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Journal of Seybold ReportISSN NO: 1533-92111. Simple Moving Average (SMA): A simple moving average (SMA) calculates the average of a selected range of prices,usually closing prices, by the number of periods in that range. It is the most widelyused of all technical indicators and it is computed by;SMA Where ‘n’ is the number of period included in the average and ‘An’ is the price of an asset atperiod n. The SMA is calculated using historical prices, this moving average doesn’t predictfuture market movements but rather lags the current price. The trading rules are: Buy when the current price is below the moving average. Sell when the current price is above the moving average.Often daily financial data can be a bit noisy, what we can do is use the rolling mean method(often called simple moving average) to get more signal about the general trend of the data. Inthis study, the data usually provide a window of a set time period and then use that to calculatethe average. The following Fig: 1 shows the example of SMA-200 (moving average of 200 days)for AAPL open price.apple['SMA-200'] apple['Open'].rolling(window 200).mean()Fig: 12. Exponential Weighted Moving Average: Exponentially weighted moving average or Exponentially moving average will allow us toreduce the lag effect from simple moving average (SMA) and it will put more weight on valuesthat occurred more recently (by applying more weight to the more recent values, thus the name).VOLUME 15 ISSUE 10 2020Page: 37

Journal of Seybold ReportISSN NO: 1533-9211So as the values get closer to the present time then apply more weight to them when calculatingthe average. Therefore, the amount of weight applied to the most recent values will depend onthe actual parameters used in the EWMA or EMA and the number of periods given in a windowsize. So the general formula of EMA is: Where is the input value,is the applied weight and is the output? Now the question ariseis how it can change from i 0 to t and how do we define theThis depends on the adjust parameters provided to the ewm () method in pandas,Case 1: when put adjust parameter is True which is in default then weighted average arecalculated using weights as follows: So we have as the actual output value on the left hand side and on the right hand side(denominator) is the sum of all the weight. But in the numerator there is decrease in weight asand needs toyou move further and further into the time series because we will havebetween 0 and 1. This means if we keep squaring the fraction to power of some other highernumber which eventually gives the smaller value and we are providing smaller weights for thedata as it gets older and older. Therefore, that very first data point has a smallest weight attachedto it and the recent data point has the most weight attached to it, essentially 1.Case 2: when put adjust parameter is False specified then moving averages are calculated asfollows:, means that first input value is the first output value, which is equivalent to using weights:When adjust False we haveand the last representation from above we have, therefore there is an assumption that is not an ordinary value butrather an exponentially weighted moment of the infinite series up to that point.While should be between 0 and 1 and it’s impossible to pass an directly so this whole thingcomes down to these three conditions:VOLUME 15 ISSUE 10 2020Page: 38

Journal of Seybold Report ISSN NO: 1533-9211Span corresponds to what is commonly called an “N-day EW moving average”.Center of mass has a more physical interpretation and can be thought of in terms of span:c and inversely proportion to span.Half-life is the period of time for the exponential weight to reduce to one half.Or specifies the smoothing factor directly.Typically, when doing EMA the best way to do is with span. In Fig: 2 shows the example ofEMA-90 (moving average of 90 days) for apple open price:apple['EMA-90'] apple['Open'].ewm(span 90).mean()Fig 2:VOLUME 15 ISSUE 10 2020Page: 39

Journal of Seybold ReportISSN NO: 1533-9211VCoding for Backtesting1. SMA Cross Over: - In this strategy we use Quantopian environment for coding this out and inthis case the strategies are as follows: Buy when 30 days MA is below the 90 days MA Sell when 30 days MA is above the 90 days MAdef initialize(context):context.apple sid(24)schedule function(check moving avg, date rules.every day(),time rules.market close(minutes 30))def check moving avg(context,data):current price data.current(context.apple, 'close')closing price 1 data.history(context.apple, 'close', 30, '1d')closing price 2 data.history(context.apple, 'close', 90, '1d')avg 1 closing price 1.mean()avg 2 closing price 2.mean()ma 1 avg 1 avg 2ma 2 avg 1 avg 2if ma 1 ma 2:order target percent(context.apple, 1.0)print('Buying')elif ma 1 ma 2:order target percent(context.apple, -1.0)print('Shorting')else:passrecord(MA30 avg 1, MA90 avg 2, closing price current price)VOLUME 15 ISSUE 10 2020Page: 40

Journal of Seybold ReportISSN NO: 1533-92112. Exponential Weighted Moving Average (EWMA) Or (EMA) Cross Over: - In this we usethe same cases as SMA cross over.import talibdef initialize(context):context.microsoft sid(5061)context.invested Falseschedule function(handle data, date rules.every day(),time rules.market close(minutes 30))def handle data(context, data):price history data.history(context.microsoft, 'price', 90, '1d')ema short talib.EMA(price history, timeperiod 30)ema long talib.EMA(price history, timeperiod 90)ema 1 ema short[-1]ema 2 ema long[-1]if ema 2 ema 1 and not context.invested:order target percent(context.microsoft, 1.0)context.invested Trueelif ema 2 ema 1 and context.invested amount 100:order target percent(context.microsoft, -1.0)context.invested Falserecord(EMA30 ema 1, EMA90 ema 2)VI RecommendationsWe just showed how to calculate the SMA and EMA based on some window/span. However,basic SMA has some "weaknesses". Smaller windows will lead to more noise, rather than signal It will always lag by the size of the window It will never reach to full peak or valley of the data due to the averaging. Does not really inform you about possible future behaviour, all it really does is describe trendsin your data. Extreme historical values can skew your SMA significantlyVOLUME 15 ISSUE 10 2020Page: 41

Journal of Seybold ReportISSN NO: 1533-9211To help fix some of these issues, we used a EWMA (Exponentially-weighted moving average) tobetter understand the market data and analyzed it more clearly.VIIConclusionThe algorithmic trading is the mixture of core statistical methods and information technology. Inthe absence of either core statistical methods or information technology, such program of tradingis not possible and cannot be executed. This paper explores two different strategies in a timeseries data. The SMA and EWMA cross over trading strategies are based on a company’s data asan example. However, the data on this paper is provided for informational purposes only anddoes not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement forany security or strategy, nor does it constitute an offer to provide investment advisory servicescorrespond to this paper. These proposed strategies could also be implemented on different pricetime series or improved by introducing leverage. Overall, this study explained the usefulness ofrelatively simple strategies in generating profits and analyze market data for investors.VIII1.2.3.4.5.LimitationsLimitation of data.More time spend on computer screen.Loss of human control.Not all strategies can be automated.Need to know the programming process.IXReferences[1] M. Souza1, D. Ramos, M. Pena, V. Sobreiro, and H. Kimura, “Examination of theprofitability of technical analysis based on moving average strategies in brics,” FinancialInnovation, vol. 4:3, 2018.[2] trading/[3] egy.asp[4] xamples.aspVOLUME 15 ISSUE 10 2020Page: 42

Any strategy for algorithmic trading requires an identified opportunity that is profitable in terms of improved earnings or cost reduction. In this paper, the study focuses on evaluating few trading strategies and trading . we used a Quantopian platform (Algorithm IDE), which is a Python

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