Short Interest Long-Short Strategy
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Quant Buffet native backtest IDEEdit and run Quant Buffet Python for Short Interest Long-Short 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: Short Interest Long-Short 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
Why Do Short Interest Levels Predict Stock Returns?
Ekkehart Boehmer; Bilal Erturk; Sorin M. Sorescu
- SGSingapore Management University
- ?Singapore Management University - Lee Kong Chian School of Business
- Texas A&M University
- ?Texas A&M University - Department of Finance
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1019309
Strategy in a nutshell
All stocks from NYSE, AMEX, and NASDAQ are part of the investment universe. Stocks are then sorted each month into short-interest deciles based on the ratio of short interest to shares outstanding. The investor then goes long on the decile with the lowest short ratio and short on the decile with the highest short ratio. The portfolio is rebalanced monthly, and stocks in the portfolio are weighted equally.
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. Those mentioned above 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.