Dynamic Asset Allocation with Asset-Specific Regime Forecasts
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Quant Buffet native backtest IDEEdit and run Quant Buffet Python for Dynamic Asset Allocation with Asset-Specific Regime Forecasts 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: Dynamic Asset Allocation with Asset-Specific Regime Forecasts
# Detected pattern: Momentum rotation
# 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: Hold top 1 by 126-day return; monthly.
def Rebalance(self):
scores = {}
for symbol in self.symbols:
hist = self.History(symbol, 126 + 5, Resolution.Daily)
if hist.empty: continue
close = hist["close"]
if hasattr(close, "unstack"):
close = close.unstack(level=0).iloc[:, 0]
if len(close) < 126 + 1: continue
scores[symbol] = float(close.iloc[-1] / close.iloc[-126 - 1] - 1)
ranked = sorted(scores.items(), key=lambda kv: kv[1], reverse=True)[:1]
for symbol in self.symbols:
self.SetHoldings(symbol, 0)
if ranked:
w = 1.0 / len(ranked)
for symbol, _ in ranked:
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
Dynamic Asset Allocation with Asset-Specific Regime Forecasts
Shu, Yizhan; Chenyu Yu; John M. Mulvey
Teaser
Rank the book by trailing return and hold the top-N names equal-weight. Universe: EWZ, FXI, EWT, EWY, EIDO, THD, EPHE, ECH, EPOL, EZA, ARGT, TUR. Parameters: lookback=126; top_n=1; rebalance=monthly. Rebalanced on the engine's template schedule with 5 bps commission and 2 bps slippage.
Strategy in a nutshell
This article introduces a novel hybrid regime identification-forecasting framework designed to enhance multi-asset portfolio construction by integrating asset-specific regime forecasts. Unlike traditional approaches that focus on broad economic regimes affecting the entire asset universe, our framework leverages both unsupervised and supervised learning to generate tailored regime forecasts for individual assets. Initially, we use the statistical jump model, a robust unsupervised regime identification model, to derive regime labels for historical periods, classifying them into bullish or bearish states based on features extracted from an asset return series. Following this, a supervised gradient-boosted decision tree classifier is trained to predict these regimes using a combination of ass
Economic rationale
Assets with stronger recent relative performance tend to continue outperforming over intermediate horizons; rotating into leaders harvests that premium. Related evidence from “Dynamic Asset Allocation with Asset-Specific Regime Forecasts”: This article introduces a novel hybrid regime identification-forecasting framework designed to enhance multi-asset portfolio construction by integrating asset-specific regime forecasts. Unlike traditional approaches that focus on broad economic regimes affecting the entire asset universe, our framework leverages both unsupervised and supervised learning to generate tailored regime forecasts for individual assets. Initially, we use the statistical jump model, a robust unsupervised regime identifi