Classical Technical Analysis Pattern Efficacy in the Cryptocurrency Markets - Gordon Finn

1

Titles you can't find anywhere else

Try Scribd FREE for 30 days to access over 125 million titles without ads or interruptions! Start Free Trial Cancel Anytime.













Table of Contents

Abstract………………………………………………… Abstract…………………… ……………………………………………………… ………………………………………....3 ……………....3 Introduction………………………………………………… Introduction…………………… ……………………………………………………… ……………………………………..4 …………..4 Literature Review…………………….………… Review…………………….……………………………………… ………………………………………………....6 …………………....6 Data……………………………………………………… Data…………………………… ……………………………………………………… …………………………………........11 ……........11 Methods…………………………………………………… Methods………………………… ……………………………………………………… ……………………………………..12 ………..12 Results………………………………………………… Results…………………… ……………………………………………………… ………………………………………....24 ……………....24 Conclusion……………………………………………………… Conclusion…………………………… ……………………………………………………… ……………………………….28 ….28 Bibliography…………………………………………………… Bibliography………………………… ……………………………………………………… ………………………………..30 …..30













Abstract

Cyrptocurrencies and blockchain technology are currently extremely relevant topics for academics and professionals alike. They offer the opportunity oppo rtunity to disrupt middlemen in every industry as well as promote a new understanding und erstanding of how funds can be transferred. With this market has come rampant speculation in the form of trading these burgeoning assets. This paper studies how these markets have developed and how effective technical analysis is at predicting price patterns. Using previous studies as a framework along with hourly and daily cryptocurrency price data, it was determined that the classical technical analysis patterns do have hav e some ability to predict future price movements; however, more research is needed on both a larger sample size and with a variety variet y of other different test parameters.













Introduction

In 2009, a mysterious figure, known only as Satoshi Nakamoto (pen name), published a ground-breaking research whitepaper entitled, “Bitcoin: A Peer -to-Peer -to-Peer Electronic Cash System.”1 Nakamoto’s goal was to create a network allowing users to bypass financial institutions using cryptography. To do so, Nakamoto built a technology called “Blockchain.”2 Blockchain technology came as a result of what is known as the “double “dou ble-spend” problem.

Cryptocurrencies have been around since the 1950s, yet there was always a main underlying issue: users could spend more than they the y currently had unless there was a trusted third party who controlled the transactions.3 All electronic transactions prior to Bitcoin have had to use financial institutions as their trusted third party. Bitcoin is different as payment records are recorded in a master public ledger where past payments are verified by a community of nodes. Each block contains the current transactions as well as all previous transactions. Nodes maintain this network and k eep roster of the decentralized ledger, or blockchain. As long as there are more honest nodes in the network than dishonest, the ledger will statistically prove the confirmation of past transaction. Bitcoin’s technology is revolutionary and has the potential to upend many industries as they are known today. Bitcoin started as an obscure payment method obtainable obta inable only from those who held it as well as rewards for operating a node. Slowly Slowl y but surely, exchanges were built b uilt out of community oriented websites. These early exchanges were not no t ready for the massive amount of volume and













interest. The largest exchange up until 2014 20 14 was Mt. Gox, now infamous for its lack of cybersecurity. The exchange was cleared out by hackers, and billions worth of the currency were stolen.4 This exchange prior to Bitcoin was designed designe d for trading Magic: The Gathering cards. Large institutions have stepped in and created a more regulated and sound environment for trading the currency. In 2018, there are now over o ver 1,558 different tradable cryptocurrencies similar to Bitcoin with a total market capitalization of $367 Billion.5 This overall market capitalization is greater than all but 10 companies globally, and in the peak of the market on January 6th, the overall market capitalization stood at roughly $800 Billion, or a greater market capitalization than any an y other company in history other than Apple Inc.6 Given the large influx of institutional and retail funds into the sector, many have looked at how cryptocurrency price formation occurs as well as what trading strategies are successful. This paper will focus on technical analysis and its application app lication with cryptocurrencies. Technical analysis is a school of trading strategies that focus on forecasting future prices trends solely on the performance of past price trends. This Th is is inherently different from fundamental analysis where traders and investors attempt to forecast the future price based on the intrinsic value of the asset. The presumed father of the Technical analysis is Charles Dow, who in a series s eries of Wall Street Journal editorials laid out his six main theories regarding overall market movements, yet













these theories dictate that trends in past prices govern gov ern future prices in the market.7 Many further expanded on these trends, but perhaps none were more influential than Robert Edwards, John Magee, and W.H.C. Basset, and their book Technical Analysis of Stock Trends. Since its first publication in 1948, this book has served as somewhat of a bible for technical traders. The book itself lays out a series of classical technical chart patterns which indicate a “bullish” (positive) or a “bearish” (negative) price movement to come in the future.8

Technical analysis and academics have been often at odds in the past, yet several researchers have attempted to cross the divide between bet ween the two. Andrew Lo, Harry Mamaysky, Mama ysky, and Jiang Wang published a paper in August 2000, entitled Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation, looking at the

empirical results of the classical chart patterns on historical data. In their study lookin g over a 31-year sample period, they found that “several tec hnical indicators do provide incremental information and may have some practical value.”9

This paper will attempt to recreate their study on the impact of classical technical chart patterns and apply it to cryptocurrency price data using their algorithm definitions for each pattern. The analysis will use python scripts to analyze historical data of the top 250 cryptocurrencies by market capitalization. Literature Review

7

Andrew Beattie, "Giants of o f Finance: Charles Dow," Investopedia, l ast modified January 5, 2018, accessed April















Technical Analysis of Stock Trends10

The book itself has been revised many man y times from its original publication, but much remains the same. The authors guide the reader through basics of technical analysis including the Dow theory. The Dow theory has five main tenants including: 1. The averages discounts everything: the price will take in all current available information, and any deviation will be corrected quickly. 2. The three trends: the “market” moves in three different trends: major, secondary, and minor. Major trends are movements of greater than 20%. 2 0%. Secondary trends are movements on an intermediate timeframe that are usually over several months. Minor trends are day to day movements and fluctuations within the market. 3. The primary trends: These large overall movements are c ategorized by “bull” (positive) and “bear” (negative) markets. According to Dow, we can and always are

in one of the two at any given point. 4. The secondary trends: These trends are defined as “intermediate ractions that interrupt the progress of prices in the Primary Direction.” These are so called “corrections” that last for three weeks to several months.













The book also focuses on numerous other important aspects of technical trading including: classical chart patterns, trend lines, moving averages, entry and exits points, and support and resistance levels. These topics are all important to the technical trader, but this paper will only focus on the efficacy of classical chart patterns p atterns which the authors define as is stated in the methods portion of this paper. Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation11

Lo, Mamaysky, and Wang looked to bridge the gap between academic finance and technical analysis. Many in this new wave of quantitative finance dismiss technical analysis entirely, yet these researchers looked to see if there th ere were any sort of statistical significance to classical chart patterns on a historical basis. This paper forms the basis of the overall exploration in how to examine these chart patterns historically. They first first determined their sample set: several hundred U.S. stocks returns from 1962 to 1966. From there, they then smoothed their data using smoothing estimators and kernel regression. For them, they assessed their smoothed price series as a result of the following equation:













Automating this process, they then defined each te chnical pattern “in terms of its geometric properties, for example, local extreme.” Applying the kernel smoother, they analyzed

the price series for each defined pattern to test for occurrences. The specific pattern definitions de finitions are located in the methods section of this paper, but they used these local maxima and minima to determine the pattern formation. Once they determined all historical patterns within the dataset, the researchers the n looked at one-day returns after each pattern was detected. To do so, they defined a trading window of 36 days where the returns were truncated after the immediate formation of the pattern. The returns were standardized by taking the returns by subtracting the mean returns of each period and dividing by the standard deviation. These returns were analyzed on an aggregate basis and showed that for all ten patterns described in the study, abnormal patterns were observed, especially for Nasdaq stocks. The researchers admitted that these returns do contain some biases. The kernel regression equation is forward looking, taking into accounts acco unts data points in the future and thus cannot lead to deceiving results. Additionally, the researchers noted that the patterns them selves may not always be the most effective patterns in terms of abnormal returns. The y conclude that further













Ciaiana, Rajcaniova, and Kancs published their paper on the economic determinants of Bitcoin price formation in Applied Economics in 2016. It is the first paper that looks at traditional economic price determinants as well as “digital currency-specific factors.” The

researchers began by taking the previously identified Barro model for gold standard and applied it towards Bitcoin daily data from 2009-2015. The adapted model is shown below where P is the price level of goods and services, G is the size of the Bitcoin economy, V is the velocity of Bitcoin circulation (frequency of turnover of one Bitcoin in terms of changing hands), and B represents the total stock of Bitcoin in circulation.

 =

 

This model looks at relationships between supply and demand as well as actual “mining” production. Expanding upon it, they created numerous coefficients to attempt to further reconcile the unexplained price movements. One such coefficient looks to additionally account for news cycles (negative, neutral, and positive). The final set of coefficients look to account for macro financial indicators such as interest rates, unemployment rates, and stock market ind ices movements. Their analysis yielded statistically significant results.













had any significant correlation with Bitcoin price formation. Stock market indices in particular showed no correlation with Bitcoin price movements. Foreign exchange forecasting models such as these do have some sort of application in terms of attempting to find some sort of pricing predictability for cryptocurrencies. In p articular, it seems that these currencies react more to crypto-currency cr ypto-currency specific factors rather than traditional equity factors. This could possibly mean that if the technical patterns work with equities, they may not necessarily correlate to abnormal abn ormal returns with cryptocurrencies. Data The data used for this paper were obtained o btained from two main sources: CryptoCompare and Quandl.1314 Quandl served as the main resource for gathering equity data. This equity equit y data was acquired using Quandl’s API. Quandl’s API is free to use and allows users to gather series of

price history of equities with ease. The main data used in the testing of the algorithms was Quandl’s data for Microsoft from January 1st, 2003 to May 1st, 2016. This data was used in

conjunction with in order to test the accuracy of the adapted model mod el written by Andrew Campbell on Quantopian. Unfortunately, the adjusted close price for most equities varies depending on the data source. Quandl’s data as well as Google and Yahoo Finance all deviated from each other.















years (1/1/2008 – 12/31/2016). 12/31/2016). The data could possibly be affected by the overall market cycle of this time sample, but these patterns are subjected to only a 36-day trading window and should not be affected by yearly trends.15 The cryptocurrency price data were found using CryptoCompare’s API. CryptoCompare

is a data source for cryptocurrencies and offers a variety of tools. The API does limit the amount of data points collected per ip address a ddress to 2,000. The price data consist of the price and timestamp of the individual coin against a Bitcoin (BTC) trading pair. This is commonplace among cryptocurrency exchanges, and very few coins are traded against fiat currency. Using Coin Market Cap, a leading source of cryptocurrency market capitalization information, the top 250 coins were selected on January 6th, 2018. 16 These coins were processed in a loop where the past 2,000 hours h ours of price data were













Campbell’s research notebook posted on Quantopian.com.1718 This paper aims to replicate Lo, Mamyskys, and Wang’s definitions for each algorithm detecting the classical chart patterns

described in Technical Analysis of Stock Trends. The algorithms had to use the new price data collected as well as translated into python that can run locally on any computer. Andrew Campbell adapted much of the research paper’s code into python and published the source code on Quantopian.com. However, Quantopian.com runs a very specific version of iPython as well, as their own proprietary suite of python packages and interfaces. The code posted on their site must be heavily adapted in order to be run on a basic python interpreter.19 Once Campbell’s source code had been thoroughly debugged and adapted to python 2.7

on Windows 10, the data were then cleaned to ensure that each data point was part of a Pandas series indexed by time. As Lo, Mama ysky, and Wang point out “The starting point for any study













Kernel smoothing estimation is a common tool for machine learning al gorithms. It works by taking in a series of past data points and applying a smoothing parameter known as bandwidth as well as a regression of previous data points. po ints. This bandwidth factor is important because it tells the model how close to fit the data. If the bandwidth is too large, not enough of the movement will be captured (too flat) and if the bandwidth is too small, too much of the movement will be captured as one can see in Figure 1.23 Figure 1. Smoothed Microsoft Price Data from 1/1/2012 to 4/31/2012













Where n is equal to the number n umber of data points in the dataset and d is the number of overall dimensions.24 For our analysis, this bandwidth factor changed dynamically as the number of data points fluctuated dynamically depending on the price history of the coin. While kernel smoothing can be helpful in terms of smoothing historical data, it does introduce a look-ahead bias as the kernel smoothing indicator will use forward data points to produce its current smoothed ones.25 Another possible method is through moving averages. Moving averages take the past data up to a certain limit and create an average that moves along with the data as they move mov e forward. While there are many different types of o f moving averages, data scientists sometimes prefer to use exponential mo ving averages when it comes to price series forecasting for the future as they they are only backward looking.26 Two specific exponential moving averages were used in this paper: exponentially weighted moving averages













impact on the current moving average. Ultimately the span has the greatest effect on the fitting of the smoothed data to the original as is seen in Figure 2.28  = 2/(  1) Figure 2. EWMA’s for Apple price data from 6/1/201 2 to 11/1/2012















bottom. These patterns are all derived from classical technical analysis, analysis, Technical Analysis of Stock Trends, and Lo, Maymasky, and Wang,29

Head and Shoulders patterns consist of the following extreme ex treme according to Lo, Mamaysky, and Wang:













Figure 2. Head and Shoulders Pattern Observed on Ethereum (ETH)













32

A Broadening Top is a bearish b earish pattern where the price rallies and then sees two higher highs as well as two lower lows. Volume falls throughout the formation, and confirmation occurs after the price falls below E4. The opposite occurs in a Broadening Bottom pattern, but the alleged













Figure 5. Broadening Bottom observed on Steemit Coin (STEEM)













psychology and pattern formation.35 Figures 6 and 7 show the Triangle Top and Triangle Bottom respectively. Figure 6. Triangle Top observed on Neo (NEO)



























upside-down “U”. Rounding Bottoms form a “U” pattern and represent bullish price action.37 Figures 8 and 9 show the Rounding Top and the Rounding Bottom respectively.

























abnormal returns as well as a large range of potential outcomes. Figure 11 shows sho ws the results of one sample T tests with the following hypotheses:













HS T Statistic P - Value

- 5. 43 0

T Test Da ta fo r 1 Hour Normalized Returns Returns (N = 250, Bandwidth Bandwidth =1.8) IHS BTOP BBOT TTOP TBOT RTOP 2. 98 0. 003

- 1 . 93 0. 055

2 . 29 0. 023

- 7. 06 0

3. 21 0. 002

- 1 . 81 0. 71

RBOT - 2. 15 0. 278































































Classical Technical Analysis Pattern Efficacy in the Cryptocurrency Markets - Gordon Finn

Recommend Documents