Managing the Downside of Active and Passive Strategies—<i>Part 1
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Quant Buffet native backtest IDEEdit and run Quant Buffet Python for Managing the Downside of Active and Passive Strategies—<i>Part 1 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: Managing the Downside of Active and Passive Strategies—<i>Part 1
# 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
Managing the Downside of Active and Passive Strategies—<i>Part 1: Convexity and Fragilities</i>
Raphaël Douady
Centre National de la Recherche Scientifique; Centre d'Économie de la Sorbonne; Canadian Nautical Research Society; Université Paris 1 Panthéon-Sorbonne
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
In this article, the author addresses the question of how to manage a large (or small) portfolio in low interest rate conditions while equity markets bear significant drawdown risk. More generally, he explains how to build an <i>antifragile</i> portfolio that can weather the most extreme market scenarios without affecting long-term performance. He also discusses how active strategies systematically create or increase existing market instabilities. By analyzing in depth market behavior during past speculative bubbles and credit crises, the author aims to address these issues. In this first part of a two-part series, the author describes as faithfully as possible the major mechanisms at stake, avoiding the trap of mapping the complexity of financial markets into a single mathematical model,
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 “Managing the Downside of Active and Passive Strategies—<i>Part 1: Convexity and Fragilities</i>”: In this article, the author addresses the question of how to manage a large (or small) portfolio in low interest rate conditions while equity markets bear significant drawdown risk. More generally, he explains how to build an <i>antifragile</i> portfolio that can weather the most extreme market scenarios without affecting long-term performance. He also discusses how active strategies systematically create or increase existing market instabilities. By analyzing in depth market behavior during pas