Payroll News Timing in FX
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Quant Buffet native backtest IDEEdit and run Quant Buffet Python for Payroll News Timing in FX 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 →
Quant Buffet syntax cheat sheet (copy / insert)
Paste these fragments into the editor. The sandbox rejects QuantConnect, os, and network libraries.
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_metricsASSETS = ["SPY", "QQQ", "TLT", "GLD", "BIL"]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, readyengine.set_target_weights(dt, {"SPY": 0.60, "BIL": 0.40})Live backtest 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.
# Generated from Quant Buffet → QuantConnect LEAN
# Strategy: Payroll News Timing in FX
# 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 = ["SPY", "TLT", "GLD", "BIL"]
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)
Exported code uses the platform’s native classes and libraries. Install dependencies in your third-party IDE, then run. Validate before live trading.
Academic paper
Strategy in a nutshell
The investment universe includes FX futures for the euro (Deutsche mark before euro), British pound, Swiss franc, Japanese yen, Canadian dollar, Australian dollar, and New Zealand dollar. Data sources include speculator FX exposures from the CFTC’s Commitments of Traders (COT) reports, bid-ask spreads from futures and the OTC market (via Refinitiv Tick History and Eikon), and futures margin data from the Chicago Mercantile Exchange (CME).
First, the long and short open interest in U.S. dollars is evaluated for each currency futures contract for the speculator investor type. The daily net open interest is defined as long minus short positions. FX exposure of speculators for each currency is calculated by dividing its net open interest by the sum of absolute net open interests across all currencies. Portfolio weights are equal to these FX exposures.
On U.S. payroll announcement days, the strategy mimics speculator FX exposure by investing in currency futures using 10-day lagged portfolio weights. Positions are opened five minutes before the announcements and closed one hour after release.
The FX target portfolio is composed of (100−α)%(100-\alpha)\%(100−α)% risky assets and α%\alpha\%α% cash holdings (between 5% and 20%), to meet futures margin requirements. The remainder is invested in risky assets such as the S&P 500 total return index, Bank of America U.S. corporate bond total return index, or JP Morgan aggregate commodity total return index. Results are presented for a strategy investing in the S&P 500 with 20% cash, achieving a Sharpe ratio gain of 0.39 relative to a strategy that ignores payroll announcements.
Economic rationale
The strategy leverages the informational content of speculators’ FX portfolio exposures, which contain predictive signals about upcoming payroll announcements. This predictive power persists due to long-lived information in FX positions, driven by timing and stealth motives:
Patience in Execution: Speculators may delay trades to avoid noise trading or take advantage of market liquidity.
Access to Private Information: Some speculators possess information unavailable to other market participants.
By mimicking informed speculators’ FX portfolio exposures, investors can capture these signals. FX exposures are measured as the net position of long and short open interests in U.S. dollars and rescaled so that portfolio weights sum to unity across different futures, reflecting the relative allocation of informed market activity.