Industry Momentum Strategy: Monthly Long Stocks in Top Industries by Price-to-52-Week High, Short Bottom Industries
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Quant Buffet native backtest IDEEdit and run Quant Buffet Python for Industry Momentum Strategy: Monthly Long Stocks in Top Industries by Price-to-52-Week High, Short Bottom Industries 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
Accent = strategy · dashed grey = buy-and-hold benchmark
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: Industry Momentum Strategy: Monthly Long Stocks in Top Industries by Price-to-52-Week High, Short Bottom Industries
# 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
Industry Information and the 52-Week High Effect
Xin Hong; Bradford D. Jordan; Mark H. Liu
- University of Kentucky
- University of Florida
- ?University of Florida - Department of Finance, Insurance and Real Estate
- ?University of Kentucky - Gatton College of Business and Economics
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1787378
Strategy in a nutshell
The investment universe consists of all stocks from NYSE, AMEX, and NASDAQ (the research paper used the CRSP database for backtesting). The ratio between the current price and 52-week high is calculated for each stock at the end of each month (PRILAG i,t = Price i,t / 52-Week High i,t). Every month, the investor then calculates the weighted average of ratios (PRILAG i,t) from all firms in each industry (20 industries are used), where the weight is the market capitalization of the stock at the end of the month t. The winners (losers) are stocks in the six industries with the highest (lowest) weighted averages of PRILAGi,t. The investor buys stocks in the winner portfolio and shorts stocks in the loser portfolio and holds them for three months. Stocks are weighted equally, and the portfolio is rebalanced monthly (which means that 1/3 of the portfolio is rebalanced each month).
Economic rationale
Academics speculate that this effect is connected to “adjustment and anchoring bias”. Anchoring is a psychological bias that says that people start with an implicitly suggested reference point (the “anchor” -> 52-week high in our example) and then make incremental adjustments based on additional information. The financial paper says that traders use the 52-week high as a reference point in which they evaluate the potential impact of news. When good news has pushed a stock’s price near or to a new 52-week high, traders are reluctant to bid the price of the stock higher even if the information warrants it. The information eventually prevails, and the price moves up, resulting in a continuation. It works similarly for 52-week lows.