Factor models and investment strategies in the renewable energy sector
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Quant Buffet native backtest IDEEdit and run Quant Buffet Python for Factor models and investment strategies in the renewable energy sector 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 models and investment strategies in the renewable energy sector
# 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
Factor models and investment strategies in the renewable energy sector
José Luís Miralles Quirós; María del Mar Miralles Quirós
Universidad de Extremadura
Teaser
Rank the book by trailing return and hold the top-N names equal-weight. Universe: XLB, XLE, XLF, XLI, XLK, XLP, XLU, XLV, XLY, XLC, XLRE. Parameters: lookback=126; top_n=3; rebalance=monthly. Rebalanced on the engine's template schedule with 5 bps commission and 2 bps slippage.
Strategy in a nutshell
The growing concern about the natural environment is getting relevance not only on institutional investors but also on individual ones. However, these retail investors face some difficulties, such as the fact that they are often not able to monitor many assets but they are also unable to use most of the methodology proposed in the empirical evidence because it is not feasible for them. We avoid these problems by proposing an investment strategy based on the different values of the alpha and beta coefficients from the Fama-French and Carhart models. We use Exchange Traded Funds (ETFs) which track companies involved on clean and renewable energy businesses and it is provided evidence that in most of the cases the best investment strategy is to follow the positive and significant values of th
Economic rationale
Assets with stronger recent relative performance tend to continue outperforming over intermediate horizons; rotating into leaders harvests that premium. Related evidence from “Factor models and investment strategies in the renewable energy sector”: The growing concern about the natural environment is getting relevance not only on institutional investors but also on individual ones. However, these retail investors face some difficulties, such as the fact that they are often not able to monitor many assets but they are also unable to use most of the methodology proposed in the empirical evidence because it is not feasible for them. We avoid these problems by proposing an investment strategy based on the different values of the alpha and beta