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American Journal of  Financial 
Technology and Innovation (AJFTI)

Review of  Recent Research Directions and Practical Implementation of  Low-Frequency 
Algorithmic Trading

Talal Al-Sulaiman1*

Volume 2 Issue 1, Year 2024
ISSN: 2996-0975 (Online)

https://doi.org/10.54536/ajfti.v2i1.2354
https://journals.e-palli.com/home/index.php/ajfti

Article Information ABSTRACT

Received: January 22, 2024

Accepted: February 24, 2024

Published: February 26, 2024

Financial trading has undergone substantial technological evolution, with automation taking 
center stage, leading to approximately 80% of  US market trades being executed by computer 
systems, predominantly by large financial institutions. The rise of  algorithmic trading, poised 
to engage smaller entities, international markets, and individual traders, drives this article’s 
exploration of  research in this field. Providing a comprehensive overview, it outlines the 
evolution of  trading practices and defines algorithmic trading as a computer-powered tool 
aiding investment decisions. The article details the steps involved in algorithmic trading, 
covering opportunity identification, quantitative research, implementation, testing phases, 
and continuous monitoring. It also examines prevalent programming languages and open-
source platforms facilitating algorithm development. Focusing on trading frequencies 
across financial instruments, it delves into high-frequency trading as a subset, alongside 
methodologies like technical and fundamental analysis, time series analysis, option trading 
strategies, and machine learning techniques used in algorithm creation. Categorized by trading 
frequencies, analytical approaches, involved financial instruments, and analysis objectives, 
the reviewed papers contribute insights into algorithmic trading’s diverse landscape and 
methodologies, offering valuable perspectives for industry participants and researchers alike.

Keywords
Financial Trading, Algorithms, 
Low Frequency, Practical 
Implementation, Technology

INTRODUCTION
As in most life aspects, technology has tremendously 
advanced financial systems. It includes many financial 
systems such as credit business, real estate, insurance, 
and financial markets. This advancement motivates more 
quantitative financial mathematics, financial engineering, 
actuarial science, and risk management.
This paper focuses on the evolution of  trading in 
financial markets. Overseas business growth during the 
industrial revolution at the beginning of  the 17th inspired 
joint-stock companies and the Dutch East India Co. to 
issue the first paper shares. The paper shares make it very 
convenient to transfer the stocks’ ownership, increasing 
the issue of  paper shares rapidly. The place where the 
buyers and sellers gathered to trade the paper shares 
is called the stock exchange, and the first established 
exchange was the Amsterdam Stock Exchange (Braudel 
& Reynolds, 1983). The worldwide exchanges continued 
until the 90s of  the previous century when it shifted 
to electronic trading (Johnson, 2014). The shift quickly 
increases the trading volume. However, with the increase 
of  computational power and cloud service availability, 
the middle of  the first decade of  this century promoted 
computers to perform trading on behalf  of  individuals. 
It allows for automated trading to be achieved through a 
finite sequence of  steps algorithms.
As of  2020, 80% of  the trading volume is effectuated 
through algorithms, and most hedge funds use algorithms 
to set up their trading strategies. H. Simon (Simon, 1955) 
prevents declare the bounded rationality the human 
from making rational decisions due to human emotions, 
the mind’s cognitive limitations, and time availability. 

Algorithmic trading (AT) allows for reducing the limitation 
on rationality. Algorithmic tradings have advantages 
over discretionary trading by removing emotions and 
coming up with consistent decisions. In addition, it can 
monitor the market all the time and implement back 
testing to ensure the strategy’s effectiveness. However, 
the algorithmic trading results depend on the quality of  
the developed model and its ability to capture the right 
signals. In other words, the algorithmic is superior to 
discretionary trading only if  the algorithm itself  besteads 
the discretionary traders.
However, the main advantage of  AT, according to 
Johnson (Simon, 1955), are its ability to minimize the 
effect of  emotions, back testing, maintain discipline 
and consistency, improve placing order speed, and 
diversification. How- ever, he stated that the main 
disadvantages of  AT centers with the possibility of  
expense increase and machine failure.
The area of  algorithmic trading requires multidisciplinary 
knowledge and skills in finance, mathematics, engineering, 
and programming. Algorithmic trading is usually executed 
through automated trading, Robot trading, and black box 
(Kissell, 2013).
The paper proceeds as follows: in Section 2, we defined 
and characterized algorithmic trading. Section 3 surveys 
the programming languages and platforms to research 
algorithmic trading. Section 4 demonstrates the domains 
of  algorithmic trading. Section 5 shows the matrix of  
performance measures for backtesting. Section 6 explores 
the main methods used to develop an algorithm for trading. 
Section 7 survey-related search on various domains. Finally, 
Section? Provides the conclusion remarks.

1 Audi Real Estate Refinancing Company and Engineering Management Department, Prince Sultan University, Riyadh, Saudi Arabia
* Corresponding author’s e-mail: talalsulaiman510@outlook.com



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LITERATURE REVIEW
The definition of  algorithmic trading (AT) and high-
frequency trading (HFT) varies among researchers. 
Jarnecic et al. (Jarnecic & Snape, 2010) define AT 
as computer algorithms executing predetermined 
trading decisions to minimize price impact. Domowitz 
(Domowitz & Yegerman, 2005) characterizes it as 
automated equity order execution via direct market-
access channels. Hendershott et al. (Hendershott et al., 
2011) describe AT as using algorithms for automatic 
trading decisions, order submissions, and management. 
HFT, a primary type of  AT, relies on speed for profits. 
Jarnecic et al. (Jarnecic & Snape, 2010) define HFT as 
high-speed algorithms generating and executing trades for 
capital returns. Cvitani et al. (Cvitanic & Kirilenko, 2010) 
define it as rapid, automated programs creating, directing, 
and executing orders in electronic markets, engaging in 
substantial order submissions and cancellations. Gomber 
et al. (Gomber & Haferkorn, 2015) highlight typical 
AT and HFT characteristics involving pre-designated 
decisions, live market data observation, and automated 
order submission and management. However, HFT 
differs with numerous orders and cancellations, profiting 
as a middleman and holding assets briefly.
The development of  HFT is chiefly by financial 
institutions, emphasizing algorithmic trading’s researcher 
development and implementation for retail investors. 
Choosing between buying or building trading software 
presents trade-offs. Johnson (Johnson, 2020) notes that 
buying existing software offers easy implementation and 
customization but can be costly and potentially contain 
loopholes. Building software, although time-consuming, 
offers control and customization. Numerous references 
like “Learn Algorithmic Trading” (Donadio & Ghosh, 
2019), “Hands-On Machine Learning for Algorithmic 
Trading” (Jansen, 2018), “Trading Evolved” (Clenow, 
2019), and “Algorithmic Trading” (Johnson, 2020) 
provide valuable hands-on experience in developing 
trading systems. Open-source trading platforms like 
Quantopian, Quant-Connect, and Quant-Insti provide 
cloud-based services for algorithm development, back-
testing, and live trading (Cohan; QuantConnec Profile, 
2011; Oberoi).
Algorithmic strategies’ domains are crucial, as strategies 
may perform differently based on financial instrument 
types or trading environments. Derivatives like forwards, 
futures, swaps, and options can impact trading strategy 
effectiveness (Hull, 2003). Back-testing using historical 
data is vital to evaluate algorithm performance. Various 
performance measures such as portfolio diversification, 
concentration, and risk-return ratios (Markowitz, 1952) 
help assess algorithm reliability and effectiveness.

Materials and Methods 
Financial markets employ technical analysis, a tool 
reliant on historical prices to predict market patterns 
and facilitate trading decisions. Originating from Charles 
Dow’s Dow Theory in 1900 (Achelis), it focuses on 

interpreting price movements through charts. The 
analysis mainly revolves around momentum and mean 
reversion strategies. Momentum strategies advocate 
following existing trends, assuming their continuation, 
whereas mean reversion anticipates securities returning 
to their average prices. Various technical indicators, such 
as moving averages (MA), exponential moving averages 
(EMA), and Bollinger Bands (BB), aid in analyzing 
market trends. For example, the Double Exponential 
Moving Average (DEMA) utilizes two EMAs for mean 
reversion signals, while Bollinger Bands offer confidence 
intervals depicting potential overbought or oversold 
situations for both strategies. However, technical analysis 
lacks adaptiveness and learning capabilities. Integrating 
modern algorithms like machine learning enhances 
pattern detection and prediction capabilities.
Contrarily, fundamental analysis estimates assets’ intrinsic 
value based on financial statements and economic 
factors, evading the Efficient Market Hypothesis (Fama, 
1970). Techniques include financial ratios, discounted 
dividends, or free cash flow models (Graham & Dodd, 
2008). Financial time series analysis evaluates and predicts 
security prices over time, including linear (AR, MA, 
ARMA, ARIMA) and nonlinear models (Threshold AR, 
Markov Switching AR). Volatility prediction models like 
ARCH and GARCH forecast variations in asset returns 
due to changing volatility. Moreover, computational 
mathematics, involving AI, machine learning, and data 
mining, supports these analyses (Overby, 2011; McMillan, 
2002; Kastenholz, 2019). Decision trees (e.g., CART, 
C4.5) merge fundamental analysis with decision-making, 
offering actionable rules for stock actions (Rokach, 2014; 
Larose & Larose, 2014). These methods complement 
each other, enhancing market understanding and trading 
strategies (Box et al., 2011; Tsay, 2005; Tsay, 2013).
Furthermore, the evolution of  algorithmic trading 
is significantly influenced by advancements in neural 
networks, particularly Long Short-Term Memory (LSTM) 
networks introduced by Hochreiter and Schmidhuber 
(Hochreiter & Schmidhuber, 1997). LSTM, a form 
of  recurrent neural network (RNN), features context 
neurons representing short-term memory dynamically 
updating during a time sequence. Differing from feed-
forward neural networks, RNNs transmit output from 
context nodes back to hidden layers, involving input 
and forget gates, and output gate mechanisms. Weight 
optimization in LSTM employs backward training 
methods like back-propagation, resilient-propagation, 
and genetic algorithms. Reinforcement learning, another 
AI branch, aims to maximize reward through iterative 
actions based on observed states, as described by Q 
learning. Studies explore AI-driven trading strategies, 
from pair-switching approaches (Maewal & Bock, 2011) 
and technical indicator-based decision support systems 
(Dash & Dash, 2016; Henrique et al., 2018) to employing 
machine learning like deep learning neural networks (Gao 
& Chai, 2018; Yu & Yan, 2020) and sentiment analysis 
(Bernile & Lyandres, 2011; Putra & Kosala, 2011). Various 



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Am. J. Financ. Technol. Innov. 2(1) 1-14, 2024

other strategies underline the diversity and complexity 
of  algorithmic trading models, ranging from leveraging 
historical market data to predicting stock trends and 
exploiting market anomalies (Cohen et al., 2010; Gerlein 
et al., 2016; Kishore et al., 2008; Nair et al., 2010).

RESULTS AND DISCUSSION
On stock options weekly or based on options covered 
by the underlying assets of  stocks with monthly data. 
Table 1 shows the most common domains of  algorithmic 
strategies.

Table 1: Common domains of algorithmic strategies
Underlying asset Derivatives Trading frequency
Equity Forward Fractions of second
Commodity futures Seconds
Bonds Options Minutes
Foreign currency Swap Days
REIT ETF Weeks
Cryptocurrencies Mutual funds Months

Years

Trend indicators attempt to detect a trend in the prices 
of  the assets. Calculating the moving average is a 
common procedure to identify the up or down trends 
by smoothing the prices. The momentum See section 6.2 
indicators estimate the speed in the changes of  prices in 

a given time-space. The volatility indicators focus on the 
trading activities, possible range, and security risk. Finally, 
the volume indicators measure the attraction of  financial 
assets. Table 2 shows a comprehensive classification of  
the technical indicators.

Table 2: Comprehensive classification of technical indicators
Trend 
indicators

Accumulative swing index 
(ASI)

Andrews pitchfork Aroon Detrended price 
oscillator

Directional movement Double exponential 
moving average

Dow theeory Elliott wave theory

Exponential moving average 
(EMA)

Fourier transform Gann angles Inertia

Linear regression indicator Mass Index Mesa sine wave Moving average 
convergence 
divergence (MACD)

Parabolic stop and reverse 
(Parabolic SAR)

Simple moving 
average (SMA)

Triangular moving 
average (TMA)

Variable moving 
average (VMA)

Weighted moving average 
(WMA)

Price channel Qstick Raff regression 
channel

Speed resistance lines Swing Index Triple exponential 
moving average 
(TEMA)

Trend lines

Vertical horizontal filter (VHF) Wilder’s smoothing
Momentum 
indicators

Absolute breadth index (ABI) Accumulation/
distribution line

Advance/decline 
ratios

Advancing declining 
issues

Advancing, declining, 
unchanged volume

Bradth thrust Chande momentum 
oscillator

Commodity channel 
index (CCI)

Commodity channel Index Commodity 
selection index

Dynamic 
momountom index

Ease of  movement

Forecast Oscillator (FO) Intaday 
momountom index

McClellan oscillator McClellan 
summation index

Member short ratio Momentum Money flow index New highs-lows 
cumulative

New highs-lows ratio Price oscillator Price-rate-of-
change (ROC)

Projection oscillator



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Relative momountom index Relative strength 
Index (RSI)

Stochastic 
momentum index

Stochastic oscillator 
(SO)

TRIX Williams 
accumulation /
distribution

Williams %R

Volatility 
Indicators

Average true range Bollinger bands Envelopes (trading 
bands)

Fibonacci

Standard deviation TRIN arms index Open-10 TRIN Relative volatility 
index

Standard deviation Standard deviation 
channel

Standard error 
bands

Standard error 
channel

Ultimate oscillator Volatility Chaikin’s
Volume 
Indicators

Market facilitation index Negative volume 
index

On balance volume Positive volume 
index

Price and volume trend STIX Trade volume index upside/downside 
ratio

upside/downside volume Volume Volume oscillator 
(VO)

Volume rate of 
change

Volume adjusted moving 
average (VAMA)

Other Japanese candlestick Kagi Large block ratio ODD lot balance 
index

ODD lot short ratio ODD proability 
cones

Overbought/
oversold

Public short ratio

Put/call ratio Random walk index Renko Spreads
Three line break Time series forecast Tirone levels Total short ratio
Typical price Weighted close Zig zag

A single firm’s outputs should be compared to other 
issues of  a similar type, such as the average of  firms in the 
same sector or the average of  leading firms in a similar 
business. Furthermore, the analyst should not look to a 
single period to assess the firm’s quality but instead see 
the ratios trending over multiple periods. For example, 

figure 5 shows the return on equity of  AAPL and MSFT 
from 2005 to 2020. As we can see from the figure, both 
companies were exposed to a drop in the ROE between 
2010 and 2013. However, starting in 2017, we can see the 
ROE trending upward.

Table 3: Financial ratios
Liquidity ratios
Current Higher values are preferred Current assets/Current liabilities
Quick Higher values are preferred Current assets−Inventories/Current liabilities
Inventory turnover Higher values are preferred Sales/Inventories
DSO Lower values are preferred Receivable/Daily sales
Fixed assets turnover Higher values are preferred Sales/Net fixed assets
Total assets turnover Higher values are preferred Sales/Total assets
Debt management ratios
Debt ratio Lower values are preferred Total liabilities/Total assets
TIE Lower values are preferred EBIT/Interest charges
EBITDA coverage Lower values are preferred EBITDA+LP/Interest+PP +LP
Profitability ratios
Profit margin on sales Higher values are proffered NIS/Sales
BEP Higher values are preferred EBIT/Total assets
ROA Higher values are preferred NIS/Total assets



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ROE Higher values are preferred NIS/Common equity
Market value ratios
P/E High value indicates overpricing Price per share/Earning per share
Price/cash flow High value indicates overpricing Price per share/Cashflowpershare
Market/book High value implies high expectations Market price per share/Book value per share

They analyze the algorithms with k = 10 before and 
after transaction costs on the stocks of  the S&P 500 for 
the period from Dec 1989 to Oct 2015 using various 
performance measures. Table 4 shows the comparative 
results after transaction costs obtained by (Krauss et al., 
2017).
In a similar design, Fisher and Krauss (Fischer & 

Krauss, 2018) utilized the LSTM type of  deep learning 
networks to compare the results of  LSTM with a set 
of  benchmarked memoryless models such as RF, deep 
neural network (DNN), and logistic regression (LOG). 
Table 5 shows the comparative results after transaction 
costs obtained by (Fischer & Krauss, 2018), where LSTM 
performs superior to the benchmarks models.

Table 4: Comparison of results obtained by Krauss et al. (Krauss et al., 2017)
Before transaction costs After transaction costs
DNN GBT RAF ENS DNN GBT RAF ENS

Daily mean return (long) 0.0033 0.0037 0.0043 0.0045 0.0013 0.0017 0.0023 0.0025
Daily mean return (short) -0.0011 -0.0013 -0.0013 -0.0015 -0.0001 -0.0003 -0.0003 -0.0005
Daily mean return 0.0022 0.0025 0.003 0.0029 0.0012 0.0015 0.002 0.0019
Standard dev. 0.0269 0.0217 0.0208 0.0239 0.0269 0.0217 0.0208 0.0239

Table 5: Comparison of results obtained by Fisher and Krauss (Fischer & Krauss, 2018)
Before transaction costs After transaction costs
LSTM RAF DNN LOG LSTM RAF DNN LOG

Daily mean return (long) 0.0029 0.003 0.0022 0.0021 0.0019 0.002 0.0012 0.0011
Daily mean return (short) 0.0017 0.0012 0.001 0.0005 0.0007 0.0002 0.0 -0.0005
Daily mean return 0.0046 0.0043 0.0032 0.0026 0.0007 0.0002 0.0012 -0.0005
Standard dev. 0.0209 0.0215 0.0262 0.0269 0.0209 0.0215 0.0262 0.0269
Max. drawdown on daily basis 0.466 0.3187 0.5594 0.5595 0.5233 0.7334 0.9162 0.9884
Annualized mean return 2.0127 1.7749 1.061 0.7721 0.8229 0.6787 0.246 0.0711
Annulaized sharpe ratio 10.0224 9.5594 4.2029 2.9614 2.3365 1.8657 0.5159 0.1024

The value strategy fundamentally values the currency, 
long undervalued, and short overvalued, assuming they 
will revert to their fundamental value. In addition, they 
developed a compounded strategy that composites the 
three strategies as a single trading strategy. The portfolio 
is balanced monthly, and the results are compared to a 
benchmark of  global bonds and stocks. The results 
exhibit a superior performance of  the composite strategy, 
as shown in table 6.
The model of  Fernandez et al. (Fernandez-Perez et 

al., 2018) takes advantage of  the skewness anomaly in 
the commodities’ future returns. The algorithm has a 
long commodity future of  negative skew and a short 
commodity future of  positive skew. Zaremba et al. 
(Zaremba et al., 2019) analyze 15 commodity factors from 
1986 to 2017 to find if  the momentum effect exists.
The results confirm the assumption that buying a 
commodity with the highest past returns or selling a 
commodity with the lowest past returns supplement a 
significant profit.

Table 6: Comparison of results obtained by Kroencke (Kroencke et al., 2014)
Mean returns standard deviation Sharpe ratio

Global bonds 4.21 5.38 0.78
Global Stocks 5.81 14.31 0.41
FX carry trade 6.18 7.5 0.82
FX momentum 5.34 7.68 0.7
FX value 4.18 6.69 0.62
FX composite 8.23 7.22 1.14



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Table 7: Research considerations
Objective
Prediction (Dash & Dash, 2016; Henrique et al., 2018; Gao & Chai, 2018; Yu & Yan, 2020; Al-

Sulaiman, 2022; Nair et al., 2010; Chen & Hao, 2017; Weng et al., 2018; Lee et al., 2019; 
Madan et al., 2015; Deng et al., 2016; Almahdi & Yang, 2017; Al-Sulaiman, 2022; Jang 
& Lee, 2017; McNally et al., 2018; Alessandretti et al., 2018; Colianni et al., 2015; Al-
Sulaiman & Al-Matouq, 2021; de Almeida et al., 2018)

Profit from stylized 
anomaly

(Maewal & Bock, 2011; Krauss et al., 2017; Fischer & Krauss, 2018; Gatev et al., 2006; 
Chen et al., 2019; Rad et al., 2016; Bernile & Lyandres, 2011; Dimic et al., 2018; Geyer-
Klingeberg et al., 2018; Berkowitz & Depken, 2018; Lev & Nissim, 2006; Cohen et al., 
2010; Frazzini & Pedersen, 2014; Kishore et al., 2008; Liu et al., 2003; Sadka, 2006; 
Garfinkel & Sokobin, 2006; Chordia & Shivakumar, 2006; Frazzini & Lamont, 2007; 
Faber, 2007; Faber, 2010; Lisauskas, 2011; Maze, 2012; Kroencke et al., 2014; Cenedese 
et al., 2012; Barroso & Santa-Clara, 2015; Baker & Haugen, 2012; Fernandez-Perez et 
al., 2018; Zaremba et al., 2019)

Financial instruments
Stocks Maewal & Bock, 2011; Dash & Dash, 2016; Henrique et al., 2018; Deng et al., 2016; 

Deng et al., 2016; Gao & Chai, 2018; Gatev et al., 2006; Chen et al., 2019; Rad et 
al., 2016; Yu & Yan, 2020; Bernile & Lyandres, 2011; Dimic et al., 2018; Berkowitz 
& Depken, 2018; Geyer-Klingeberg et al., 2018; Lev & Nissim, 2006; Al-Sulaiman, 
2022; Cohen et al., 2010; Frazzini & Pedersen, 2014; Kishore et al., 2008; Liu et al., 
2003; Sadka, 2006; Garfinkel & Sokobin, 2006; Chordia & Shivakumar, 2006; Nair 
et al., 2010; Chen & Hao, 2017; Weng et al., 2018; Lee et al., 2019; Frazzini & Lamont, 
2007; Faber, 2007; Lisauskas, 2011; Al-Sulaiman & Al-Matouq, 2021)

Bonds (Maewal & Bock, 2011; Faber, 2007)
FX (Kroencke et al., 2014; Cenedese et al., 2012; Barroso & Santa-Clara, 2015; de Almeida 

et al., 2018)
Options (Maze, 2012; Baker & Haugen, 2012)
Commodity futures (Fernandez-Perez et al., 2018; Zaremba et al., 2019)
Bitcoin (Madan et al., 2015; Jang & Lee, 2017; McNally et al., 2018; Alessandretti et al., 2018; 

Colianni et al., 2015)
Resolution
Daily (Dash & Dash, 2016; Henrique et al., 2018; Krauss et al., 2017; Deng et al., 2016; Gao & 

Chai, 2018; Gatev et al., 2006; Chen et al., 2019; Rad et al., 2016; Yu & Yan, 2020; Bernile 
& Lyandres, 2011; Dimic et al., 2018; Berkowitz & Depken, 2018; Geyer-Klingeberg 
et al., 2018; Al-Sulaiman, 2022; Liu et al., 2003; Sadka, 2006; Garfinkel & Sokobin, 
2006; Chordia & Shivakumar, 2006; Nair et al., 2010; Chen & Hao, 2017; Weng et al., 
2018; Lee et al., 2019; Deng et al., 2016; Almahdi & Yang, 2017; Barroso & Santa-Clara, 
2015; Madan et al., 2015; Jang & Lee, 2017; McNally et al., 2018; Alessandretti et al., 
2018; Colianni et al., 2015; Al-Sulaiman & Al-Matouq, 2021; de Almeida et al., 2018)

Monthly (Frazzini & Pedersen, 2014; Faber, 2007; Engle, 1982; Lisauskas, 2011; Maze, 2012; 
Kroencke et al., 2014; Cenedese et al., 2012; Baker & Haugen, 2012; Fernandez-Perez 
et al., 2018; Zaremba et al., 2019)

Quarterly (Maewal & Bock, 2011; Cohen et al., 2010; Frazzini & Pedersen, 2014; Kishore et al., 2008)
Yearly (Lev & Nissim, 2006)
Methods
Pair trading and 
statistical arbitrage

(Gatev et al., 2006; Chen et al., 2019; Rad et al., 2016; Bernile & Lyandres, 2011; Dimic 
et al., 2018; Berkowitz & Depken, 2018; Geyer-Klingeberg et al., 2018; Fernandez-
Perez et al., 2018)

Fundamental Methods (Lev & Nissim, 2006; Cohen et al., 2010; Frazzini & Pedersen, 2014)
Momentum (Kishore et al., 2008; Liu et al., 2003; Sadka, 2006; Garfinkel & Sokobin, 2006; Chordia 

& Shivakumar, 2006; Frazzini & Pedersen, 2014; Zaremba et al., 2019)



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AI-ML methods
(LSTM, ANNT, RL, CF 
and GA)

(Dash & Dash, 2016; Krauss et al., 2017; Deng et al., 2016; Gao & Chai, 2018; Yu & 
Yan, 2020; Al-Sulaiman, 2022; Lee et al., 2019; Deng et al., 2016; Almahdi & Yang, 
2017; Jang & Lee, 2017; Al-Sulaiman & Al-Matouq, 2021; de Almeida et al., 2018)

AI-ML methods
(SVM and SVR and 
logistic regression)

(Henrique et al., 2018; Chen & Hao, 2017; Lee et al., 2019; Madan et al., 2015; McNally 
et al., 2018; Alessandretti et al., 2018; Colianni et al., 2015; de Almeida et al., 2018)

AI-ML methods
(Decision tree, K-nearest, 
and random forest)

(Nair et al., 2010; Chen & Hao, 2017; Lee et al., 2019; Weng et al., 2018; Madan et al., 
2015)

The cycle of  algorithmic trading starts with an investment 
idea followed by quantitative research and model 
development. After that, the model’s implementation 
using a programming language is needed, and then 
perform a back testing to measure the algorithm’s 
performance in the past. Once we ensure the model 
validity, we test the algorithm’s performance on the live 
stream using paper trading. Finally, as we are asserting 

the algorithm’s quality, we may deploy it for live trading 
and monitor its performance. Figure 1 shows the cycle of  
algorithmic trading.
Figure 2 shows DEMA’s extracted signals with nf  = 20 
for the fast EMA and ns = 100 for the slow EMA on 
the Google Inc. (GOOG) historical prices for the period 
from 2001 to 2018.
Relative strength index (RSI) is a popular momentum 

Figure 1: Cycle of algorithmic trading

Figure 2: Signal of double exponential moving average on google from 2001- 2018



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indicator proposed by Welles Wilder (Wilder, 1978). 
The RSI compares upward and downward movements 
over a specific period n and returns a range of  oscillator 
values between 0 and 100. Often, the value below 30 is 
an indication of  oversold activities, and the value over 
70 indicates overbought actions. A popular value of  
parameter n is n = 14, and the resolution is based on 
the trading frequency. In addition, n values of  9 and 25 
are predominantly in use. The RSI value is determined as 
follows:

Where Ut and Dt are the upward and downward changes, 
respectively, and they following:
Ut={pt - p(t-1) ifpt - p(t-1) > 0 0otherwise }
Dt={|pt - p(t-1) |ifpt - p(t-1) < 0 0otherwise }
The financial statements contain considerable information 
about company performance divided into the balance 
sheet, income statement, and cash flow statement. The 
balance sheet provides an overlook of  the assets, liabilities, 
and equity of  the company. The income statement 
shows the net sales, operating costs, interest (I), taxes (T), 
depreciation (D) and amor- tization (A), and the earnings 
(E) before and after these costs (EBITDA, EBIT, EBT, 
net income). Finally, the cash flow statement measures the

Figure 3: MACD for Microsoft from 2018-2019

Figure 4: RSI for Microsoft for 2020



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The liquidity ratio measures the firm’s ability to meet 
its obligations. The asset management ratio identifies 
the efficiency of  managing the in- vestment on assets 
compared to sales revenue. The debt management ratios 
aim to measure the firm’s exposure to financial leverage. 
Profitability ratios measure the effects of  the other class 

ratio on the operating income. Table 3 shows common 
ratios in each class5.
Figure 6 shows the co-movement of  Home Depo. (HD) 
and Wall-Mart (WMT) along with the normalized spread 
with trading signal given ∆ = 1. For more on statistical 
arbitrage pairs trading strategies, see (Krauss, 2017).

Figure 5: Return on equity from 2005 to 2020 for AAPL and MSFT

Figure 6: Signal for sample pair trading



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Am. J. Financ. Technol. Innov. 2(1) 1-14, 2024

Figure 7 shows the profits of  the options strategies over 
a variety of  possible prices at maturity.
TIn this paper, we do not aim to define, classify, 
differentiate or illustrate the methods in these areas, but 
alternatively, we focus on their application in trading and 
explore some of  the standard methods used to develop 
algorithms for trading. However, machine learning 
and data mining algorithms aim to solve estimations, 

predictions, classifications, associations, and clustering 
problems.  The problems can be classified into supervised 
learning, unsupervised learning, semi-supervised learn- 
ing, and reinforcement learning. This paper discusses the 
decision tree methods, the long short-term memory neural 
network, and reinforcement Q-learning. Nevertheless, 
figure 8 shows a social network of  the universal methods 
of  machine learning used in algorithmic tradings.

Figure 7: Possible Profits of various option strategies

Figure 8: Social network of machine learning methods



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Am. J. Financ. Technol. Innov. 2(1) 1-14, 2024

Figure 9 shows an example of  simplified decision tree 
rules. 
The rules are achieved through constructing a path 
for the tree, starting the root node to leaves through 
the branches of  decision nodes. The classification and 
regression algorithm (CART) is a classical decision tree 
algorithm. CART partitions the tree in a binary manner 
by splitting the tree into two branches at each decision 

node. The splitting is based on the maximum value of  
the optimality measure function among all possible split 
candidates.
Similar arguments apply to all the nodes in all hidden layers 
and the output layer. For example, figure 10 illustrates the 
forward path of  a simplified LSTM consist of  three input 
nodes, three nodes in the first hidden layer, two nodes in 
the second hidden layer, and one output node.

Figure 9: Example of decision tree rules

Figure 10: Illustration of LSTM neural network

CONCLUSION
In conclusion, the dominance of  computer-based 
auto trading, representing 80% of  Wall Street activity, 
is poised to extend to individual investors. This 

research comprehensively reviews algorithmic trading, 
encompassing definitions, project life cycles, platforms, 
languages, strategy classification, performance measures, 
and development methodologies. Primarily focusing on 



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lower-frequency trading, it categorizes reviewed papers 
by objectives, financial instruments, trading periods, and 
methodologies. The findings highlight a focus on shorter 
investment periods for popular assets like stocks, with 
limited attention to derivatives due to complexity. Serving 
as a valuable resource, this paper also charts a course for 
future research in algorithmic trading.

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