Overview
Use this skill when the user wants SuperCandles through the moexalgo Python package and DataFrame analysis.
Access note: Promo includes SuperCandles. Стартовый / Starter free-token availability is planned at T - 1 day; verify current access before promising it.
Quick Start
python -m pip install "moexalgo[dataframe]" python-dotenv
import os
from dotenv import load_dotenv
from moexalgo import session, Market, Ticker
load_dotenv() session.TOKEN = os.environ["APIKEY"]
eq = Market("EQ") trades_all = eq.tradestats(date="2025-01-10") orders_all = eq.orderstats(date="2025-01-10") book_latest = eq.obstats(date="2025-01-10", latest=True)
sber = Ticker("SBER") trades = sber.tradestats(start="2025-01-01", end="2025-01-31") orders = sber.orderstats(start="2025-01-01", end="2025-01-31") book = sber.obstats(start="2025-01-01", end="2025-01-31")
## Core Workflow
1. Use `Market("EQ"|"FO"|"FX")` for all instruments by date.
2. Use `Ticker(...)` for one instrument over `start` and `end`.
3. Choose `tradestats`, `orderstats`, or `obstats` based on the metric the user needs.
4. Analyze with pandas groupbys, pivots, ratios, or plots according to the user's requested output.
5. Use library `offset` for pagination where supported; do not pass raw REST `start` to library methods.
6. Keep DataFrames as the default; mention `native=True` only when the user explicitly asks for dict iterators.
## References
Read `references/supercandles.md` for method signatures, fields, interpretation notes, and DataFrame analysis examples.
## Boundary
For raw endpoint URLs, curl, JSON/CSV, or no-Python answers, use the direct SuperCandles skill instead.