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
DNSE REST	1m/5m/15m/30m/1H	Vietnam stocks — directly via the DNSE Open API; requires DNSE_API_KEY + DNSE_API_SECRET in .env
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)

DNSE Intraday API (Vietnam Stocks)

from app.services.dnse.intraday_tool import get_intraday_tool

tool = get_intraday_tool()
# Fetch 5-minute bars for VIC (last 5 trading days)
from datetime import datetime, timedelta, timezone
TZ_VN = timezone(timedelta(hours=7))
now = datetime.now(TZ_VN)
from_ts = int((now - timedelta(days=5)).timestamp())
to_ts = int(now.timestamp())

candles = tool.fetch("VIC", resolution="5", from_ts=from_ts, to_ts=to_ts)
# Returns [{time, open, high, low, close, volume}, ...]

The DNSE REST endpoint (GET /price/ohlc) returns TradingView-format arrays (t, o, h, l, c, v) and supports resolutions 1, 5, 15, 30, 1H, 1D. Use the DnseIntradayTool for direct access independent of the WebSocket stream hub.

You can also call the FastAPI endpoint directly:

GET /api/stock/intraday/VIC?resolution=5&start=2026-05-28&end=2026-06-02

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()

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