Investment implications of Industry 4.0: evidence from smart manufacturing ETFs
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Quant Buffet native backtest IDEEdit and run Quant Buffet Python for Investment implications of Industry 4.0: evidence from smart manufacturing ETFs 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: Investment implications of Industry 4.0: evidence from smart manufacturing ETFs
# 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
Investment implications of Industry 4.0: evidence from smart manufacturing ETFs
Cristiana Tudor; Alexandra Horobeţ; Zeno Dinca; L. Belascu; Robert Şova
Bucharest University of Economic Studies; Lucian Blaga University of Sibiu
Teaser
Hold each liquid ETF only when its price is above a long SMA; equal-weight the longs, cash otherwise. Universe: XLB, XLE, XLF, XLI, XLK, XLP, XLU, XLV, XLY, XLC, XLRE. Parameters: sma_days=200; rebalance=monthly. Rebalanced on the engine's template schedule with 5 bps commission and 2 bps slippage.
Strategy in a nutshell
The rise of smart manufacturing, driven by digital transformation and Industry 4.0, has introduced new opportunities for investors seeking to diversify their portfolios. Smart manufacturing ETFs offer a unique risk-return profile tailored to the evolving landscape of industrial automation and data-driven processes. This paper explores the comparative risk-adjusted performance of a smart manufacturing ETF, a conventional industrial portfolio, and a broad-market SP500 tracking portfolio, utilizing daily data from October 2019 to October 2022. By deconstructing the excess returns of these portfolios through one-factor, three-factor, and five-factor asset pricing models, we provide insights into the risk exposure and performance drivers of smart manufacturing investments. Results indicate that
Economic rationale
Trend filters exploit persistent serial correlation in asset returns and reduce exposure when prices fall below a long-horizon average, cutting left-tail risk. Related evidence from “Investment implications of Industry 4.0: evidence from smart manufacturing ETFs”: The rise of smart manufacturing, driven by digital transformation and Industry 4.0, has introduced new opportunities for investors seeking to diversify their portfolios. Smart manufacturing ETFs offer a unique risk-return profile tailored to the evolving landscape of industrial automation and data-driven processes. This paper explores the comparative risk-adjusted performance of a smart manufacturing ETF, a conventional industrial portfolio, and a broad-market SP500 tracking portfolio, utilizing