US Equity Value Factor Strategy: Monthly Long High and Short Low Book-to-Price Stocks
Log in to collectOnsite backtest IDE
Quant Buffet native backtest IDEEdit and run Quant Buffet Python for US Equity Value Factor Strategy: Monthly Long High and Short Low Book-to-Price Stocks 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: US Equity Value Factor Strategy: Monthly Long High and Short Low Book-to-Price Stocks
# Detected pattern: SMA trend
# 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 where close > SMA(200); equal-weight; monthly.
def Rebalance(self):
longs = []
for symbol in self.symbols:
hist = self.History(symbol, 200 + 5, Resolution.Daily)
if hist.empty: continue
close = hist["close"].unstack(level=0).iloc[:, 0] if hasattr(hist["close"], "unstack") else hist["close"]
if len(close) < 200: continue
if float(close.iloc[-1]) > float(close.iloc[-200:].mean()):
longs.append(symbol)
weight = 1.0 / len(longs) if longs else 0.0
for symbol in self.symbols:
self.SetHoldings(symbol, weight if symbol in longs else 0.0)
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
Fact, Fiction, and the Size Effect
Ron Alquist; Ronen Israel; Tobias J. Moskowitz
- United States Department of the Treasury
- ?Financial Stability Oversight Council, U.S. Treasury
- Capital University
- ?AQR Capital Management, LLC
- National Bureau of Economic Research
- ATAgency for Quality Assurance and Accreditation Austria
- Yale University
- ?AQR Capital
- ?National Bureau of Economic Research (NBER)
- ?Yale University, Yale SOM
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3177539
Strategy in a nutshell
The investment universe encompasses NYSE, AMEX, and NASDAQ stocks, targeting "value" investing through the HML portfolio, which longs stocks with high book-to-price ratios and shorts those with low ratios. This approach includes analyzing the average returns of two subsets: HML small, focusing on small-cap stocks, and HML large, targeting large-cap stocks, both adopting a value investing stance. The portfolio, equally weighted across all holdings, undergoes monthly rebalancing to maintain its strategy alignment and capture the essence of value investing through a diversified approach across market capitalizations.
Economic rationale
One theory suggests investors excessively favor growth stocks due to their growth potential, leading to value stocks being undervalued. Some scholars argue that the market value to book value ratio acts as a risk indicator, positing that the higher returns from low MV/BV stocks serve as a reward for bearing additional risk. Typically, stocks with low MV/BV ratios are in financial distress, further reinforcing the concept that their higher returns compensate investors for the increased risk associated with these stocks.