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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.
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
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.
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.
格兰书院三位一体交易系统。均线(55+233)+结构(ABCD)+时空(级别嵌套)的完整交易方法论，含仓位管理、做T/加仓抉择、止盈止损、集合竞价、主涨段判断、关键K、维科夫法。
| name | visualization |
| description | 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. |
| metadata | {"hermes":{"tags":["visualization","charts","plotting","finance","matplotlib"],"category":"analysis","requires_toolsets":["terminal"]}} |
Creates financial charts using matplotlib/mplfinance.
tradinglearn/backtest/backtester.py — plot_results() shows price vs portfolio overlay with buy/sell markerstradinglearn/utils/parameter_optimizer.py — plot_optimization_results() generates 4-panel heatmap figurefrom backtest.backtester import Backtester
bt = Backtester()
bt.run_backtest(data, strategy, params)
bt.plot_results() # Price chart + portfolio value + buy/sell signals
For custom charts:
import matplotlib.pyplot as plt
fig, axes = plt.subplots(2, 1, figsize=(12, 8))
axes[0].plot(portfolio['date'], portfolio['portfolio_value'])
axes[1].bar(trade_log['exit_date'], trade_log['pnl'])