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Bond · United States · bonds · Monthly · United States Free

Fair Spread Value Factor in Corporate Bonds

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Onsite backtest IDE

Quant Buffet native backtest IDE

Edit and run Quant Buffet Python for Fair Spread Value Factor in Corporate Bonds 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 · 42 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
6.75%
Sharpe
0.45
Max DD
-53.11%
Vol
18.22%
Sortino
0.69
Beta
0.78

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: Momentum rotationAssets: SPY, TLT, GLD, BIL
# Generated from Quant Buffet → QuantConnect LEAN
# Strategy: Fair Spread Value Factor in Corporate Bonds
# 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 3 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)[:3]
        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

Putting Credit Factor Investing into Practice

AuthorsHendrik Kaufmann; Philip Messow

Institute
  • ?Quoniam Asset Management GmbH
  • ?Robeco Quantitative Investments

Paper linkhttps://papers.ssrn.com/sol3/papers.cfm?abstract_id=3791602

Screenshot from the original paper

Screenshot from the original paper

Strategy in a nutshell

Universe: USD-denominated IG bonds (Financials, Utilities, Industrials) from ICE G0BC index, excluding government and securitized bonds. Estimate fair OAS using ridge + adaptive LASSO regressions on bond and issuer characteristics. Value signal = market OAS – fair OAS. Long top quintile, equally weighted, monthly rebalanced.

Economic rationale

Captures undervalued bonds: market OAS > fair OAS signals underpricing. The strategy relies on robust ML-based fair value estimation, using multiple financial and market variables to identify mispriced bonds reliably.

Backtest performance

Annualised return6.75%
Volatility18.22%
Beta0.78
Sharpe ratio0.45
Sortino ratio0.69
Maximum drawdown-53.11%