Backtesting the Gold Macro Scorecard: Does the Signal Deliver?

なぜゴールドマクロスコアカードをバックテストする?

関連記事 マクロデータを使用して金価格を予測する, we built a composite macro scorecard that assigns directional signals to six US macro indicators — TIPS 10Y real yield, breakeven inflation, Fed policy rate, Fed total assets, M2 money supply, and the trade-weighted dollar — and aggregates them into a net gold bias. The scorecard tells you whether the macro regime favours gold. But does it actually work?

この記事では,この質問に回答するために, システム的なバックテストを行います. 日常 通貨の価格について 商品の最終点ネットワーク信号に基づいて 金貨のシンプルなロング/フラットポジションを保持し, その信号が 買取・保有値以上の 有意義なリターンを 提供しているか測定します.

ステップ1: 日々の金価格とマクロシリーズを取得

バックテストの基礎はFXMacroDataからの日々の金価格です. 商品/金 endpoint — LBMA PM Fix prices in USD per troy ounce. Unlike monthly or weekly aggregated data, daily prices let us measure the precise impact of each macro signal transition.

import requests
import pandas as pd
from datetime import date

BASE = "https://fxmacrodata.com/api/v1"
KEY  = "YOUR_API_KEY"

def get_series(path: str, start: str = "2020-01-01") -> pd.DataFrame:
    """Fetch a time series and return as a DataFrame with date index."""
    r = requests.get(f"{BASE}{path}", params={"api_key": KEY, "start_date": start})
    r.raise_for_status()
    data = r.json().get("data", [])
    df = pd.DataFrame(data)
    if not df.empty:
        df["date"] = pd.to_datetime(df["date"])
        df = df.set_index("date").sort_index()
    return df

# Daily gold prices
gold = get_series("/commodities/gold")
print(f"Gold: {len(gold)} daily observations, {gold.index[0].date()} to {gold.index[-1].date()}")
# Gold: ~1350 daily observations, 2020-01-02 to 2026-04-15

Next, pull the six macro indicator series that feed the scorecard. These are published at different frequencies — some weekly (TIPS yield, breakeven), some monthly (CPI, M2), some on FOMC dates (policy rate) — but each observation persists as the "current" value until the next release.

# Macro indicator series
series = {
    "tips":       get_series("/announcements/usd/inflation_linked_bond"),
    "breakeven":  get_series("/announcements/usd/breakeven_inflation_rate"),
    "policy":     get_series("/announcements/usd/policy_rate"),
    "cb_assets":  get_series("/announcements/usd/cb_assets"),
    "m2":         get_series("/announcements/usd/m2"),
    "twi":        get_series("/announcements/usd/trade_weighted_index"),
}

for name, df in series.items():
    print(f"  {name:12s}: {len(df):4d} obs  ({df.index[0].date()} – {df.index[-1].date()})")

ステップ2: 順番を並べ,前記を記入する

すべてのマクロシリーズを日々のゴールド日付インデックスに合併する.各マクロ値は,リリース日から次のリリースまで前進させられる.

# Align all series to the daily gold date index
aligned = gold[["val"]].rename(columns={"val": "gold"}).copy()

for name, df in series.items():
    # Reindex to gold dates and forward-fill
    macro = df[["val"]].rename(columns={"val": name})
    macro = macro.reindex(aligned.index, method="ffill")
    aligned = aligned.join(macro)

# Drop rows where any macro series hasn't started yet
aligned = aligned.dropna()
print(f"Aligned dataset: {len(aligned)} trading days")
print(aligned.tail())

ステップ3: 日々のスコアカード信号を計算する

On each trading day, we compute the same scorecard from the original article — but instead of comparing the latest two observations, we compare the current forward-filled value against the value from 30 calendar days prior. This gives a more robust measure of direction than day-over-day noise.

LOOKBACK = 30  # calendar days for direction detection

def score_column(col: pd.Series, mode: str) -> pd.Series:
    """Score a macro series: +1 bullish gold, 0 neutral, -1 bearish."""
    prev = col.shift(LOOKBACK)
    change = col - prev

    if mode == "falling":
        return pd.Series(
            [1.0 if c < -0.05 else (-1.0 if c > 0.05 else 0.0) for c in change],
            index=col.index
        )
    elif mode == "rising":
        return pd.Series(
            [1.0 if c > 0.05 else (-1.0 if c < -0.05 else 0.0) for c in change],
            index=col.index
        )
    elif mode == "negative":
        return pd.Series(
            [1.0 if v < 0 else (-1.0 if v > 1.0 else 0.0) for v in col],
            index=col.index
        )
    return pd.Series(0.0, index=col.index)


scoring_rules = {
    "tips":      "negative",   # low/negative real rates = bullish gold
    "breakeven": "rising",     # rising inflation expectations = bullish
    "policy":    "falling",    # falling policy rate = bullish
    "cb_assets": "rising",     # expanding balance sheet = bullish
    "m2":        "rising",     # growing money supply = bullish
    "twi":       "falling",    # weakening dollar = bullish
}

for name, mode in scoring_rules.items():
    aligned[f"sig_{name}"] = score_column(aligned[name], mode)

signal_cols = [f"sig_{name}" for name in scoring_rules]
aligned["net_score"] = aligned[signal_cols].sum(axis=1)

print(aligned[["gold", "net_score"]].tail(10))

ステップ4: 取引規則を定義する

バックテストはシンプルで現実的なルールを使います

• 信号は長かった 純スコアカード ≥ +2 となると,ゴールドにロングする (ゴールドの値上げを期待する).
• 信号が表示されない 純スコアカード < +2 の場合,現金保持 (金貨のポジションがない)
• 短売りは禁止 マクロスコアカードは,金貨の有利な体制を特定する.
• 負債は負けない ポジションは100%金か100%現金か
• 日々の再バランス: 信号は日末に評価され,ポジション変更は次の取引日の収益に適用されます.
• 取引費用 円回取引 (入場+出場) につき 5 ベースポイントを引いて 金ETFや先物取引のスプリッドとスリップを計算します

# Trading rules
THRESHOLD = 2.0     # net score threshold to go long
COST_BPS  = 5       # round-trip cost in basis points

# Daily gold returns
aligned["gold_ret"] = aligned["gold"].pct_change()

# Position: 1 = long gold, 0 = flat (cash)
# Signal on day t is based on data available at close of day t
# Position applies to day t+1's return
aligned["position"] = (aligned["net_score"] >= THRESHOLD).astype(float)

# Detect trade events (position changes)
aligned["trade"] = aligned["position"].diff().abs()
aligned.loc[aligned.index[0], "trade"] = 0  # no trade on first day

# Strategy return: position from previous day * today's gold return, minus costs
aligned["strat_ret"] = (
    aligned["position"].shift(1) * aligned["gold_ret"]
    - aligned["trade"].shift(1) * (COST_BPS / 10_000)
)

# Cumulative returns
aligned["gold_cum"]  = (1 + aligned["gold_ret"]).cumprod()
aligned["strat_cum"] = (1 + aligned["strat_ret"].fillna(0)).cumprod()

print(f"Buy-and-hold return: {(aligned['gold_cum'].iloc[-1] - 1) * 100:.1f}%")
print(f"Strategy return:     {(aligned['strat_cum'].iloc[-1] - 1) * 100:.1f}%")

ステップ 5: 測定性能

リスク調整指標は,戦略の優位性はスキル (マクロ体制のタイミング) によるものなのか,それとも単にリスクの引き上げによるものかを教えてくれます.

import numpy as np

def performance_stats(returns: pd.Series, trades: pd.Series, label: str) -> dict:
    """Compute key performance stats for a return series."""
    total_ret = (1 + returns).prod() - 1
    ann_ret   = (1 + total_ret) ** (252 / len(returns)) - 1
    ann_vol   = returns.std() * np.sqrt(252)
    sharpe    = ann_ret / ann_vol if ann_vol > 0 else 0
    # Maximum drawdown
    cum = (1 + returns).cumprod()
    peak = cum.cummax()
    dd = (cum - peak) / peak
    max_dd = dd.min()
    # Win rate
    invested_days = returns[returns != 0]
    win_rate = (invested_days > 0).mean() if len(invested_days) > 0 else 0
    n_trades = int(trades.sum() / 2)  # round trips

    return {
        "label":        label,
        "total_return":  f"{total_ret * 100:.1f}%",
        "annual_return": f"{ann_ret * 100:.1f}%",
        "annual_vol":    f"{ann_vol * 100:.1f}%",
        "sharpe_ratio":  f"{sharpe:.2f}",
        "max_drawdown":  f"{max_dd * 100:.1f}%",
        "win_rate":      f"{win_rate * 100:.1f}%",
        "trades":        n_trades,
    }

strat_stats = performance_stats(
    aligned["strat_ret"].dropna(),
    aligned["trade"].fillna(0),
    "Macro Scorecard"
)
bnh_stats = performance_stats(
    aligned["gold_ret"].dropna(),
    pd.Series(0, index=aligned.index),
    "Buy & Hold"
)

for k in strat_stats:
    if k == "label":
        print(f"{'Metric':<20s} {strat_stats[k]:>20s} {bnh_stats[k]:>20s}")
        print("-" * 62)
    else:
        print(f"  {k:<18s} {strat_stats[k]:>20s} {bnh_stats[k]:>20s}")

ステップ 6: 引き上げと信号品質を分析する

マクロタイムリングモデルの最も重要な価値提案は,すべての上昇動きを把握することではなく,最悪の下落動きを避けることです.戦略が平らだった (金貨がなくなっていた) 期間と,それが有意義な引き下げに対応していたかどうかを調べましょう.

# Identify flat periods and their gold returns
flat_mask = aligned["position"].shift(1) == 0
flat_gold_ret = aligned.loc[flat_mask, "gold_ret"]
long_gold_ret = aligned.loc[~flat_mask, "gold_ret"]

print(f"Days long gold:       {(~flat_mask).sum()}")
print(f"Days flat (cash):     {flat_mask.sum()}")
print(f"Avg daily ret (long): {long_gold_ret.mean()*100:.3f}%")
print(f"Avg daily ret (flat): {flat_gold_ret.mean()*100:.3f}%")
print(f"Avoided loss days:    {(flat_gold_ret < 0).sum()} "
      f"(total loss: {flat_gold_ret[flat_gold_ret < 0].sum()*100:.1f}%)")

The 2022 drawdown is the clearest example. As the Fed raised rates aggressively from March to October 2022, the TIPS 10Y yield surged from near zero to +1.6%, the trade-weighted dollar appreciated sharply, and M2 growth turned negative. The scorecard correctly read all three signals as bearish and moved to cash, avoiding the bulk of gold's ~20% decline from peak to trough.

ステップ7:信号の調節分別

すべてのスコアカードレベルが等しくありません. 平均先物金利を純スコアレベルに分割すると,シグナルが有利な制度と不利な制度を区別する方法が明らかになります.

# Forward 20-day gold return by score level
aligned["fwd_20d"] = aligned["gold"].pct_change(20).shift(-20)

regime_stats = (
    aligned.groupby("net_score")["fwd_20d"]
    .agg(["mean", "std", "count"])
    .rename(columns={"mean": "avg_20d_ret", "std": "vol_20d", "count": "days"})
)
regime_stats["avg_20d_ret"] *= 100
regime_stats["vol_20d"] *= 100
print(regime_stats.round(2))

ステップ 8: 安定性 チェック

単一のバックテスト構成が過剰に適合する.ここで,キーパラメータを変化させることで結果が脆弱でないことを確認します.

限界感度

results = []
for thresh in range(-2, 6):
    pos = (aligned["net_score"] >= thresh).astype(float)
    ret = pos.shift(1) * aligned["gold_ret"]
    trades = pos.diff().abs().fillna(0)
    ret -= trades.shift(1) * (COST_BPS / 10_000)
    cum = (1 + ret.fillna(0)).prod()
    vol = ret.std() * np.sqrt(252)
    ann = cum ** (252 / len(ret)) - 1
    sharpe = ann / vol if vol > 0 else 0
    results.append({"threshold": thresh, "total_ret": f"{(cum-1)*100:.1f}%",
                    "sharpe": round(sharpe, 2), "pct_invested": f"{pos.mean()*100:.0f}%"})

pd.DataFrame(results).set_index("threshold")

振り返る時の感受性

for lb in [15, 30, 60, 90]:
    # Recompute scores with different lookback
    sig_sum = pd.Series(0.0, index=aligned.index)
    for name, mode in scoring_rules.items():
        prev = aligned[name].shift(lb)
        chg  = aligned[name] - prev
        if mode == "falling":
            sig = pd.Series([1 if c < -0.05 else (-1 if c > 0.05 else 0) for c in chg], index=aligned.index)
        elif mode == "rising":
            sig = pd.Series([1 if c > 0.05 else (-1 if c < -0.05 else 0) for c in chg], index=aligned.index)
        elif mode == "negative":
            sig = pd.Series([1 if v < 0 else (-1 if v > 1 else 0) for v in aligned[name]], index=aligned.index)
        else:
            sig = pd.Series(0, index=aligned.index)
        sig_sum += sig
    pos = (sig_sum >= THRESHOLD).astype(float)
    ret = pos.shift(1) * aligned["gold_ret"]
    cum = (1 + ret.fillna(0)).prod()
    vol = ret.std() * np.sqrt(252)
    ann = cum ** (252/len(ret)) - 1
    print(f"  Lookback {lb:3d}d: return {(cum-1)*100:+.1f}%  Sharpe {ann/vol:.2f}")

ステップ9 バックテストのスクリプトを完了

FXMacroDataからすべてのデータを取得し,スコアカード戦略を実行し,チャート準備済みの出力でパフォーマンス要約を印刷する完全な,自立したバックテストです

"""
Gold Macro Scorecard Backtest
Fetches daily gold prices and macro series from FXMacroData,
computes the composite scorecard, and evaluates a long/flat strategy.
"""
import requests
import pandas as pd
import numpy as np
from datetime import date

BASE = "https://fxmacrodata.com/api/v1"
KEY  = "YOUR_API_KEY"
START = "2020-01-01"
THRESHOLD = 2
LOOKBACK  = 30
COST_BPS  = 5

def get(path: str) -> pd.DataFrame:
    r = requests.get(f"{BASE}{path}", params={"api_key": KEY, "start_date": START})
    r.raise_for_status()
    df = pd.DataFrame(r.json().get("data", []))
    df["date"] = pd.to_datetime(df["date"])
    return df.set_index("date").sort_index()

# ── Fetch data ──
gold = get("/commodities/gold")[["val"]].rename(columns={"val": "gold"})

macro = {
    "tips":      (get("/announcements/usd/inflation_linked_bond"),  "negative"),
    "breakeven": (get("/announcements/usd/breakeven_inflation_rate"), "rising"),
    "policy":    (get("/announcements/usd/policy_rate"),             "falling"),
    "cb_assets": (get("/announcements/usd/cb_assets"),               "rising"),
    "m2":        (get("/announcements/usd/m2"),                      "rising"),
    "twi":       (get("/announcements/usd/trade_weighted_index"),    "falling"),
}

# ── Align and forward-fill ──
df = gold.copy()
for name, (series, _) in macro.items():
    s = series[["val"]].rename(columns={"val": name})
    df = df.join(s.reindex(df.index, method="ffill"))
df = df.dropna()

# ── Score ──
def score(col, mode):
    prev = col.shift(LOOKBACK)
    chg  = col - prev
    if mode == "negative":
        return col.apply(lambda v: 1 if v < 0 else (-1 if v > 1 else 0)).astype(float)
    if mode == "falling":
        return chg.apply(lambda c: 1 if c < -0.05 else (-1 if c > 0.05 else 0)).astype(float)
    if mode == "rising":
        return chg.apply(lambda c: 1 if c > 0.05 else (-1 if c < -0.05 else 0)).astype(float)
    return pd.Series(0.0, index=col.index)

df["net_score"] = sum(score(df[n], m) for n, (_, m) in macro.items())

# ── Trade ──
df["ret"] = df["gold"].pct_change()
df["pos"] = (df["net_score"] >= THRESHOLD).astype(float)
df["trade"] = df["pos"].diff().abs().fillna(0)
df["strat_ret"] = df["pos"].shift(1) * df["ret"] - df["trade"].shift(1) * (COST_BPS/1e4)
df["gold_cum"]  = (1 + df["ret"].fillna(0)).cumprod()
df["strat_cum"] = (1 + df["strat_ret"].fillna(0)).cumprod()

# ── Report ──
for label, cum_col, ret_col in [("Strategy", "strat_cum", "strat_ret"),
                                  ("Buy&Hold", "gold_cum", "ret")]:
    total = df[cum_col].iloc[-1] - 1
    vol   = df[ret_col].std() * np.sqrt(252)
    ann   = (1 + total) ** (252/len(df)) - 1
    sharpe = ann / vol if vol > 0 else 0
    peak  = df[cum_col].cummax()
    mdd   = ((df[cum_col] - peak) / peak).min()
    print(f"{label:12s}  Return: {total*100:+.1f}%  Sharpe: {sharpe:.2f}  MaxDD: {mdd*100:.1f}%")

print(f"\nDays invested: {df['pos'].mean()*100:.0f}%  |  Round-trips: {int(df['trade'].sum()/2)}")

重要 な 発見 と 実践 的 な 教訓

制限 と 警告

• 裏付けのテストです この記事に示されているサンプル結果は,方法論を説明するために代表的なデータを使用しています. 選択した日付範囲で検証された結果を生成するために,完全なスクリプトをライブ API に比べて,独自のキーで実行する必要があります.
• 指標の選択における生存率偏差 We chose these six indicators because they have strong theoretical priors for gold — but the selection itself is a form of implicit curve-fitting. A truly out-of-sample test would require selecting indicators before seeing the gold data.
• 位置のサイズも バイナリー・ロング/フラットアプローチは意図的にシンプルである.より洗練されたポジションサイズ (例えば,純スコア大きさによるエクスポージャーのスケーリング) はリスク調整回帰を向上させることができるが,過剰に適合する自由パラメータを追加する.
• 現金回収は無視 During flat periods, the strategy earns zero. In practice, short-term rates have been 0–5.5% over this period — including risk-free yield on idle cash would further improve the strategy's risk-adjusted edge.
• 取引コストモデルを 未来期間のモデル化には使わない 取引先の代わりに金先の先物取引先で実施される場合,ロールコストとマーージン要件が適用されます.回帰コスト5bpsの仮定は,金先物交易所のスプレッドを代表しますが,先物売買取引所の実行コストを過小評価することがあります.
• マクロデータ公開遅延 バックテストは実際の公開日数を使用します.前向きな偏見はありません. しかし,ライブ取引では,データリリースとシステム処理の間には数時間かかることがあります. 日々の再バランスペースは,この戦略にとって無関係です.

All data used in this backtest — daily gold prices and the six US macro indicators — is available from the FXMacroData API. The 金貨の最終点 固定価格を2020年まで提供し, 総額で評価される 利率,インフレ,通貨指標の全範囲をカバーします. fxmacrodata.com/subscribe 登録するわかった