Ultimate Algorithmic Trading System

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If close crosses below myBotBand, thenSellShort MOCIf position is long, thenIf close crosses below myAvg, thenSell MOCIf position is short, thenIf close crosses above myAvg, thenBuyToCover MOC64STOCHASTICS AND AVERAGES AND RSI! OH, MY!The beauty of this algorithm is in its simplicity; we don’t even need to sketcha flowchart. There is another facet of this algorithm that makes it a winner—it’sself-adaptive. Meaning the bands are moving (adapting) around the average based onan intrinsic property of the underlying instrument. And that property is volatility.Volatility reveals one of the most important things we need to know about aninstrument, and that is risk. Risk is directly proportional to volatility—the largerthe swings, the riskier the market. I am jumping the gun here, but we will use thisrisk measure as a filter in version 2 of the Bollinger Band trend algorithm. Volatilityalso informs us of market noise. When the Bollinger Bands expand during highlevels of volatility, they are indicating a chaotic situation—a situation where entrycriteria should be made more difficult to trigger. The following is the pseudocodefor version 1 of the trading algorithm:‘Trend Following with Bollinger Bands‘Utilizing 60-day average and 2 standard deviations‘Version 1myAVG = SMA(C,60)myTopBand = BollingerBand(C,60,+2)myBotBand = BollingerBand(C,60,-2)If close > myTopBand thenBuy this bar on closeIf close < myBotBand thenSellShort this bar on closeIf marketPosition = 1 and c < myAvg then sell this bar on closeIf marketPosition =-1 and c > myAvg then buyToCover this baron closeTable 2.8 shows the individual results of Bollinger Band version 1 across 35futures markets.In the prior paragraphs, I hinted of a version 2 of this algorithm utilizing thevolatility as trading filter. Risk aversion is most often the number-one considerationin the development of a trading algorithm, and we can use the volatility as awww.rasabourse.com
TABLE 2.8 Performance of Bollinger Band Algorithm Version 1Bollinger Band [60 Days 2 Standard Deviations]Ticker Net Profit Max DD # Trades Avg P/L Avg Bars Held % Winners<Portfolio> 1319369 −380897 3580 369 44 36AD0_E0B 30100 −25600 95 317 45 35BP0_E0B 86888 −90825 107 812 41 34C20_E0B −1263 −23038 119 −11 42 35CC20_E0B −67870 −69300 123 −552 33 22CL20_E0B 168160 −55940 101 1665 47 47CT20_E0B 54050 −34175 112 483 40 31CU0_E0B 167738 −27425 91 1843 50 44DX20_E0B 84575 −13820 94 900 49 47ED0_E0B 4997 −3363 105 48 48 31EMD0_E0B −18880 −38940 92 −205 39 29ES0_E0B −41925 −52213 120 −349 36 31FC0_E0B 55438 −25650 101 549 49 41FV0_E0B 23631 −15335 89 266 45 39GC20_E0B −19280 −59660 111 −174 39 32HG20_E0B 76650 −42513 111 691 44 37HO20_E0B 74941 71391 116 646 39 37KC20_E0B 100294 −108269 99 1013 45 38KW20_E0B 10800 −21350 109 99 41 31LB0_E0B 37973 −24997 109 348 44 38LC0_E0B −11770 −30010 121 −97 39 28LH0_E0B 10880 −25480 113 96 42 37MP0_E0B 6755 −25070 68 99 45 37NG20_E0B 175690 −99650 87 2019 47 45NK0_E0B 48150 −41675 85 566 46 36OJ20_E0B −10130 −45408 110 −92 43 36PL20_E0B 55340 −34340 109 508 41 31RB0_E0B 124147 −58171 109 1139 44 39S20_E0B 19475 −38125 115 169 41 36SB20_E0B 31944 −13788 100 319 48 42SI20_E0B −25660 −113680 111 −231 39 29TF0_E0B −14570 −51590 49 −297 41 37TU0_E0B 37075 −6975 78 475 50 42TY0_E0B 42466 −19490 98 433 46 40US0_E0B 22038 −42338 109 202 42 39W20_E0B −19475 −34225 114 −171 38 3265STOCHASTICS 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,
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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 and 78: LC0_E0B −25000 −41790 398 −62
Page 79: FIGURE 2.14Source: TradeStationA 60
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
Page 127 and 128: range day and initially puts on two
Page 129 and 130: 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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