January Return-Based Equity Timing Strategy
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Quant Buffet native backtest IDEEdit and run Quant Buffet Python for January Return-Based Equity Timing 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
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: January Return-Based Equity Timing Strategy
# Detected pattern: Absolute momentum
# 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 with positive 252-day return; equal-weight; monthly.
def Rebalance(self):
# Pattern: abs_momentum — Long assets with positive 252-day return; equal-weight; monthly.
# Default: equal-weight. Port your make_on_day weights here via SetHoldings.
w = 1.0 / len(self.symbols) if self.symbols else 0.0
for symbol in self.symbols:
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
What's the Best Way to Trade Using the January Barometer?
Michael J. Cooper; John J. McConnell; Alexei V. Ovtchinnikov
- University of Utah
- ?University of Utah - David Eccles School of Business
- Purdue University West Lafayette
- ?Purdue University
- HEC Paris
- ?HEC Paris - Finance Department
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1436516
Strategy in a nutshell
Invest in the equity market in each January. Stay invested in equity markets (via ETF, fund, or futures) only if January return is positive; otherwise, switch investments to T-Bills.
Economic rationale
The fundamental reasons for the persistence of this anomaly in the future are very weak, and any rational explanation cannot be found. However, the whole anomaly is probably only a consequence of data mining. Additionally, the spread between the market timing using the January Barometer and passive investment in the equity market is so small that it is probably not interesting (or wise) to pursue this strategy. On the other hand, there is a large amount of research that does not support this strategy, for example, Huang: “Real-Time Profitability of Published Anomalies: An Out-of-Sample Test“: The Other January Effect (OJE), which suggests positive (negative) equity market returns in January predict positive (negative) returns in the following 11 months of the year, does not outperform a buy-and-hold approach in the US equity market and therefore adds no value to market timers. There is also no evidence of the OJE working consistently on individual stocks or international markets.” or the work of Marshall and Visaltanachoti, “The Other January Effect: Evidence Against Market Efficiency?“: “The Other January Effect (OJE), which suggests positive (negative) equity market returns in January predict positive (negative) returns in the following 11 months of the year, underperforms a simple buy-and-hold strategy before and after risk-adjustment. Even the best modified OJE strategy, which benefits from several ex-post adjustments, does not generate statistically or economically significant excess returns.”
Last but not least, Stivers, Sun and Sun in their work: “The Other January Effect: International, Style, and Subperiod Evidence” state that: “Our evidence indicates that the OJE is primarily a US market-level-based phenomenon that has diminished over time, which suggests a `temporary anomaly’ interpretation.”Therefore, this strategy should be considered with caution or maybe not traded at all, although the popular press tends to like it.