Top 100 Market Cap Weekly Reversal Strategy
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Quant Buffet native backtest IDEEdit and run Quant Buffet Python for Top 100 Market Cap Weekly Reversal Strategy 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: Top 100 Market Cap Weekly Reversal Strategy
# 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
Another Look at Trading Costs and Short-Term Reversal Profits
Wilma de Groot; Joop Huij; Weili Zhou
- ?Robeco Asset Management
- NLErasmus University Rotterdam
- ?Erasmus University - Rotterdam School of Management
- ?Erasmus University Rotterdam (EUR) - Erasmus Research Institute of Management (ERIM)
- ?Robeco
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1605049
Strategy in a nutshell
The strategy targets the 100 largest companies by market cap. Each week, it invests in the ten stocks that had the weakest performance over the past week and shorts the ten that performed best in the previous month. This approach aims to capitalize on mean reversion, betting that stocks that recently underperformed will rebound and those that overperformed will regress. The portfolio undergoes weekly rebalancing to adjust positions and align with the latest performance data.
Economic rationale
Research suggests the reversal anomaly in equity markets—where past underperformers rebound and overperformers regress—stems from investors overreacting to past news, then correcting. Stefan Nagel's study, "Evaporating Liquidity," interprets these anomaly returns as akin to earnings from providing liquidity, closely paralleling gains of liquidity providers. While the reversal strategy is theoretically sound, transaction costs have posed challenges to its practical application. Yet, focusing on larger stocks can mitigate these costs, as highlighted by research from de Groot, Wilma, Huij, Joop, and Zhou, Weili. Their findings prefer Nomura's cost estimates over the potentially understated or negative costs in the Keim and Madhavan model. Notably, the Nomura model, calibrated with European trade data, facilitates analysis of European equities. Recent studies confirm significant net reversal profits among large-cap stocks, underscoring that market liquidity enhancements have not made these profits mere compensations for inventory risks borne by market makers.