使用FXMacroData与预测市场:卡尔希和多市场

宏观数据与预测市场相匹配的地方

预测市场已经从好奇心转向基础设施. 卡尔希 美国首家由CFTC监管的预测交易所, 允许您交易"will 美国CPI exceed 3.5% in April?" or "Will the 美国联邦储备 Polymarket 在 Polygon 区块链上运行,提供类似的二进制结果市场,在全球开放式访问下进行宏观事件. 美元/日元没有人知道. 欧元/美元其他外汇公司.

如果您已经使用FXMacroData来跟踪央行日历,监控CPI惊喜,并抽取政策利率历史,您有必要的原材料来建立这些市场的系统优势.本指南将向您展示如何连接点:从FXMacrodata抽取结构化宏观数据,将其映射到开放预测市场合约,并在Python中构建可重复的决策工作流.

预先要求

• 一个FXMacroData API 键 在这里订阅 免费试用.
• 通过Python 3.10或更高版本 requests 已经安装 (pip install requests) 没有.
• 卡尔希账户启用API访问,或者是Polymarket钱包进行手动交叉引用.
• 基本熟悉CPI的内容, 农业以外的工资没有 政策利率 决策是这样的.

步骤1 拉下一个发布日历

首先,您需要清楚地了解什么发布会和何时发布.FXMacroData的发布日历终点返回货币的所有预定宏观事件,以及预期日期和发布时间.

import requests

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

def get_upcoming_releases(currency: str) -> list[dict]:
    url = f"{BASE}/calendar/{currency}?api_key={API_KEY}"
    resp = requests.get(url, timeout=10)
    resp.raise_for_status()
    return resp.json().get("data", [])

usd_calendar = get_upcoming_releases("usd")

for event in usd_calendar[:5]:
    print(event["indicator"], event.get("announcement_datetime"), event.get("next_release_date"))

每个条目都带有一个 indicator (例如: inflation没有人知道. non_farm_payrolls没有人知道. policy_rate), the scheduled announcement time as a Unix timestamp, and the next expected release date. This is your primary input for filtering open prediction market contracts — if a contract resolves on "CPI for March 2026" you need the exact announcement date to size your time horizon correctly.

步骤2 获取历史公告数据并计算惊喜序列

预测市场价格概率基于可用信息. 如果您可以计算给定指标的近期惊喜指数,实际打印的共识是多少次,以及您有多少的基线来校准您的位置. 从公告终点从过去十二个月的实际与预测数据中提取:

def get_announcement_history(currency: str, indicator: str, limit: int = 24) -> list[dict]:
    url = f"{BASE}/announcements/{currency}/{indicator}?api_key={API_KEY}&limit={limit}"
    resp = requests.get(url, timeout=10)
    resp.raise_for_status()
    return resp.json().get("data", [])

def compute_surprise_series(records: list[dict]) -> list[dict]:
    surprises = []
    for r in records:
        actual = r.get("actual_value")
        consensus = r.get("predicted_value")
        if actual is not None and consensus is not None:
            surprises.append({
                "date": r["date"],
                "actual": actual,
                "consensus": consensus,
                "surprise": actual - consensus,
                "direction": "beat" if actual > consensus else "miss",
            })
    return surprises

cpi_history = get_announcement_history("usd", "inflation", limit=24)
cpi_surprises = compute_surprise_series(cpi_history)

beat_count = sum(1 for s in cpi_surprises if s["direction"] == "beat")
miss_count = len(cpi_surprises) - beat_count
print(f"CPI beat rate (last 24 releases): {beat_count}/{len(cpi_surprises)} = {beat_count/len(cpi_surprises):.0%}")

没有什么. predicted_value 每个公告记录上的字段存储了权威来源的市场共识 (费城联邦储备局对美元指标的专业预测者调查). 这也是同一个准预测市场定价的共识信号,因此您的惊喜系列将与卡尔希CPI合约中的隐含概率直接相比.

步骤3 预测市场合约的地图指标

卡尔希和波利马克特都将其宏观合约围绕特定指标值构建起来.一旦您了解FXMacroData指标的,映射就很简单.下面是一个最流动合约的参考表:

没有什么. 发布日程 查看所有这些指标表单一视图. 当卡尔希合约列出解决日期时,与 next_release_date 根据FXMacroData日历确认他们根据相同的基本公告解决.不匹配,即合同根据预估和最终修订解决是误价的常见来源.

步骤4 查询共识定预测终点

汇丰MacroData的预测终点提供了来自官方调查数据的前性共识值,这些调查是预测市场参与者用来固他们的先验的. 抽取下一次CPI发布的当前共识,并将其与开放卡尔希合约的门进行比较:

def get_predictions(currency: str, indicator: str) -> list[dict]:
    url = f"{BASE}/predictions/{currency}/{indicator}?api_key={API_KEY}"
    resp = requests.get(url, timeout=10)
    resp.raise_for_status()
    return resp.json().get("data", [])

cpi_predictions = get_predictions("usd", "inflation")

# Most recent upcoming prediction
if cpi_predictions:
    next_pred = cpi_predictions[0]
    for p in next_pred.get("predictions", []):
        print(f"Source: {p['prediction_source_label']}")
        print(f"Consensus: {p['predicted_value']}%")
        print(f"For release date: {next_pred['date']}")

样本反应的形状:

{
  "currency": "USD",
  "indicator": "inflation",
  "count": 1,
  "prediction_count": 1,
  "data": [
    {
      "announcement_id": "usd_inflation_2026-05-14",
      "currency": "usd",
      "indicator": "inflation",
      "date": "2026-05-14",
      "announcement_datetime": 1747216200,
      "predictions": [
        {
          "predicted_value": 2.8,
          "prediction_type": "market_consensus",
          "prediction_source": "philly_fed_spf",
          "prediction_source_label": "Philadelphia Fed Survey of Professional Forecasters"
        }
      ]
    }
  ]
}

If a Kalshi contract asks "Will April CPI print above 2.9%?" and the SPF consensus is 2.8%, you now have a quantified starting point: the consensus says no with a 0.1 percentage point buffer. Your historical surprise series from Step 2 then tells you how often CPI has beaten consensus by more than 0.1 percentage point, giving you an empirical base rate to compare against the contract's implied probability.

步骤5 建立完整的决策信号

将三个输入组合成一个单一的决策函数.逻辑是故意简单的目标是可重复的,数据基础的信号,而不是一个黑盒:

def prediction_market_signal(
    currency: str,
    indicator: str,
    contract_threshold: float,
    contract_direction: str,  # "above" or "below"
    lookback: int = 24,
) -> dict:
    """
    Returns a signal dict for a prediction market contract.

    contract_threshold: the numeric threshold in the contract question
                        (e.g. 2.9 for "Will CPI exceed 2.9%?")
    contract_direction: "above" means YES if actual > threshold
    """
    history = get_announcement_history(currency, indicator, limit=lookback)
    surprises = compute_surprise_series(history)
    predictions = get_predictions(currency, indicator)

    consensus = None
    release_date = None
    if predictions:
        latest_preds = predictions[0].get("predictions", [])
        if latest_preds:
            consensus = latest_preds[0]["predicted_value"]
            release_date = predictions[0]["date"]

    # Base rate: how often did actual exceed the threshold historically?
    actuals_above = sum(1 for r in history if r.get("actual_value") is not None
                        and r["actual_value"] > contract_threshold)
    base_rate = actuals_above / len(history) if history else None

    # Surprise bias: mean surprise (positive = beat)
    mean_surprise = (sum(s["surprise"] for s in surprises) / len(surprises)
                     if surprises else None)

    # Directional signal
    if consensus is not None:
        buffer = consensus - contract_threshold  # positive = consensus above threshold
        signal = "NO" if (contract_direction == "above" and buffer > 0) else "YES"
    else:
        signal = "NEUTRAL"

    return {
        "currency": currency.upper(),
        "indicator": indicator,
        "contract_threshold": contract_threshold,
        "contract_direction": contract_direction,
        "consensus": consensus,
        "release_date": release_date,
        "historical_base_rate_above_threshold": base_rate,
        "mean_surprise_last_n": mean_surprise,
        "lookback": lookback,
        "signal": signal,
    }


result = prediction_market_signal(
    currency="usd",
    indicator="inflation",
    contract_threshold=2.9,
    contract_direction="above",
    lookback=24,
)

import json
print(json.dumps(result, indent=2))

输出示例:

{
  "currency": "USD",
  "indicator": "inflation",
  "contract_threshold": 2.9,
  "contract_direction": "above",
  "consensus": 2.8,
  "release_date": "2026-05-14",
  "historical_base_rate_above_threshold": 0.33,
  "mean_surprise_last_n": 0.04,
  "lookback": 24,
  "signal": "NO"
}

阅读:共识为2.8% (低于2.9%的门),CPI打印超过2.9%24次发布的历史基率为33%,平均惊喜是温和的0.04ppt上升偏差.原始信号是NO,但基率和惊喜偏差的组合告诉你,这不是一个高信念的瘦调整位置大小.

Step 6 — Extend to non-USD and cross-market contracts

预测市场越来越多地列出非美元宏观事件的合约:欧洲央行决定,英国CPI,日本银行政策和澳大利亚就业.FXMacroData通过相同的终点结构覆盖所有这些.

# ECB rate decision signal
ecb_signal = prediction_market_signal(
    currency="eur",
    indicator="policy_rate",
    contract_threshold=3.15,   # "Will ECB rate fall below 3.15%?"
    contract_direction="below",
    lookback=12,
)

# UK inflation signal
uk_cpi_signal = prediction_market_signal(
    currency="gbp",
    indicator="inflation",
    contract_threshold=2.5,
    contract_direction="above",
    lookback=18,
)

# Australian employment signal
aus_employment_signal = prediction_market_signal(
    currency="aud",
    indicator="employment",
    contract_threshold=30.0,   # thousands, "Will employment add 30K+?"
    contract_direction="above",
    lookback=18,
)

没有什么. 欧元/美元 现在我 汇率汇率 仪表板显示每个货币对的政策利率历史和CPI趋势,在承诺之前给您视觉上下文检查. 欧洲央行 has been cutting consistently over the last four meetings, a contract asking "Will the ECB cut in June?" has very different base-rate dynamics than one asking "Will the ECB hike?"

步骤7 监控COT定位作为确认层

期货投机往往反映了相同的方向观点. 机器人机器人的仪表板 对于美元来说,指数显示大投机者是否是净长或短美元,即将进入CPI打印.如果规格是大量净长美元,CPI信号为NO (低于门),则有可能出现双边:预测市场合约可能值得消退,外汇仓位也可能容易受到挤压.

通过程序来提取COT数据,将其纳入您的信号框架:

def get_cot(currency: str) -> list[dict]:
    url = f"{BASE}/cot/{currency}?api_key={API_KEY}&limit=4"
    resp = requests.get(url, timeout=10)
    resp.raise_for_status()
    return resp.json().get("data", [])

usd_cot = get_cot("usd")
if usd_cot:
    latest = usd_cot[0]
    net_position = latest.get("net_position")
    print(f"USD spec net position (latest week): {net_position:+,.0f} contracts")

让我们一起做

整个工作流程看起来是这样:

1. 拉起这个 发布日程 确定即将到来的公告和预定时间.
2. 赛事公告日期为 开放预测市场合同 在卡尔希或聚市场上.
3. 拿来一个 预测终点 对于当前的共识值.
4. 计算一个 历史惊喜系列 为了测量方向偏差和基率.
5. 产生一个 方向信号 通过将共识与合同值进行比较.
6. 选择性地加上 机器人位置 作为确认信号.
7. 根据合同中暗示的概率与实证基准利率估计之间的差距,

开始吧

本指南中使用的所有终点均可免费试用,不需要信用卡.包括CPI,NFP,GDP,失业率,核心PCE和政策利率在内的美元指标均可在免费层面上访问.非美元指数和COT数据需要专业计划.

• 订阅并获得您的API密钥没有人知道. 订阅
• 发布日程没有人知道. 发布日程
• 美元指标目录没有人知道. 美国人民币
• 机器人机器人的仪表板没有人知道. 仪表板/婴儿床