Weekly US Futures Reversal Strategy Based on Volume, Open Interest, and Past Returns
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Quant Buffet native backtest IDEEdit and run Quant Buffet Python for Weekly US Futures Reversal Strategy Based on Volume, Open Interest, and Past Returns 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: Weekly US Futures Reversal Strategy Based on Volume, Open Interest, and Past Returns
# 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 1 by 21-day return; monthly.
def Rebalance(self):
scores = {}
for symbol in self.symbols:
hist = self.History(symbol, 21 + 5, Resolution.Daily)
if hist.empty: continue
close = hist["close"]
if hasattr(close, "unstack"):
close = close.unstack(level=0).iloc[:, 0]
if len(close) < 21 + 1: continue
scores[symbol] = float(close.iloc[-1] / close.iloc[-21 - 1] - 1)
ranked = sorted(scores.items(), key=lambda kv: kv[1], reverse=True)[:1]
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
A Comparison of Short-Term Mean-Reversion Indicators for Global Equities
Raymond Micaletti
- ?Relative Sentiment Technologies, LLC
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4339128
Strategy in a nutshell
The investment universe consists of 24 types of US futures contracts (4 currencies, five financials, eight agricultural, seven commodities). A weekly time frame is used – a Wednesday- Wednesday interval. The contract closest to expiration is used, except within the delivery month, in which the second-nearest contract is used. Rolling into the second nearest contract is done at the beginning of the delivery month.
The contract is defined as the high- (low-) volume contract if the contract’s volume changes between period from t-1 to t and period from t-2 to t-1 is above (below) the median volume change of all contracts (weekly trading volume is detrended by dividing the trading volume by its sample mean to make the volume measure comparable across markets).
All contracts are also assigned to either high-open interest (top 50% of changes in open interest) or low-open interest groups (bottom 50% of changes in open interest) based on lagged changes in open interest between the period from t-1 to t and period from t-2 to t-1. The investor goes long (short) on futures from the high-volume, low-open interest group with the lowest (greatest) returns in the previous week. The weight of each contract is proportional to the difference between the return of the contract over the past one week and the equal-weighted average of returns on the N (number of contracts in a group) contracts during that period.
Economic rationale
Evidence of short-horizon return predictability is consistent with the overreaction hypothesis; namely, traders over-adjust their posterior beliefs to news more than it is warranted by fundamentals. Overconfidence and overreaction themselves imply a large volume of trading, and they are thus positively related to the magnitude of price reversals. Therefore an irrationality-induced market inefficiency gives rise to a negative relation between volume and expected returns. Open interest represents uninformed trading by hedgers or hedging activity and thus is also an important determinant of the market state.