Quantitative Trading

Introduction The dual moving average crossover is arguably the simplest systematic trading strategy that captures the essence of trend following: when a shorter-period average rises above a longer-period average, an uptrend is inferred; when it falls below, a downtrend is signaled. Despite its apparent naivety, this strategy serves as the theoretical backbone for a broad class of trend-following systems, from the classic Donchian channel to modern time-series momentum factors. Its simplicity makes it an ideal vehicle for understanding the tradeoff between signal lag and noise filtering that lies at the heart of all technical indicator design. ...

Introduction Financial markets alternate between periods of persistent directional movement and periods of consolidation around a mean. This distinction — between trending and mean-reverting regimes — is among the most consequential facts a trader or portfolio manager must confront, for the optimal strategy in one regime is often the worst in the other. A moving-average crossover that generates steady profits in a trending market will be whipsawed to extinction in a range-bound one; conversely, a mean-reversion strategy that profits from oscillations will suffer catastrophic drawdowns when a trend emerges. ...

In discretionary trading, indicators serve as auxiliary decision-making tools; in systematic trading (CTA), they become the raw material for constructing factors. For Chinese government bond futures (T/TF/TS), naively applying generic technical analysis is insufficient. Instead, one should build an indicator system grounded in three dimensions: macro inertia, volatility regime, and term structure. Trend Factors: Capturing Macro Inertia The underlying asset of government bond futures is the risk-free interest rate. Interest rate cycles are driven by macroeconomic fundamentals (GDP, CPI) and monetary policy operations (MLF, OMO), exhibiting strong trend persistence. Trend-following factors aim to capture this structural momentum. ...

Designing a production-grade quantitative trading system demands careful decomposition of responsibilities across data ingestion, order execution, strategy computation, and operational monitoring. This article presents a layered architecture that separates these concerns, followed by a systematic taxonomy of trading strategies with particular attention to treasury and index futures markets. The discussion extends to machine learning and reinforcement learning frameworks, and concludes with practical considerations for live deployment and strategy evaluation. Layered System Architecture A well-structured quantitative trading platform should adopt a layered architecture where each layer encapsulates a distinct domain of responsibility and communicates with adjacent layers through well-defined interfaces. This separation not only improves maintainability but also enables independent evolution of each component — a critical property when market conditions or regulatory requirements shift. ...