Portfolio Diversification and Dynamic Interactions between Clean and Dirty Energy Assets
Log in to collectOnsite backtest IDE
Quant Buffet native backtest IDEEdit and run Quant Buffet Python for Portfolio Diversification and Dynamic Interactions between Clean and Dirty Energy Assets 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: Portfolio Diversification and Dynamic Interactions between Clean and Dirty Energy Assets
# 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
Portfolio Diversification and Dynamic Interactions between Clean and Dirty Energy Assets
Nadia Belkhir; Wafa Kammoun Masmoudi; Sahar Loukil; Rihab Belguith
Imam Mohammad ibn Saud Islamic University; University of Carthage; University of Sfax
Teaser
Allocate inversely to asset volatility so risk contributions are roughly equal. Universe: GLD, SLV, DBC, GSG, USO, UNG, DBA. 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
Clean energy, with its focus on environmental sustainability and efficiency, has gained significance as concerns over the impact of traditional energy growth. However, there is limited evidence on the value of clean energy investments. This paper explores the role of clean energy in a balanced investment portfolio by examining two traditional energy assets (crude oil and natural gas) and two clean energy assets (SPDR S&P Kensho Clean Power ETF and iShares Global Clean Energy ETF). Using a time-varying parameter vector autoregression (TVP-VAR) model on daily data from October 2021 to January 2024, we analyze the evolving connectedness between these assets. Our results highlight dynamic interactions, with green finance indices like CNRG acting as net shock transmitters, while traditional
Economic rationale
Equalizing risk contributions avoids concentration in the noisiest assets and stabilizes multi-asset drawdowns. Related evidence from “Portfolio Diversification and Dynamic Interactions between Clean and Dirty Energy Assets”: Clean energy, with its focus on environmental sustainability and efficiency, has gained significance as concerns over the impact of traditional energy growth. However, there is limited evidence on the value of clean energy investments. This paper explores the role of clean energy in a balanced investment portfolio by examining two traditional energy assets (crude oil and natural gas) and two clean energy assets (SPDR S&P Kensho Clean Power ETF and iShares Global Clean Energy ETF). Using a ti