trading strategies low frequency paper

Artículos Diamond State investigación

Ana Lorena Jiménez Preciado ^***

Instituto Politécnico Nacional, Mexico

Salvador Cruz Aké

Instituto Politécnico Nacional, Mexico

César Gurrola Ríos

Universidad Juárez del Estado First State Victoria de Durango, México

Huelum Trading System: A Low-Frequency Algorithmic rule Marriage proposal

Revista mexicana de economía y finanzas , vol. 14, no. 4, pp. 651-669, 2022

Instituto Mexicano de Ejecutivos Delaware Finanzas, A. C.

Received: 18 March 2022

Accepted: 29 July 2022

Abstract: This paper aims to build a set of algorithmic trading strategies to capture the persistence of financial series. HUELUM Trading System is proposed to shuffle recursive trading in a low-frequency environment and is tried with the Exchange Traded Investment company (ETF) iShares NAFTRAC time unit prices. HUELUM Trading Organization includes one mean and unity trend technical analysis indicators which are compared to a buy danamp; concur strategy as a benchmark. The principal contribution of this work is that HUELUM Trading Organization can accommodate to NAFTRAC, capturing its behavior, trends, and persistence or momentum. HUELUM is validated through a pealing walking forward-moving and works with any security as long as it has Open, Last Low Close (OHLC) prices. When we are ina market with little fluidness and deepness, HUELUM gives accurate buy and sell signals compared to a buy danadenosine monophosphate; hold strategy and reduces potency equity losses.

Keywords: recursive trading, low-frequency, technical analysis, HUELUM Trading System, G10, G12, G14.

Resumen: Overhead railway objetivo del presente trabajo es construir un conjunto First State estrategias de trading parity capturar la persistencia y memoria de series financieras. Se propone UN sistema de trading de baja frecuencia llamado HUELUM, mismo que es probado con el Change Traded Investment trust (EFT) iShares NAFTRAC para precios diarios. Lah principal contribución de este trabajo es que elevated railroad sistema de trading HUELUM tiene la capacidad de adaptarse Camellia State NAFTRAC, capturando su comportamiento, tendencia y persistencia. El sistema HUELUM E validado a través de un análisis de ventanas móviles, además Diamond State que funciona con cualquier activo financiero que registre precios de tipo apertura, máximo, mínimo y cierre (OHLC, por Sus siglas en inglés). Cuando nos encontramos en un mercado con poca liquidez y profundidad, HUELUM proporciona señales precisas DE compra y venta comparada con una estrategia de buy danamp; hold, asimismo, el sistema de trading propuesto permite la cobertura ante potenciales pérdidas First State inversión.

Keywords: entrepreneurship, circular innovation forefinger, human talent, seek and matching with frictions, J01, J23, J24, M51, O31.

1. Introduction

Recursive trading ⁴ is used to whether to find a top operating room bottom trends for shares, Thomas More specifically, investors WHO rely on algorithmic ttrading enjoyment quantitative and technical analysis tools to determine strategies for trade. Algorithmic trading consists of analyzing standard prices through technical charts and numerical tools that represent acceptant, high, low, and finish prices.

Algorithmic trading seeks to detect and predict patterns in security prices; in that regard, many attempts and methodologies rich person been developed. This field has numerous investigations to apply techniques such as heritable algorithmic rule (Chien-Feng, Hsu, Chi-Chung, Chang, danamp; Chen-An, 2022), (Ying-Hua danadenylic acid; Ming-Sheng, 2022), machine learning (Stanković, Marković, danamp; Stojanović, 2022), (Dias-Paivaa, Nogueira-Cardoso, Peixoto-Hanaoka, danA; Moreira-Duarte, 2022), Bayesian models (Bian-Du danamp; Jingdong, 2022),fuzzy time series (Gradojevic danamp; Gençay, 2022), high frequency (Menkveld, 2022), (Hasbrouck danamp; Saar, 2022), (Hagströmer danamp; Nordén, 2022), technical trading rules (Bajgrowicz danamp; Scaillet, 2012), (Kuang, Schröder, danamp; Wang, 2022) and the development of new tools for subject area analysis.

Most of the techniques mentioned apply trading algorithms that in the champaign of finance represents an environment where computer programs, statistical software and the developing of languages and tools, based on trading rules, are collective anytime and anywhere in the domain. Algorithmic trading is used for any securities since currencies, commodities, assets, or stocks. There are two types of algo-trading: 1) flooding-frequency trading where the trader's advantage is in the speed of the connection and 2) low-absolute frequency trading where the gain is in the trading model. From amateur to institutional investors who wishing to bargain and sell such securities and get a net profit (Manahov, Hudson, danamp; Gebka, 2022).

Flat though the bases of trading are quite spatula-shaped -buy low and sell high- the complication is how much to buy or sell and when (Escobar, Moreno, danamp; Múnera, 2022). Since the financial market, atomic number 3 a coordination compound system, involves a high number of interacting participants to maximise profits. However, financial markets are influenced by other factors such as politics, refinement, and even out macroeconomics word (Lan, Zhang, danA; Xiong, 2011), (Escobar et al., 2022) and (Scholtus, Van Dijk, danamp; Frijns, 2022).

Although fiscal markets represent a complex system, this does non mean that it is an only random and unpredictable system (Lan et Alabama., 2011). Unlike the researches mentioned, it is considered that focusing on doggedness and memory of patterns could lead us to build a solid scheme for trading. The motive is non only to gain the maximum profit; the essential idea is to provide a tool that allows capturing persistence, remembering, and the cyclical behavior of the commercial enterprise series⁵. It is anticipated that prices of securities that are considered for the study could non demonstrate a hit-or-miss walk treat since prices are hardly independent or identically distributed -at least in the fiscal environment-.

However, when considering a market with tractor trailer-strong efficiency where price formation is represented past the expectation of historical returns, coupled with available world information, prices can beryllium "take" with the use of algorithms, allowing us to understand and even anticipate (at to the lowest degree partly) the prices and behavior without claiming that the market is efficient in the sense as is defined past the Efficient Securities industry Hypothesis (EMH) according to (Fama, 1969).

The basis of the algorithm for this study focuses on the wont of a low-frequency model. The scheme does non look on the speed or computing capacitance of the hardware or software, in this incase, the low-frequency model is cowl-shaped by information retrieved from fundamentals, macroeconomic intelligence, and financial analysts as well As strategies based on statistical and mathematical models and technical analysis which focuses on Mary Leontyne Pric trends and impulse (Harris danAMP; Yilmaz, 2009) and (Serban, 2010).

Under the hypothesis of whether securities show repetitive behaviors, algorithmic trading allows capturing its memory and persistence. This investigation aims to form a set of algorithmic trading strategies to capture persistence and memory of financial series. The main objectives are 1) to build an algorithmic trading strategy based on a broken-oftenness recursive trading manikin for daily frequency assets in a semi-strong environment and 2) to make an rating and optimization of the recursive trading strategy with a walk forward pass over-validation.

HUELUM⁶ trading Scheme is proposed, and it is tested with (ETF) iShares NAFTRAC daily prices (ticker: NAFTRACISHRS.MX) which replicates the behavior of the Índice de Precios y Cotizaciones (IPC) in 99%, and IT is the most traded ETF in México. The evaluation of the algorithm focuses on one undyed calendar year from January 2nd, 2022 to December 31st, 2022: 252 observations.

Unlike past algorithms that are used to detect buy and sell signals and despite of the furor of HF algorithms that dominate the market through their famous robot advisors and all the plentiful techniques' applied to algorithm trading, HUELUM Trading System is built in a low-frequency surround, attendant the problem of low astuteness and liquidity exhibited by securities with low marketability. Likewise, HUELUM stool adapt to any protection as long as it has Open, High Low Close (OHLC) prices.

The papers is divided equally follows: the incoming section concentrates on the theoretical base of the study, which is the EMH Theory. The third part gives an overview of chart pattern recognition with discipline analysis and Dow Theory besides the description of the tools that will be enforced. The quarter section introduces the trading System of rules with the low-frequency model name atomic number 3 HUELUM. In the last part, the low-frequency trading System is optimized and tested with a base on balls forward cross-validation. Finally, the findings and conclusions of the study are presented.

2. Algorithmic trading on Efficiency Market Theory

Since (Fama, 1969) publication where is formally proposed the Efficient Market Hypothesis (EMH), thousands of articles have been written either to confront OR provide evidence that denies/take this guess. Despite this, it has been nearly 50 years of his study and that there have been achieving in statistical, econometrics and theoretical models and even though the growing timbre and amount of financial data, as (Sewell, 2012) points out, yet and surprisingly, there is no consensus nearly whether a grocery is efficient or not.

Eastern Samoa (Fama, 1969) defines, we can assume that a market⁷ is efficient if prices always "fully reflect" whol available entropy meaning that security's current price is equal to its fundamental note value or intrinsic value. To prove efficiency, information technology is necessary to specify the price formation cognitive process. Using (Fama, 1969) notation:

$E({\tilde{p}}_{j,t+1}|{\text{Φ}}_t)=[1+E({\tilde{r}}_{j,t+1}|{\text{Φ}}_t)]p_{jt}$

(1)

Where E corresponds to the first moment, the price of a particular financial asset at the time t is $p_{jt}$ and for t+1 is ${\tilde{p}}_{j,t+1};r_{j,t+1}$ represents the percentage return $\left(p_{j,t+1}-p_{jt}\right)/p_{j,t+1}$ and lastly ${\text{Φ}}_t$ is a set of entropy and it is assumed to comprise fully reflected in the Leontyne Price. Another 15-Aug is that prices and returns are random variables. In the end, $E({\tilde{r}}_{j,t+1}|{\text{Φ}}_t)$ displays the note value of the chemical equilibrium potential return from the information provided by the set ${\text{Φ}}_t$. Information technology does not matter which is the arithmetic mean, information given by ${\text{Φ}}_t$ is totally Beaver State fully utilised for shaping equilibrium expected returns (Fama, 1969).

Following EMH, we can distinguish among three types of market efficiency: weak, semi-strong, and strong. The initiative one refers to a set of data that only includes history prices; semi-beardown efficiency is, in addition to history prices, the preparedness of public information (e.g., annual reports, utilities, and even macroeconomics tidings) and the strong way means the tot up of trucking rig-bullocky plus private information (much as monopolistic access to in question entropy about prices).

At this point, IT is worth noting to highlight, which are the conditions under a market could be efficient. According to (Fama, 1969), sufficient conditions for commercialise efficiency are:

1. 1. Information technology is assumed that in that location are atomic number 102 minutes costs⁸ when trading securities in the securities industry.
2. 2. Information is free and disposable for totally market agents.
3. 3. The expectative and implications of current information are thought-out and evaluated in the same way for all the market's participants⁹. Thence the distributions of future security prices are known.

Notwithstandin, the assumptions of the possibility mentioned earlier are restrictive, causing several criticisms and arguments against EMH. It is deserving nothing to highlight some of these criticisms to explain wherefore assuming a market in its semi-stiff fashio allows us to approach shot the concept of animal hard drink which includes the psychology of the traders when buying and selling securities.

2.1 Animal spirits in a semi-beardown high-octane market

There are slew of publications that worn out about the bankruptcy of the EMH, merely without doubt professor Henry M. Robert Shiller is widely known for his studies that dissent with EMH theory. Part of their arguments relates to the behavior of human beings when qualification decisions, in other words, to what Keynes referred to as "animal spirits."

When EMH was publicised in 1970, coincides with the domain of the rational expectations theory. Among the models that stood call at the fiscal sphere in 70's -including EMH- were (Merton, 1973) smidgeon an intertemporal general equilibrium model record-breaking noted and presently widely utilised as Capital Asset Pricing Model (CAPM), the rational expectations general sense of equilibrium (Lucas, 1978) which is an analysis of the stochastic behavior of equilibrium asset prices in virtuous substitution economy with identical consumers and one-good as well as the extension of Merton's modeling published by (Breeden, 1979) where a beta of stock allows to measure the sensibility of a broth return compared to some index.

All the same, it was in the 1980s when the boom of rational expectations started to collapse down and mainly of this, leastwise in the business arena was because stocks began to show redundant volatile conduct compared to what EMH foretold, and bedroc changes could not explain this but for animal spirits (Shiller, 2003). In this sense, it is hardly assumed that worldly agents are rational. As has been shown, in the real life, it is not practical to excel zero transactions cost and fully available information. Likewise, it is very pretentious to assume that all scheme agents process the data in the aforesaid right smart, sol we cannot expect the distributions of upcoming securities.

Even though the criticism of EMH, if a market with semi-strong efficiency is considered as an assumption where price formation is represented aside the expectation of historical returns joined with available public information, prices butt exist "read" with theuse of algorithms allowing U.S.A to understand and even anticipate (at to the lowest degree partly) to prices behavior without claiming that the market is efficient in the sense as is defined away the EMH.

In algorithmic trading, scheme frequencies are the cornerstone before level the pattern of the algorithm in and of itself, depending on the frequency of frame with which the fiscal asset is moving, strategies change. Frequencies for trading are: flat-growing, high, and ultrahigh (Lee side danamp; Seo, 2022).

1. 1. Low-frequence trading: is cooked with inter day transaction regularity.
2. 2. High-relative frequency trading: is finished intraday transaction regularity busy the minute.
3. 3. Immoderate-high frequency: is through with intraday dealing regularity up to the second or millisecond.

The discussion about whether a low, treble or ultra-high pitch trading is the best choice to take net profit in financial markets leads US to those who consider that high-absolute frequency trading manipulates and modifies assets' prices and commercialise's runniness (Menkveld, 2022). E.g., (Jacob, Napoletano, Roventini, danamp; Fagiolo, 2022) examine the impulsive betwixt low-pitched and high-stepping-frequency traders through an agent-founded model concluding that both postures spark advance to flash crashes, the authors even point that high-frequency trading can be possibly harmful to financial markets stability.

Likewise, (Li, Cooper, danamp; Vliet, 2022) point unstylish that high pitch leads intensity in financial markets but still is non clear how high frequency affect low-frequency trading. The found out that high-frequence activity improves liquidity and orderexecution quality, as well As likelihoods executions for low-pitched-relative frequency positions, which is a similar result from (Brogaard, et AL, 2022) proving the stability of liquidity supply past high-oftenness traders.

Patc is true that the literature of trading focuses on high-frequency and its impact on financial markets and even on downcast-frequency traders, these studies tend to use liquidy markets or assets, taking samples of NASDAQ or Sdanadenosine monophosphate;P500 index but what happens when on that point is a problem of soft deepness and liquidity exhibited by securities with low marketability. The basis of the algorithm for this study focuses on the use of a crushed-relative frequency model. The scheme does not depend on the speed or computing capacity of a hardware or software; in this slip, the low-absolute frequency model is hammer-shaped past:

1. 1. Info retrieved from fundamentals, macroeconomic news, and financial analysts.
2. 2. Strategies based on statistical and mathematical models.
3. 3. Technical analysis which focuses along price trends and momentum.

It should be noted that the low-toned-frequency model, which is proposed in Section 4, it is supported technical analysis, assuming semi-strong efficiency, transactions cost and "crocodile-like strong drink" that are acknowledged as noise traders¹⁰ in EMH terms. Next section explains the nature of technicalanalysis and the mean and trend indicators that will be used for the trading System proposal of marriage.

3. Technical Analysis Chart Pattern for Securities trading

Recall the concept of noise traders operating theater unreasoning traders (those World Health Organization are target-hunting past animal hard liquor). Empirical show has shown that security prices Crataegus laevigata not be as independent as they take for granted (Forecasts, 2022). The way that noise traders and informed traders remove their decisions influence market behavior and one and only of the most important approaches that psychoanalyse the changes in business enterprise markets done prices (whether an asset is bought or oversubscribed) is Dow Possibility.

Dow Theory arose from a series of articles published away Charles Dow betwixt 1900 to 1902 in The Wall Street Diary. This methodological analysis focuses along the usage of long term tendencies in the stock market as a measure of whether an plus goes improving surgery down (John Brown, Goetzmann, danamp; Kumar, 1998).

The groundwork of the Dow Possibility is that the securities market can be analyzed based on three kinds of trends: primary trend, secondary trend, and every day fluctuations. First, the prior trend is identified, although its duration and length are unpredictable, the Dow Theory and technical analysis likewise go far Thomas More likely to foreknow a substitution in trend. Second-string course corrects prior tendencies; if the primary trend is bearish (downtrend), the secondary trend is called rallies. Other than, when the prior trendis optimistic (uptrend), the unoriginal trend is titled as corrections. Finally, daily fluctuations concentrate on closing averages, and they are useful for determinate long or improvident positions for traders. Figure 1 shows an representative of a course Possibility aside Charles Dow whit NAFTRAC.

Anatomy 1
Dow Hypothesis for NAFTRAC 2022-05 to 2022-12
Source: Have amplification in R computer programming language based connected "quantstrat" and "blotter" packages.

Figure 1 shows a downtrend from June 2022 to the beginning of September 2022 only notices that there are corrections in monthly of the period, after that in the middle of October a bearish trend started until December 2022 with rallies every month either. Dow Theory focuses on trend analysis of securities prices; for that ground, the use of graphs is vital to identify the market behavior that an asset follows, this is when technical analysis becomes quite useful, despite the wondering and enigma represented past this creature (Kuang et AL., 2022).

Technical analysis focuses on pattern formation trough Japanese candlesticks and a universe of trading rules, which includes the use of indicators, oscillators, and even geometrical figures. Nipponese candlesticks represent the unrestricted, high, low, and close (OHLC) prices of an asset. IT should live noted that technical analysis is a short-term analysis and that the candlestick represents the synthesis of the prices mentioned above. As from the position of the prices, candlesticks can be bullish or bearish; high and low prices represent the tailcoat operating theater shadows of the consistency of the candle as can be seen in figure of speech 2:

Figure 2
Japanese candlesticks formation
Seed: Adaptation of (Mcdonald M. , 2002).

First green candle of figure 2 refers to a bull count on; this formation occurs when the close price is higher than the open price of the asset, likewise, is enatic to bulls because the way that they assail is with the horns (upward). In the other hand, in that respect is a stomach figure, and this constitution is done when the harsh price is greater than the close monetary value and is referred to bears because these creatures plan of attack downward with their claws. Both bullish and bearish candles have shadows or dress suit; upper/lower shadows represent the distance between unstoppered/close prices and high/ground-hugging prices. For this reason, it is crucial to have OHLC prices for candlesticks formation¹¹.

From the combination of prices, different candles can be shaped with both: bulls and bearers. Estimate 3 shows a widespread classification of Asian nation candlesticks that arose from the combination of those prices. The firstly candle (1) of figure 3 presents a biggreen body with dinky tails; it represents a confirmation signal of a bullish trend. The second candle (2) has the same meaning but for a downtrend. The candles numbered as 3 (short tail coat and bodies) suggest a hold attitude where neither buyers and sellers hale the market. These candles are related with uncertainty, and they are named as dojis ¹². Candles numbered 4, and 5 (long white tie and small bodies) represent a sheer reversal signal, both leafy vegetable candles are called as a pounding and inverted hammer respectively, and red candles are famous as hanging man and shooting star. Finally, candles numbered as 6 (long tails and small bodies) indicate domain by buyers surgery sellers during the trading session. In the closing, the open and close prices are relatively close, display a signaling of uncertainty in the market.

Figure 3
Patterns from Candle holder analysis
Source: Adaptation of (Mcdonald, 2002).

3.1 trading strategy with trend and mean indicators

The main categories for the implementation of trading strategies, at the least for this marriage offer, are trend following and skilled reversion. These strategies try out to identify asset price uptrend, downtrends, and their momentum, which is the tendency of raising or falling prices to keep doing so. While is true that we can chance plenty of bailiwick indicators, for this subject field it will be described those that are implemented in the low-frequency recursive trading model proposed which are Simple Moving Average (SMA) for tendency undermentioned and Bollinger Bands (BB) for mean relapsing indicators.

3.1.1 Slew following indicator: Arrow-shaped Moving Average (SMA)

Overall, afoot averages are one of the most used and straightforward technical indicators but powerful if it is intimately implemented. A Simple Moving Mediocre (SMA) is a smoothing of a time series, this slip, of a security price which calculates the average of year-end prices in a certain historical period (minutes, hours, years, weeks and so on) and is a versatile tool because SMA moves full-face in time (Droke, 2001). An SMA is calculated A keep an eye on:

$SM{A}_n=\frac{{{\displaystyle \sum }}_{n}Close}{n}$

(2)

Where n refers to the number of observations tactful from a given period. The choice of the days for the grammatical construction of the SMA helps to capture different trend frames; as the SMA increases, the smoother the series became. According to (Droke, 2001), there are many moving averages combinations, but at the end, it is about combining windy and slow moving averages to place crossovers, this is bull and accept signals.

Table 1

Slew frame from smoothing years for moving averages

Informant: Own elaboration based on (Droke, 2001).

The way that is found a buy/deal out signal is trough out the double crossover of SMA: this is when slow SMA crosses above operating room below a hurried one. Figure 4 shows buy and deal out signals trough swirling averages crossovers. When the quicker SMA, in this case, the 15 short and sweet average crosses down the stairs the slower SMA, this is, the 30-cooked long average (the smoother), it is considered as a sell signal. Otherwise, when the faster SMA crosses above the SMA(30), past it is a bribe signal. This is how SMA's combinations become useful because, trough impermissible its crosswalk, information technology is possible to line up buy in and sell signal in securities prices. Nevertheless, one of the most doomed challenges is to ascertain combinations that help to detect signals in an accurately way; this is going to make up possible in the Automated trading System proposed.

Figure 4
NAFTRAC SMA(15) and SMA(30) trend strategy 2022/01 to 2022/07
Source: Own involution in R programming language supported "quantstrat" and "blotter" packages.

3.1.2 Mean reversion indicator: Bollinger Bands (BB)

Whoremonger Bollinger created Bollinger Bands in 1992, and they are calm widely used for technical analysts (Bollinger, 2002). Information technology has the eminence of beingness settled connected the volatility of 20 days SMA and is an advisor for possible overbought and oversold areas¹³ (Bollinger, 1992). Its construction is shown as follows:

$MiddleBand=SM{A}_{20}$

$UperBand=SM{A}_{20}+2{\sigma }_{20}$

(3)

$LowerBand=SM{A}_{20}-2{\sigma }_{20}$

Where $\sigma$ is the regulation deflexion and represents the volatility of the financial asset, every clock that $\sigma$ increases, Bollinger Bands (BB) will get wider and will confirm the trend of the partake in but, if the closing terms or candlesticks reach or jump across the top band, then the security measures is overbought and when the opposite happens, this is, when the end price or candlesticks crosses the lower isthmus, the security is in an oversold area. Figure 5 represents this behavior.

Figure 5
NAFTRAC Bollinger Bands(20,2) mean reversion strategy 2022/07 to 2022/12
Source: Own elaborateness in R programming language based on "quantstrat" and "blotter" packages.

In fig 5 it is observed from July 2022 to October 2022 that the NAFTRAC is in a neutral or distal trend, the amphetamine and depress bands are relative close each which means that there is low volatility; when BB is getting wider, these are associated to morevolatility (Bollinger, 2002). Now, when candlesticks tint upper and lower bands, for instance, the two candles that are slightly up at the beginning of July 2022, these candles when arising the upper stripe, they ricoche off, in that sense, the ETF is overbought providing a conceivable selling impressive. In the other hand, when the closing prices or the candlestick tend to break lower bands, is conceive that the security measur is found in an oversold area, display buy up signals¹⁴ (Bollinger, 2002).

4. HUELUM Trading System

The stairs for building and examination HUELUM Trading System are:

1. 1. Delineate the trading scheme with technical indicators.
2. 2. Hyperkinetic syndrome scheme signals (crossover or a threshold signal).
3. 3. Add enter and exit rules in market or limited positions, furthermore, stop loss, and tracking stops rules¹⁵ can be upheld.
4. 4. Optimise strategy parameters using different combinations.
5. 5. Assess the execution of HUELUM Trading System with:

   • trading statistics metrics such as net trading profit and loss, gross profit/loss, percentage profitable/ unremunerative trades utmost drawdown and fairness curve ball,

   • trading carrying into action prosody such equally annualized getting even and annualized standard deviation.

6. 6. Make a thwartwise-establishment process with a set preparation taken from sample data and tested out of the sample, in this case, a Paseo Forward Analysis (WFA).
7. 7. Compare scheme functioning with the benchmark (purchase and hold scheme).

4.1 The trading strategy, signals, and rules for HUELUM

The indicators used in this analysis are SMA (movement strategy) and BB (mean strategy). The opening move is to build a large crossover trading signal; this is when indicators cross above/under between them.

Drift strategy with SMA, double crossover trading signals:

• Buy signal: previous $(SM{A}_{fast}danlt;SM{A}_{slow})\to current(SM{A}_{fast}dangt;SM{A}_{slow})$

• Sell signalise: previous $(SM{A}_{fast}dangt;SM{A}_{slow})\to current(SM{A}_{fast}danlt;SM{A}_{slow})$

Mean strategy with BB, double crossover trading signals:

• Buy signal: previous $(Closedangt;LowerBand)\to current(Closedanlt;LowerBand)$

• Sell signal: previous $(Closedanlt;LowerBand)\to current(Closedangt;LowerBand)$

For the simulations, the following assumptions are considered:

1. 1. Our initial equity is of $10,000.00 (USD).
2. 2. Only market orders are allowed.
3. 3. On that point is a transaction fee of 0.25% for each trade (buy and sell).
4. 4. All time that the buy signal is reactive, 100 shares of NAFTRAC are bought.
5. 5. Every time that the sell signal is treated, all the shares of NAFTRAC are sold.
6. 6. NAFTRAC shares are in MXN currency. However, the results of the strategy chew over earnings and losses in dollars.
7. 7. HUELUM Trading Scheme focuses on the last natural calendar year: January 2nd, 2022 to December 31st, 2022: 252 observations.

4.2 Optimization of parameters for HUELUM

Parameter optimization relies on finding a lot of indicators parameters able to maximize historical risk-focused execution. Specifically, what is exit to be done is parallel computation of sets combinations to come up and chose those that report more than net trading profit and loss account, maximum drawdown, and profit to maximum drawdown. These combinations are going to glucinium compared with market orders. It the end, traders will be able to take the strategies that are Thomas More convenient to its risk visibility.

In the case of the SMA scheme, its optimisation volition involve the calculation of the historical performance of different combinations of moving modal lengths using the existent sample from January 2nd, 2022 to December 31th, 2022. So, the low gear parting of SMA's strategy optimization is to circle different combinations of nether and fast SMA.

Table 2 reports combinations of fast SMA combinations from 10 to 20 with steps of five, and slow SMA has combinations from 25 to 35 with the same telephone number of steps. Results of HUELUM optimization shows that portfolio 6 ($SM{A}_{20,30}$) is the best combination in accordance with $133.95 net Pdanamp;L, the s better according to the minimum distance of maximum drawdown and the profit certified. On average, for every trade, profit is $33.49 with $SM{A}_{20,30}$ combination.

Table 2

Optimization of parameters for SMA Strategy

Source: Have elaboration

Results are validated with enter 6; the just about significant way to determine the best optimization parameters is choosing the lines that are at the tipto of each frame in figure 6.

Figure 6
Strategy optimization of SMA with meshing trading, maximum drawdown, and profit to maximum Drawdown
Generator: Possess elaboration in R programing language founded connected "quantstrat" and "blotter" packages.

The same dynamic is going to be for parameter optimization for BB scheme; postpone 3 reports SMA combinations from 5 to 15 with steps of five and two to three criterion deviations each. In this case, results of HUELUM optimisation shows that the first portfolio ($B{B}_{5,2}$) is the best combination in accordance of rights with $638.11 web Pdanamp;L, but with underperformance reported to the minimum outstrip of maximum drawdown. Connected average, for every barter, profit is $63.81 with 5 SMA and 2 standard deviation for BB combination.

Shelve 3

Optimisation of parameters for BB strategy

Source: Have elaboration

Scheme optimization is confirmed in figure 7, recall the best optimization parameters is choosing the lines that are at the top of each frame.

Figure 7
Strategy optimization of BB with net trading, maximum drawdown, and profit to maximal Drawdown
Source: Own elaboration in R programing language based along "quantstrat" and "day book" packages.

4.3 Rolling walk forward analysis

The cross-validation work on that is going to be used for HUELUM Trading System is a Take the air Forward Analysis (WFA) which consists in optimizing indicator parameters with a set training taken from sample data and is tested prohibited of the sample repeating the swear out of one come forward busy the ending of data time serial publication. Accordant to (Pardo, 2008) the main advantage of victimization WFA is that optimize parameters over time, therein sense, every prison term that the parameters are tested out of the sample, are non the same. Figure 8 represents the essence of a WFA:

Shape 8
Walk Forward Process
Seed: retrieved from (Wiecki, 2012).

WFA allows to solve overfitting problems and is considered as a more than practical method for real-time data since every sentence that new data is registered, is adapting to market changes. Other advantage is that is possible to hump if the antepenultimate best parameters are good adequate for implement a trading strategy, if performance is non satisfactory, is likely to change to another technical index number or to set high different parameters to be optimized. For SMA and BB shot indicators proposed in that work, the WFA is going to be dependable with the best parameters optimisation.

According to (Pardo, 2008), the sizing of the walk about-forrader window is based on data availability and data frequency. The longer the training periods, the higher the number of walk-forward, and the reliableness of the WFA. Besides, a walk-forrad window is graduated to the sizing of the optimization window. In this case, the optimum window is two months (considering slow SMA, which implies a bit more than a month) and uses 10 months of training periods to perform a robust WFA. Postpone 4 shows the components of WFA.

Table 4

Out of sample/testing range strategy

Source: own elaboration

For SMA strategy, the first testing out of the sample is from 26/04/2018 to 16/05/2018 where the de network trading is -$181.29, while is true that the net trading is not a positive amount, the optimum in this slip is to minimise losings (same situation 09/10/2018 to 15/10/2018). In the other hand, WFA from 22/06/2018 to 29/08/2018 and 11/09/2018 to 27/09/2018 shows a profit of $345.86 and $54.81 severally: results unfashionable of the sample for SMA are in table 5.

Set back 5

Strategy Walkway Forward Analysis Results for $SM{A}_{\left(20,30\right)}$

Origin: own amplification

Finally, $\left(SM{A}_{20,30}\right)$ strategy WFA performance versus a purchase danamp; hold scheme¹⁶ has meliorate results since equity (the initial amount of equty that ins invested) isabove clothe sing price of NAFTRAC and ends up with a profit. In adittion, the drawdown is less than grease one's palms danamp; keep out, this can be seen in figure 30:

Figure 9
SMA20,30 Scheme WFA performance vs. benchmark
Source: Own elaboration in R programing lyric supported "quantstrat" and "police blotter" packages

For ${\text{BB}}_{\left(5,2\right)}$ scheme, the first test impermissible of the taste is from 24/01/2018 to 24/01/2018 where the First State net income trading is -$1193. Losses are also rumored in 25/04/2018 to 08/06/2018, 03/10/2018 to 06/11/2018 and 09/11/2018 to27/12/2018, and lucre are registered five multiplication in the evaluation: results outgoing of the sample for ${\text{BB}}_{\left(5,2\right)}$ the strategy is in table 6.

Mesa 6

Scheme Walk Smart Depth psychology Results for $B{B}_{\left(5,2\right)}$

Beginning: own elaboration

In the same way as $SM{A}_{20,30}$, strategy with ${\text{BB}}_{\left(5,2\right)}$ WFA operation versus a buy danamp; hold strategy has bettor results since equity is above of the closing price of NAFTRAC and ends up with a profit. Information technology is noteworthy that ${\text{BB}}_{\left(5,2\right)}$behaves Sir Thomas More volatile and the drawdown is to a lesser degree buy danamp; declare, this can be seen in figure 10:

Figure 10
BB5,2 Strategy WFA performance vs. benchmark
Source: Own elaboration in R programing language based on "quantstrat" and "blotter" packages

Lastly, both strategies display better trading execution metrics compared with the buy and harbour strategy. $SM{A}_{20,30}$ scheme exhibits a higher annualized return of 1.31% and 1.93% with ${\text{BB}}_{\left(5,2\right)}$ scheme compared with a veto return of -16.73% registered by the buy and go for strategy. Annualized standard deviation represents a evaluate of volatility and risk performance, in this case, the $SM{A}_{20,30}$ combination is less risky than the benchmark (buy and hold) only not with${\text{BB}}_{\left(5,2\right)}$ scheme atomic number 3 IT is presented in table 7.

Table 7

trading performance prosody

Source: own involution

Spell NAFTRAC ended up with a negative return in 2022, HUELUM hind end take advantage of NAFTRAC doings optimizing the strategies presenting a benefit.

5. Conclusions

Algorithmic trading is used to whether to find a top or keister trends for share prices, more specifically, investors who rely on recursive trading use quantitative and technical analysis tools to determine strategies for trade. Algorithmic trading consists of analyzing standard prices through charts and numerical tools that represent open, treble, low, and close prices. In this regard, the objective of this work is to build a set of algorithmic trading strategies to fascinate persistence and memory of financial series, more specifically, to shape an algorithmic trading strategy based happening a low-relative frequency algorithmic trading model for time unit frequency assets in a semi-strong surround.

HUELUM Trading System low-frequency model was proposed to make algorithmic trading tested with the ETF NAFTRAC daily prices which replicate the behaviour of the Índice de Precios y Cotizaciones (IPC) of Mexican Stock Exchange. In that first version of HUELUM it was tested one mean indicator (Bollinger Bands), and one trend indicator (SMA) and they were compared to a benchmark, in this case, with a buy danamp; nurse strategy. Assuming initial equity of $10,000.00 (USD), commercial indicators were probed to find buying and selling signals: both, SMA and BB shot were tested applying different combinations and validated through a rolling walk fore analysis.

To select the unexceeded portfolio with SMA and BB shot combinations, we searched for parameters able to maximize historical risk-adjusted performance such as net trading, profits and loss, maximum drawdown, and gain to maximum drawdown. In the end, the best combinations were those its end fairness exhibited the highest profit. Likewise, it is possible to know the number of trades and transactions of from each one combination arsenic well as the average gain/loss per trade.

For NAFTRAC, the best technical indicators are $SM{A}_{20,30}$ and ${\text{BB}}_{\left(5,2\right)}$ combinations, with $133.95 net Pdanamp;L while ${\text{BB}}_{\left(5,2\right)}$ is the best mix in accordance with $638.11 net Pdanadenylic acid;L. Both strategies display better trading performance metrics compared with the grease one's palms and hold strategy with a higher annualized return of 1.31% for $SM{A}_{20,30}$ and 1.93% return for ${\text{BB}}_{\left(5,2\right)}$. Nonetheless, ${\text{BB}}_{\left(5,2\right)}$ exhibits a high risk due its 17.40% annualized common deviation connate the 16.65% for buy and concord and 3.44% of $SM{A}_{20,30}$.

In the end, these strategies help to pass out the maximum profit even when NAFTRAC ended up with -16.73% annualized return. The transverse-validation unconscious process implemented was a WFA which consists in optimizing indicator parameters with a set education (10 months in this case) and two months tested stunned of the try repeating the process of one abuse forward skyward to the end of NAFTRAC series. WFA allows to solve overfitting problems and shows the net trading profit/loss for each come out of sample and trades. WFA provides unexpendable data about the strategy execution for all window tested.

In that sensation, HUELUM could be used for a general strategy or could be tested in every time frame elect from the trader to take the about juicy indicator or a mix of technical indicators which is a notable advantage since is possible to track trades and strategy performance through an fairness curve graph whether organize general strategy or WFA windows.

The briny of this work is that the HUELUM Trading Scheme has the capability to adapt to any plus (as long A IT has OHLC prices), to enamour its conduct, trends and impulse and even better, HUELUM gives accurate buy and sell signals allowing trading strategies, all of this, in a low-frequency environment. It is worth noting to remark that while it is geographical that this research only reported market positions, HUELUM has the flexibility to include limited, stop loss and trailing Michigan positions according to trader's preference. Recall the hypothesis to change cost transactions in HUELUM, which allows comparison unusual fees, another advantage of this trading System.

Although high-frequency algorithms take over become the sensation for many analysts and traders, keep in mind that not all the markets have the profundity and liquidity to make that squeaking-frequency algorithm works with efficiency, especially securities that are listed in emerging countries much like México. This is when algorithmic rule trading for underslung frequency like HUELUM, helps to traders, to analyst and anyone World Health Organization has an investment in financial assets, to stimulate a break an precise decision compared to a buy in danAMP;hold strategy, to reach more win and last not least, to reduce potential fairness losings.

Now, this is not the first and last version of HUELUM; this trading System has the flexibility to admit other indicators, not necessarily technical ones. For future research, the creation of new tools and indicators will be enforced in HUELUM Trading System.

Referencias

Bajgrowicz, P., danadenylic acid; Scaillet, O. (2012). Technical trading revisited: False discoveries, persistence tests, and transaction costs. Journal of Business Economic science, 106(3), 473-491. doi: 10.1016/j.jfineco.2012.06.001

Bian-Du, H., danadenosine monophosphate; Jingdong, Z. (2016). Optimal execution in high-frequency trading with Bayesian eruditeness. Physica A: Statistical Mechanics and its Applications, 461, 767-777. Interior: 10.1016/j.physa.2016.06.021

Black, F. (1986). Noise. The Daybook of Finance, 41(3), 528-543. doi: 10.1111/j.1540-6261.1986.tb04513.x

Bollinger, J. (1992). Using Bollinger Bands. Stocks danamp; Commodities, 10(2), 47-51.

Bollinger, J. (2002). Bollinger on Bollinger Bands. New York: McGraw-Hill.

Breeden, D. T. (1979). An Intertemporal Asset Pricing Model With Random Consumption and Investment funds Opportunities. Journal of Fiscal Economics , 7(3), 265-296. doi: 10.1142/9789812701022_0003

Brogaard, J., Carrion, A., Thibaut, M., Ryan, R., Shkilko, A., danamp; Konstantin, S. (2018). High frequence trading and intense price movements. Journal of Financial Economics , 128(2), 1-13. Department of the Interior: 10.1016/j.jfineco.2018.02.002

Chromatic, S. J., Goetzmann, W. N., danamp; Kumar, A. (1998). The Dow Theory: William Peter Hamilton's track record reconsidered. Journal of Finance, 53(4), 1311-1333. doi: 10.1111/0022-1082.00054

Bulkowski, T. N. (2005). Cyclopaedia of Graph Patterns. New Jersey: John Wiley danamp; Sons, Inc.

Chien-Feng, H., Hsu, C.-J., Chi-Chung, C., Chang, B. R., danampere; Chen-An, L. (2015). An sound model for pairs trading victimization genetic algorithms. Computational Intelligence and Neuroscience: CIN, 1-10. doi: 10.1155/2015/939606

Diaz-Paivaa, F., Nogueira-Cardoso, R., Peixoto-Hanaoka, G., danamp; Moreira-Duarte, W. (2019). Decision-making for business enterprise trading: A coalition approach of machine learning and portfolio excerpt. Expert Systems with Applications, 115, 35-655. doi: 10.1016/j.eswa.2018.08.003

Droke, C. (2001). Gann Easy (First ed.). New York State: Marketplace Books.

Escobar, A., Moreno, J., danamp; Múnera, S. (2013). A technical analysis indicator based on fuzzy logical system. Electronic Notes in Theoretical Calculator Scientific discipline, 292, 27-37. doi: 10.1016/j.entcs.2013.02.003

Fama, E. F. (1969). American Finance Association Economic Capital Markets: A Review of Possibility and Empirical Work. The Diary of Finance , 25(2), 28-30.

Forecasts, D. T. (2015). The Dow Theory Explained. Indiana: Horizon Publication Party.

Gradojevic, N., danamp; Gençay, R. (2013). Fuzzy logic, trading uncertainty and technical trading. Journal of Banking and Finance, 37(2), 578-586. Interior Department: 10.1016/j.jbankfin.2012.09.012

Grossman, S. J., danAMP; Stiglitz, J. E. (1980). On the Impossibility of Informationally Efficient Markets. American Efficient Review, 70(3), 393-408. doi: 10.1177/0003065108330523

Hagströmer, B., danAMP; Nordén, L. (2013). The diverseness of full-frequency traders. Journal of Business Markets, 16(4), 741-770. doi: 10.1016 /j.finmar.2013.05.009

Harris, R. D., danamp; Yilmaz, F. (2009). A momentum trading scheme based on the low absolute frequency portion of the rate of exchange. Diary of Banking and Finance , 33(9), 1575-1585. doi: 10.1016 /j.jbankfin.2009.03.003

Hasbrouck, J., danamp; Saar, G. (2013). Low-latency trading. Journal of Financial Markets , 16(4), 646-679. Interior: 10.1016/j.finmar.2013.05.003

Jacob, S., Napoletano, M., Roventini, A., danamp; Fagiolo, G. (2016). Rock around the clock: An agent-based model of low- and high-frequency trading. Journal of Evolutionary Political economy, 26(1), 49-76. DoI: 10.1007/s00191-015-0418-4

Kuang, P., Schröder, M., danAMP; Wang, Q. (2014). Illusory profitability of technical analysis in emerging foreign rally markets. International Journal of Forecasting, 30(2). doi: 10.1016/j.ijforecast.2013.07.015

Local area network, Q., Zhang, D., danamp; Xiong, L. (2011). Reversal Pattern Discovery in Business Time Series Supported Fuzzy Candlestick Lines. Systems Engineering Procedia,, 2, 182-190. doi: 10.1016/j.sepro.2011.10.021

Lee, K., danadenosine monophosphate; Seo, B. K. (2017). Marked Hawkes process modeling of price kinetics and volatility appraisal. Journal of Empirical Finance, 40, 174-200. Department of the Interior: 10.1016 /j.jempfin.2016.08.004

51, K., James Fenimore Cooper, R., danadenosine monophosphate; Vliet, B. V. (2017). How Does Senior high-Frequency trading Affect LowFrequency trading? Journal of Behavioral Finance, 1-15.

Lucas, R. E. (1978). Asset Prices in an Telephone exchange Economy. Econometrica, 46(6), 1429-1445. doi: 10.2307/1913837

Manahov, V., Hudson, R., danamp; Gebka, B. (2014). Does HF trading affect technical analysis and market efficiency? And if thusly, how? Diary of International Commercial enterprise Markets, Institutions and Money, 28(1), 131-157. Department of the Interior: 10.1016/j.intfin.2013.11.002

Mcdonald, M. (2002). Predict market swings with subject field analysis. New York: John Wiley and Sons Inc.

Menkveld, A. J. (2013). High oftenness trading and the inexperienced market makers. Journal of Business Markets , 16(4), 712-740. doi: 10.1016/j.finmar.2013.06.006

Merton, R. (1973). An Intertemporal Capital Asset Pricing Model. Econometrica , 41(5), 867-887.

Pardo, R. (2008). The Rating and Optimization of trading Strategies. Garden State: John Lackland Wiley danamp; Sons, Inc .

Scholtus, M., Van Dijk, D., danamp; Frijns, B. (2014). Speed, algorithmic trading, and market quality around macroeconomic newsworthiness announcements. Journal of Banking and Finance , 38(1), 89-105. doi: 10.1016/j.jbankfin.2013.09.016

Serban, A. F. (2010). Combination poor reversion and momentum trading strategies in unnaturalized exchange markets. Daybook of Banking and Finance , 34(11), 2720-2727. doi: 10.1016/j.jbankfin.2010.05.011

Sewell, M. (2012). The Efficient Grocery store Hypothesis: Empirical Evidence. International Daybook of Statistics and Probability, 1(2), 164-178. doi: 10.5539/ijsp.v1n2p16 4

Shiller, R. J.(2003). From Cost-efficient Markets Theory to Behavioural Finance. Journal of Economic Perspectives, 17(1), 83-104. doi: 10.1002/9781118258415

Stanković, J., Marković, I., danamp; Stojanović, M. (2015). Investment Strategy Optimisation Using Technical Analysis and Prognostic Model in Emerging Markets. Procedia Economics and Finance, 19(15), 51-62. doi: 10.1016/S2212-5671(15)00007-6

Tam, F. K. (2015). The power of Japanese candlestick charts. Advance Filtering Techniques for trading Stocks, Futures, and Forex. Singapore: John Wiley danAMP; Sons Singapore Pte. Ltd.

Wiecki, T. (2012, March 2). Parametric quantity Optimization in Recursive Finance Split up I: Walk overbold optimization. Retrieved from http://blog.quantopian.com/parametric quantity-optimisation/

Ying-Hua, C., danadenylic acid; Ming-Sheng, L. (2017). Incorporating Markov determination process on beginning algorithms to formulate trading strategies for stock markets. Practical Soft Computation, 52, 1143-1153. doi: 10.1016/j.asoc.2016.09.016

Notes

⁴ Also known as black-box trading, algo-trading or automated trading.

⁵ Even in a downtrend, financial grocery ever offers an opportunity to make a paying swap.

⁶ HUELUM refers to a shouting verbalism exploited by the community of Instituto Politécnico Nacional. Originally, HUELUM expression was wont to gather students and invite them to skip classes.

⁷ Where a market information technology's made up of firms that make production-investment conclusion and investors that select among firms' securities.

⁸ In fact, the main criticisms of EMH focus connected the lack of determinate risk preferences and the cost of information, if we take into consideration this argue, is not possible to reach efficiency because economic agents have no longer access to the same information. In this sensation, the EMH is untestable and unattainable (Sewell, 2012).

⁹ And straight (Fama, 1969) refers that if one of these assumptions is broken, a market is no inefficient unless that with the available information, a better judgment is made. As wel, if information is non costless for all investors this is not enough to reckon a grocery inefficient.

¹⁰ There's plenty literature that discuss whether the behavior of randomness traders may shape share prices contempt of sophisticated investors or non. However, it is thoughtful that interference traders are essentials if it is desired the existence of liquid markets (Covert, 1986) as symptomless as they play a main part in trading Sessions (Grossman danamp; Stiglitz, 1980) in spite of they adjudicate to replicate the behavior of other traders in an irrational number way and their techniques.

¹¹ Japanese candlesticks are commonly represented with green or flannel color for bull's figures and flushed operating theatre black color for bearish candles. However, they can be represented with the colors that the trader considers most ready to hand.

¹² At this dot, IT is worth notingto citation than the use of candles first appeared at the end of 1800 in Japan. The credits are attributed to a Elmer Leopold Rice dealer named Munehisa Homma. This Japanese trader had such a good performance that became the financial consultant to the Japanese politics and was given the title of Samurai, he achieved more than 100 winning trades in a road and accordant to (Tammy, 2022), their ideas where perfectioned over some years of trading to finally culminate in the system of candlestick charts that are currently being attributed to Charles Dow as the pioneer of study psychoanalysis in the United States.

¹³ An overbought area relates to a constant uptrend of the security department's prices smidgin a few department of corrections and an oversold orbit is when there is a constant downtrend of the protection's prices whit a couple of rallies.

¹⁴ BB send away embody utilised equally support and resistor as fountainhead. A back up level is where the concluding price or the candles tends to find an complex number barrier in varied bouncing price's levels when the price is dropping. Opposite to the support, a resistance is when the closing price operating theatre the candles tends to get hold an imaginary barrier in different bouncing price's levels when the price is revolt (Bulkowski, 2005).

¹⁵ A discontinue loss ordering is a specified threshold related to first trade asset Leontyne Price where a market operating theater limit order is activated, and a trailing stop-loss order is a specified threshold related to current plus price where a market or limit order is active.

¹⁶ Assuming purchasing NAFTRAC at the beginning of 2022 and held it to the end of 2022.

Author notes

** Segundo Lugar, Categoría Investigación Financiera Empresarial, XXXIV Premio de Investigación Financiera IMEF-EY 2022

*Contacto de correspondencia. Correo: ajimenezp@ipn.mx

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