| """Download Deribit's BTC implied-volatility index (DVOL), hourly, full history. |
| |
| DVOL is the options market's own 30-day expectation of BTC volatility, in |
| annualised percent, published since March 2021. It is information the price |
| series does not contain directly: what option traders are paying for future |
| movement. Public endpoint, no key needed. |
| |
| Rows are hourly OHLC of the index keyed by the hour's open time (UTC), the |
| same convention as the Binance candles, so the close of row t is known when |
| candle t closes and may be used for the forecast issued then. |
| |
| Writes outputs/volatility/btc_dvol_1h.csv. |
| |
| Usage: |
| python scripts/fetch_dvol.py |
| """ |
| from __future__ import annotations |
|
|
| import json |
| import time |
| import urllib.request |
| from pathlib import Path |
|
|
| import pandas as pd |
|
|
| ROOT = Path(__file__).resolve().parents[1] |
| OUT = ROOT / "outputs" / "volatility" / "btc_dvol_1h.csv" |
| URL = ("https://www.deribit.com/api/v2/public/get_volatility_index_data" |
| "?currency=BTC&start_timestamp={start}&end_timestamp={end}&resolution=3600") |
| START_MS = 1609459200000 |
|
|
|
|
| def fetch() -> pd.DataFrame: |
| end, rows = int(time.time() * 1000), [] |
| while end > START_MS: |
| with urllib.request.urlopen(URL.format(start=START_MS, end=end), timeout=30) as resp: |
| result = json.load(resp)["result"] |
| batch = result.get("data") or [] |
| if not batch: |
| break |
| rows += batch |
| nxt = result.get("continuation") |
| if not nxt or nxt >= end: |
| break |
| end = nxt |
| time.sleep(0.2) |
| df = pd.DataFrame(rows, columns=["ts", "open", "high", "low", "close"]) |
| df["timestamp"] = pd.to_datetime(df["ts"], unit="ms", utc=True) |
| return (df.drop(columns="ts").drop_duplicates("timestamp") |
| .sort_values("timestamp").reset_index(drop=True)) |
|
|
|
|
| def main() -> None: |
| df = fetch() |
| OUT.parent.mkdir(parents=True, exist_ok=True) |
| df.to_csv(OUT, index=False) |
| full = pd.date_range(df["timestamp"].min(), df["timestamp"].max(), freq="1h") |
| print(f"{len(df):,} hours, {df['timestamp'].min()} -> {df['timestamp'].max()}; " |
| f"{len(full) - len(df):,} missing hours; DVOL range {df['close'].min():.1f}-{df['close'].max():.1f}") |
| print(f"written {OUT.relative_to(ROOT)}") |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
