Option Trading and Returns versus the 52-Week High
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Quant Buffet native backtest IDEEdit and run Quant Buffet Python for Option Trading and Returns versus the 52-Week High 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: Option Trading and Returns versus the 52-Week High
# 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 Trading and Returns versus the 52-Week High and Low
Siu Kai Choy; Jason Zhanshun Wei
- King's College London
- CAUniversity of Toronto
- ?University of Toronto - Rotman School of Management
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4119040
Strategy in a nutshell
The investment universe consists of all stocks from the CRSP database and their underlying options contracts. First, apply the following screening procedure to the options universe and retain only those which meet the following criteria: trading volume is non-zero, maturity is between 30 and 60 days (i.e., only options maturing in the month following the next), moneyness (defined as the exercise price over the stock price) is in the range of (0.8, 1.2), the bid quote and bid-ask spread are positive, and the percentage bid-ask spread (i.e., bid-ask spread divided by the midpoint) is less than 100%. Second, on the last trading day of each month, calculate the price-to-high (PTH) ratio for each stock, defined as the stock price divided by its 52-week high. Third, sort the stocks into quintiles based on their PTH ratio, where the highest quintile consists of the highest PTH stocks, and the lowest quintile consists of the lowest PTH stocks. Fourth, for each stock in the highest quintile, buy one call option and delta-hedge in the next month with daily rebalancing. Conversely, for each stock in the lowest quintile, sell one call option and delta-hedge in the next month with daily rebalancing. Invest/borrow the net balance at the risk-free rate. Positions are value-weighted based on the dollar value of open interest and rebalanced monthly.
Economic rationale
Driessen, Lin, and Van Hemert (2012) found that the implied volatility in both call and put options decreases when the stock price approaches the 52-week extremes, which indicates anchoring-induced sluggish incorporation of news into stock prices. Consequently, this downward-biased volatility forecast leads to the undervaluation of both calls and puts when the stock price is at its 52-week high and low. However, the demand-pressure theory of Garleanu, Pedersen, and Poteshman (2009) implies mispricing of calls and puts in the opposite direction. More specifically, calls (puts) become undervalued and puts (calls) overvalued when the stock price approaches its 52-week high (low). The demand-pressure effect and the volatility effect reinforce each other for calls (puts) when the stock price approaches its 52-week high (low), but they offset each other for calls (puts) when the stock price approaches its 52-week low (high). Translating prices into returns, when the stock price approaches its 52-week high (low), the subsequent delta-hedged returns are unambiguously higher for calls (puts).