The Alpha Engine: Designing an Automated Trading Algorithm

Golub, Anton; Glattfelder, James B.; Olsen, Richard B.

doi:10.1201/9781315372006-3

Cited by 18 publications

(22 citation statements)

References 15 publications

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“…We study in this section the distribution of maximal gains 4 of processes, prior to the occurrence of a fixed loss δ > 0. Golub et al (2016Golub et al ( , 2018 claim that for a Brownian motion (the toy model of a fair game), this gain is exponentially distributed, with parameter δ; thus in average, one gains δ before experiencing a loss of size δ. This result is independent of the volatility of the Brownian motion.…”

Section: Lehoczky's Proof For Spectrally Negative Lévy Martingalesmentioning

confidence: 99%

Three Essays on Stopping

Mayerhofer

2019

Risks

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First, we give a closed-form formula for first passage time of a reflected Brownian motion with drift. This corrects a formula by Perry et al. (2004). Second, we show that the maximum before a fixed drawdown is exponentially distributed for any drawdown, if and only if the diffusion characteristic μ / σ 2 is constant. This complements the sufficient condition formulated by Lehoczky (1977). Third, we give an alternative proof for the fact that the maximum before a fixed drawdown is exponentially distributed for any spectrally negative Lévy process, a result due to Mijatović and Pistorius (2012). Our proof is similar, but simpler than Lehoczky (1977) or Landriault et al. (2017).

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Section: Lehoczky's Proof For Spectrally Negative Lévy Martingalesmentioning

confidence: 99%

Three Essays on Stopping

Mayerhofer

2019

Risks

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“…In 2017, Golub, Glattfelder, and Olsen () presented a DC‐based trading strategy called ‘Alpha Engine’. The Alpha Engine is a counter‐trend trading strategy.…”

Section: Directional Changesmentioning

confidence: 99%

“…Here, the ‘coastline’ denotes the estimated maximum profits that can be produced under the DC context. In other words, the authors suggested that most of the potentials of the DC framework as basis of trading strategies is not exploited yet (Golub et al, ). Finally, we want to note that our proposed trading strategy in this paper, named TSFDC, has no common features with any of these DC‐based strategies as we should explain in Section 4.…”

Section: Directional Changesmentioning

confidence: 99%

“…In this section, we compare TSFDC with the trading strategy named ‘DC + GA’ established by Golub et al (). DC + GA runs N theta DC summaries concurrently (using N theta thresholds; where N theta is a parameter to be chosen by the investor).…”

Section: Comparing Tsfdc To Other Trading Strategiesmentioning

confidence: 99%

“…Kampouridis and Otero report the average monthly returns of applying DC + GA to five currency pairs (shown in Table Kampouridis & Otero, , page 158). We note that DC + GA incurs overall losses in two out of the five cases.…”

Section: Comparing Tsfdc To Other Trading Strategiesmentioning

confidence: 99%

See 2 more Smart Citations

TSFDC: A trading strategy based on forecasting directional change

Bakhach

Tsang

Chinthalapati

2018

Intell Sys Acc Fin Mgmt

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Directional Change (DC) is a technique to summarize price movements in a financial market. According to the DC concept, data is sampled only when the magnitude of price change is significant according to the investor. In this paper, we develop a contrarian trading strategy named TSFDC. TSFDC is based on a forecasting model which aims to predict the change of the direction of market's trend under the DC context. We examine the profitability, risk and risk-adjusted return of TSFDC in the FX market using eight currency pairs. The results suggest that TSFDC outperforms the buy and hold approach and another DC-based trading strategy.

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Measuring relative volatility in high‐frequency data under the directional change approach

Tsang

O’Hara

2022

Intell Sys Acc Fin Mgmt

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Summary We introduce a new approach in measuring relative volatility between two markets based on the directional change (DC) method. DC is a data‐driven approach for sampling financial market data such that the data are recorded when the price changes have reached a significant amplitude rather than recording data under a predetermined timescale. Under the DC framework, we propose a new concept of DC micro‐market relative volatility to evaluate relative volatility between two markets. Unlike the time‐series method, micro‐market relative volatility redefines the timescale based on the frequency of the observed DC data between the two markets. We show that it is useful for measuring the relative volatility in micro‐market activities (high‐frequency data).

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The Alpha Engine: Designing an Automated Trading Algorithm

Cited by 18 publications

References 15 publications

Three Essays on Stopping

Three Essays on Stopping

TSFDC: A trading strategy based on forecasting directional change

Measuring relative volatility in high‐frequency data under the directional change approach

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