Expected Options Return Predictability Using Machine Learning
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Quant Buffet native backtest IDEEdit and run Quant Buffet Python for Expected Options Return Predictability Using Machine Learning 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: Expected Options Return Predictability Using Machine Learning
# 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
Option Return Predictability with Machine Learning and Big Data
Turan G. Bali; Heiner Beckmeyer; Mathis Moerke; Florian Weigert
- Georgetown University
- ?Georgetown University - McDonough School of Business
- DEUniversity of Münster
- ?University of Münster - Finance Center Muenster
- CHSwiss Finance Institute
- CHUniversity of St. Gallen
- ?University of St. Gallen - School of Finance
- ?University of St. Gallen - Swiss Institute of Banking and Finance
- CHUniversity of Neuchâtel
- DEUniversity of Cologne
- ?University of Cologne - Centre for Financial Research (CFR)
- ?University of Neuchatel - Institute of Financial Analysis
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3895984
Strategy in a nutshell
The strategy targets all U.S. optionable equities (share codes 10 and 11) from NYSE, AMEX, and NASDAQ, using options and underlying stock data from IvyDB, CRSP, and Compustat (1996–2020). After filtering out incomplete options and dividend-affected stocks, a rich feature set—including liquidity, value, and others—is constructed. Five nonlinear models (Random Forest, Gradient Boosted Trees, GBT with dropout, and Feed-Forward Nets) are trained on rolling 5-year windows, validated for hyperparameter tuning, and tested annually. Predicted excess returns are sorted into deciles, forming equally weighted long-short portfolios that buy options in the top decile and sell those in the bottom decile, fully funded and rebalanced monthly.
Economic rationale
Feature importance analysis using Shapley Additive Explanations shows the primary driver is an option’s position on the implied volatility surface, reflecting a relative value factor. Volatility and liquidity risk premia are secondary, with momentum, quality, and informed trading contributing. The ensemble captures nonlinear mispricing signals, enabling the exploitation of complex relationships between options and underlying stocks to generate systematic alpha.