VIX Put-Call Volume Ratio
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Quant Buffet native backtest IDEEdit and run Quant Buffet Python for VIX Put-Call Volume Ratio 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: VIX Put-Call Volume Ratio
# Detected pattern: Mean reversion
# 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: Buy when return z-score < -1 over 20 days.
def Rebalance(self):
import numpy as np
picks = []
for symbol in self.symbols:
hist = self.History(symbol, 20 + 5, Resolution.Daily)
if hist.empty: continue
close = hist["close"]
if hasattr(close, "unstack"):
close = close.unstack(level=0).iloc[:, 0]
rets = close.pct_change().dropna()
if len(rets) < 20: continue
window = rets.iloc[-20:]
z = (window.iloc[-1] - window.mean()) / (window.std() or 1e-9)
if z < -1:
picks.append(symbol)
w = 1.0 / len(picks) if picks else 0.0
for symbol in self.symbols:
self.SetHoldings(symbol, w if symbol in picks else 0.0)
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 Information Content of the VIX Options Trading Volume
Chen Gu; Xu Guo; Alexander Kurov; Raluca Stan
- Shanghai Business School
- ?Shanghai Business School - Research Center of Finance
- Shenzhen University
- ?Shenzhen University - College of Economics
- West Virginia University
- ?West Virginia University - College of Business & Economics
- University of Minnesota, Duluth
- ?University of Minnesota Duluth
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3975868
Strategy in a nutshell
The investment universe consists of nearby VIX futures contracts, with the next-to-maturity contract selected when its daily trading volume is higher. Volume data are sourced from CBOE, and futures data from Genesis Financial Technologies. First, compute the daily aggregated put-call volume ratio using the trading volume of VIX puts and calls initiated by buyers opening new positions. The ratio is calculated as put volume divided by the sum of put and call volumes. Next, calculate the z-score of the put-call ratio using an expanding timeframe (initial normalization performed after 600 days). If the z-score exceeds 0.75, take a short position in VIX futures, which is closed the following day.
Economic rationale
Research indicates that sophisticated and informed investors actively use options. Consistent with prior literature, the strategy assumes that informed traders use VIX options to express their market views. The aggregated trading volume of these traders reflects valuable information, allowing the put-call ratio to predict subsequent VIX movements. This predictive power remains robust even after controlling for economic and financial variables such as term spreads, credit spreads, T-bill spreads, or lagged S&P 500 returns. The effect is persistent, stronger when VIX levels are high, and holds across both recessionary and expansionary periods.