The analysis of investment into industries based on portfolio managers
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Quant Buffet native backtest IDEEdit and run Quant Buffet Python for The analysis of investment into industries based on portfolio managers 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: The analysis of investment into industries based on portfolio managers
# Detected pattern: Volatility targeting
# 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: Scale weights to target vol 0.1 using 63-day vol.
def Rebalance(self):
# Pattern: vol_target — Scale weights to target vol 0.1 using 63-day vol.
# 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
Teaser
Scale exposure inversely to realized volatility toward a target annual vol. Universe: SPY, BIL. Parameters: target_vol=0.1; vol_lookback=63; rebalance=monthly. Rebalanced on the engine's template schedule with 5 bps commission and 2 bps slippage.
Strategy in a nutshell
The main goal of each investor when investing capital is to recover funds. The current market brings unlimited opportunities in creating an investment strategy. This strategy is highly dependent on the investor's preferences, his attitude to risk, financial capacity, expected return, self-interest, and many other factors that determine what is best for the investor. For this reason, the submitted work concerns investors who will comply with strict restrictions. In this way, an investment strategy for a selected group of investors can be created. The paper focuses on investors in Slovakia. The art of investment decisions to make the greatest possible return is portfolio management. The studies presented in this work are designed to explore different models and to achieve investor goals to m
Economic rationale
Volatility is more forecastable than expected returns; scaling down when vol is high improves risk-adjusted outcomes (volatility-managed portfolios). Related evidence from “The analysis of investment into industries based on portfolio managers”: The main goal of each investor when investing capital is to recover funds. The current market brings unlimited opportunities in creating an investment strategy. This strategy is highly dependent on the investor's preferences, his attitude to risk, financial capacity, expected return, self-interest, and many other factors that determine what is best for the investor. For this reason, the submitted work concerns investors who will comply with strict restrictions. In this way, an investment strateg