minute-analysis

name	minute-analysis
description	Minute-level data analysis and backtesting. Retrieves minute candlesticks through OKX/Tushare/yfinance and can be used both for analysis and as input to the backtest engine.
category	strategy

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={  # see _constants.OKX_API_BASE
    "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()

TWAP (Time-Weighted Average Price)

df["twap"] = df["close"].expanding().mean()

Volume Distribution

df["vol_pct"] = df["vol"] / df["vol"].sum() * 100
hourly_vol = df.set_index("ts").resample("1h")["vol"].sum()

Parameters

Parameter	Description
inst_id	Trading pair, such as "BTC-USDT"
bar / interval	Candlestick interval: 1m/5m/15m/30m/1H/4H
limit	Number of records to retrieve (OKX returns at most 300 per request)

Common Pitfalls

• OKX returns at most 300 rows per request. The loader paginates automatically, but 1m datasets are still very large
• The time range for minute-level backtests should not be too long, otherwise both data retrieval and backtesting will become slow or time out
• Tushare minute endpoints require a score >= 2000. If the score is insufficient, the API returns empty data
• Timestamps are Unix timestamps in milliseconds and should be converted with unit="ms"
• Transaction costs for minute strategies should be set lower (for example 0.05% instead of 0.1%) because intraday trading is frequent

Dependencies

pip install pandas numpy requests