Equity · United States · ETFs, funds · Every 3 Years Free

Country ETF Long-Short Reversal Strategy Based on 36-Month Returns with Triennial Rebalance

Academic paper

Winner-Loser Reversals in National Stock Market Indices: Can They Be Explained?

AuthorsAnthony J. Richards

Institute

Reserve Bank of Australia
Reserve Bank of Australia - Economic Research

Paper linkhttps://papers.ssrn.com/sol3/papers.cfm?abstract_id=883937

Screenshot from the original paper

Strategy in a nutshell

The strategy involves 16 ETFs, each representing a different country's equity index. The approach is to invest long in the four ETFs representing countries with the lowest 36-month returns and short the four with the highest returns over the same period. This portfolio is adjusted every three years to account for changes in market performance, aiming to capitalize on the potential for reversal in the fortunes of underperforming and outperforming countries. This contrarian strategy seeks to exploit long-term cyclical shifts in market valuations.

Economic rationale

The study investigates why national stock markets experience performance reversals over years, highlighting challenges in pinpointing a definitive cause due to short data samples, limited country numbers, market integration issues, and the absence of a universal asset pricing model. This complexity mirrors the ongoing debate about price anomalies in the U.S. market. Crucially, the research finds no evidence linking these reversals to risk differentials, dismissing the idea that prior underperformers were riskier based on their volatility or correlation with global market returns or other risk factors. However, reversals were more pronounced in smaller markets, suggesting a potential "small-country effect" or market imperfections. Additionally, the limited impact of cross-border equity flows on correcting mispricings, possibly due to fears of expropriation or capital controls, offers another explanation. The study also entertains the possibility that arbitrage might not fully eliminate price discrepancies due to equities' uncertain valuation, return volatility, and the time needed for market correction. Interestingly, increased cross-border flows might exacerbate mispricings by attracting more momentum investors, further complicating the market dynamics.

Entry & exit

Entry

On each month, rank EWA, EWK, EWZ, EWC, FXI, EWQ, EWG, EWH, EWI, EWJ (+2 more) by 21-day return and invert the sort (reversal). Equal-weight the weakest 3 name(s), expecting a bounce.

Exit

At the next rebalance, sell names that are no longer in the bottom 3 and rotate into the new laggards.

Market data required

Needs at least 21 adjusted daily closes on EWA, EWK, EWZ, EWC, FXI, EWQ (+6 more) 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

EWA, EWK, EWZ, EWC, FXI, EWQ, EWG, EWH, EWI, EWJ, EWN, EWS

Frequency

Every 3 Years · Daily adjusted close; signals rebalance on the month change (last available session)

History needed

≥ 21 trading days per symbol (about 1 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 Country ETF Long-Short Reversal Strategy Based on 36-Month Returns with Triennial Rebalance 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 · 42 lines

"""
Quant Buffet interactive lab — reversal-effect-in-international-equity-etfs
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 = ['EWA', 'EWK', 'EWZ', 'EWC', 'FXI', 'EWQ', 'EWG', 'EWH', 'EWI', 'EWJ', 'EWN', 'EWS']
LOOKBACK = 21
TOP_N = 3
INVERT = True  # True = short-term reversal (rank ascending)

def make_on_day(prices: pd.DataFrame):
    cols = [c for c in ASSETS if c in prices.columns]
    rets = prices[cols].pct_change(LOOKBACK)
    state = {"last": None}

    def on_day(engine: PortfolioEngine, dt: pd.Timestamp) -> None:
        row = rets.loc[dt]
        if row.isna().all():
            return
        key = (dt.year, dt.month)
        if state["last"] == key:
            return
        state["last"] = key
        ranked = row.dropna().sort_values(ascending=INVERT)
        picks = list(ranked.head(TOP_N).index)
        if not INVERT:
            picks = [s for s in picks if ranked[s] > 0] or picks[:1]
        w = {} if not picks else {s: 1.0 / len(picks) for s in picks}
        engine.set_target_weights(dt, w)

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

"""
Quant Buffet interactive lab — reversal-effect-in-international-equity-etfs
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 = ['EWA', 'EWK', 'EWZ', 'EWC', 'FXI', 'EWQ', 'EWG', 'EWH', 'EWI', 'EWJ', 'EWN', 'EWS']
LOOKBACK = 21
TOP_N = 3
INVERT = True  # True = short-term reversal (rank ascending)

def make_on_day(prices: pd.DataFrame):
    cols = [c for c in ASSETS if c in prices.columns]
    rets = prices[cols].pct_change(LOOKBACK)
    state = {"last": None}

def on_day(engine: PortfolioEngine, dt: pd.Timestamp) -> None:
        row = rets.loc[dt]
        if row.isna().all():
            return
        key = (dt.year, dt.month)
        if state["last"] == key:
            return
        state["last"] = key
        ranked = row.dropna().sort_values(ascending=INVERT)
        picks = list(ranked.head(TOP_N).index)
        if not INVERT:
            picks = [s for s in picks if ranked[s] > 0] or picks[:1]
        w = {} if not picks else {s: 1.0 / len(picks) for s in picks}
        engine.set_target_weights(dt, w)

ready = rets.dropna(how="all").index.min() if rets.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

6.57%

Sharpe

0.38

Max DD

-67.75%

Vol

24.38%

Sortino

0.59

Beta

0.99

Up days

41%

Stored preview only. Run backtest to simulate every daily session from Yahoo Finance adjusted closes.

Run the backtest to load orders, then mark them on the equity chart.

Equity curve (indexed = 100)

Accent = strategy · dashed grey = buy-and-hold benchmark. Hover or tap any point for its date and values.

Drawdown

Metrics bar chart

Monthly returns

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: Momentum rotationAssets: EWA, EWK, EWZ, EWC, FXI, EWQ, EWG, EWH, EWI, EWJ, EWN, EWS

# Generated from Quant Buffet → QuantConnect LEAN
# Strategy: Country ETF Long-Short Reversal Strategy Based on 36-Month Returns with Triennial Rebalance
# Detected pattern: Momentum rotation
# 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 = ["EWA", "EWK", "EWZ", "EWC", "FXI", "EWQ", "EWG", "EWH", "EWI", "EWJ", "EWN", "EWS"]
        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: Hold top 3 by 21-day return; monthly.

    def Rebalance(self):
        scores = {}
        for symbol in self.symbols:
            hist = self.History(symbol, 21 + 5, Resolution.Daily)
            if hist.empty: continue
            close = hist["close"]
            if hasattr(close, "unstack"):
                close = close.unstack(level=0).iloc[:, 0]
            if len(close) < 21 + 1: continue
            scores[symbol] = float(close.iloc[-1] / close.iloc[-21 - 1] - 1)
        ranked = sorted(scores.items(), key=lambda kv: kv[1], reverse=True)[:3]
        for symbol in self.symbols:
            self.SetHoldings(symbol, 0)
        if ranked:
            w = 1.0 / len(ranked)
            for symbol, _ in ranked:
                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.