Combining Smart Factors Momentum and Market Portfolio
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Quant Buffet native backtest IDEEdit and run Quant Buffet Python for Combining Smart Factors Momentum and Market Portfolio 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
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: Combining Smart Factors Momentum and Market Portfolio
# 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
The Active vs Passive: Smart Factors, Market Portfolio or Both?
Matus Padysak
- SKComenius University Bratislava
- ?Comenius University - Faculty of Mathematics, Physics and Informatics
- ?Quantpedia.com
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3745517
Strategy in a nutshell
The investment universe consists of factors from the Alpha Architect’s Factor Investing Data Library (factor for all major investment styles such as Value, Quality, Momentum, Size and Volatility) based on the top 1500 US stocks. Firstly construct the fast and slow signals for each factor. The fast signal is the past one-month return, and the slow signal is the past twelve-months return. For each type of signal, to obtain the weights, cross-sectionally rank signals’ based on their absolute values. The weight for the individual slow or fast signal is equal to the corresponding rank divided by the sum of all ranks and multiplied by the signal’s sign (equations 3 and 4 in the paper). For the dynamically blended strategy (smart factors strategy), each factor has a final weight of three-quarters of the weight of fast signal plus one-quarter of the weight of slow signal (equation 12). Nextly, consider the top 1500 US stocks as the market portfolio. The combined smart factors and market strategy finds the weights of the market and factor portfolio using past moving averages of the returns. The combined strategy looks back on the past twelve months, and twelve MAs of the returns. Suppose the MA for active investing (factor momentum) is larger than MA for market portfolio, then the active investing scores one point. Otherwise, the market portfolio gets one point. Therefore, each month, the weight of the factor momentum and market portfolio is determined by the number of “winning” (loosing) moving averages (equations 13 and 14). The strategy is rebalanced monthly.
Economic rationale
Firstly, the functionality of factor strategies was proven by numerous academic researches. The same could be said about the momentum in factors since both the time-series and cross-sectional momentum strategies are well-examined and proved to be functional. The factor momentum also solves the problem of underperforming factors because of the wrong portfolio sort (for example, when growth outperforms value or big size outperforms small size).
The blending of the factor seems to be important because the slow signals tend to be unreactive to changes in trend, and fast signals are often false alarms. Therefore, the weight of the factors should be adjusted based on signal interreactions. Lastly, the dynamical weights based on the strength of the signals is also a widely utilized approach that was found to be effective also in the factor universe.
Although the active factor strategy largely outperforms naive equal-weighting of the factors or signals alone, it would have been largely beaten by the market. However, the active factor strategy and market are negatively correlated. This correlation is statistically significant using a robust non-parametric test, and this result suggests that the two portfolios could be combined to achieve the best of the two approaches. The backtest confirms this theory, since the combined strategy using moving averages, has the largest return, the lowest volatility or drawdown, and the returns distribution is much more favourable.