Ultimate Algorithmic Trading System

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indicator of trend and it will put you into the market in the direction it thinks thetrend is going. It is self-adapting. The following is the pseudocode used in the testingof these three simplistic methodologies:‘Trend Following with Different Averaging Methods‘Simple Price MAV Cross OvermyAVG = SMA(C,80) ‘Comment out the two you do not want to use‘myAVG = WMA(C,80)‘myAVG = EMA(C,80)If close of today > myAVG thenBuy this bar on closeIf close of today < myAVG then‘by placing a single quote at thestart of the lineSellShort this bar on close62STOCHASTICS AND AVERAGES AND RSI! OH, MY!Bollinger BandsJohn Bollinger is the originator of this volatility-based channel indicator. It isconsidered a channel because the indicator consists of two lines that never intersect.The channel width is based on a standard deviation value, which is derived from anN-length moving average. Most Bollinger Bands are one or two standard deviationsabove/below the moving average. In a perfectly ‘‘normal’’ world, 95 percentof values from a sample will stay within two standard deviations of its mean.Theoretically speaking, would it not make sense that once price rises to the upperchannel/band there exists a higher probability of price reverting to its mean? Ordoes it make more sense that a penetration of a band indicates strong momentumand the beginning of a trend. Like many indicators, Bollinger Bands can be usedas mean reversion as well as trend following. The most successful trend-followingsystems use a longer-length Bollinger Band penetration algorithm for trade entry.Mean-reversion traders use a much shorter length input for this indicator.If the market demonstrates enough momentum to carry price through twostandard deviations, it seems there is a real good chance it has enough gusto tocontinue on in that direction. Well, this is what the trend followers of the 1990sthought, and they turned out to be right.Figure 2.14 shows a 60-day two-standard-deviation (SD) Bollinger Band on crudeoil. The Bollinger Bands are the outside lines and the centerline is the movingaverage. This is a perfect example of why this trend entry technique gained so muchpopularity in the 1990s and in the new millennium. Crude penetrated the lowerband and never looked back until the trend entered into a congestive phase—a $50move or $50,000 per contract. That’s right, $50,000! And you thought I wasn’tgoing to reveal the Holy Grail! Remember, one monster trade doesn’t make atrading algorithm.www.rasabourse.com
FIGURE 2.14Source: TradeStationA 60-day, two-SD Bollinger Band on crude oil.Here’s how you calculate Bollinger Bands:Calculation of Bollinger Bands using 60 days and 2 standard deviations:1. Calculate a 60-period SMA of price for the chosen time period.2. Calculate the standard deviation of the 60-day sample (use a computer).3. The top or upper band is the 60-period SMA + 2 standard deviations.4. The bottom or lower band is the 60-period SMA − 2 standard deviations.Let’s put the Bollinger Band indicator to a test utilizing a common trend-followingalgorithm (Box 2.8).Box 2.8 Using John Bollinger’s Bands as a Trend FollowerBuy when market closes above top band.Sell short when market closes below bottom band.Sell when market position is long and market closes below average.Cover when market position is short and market closes above average.myAvg = Average(C,60) ‘ 60-day moving average calculationmyTopBand = BollingerBand(C,60,+2) ‘ 2 std dev above averagemyBotBand = BollingerBand(C,60, −2) ‘ the − 2 means below averageIf close crosses above myTopBand, thenBuy MOC63STOCHASTICS AND AVERAGES AND RSI! OH, MY!www.rasabourse.com
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THE ULTIMATEALGORITHMICTRADING SYST
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THE ULTIMATEALGORITHMICTRADING SYST
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CONTENTSAbout the authorIntroductio
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Reproduction 238Mutation 240Using G
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ABOUT THE AUTHORIt was March of 198
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Well that was 27 years ago and I di
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xivINTRODUCTION TO THE ULTIMATE ALG
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xviINTRODUCTION TO THE ULTIMATE ALG
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CHAPTER 1Introductionto TradingAlgo
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However, at this introductory stage
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completely above the tableau. If th
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Don’t get bogged down trying to u
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checking account was down the $10K,
Page 27 and 28: Short Liquidation Condition—What
Page 29 and 30: One BarStrengthTwo BarStrengthThree
Page 31 and 32: and inexperienced trading system pr
Page 33 and 34: Input = 6StartInput = 4Input <> 6In
Page 35 and 36: The diagram in Figure 1.4 looks cle
Page 37 and 38: observed. If the latter criterion i
Page 39 and 40: Start or NewBarIs C >AVG(C,200)YesN
Page 41 and 42: CHAPTER 2Stochastics andAverages an
Page 43 and 44: The basic theory behind the ADX is
Page 45 and 46: 4. Divide the 14-day smoothed plus
Page 47 and 48: if myADX < 35 thenif MarketPosition
Page 49 and 50: TABLE 2.1Performance of Directional
Page 51 and 52: FIGURE 2.4Source: TradeStationExamp
Page 53 and 54: Start orNew BarIs RSI < 30YesBuy Ma
Page 55 and 56: RSI DivergenceMarket Continuing to
Page 57 and 58: If marketPosition =-1 and close > e
Page 59 and 60: TABLE 2.3Performance of RSI Failure
Page 61 and 62: Start orNew BarIs Slow %K CrossingA
Page 63 and 64: TABLE 2.4Performance of Stochastic
Page 65 and 66: it is boundless extended readings a
Page 67 and 68: TABLE 2.5Performance of CCI algorit
Page 69 and 70: FIGURE 2.11Source: TradeStationThe
Page 71 and 72: We will discuss moving averages in
Page 73 and 74: MACDAvg = XAverage( MyMACD,9)MACDDi
Page 75 and 76: FIGURE 2.13Source: TradeStationThre
Page 77: LC0_E0B −25000 −41790 398 −62
Page 81 and 82: TABLE 2.8 Performance of Bollinger
Page 83 and 84: If filterTrade = false thenIf close
Page 85 and 86: Keltner ChannelChester Keltner brou
Page 87 and 88: FIGURE 2.15Source: TradeStationKelt
Page 89 and 90: 35000300002500020000150001000050000
Page 91 and 92: TABLE 2.12Best Parameter Sets of Ke
Page 93 and 94: CHAPTER 3Complete TradingAlgorithms
Page 95 and 96: Box 3.1 Turtle System #1Buy one tic
Page 97 and 98: TABLE 3.1Turtle System 1 without LT
Page 99 and 100: TABLE 3.3Turtle System 1 with LTLF
Page 101 and 102: TABLE 3.4Tandem Performance of Turt
Page 103 and 104: Pause the Battle—Let’s Talk Ris
Page 105 and 106: Box 3.3 Battle Royale—And the Com
Page 107 and 108: TABLE 3.5Single Moving Average (SMA
Page 109 and 110: TABLE 3.8Donchian Channel Breakout
Page 111 and 112: we have, because who has the time t
Page 113 and 114: %Winners ShortLen InterLen StopDoll
Page 115 and 116: on a peak. This demonstrates a lack
Page 117 and 118: The correlation coefficients range
Page 119 and 120: TABLE 3.13The Best Results for the
Page 121 and 122: TABLE 3.17Results for the Donchian
Page 123 and 124: FIGURE 3.5AmiBroker’s walk-forwar
Page 125 and 126: direction of the 200-day moving ave
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CHAPTER 4Introduction toAmiBroker
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Things All Programmers Need to Know
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expressions deal with a string of c
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AFL uses functions DateNum(), DateT
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to Short and Cover as well. If you
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FIGURE 4.2The AmiBroker Editor wind
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to the user. Instead of having to r
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This is where you can change the va
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shortMav = MA(C,avgLen1);longMav =
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code. This is a very difficult thin
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the order of the arrays. I admit th
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FIGURE 4.17The AFL Code Wizard ‘
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■■■This time click on is grea
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This should look very similar to th
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before a single line of code is typ
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COND3 = C > MA(C,100);COND1 = cente
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’********************************
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FIGURE 5.1trading.Once you define t
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GetDataGET DATACOMPONENTLoad Data i
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152USING MICROSOFT EXCEL TO BACKTES
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performance of the trading system.
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156 FIGURE 5.9 The Visual Basic Edi
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FIGURE 5.12This error message appea
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End IfIf marketPosition = -1 ThenCa
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■■moc—Market On Close order.
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Simple Moving Average Crossover usi
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Call Trade(ExitShort, "S-ATR Prof",
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168USING PYTHON TO BACKTEST YOUR AL
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It works well on both Unix and Wind
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function modules and some are a lit
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inclusive, the number of iterations
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176This indicator is programmed as
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if prices[curBar - offset] < prices
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All these files deal with opening a
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entryPrice,fileList,entryPrice,entr
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Python has a ton of reusable code a
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186USING PYTHON TO BACKTEST YOUR AL
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portfolioClass. These classes are n
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percent wins, number of trades, and
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>>> c = a + b>>> cAfter you hit ent
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you don’t need to know exactly ho
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Shorting occurs when the low of the
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That was the long entry logic only
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FIGURE 6.4brackets.A Python run-tim
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prevAvg2 and today’s avg1 must be
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■ TradeStation IDE204AN INTRODUCT
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206AN INTRODUCTION TO EASYLANGUAGEt
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FIGURE 7.5The algorithm with a sing
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the Vars statement and initialize t
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TABLE 7.1EasyLanguage Keywords and
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214AN INTRODUCTION TO EASYLANGUAGEF
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standard deviations. In our example
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218AN INTRODUCTION TO EASYLANGUAGEt
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into a project makes it easier to a
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If marketPosition =-1 thenbeginend;
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or program any trading idea. Practi
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CHAPTER 8Genetic Optimization,Walk
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superfast processors, searching a l
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This equation involves four unknown
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For generation = 1 To 100 ' total g
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Converting Fitness in Terms of Slic
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'the 6 darts landedRandomizeFor l =
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roulette(i) = Rnd(1#)If roulette(i)
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selection and reproduction may even
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This is just one possible solution.
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history of each market. This test w
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Crossover RateThe rate that determi
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TradeStation’s and AmiBroker’s
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TABLE 8.3In-Sample Testing TA2Le Se
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Here are the various calculations t
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1. movAvgLen { 50 to 150 by 10 incr
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TABLE 8.6The Out-of-Sample (OOS) Re
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your algorithm are each written on
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That’s it! These seven lines of c
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[Equity]350003000025000200001500010
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Calculating Start Trade Drawdown Wi
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A loop is used to flow through each
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The maximum drawdown of this algori
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■ Fixed Fractional272PORTFOLIO AN
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FIGURE 9.2Add a strategy to your st
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276FIGURE 9.6The UGTATS markets and
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278FIGURE 9.9 How to adjust the set
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FIGURE 9.12 The Trade Analysis wind
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282PORTFOLIO ANALYSISFIGURE 9.15thi
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FIGURE 9.18 Profit and drawdown are
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FIGURE 9.20 To set the portfolio st
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FIGURE 9.24Source: TradeStationThe
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count makes sense; trades are turne
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APPENDIX AAmiBrokerHere is a brief
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switchbreak (part of the switch sta
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Here is the source code from Chapte
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PctSize = PositionRisk * MarginDepo
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■ KeywordsprevMarketPositionmarke
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upSumAvg = upSum / lengthdnSumAvg =
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Sub BollingerBand(dataList, length,
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308EXCEL SYSTEM BACKTESTERIf (kVal
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■ Keywords and Identifierscurrent
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312PYTHON SYSTEM BACKTESTERdef calc
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sysMark =self.systemMarkets[i]avgLo
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class stochClass(object):class stoc
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318PYTHON SYSTEM BACKTESTERfor i in
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■ Python-Specific Keywordsandasas
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Monte Carlo Analysis# Monte Carlo A
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FIGURE D.1324TRADESTATION AND EASYL
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Simple RSI with Profit Objective an
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If condition1 and condition2 then b
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330TRADESTATION AND EASYLANGUAGEIf(
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if lastTradeLoser thenbeginif curre
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Value1 = swingHighBar(1,h,pivotHiSt
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■■■■■■■Statistically
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INDEXNote: Page references followed
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calculation2 standard deviations, u
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ES. See Exhaustive searchESB. See E
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Mini Russell day trading system, na
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PPO. See Percentage price oscillato
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Time-based market order (MOC), exit
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Ultimate Algorithmic Trading System

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