Towards New Strategies for Investing: Insights on Sustainable Exchange-Traded Funds (ETFs)
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Quant Buffet native backtest IDEEdit and run Quant Buffet Python for Towards New Strategies for Investing: Insights on Sustainable Exchange-Traded Funds (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: Towards New Strategies for Investing: Insights on Sustainable Exchange-Traded Funds (ETFs)
# 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 3 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)[:3]
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
Towards New Strategies for Investing: Insights on Sustainable Exchange-Traded Funds (ETFs)
Nini Johana Marín‐Rodríguez; Juan David González-Ruíz; Sergio Botero-Botero
Universidad de Medellín; Universidad Nacional de Colombia
Teaser
Rank the book by trailing return and hold the top-N names equal-weight. Universe: SPY, EFA, EEM, VNQ, DBC, GLD, TLT, IEF, HYG. Parameters: lookback=126; top_n=3; 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
As investors increasingly incorporate environmental, social, and governance (ESG) factors into their decision-making, sustainable Exchange-Traded Funds (ETFs) have gained prominence in both investment portfolios and financial research. This study aims to provide a comprehensive analysis of the Sustainable ETF research landscape by utilizing scientometric and bibliometric methods with tools such as VOSviewer, Bibliometrix, and CiteSpace. Drawing from the Web of Science and Scopus databases, the study identifies key thematic areas, influential authors, and emerging trends. The findings highlight the conceptual evolution of Green ETFs, from early definitions focused on ESG-aligned investments to more complex instruments incorporating diversified screening criteria and advanced technologies li
Economic rationale
Assets with stronger recent relative performance tend to continue outperforming over intermediate horizons; rotating into leaders harvests that premium. Related evidence from “Towards New Strategies for Investing: Insights on Sustainable Exchange-Traded Funds (ETFs)”: As investors increasingly incorporate environmental, social, and governance (ESG) factors into their decision-making, sustainable Exchange-Traded Funds (ETFs) have gained prominence in both investment portfolios and financial research. This study aims to provide a comprehensive analysis of the Sustainable ETF research landscape by utilizing scientometric and bibliometric methods with tools such as VOSviewer, Bibliometrix, and CiteSpace. Drawing from the Web of Science and Scopus databases, the s