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Equity · United States · stocks · Monthly · United States Free

Accruals Seasonality

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Onsite backtest IDE

Quant Buffet native backtest IDE

Edit and run Quant Buffet Python for Accruals Seasonality 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 →

Ready — edit code, then Run backtest.
IDE · 50 lines
Quant Buffet syntax cheat sheet (copy / insert)

Paste these fragments into the editor. The sandbox rejects QuantConnect, os, and network libraries.

Required imports
Only these libraries are allowed in the sandbox.
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_metrics
ASSETS list (whitelisted ETFs)
Module-level list. Tickers must be in the Quant Buffet whitelist.
ASSETS = ["SPY", "QQQ", "TLT", "GLD", "BIL"]
make_on_day contract
Must return (on_day, ready). on_day calls engine.set_target_weights.
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, ready
Set target weights
Weights should sum to about 1.0. Empty dict = 100% cash.
engine.set_target_weights(dt, {"SPY": 0.60, "BIL": 0.40})

Live backtest performance

CAGR
7.89%
Sharpe
0.63
Max DD
-33.72%
Vol
13.60%
Sortino
0.93
Beta
0.51
Up days
49%

Run the backtest to populate charts.

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.

Run in: QuantConnect Cloud or LEAN CLI · QCAlgorithm with Equity securities and monthly rebalance.

Detected pattern: Absolute momentumAssets: SPY, TLT, GLD, BIL
# Generated from Quant Buffet → QuantConnect LEAN
# Strategy: Accruals Seasonality
# 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 Rise of Accruals Seasonality Spread

AuthorsSiu Kai Choy; Gerald J. Lobo; Yongxian Tan

Institute
  • King's College London
  • ?affiliation not provided to SSRN
  • University of Houston
  • ?University of Houston - C.T. Bauer College of Business
  • NZUniversity of Otago

Paper linkhttps://papers.ssrn.com/sol3/papers.cfm?abstract_id=3983051

Screenshot from the original paper

Screenshot from the original paper
Screenshot from the original paper

Strategy in a nutshell

Universe: NYSE, AMEX, NASDAQ non-financial stocks from CRSP.

Exclusions: Share price < $5 and missing market cap (end of previous month).

Sorting Measure:

Accruals Seasonality (AS) over past 5 years.

For quarter q, take last 20 quarters (q-23 to q-4).

Rank accruals in ascending order (higher accruals → higher rank).

AS = average of ranks from quarters q-4, q-8, q-12, q-16, q-20.

Portfolio Formation:

Each month, include only stocks with expected accruals that month.

Expected accrual = 12 months after last actual accrual date (from 10-K/Q filing or earlier report).

Sort into quintiles by AS.

Long lowest quintile, short highest quintile.

Value-weighted, capped at 95th percentile of NYSE market cap.

Rebalancing: Monthly (with varying stock sets based on reporting dates).

Economic rationale

Accruals anomaly: Investors underreact to accruals information.

Post-2001 awareness: Investors learned about accruals effect but still don’t forecast it—only react when published.

Implication: Seasonal accrual predictability should be priced in efficient markets, but isn’t.

No risk-based story: Return spread is due to investor inattention / unsophisticated arbitrage, not compensation for risk.

Backtest performance

Annualised return7.89%
Volatility13.60%
Beta0.51
Sharpe ratio0.63
Sortino ratio0.93
Maximum drawdown-33.72%
Win rate49%