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Use when tasks need HTML reports, Markdown strategy reports, PNG chart helpers, or report files under the research reports directory.
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 | report |
| description | Use when tasks need HTML reports, Markdown strategy reports, PNG chart helpers, or report files under the research reports directory. |
Render research outputs into self-contained HTML (optionally PDF) using Jinja2 templates + matplotlib charts.
| Component | Purpose |
|---|---|
ReportRenderer | Load Jinja2 templates from skills/report/templates/, render with context, save to reports/ |
charts module | Return matplotlib figures as PNG bytes for inline embedding |
strategy_markdown | Write public Markdown strategy reports plus PNG performance charts |
| Templates | factor_report.html, backtest_report.html, signal_digest.html |
Charts are inlined as base64 data URIs via the png_data_uri Jinja filter, so
every report is a single portable HTML file.
from skills.report import ReportRenderer, charts
renderer = ReportRenderer()
ranking_png = charts.plot_factor_ranking(ranking_df, value_col="IC_IR")
html = renderer.render(
"factor_report",
{
"title": "Macro pool — weekly factor screen",
"pool": "macro_pool",
"n": 5,
"as_of": "2026-05-08",
"ranking_chart": ranking_png, # bytes
"ranking_html": ranking_df.to_html(), # pre-rendered pandas table
},
)
path = renderer.save(html, "macro_pool_2026-05-08.html")
Strategy Markdown reports:
import pandas as pd
from skills.report.strategy_markdown import StrategyReport, write_strategy_report
result_df = pd.DataFrame(
{
"return": [0.01, -0.02],
"cum_return": [0.01, -0.0102],
"drawdown": [0.0, -0.02],
"turnover": [1.0, 0.0],
},
index=pd.date_range("2024-01-01", periods=2, name="eob"),
)
report = StrategyReport(
slug="demo",
title="Demo Strategy",
domain="time_series",
strategy_type="Rule-based",
label="none",
description="Demo description.",
metrics={"sharpe_ratio": 1.5, "total_return": -0.0102},
result_df=result_df,
notes=["Uses date x symbol vector weights."],
)
path = write_strategy_report(report, "reports/strategy_examples")
Pass a template name with or without .html; both work. Relative output
paths resolve against reports/ at the repo root; absolute paths are
respected as-is.
| Function | Returns |
|---|---|
plot_equity_curve(returns, title) | Cumulative (1+r).cumprod() equity curve |
plot_backtest_performance(result_df, title) | Backtest equity curve plus drawdown |
plot_ic_heatmap(ic_df, title) | RdBu_r symmetric heatmap (rows=factors, cols=pools/periods) |
plot_factor_ranking(ranking_df, value_col, label_col, title, top_n) | Horizontal bar chart of top factors, colored by sign |
plot_regime_states(prices, states, title) | Price line with colored bands per regime |
All helpers return bytes (PNG). The headless Agg backend is pinned at
import time so reports render inside cron jobs and SSH sessions without a
display.
#4f81bd; table header background #f4f4f4.{% if chart %} … {% endif %}; the
png_data_uri filter returns an empty string when the chart is None, but
guarding with {% if %} avoids rendering an empty <img> tag.{{ df.to_html() | safe }}.renderer.to_pdf(html, "macro_pool_2026-05-08.pdf")
Requires weasyprint (install via uv pip install weasyprint). Keep this as
an optional dependency — core reports should work without it.
Use when tasks need factor diagnostics, IC/grouped return analysis, attribution, robustness checks, or time-series distribution and stationarity checks.
Use when tasks need vectorized strategy execution, portfolio weighting, portfolio-level filters, transaction cost helpers, exit A/B analysis, overlay metrics, or multi-strategy return blending.
Use when tasks need strategy-agnostic OHLCV indicators, math utilities, generic factor examples, regime slicing, resampling, or label makers.
Use when tasks need optional PyCaret model training, ML factor generation, inference wrappers, feature importance, or sparse LASSO weight generation.
Use when tasks need PandaData/PandaAI market data, reference data, adjustment factors, futures tick downloads, or symbol conversion.
Use when tasks need reusable research pipeline templates, factor screening, parameter sensitivity sweeps, or strategy comparison.