Equity · United States · CFDs, ETFs, funds, futures · Daily · United States Free

US Pre-Holiday Equity Exposure Strategy

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Quant Buffet native backtest IDE

Edit and run Quant Buffet Python for US Pre-Holiday Equity Exposure Strategy 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 · 50 lines

"""
Quant Buffet interactive lab — pre-holiday-effect
Allowed: backtest.data / backtest.engine / backtest.metrics, numpy, pandas.
Required: ASSETS = [...] and def make_on_day(prices): -> (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 = ['SPY', 'BIL']
LOOKBACK = 252
CASH = 'BIL'

def make_on_day(prices: pd.DataFrame):
    cols = [c for c in ASSETS if c in prices.columns]
    if CASH not in cols and CASH in prices.columns:
        cols = cols + [CASH]
    risky = [c for c in cols if c != CASH]
    rets = prices[cols].pct_change(LOOKBACK)
    state = {"last": None}

def on_day(engine: PortfolioEngine, dt: pd.Timestamp) -> None:
        key = (dt.year, dt.month)
        if state["last"] == key:
            return
        state["last"] = key
        scores = {
            s: float(rets.at[dt, s])
            for s in risky
            if s in rets.columns and pd.notna(rets.at[dt, s])
        }
        if not scores:
            engine.set_target_weights(dt, {CASH: 1.0} if CASH in cols else {})
            return
        best = max(scores, key=scores.get)
        if scores[best] > 0:
            engine.set_target_weights(dt, {best: 1.0})
        elif CASH in cols:
            engine.set_target_weights(dt, {CASH: 1.0})
        else:
            engine.set_target_weights(dt, {})

ready = rets.dropna(how="all").index.min() if rets.notna().any().any() 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

7.89%

Sharpe

0.63

Max DD

-33.72%

Vol

13.60%

Sortino

0.93

Beta

0.51

Up days

53%

Run the backtest to populate charts.

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: Absolute momentumAssets: SPY, TLT, GLD, BIL

# Generated from Quant Buffet → QuantConnect LEAN
# Strategy: US Pre-Holiday Equity Exposure Strategy
# Detected pattern: Absolute momentum
# 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: Long assets with positive 252-day return; equal-weight; monthly.

    def Rebalance(self):
        # Pattern: abs_momentum — Long assets with positive 252-day return; equal-weight; monthly.
        # 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

Testing the Significance of Calendar Effects

AuthorsPeter Reinhard Hansen; Asger Lunde; James M. Nason

Institute

DKCopenhagen Business School
DKAarhus University
University of North Carolina at Chapel Hill
?Aarhus University - CREATES
?Copenhagen Business School, Finance
?University of North Carolina (UNC) at Chapel Hill - Department of Economics
?Aarhus University - School of Business and Social Sciences
?CREATES
North Carolina State University
?North Carolina State University - Department of Economics

Paper linkhttps://papers.ssrn.com/sol3/papers.cfm?abstract_id=388601

Screenshot from the original paper

Strategy in a nutshell

Investors use some simple investment vehicles to gain exposure to US equity market (ETF, fund, CFD or future) only during days preceding holiday days (New Year’s Day, Martin Luther King Jr. Day, President’s Day, Good Friday, Memorial Day, Independence Day, Labor Day, Election Day, Thanksgiving Day, Christmas Day). Investors stay in cash during other trading days. The anomaly isn’t limited only to the US market but seems to work well also in other countries; therefore, it could be broadened to include pre-holiday days for local holidays in other markets.

Economic rationale

The main explanatory factors for this anomaly are behavioral. One explanation states that short-sellers close their risky positions prior to holidays. Another reason could be investors’ good mood around holidays, indicating greater optimism about future prospects and, therefore, a high probability of positive market moves.

Backtest performance

Annualised return7.89%

Volatility13.60%

Beta0.51

Sharpe ratio0.63

Sortino ratio0.93

Maximum drawdown-33.72%

Win rate53%