Stock Trading Rule that Produces Higher Returns with Lower Risk
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Quant Buffet native backtest IDEEdit and run Quant Buffet Python for Stock Trading Rule that Produces Higher Returns with Lower Risk 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: Stock Trading Rule that Produces Higher Returns with Lower Risk
# 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(189); equal-weight; monthly.
def Rebalance(self):
longs = []
for symbol in self.symbols:
hist = self.History(symbol, 189 + 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) < 189: continue
if float(close.iloc[-1]) > float(close.iloc[-189:].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
Strategy in a nutshell
The investment universe consists of the S&P 500 (via mutual funds or ETFs) and 3-month Treasury bills. The strategy involves selling the index at relative maxima and buying at relative minima. At the close of the last trading day each month, calculate the nine-month simple moving average (SMA) trend line and its first derivative.
Sell Signal: Triggered if the derivative is negative, the nine-month SMA slope ≤ tangent of 355°, the two-month SMA slope ≤ tangent of 353°, and either the S&P 500 opening or closing price is below the nine-month SMA. On the first trading day of the next month, close the S&P 500 position and invest in 3-month Treasury bills. This allocation remains for the following two months regardless of a new buy signal.
Buy Signal: Triggered if the derivative is positive and the nine-month SMA slope ≥ tangent of 5°. On the first trading day of the next month, close the bill position and buy the S&P 500. Allocation decisions are made monthly.
Economic rationale
The strategy is a risk-on/risk-off trend-following system, aiming to capture upward equity trends while switching to safe Treasury bills at market peaks. Conversely, it re-enters equities at market troughs. The underlying logic is not fully transparent, and the paper does not justify why it should work. There is a risk that the strategy is overfitted, so a thorough out-of-sample analysis is recommended to validate its effectiveness.