為替のルックアヘッド・バイアスを避けて現地通貨建てゴールドをバックテストする

local_gold_close = gold_usd_close × usd_to_local_fx

curl -G "https://api.goldprice.dev/v1/bars" \
  -H "Authorization: Bearer $GOLDPRICE_API_KEY" \
  --data-urlencode "symbol=XAU-USD-SPOT" \
  --data-urlencode "interval=1d" \
  --data-urlencode "from=2025-07-10T00:00:00Z" \
  --data-urlencode "to=2026-07-10T00:00:00Z" \
  --data-urlencode "limit=400"

curl -G "https://api.exchangerate.dev/v1/range" \
  -H "Authorization: Bearer $EXCHANGERATE_API_KEY" \
  --data-urlencode "base=USD" \
  --data-urlencode "symbols=IDR" \
  --data-urlencode "start_date=2025-07-10" \
  --data-urlencode "end_date=2026-07-10"

import os
from decimal import Decimal

import pandas as pd
import requests

GOLD_API = "https://api.goldprice.dev/v1"
FX_API = "https://api.exchangerate.dev/v1"

START = "2025-07-10"
END = "2026-07-10"
LOCAL_CURRENCY = "IDR"

# This one-year example uses Pro history. Free keys can request 30 days.


def fetch_gold_daily(start: str, end: str) -> pd.DataFrame:
    response = requests.get(
        f"{GOLD_API}/bars",
        headers={"Authorization": f"Bearer {os.environ['GOLDPRICE_API_KEY']}"},
        params={
            "symbol": "XAU-USD-SPOT",
            "interval": "1d",
            "from": f"{start}T00:00:00Z",
            "to": f"{end}T00:00:00Z",
            "limit": 400,
        },
        timeout=15,
    )
    response.raise_for_status()
    rows = response.json()["bars"]

    gold = pd.DataFrame(rows)
    gold = gold[gold["is_closed"] & gold["close"].notna()].copy()
    gold["date"] = pd.to_datetime(gold["bar_start"], utc=True).dt.date
    gold["gold_usd_close"] = gold["close"].map(Decimal)
    return gold.set_index("date")[["gold_usd_close"]].sort_index()


def fetch_fx_daily(start: str, end: str, currency: str) -> pd.DataFrame:
    response = requests.get(
        f"{FX_API}/range",
        headers={"Authorization": f"Bearer {os.environ['EXCHANGERATE_API_KEY']}"},
        params={
            "base": "USD",
            "symbols": currency,
            "start_date": start,
            "end_date": end,
        },
        timeout=15,
    )
    response.raise_for_status()
    # The range endpoint contains published business-day rows only. Keep its
    # missing weekends and holidays missing rather than carrying a later rate.
    fx = pd.DataFrame(response.json()["data"])
    fx["date"] = pd.to_datetime(fx["date"]).dt.date
    fx["usd_to_local"] = fx["rates"].map(lambda rates: Decimal(str(rates[currency])))
    return fx.set_index("date")[["usd_to_local"]].sort_index()


gold = fetch_gold_daily(START, END)
fx = fetch_fx_daily(START, END, LOCAL_CURRENCY)

# Only use dates with an actual settled gold close and a fresh FX observation.
series = gold.join(fx, how="inner")
series["local_gold_close"] = series["gold_usd_close"] * series["usd_to_local"]
series["local_return"] = series["local_gold_close"].pct_change()

print(series.tail())

series["fast_ma"] = series["local_gold_close"].rolling(50).mean()
series["slow_ma"] = series["local_gold_close"].rolling(200).mean()
series["signal"] = (series["fast_ma"] > series["slow_ma"]).astype(int)

# A signal formed from today's close may only affect the next row's return.
series["strategy_return"] = series["local_return"] * series["signal"].shift(1)
series["strategy_value"] = (1 + series["strategy_return"].fillna(0)).cumprod()