and processes real-time and historical stock market data from TDX protocol servers. Provides K-line data at multiple intervals, real-time quotes, and market overviews. Trigger when the user requests stock prices, market data, or financial instrument data.
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
| name | market-data |
| description | Fetches and processes real-time and historical stock market data from TDX protocol servers. Provides K-line data at multiple intervals, real-time quotes, and market overviews. Trigger when the user requests stock prices, market data, or financial instrument data. |
| metadata | {"hermes":{"tags":["market-data","tdx","stock","kline","quotes"],"category":"trading","requires_toolsets":["terminal"]}} |
Fetches stock market data from TDX (通达信) protocol servers.
tradinglearn/pytdx2/client/quotationClient.py — QuotationClient with get_KLine_data(), get_security_quotes(), etc.tradinglearn/pytdx2/client/baseStockClient.py — BaseStockClient socket layer with auto-retrytradinglearn/pytdx2/const.py — MARKET, CATEGORY, PERIOD enums, 43+ server hostsget_security_quotes() / get_security_quotes_details() (with 5-level depth)get_KLine_data(market, code, period, start, count) — 1min to yearlyget_top_stock_board(category) — top gainers/losers/amplitude, get_index_info()from pytdx2.client.quotationClient import QuotationClient
from pytdx2.const import MARKET, PERIOD
client = QuotationClient()
client.connect().login()
# Daily K-line for 平安银行
klines = client.get_KLine_data(MARKET.SZ, "000001", PERIOD.DAY, start=0, count=800)
# Real-time quotes for a batch
quotes = client.get_security_quotes(["000001", "000002"])
client.disconnect()
client.disconnect() — sockets leak otherwisedata_fetcher.py wraps this with retry logic and DataFrame normalization燕赤霞短线打板体系 — 一进二/二进三/反包/弱转强/竞价/分歧转一致/涨停金凤凰。基于抖音口播稿蒸馏，适用于A股短线打板、龙头战法、情绪周期判断。
Generates structured trading performance reports. Includes performance metrics summary, equity curve, trade-by-trade log, monthly returns heatmap, and comparative benchmark analysis. Trigger when the user wants a summary or report of trading results.
Creates financial charts and visualizations. Supports candlestick charts, equity curves, indicator overlays, correlation heatmaps, and distribution plots. Trigger when the user wants to visualize financial data or trading results.
Cleans and preprocesses financial time series data. Handles missing values, outliers, corporate actions (splits, dividends), date alignment, and data quality checks. Trigger when the user has messy financial data or needs to prepare data for analysis.
Reviews code with awareness of financial domain pitfalls: lookahead bias, data leakage, proper decimal handling, survivorship bias, overfitting, and correct backtesting methodology. Trigger when the user requests code review of trading or financial code.
Generates documentation for trading and financial code: strategy documentation, API references, usage examples, README files, and inline code comments. Trigger when the user wants to document trading code or generate project documentation.