Equity · Global · stocks · Weekly Free

Top 100 Market Cap Weekly Reversal Strategy

Academic paper

Another Look at Trading Costs and Short-Term Reversal Profits

AuthorsWilma de Groot; Joop Huij; Weili Zhou

Institute

Robeco Asset Management
Erasmus University Rotterdam
Erasmus University - Rotterdam School of Management
Erasmus University Rotterdam (EUR) - Erasmus Research Institute of Management (ERIM)
Robeco

Paper linkhttps://papers.ssrn.com/sol3/papers.cfm?abstract_id=1605049

Screenshot from the original paper

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.

Entry & exit

Entry

Compute a 20-day z-score on each name in SPY, BIL. Enter when z ≤ -1 (stretched to the downside).

Exit

Exit when the z-score mean-reverts back to ≥ 0. Positions are not held through a full trend — only the snap-back.

Market data required

Needs at least 25 adjusted daily closes on SPY, BIL before the first signal is ready. ETF columns print on exchange sessions only. The lab loads Yahoo Finance via backtest.data.load_daily_prices.

Symbols

SPY, BIL

Frequency

Weekly · Daily adjusted close; signals rebalance on the month change (last available session)

History needed

≥ 25 trading days per symbol (about 2 months), plus a few extra bars so the first signal can fire.

Fields

Adjusted close (Yahoo auto_adjust)
Session date index

Credentials

None for paper and the onsite lab
Broker or exchange API keys only for live orders

APIs & providers

Quant Buffet Data API

The lab calls load_daily_prices for the ASSETS panel of adjusted closes, then caches CSV so backtests replay the same series.

Provider: backtest.data.load_daily_prices
Auth: None for paper / sandbox backtests
Endpoints: load_daily_prices(ASSETS, start="2000-01-01")

API / docs → /en/docs/data-api

Yahoo Finance via yfinance

US equities and ETFs, adjusted OHLCV, free for research (rate limits apply).

Provider: yfinance → Yahoo Finance
Auth: None for public history
Endpoints: Ticker.history / yf.download(auto_adjust=True)

API / docs → github.com/ranaroussi/yfinance

Onsite backtest IDE

Quant Buffet native backtest IDE

Edit 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 →

IDE · 44 lines

"""
Quant Buffet interactive lab — short-term-reversal-effect-in-stocks
Allowed: backtest.data / backtest.engine / backtest.metrics, numpy, pandas.
Required: a module-level ASSETS list, and def make_on_day(prices) returning (on_day, ready)
"""
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_metrics

ASSETS = ['SPY', 'BIL']
LOOKBACK = 20
ENTRY_Z = -1.0
EXIT_Z = 0.0

def make_on_day(prices: pd.DataFrame):
    cols = [c for c in ASSETS if c in prices.columns]
    mu = prices[cols].rolling(LOOKBACK, min_periods=LOOKBACK).mean()
    sd = prices[cols].rolling(LOOKBACK, min_periods=LOOKBACK).std(ddof=0)
    z = (prices[cols] - mu) / sd.replace(0, np.nan)
    held = {s: False for s in cols}

    def on_day(engine: PortfolioEngine, dt: pd.Timestamp) -> None:
        long = []
        for s in cols:
            zv = z.at[dt, s]
            if pd.isna(zv):
                continue
            if not held[s] and zv <= ENTRY_Z:
                held[s] = True
            elif held[s] and zv >= EXIT_Z:
                held[s] = False
            if held[s]:
                long.append(s)
        w = {} if not long else {s: 1.0 / len(long) for s in long}
        engine.set_target_weights(dt, w)

    ready = z.dropna(how="all").index.min() if z.notna().any().any() else None
    return on_day, ready

"""
Quant Buffet interactive lab — short-term-reversal-effect-in-stocks
Allowed: backtest.data / backtest.engine / backtest.metrics, numpy, pandas.
Required: a module-level ASSETS list, and def make_on_day(prices) returning (on_day, ready)
"""
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_metrics

ASSETS = ['SPY', 'BIL']
LOOKBACK = 20
ENTRY_Z = -1.0
EXIT_Z = 0.0

def make_on_day(prices: pd.DataFrame):
    cols = [c for c in ASSETS if c in prices.columns]
    mu = prices[cols].rolling(LOOKBACK, min_periods=LOOKBACK).mean()
    sd = prices[cols].rolling(LOOKBACK, min_periods=LOOKBACK).std(ddof=0)
    z = (prices[cols] - mu) / sd.replace(0, np.nan)
    held = {s: False for s in cols}

def on_day(engine: PortfolioEngine, dt: pd.Timestamp) -> None:
        long = []
        for s in cols:
            zv = z.at[dt, s]
            if pd.isna(zv):
                continue
            if not held[s] and zv <= ENTRY_Z:
                held[s] = True
            elif held[s] and zv >= EXIT_Z:
                held[s] = False
            if held[s]:
                long.append(s)
        w = {} if not long else {s: 1.0 / len(long) for s in long}
        engine.set_target_weights(dt, w)

ready = z.dropna(how="all").index.min() if z.notna().any().any() else None
    return on_day, ready

Quant Buffet syntax cheat sheet (copy / insert)

Paste these fragments into the editor. The sandbox rejects QuantConnect, os, and network libraries.

Required imports

Only these libraries are allowed in the sandbox.

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_metrics

ASSETS list (whitelisted ETFs)

Module-level list. Tickers must be in the Quant Buffet whitelist.

ASSETS = ["SPY", "QQQ", "TLT", "GLD", "BIL"]

make_on_day contract

Must return (on_day, ready). on_day calls engine.set_target_weights.

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, ready

Set target weights

Weights should sum to about 1.0. Empty dict = 100% cash.

engine.set_target_weights(dt, {"SPY": 0.60, "BIL": 0.40})

Live backtest performance

CAGR

1.44%

Sharpe

0.17

Max DD

-36.97%

Vol

14.04%

Sortino

0.26

Beta

0.55

Up days

45%

Run the backtest to populate charts.

Order records

Run the backtest to load every order fill alongside 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.

Target platform

Run in: QuantConnect Cloud or LEAN CLI · QCAlgorithm with Equity securities and monthly rebalance.

Detected pattern: Mean reversionAssets: SPY, BIL

# 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", "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.