minute-analysis

minute-analysis is a skill for Claude Code, Codex from skloxo/TideTrading. It costs 40 tokens per session (1,008 once invoked), scanned A, a copy of minute-analysis, MIT.

A tool for working with price data recorded every minute and testing trading strategies against it. It gets candlestick data from OKX, Tushare, or yfinance.

In plain words
What is it for?
Use it to calculate intraday measures such as VWAP and TWAP, inspect trading volume, and backtest strategies using intervals such as 1, 5, or 15 minutes.
Why use it?
Daily prices can hide important changes within a trading day. This helps you study intraday signals and see how a strategy would have performed at shorter time intervals.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to calculate intraday measures such as VWAP and TWAP, inspect trading volume, and backtest strategies using intervals such as 1, 5, or 15 minutes.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/skloxo/tidetrading/minute-analysis
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add skloxo/TideTrading --skill minute-analysis
Clone the repo
git clone --depth 1 https://github.com/skloxo/TideTrading

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for minute-analysis

README.md
[![agentmods](https://agentmods.dev/badge/skills/skloxo/tidetrading/minute-analysis.svg)](https://agentmods.dev/skills/skloxo/tidetrading/minute-analysis)
Your own site
<a href="https://agentmods.dev/skills/skloxo/tidetrading/minute-analysis"><img src="https://agentmods.dev/badge/skills/skloxo/tidetrading/minute-analysis.svg" alt="Measured on agentmods" height="20"></a>
Per session 40 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,008 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 1 finding. A grade says what 26 rules found in the file — not that it is safe.
Origin 100% copy Near-identical to another mod in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00040 $0.01008
Opus 5 $0.00020 $0.00504
Sonnet 5 $0.00008 $0.00202
Haiku 4.5 $0.00004 $0.00101

Measured 8d ago against content hash 07738c13d374, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

minute-analysis scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.

The scan reads SKILL.md. This mod also ships 1 executable file (example_signal_engine.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Makes network callslowCapability

Not a fault in itself. Listed so you know the mod talks to something, and to what.

resp = requests.get("https://www.okx.com/api/v5/market/candles", params={
Origin

This is a copy

100% identical to minute-analysis — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

agent/src/skills/minute-analysis/SKILL.md · 102 lines

How it starts

The opening of the file, as written. The whole thing — 102 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Minute-Level Data Analysis and Backtesting

Purpose

Retrieve minute-level candlestick data through data-source APIs and calculate intraday indicators (VWAP, TWAP, volume distribution, and more). Supports minute-level backtesting: set "interval": "5m" in config.json and use the backtest tool to run intraday strategies.

Backtest Configuration

For minute-level backtests, simply add the interval field in config.json:

{
  "source": "okx",
  "codes": ["BTC-USDT"],
  "start_date": "2026-03-01",
  "end_date": "2026-03-15",
  "interval": "5m",
  "initial_cash": 1000000,
  "commission": 0.0005
}
  • The annualization factor is inferred automatically from source + interval (OKX 5m = 365 x 288 = 105120)
  • Minute-level datasets are large. Recommended time limits: no more than 7 days for 1m, no more than 30 days for 5m, and no more than 1 year for 1H

Supported Data Sources and Intervals

Data Source Supported Intervals Notes
OKX 1m/5m/15m/30m/1H/4H Cryptocurrency, trades 7x24
Tushare 1m/5m/15m/30m/1H China A-shares, requires score >= 2000
yfinance 1m/5m/15m/30m/1H Hong Kong / US equities (free, no key required)

OKX Minute Candlestick API

import requests
import pandas as pd

resp = requests.get("https://www.okx.com/api/v5/market/candles", params={
    "instId": "BTC-USDT",
    "bar": "1m",       # 1m/5m/15m/30m/1H/4H
    "limit": "300",    # At most 300 rows per request
})
data = resp.json()["data"]
columns = ["ts", "open", "high", "low", "close", "vol", "volCcy", "volCcyQuote", "confirm"]
df = pd.DataFrame(reversed(data), columns=columns)
df["ts"] = pd.to_datetime(df["ts"].astype("int64"), unit="ms")
for col in ["open", "high", "low", "close", "vol"]:
    df[col] = df[col].astype(float)

Indicator Calculation Templates

VWAP (Volume-Weighted Average Price)

typical_price = (df["high"] + df["low"] + df["close"]) / 3
df["vwap"] = (typical_price * df["vol"]).cumsum() / df["vol"].cumsum()

Read the full file on GitHub · 102 lines

Files

What ships with it

1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 8d ago First seen · 102 lines · 40 tokens per session scan A 07738c13d374

Subscribe to this mod's changes

minute-analysis is a skill published in the GitHub repository skloxo/TideTrading (10 stars, last pushed yesterday), licensed MIT. It adds 40 tokens to every session and 1,008 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). It is 100% identical to minute-analysis, differing in 0 lines, and is treated as a copy.

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