High-Volatility Momentum-Reversal Strategy in Large-Cap Stocks
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Quant Buffet native backtest IDEEdit and run Quant Buffet Python for High-Volatility Momentum-Reversal Strategy in Large-Cap 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: High-Volatility Momentum-Reversal Strategy in Large-Cap Stocks
# Detected pattern: Mean reversion
# 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: Buy when return z-score < -1 over 20 days.
def Rebalance(self):
import numpy as np
picks = []
for symbol in self.symbols:
hist = self.History(symbol, 20 + 5, Resolution.Daily)
if hist.empty: continue
close = hist["close"]
if hasattr(close, "unstack"):
close = close.unstack(level=0).iloc[:, 0]
rets = close.pct_change().dropna()
if len(rets) < 20: continue
window = rets.iloc[-20:]
z = (window.iloc[-1] - window.mean()) / (window.std() or 1e-9)
if z < -1:
picks.append(symbol)
w = 1.0 / len(picks) if picks else 0.0
for symbol in self.symbols:
self.SetHoldings(symbol, w if symbol in picks 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
Do Momentum and Reversals Coexist?
Jason Zhanshun Wei
- CAUniversity of Toronto
- ?University of Toronto - Rotman School of Management
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1679464
Strategy in a nutshell
The investment universe consists of NYSE, AMEX, and NASDAQ stocks with prices higher than $5 per share. At the beginning of each month, the sample is divided into equal halves, at the size median, and only larger stocks are used. Then each month, realized returns and realized (annualized) volatilities are calculated for each stock for the past six months. One week (seven calendar days) prior to the beginning of each month is skipped to avoid biases due to microstructures. Stocks are then sorted into quintiles based on their realized past returns and past volatility. The investor goes long on stocks from the highest performing quintile from the highest volatility group and short on stocks from the lowest-performing quintile from the highest volatility group. Stocks are equally weighted and held for six months (therefore, 1/6 of the portfolio is rebalanced every month).
Economic rationale
Academic research postulates that the medium-term momentum is rationalized largely along the behavioral avenue. Gradual information diffusion and/or investor under-reaction leads to momentum (Chan, Jegadeesh and Lakonishok, 1996; and Hong, Lim and Stein, 2000). Some researchers show that information uncertainty can intensify return continuations under the postulation that investors under-react more (due to overconfidence) when presented with vague information. Following this line of thinking, investors should see stronger momentum in securities with greater information uncertainty, such as in smaller stocks and stocks with higher volatility.