Earnings Disagreement Put Spread Strategy on S&P 100 Options
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Quant Buffet native backtest IDEEdit and run Quant Buffet Python for Earnings Disagreement Put Spread Strategy on S&P 100 Options 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: Earnings Disagreement Put Spread Strategy on S&P 100 Options
# 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
Option-Implied Correlations and the Price of Correlation Risk
Joost Driessen; Pascal J. Maenhout; Grigory Vilkov
- NLTilburg University
- ?Tilburg University - Center for Economic Research (CentER)
- ?Tilburg University - Tilburg University School of Economics and Management
- INSEAD
- ?INSEAD - Finance
- DEFrankfurt School of Finance & Management
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2359380
Strategy in a nutshell
The investment universe consists of stocks from the S&P 100 index. Trading vehicles are options on stocks from this index and also options on the index itself. The investor uses analyst forecasts of earnings per share from the Institutional Brokers Estimate System (I/B/E/S) database and computes for each firm the mean absolute difference scaled by an indicator of earnings uncertainty (see page 24 in the source academic paper for detailed methodology). Each month, investor sorts stocks into quintiles based on the size of belief disagreement. He buys puts of stocks with the highest belief disagreement and sells the index puts with Black-Scholes deltas ranging from -0.8 to -0.2.
Economic rationale
The academic paper shows that dispersion in analysts’ forecasts is strongly related to the implied volatility of index and single-name options. Research shows that option excess returns reflect the different exposure to disagreement risk. Investors who buy options of firms that are more prone to heterogeneity in beliefs are compensated in equilibrium for holding this risk. Volatility risk premia of individual and index options represent compensation for the priced disagreement risk. Hence, in the cross-section of options, the volatility risk premium depends on the size of the belief heterogeneity of this particular firm and the business cycle indicator. As the risk-neutral skewness, the volatility risk premium for index options can be larger or smaller depending on the size of disagreement and of the firm’s share.