Equity · United States · ETFs, stocks · Monthly · United States Free

Short Interest Strategy

Onsite backtest IDE

Quant Buffet native backtest IDE

Edit and run Quant Buffet Python for Short Interest 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 →

IDE · 43 lines

"""
Quant Buffet interactive lab — short-interest-effect-long-short-version
Allowed: backtest.data / backtest.engine / backtest.metrics, numpy, pandas.
Required: ASSETS = [...] and def make_on_day(prices): -> (on_day, ready)
"""
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 = ['VNQ', 'IYR', 'RWX', 'SPY', 'BIL']
REBALANCE_ONCE = False

def make_on_day(prices: pd.DataFrame):
    cols = [c for c in ASSETS if c in prices.columns]
    state = {"last": None, "done": False}

def on_day(engine: PortfolioEngine, dt: pd.Timestamp) -> None:
        available = [s for s in cols if pd.notna(prices.at[dt, s])]
        if not available:
            return
        if REBALANCE_ONCE:
            if state["done"]:
                return
            state["done"] = True
            engine.set_target_weights(dt, {s: 1.0 / len(available) for s in available})
            return
        key = (dt.year, dt.month)
        if state["last"] == key:
            return
        state["last"] = key
        engine.set_target_weights(dt, {s: 1.0 / len(available) for s in available})

counts = prices[cols].notna().sum(axis=1)
    need = max(1, len(cols) // 2)
    eligible = counts[counts >= need]
    ready = eligible.index.min() if not eligible.empty else None
    return on_day, ready

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

0.47

Max DD

-57.95%

Vol

16.96%

Sortino

0.72

Beta

0.77

Up days

62%

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.

Target platform

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

Detected pattern: Custom / hybridAssets: SPY, TLT, GLD, BIL

# Generated from Quant Buffet → QuantConnect LEAN
# Strategy: Short Interest Strategy
# Detected pattern: Custom / hybrid
# 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: Custom Quant Buffet logic — adapt the signal block to match your lab on_day().

    def Rebalance(self):
        # Pattern: custom — Custom Quant Buffet logic — adapt the signal block to match your lab on_day().
        # 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 Good News in Short Interest

AuthorsEkkehart Boehmer; Zsuzsa R. Huszár; Bradford D. Jordan

Institute

SGSingapore Management University
?Singapore Management University - Lee Kong Chian School of Business
ATCentral European University
SGNational University of Singapore
University of Florida
?University of Florida - Department of Finance, Insurance and Real Estate

Paper linkhttps://papers.ssrn.com/sol3/papers.cfm?abstract_id=1405511

Screenshot from the original paper

Strategy in a nutshell

All stocks from NYSE, AMEX, and NASDAQ are part of the investment universe. The short-interest ratio is used as the predictor variable. Stocks are sorted based on their short interest ratio, and the first percentile is held. The portfolio is equally weighted and rebalanced monthly.

Economic rationale

The literature offers two popular explanations for this predictability, namely the overvaluation hypothesis and the information hypothesis. The first possible explanation for the short interest effect – the overvaluation hypothesis stems from the work of Miller (1977). His theory says that stocks with high levels of short interest are overvalued because pessimistic investors are unable to establish short positions, leaving only the optimists to participate in the pricing process. In this model, market forces are unable to prevent overpricing in the amount of shorting costs when these costs are high. The greater the shorting costs, the greater the possible overpricing, and therefore, the lower the subsequent stock returns.

The second and probably more valid explanation is the information hypothesis. The information hypothesis builds on a broadening base of empirical research that demonstrates that short sellers are well-informed traders. The aforementioned could be the reason for the functionality because if one follows the decisions of the short-sale practitioners, who tend to be investors with superior analytical skills (for example, according to the research of Gutfleish and Atzil, 2004). The main idea is simple; the research says, that these investors typically initiate short positions only if they can infer low fundamental valuation from public sources. For example, short-sellers may engage in forensic accounting, looking for high levels of accrual as evidence of hidden bad news. Still, there is a large number of other possibilities than just accruals.

Backtest performance

Annualised return6.75%

Volatility16.96%

Beta0.77

Sharpe ratio0.47

Sortino ratio0.72

Maximum drawdown-57.95%

Win rate62%