code-analysis-playbook

name	code-analysis-playbook
description	How to use the code execution sandbox well — parse the JSON payload (not disk files), compute a couple of useful diagnostics before forecasting, and keep the session stateful within a turn. Load this before writing code. No scripts.

Code-analysis playbook

A short guide to using the run_code sandbox productively. This is a starter skill — extend it with the diagnostics that matter for your problem.

Where your data lives

All data comes from the JSON payload in your context — there are no disk files and no network. The history arrives as a CSV string (e.g. target_history_csv). Parse it with io.StringIO, never as a file path:

import io, pandas as pd
df = pd.read_csv(io.StringIO(payload["target_history_csv"]))

The sandbox is stateful within a turn: parse once in your first code block, then reuse the DataFrame in later blocks instead of re-parsing.

Compute before you forecast

Run a couple of cheap diagnostics so your forecast is grounded in arithmetic, not vibes:

1. Recent trend — slope/return over the last N observations.
2. Volatility — recent standard deviation of changes; it sets how wide your quantile bands should be.
3. Sanity check — does your point forecast sit within a plausible multiple of recent moves? If not, revisit it.

Use the printed numbers to set the point forecast and to calibrate the spread between your low and high quantiles — wider when recent volatility is high.

Domain focus (edit this for your use case)

For S&P 500 log-returns, the series is near-unforecastable in the mean: recent realised volatility is far more predictable than direction. Let recent vol, not a directional hunch, set the width of your quantile bands, and keep the point forecast close to zero unless you have real signal.

Room to grow

• Add your own diagnostic patterns (regime detection, seasonality, covariates).
• Drop reusable reference values into a references/ file and load_skill_resource them.