Consistent Momentum Long-Short Portfolio with Six-Month Hold
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Quant Buffet native backtest IDEEdit and run Quant Buffet Python for Consistent Momentum Long-Short Portfolio with Six-Month Hold 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
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: Consistent Momentum Long-Short Portfolio with Six-Month Hold
# Detected pattern: Absolute momentum
# 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 with positive 252-day return; equal-weight; monthly.
def Rebalance(self):
# Pattern: abs_momentum — Long assets with positive 252-day return; equal-weight; monthly.
# 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
Persistency of the Momentum Effect: The Role of Consistent Winners and Losers
Hong‐Yi Chen; Pin‐Huang Chou; Chia‐Hsun Hsieh
- TWNational Chengchi University
- ?National Chengchi University - Department of Finance
- TWNational Central University
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2652592
Strategy in a nutshell
The investment universe consists of stocks listed at NYSE, AMEX, and NASDAQ, whose price data (at least for the past seven months) are available at the CRSP database. The investor creates a zero-investment portfolio at the end of the month t, longing stocks that are in the top decile in terms of returns both in the period from t-7 to t-1 and from t-6 to t, while shorting stocks in the bottom decile in both periods (i.e. longing consistent winners and shorting consistent losers). The stocks in the portfolio are weighted equally. The holding period is six months, with no rebalancing during the period. There is a one-month skip between the formation and holding period.
Economic rationale
Momentum anomaly is, in general, related to investors’ irrationality – they underreact to new information as they do not incorporate news in their transaction prices sufficiently. Under the information asymmetry and the heterogeneous beliefs hypotheses, the persistence of momentum effect depends on size, idiosyncratic volatility, % of institutional ownership, and trading volume. According to the former, investors tend to be conservative in the case of stocks with higher idiosyncratic volatilities and a lower percentage of outstanding stocks owned by institutional investors (they become consistent winners/losers due to slow price adjustment). The latter suggests that higher trading volume on stock (a proxy for disagreement among investors) should produce a stronger momentum effect.