Quant Mashup - Trading the Breaking Switch-off: Robust changepoint protocol [Trading the Breaking]Introduction. The drift in algorithm performance. Taxonomy of performance drift. Model risks and limitations. Robust estimation and the biweight loss. M-estimators and the geometry of influence. Tukey’s Biweight (bisquare) loss. Iteratively Reweighted Least Squares (IRLS). Regime segmentation via(...) Switch-Off: Bayesian online changepoint detection [Trading the Breaking]Every quantitative trading operation, from a single retail algorithmist to a multi-billion dollar systematic fund, faces an identical, recurring, and fundamental dilemma: When to deactivate a failing strategy. This problem is, in many ways, the only problem that matters for long-term survival. Alpha(...) Optimization: Adaptive regret for regime-shifting markets [Trading the Breaking]In our preceding discourse, we talked about the features of parameter-free optimization, a methodology designed to liberate quantitative strategists from the sinister task of parameter tuning. The allure was undeniable: escape the perilous cycle of tweaking lookback windows, volatility thresholds,(...) Parameter-free optimization [Trading the Breaking]Let’s talk plainly. Quant finance has spent years chasing complexity—layering indicators, stacking models, scaling clusters—anything that might tease out an edge. We’ve built whole infrastructures around that chase: faster data, bigger grids, deeper nets. Yet under all that polish sits an(...) Robust optimization protocol [Trading the Breaking]Parameter optimization is where good ideas go to either earn their keep or quietly fail. Given a fixed modeling recipe, the optimizer will always return a winner; what it cannot tell you—unless you force it to—is whether that winner is real. Financial data are dependent, heteroskedastic,(...) Combinatorial Purged Cross Validation for Optimization [Trading the Breaking]Traditional grid or Bayesian searches conducted on a single path reward parameters that overfit to this specific historical path. This inflates performance metrics through selection bias and temporal leakage. Combinatorial Purged Cross-Validation (CPCV) addresses this flaw by generating a multitude(...) Walk-Forward optimization [Trading the Breaking]I want to start by saying that the key is in the data, not in the model or its parameters. Therefore, if your data is garbage, no matter how much you parameterize it, the results will still be garbage. If you parameterize a model, it's to fine-tune something that already works. Period. Knowing(...) Options: Iron Butterfly [Trading the Breaking]In the previous article, we deconstructed the Iron Condor, a robust strategy for harvesting the variance risk premium in markets characterized by range-bound behavior. The Condor, with its constituent out-of-the-money credit spreads, offers a wide plateau of profitability, making it a forgiving(...) Options: Iron Condor Strategy [Trading the Breaking]The iron condor’s appeal is statistically seductive: a high-probability, defined-risk structure promising steady income from time decay and volatility erosion. Yet beneath its deceptively flat payoff profile lies a quantitatively intricate reality—one where theoretical win rates often mask a(...) Testing Strategies [Trading the Breaking]Introduction. Risks and method limitations. Circular-shift (lag-invariant) permutation test. Random sign-flip (direction-neutral) test. Stationary bootstrap of returns. White's reality check and Hansen's superior predictive ability. Jittered-entry (temporal perturbation) test. Parameter(...) Backtesting [Trading the Breaking]Introduction You know, after more than a decade in this business, I've come to think of backtesting as the ultimate paradox of our profession. It's like being handed the top one lie detector in the world, only to discover it's been calibrated exclusively on your own personal brand of(...) Model: Advances in clustering [Trading the Breaking]Look, here’s the thing—we’ve all been drinking the correlation Kool-Aid for decades, right? It’s elegant, sure. Clean math. Easy to explain to the PMs. But let’s get real: relying on a correlation matrix in today’s markets is like trying to sail a speedboat with an anchor chained to your(...) Model: Clustering [Trading the Breaking]Alright, let’s establish first principles. Before deploying capital into algorithmic strategies, one must confront the paradigm shift that distinguishes durable firms from those erased by structural blind spots: financial markets are not monolithic stochastic processes but non-stationary systems(...) Data: Range, Renko, Filter and Volatility bars [Trading the Breaking]You are observing the markets in real-time—thousands of price ticks cascading across your screen, each reflecting a momentary shift in supply, demand, and sentiment. At first glance, the data appears evenly spaced, structured, and regular. Yet beneath this surface lies a deeper asymmetry: the(...) What are your bars hiding from you? [Trading the Breaking]The electronic marketplace generates vast amount of data—billions of timestamped trades, quotes, and cancellations—that demand processing to extract actionable insights. For quantitative traders, the central challenge lies not in designing strategies but in constructing a robust framework to(...) Could data drift be silently sabotaging your PnL? [Trading the Breaking]In the day-to-day grind of systematic trading, volatility isn’t just a market feature—it’s the atmosphere we operate in. It drives the edge, defines the risk, and sets the tempo. But while volatility creates the conditions for profit, it also contains the seeds of our destruction. That(...) Is your strategy built on distributional lies? [Trading the Breaking]During the previous optimization cycle, I was tasked with enhancing inventory management protocols for a legacy trading system operating under low-latency constraints—order cycle times ≥ 500ms. While the academic corpus fixates on high-frequency trading paradigms—microsecond latency(...) Are you blind to the tail risks lurking in calm markets? [Trading the Breaking]Algorithmic trading systems can give you this sleek, high-tech confidence—like the robots have everything under control. They’re fast, precise, and backtested to death, right? But that’s where the trap snaps shut. When your risk metrics are built on things like standard deviation or recent(...)