---
name: minute-analysis-07738c13d374
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`:

```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

```python
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)

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

### TWAP (Time-Weighted Average Price)

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

### Volume Distribution

```python
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

```bash
pip install pandas numpy requests
```


---
Source attribution: HKUDS
Original project: https://github.com/HKUDS/Vibe-Trading
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Mega AI Hub.

## Upstream license notice

MIT License

Copyright (c) 2026 Gabriel Moreira

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.



# Mega AI Hub resource metadata

Creator / publisher: HKUDS
Original project: https://github.com/HKUDS/Vibe-Trading
Attribution basis: Upstream repository explicitly recorded in package mirror path

Fields of work: Data & Analytics, Finance & Accounting, Marketing, Product Management
Roles: Data Analyst, Financial Analyst, Growth Marketer, Market Research Analyst, Product Manager
Specialties: API, Database
Tools: Okx, Tushare, Yfinance
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: 860c289e402102cd408b07adeb8e06213724843d122ccb5fe3f82a9170de2f13