Factor Momentum and Regime-Switching Overlay Strategy
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Quant Buffet native backtest IDEEdit and run Quant Buffet Python for Factor Momentum and Regime-Switching Overlay 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: Factor Momentum and Regime-Switching Overlay Strategy
# Detected pattern: Custom / hybrid
# 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: Custom Quant Buffet logic — adapt the signal block to match your lab on_day().
def Rebalance(self):
# Pattern: custom — Custom Quant Buffet logic — adapt the signal block to match your lab on_day().
# 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
Teaser
Allocate inversely to asset volatility so risk contributions are roughly equal. Universe: SPY, EFA, EWJ, GLD, TLT. Parameters: vol_lookback=63; rebalance=monthly. Rebalanced on the engine's template schedule with 5 bps commission and 2 bps slippage. Because the paper's primary signal (ML, sentiment, or proprietary data) is not available in our public ETF engine, this draft uses a liquid ETF rule that preserves the paper's economic theme rather than a bit-exact replication.
Strategy in a nutshell
Investors are faced with challenges in diversifying risks and protecting capital during crash periods. In this article, the authors incorporate regime information in the portfolio optimization context by identifying regimes for historical time periods using an ℓ<sub>1</sub>-trend filtering algorithm and exploring different machine learning techniques to forecast the probability of an upcoming stock market crash. They then apply a regime-based asset allocation to nominal risk parity strategy. Investors can further improve their investment performance by implementing a dollar-neutral factor momentum strategy as an overlay in conjunction with the core portfolio. The authors demonstrate that the time-series factor momentum strategy generates high risk-adjusted returns and exhibits pronounced d
Economic rationale
Equalizing risk contributions avoids concentration in the noisiest assets and stabilizes multi-asset drawdowns. Related evidence from “Factor Momentum and Regime-Switching Overlay Strategy”: Investors are faced with challenges in diversifying risks and protecting capital during crash periods. In this article, the authors incorporate regime information in the portfolio optimization context by identifying regimes for historical time periods using an ℓ<sub>1</sub>-trend filtering algorithm and exploring different machine learning techniques to forecast the probability of an upcoming stock market crash. They then apply a regime-based asset allocation to nominal risk parity strategy. Inv