Using Machine Learning to Identify Mispricing in European Stock Markets
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Quant Buffet native backtest IDEEdit and run Quant Buffet Python for Using Machine Learning to Identify Mispricing in European Stock Markets 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: Using Machine Learning to Identify Mispricing in European Stock Markets
# Detected pattern: Momentum rotation
# 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: Hold top 2 by 126-day return; monthly.
def Rebalance(self):
scores = {}
for symbol in self.symbols:
hist = self.History(symbol, 126 + 5, Resolution.Daily)
if hist.empty: continue
close = hist["close"]
if hasattr(close, "unstack"):
close = close.unstack(level=0).iloc[:, 0]
if len(close) < 126 + 1: continue
scores[symbol] = float(close.iloc[-1] / close.iloc[-126 - 1] - 1)
ranked = sorted(scores.items(), key=lambda kv: kv[1], reverse=True)[:2]
for symbol in self.symbols:
self.SetHoldings(symbol, 0)
if ranked:
w = 1.0 / len(ranked)
for symbol, _ in ranked:
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
Matthias X. Hanauer; Marina Kononova; Marc Steffen Rapp
- DETechnical University of Munich
- ?Robeco Quantitative Investments
- ?Technische Universität München (TUM)
- DEPhilipps University of Marburg
- ?University of Marburg - School of Business & Economics
- ?University of Marburg - Marburg Centre for Institutional Economics (MACIE)
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3977872
Strategy in a nutshell
This strategy invests in EU17 stocks (EU15 plus Switzerland and Norway), excluding financial firms, non-common equities, secondary listings, and companies with missing or invalid data. Stocks with market capitalization below USD 10 million are also excluded. Using accounting variables from the previous 48 months—such as total assets, sales, and long-term debt (as defined in Table A)—each variable is standardized by cross-sectional ranking into a [-1, 1] range. Market capitalization is deflated by the total market value to control for shifts in market-wide valuation norms. Random forest and gradient boosting models are trained to estimate each firm’s fair value, and the final estimate is the average of both. The mispricing signal is defined as the difference between the estimated fair value and market capitalization, scaled by market capitalization. Each month, firms are sorted into quintiles based on this signal. The portfolio goes long the most undervalued quintile and short the most overvalued, with monthly rebalancing and value-weighted positions.
Economic rationale
The strategy’s profitability stems from two core drivers: mispricing detection and model design. By leveraging fundamental accounting data, it systematically identifies undervalued and overvalued firms, capitalizing on valuation inefficiencies. Moreover, advanced machine learning methods—random forest and gradient boosting—capture complex, nonlinear relationships that traditional linear models fail to detect. The ensemble approach enhances prediction accuracy, making both the conceptual idea of exploiting mispricing and the technological sophistication of the model equally crucial to its success.