This is a summary of links recently featured on Quantocracy as of Sunday, 05/03/2026. To see our most recent links, visit the Quant Mashup. Read on readers!
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I paper-traded 22 popular crypto strategies on real fees for 10 days. Here’s the data. [Strat Proof]Why I'm publishing this I wanted to build a trading bot like a lot of people did once Claude integrated with TradingView. Took the leap, my strategies kept failing, and the backtests kept being way too optimistic compared to what happened when I actually ran them. Started digging into why. This post is what 10 days of running 22 popular strategies on real Binance fees with real L2 spread
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Where Risk Parity Hurts: A 58-Year Audit of Tails and Drawdowns [Beyond Passive]The previous article extended the inverse-volatility allocation across SPY, TLT, and GLD back to 1968 using a synthetic price construction. Over fifty-eight years the strategy delivered a CAGR of 7.1%, volatility of 7.5%, a Sharpe of 0.97, and a maximum drawdown of 22%. The volatility-targeting overlay, justified by the persistence of volatility across the same window, kept realised vol close to
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Almost Explicit Implied Volatility [Chase the Devil]Several years ago, I had explored accuracy and performance of different ways to imply the Black-Scholes volatility. Jherek Healy proposed some improvements over my naive algorithm on his blog. Recently, a Linkedin post mentioned a new paper from Wolfgang Schadner which presents an almost explicit formula for the implied volatility. Almost because it actually relies on some implementation of the
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Rethinking Trend Following: Optimal Regime-Dependent Allocation [Alpha Architect]Most trend-following research focuses on signal construction: how to detect trends better, faster, or earlier. The paper asks a different question, and arguably a more important one for investors: once a market regime has been identified, what is the optimal portfolio exposure in that regime? That is the central novelty of the paper which is available here. Traditional time-series momentum
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Curve trades with macroeconomic signals [Macrosynergy]The shape of yield curves in developed swap markets reflects the state of growth, inflation, and credit supply. This is primarily because central banks adjust short-term policy rates in response to evolving economic conditions, while their credibility helps anchor longer-term forward rates. In monetary policy regimes committed to price stability, and when short rates are above the zero lower