Balance Sheet Accruals Strategy
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Quant Buffet native backtest IDEEdit and run Quant Buffet Python for Balance Sheet Accruals 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: Balance Sheet Accruals 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
The Persistence of the Accruals Anomaly
Baruch Lev; Doron Nissim
- New York University
- ?New York University - Stern School of Business
- Columbia University
- ?Columbia University - Columbia Business School
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=546108
Strategy in a nutshell
The investment universe includes stocks from NYSE, AMEX, and NASDAQ. Accruals, a non-cash earnings component, are calculated using the formula: BS_ACC = (∆CA - ∆Cash) - (∆CL - ∆STD - ∆ITP) - Dep, with ∆CA representing the annual change in current assets, ∆Cash the change in cash equivalents, ∆CL the change in current liabilities, ∆STD the change in short-term debt, ∆ITP the change in income taxes payable, and Dep the depreciation expense. Stocks are ranked into deciles based on accruals, with investments in the lowest and shorts in the highest. Portfolios are rebalanced annually in May, post-earnings publication.
Economic rationale
The accrual anomaly, attributed to the earnings fixation hypothesis, suggests investors overly focus on earnings, neglecting to separately evaluate cash-flow and accrual elements. This oversight leads to misplaced optimism for companies with high accruals and undue pessimism for those with low accruals, causing valuation errors: high accrual companies become overvalued and underperform, while low accrual ones are undervalued but yield high returns. Recent studies, including Detzel, Schabel, and Strauss's "There are Two Very Different Accruals Anomalies," highlight the distinction between investment-related and non-investment accruals. They found that investment accruals better predict returns, are influenced by market sentiment, and have a risk-associated premium, unlike non-investment accruals. This differentiation clarifies previous mixed findings, indicating two separate phenomena within the accrual anomaly: a risk-based investment accruals premium and mispricing of non-investment accruals.