Technical Indicators Predict Cross-Sectional Expected Stock Returns
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Quant Buffet native backtest IDEEdit and run Quant Buffet Python for Technical Indicators Predict Cross-Sectional Expected Stock Returns 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: Technical Indicators Predict Cross-Sectional Expected Stock Returns
# 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
Technical Indicators and Cross-Sectional Expected Returns
Hui Zeng; Ben R. Marshall; Nhut H. Nguyen; Nuttawat Visaltanachoti
- NZMassey University
- ?Massey University - Department of Economics and Finance
- ?Massey University - School of Economics and Finance
- NZAuckland University of Technology
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3992035
Strategy in a nutshell
The investment universe includes all firms listed on NYSE, AMEX, and NASDAQ from the CRSP database. Firms with fewer than 60 monthly return observations are excluded. Fourteen firm-level technical indicators are constructed based on three trend-following strategies: moving average, momentum, and volume-based rules.
Moving Average Rule: Trading signals are generated by comparing short- and long-term moving averages.
Momentum Rule: Signals arise from comparing the current stock price with its level n months ago.
On-Balance Volume Rule: Signals are based on changes in trading volume.
Each month ttt, stock iii’s return is regressed on the 14 technical indicators from month t−1t-1t−1, using a rolling 60-month window to estimate the next month’s return. To mitigate overfitting, the time-series average of the cross-sectional OLS coefficients is calculated using a 60-month smoothing window. At month-end, stocks are sorted into value-weighted deciles based on their estimated returns. The top decile is bought and the bottom decile is sold. The resulting long-short portfolio is value-weighted and rebalanced monthly.
Economic rationale
Trend-following strategies generate buy (sell) signals in response to positive (negative) market trends, reflected by recent price increases (decreases). Technical indicators have been shown to predict stock returns effectively. Zhu and Zhou (2009) theoretically demonstrate how technical analysis enhances asset allocation between risk-free bonds and predictable stocks. Empirical studies, such as Zeng, Marshall, Nguyen, and Visaltanachoti (2021), find that technical indicators have significant predictive power, especially for firms with high limits to arbitrage. Combining multiple trend-following indicators improves the model’s ability to detect stock price trends and explains cross-sectional variations in stock returns.