Quant Mashup - Aligrithm

Stock-Bond Correlation: The Sign Flips and So Do Its Drivers [Aligrithm]

A 50/50 stock-bond portfolio that earned its target return with moderate volatility before 2000 requires an 80/20 allocation after 2000 to maintain the same profile. That 30-point shift is not a style choice or a bet on equities. It is the portfolio adjustment needed to compensate for the stock-bond(...)

Volatility Clustering in Bitcoin: Regime Persistence as a Forecast [Aligrithm]

Bitcoin's volatility does not mix randomly across time. High-vol periods follow high-vol periods, low-vol stretches extend themselves, and the transition probabilities are stable enough to build a forecast from. Borrego Roldán's 2024 study quantifies this with hourly and daily data from(...)

Crypto Isn't Structurally Alien: Roll/VPIN/Amihud Predict Distribution Shifts [Aligrithm]

Easley, O'Hara, Yang and Zhang take five textbook microstructure measures, compute them on Binance one-minute bars for BTC, ETH, XRP, SOL and ADA over January 2021 to July 2023, and ask a random forest to call the sign of tomorrow's change in the return distribution. Two numbers carry the(...)

FX Edge Lives in Other Markets (cross-asset series) [Aligrithm]

A currency futures book built from other currency futures scores a Sharpe of 0.16. The same contracts, wired into a graph that also contains equities, bonds, and commodities, jump the FX sleeve to 0.66. That is Pu, Roberts, Dong, and Zohren, 64 futures, 2000 to 2022, volatility-targeted to 15%.(...)

Does Complexity Actually Help? The Virtue-of-Complexity Autopsy [Aligrithm]

Kelly, Malamud and Zhou opened their Journal of Finance paper by reporting out-of-sample market timing Sharpe ratio improvements "relative to market buy-and-hold" of roughly 0.47 per year, earned by a model with 12,000 predictors trained on 12 monthly observations. Daniel Buncic re-ran it(...)

Metaheuristics for Rule Optimization — With Diversity as the Guardrail [Aligrithm]

Hernández-Romo and co-authors ran four population-based optimizers 31 times each, 1000 generations per run, population of 50, tuning an eight-parameter moving-average strategy on five-minute BTC/USDC bars from January 2020 to March 2025. Differential Evolution won on average annualized return with(...)

Model-Based or Data-Mined: Lotter's Framing of the Whole Problem [Aligrithm]

Johann Christian Lotter opens his workshop deck with four lines that do more work than most methodology chapters. A model is not the reality. The reality is unknown. The same reality can be described with many different models. The best model must be selected by experiment. Every argument in Pillar(...)

Price-Path Convexity: A New Cross-Sectional Anomaly [Aligrithm]

Take two stocks that both end the month flat. The first bled lower for two weeks, then clawed all the way back. The second ran up for two weeks, then gave it all back. Same start, same finish, same zero return. Sort every stock in the market by past return and these two land in the identical bucket,(...)

Dual Momentum Between Gold and Bitcoin (Two Stores of Value) [Aligrithm]

An 8-week dual-momentum switch between GLD and IBIT returns 79.91% a year at a Sharpe of 1.64. Vojtko and Dujava report that number on a Quantpedia note covering 31 December 2018 through April 2026, weekly rebalance at Wednesday's close, cash when both lookbacks print negative. Hold the other(...)

Factor Timing Mostly Fails — the Honest Version [Aligrithm]

Factor timing is the seductive idea that you can do better than holding a basket of factors: lean into value when value looks cheap, load momentum when momentum is running, cut a factor when its volatility spikes. Every one of those moves has a tidy formula and a plausible story. And when you run(...)

How to Spot a Fake ML Trading Paper [Aligrithm]

Three papers crossed my desk this month. One detects market manipulation with graph neural networks and reports 98.7% accuracy at 8.3 milliseconds per decision. A second, same author cluster, detects anomalies in high-frequency trading and reports a 15% accuracy improvement with an F1 of 0.915. A(...)

Stop Using Pairwise Granger: PCMCI for Financial Causality [Aligrithm]

Run pairwise Granger tests over a basket of thirty instruments and you get a causal network that looks like a plate of spaghetti. AUD/USD drives copper, copper drives the Aussie back, oil leads the loonie, the loonie leads oil, and somewhere in the mess a small-cap ETF appears to Granger-cause the(...)

Bid-Ask Spread From OHLC: The GMM Estimator That Beats Roll/CS [Aligrithm]

You backtest a strategy, subtract "a spread" for costs, and move on. Where did that number come from? If you typed in a guess, or pulled a single quoted spread from a vendor, you are almost certainly wrong, and wrong in the one direction that flatters your worst assets. The effective(...)

The Mathematics of Machine Learning, for Traders [Aligrithm]

You already ran the models. The old article "From One Tree to Forests to Boosting" walked you from a single decision tree to XGBoost, "How a Decision Tree Engineers a New Alpha" showed a tree carving conditional edges out of order-book features, and "Ridge Above 1h, XGBoost(...)

The Signal Ceiling: Why No Single-Bar OHLCV Edge Beats Costs in MNQ [Aligrithm]

Open any retail futures forum and you will find the same fourteen setups traded as gospel: the opening range breakout, the gap fade, the gap continuation, the volume spike, the liquidity grab reversal, the Asia session expansion. Each comes with a chart, a win rate, and a story about why it works.(...)

Fair Value as an Adaptive Low-Pass Filter: LAFO for Mean Reversion [Aligrithm]

Every mean-reversion trade starts with a lie you tell yourself about where price "should" be. You call it fair value, you subtract it from the spot price, and you bet the gap closes. Most traders reach for a moving average and stop thinking. Xu, Firoozye, Koukorinis, Treleaven, and Zhu at(...)

Bitcoin's Overnight Returns Forecast the VIX [Aligrithm]

Split one Bitcoin day into two pieces and only one of them predicts anything. The piece that runs while US stock exchanges are closed, from yesterday's 4pm close to today's 9:30am open, carries a signal for tomorrow's VIX. The piece that runs while those exchanges are open carries(...)

Chicken and Egg: Use the SPX to Time the VIX, Not Vice Versa [Aligrithm]

Twenty years of retail research points the arrow one way. You read the VIX, and the VIX tells you where the S&P 500 is going. Oversold VIX means complacency, sell stocks; spiked VIX means panic, buy the dip. Connors built a cottage industry on it, and every trading forum still runs some version(...)

Momentum Is a Ranking Problem: Learning-to-Rank vs Regress-then-Rank [Aligrithm]

Cross-sectional momentum has one job: at each rebalance, order a universe of assets from worst to best, buy the top, sell the bottom. Everyone agrees on that. Where strategies quietly disagree is on how they produce the order. Classic momentum sorts on the past twelve-month return. A neural net(...)

Can Machines Learn Weak Signals? Ridge > Zero > Lasso [Aligrithm]

Feed 920 firm characteristics into a Lasso to predict next month's stock returns and it will do something that should stop you cold: it loses to a model that predicts zero for every stock. Not "underperforms a good benchmark." Loses to the number 0. Shen and Xiu prove this is not bad(...)

Percentile-Rank Momentum With Hysteresis: Low-Churn Signals [Aligrithm]

Momentum is the oldest anomaly in the book, and a new momentum paper has to justify why it exists. Landolfi's percentile-rank framework does not sell you the momentum. It sells the plumbing around it: rank each move against its own sign-consistent history instead of a raw threshold, gate(...)

State-Space Models for Price: CryptoMamba vs Transformers (Skeptical) [Aligrithm]

Every few years a new architecture gets pointed at Bitcoin and a paper announces it won. This round it is Mamba, the selective state-space model that is genuinely reshaping language and vision. Sepehri, Mehradfar, Soltanolkotabi, and Avestimehr at USC built CryptoMamba, a compact Mamba network that(...)

Network Momentum as a Cross-Asset Factor [Aligrithm]

Momentum is the one factor nobody argues about. Winners keep winning, losers keep losing, and the effect shows up in stocks, bonds, commodities, and currencies across a century of data. The old article "From Intermarket Analysis to Network Momentum" pushed a harder claim: an asset's(...)

Trend-Following P&L Is a Function of Autocorrelation (Closed Form) [Aligrithm]

Ask a CTA salesperson why their fund makes money and you get a story: markets trend, we ride the trend, we cut losers and let winners run. That story is untestable. Sepp and Lucic did something the industry rarely does. They wrote down the exact profit-and-loss of a standard European trend-follower(...)