Pre-Earnings Buyback Announcement Momentum Strategy
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Quant Buffet native backtest IDEEdit and run Quant Buffet Python for Pre-Earnings Buyback Announcement Momentum Strategy 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: Pre-Earnings Buyback Announcement Momentum Strategy
# 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
Nadja Guenster; Erik Kole; Ben Jacobsen
- University of California, Berkeley
- ?University of Muenster - Finance Center Muenster
- NLTinbergen Institute
- NLErasmus University Rotterdam
- Environmental Research Institute of Michigan
- ?ERIM
- ?Erasmus University Rotterdam - Erasmus School of Economics - Econometric Institute
- NLTilburg University
- NZMassey University
- NLTIAS School for Business and Society
- ?Tilburg University - TIAS School for Business and Society
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1071670
Strategy in a nutshell
The investment universe consists of stocks from NYSE/AMEX/Nasdaq (no ADRs, CEFs or REITs), bottom 25% of firms by market cap are dropped. Each quarter, the investor looks for companies that announce a stock repurchase program (with announced buyback for at least 5% of outstanding stocks) during days -30 to -15 before the earnings announcement date for each company. Investor goes long stocks with announced buybacks during days -10 to +15 around an earnings announcement. The portfolio is equally weighted and rebalanced daily.
Economic rationale
The academic paper states that it is generally accepted that managers have more information about the firm than investors. Given this information asymmetry, managers can make informed decisions about corporate actions such as equity offerings or repurchases. The announcement of stock repurchase or secondary equity offering is voluntary and can be easily moved by a few weeks or months. Therefore the timing of SEO or repurchase announcement before earnings announcement could be perceived as important information about the future performance of stock during the earnings announcement period.