使用 FXMacroData 欧元/美元宏观信号在 Kraken 上进行算法交易

为什么欧元/美元宏观分歧推动加密货币在克拉克恩上

克拉肯在加密货币交易场景中占据着独特的地位:它被欧洲交易者大量使用,接受直接欧元存款,并对欧元和美元报价许多对.这意味着控制欧元/美元的宏观力量,例如欧洲央行与美联储政策分歧,欧元区与美国通胀差异,以及实际收益率差距,创造了方向上的后风和逆风,这些因素在克拉根上市的对中显著表现出来,如XBT/USD和XB T/EUR.

当美联储收紧,而欧洲央行持有时,美元会增强,实际收益率上升,风险资产 (包括加密货币) 通常面临销售压力. 当欧洲央行的与美联儲相反方向失调时,例如,欧元强和压缩的美国实际收成倾向于支持BTC的风险定位. 这些模式持续数周和数月,并且可以通过FXMacroData的指标终点预先完全观察.

本指南介绍了在 Kraken 上建立一个基于宏信号的算法交易机器人.

• 从FXMacroData中获取欧元和美元宏观信号 (政策利率,CPI,核心通胀和欧元/美元现货)
• 计算EUR/USD差异分数,以对宏观制度进行分类
• 通过FXMacroData发布日历安排执行窗口,围绕欧洲央行和美联储发布日期
• 通过官方的REST API提交Kraken上的市场和限额订单
• 应用简单的仓位规模和风险控制

预先要求

在开始之前,请确保您准备好以下内容:

• Python 3.9+ 所有片段使用标准类型注释
• 汇率数据API键 登记在 订阅 并从帐户仪表板复制您的密钥
• 克拉克恩账户 在 Kraken 仪表板中生成 API 键 安全 → API 随着 创建和修改订单 现在我 查询基金 许可证
• Python 包没有人知道. requests没有人知道. krakenex没有人知道. pandas没有人知道. schedule

pip install requests krakenex pandas schedule

存储所有凭证作为环境变量 永远不要硬代码密钥:

export FXMACRO_API_KEY="YOUR_FXMACRODATA_KEY"
export KRAKEN_API_KEY="YOUR_KRAKEN_API_KEY"
export KRAKEN_PRIVATE_KEY="YOUR_KRAKEN_PRIVATE_KEY"

步骤1:获取欧元和美元宏观信号

差异模型使用四个序列: 欧洲央行政策利率没有 美联储政策利率没有人知道. 欧元区的CPI没有 美国CPI它们一起描述了每个央行在上升或放宽周期中的位置,以及通货膨胀差异是否有利于欧元或美元的强.

import os
import requests

BASE_URL = "https://fxmacrodata.com/api/v1"
FXMACRO_KEY = os.environ["FXMACRO_API_KEY"]


def get_series(path: str, start: str = "2023-01-01") -> list[dict]:
    """Fetch a time-series from FXMacroData."""
    resp = requests.get(
        f"{BASE_URL}{path}",
        params={"api_key": FXMACRO_KEY, "start": start},
        timeout=10,
    )
    resp.raise_for_status()
    return resp.json()["data"]


# Macro inputs
ecb_rate   = get_series("/announcements/eur/policy_rate")
fed_rate   = get_series("/announcements/usd/policy_rate")
eur_cpi    = get_series("/announcements/eur/inflation")
usd_cpi    = get_series("/announcements/usd/inflation")

# Forex confirmation: EUR/USD spot trend
eurusd     = get_series("/forex/eur/usd", start="2024-01-01")

# Each item: {"date": "2025-04-08", "val": 4.0, "announcement_datetime": "2025-04-08T12:15:00Z"}
print(f"ECB rate:  {ecb_rate[0]['val']}%")
print(f"Fed rate:  {fed_rate[0]['val']}%")
print(f"EUR CPI:   {eur_cpi[0]['val']}%")
print(f"USD CPI:   {usd_cpi[0]['val']}%")
print(f"EUR/USD:   {eurusd[0]['val']}")

没有什么. announcement_datetime 您将在步骤4中使用它暂停交易,并在发布窗口关闭后立即恢复.

步骤2:计算欧元/美元宏观差异分数

差异分数不仅仅是指交易从单一指标,而且将所有四个系列综合成 -1 (强美元模式 BTC 的风险减退) 和 +1 (弱美元/风险加息模式 支持 BTC) 之间的方向数字.积极分数表明长期的条件;负分数建议保持平稳或短线.

def divergence_score(
    ecb_rate_pct: float,
    fed_rate_pct: float,
    eur_cpi_pct: float,
    usd_cpi_pct: float,
    eurusd_rate: float,
    *,
    eurusd_neutral: float = 1.08,
) -> float:
    """
    Returns a composite EUR/USD macro divergence score in [-1, +1].

    Positive  → USD relatively weak, risk-on environment, BTC bullish.
    Negative  → USD relatively strong, risk-off environment, BTC bearish.
    """
    score = 0.0

    # Component 1: rate spread (ECB rate − Fed rate)
    # Positive spread → ECB tighter than Fed → EUR-supportive → +score
    rate_spread = ecb_rate_pct - fed_rate_pct
    score += max(-1.0, min(1.0, rate_spread / 2.5)) * 0.35

    # Component 2: inflation differential (EUR CPI − USD CPI)
    # Higher EUR inflation → ECB forced to stay hawkish → EUR-supportive
    infl_diff = eur_cpi_pct - usd_cpi_pct
    score += max(-1.0, min(1.0, infl_diff / 3.0)) * 0.25

    # Component 3: Fed hawkishness drag
    # High Fed rate in absolute terms → USD strength → negative for BTC
    fed_drag = (fed_rate_pct - 3.0) / 3.0   # neutral at 3 %
    score -= max(-1.0, min(1.0, fed_drag)) * 0.20

    # Component 4: EUR/USD spot vs neutral (1.08)
    # EUR/USD above neutral → dollar relatively weak → +score
    fx_signal = (eurusd_rate - eurusd_neutral) / 0.08
    score += max(-1.0, min(1.0, fx_signal)) * 0.20

    return max(-1.0, min(1.0, score))


score = divergence_score(
    ecb_rate_pct=ecb_rate[0]["val"],
    fed_rate_pct=fed_rate[0]["val"],
    eur_cpi_pct=eur_cpi[0]["val"],
    usd_cpi_pct=usd_cpi[0]["val"],
    eurusd_rate=eurusd[0]["val"],
)

print(f"Divergence score: {score:+.3f}")
if score >= 0.25:
    print("Regime: RISK-ON → consider long XBT/USD")
elif score <= -0.25:
    print("Regime: RISK-OFF → consider flat / short")
else:
    print("Regime: NEUTRAL → no directional edge")

步骤3:连接到Kraken

没有什么. krakenex 库将Kraken的REST API包裹在一个简单的 query_public 现在我 query_private 私有端点 (订单,查询余额) 需要您的API键对.

import krakenex

kraken = krakenex.API(
    key=os.environ["KRAKEN_API_KEY"],
    secret=os.environ["KRAKEN_PRIVATE_KEY"],
)


def get_balance() -> dict[str, float]:
    """Return current account balance as {asset: amount}."""
    result = kraken.query_private("Balance")
    if result.get("error"):
        raise RuntimeError(f"Kraken balance error: {result['error']}")
    return {k: float(v) for k, v in result["result"].items()}


def get_xbt_price() -> float:
    """Return latest BTC/USD mid-price from Kraken ticker."""
    result = kraken.query_public("Ticker", {"pair": "XBTUSD"})
    if result.get("error"):
        raise RuntimeError(f"Kraken ticker error: {result['error']}")
    ticker = result["result"]["XXBTZUSD"]
    bid = float(ticker["b"][0])
    ask = float(ticker["a"][0])
    return (bid + ask) / 2.0


balance = get_balance()
usd_balance = balance.get("ZUSD", 0.0)
xbt_balance = balance.get("XXBT", 0.0)
xbt_price   = get_xbt_price()

print(f"USD balance: ${usd_balance:,.2f}")
print(f"XBT balance: {xbt_balance:.6f} BTC")
print(f"XBT/USD price: ${xbt_price:,.2f}")

第四步:围绕欧洲央行和美联储发布的时间表

高影响力宏观发布欧洲央行利率决定,FOMC声明,欧元区CPI打印通常会对外汇和加密货币市场造成急剧波动.最安全的方法是在每次发布后30分钟内停止任何开放订单活动,并立即重新评估宏观评分,因此您可以在新制度上交易,而不是发布前的噪音.

据外媒报道, 发布日程 显示每个货币/指标对的下一个预定公告日期时间,使得建立一个黑幕调度器变得简单:

from datetime import datetime, timezone, timedelta


def get_next_releases(currencies: list[str], indicators: list[str]) -> list[datetime]:
    """
    Fetch upcoming announcement datetimes for the given
    currency/indicator combinations via FXMacroData.
    """
    upcoming: list[datetime] = []
    for currency in currencies:
        for indicator in indicators:
            try:
                # Fetch recent releases; announcement_datetime on future entries
                # will be greater than now, while past entries will be skipped below.
                data = get_series(f"/announcements/{currency}/{indicator}", start="2024-01-01")
                for item in data[:6]:   # scan the six most-recent entries for future datetimes
                    dt_str = item.get("announcement_datetime")
                    if dt_str:
                        dt = datetime.fromisoformat(dt_str.replace("Z", "+00:00"))
                        if dt > datetime.now(tz=timezone.utc):
                            upcoming.append(dt)
                            break
            except (requests.RequestException, KeyError, ValueError) as exc:
                log.warning("Skipping %s/%s in release calendar: %s", currency, indicator, exc)
    return sorted(upcoming)


def in_blackout_window(
    release_times: list[datetime],
    buffer_minutes: int = 30,
) -> bool:
    """Return True if we are within buffer_minutes of any scheduled release."""
    now = datetime.now(tz=timezone.utc)
    for rt in release_times:
        if abs((rt - now).total_seconds()) < buffer_minutes * 60:
            return True
    return False


# Watch ECB and Fed policy rates plus both CPI prints
WATCH_CURRENCIES  = ["eur", "usd"]
WATCH_INDICATORS  = ["policy_rate", "inflation", "core_inflation"]

release_times = get_next_releases(WATCH_CURRENCIES, WATCH_INDICATORS)
print("Upcoming release windows:")
for rt in release_times:
    print(f"  {rt.strftime('%Y-%m-%d %H:%M UTC')}")

if in_blackout_window(release_times):
    print("⚠  BLACKOUT — halting order activity")
else:
    print("✓  Clear to trade")

步骤5:在Kraken上进行宏观驱动订单

随着宏观分数和黑色检查的实施,订单逻辑是直接的.当分数超过长门并且没有开放的位置时,提交一个限额购买.当得分转变负或位置达到利目标时,提出一个限售. 位置大小以可用美元余额的小部分表示,限于硬最大.

import math


# ── Risk parameters ──────────────────────────────────────────────────────────
LONG_THRESHOLD   = 0.25    # score above this → open long
FLAT_THRESHOLD   = -0.10   # score below this → close long / stay flat
MAX_POSITION_USD = 2_000   # absolute cap per trade
RISK_FRACTION    = 0.10    # 10 % of USD balance per signal
LIMIT_SLIP_BPS   = 5       # place limit 5 bps above mid to improve fill rate
MIN_XBT_POSITION = 0.0001  # Kraken minimum order size for XBT


def open_long(usd_amount: float, xbt_price: float) -> dict:
    """Submit a limit buy order for XBT/USD on Kraken."""
    volume = round(usd_amount / xbt_price, 5)
    limit_price = round(xbt_price * (1 + LIMIT_SLIP_BPS / 10_000), 2)

    result = kraken.query_private("AddOrder", {
        "pair":      "XBTUSD",
        "type":      "buy",
        "ordertype": "limit",
        "price":     str(limit_price),
        "volume":    str(volume),
        "oflags":    "post",          # post-only: never pays taker fee
    })
    if result.get("error"):
        raise RuntimeError(f"Kraken order error: {result['error']}")
    return result["result"]


def close_long(xbt_volume: float, xbt_price: float) -> dict:
    """Submit a limit sell order to close an existing long."""
    limit_price = round(xbt_price * (1 - LIMIT_SLIP_BPS / 10_000), 2)
    result = kraken.query_private("AddOrder", {
        "pair":      "XBTUSD",
        "type":      "sell",
        "ordertype": "limit",
        "price":     str(limit_price),
        "volume":    str(round(xbt_volume, 5)),
        "oflags":    "post",
    })
    if result.get("error"):
        raise RuntimeError(f"Kraken order error: {result['error']}")
    return result["result"]


def run_signal(score: float) -> None:
    """Execute the macro signal: open, hold, or close a XBT/USD position."""
    balance   = get_balance()
    usd_bal   = balance.get("ZUSD", 0.0)
    xbt_bal   = balance.get("XXBT", 0.0)
    xbt_price = get_xbt_price()

    has_position = xbt_bal >= MIN_XBT_POSITION  # treat dust as no position

    if score >= LONG_THRESHOLD and not has_position:
        usd_to_deploy = min(usd_bal * RISK_FRACTION, MAX_POSITION_USD)
        if usd_to_deploy < 10:
            print("Insufficient USD balance for trade.")
            return
        result = open_long(usd_to_deploy, xbt_price)
        print(f"Long opened — txid: {result.get('txid')}, "
              f"volume: {result.get('descr', {}).get('order')}")

    elif score <= FLAT_THRESHOLD and has_position:
        result = close_long(xbt_bal, xbt_price)
        print(f"Long closed — txid: {result.get('txid')}")

    else:
        print(f"Score {score:+.3f} — no action (position={'open' if has_position else 'flat'})")

步骤6:组装完整的机器人循环

最后一步将所有内容都连接到一个每小时运行一次的计划循环中. 在每次点击时,它会刷新宏数据,检查停电窗口,重新计算分差分数,并执行信号. schedule 图书馆保持这种轻量, 不需要一个完整的任务队列.

import schedule
import time
import logging

logging.basicConfig(
    level=logging.INFO,
    format="%(asctime)s [%(levelname)s] %(message)s",
)
log = logging.getLogger(__name__)


def bot_tick() -> None:
    """Single iteration of the macro bot."""
    log.info("── Macro bot tick ──────────────────────────────────────────")

    # 1. Fetch fresh macro data
    try:
        ecb   = get_series("/announcements/eur/policy_rate")[0]["val"]
        fed   = get_series("/announcements/usd/policy_rate")[0]["val"]
        ecpi  = get_series("/announcements/eur/inflation")[0]["val"]
        ucpi  = get_series("/announcements/usd/inflation")[0]["val"]
        fx    = get_series("/forex/eur/usd")[0]["val"]
    except Exception as exc:
        log.error("Failed to fetch macro data: %s", exc)
        return

    score = divergence_score(ecb, fed, ecpi, ucpi, fx)
    log.info("ECB %.2f%%  Fed %.2f%%  EUR CPI %.1f%%  USD CPI %.1f%%  EUR/USD %.4f",
             ecb, fed, ecpi, ucpi, fx)
    log.info("Divergence score: %+.3f", score)

    # 2. Check release calendar blackout
    try:
        releases = get_next_releases(WATCH_CURRENCIES, WATCH_INDICATORS)
    except Exception as exc:
        log.warning("Release calendar fetch failed: %s — proceeding without blackout", exc)
        releases = []

    if in_blackout_window(releases, buffer_minutes=30):
        log.warning("BLACKOUT window active — skipping order activity")
        return

    # 3. Execute signal
    try:
        run_signal(score)
    except Exception as exc:
        log.error("Order execution failed: %s", exc)


# Run immediately on start, then every hour
bot_tick()
schedule.every(1).hours.do(bot_tick)

log.info("Bot running — press Ctrl+C to stop")
while True:
    schedule.run_pending()
    time.sleep(30)  # poll every 30 s so scheduled hourly tasks fire on time

步骤7:添加止损和获利保护

模式分数告诉你什么时候开盘,什么时候退出宏观轴心,但这可能需要几天. 在此期间,你需要价格水平的保护,以便在退出信号发射之前,

STOP_LOSS_PCT   = 0.05    # exit if price drops 5 % below entry
TAKE_PROFIT_PCT = 0.12    # exit if price rises 12 % above entry

# Store entry price in a lightweight state file
import json, pathlib

STATE_FILE = pathlib.Path("bot_state.json")


def load_state() -> dict:
    if STATE_FILE.exists():
        try:
            return json.loads(STATE_FILE.read_text())
        except (json.JSONDecodeError, OSError):
            log.warning("bot_state.json is corrupt or unreadable — resetting state")
    return {"entry_price": None}


def save_state(state: dict) -> None:
    STATE_FILE.write_text(json.dumps(state))


def check_price_guards(xbt_price: float, xbt_bal: float) -> bool:
    """
    Returns True if a stop or take-profit was triggered (position closed).
    Call this before evaluating the macro score so price guards take priority.
    """
    state = load_state()
    entry = state.get("entry_price")
    if entry is None or xbt_bal < 0.0001:
        return False

    pnl_pct = (xbt_price - entry) / entry

    if pnl_pct <= -STOP_LOSS_PCT:
        log.warning("Stop-loss triggered at %.2f%% loss — closing position", pnl_pct * 100)
        close_long(xbt_bal, xbt_price)
        save_state({"entry_price": None})
        return True

    if pnl_pct >= TAKE_PROFIT_PCT:
        log.info("Take-profit triggered at %.2f%% gain — closing position", pnl_pct * 100)
        close_long(xbt_bal, xbt_price)
        save_state({"entry_price": None})
        return True

    return False

什么时候? open_long 记录填充价格在 bot_state.json在每一个声,打电话 check_price_guards 在宏观评分评估之前,如果返回 True, 跳过剩余的标记,因为该位置已经在价格水平上关闭.

扩大战略

一旦核心机器人运行可靠, 值得考虑的扩展程序有几个:

• 增加欧元核心通胀 没有 欧元核心CPI 现在我 美元的PCE 它们与主要的CPI一起插入分数,可以改善转折点的制度分类.
• 克拉克恩保证金交易 Kraken支持XBT/USD的杆率高达5倍,并加上杆值. "leverage": "2:1" 参数到 AddOrder 电话放大正态信号 (只有在比例更紧的止损时才适用).
• 多对旋转 重复同样的模式逻辑ETH/USD (XETHZUSD两者都处于风险增长状态时,将资本转移到任何显示较强动力的货币对.
• 内部外汇覆盖 从 汇率数据中获取欧元/美元现货数据 欧元贸易加权指数 终点,并将日内动力作为积极宏观体制中的短期入口过器.
• 网络软件订单簿 用Kraken的WebSocket源来替换预定投票循环,以便实时更新价格,将延迟从几分钟减少到几毫秒,以便进行入口和退出精炼.

摘要及下一步步骤

现在你有一个完整的Python交易机器人,它将EUR/USD宏观分歧转化为可操作的长/平信号.

1. 汇率指标拉动 欧洲央行和美联储利率,欧元和美元CPI,欧美美元现货,所有通过 /announcements/ 现在我 /forex/ 具有第二级时间的终点
2. 差异分数 权重复合材料,将多个指示器映射到单个方向信号上
3. 停电计划器 在使用 时间间暂停订单活动 汇率数据发布日历
4. 克拉克恩订单管理 限制仅使用邮件标志的买卖,以尽量减少费用
5. 价格监督 停止损失和获利水平保护宏观制度轴心之间的位置

作为自然的下一步,探索 美元的PCE 现在我 欧元核心CPI 为了提高分数的通货膨胀成分,或者扩展机器人直接在 Kraken 上交易EUR/USD,使用与现货外汇仓位相同的模式信号.

详细的API参考和所有可用的货币/指标组合在 没有任何参考为了获得API密钥,请访问 订阅现在我们要做什么?