Country ETF Long-Short Reversal Strategy Based on 36-Month Returns with Triennial Rebalance
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Quant Buffet native backtest IDEEdit 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 →
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: 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 = ["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: 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.
Academic paper
Winner-Loser Reversals in National Stock Market Indices: Can They Be Explained?
Anthony J. Richards
- Reserve Bank of Australia
- ?Reserve Bank of Australia - Economic Research
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=883937
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.