Global 5-Asset Trend-Following Strategy Using 10-Month SMA Filter
Log in to collectOnsite backtest IDE
Quant Buffet native backtest IDEEdit and run Quant Buffet Python for Global 5-Asset Trend-Following Strategy Using 10-Month SMA Filter 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: Global 5-Asset Trend-Following Strategy Using 10-Month SMA Filter
# 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(210); equal-weight; monthly.
def Rebalance(self):
longs = []
for symbol in self.symbols:
hist = self.History(symbol, 210 + 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) < 210: continue
if float(close.iloc[-1]) > float(close.iloc[-210:].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
Teaser
Investment universe: 5 ETFs (SPY, EFA, BND, VNQ, GSG). Portfolio equally weighted. Hold if over 10-month SMA, else cash.
Strategy in a nutshell
The investment universe comprises five ETFs: SPY (US stocks), EFA (foreign stocks), BND (bonds), VNQ (REITs), and GSG (commodities). The portfolio maintains equal weighting across these ETFs. Each asset class ETF is held only when its value exceeds its ten-month Simple Moving Average (SMA); otherwise, the portfolio remains in cash. This strategy aims to capitalize on favourable market conditions indicated by the SMA while avoiding exposure to asset classes experiencing downward trends, thereby optimizing investment performance.
Economic rationale
The strategy operates on a simple premise: momentum or trend-following filters identify periods of lower performance coupled with higher volatility, as well as periods of higher performance with lower volatility. Successful implementation naturally leads to outperforming a passive buy-and-hold approach. This strategy consistently outperforms passive holding with lower drawdowns, as demonstrated in research findings. By timing each asset class appropriately, it surpasses passive strategies while minimizing risks. Diversification benefits from low asset correlation are leveraged in a multi-asset portfolio with momentum filters, enabling investment in favorable asset classes while avoiding unfavorable ones. Practically, since 1973, adopting this strategy would have enhanced risk-adjusted returns through diversified assets and market timing, avoiding significant losses during bear markets. This approach offers equity-like returns with bond-like volatility and drawdowns, as highlighted by Collie, Sylvanus, and Thomas. They emphasize the volatility of market volatility, advocating for adaptable asset allocation to navigate market fluctuations effectively.