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 osfrom decimal import Decimalimport pandas as pdimport requestsGOLD_API = "https://api.goldprice.dev/v1"FX_API = "https://api.exchangerate.dev/v1"START = "2025-07-10"END = "2026-07-10"LOCAL_CURRENCY = "IDR"# These older dates need Plus or Pro on both APIs; Basic covers the last year.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()