Empirical investigation of state-of-the-art mean reversion strategies for equity markets
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Quant Buffet native backtest IDEEdit and run Quant Buffet Python for Empirical investigation of state-of-the-art mean reversion strategies for equity markets 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: Empirical investigation of state-of-the-art mean reversion strategies for equity markets
# Detected pattern: SMA trend
# 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: Long assets where close > SMA(200); equal-weight; monthly.
def Rebalance(self):
longs = []
for symbol in self.symbols:
hist = self.History(symbol, 200 + 5, Resolution.Daily)
if hist.empty: continue
close = hist["close"].unstack(level=0).iloc[:, 0] if hasattr(hist["close"], "unstack") else hist["close"]
if len(close) < 200: continue
if float(close.iloc[-1]) > float(close.iloc[-200:].mean()):
longs.append(symbol)
weight = 1.0 / len(longs) if longs else 0.0
for symbol in self.symbols:
self.SetHoldings(symbol, weight if symbol in longs 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
Empirical investigation of state-of-the-art mean reversion strategies for equity markets
Seung-Hyun Moon; Yong-Hyuk Kim; Byung-Ro Moon
Teaser
Hold each liquid ETF only when its price is above a long SMA; equal-weight the longs, cash otherwise. Universe: VNQ, IYR, RWX, SPY, BIL. Parameters: sma_days=200; rebalance=monthly. Rebalanced on the engine's template schedule with 5 bps commission and 2 bps slippage.
Strategy in a nutshell
Recent studies have shown that online portfolio selection strategies that exploit the mean reversion property can achieve excess return from equity markets. This paper empirically investigates the performance of state-of-the-art mean reversion strategies on real market data. The aims of the study are twofold. The first is to find out why the mean reversion strategies perform extremely well on well-known benchmark datasets, and the second is to test whether or not the mean reversion strategies work well on recent market data. The mean reversion strategies used in this study are the passive aggressive mean reversion (PAMR) strategy, the on-line moving average reversion (OLMAR) strategy, and the transaction cost optimization (TCO) strategies. To test the strategies, we use the historical pric
Economic rationale
Trend filters exploit persistent serial correlation in asset returns and reduce exposure when prices fall below a long-horizon average, cutting left-tail risk. Related evidence from “Empirical investigation of state-of-the-art mean reversion strategies for equity markets”: Recent studies have shown that online portfolio selection strategies that exploit the mean reversion property can achieve excess return from equity markets. This paper empirically investigates the performance of state-of-the-art mean reversion strategies on real market data. The aims of the study are twofold. The first is to find out why the mean reversion strategies perform extremely well on well-known benchmark datasets, and the second is to test whether or not the mean reversion strategies wo