Retrieve minute-level price data for stocks and cryptocurrencies, calculate average prices and volume patterns, and test intraday trading strategies. Configure the candle interval and trading costs to examine results using historical market data.
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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.
설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
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.
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
}
source + interval (OKX 5m = 365 x 288 = 105120)1m, no more than 30 days for 5m, and no more than 1 year for 1H| 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) |
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)
typical_price = (df["high"] + df["low"] + df["close"]) / 3
df["vwap"] = (typical_price * df["vol"]).cumsum() / df["vol"].cumsum()
df["twap"] = df["close"].expanding().mean()
df["vol_pct"] = df["vol"] / df["vol"].sum() * 100
hourly_vol = df.set_index("ts").resample("1h")["vol"].sum()
| 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) |
1m datasets are still very largeunit="ms"pip install pandas numpy requests
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Compare a Chinese company's US depositary receipts, Hong Kong shares, and mainland shares after adjusting for exchange rates and share ratios. Analyze pricing gaps, valuation differences, and delisting risks to support research into its different listings.
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Retrieve stock prices, futures quotes, exchange rates, and economic indicators with the free Python library AKShare. The skill explains market-specific functions, symbol formats, and data preparation for analysis and testing strategies against historical data.
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Assess whether a Chinese A-share company may receive ST or *ST risk warnings next financial year. Review financial forecasts, dividends, audit opinions, and regulatory penalties, then report risk severity separately from confidence in the prediction.
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Diagnose failed or abnormal backtests, which test trading strategies on historical data. Inspect result files, strategy code, and configuration to identify bugs, make targeted repairs, rerun tests, and verify the updated metrics.
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