scholarly journals Using algorithmic trading to analyze short term profitability of Bitcoin

2021 ◽  
Vol 7 ◽  
pp. e337
Author(s):  
Iftikhar Ahmad ◽  
Muhammad Ovais Ahmad ◽  
Mohammed A. Alqarni ◽  
Abdulwahab Ali Almazroi ◽  
Muhammad Imran Khan Khalil
Keyword(s):  
Trading Strategy ◽  
Trading Strategies ◽  
Reservation Price ◽  
Algorithmic Trading ◽  
Recent Past ◽  
Investment Opportunity ◽  
Short Term ◽  
Trading Period

Cryptocurrencies such as Bitcoin (BTC) have seen a surge in value in the recent past and appeared as a useful investment opportunity for traders. However, their short term profitability using algorithmic trading strategies remains unanswered. In this work, we focus on the short term profitability of BTC against the euro and the yen for an eight-year period using seven trading algorithms over trading periods of length 15 and 30 days. We use the classical buy and hold (BH) as a benchmark strategy. Rather surprisingly, we found that on average, the yen is more profitable than BTC and the euro; however the answer also depends on the choice of algorithm. Reservation price algorithms result in 7.5% and 10% of average returns over 15 and 30 days respectively which is the highest for all the algorithms for the three assets. For BTC, all algorithms outperform the BH strategy. We also analyze the effect of transaction fee on the profitability of algorithms for BTC and observe that for trading period of length 15 no trading strategy is profitable for BTC. For trading period of length 30, only two strategies are profitable.

FINRA proposes requiring registration of associated persons who develop algorithmic trading strategies

2015 ◽  
Vol 16 (3) ◽  
pp. 33-36
Author(s):  
Janet M. Angstadt ◽  
Michael T. Foley ◽  
Ross Pazzol ◽  
James D. Van De Graaff
Keyword(s):  
Design Methodology ◽  
Trading Strategy ◽  
Trading Strategies ◽  
Algorithmic Trading ◽  
Design Development ◽  
Significant Modification ◽  
Current Form ◽  
Content Type ◽  
Rule Making

Purpose – To analyze FINRA’s proposal that would require registration with FINRA of associated persons of FINRA-member firms who are primarily responsible for the design, development or significant modification of an algorithmic trading strategy. Design/methodology/approach – This article discusses the rationale and details of the proposed requirements. Findings – If adopted in its current form, the proposed rule-making, particularly when combined with the SEC’s proposed amendments to Rule 15b9-1 under the Securities and Exchange Act, would result in many various individuals who currently are not subject to a FINRA registration requirement, to pass a qualification examination and register. Originality/value – This article contains valuable information about important FINRA rule making activity.

Algorithmic Trading Strategies Berbasis Artificial Neural Network Sebagai Alat Bantu Analisis Teknikal Pada Perdagangan Valuta Asing

2018 ◽  
Vol 3 (2) ◽  
Author(s):  
Irwan Cahyadi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Foreign Exchange Market ◽  
Trading Strategy ◽  
Trading Strategies ◽  
Algorithmic Trading ◽  
Market Strategy ◽  
Strategy Model ◽  
Artificial Neural ◽  
Investment System

Pasar valuta asing atau foreign exchange market adalah pasar yang memfasilitasi pertukaran (tempat bertemunya penawaran dan permintaan) valuta untuk mempermudah transaksi-transaksi perdagangan dan keuangan internasional. Untuk melakukan perdagangan valas (valuta asing) diperlukan metode atau alat-alat bantuan untuk menganalisa pergerakan harga valas dan mengambil keputusan berdasarkan analisa tersebut. Penelitian dilakukan untuk membangun algorithmic trading strategy (strategi dalam kaitannya dengan entry–exit pada market) yang terautomatisasi menggunakan teknologi komputer (dan bisa digunakan pada sub-jenis pasar finansial lainnya). Metodologi penelitian menggunakan KV methodology untuk pengembangan trading/investment system. Beberapa pertimbangan yang menjadikan KV methodology sebagai metodologi pada penelitian, KV methodology bisa diaplikasikan untuk pengembangan dan evaluasi trading/ investment system terlepas adanya perbedaan strategy, holding periods, benchmarks, atau market, strategy independent, dan mendukung continuous improvement / perbaikan berkelanjutan.KV methodology akan menghasilkan keluaran berupa trading strategy model dan diaplikasikan ke dalam automated trading system pada trading platform MT4 menggunakan bahasa pemrograman MQL untuk mengungkap / memahami kinerja dan potensi penuh trading strategy. Lebih jauh trading strategy model dikembangkan menggunakan artificial neural network ditujukan sebagai alat bantu untuk analisis (berupa prediksi harga ke depan, dan optimisasi strategy (signal) entry (buy-sell) / exit) ataupun sebagai full automated trading strategies. 

FINRA rule amendment requires registration of associated persons who develop algorithmic trading strategies

2016 ◽  
Vol 17 (3) ◽  
pp. 39-41
Author(s):  
Michael T. Foley ◽  
Janet M. Angstadt ◽  
Ross Pazzol ◽  
James D. Van De Graaff
Keyword(s):  
Design Methodology ◽  
Trading Strategy ◽  
Trading Strategies ◽  
Algorithmic Trading ◽  
Design Development ◽  
Significant Modification ◽  
Content Type ◽  
Rule Making

Purpose To analyze a recently approved FINRA rule amendment that will require registration with FINRA of associated persons of FINRA-member firms who are primarily responsible for the design, development or significant modification of an algorithmic trading strategy. Design/methodology/approach This article discusses the rationale and details of the proposed requirements. Findings The amended FINRA rule, particularly when combined with the SEC’s proposed amendments to Rule 15b9-1 under the Securities and Exchange Act of 1934, will result in many individuals who currently are not subject to a FINRA registration requirement to pass a qualification examination and register. Originality/value This article contains valuable information about important FINRA rule-making activity.

Cluster Analysis for Evaluating Trading Strategies

2018 ◽  
Keyword(s):  
Cluster Analysis ◽  
Cost Analysis ◽  
Trading Strategy ◽  
Trading Strategies ◽  
Trading Cost ◽  
Clustering Technique ◽  
Statistical Clustering ◽  
Trader Performance

In this article, we introduce a new methodology to empirically identify the primary strategies used by a trader using only post-trade fill data. To do this, we apply a well-established statistical clustering technique called k-means to a sample of progress charts, representing the portion of the order completed by each point in the day as a measure of a trade’s aggressiveness. Our methodology identifies the primary strategies used by a trader and determines which strategy the trader used for each order in the sample. Having identified the strategy used for each order, trading cost analysis can be performed by strategy. We also discuss ways to exploit this technique to characterize trader behavior, assess trader performance, and suggest the appropriate benchmarks for each distinct trading strategy.

scholarly journals Is working memory stored along a logarithmic timeline? Converging evidence from neuroscience, behavior and models

2018 ◽  
Author(s):  
Inder Singh ◽  
Zoran Tiganj ◽  
Marc Howard
Keyword(s):  
Short Term Memory ◽  
Temporal Organization ◽  
Search Model ◽  
Recent Past ◽  
Short Term ◽  
Term Memory ◽  
Growing Body ◽  
Neural Underpinnings ◽  
Behavioral Evidence ◽  
Lines Of Evidence

A growing body of evidence suggests that short-term memory does not only store the identity of recently experienced stimuli, but also information about when they were presented. This representation of ‘what’ happened ‘when’ constitutes a neural timeline of recent past. Behavioral results suggest that people can sequentially access memories for the recent past, as if they were stored along a timeline to which attention is sequentially directed. In the short-term judgment of recency (JOR) task, the time to choose between two probe items depends on the recency of the more recent probe but not on the recency of the more remote probe. This pattern of results suggests a backward self-terminating search model. We review recent neural evidence from the macaque lateral prefrontal cortex (lPFC) (Tiganj, Cromer, Roy, Miller, & Howard, in press) and behavioral evidence from human JOR task (Singh & Howard, 2017) bearing on this question. Notably, both lines of evidence suggest that the timeline is logarithmically compressed as predicted by Weber-Fechner scaling. Taken together, these findings provide an integrative perspective on temporal organization and neural underpinnings of short-term memory.

scholarly journals Risks of Traders on the World Stock Market on the Example of Ukraine

2019 ◽  
Vol 67 ◽  
pp. 06001 ◽  
Author(s):  
George Abuselidze ◽  
Olga Mohylevska ◽  
Nina Merezhko ◽  
Nadiia Reznik ◽  
Anna Slobodianyk
Keyword(s):  
Stock Market ◽  
Financial Markets ◽  
World Market ◽  
Trading Strategy ◽  
Trading Strategies ◽  
Practical Significance ◽  
External Shocks ◽  
Domino Effect ◽  
The World ◽  
The Impact

The article reveals the essence and features of the development of the stock market in Ukraine. It was established that the vigorous activity of countries in the world financial markets means that they also face a risk of global financial turmoil (the so-called “domino effect”). It is determined that the impact of global financial instability on the country depends on the openness of its economy that will lead to significant external “shocks”. The possibility of providing effective influence on domestic stock market activity with taking into account the changing world situation, development of perfect trading strategies for each participant is substantiated. The conducted analysis of the world market conditions of stock markets in recent years has made it possible to assess the real risks for new participants in the stock market and become the basis for the development of an appropriate effective trading strategy. The practical significance of the results is that they allow for a measurable approach to assessing the existing risk when choosing one or another trading strategy to move to the world stock market.

scholarly journals Optimizing the Pairs-Trading Strategy Using Deep Reinforcement Learning with Trading and Stop-Loss Boundaries

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-20 ◽  
Author(s):  
Taewook Kim ◽  
Ha Young Kim
Keyword(s):  
Reinforcement Learning ◽  
Trading Strategy ◽  
Trading Strategies ◽  
Cointegration Test ◽  
Optimum Level ◽  
Pairs Trading ◽  
Proposed Model ◽  
The Mean ◽  
Stop Loss ◽  
The Given

Many researchers have tried to optimize pairs trading as the numbers of opportunities for arbitrage profit have gradually decreased. Pairs trading is a market-neutral strategy; it profits if the given condition is satisfied within a given trading window, and if not, there is a risk of loss. In this study, we propose an optimized pairs-trading strategy using deep reinforcement learning—particularly with the deep Q-network—utilizing various trading and stop-loss boundaries. More specifically, if spreads hit trading thresholds and reverse to the mean, the agent receives a positive reward. However, if spreads hit stop-loss thresholds or fail to reverse to the mean after hitting the trading thresholds, the agent receives a negative reward. The agent is trained to select the optimum level of discretized trading and stop-loss boundaries given a spread to maximize the expected sum of discounted future profits. Pairs are selected from stocks on the S&P 500 Index using a cointegration test. We compared our proposed method with traditional pairs-trading strategies which use constant trading and stop-loss boundaries. We find that our proposed model is trained well and outperforms traditional pairs-trading strategies.

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