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moexalgo-supercandles

moexalgo-supercandles

github.com/moexalgoData & databases440 installsUpdated 19w ago
Repository

About moexalgo-supercandles

A skill for retrieving MOEXAlgo SuperCandles through the `moexalgo` Python package and analyzing the results with DataFrames. It directs users to select statistics by metric and supports analysis across markets or individual instruments.

Key features

  1. 01Query markets or individual instruments
  2. 02Choose trade, order, or order-book statistics
  3. 03Analyze DataFrames with pandas
  4. 04Use library `offset` for pagination
  5. 05Keep DataFrames as the default output

Use cases

  1. 01Analyze market data by date
  2. 02Study statistics for one instrument
  3. 03Build pandas pivots, ratios, or plots

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/moexalgo/algopack-skills --skill moexalgo-supercandles

After installing, type/moexalgo-supercandlesin Claude Code to confirm it loaded.

Bundled filesreferences/supercandles.md

Details

From the project README

MOEXAlgo SuperCandles

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.

Comments

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