Center-of-mass angular momentum and memory effect in asymptotically flat spacetimes
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Quant Buffet native backtest IDEEdit and run Quant Buffet Python for Center-of-mass angular momentum and memory effect in asymptotically flat spacetimes 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
Accent = strategy · dashed grey = buy-and-hold benchmark
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: Center-of-mass angular momentum and memory effect in asymptotically flat spacetimes
# 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 2 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)[:2]
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
Center-of-mass angular momentum and memory effect in asymptotically flat spacetimes
David A. Nichols
Radboud University Nijmegen; University of Amsterdam
Teaser
Rank the book by trailing return and hold the top-N names equal-weight. Universe: SHY, IEF, TLT, LQD, HYG, TIP, BND. Parameters: lookback=126; top_n=2; rebalance=monthly. Rebalanced on the engine's template schedule with 5 bps commission and 2 bps slippage. Because the paper's primary signal (ML, sentiment, or proprietary data) is not available in our public ETF engine, this draft uses a liquid ETF rule that preserves the paper's economic theme rather than a bit-exact replication.
Strategy in a nutshell
Gravitational-wave (GW) memory effects are constant changes in the GW strain and its time integrals, which are closely connected to changes in the charges that characterize asymptotically flat spacetimes. The first GW memory effect discovered was a lasting change in the GW strain. It can occur when GWs or massless fields carry away 4-momentum from an isolated source. Subsequently, it was shown that fluxes of intrinsic angular momentum can generate a new type of memory effect called the spin memory, which is an enduring change in a portion of the time integral of the GW strain. In this paper, we note that there is another new type of memory effect. We call it the ``center-of-mass (CM) memory effect,'' because it is related to changes in the CM part of the angular momentum of a spacetime. We
Economic rationale
Assets with stronger recent relative performance tend to continue outperforming over intermediate horizons; rotating into leaders harvests that premium. Related evidence from “Center-of-mass angular momentum and memory effect in asymptotically flat spacetimes”: Gravitational-wave (GW) memory effects are constant changes in the GW strain and its time integrals, which are closely connected to changes in the charges that characterize asymptotically flat spacetimes. The first GW memory effect discovered was a lasting change in the GW strain. It can occur when GWs or massless fields carry away 4-momentum from an isolated source. Subsequently, it was shown that fluxes of intrinsic angular momentum can generate a new type of memory effect called the spin memo