minute-analysis

# 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 ```