Predicting the Delta-Hedged Option Returns Using LASSO
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Quant Buffet native backtest IDEEdit and run Quant Buffet Python for Predicting the Delta-Hedged Option Returns Using LASSO 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: Predicting the Delta-Hedged Option Returns Using LASSO
# 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
The Cross-Section of Individual Equity Option Returns
Mobina Shafaati; Don M. Chance; Robert Brooks
- GHDominion University College
- Old Dominion University
- Louisiana State University
- ?Louisiana State University, Baton Rouge - Department of Finance
- ?Louisiana State University, Baton Rouge - E.J. Ourso College of Business Administration
- University of Alabama
- ?University of Alabama - Department of Economics, Finance and Legal Studies
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3927410
Strategy in a nutshell
This strategy focuses on American equity options and their underlying stocks, using data from OptionMetrics, CRSP, Compustat, and I/B/E/S. After filtering out options with non-standard settlement, early exercise, extreme prices, or arbitrage violations, 107 explanatory variables are constructed—8 option-related (including open interest, trading volume, volatility measures, and skewness/kurtosis) and 99 stock-related characteristics. Delta-hedged portfolios are formed by taking a long call option position hedged with a short position in the underlying stock. Each month, portfolios are sorted into deciles based on predicted returns for the following month, estimated via LASSO regression using the past ten years of data, with the penalty parameter selected through five-fold cross-validation. The rolling 10-year window updates coefficients monthly. The trading rule is to buy the top decile portfolios with the highest forecasted returns and sell the bottom decile with the lowest, equally weighted and rebalanced monthly.
Economic rationale
Theoretically, delta-hedged option returns should be unpredictable if options were perfectly replicable by their underlying stocks and risk-free bonds. However, empirical evidence shows systematic patterns in the cross-section of delta-hedged returns. Using LASSO regression allows the identification of a subset of option- and stock-level characteristics with predictive power, while zeroing out irrelevant variables. The rolling estimation approach captures time variation in the selected characteristics, enabling the strategy to exploit persistent cross-sectional predictability in option returns, thereby generating potential economic gains.