Quantitative Trading Strategy Library

1. Introduction In spread trading, the central challenge is often not deciding what to trade, but executing the trade. A spread position requires simultaneous entry into two legs — buying one contract and selling another — and any failure to fill both legs at the intended prices exposes the trader to leg-out risk: an unwanted directional exposure on the unfilled leg. This article analyzes a price-level order management framework for spread execution. The approach is parameterized by fixed trigger prices for each direction, combined with time-based filtering and algorithmic lifecycle management. While simple in specification, the design reveals important principles about execution risk and state management in multi-leg trading. ...

1. Introduction Statistical arbitrage is a class of trading strategies that exploit temporary deviations from equilibrium relationships between correlated assets. The simplest and most widely practiced variant operates on the spread — the price difference (or a linear combination) of two cointegrated instruments — and bets on its reversion to a historical mean. This article examines a Bollinger Band–based mean reversion strategy applied to the spread. We develop the statistical foundations, formalize the trading logic, analyze parameter selection, and discuss the critical risk of non-reversion. ...

1. Introduction In the early 1980s, Richard Dennis and William Eckhardt conducted one of the most famous experiments in trading history. They recruited a group of novices – dubbed the “Turtles” – and taught them a fully mechanical trend-following system. Over the following years, the Turtles collectively generated hundreds of millions of dollars in profits, demonstrating that a rules-based approach could succeed even in the hands of traders with no prior market experience. ...

The ATR Indicator True Range Conventional volatility measures based on the high-low range fail to account for gap openings. The True Range (TR), introduced by Wilder (1978), resolves this limitation by considering three sources of price movement: TRt=max⁡{Ht−Lt∣Ht−Ct−1∣∣Lt−Ct−1∣ TR_t = \max \begin{cases} H_t - L_t \\ |H_t - C_{t-1}| \\ |L_t - C_{t-1}| \end{cases} where HtH_t denotes the current period high, LtL_t the current period low, and Ct−1C_{t-1} the previous period close. The three components of TR capture distinct volatility sources: intraday range (Ht−LtH_t - L_t), upward gaps (Ht−Ct−1H_t - C_{t-1}), and downward gaps (Lt−Ct−1L_t - C_{t-1}). By taking the maximum across these three values, TR ensures that gap-driven volatility is never understated, a property particularly important for instruments that frequently exhibit overnight jumps such as index futures and government bond futures. ...

1. Introduction Most systematic trading strategies operate on a single timeframe. A moving-average crossover on daily bars, an RSI oscillator on hourly data, or a breakout on 15-minute candles – each confines its logic to one temporal resolution. Yet practitioners have long observed that the most reliable signals arise when multiple timeframes concur: a bullish trend on the weekly chart coinciding with an oversold bounce on the hourly chart, for instance. This idea, often called multi-timeframe resonance, is intuitive – but what exactly makes it work, and under what conditions does it fail? ...

1. Introduction Single-indicator trading strategies are easy to construct but fragile in practice: each indicator has regimes where it fails, and a strategy that relies on one signal alone will suffer extended drawdowns during those regimes. The natural remedy is to combine multiple signals, hoping that their errors are at least partially independent. This is the ensemble approach, borrowed from statistical learning, applied to CTA signal construction. This article develops the mathematical framework for multi-signal combination, focusing on two canonical mechanisms—linear weighting and majority voting—and analyzes the conditions under which adding signals improves rather than degrades performance. ...

1. Introduction Dual Thrust is a classical intraday range breakout strategy developed by Michael Chalek in the 1980s, once ranked among the most profitable systems by Future Truth magazine. Its elegance lies in the minimal information requirement—only the previous day’s high, low, and close prices—and in the deliberate asymmetry embedded in its breakout thresholds. Unlike symmetric channel breakout systems (e.g., Donchian channels), Dual Thrust permits independent tuning of the upside and downside trigger distances, which introduces a directional bias that can be calibrated to the prevailing market regime. ...

1. Introduction The Keltner Channel, first described by Chester Keltner in 1960 and later refined by Linda Raschke, is a volatility-envelope indicator that shares structural similarity with Bollinger Bands but differs fundamentally in how it measures dispersion. Where Bollinger Bands use the standard deviation of closing prices, Keltner Channels employ the Average True Range (ATR), yielding a smoother and often more stable envelope. This article examines a breakout strategy built on the Keltner Channel, with particular emphasis on: ...

1. Introduction Bollinger Bands, introduced by John Bollinger in the 1980s, remain one of the most widely used volatility-based indicators in quantitative trading. The core idea is straightforward: price tends to oscillate within a range defined by its own recent volatility, and extreme deviations from the mean often signal the beginning of a sustained trend rather than a mere aberration. This article examines a breakout strategy that combines three technical indicators in a coherent framework: ...

Introduction The Relative Strength Index (RSI) is one of the most widely used momentum oscillators in technical analysis. As a standalone signal generator, however, it suffers from a chronic problem: in low-volatility, mean-reverting markets, RSI oscillates frequently around its midpoint, producing a stream of false breakout signals that accumulate losses through transaction costs and adverse fills. The ATR-filtered RSI breakout strategy addresses this deficiency by requiring that market volatility, as measured by the Average True Range (ATR), exceed its recent average before any RSI signal is acted upon. The intuition is straightforward: breakouts that occur in low-volatility environments are likely to be noise, while breakouts accompanied by expanding volatility carry a higher probability of persistence. ...