Ultimate Algorithmic Trading System

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Risk of Ruin CalculationAverage Win $$400 ≪--Input HereAverage Loss $$200 ≪--Input HerePercent Wins35.0% ≪--Input HereInitial Capital $$35,000 ≪--Input HereAmount of Pain % (% of Capital)50.0% ≪--Input HerePayoff Ratio 2Percent Losers 65.0%Average Win % 1.1%Average Loss % 0.6%Sum of Possible Events (SoPE) 0.00029Sq.Root of Sum of Squares of (SoPE) 0.00818‘‘P’’ 0.517460757RoR 1.39814%88COMPLETE TRADING ALGORITHMSThis trend-following system wins only 35% of the time but has a payoff ratioof 2 (Average Win$ / Average Loss$). If your pain threshold is 50% of yourstarting capital, then you run a risk of ruin of nearly 1.4%. With these algorithmperformance metrics and RoR you shouldn’t have a problem pulling the triggeron this system. Before you start trading be sure to check the risk of ruin and seeif it fits your expectations. One more note before we resume the battle: Moneymanagement will not be utilized so that we can see the raw capabilities of eachsystem.Resume the Battle There will be a total of six popular trend-followingalgorithms tested, the Turtle system and the five shown in Box 3.3. Each test willconsist of three subtests. The subtests will reflect three different parameter setsthat will cause the algorithms to trade in a shorter-term, intermediate-term, andlonger-term manner. For example, a simple moving average crossover algorithmwill be tested utilizing moving average lengths of 19, 39, and 100. These parametersshould cover the three time horizons—short, intermediate, and long. The parameterlengths were chosen without the benefit of hindsight—in other words, they werechosen based on my perceived applicability to obtain the three time horizons withoutlooking at prior test results. This isn’t really a true statement, because my experiencethrough testing systems over the years does introduce a slight hindsight bias. Allentries and exits will be executed on the closing price of the bar that generates thetrade signal.www.rasabourse.com
Box 3.3 Battle Royale—And the Competitors Are:Single Simple Moving Average Crossover (SMA)Buy when close crosses above moving averageSell short when close crosses below moving averageLiquidate long if close < entryPrice − 3000/bigPointValueLiquidate short if close > entryPrice + 3000/bigPointValueUtilize moving average lengths of 19, 39, and 100Double Simple Moving Average Crossover (DMA)Buy when shorter moving average crosses above longer moving averageSell short when short moving average crosses below longer moving averageLiquidate long if close < entryPrice – 3000/bigPointValueLiquidate short if close > entryPrice + 3000/bigPointValueUtilize moving average lengths:Short lengths: 9, 19, 49Long lengths: 19 ,49, 99Triple Simple Moving Average Crossover (TMA)Triple moving averages require two criteria to be met before a trade entry:Buy:shortest moving average and intermediate moving average both must be greaterthan longer-term averageBuy when shortest moving average crosses above intermediate moving averageSell short:shortest moving average and intermediate moving average both must be lessthan longer-term averageSell short when shortest moving average crosses below intermediate movingaverageLiquidate long when shortest moving average crosses below intermediate movingaverage or if close < entryPrice – 3000/bigPointValueLiquidate short when shortest moving average crosses above intermediate movingaverage or if close > entryPrice + 3000/bigPointValueUtilize the following moving average lengths:89COMPLETE TRADING ALGORITHMSwww.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
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One BarStrengthTwo BarStrengthThree
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and inexperienced trading system pr
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Input = 6StartInput = 4Input <> 6In
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The diagram in Figure 1.4 looks cle
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observed. If the latter criterion i
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Start or NewBarIs C >AVG(C,200)YesN
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CHAPTER 2Stochastics andAverages an
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The basic theory behind the ADX is
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4. Divide the 14-day smoothed plus
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if myADX < 35 thenif MarketPosition
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TABLE 2.1Performance of Directional
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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 and 80: FIGURE 2.14Source: TradeStationA 60
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: Pause the Battle—Let’s Talk Ris
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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Page 129 and 130: CHAPTER 4Introduction toAmiBroker
Page 131 and 132: Things All Programmers Need to Know
Page 133 and 134: expressions deal with a string of c
Page 135 and 136: AFL uses functions DateNum(), DateT
Page 137 and 138: to Short and Cover as well. If you
Page 139 and 140: FIGURE 4.2The AmiBroker Editor wind
Page 141 and 142: to the user. Instead of having to r
Page 143 and 144: This is where you can change the va
Page 145 and 146: shortMav = MA(C,avgLen1);longMav =
Page 147 and 148: code. This is a very difficult thin
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Page 151 and 152: FIGURE 4.17The AFL Code Wizard ‘
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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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