Quant BuffetRelax, Not Over Thinking
Log InLog In

Equity · United States · CFDs, ETFs, futures, stocks · Weekly · United States Free

S&P 100 Long Strategy During Option Expiry Week

Log in to collect

Onsite backtest IDE

Quant Buffet native backtest IDE

Edit and run Quant Buffet Python for S&P 100 Long Strategy During Option Expiry Week 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 →

Ready — edit code, then Run backtest.
IDE · 50 lines
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
9.01%
Sharpe
0.69
Max DD
-31.44%
Vol
14.02%
Sortino
1.03
Beta
0.53
Up days
61%

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.

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: S&P 100 Long Strategy During Option Expiry Week
# 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

Stock Returns and Option Activity over the Option-Expiration Week for S&P 100 Stocks

AuthorsChris T. Stivers; Licheng Sun

Institute
  • University of Louisville Hospital
  • ?University of Louisville
  • GHDominion University College
  • Old Dominion University

Paper linkhttps://papers.ssrn.com/sol3/papers.cfm?abstract_id=1571786

Screenshot from the original paper

Screenshot from the original paper

Strategy in a nutshell

Investors choose stocks from the S&P 100 index as his/her investment universe (stocks could be easily tracked via ETF or index fund). He/she then goes long S&P 100 stocks during the option-expiration week and stays in cash during other days.

Economic rationale

Academic research suggests that intra-month weekly patterns in call-related activity contribute to patterns in weekly average equity returns. Hedge rebalancing by option market makers in the largest stocks with the most actively traded options is the main reason for the abnormal stock’s returns. During option-expiration weeks, a sizable reduction occurs in option-open interest as the near-term options approach expiration and then expire. A reduction in call-open interest should be associated with a reduction in the net long call position of market makers. This implies a decrease in the short-stock positions being held by market makers to delta hedge their long call holdings.

Backtest performance

Annualised return9.01%
Volatility14.02%
Beta0.53
Sharpe ratio0.69
Sortino ratio1.03
Maximum drawdown-31.44%
Win rate61%