Development of a cryptocurrency bot

As an emerging market and research direction, cryptocurrencies and cryptocurrency trading have seen considerable progress and a notable upturn in interest and activity, even entering the market people without experience or sufficient knowledge. In addition, the tremendous volatility and the fact that this market never closes make the human factor affect crypto asset trading too much. Hence, in this project a cryptocurrency trading bot is developed. To be exact, the algorithm consists of two distinguishable parts: the bot itself and the backtesting. Notwithstanding that both parts departs from the analysis of financial markets in general, and cryptocurrencies in particular, both present clear differences in terms of code and pretext. On the one hand, the bot’s algorithm is used to trade in reality, specifically through the Binance exchange. Here the user plays risks their monetary capital. On the other hand, backtesting consists of verifying the trading strategy based on historical data. Backtesting serves, then, as validation of the strategy to be followed by the bot. Thus, all the necessary fundamentals to understand both the general cryptocurrency context and technical analysis relevant concepts are presented, along with a detailed explanation of the implemented algorithm and a proper analysis of the obtained results. Finally, further code improvements and new ideas to develop in the future are suggested, apart from presenting the code developed

A dynamic trading rule based on filtered flag pattern recognition for stock market price forecasting

[EN] In this paper we propose and validate a trading rule based on flag pattern recognition, incorporating im- portant innovations with respect to the previous research. Firstly, we propose a dynamic window scheme that allows the stop loss and take profit to be updated on a quarterly basis. In addition, since the flag pat- tern is a trend-following pattern, we have added the EMA indicator to filter trades. This technical analysis indicator is calculated both for 15-min and 1-day timeframes, which enables short and medium terms to be considered simultaneously. We also filter the flags according to the price range on which they are de- veloped and have limited the maximum loss of each trade to 100 points. The proposed methodology was applied to 91,309 intraday observations of the DJIA index, considerably improving the results obtained in the previous proposals and those obtained by the buy & hold strategy, both for profitability and risk, and also after taking into account the transaction costs. These results seem to challenge market efficiency in line with other similar studies, in the specific analysis carried out on the DJIA index and is also limited to the setup considered.The fourth author of this work was partially supported by MINECO, Project MTM2016-75963-P.Arévalo, R.; García, J.; Guijarro, F.; Peris Manguillot, A. (2017). A dynamic trading rule based on filtered flag pattern recognition for stock market price forecasting. Expert Systems with Applications. 81:177-192. https://doi.org/10.1016/j.eswa.2017.03.0281771928

The statistical properties of technical trading rules

A portfolio of 200 heterogeneous technical trading rules is tested for their directional predictabilities on the DJIAI from 1988 to 1999. We also explore several nonparametric techniques designed for brain research, and detected possibly other forms of dependencies more significant than the traditional linear autocorrelation for the time series. The overall conditional mean directional predictability is 46%. 36 percent of the rules have more than 50% directional predictability, and the top 20 percent rules has a 73% directional predictability, whereas the bottom 80 percent has a directional predictability of 40%. Buy signals consistently generate higher predictability than sell signals but do not commensurate with their respective risk levels. The relationship between two sub-periods is not stable, while the difference between the conditional mean directional predictability of buy only and sell only signals is highly significance. The belief that most successful rules have a directional predictability of 25% to 50% coincides with the mode of distribution. We observe counter intuitive relationship between volatility and directional predictability. The results of directional predictability in a downtrend concur with the argument that buy-and-hold strategy is not a suitable benchmark. Attempts are made to tackle the issues of small sample bias, data snooping, size of test window, bootstrap or t-test, and homogeneity. Issues are discussed on empirical testing for their real world applications, statistical and non-statistical interpretations; also randomness test; physical or biological science approach

The Obstinate Passion of Foreign Exchange Professionals : Technical Analysis

Technical analysis involves the prediction of future exchange rate (or other assetprice) movements from an inductive analysis of past movements. A reading of the large literature on this topic allows us to establish a set of stylised facts, including the facts that technical analysis is an important and widely used method of analysis in the foreign exchange market and that applying certain technical trading rules over a sustained period may lead to significant positive excess returns. We then analyze four arguments that have been put forward to explain the continuing widespread use of technical analysis and its apparent profitability: that the foreign exchange market may be characterised by not-fully-rational behaviour; that technical analysis may exploit the influence of central bank interventions; that technical analysis may be an efficient form of information processing ; and finally that it may provide information on nonfundamental influences on foreign exchange movements. Although all of these positions may be relevant to some degree, neither non-rationality nor official interventions seem to be widespread and persistent enough to explain the obstinate passion of foreign exchange professionals for technical analysis.foreign exchange market ; technical analysis ; market microstructure

An empirical investigation of technical analysis in fixed income markets

The aim of this thesis is to evaluate the effectiveness of technical analytic indicators in the fixed income markets. Technical analysis is a widely used methodology by investors in the equity and foreign exchange markets, but the empirical evidence on the profltability of technical trading systems in the bond markets is sparse. Therefore, this thesis serves as a coherent and systematic examination of technical trading systems in the government bond futures and bond yield markets. We investigate three aspects of technical analysis. First, we evaluate the profitability of 7,991 technical trading systems in eight bond futures contracts. Our results provide mixed conclusions on the profitability these technical systems, since the results vary across different futures markets, even adjusting for data snooping effects and transaction costs. In addition, we find the profitability of the trading systems has declined in recent periods. Second, we examine the informativeness of technical chart patterns in the government benchmark bond yield and yield spread markets. We apply the nonparametric regression methodology, including the Nadaraya-Watson and local polynomial regression, to identify twelve chart patterns commonly taught by chartists. The empirical results show no incremental information are contained within these chart patterns that investors can systematically exploit to earn excess returns. Furthermore, we find that bond yield spreads are fundamentally different to price series such as equity prices or currencies. Lastly, we categorize and evaluate five type of price gaps in the financial markets for the first time. We apply our price gap categorisation to twenty-eight futures contracts. Our results support the Gap- Fill hypothesis and find that some price gaps may provide additional information to investors by exhibiting returns that are statistically different to the unconditional returns over a short period of time. ՝In conclusion, this thesis provides empirical evidence that broadly support the usage of technical analysis in the financial markets

Automated system trading, algorithms and programming - To buy or to sell the trend?

Tämän tutkielman päätavoitteena on tutkia millaisiin tuottoihin Bollinger band -työkalua hyväksikäyttämällä teknisessä analyysissä rahoitusmarkkinoilla voidaan päästä, ja siten myös markkinoiden tehokkuutta. Käytännössä tämä on toteutettu ottamalla huomioon useita eri parametreja (kaupankäyntikulut, kaupankäyntipäivä, trendin mukainen ja vastainen strategia, erilaiset variaatiot liukuvan keskiarvon laskennassa), joiden tuloksena on 5400 lopputulosta, jotka on analysoitu yhdessä muiden saman omaisuusluokan omaavien instrumenttien kanssa (osake, valuutta, energia, korko, metalli). Data on kerätty Bloombergiltä käyttäen geneeristen futuurikontrahtien päivähintoja ja valiten jokaiselle kuukaudelle/kvartaalille likvidein konrahti instrumentista riippuen aikavälille 31.12.2009-31.12.2012. Analysointi on toteutettu Excelissä käyttäen VBA-koodia, joka alkaa hintatekstitiedostojen avaamisesta ja loppuu tuloksiin. Yleisesti tulokset indikoivat, että valuutta- ja hyödykemarkkinat ovat tehokkaita, mutta ylituottoja voidaan saavuttaa osakemarkkinoilla Bollinger band -työkalua hyväksikäyttämällä

The performance of technical analysts and technical forecasting

The aim of the thesis is to evaluate the ability of technical analysis to predict movements in financial markets. This study is of obvious practical interest in that technical analysis is used intensively by market practitioners. It is useful to know whether there is any objective evidence that it works. The study has also proved timely in that technical analysis has stimulated a small but insightful programme of academic research in the past decade, and our work adds to that line of research. This study differs from previous academic research in one important way. All of our empirical work derives from information on the forecasts and trading recommendations of analysts themselves. This contrasts with most earlier studies, which try to mimic the forecasts of technical analysts by applying mechanical trading rules. Specifically, we utilise data from (a) a specially conducted survey of a group of analysts through 1998, and (b) unique data sets on daily published forecasts and trading recommendations by a leading provider of technical commentary through the years 2000-2001. Our analysis of this data strongly suggests that technical analysis does have value, and that the behaviour of technical analysts cannot be modelled using simple (or even quite complex) mechanical trading rules. A widely accepted definition is that “Technical Analysis is the study of market action, primarily through the use of charts, for the purpose of forecasting future price trends” (J.J.Murphy, 1986). In contrast to “Fundamental Analysis”, technical buying and selling strategies are based on the observation of past history activities, extracting market psychology from price patterns. The topic is important because technical analysis is by far the most common method used for short term forecasting by traders in financial markets. In spite of this, until recently there has been very little serious academic work on the value of technical analysis. The prime reason for the paucity of academic work is the fact that technical analysis does not involve well-defined statistical procedures. Rather, technical analysis is an umbrella term for a very diverse collection of techniques, some quantitative and some judgmental, most with little scientific basis, and often sold with exaggerated claims about their likely success. This has made technical analysis an easy target for ridicule, most notably in Burton Malkiefs classic Random Walk down Wall Street (Malkiel, 1973). In recent years, several factors have caused researchers to take technical analysis more seriously. From a number of “forecasting competitions” it is now well understood that a catholic approach helps improve forecast accuracy in a wide variety of business applications. The poor performance of many popular linear time series forecasting methods, notably Box-Jenkins analysis, in these competitions has shown that overselling is not unique to the world of technical analysis. Nonlinear modeling and forecasting methods are now in vogue. Indeed, in the financial markets, it is now recognised that many different regimes can be at work across a single time series of market prices, so it looks much more reasonable to use a set of tools rather than search for a single underlying model. The availability of time series data based on high-frequency financial market prices has made it easier to make objective assessments of competing forecasting methods, and a number of academic studies have exploited this to evaluate technical analysis. However, this work has focussed on the narrow area of easily-replicated mechanical trading rules, such as moving averages and filters (for example, Brock et. al. 1992), and a few well-defined turning point patterns, such as the “Head-and-Shoulders” (Osier and Chang, 1995). Just as it would be hard to argue that a single-equation regression analysis would provide a good test of the value of conventional econometric forecasting, we argue that this focus on quantitative rules does not adequately reflect the complexity of the way technical analysis is applied in practice

Online algorithms for conversion problems : an approach to conjoin worst-case analysis and empirical-case analysis

A conversion problem deals with the scenario of converting an asset into another asset and possibly back. This work considers financial assets and investigates online algorithms to perform the conversion. When analyzing the performance of online conversion algorithms, as yet the common approach is to analyze heuristic conversion algorithms from an experimental perspective, and to analyze guaranteeing conversion algorithms from an analytical perspective. This work conjoins these two approaches in order to verify an algorithms\u27 applicability to practical problems. We focus on the analysis of preemptive and non-preemptive online conversion problems from the literature. We derive both, empirical-case as well as worst-case results. Competitive analysis is done by considering worst-case scenarios. First, the question whether the applicability of heuristic conversion algorithms can be verified through competitive analysis is to be answered. The competitive ratio of selected heuristic algorithms is derived using competitive analysis. Second, the question whether the applicability of guaranteeing conversion algorithms can be verified through experiments is to be answered. Empirical-case results of selected guaranteeing algorithms are derived using exploratory data analysis. Backtesting is done assuming uncertainty about asset prices, and the results are analyzed statistically. Empirical-case analysis quantifies the return to be expected based on historical data. In contrast, the worst-case competitive analysis approach minimizes the maximum regret based on worst-case scenarios. Hence the results, presented in the form of research papers, show that combining this optimistic view with this pessimistic view provides an insight into the applicability of online conversion algorithms to practical problems. The work concludes giving directions for future work.Ein Conversion Problem befasst sich mit dem Eintausch eines Vermögenswertes in einen anderen Vermögenswert unter Berücksichtigung eines möglichen Rücktausches. Diese Arbeit untersucht Online-Algorithmen, die diesen Eintausch vornehmen. Der klassische Ansatz zur Performanceanalyse von Online Conversion Algorithmen ist, heuristische Algorithmen aus einer experimentellen Perspektive zu untersuchen; garantierende Algorithmen jedoch aus einer analytischen. Die vorliegende Arbeit verbindet diese beiden Ansätze mit dem Ziel, die praktische Anwendbarkeit der Algorithmen zu überprüfen. Wir konzentrieren uns auf die Analyse des präemtiven und des nicht-präemtiven Online Conversion Problems aus der Literatur und ermitteln empirische sowie analytische Ergebnisse. Kompetitive Analyse wird unter Berücksichtigung von worst-case Szenarien durchgeführt. Erstens soll die Frage beantwortet werden, ob die Anwendbarkeit heuristischer Algorithmen durch Kompetitive Analyse verifiziert werden kann. Dazu wird der kompetitive Faktor von ausgewählten heuristischen Algorithmen mittels worst-case Analyse abgeleitet. Zweitens soll die Frage beantwortet werden, ob die Anwendbarkeit garantierender Algorithmen durch Experimente überprüft werden kann. Empirische Ergebnisse ausgewählter Algorithmen werden mit Hilfe der Explorativen Datenanalyse ermittelt. Backtesting wird unter der Annahme der Unsicherheit über zukünftige Preise der Vermögenswerte durchgeführt und die Ergebnisse statistisch ausgewertet. Die empirische Analyse quantifiziert die zu erwartende Rendite auf Basis historischer Daten. Im Gegensatz dazu, minimiert die Kompetitive Analyse das maximale Bedauern auf Basis von worst-case Szenarien. Die Ergebnisse, welche in Form von Publikationen präsentiert werden, zeigen, dass die Kombination der optimistischen mit der pessimistischen Sichtweise einen Rückschluss auf die praktische Anwendbarkeit der untersuchten Online-Algorithmen zulässt. Abschließend werden offene Forschungsfragen genannt