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非农薪资新闻的外汇择时

学术论文

论文链接https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3983210

导读

投资标的：GLD、SLV、DBC、GSG、USO、UNG、DBA。再平衡：每日。Universe: GLD, SLV, DBC, GSG, USO, UNG, DBA. Parameters: rebalance=daily.

策略概要

该策略投资于欧元（欧元前为德国马克）、英镑、瑞士法郎、日元、加元、澳元与新西兰元的外汇期货。数据包括CFTC持仓报告（COT）中的投机者外汇敞口、期货与OTC市场买卖价差（经Refinitiv Tick History与Eikon）以及芝加哥商品交易所（CME）保证金。先评估各货币期货上投机者类型的美元多空持仓量，日度净持仓为多头减空头。各货币投机者外汇敞口等于其净持仓除以全部货币绝对净持仓之和，组合权重等于这些敞口。在美国非农公布日，策略用滞后10日的组合权重模仿投机者外汇敞口投资货币期货；头寸在公布前五分钟开立，公布后一小时平仓。外汇目标组合由(100−α)%(100-\alpha)\%(100−α)%风险资产与α%\alpha\%α%现金（介于5%与20%）构成，以满足期货保证金，其余投资于标普500全收益指数、美国银行美国公司债全收益指数或摩根大通综合商品全收益指数等风险资产。结果给出投资标普500并持有20%现金的策略，相对忽略非农公布的策略夏普比率提高0.39。

经济逻辑

策略利用投机者外汇组合敞口中的信息含量，这些敞口对即将公布的非农数据具有预测信号。预测力因外汇仓位中的长寿命信息而持续，并由择时与隐蔽动机驱动：执行上的耐心——投机者可能推迟交易以避免噪声或利用流动性；获取私人信息——部分投机者拥有其他参与者没有的信息。通过模仿知情投机者的外汇敞口，投资者可捕捉这些信号。外汇敞口以美元多空净持仓度量，并重新缩放使不同期货权重之和为1，以反映知情市场活动的相对配置。

入场与出场

入场

每个交易日由 make_on_day 决定目标权重。当前宇宙：GLD, SLV, DBC, GSG, USO, UNG, DBA。请对照实验室代码中的 engine.set_target_weights。

出场

权重被设为空或该标的权重为 0 即视为出场。引擎按 5 bps 佣金 + 2 bps 滑点执行。

所需行情数据

需要 GLD, SLV, DBC, GSG, USO, UNG (+1 more) 至少 60 根复权收盘价，才能形成第一个有效信号。ETF 仅交易日有行情。实验室通过 backtest.data.load_daily_prices 读取 Yahoo Finance。

标的

GLD, SLV, DBC, GSG, USO, UNG, DBA

频率

日内 · 日频复权收盘价；信号按每个交易日检查

所需历史

每个标的至少 60 根交易日 K 线（约 3 个交易月），外加数根缓冲才能发出第一个信号。

字段

复权收盘价（Adj Close / auto_adjust Close）
交易日日期索引

凭证

回测与实验室：无需密钥
实盘下单才需要券商 / 交易所 API

API 与数据源

Quant Buffet 数据 API

实验室用 load_daily_prices 拉取 ASSETS 的复权收盘价面板，磁盘缓存后回测可复现。

提供方: backtest.data.load_daily_prices
鉴权: 回测无需密钥
接口: load_daily_prices(ASSETS, start="2000-01-01")

API / docs → /zh/docs/data-api

Yahoo Finance（yfinance）

美股 / ETF 复权 OHLCV，免费研究接口，有速率限制。

提供方: yfinance → Yahoo Finance
鉴权: 公开历史无需密钥
接口: Ticker.history / yf.download(auto_adjust=True)

API / docs → github.com/ranaroussi/yfinance

Onsite backtest IDE

Quant Buffet 原生回测 IDE

Edit and run Quant Buffet Python for 非农薪资新闻的外汇择时 in the browser. Results update live with equity, drawdown, and metrics charts. Allowed: backtest.data, backtest.engine, backtest.metrics, numpy, pandas. Define ASSETS and make_on_day(prices). Shortcut: Ctrl+Enter. API docs →

IDE · 43 lines

"""
Quant Buffet interactive lab — 非农薪资新闻的外汇择时
Allowed: backtest.data / backtest.engine / backtest.metrics, numpy, pandas.
Required: a module-level ASSETS list, and def make_on_day(prices) returning (on_day, ready)
"""
from __future__ import annotations

import numpy as np
import pandas as pd

from backtest.data import load_daily_prices
from backtest.engine import EngineConfig, PortfolioEngine
from backtest.metrics import compute_metrics

ASSETS = ['GLD', 'SLV', 'DBC', 'GSG', 'USO', 'UNG', 'DBA']
REBALANCE_ONCE = False

def make_on_day(prices: pd.DataFrame):
    cols = [c for c in ASSETS if c in prices.columns]
    state = {"last": None, "done": False}

    def on_day(engine: PortfolioEngine, dt: pd.Timestamp) -> None:
        available = [s for s in cols if pd.notna(prices.at[dt, s])]
        if not available:
            return
        if REBALANCE_ONCE:
            if state["done"]:
                return
            state["done"] = True
            engine.set_target_weights(dt, {s: 1.0 / len(available) for s in available})
            return
        key = (dt.year, dt.month)
        if state["last"] == key:
            return
        state["last"] = key
        engine.set_target_weights(dt, {s: 1.0 / len(available) for s in available})

    counts = prices[cols].notna().sum(axis=1)
    need = max(1, len(cols) // 2)
    eligible = counts[counts >= need]
    ready = eligible.index.min() if not eligible.empty else None
    return on_day, ready

"""
Quant Buffet interactive lab — 非农薪资新闻的外汇择时
Allowed: backtest.data / backtest.engine / backtest.metrics, numpy, pandas.
Required: a module-level ASSETS list, and def make_on_day(prices) returning (on_day, ready)
"""
from __future__ import annotations

import numpy as np
import pandas as pd

from backtest.data import load_daily_prices
from backtest.engine import EngineConfig, PortfolioEngine
from backtest.metrics import compute_metrics

ASSETS = ['GLD', 'SLV', 'DBC', 'GSG', 'USO', 'UNG', 'DBA']
REBALANCE_ONCE = False

def make_on_day(prices: pd.DataFrame):
    cols = [c for c in ASSETS if c in prices.columns]
    state = {"last": None, "done": False}

def on_day(engine: PortfolioEngine, dt: pd.Timestamp) -> None:
        available = [s for s in cols if pd.notna(prices.at[dt, s])]
        if not available:
            return
        if REBALANCE_ONCE:
            if state["done"]:
                return
            state["done"] = True
            engine.set_target_weights(dt, {s: 1.0 / len(available) for s in available})
            return
        key = (dt.year, dt.month)
        if state["last"] == key:
            return
        state["last"] = key
        engine.set_target_weights(dt, {s: 1.0 / len(available) for s in available})

counts = prices[cols].notna().sum(axis=1)
    need = max(1, len(cols) // 2)
    eligible = counts[counts >= need]
    ready = eligible.index.min() if not eligible.empty else None
    return on_day, ready

Quant Buffet syntax cheat sheet (copy / insert)

Paste these fragments into the editor. The sandbox rejects QuantConnect, os, and network libraries.

Required imports

Only these libraries are allowed in the sandbox.

from __future__ import annotations

import numpy as np
import pandas as pd

from backtest.data import load_daily_prices
from backtest.engine import EngineConfig, PortfolioEngine
from backtest.metrics import compute_metrics

ASSETS list (whitelisted ETFs)

Module-level list. Tickers must be in the Quant Buffet whitelist.

ASSETS = ["SPY", "QQQ", "TLT", "GLD", "BIL"]

make_on_day contract

Must return (on_day, ready). on_day calls engine.set_target_weights.

def make_on_day(prices: pd.DataFrame):
    cols = [c for c in ASSETS if c in prices.columns]
    sma = prices[cols].rolling(200, min_periods=200).mean()
    state = {"last": None}

    def on_day(engine: PortfolioEngine, dt: pd.Timestamp) -> None:
        if sma.loc[dt].isna().all():
            return
        key = (dt.year, dt.month)
        if state["last"] == key:
            return
        state["last"] = key
        long = [
            s for s in cols
            if pd.notna(prices.at[dt, s]) and pd.notna(sma.at[dt, s])
            and prices.at[dt, s] > sma.at[dt, s]
        ]
        weights = {} if not long else {s: 1.0 / len(long) for s in long}
        engine.set_target_weights(dt, weights)

    ready = sma.dropna(how="all").index.min() if sma.notna().any().any() else None
    return on_day, ready

Set target weights

Weights should sum to about 1.0. Empty dict = 100% cash.

engine.set_target_weights(dt, {"SPY": 0.60, "BIL": 0.40})

Live backtest performance

CAGR

-0.48%

Sharpe

0.07

Max DD

-80.40%

Vol

18.94%

Sortino

0.11

Beta

0.34

Up days

32%

Run the backtest to populate charts.

Order records

Run the backtest to load every order fill alongside performance.

Export to your platform

Transform Quant Buffet lab code (ASSETS + make_on_day / PortfolioEngine) into native classes for a third-party IDE — then copy and paste.

Target platform

Run in: QuantConnect Cloud or LEAN CLI · QCAlgorithm with Equity securities and monthly rebalance.

Detected pattern: Custom / hybridAssets: GLD, SLV, DBC, GSG, USO, UNG, DBA

# Generated from Quant Buffet → QuantConnect LEAN
# Strategy: 非农薪资新闻的外汇择时
# Detected pattern: Custom / hybrid
# Source uses Quant Buffet lab APIs (ASSETS + make_on_day / PortfolioEngine).
# Review fees, data, and risk before live trading — educational export only.

from AlgorithmImports import *


class QuantBuffetExport(QCAlgorithm):
    def Initialize(self):
        self.SetStartDate(2010, 1, 1)
        self.SetCash(100000)
        tickers = ["GLD", "SLV", "DBC", "GSG", "USO", "UNG", "DBA"]
        self.symbols = []
        for t in tickers:
            if "-" in t:  # crypto proxy e.g. BTC-USD
                self.symbols.append(self.AddCrypto(t.replace("-USD", ""), Resolution.Daily).Symbol)
            else:
                self.symbols.append(self.AddEquity(t, Resolution.Daily).Symbol)
        self.Schedule.On(
            self.DateRules.MonthStart(self.symbols[0]),
            self.TimeRules.AfterMarketOpen(self.symbols[0], 30),
            self.Rebalance,
        )
        # Logic: Custom Quant Buffet logic — adapt the signal block to match your lab on_day().

    def Rebalance(self):
        # Pattern: custom — Custom Quant Buffet logic — adapt the signal block to match your lab on_day().
        # Default: equal-weight. Port your make_on_day weights here via SetHoldings.
        w = 1.0 / len(self.symbols) if self.symbols else 0.0
        for symbol in self.symbols:
            self.SetHoldings(symbol, w)

导出代码使用目标平台的原生类与库。请在第三方 IDE 中安装依赖后运行；实盘前请自行验证。