Ultimate Algorithmic Trading System

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344INDEXHhead (variable), 185Helper functions (PSB), 180–181Hermawanto, Denny, 228, 230Highest Function/Module, 318Hindsight, usage, 253fHistogram pivot points, usage, 55fHistogram system. See Moving AverageConvergence DivergenceHolland, John, 230How to Make Money in Commodities (Keltner), 69IIDE. See Integrated development environmentIdentifiers (PSB), 310IDLE. See Integrated DeveLopment EnvironmentIndicator.py, 171–179Indicatorscategories, 25classes/functions (PSB), 315–319input (algorithm criteria), 1InSampleBarsPerRun, 253In-sample (IS) testing, 250–251In-sample (IS) time periodBollinger algorithm, optimization, 104tDonchian algorithm, results, 103tportfolio, parameters, 104tInstruction ste, variability, 14–15Integrated development environment (IDE), 114Integrated DeveLopment Environment (IDLE),usage, 169–170IS. See In-sampleIteration growth rate, parameters (example),229fJJaneczko, Tomasz, 114KKaufman, Perry, 86Keltner Challengers, Bollinger Benchmark(contrast), 74tKeltner Channel, 69–75algorithm, best parameter sets, 75tcalculation2ATR,usage,6960 days, usage, 69data, Bollinger Bands data (comparison), 71foptimization, 73ftrend follower function, 69b–70bKeltner, Chester, 69Keywords (EasyLanguage), 212tLLagging indicator, 25Lambert, Donald, 48Lane, George C., 42LastBarOfMonth (LBM) pattern, 110bLast trade was a loser filter (LTLF), 79, 262absence, 80examples, 81t, 82tengagement, 86inclusion, examples, 83t, 85tusage, 328–332LBM. See LastBarOfMonthLimit (lmt) order, 161Lists, initiation (PSB), 181–182load (keyword), recognition, 4Logical expressions, 116Long division procedure, example, 4–5Long entries, 9b–10bLong entry signal description, 13–14usage, 14fLONG position, establishment, 16Long position exit instruction (TradeStation), 218lookback (sAverage function parameter), 172Loops, 294programming. See AmiBroker loop programming.Lowest Function/Module, 319LTLF. See Last trade was a loser filterMMACD. See Moving Average ConvergenceDivergenceMacro, ESB launching, 153fMarginDeposit, 123margindeposit (AmiBroker keyword), 294marketDataClass, 187, 311objects, 186–187marketDataClass() method, 185Market on Close (moc), 21order, 162Market (mkt) order, 162Markets To Test, opening, 171marketSystemClass, 188MAS. See Multi-algorithm strategymaxContractPoints, 223maxContractProfit, 223Maximum drawdown, display, 280f, 282fMaximum quantity, parameter (change), 282Mean reversion (MR), 109balgorithm, equity curve (usage), 108fstrategy (change), input variables (usage),215fMeanReversion (name), 220www.rasabourse.com
Mini Russell day trading system, narrow range day(usage), 110fmmStop defaults, 132MOC. See Time-based market ordermoc. See Market on CloseMoney management, 271Fixed Fractional money management overlay,281–284stop, exit procedure, 162Money Management window, 281parameters, change, 283fMonte Carlo analysis, 227, 258–264, 322Monte Carlo settings, AmiBroker Monte Carlosettings dialog, 262fMonte Carlo simulationAmiBroker Monte Carlo simulation,261–264distribution statistics, 264timplementation, Python (usage), 259–261,265–269Monte Carlo source code, 320–322Monte Carlo straw broom chart, 263fMoving Average Convergence Divergence(MACD), 53–58calculation, 12-day/26-day moving averages(usage), 55histogram system, 56b–57bMoving averagescrossover algorithm, 88double simple moving average (DMA) crossover,89balgorithm, 92texponential moving averages, 58–62simple moving averages, 58–62single simple moving average (SMA) crossover,89balgorithm, 91ttriple simple moving average (TMA) crossover,89b–90balgorithm, 92t20-day moving averages, examples, 59f200-day moving average, 5weighted moving averages, 58–62MR. See Mean reversionMulti-algorithm strategy (MAS), 108–111Multiple randomized equity curves,chart, 263fMutation, 240–243concepts, 230occurrence, 240–241rate, 246VBA Excel, usage, 242–243myAvg, 115myCondition, variable (initiation), 210myDayCount, 158MyFirstPortfolio, 277f, 288MyFirstStrategyGrouplist, 277fselection, result, 277fmyProfitTarg, 158MySecondAlgoWizard, 137mySum, 115myTickValue, 163myTrueRange, 148myValue, 130–131NNarrow range day, usage, 110fNet profit, performance metric, 90New Concepts In Technical Trading Systems(Wilder), 26New Trading Systems and Methods (Kaufman), 86Number of trades, performance metric, 91numcolumns (AmiBroker keyword), 294OOB/OS system description. See CommodityChannel Indexoffset (sAverage function parameter), 172OOS. See Out-of-sampleOperatorsAmiBroker Function Language (AFL), 116–117precedence, 117–118Optimizationgenetic optimization, 227parameter sets, 98t3-D optimization chart, 130fOptimize button, 129Orders, types, 161–162Oscillators, 25, 26crossover, entry signal usage. See Stochastics.Outlining, 206Out-of-sample (OOS)in-sample (IS), ratio, 102number of bars, 253percentage, 256report, 257ttesting, 251–252example, 252ttime period, Donchian algorithm results, 103t,105ttimespan, 104–105OutOfSampleBarsPerRun, 253output (algorithm criteria), 1345INDEXwww.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
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Start orNew BarIs RSI < 30YesBuy Ma
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RSI DivergenceMarket Continuing to
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If marketPosition =-1 and close > e
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TABLE 2.3Performance of RSI Failure
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Start orNew BarIs Slow %K CrossingA
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TABLE 2.4Performance of Stochastic
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it is boundless extended readings a
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TABLE 2.5Performance of CCI algorit
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FIGURE 2.11Source: TradeStationThe
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We will discuss moving averages in
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MACDAvg = XAverage( MyMACD,9)MACDDi
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FIGURE 2.13Source: TradeStationThre
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LC0_E0B −25000 −41790 398 −62
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FIGURE 2.14Source: TradeStationA 60
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TABLE 2.8 Performance of Bollinger
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If filterTrade = false thenIf close
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Keltner ChannelChester Keltner brou
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FIGURE 2.15Source: TradeStationKelt
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35000300002500020000150001000050000
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TABLE 2.12Best Parameter Sets of Ke
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CHAPTER 3Complete TradingAlgorithms
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Box 3.1 Turtle System #1Buy one tic
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TABLE 3.1Turtle System 1 without LT
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TABLE 3.3Turtle System 1 with LTLF
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TABLE 3.4Tandem Performance of Turt
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Pause the Battle—Let’s Talk Ris
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Box 3.3 Battle Royale—And the Com
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TABLE 3.5Single Moving Average (SMA
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TABLE 3.8Donchian Channel Breakout
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we have, because who has the time t
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%Winners ShortLen InterLen StopDoll
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on a peak. This demonstrates a lack
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The correlation coefficients range
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TABLE 3.13The Best Results for the
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TABLE 3.17Results for the Donchian
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FIGURE 3.5AmiBroker’s walk-forwar
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direction of the 200-day moving ave
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range day and initially puts on two
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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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Page 305 and 306: APPENDIX AAmiBrokerHere is a brief
Page 307 and 308: switchbreak (part of the switch sta
Page 309 and 310: Here is the source code from Chapte
Page 311 and 312: PctSize = PositionRisk * MarginDepo
Page 313 and 314: ■ KeywordsprevMarketPositionmarke
Page 315 and 316: upSumAvg = upSum / lengthdnSumAvg =
Page 317 and 318: Sub BollingerBand(dataList, length,
Page 319 and 320: 308EXCEL SYSTEM BACKTESTERIf (kVal
Page 321 and 322: ■ Keywords and Identifierscurrent
Page 323 and 324: 312PYTHON SYSTEM BACKTESTERdef calc
Page 325 and 326: sysMark =self.systemMarkets[i]avgLo
Page 327 and 328: class stochClass(object):class stoc
Page 329 and 330: 318PYTHON SYSTEM BACKTESTERfor i in
Page 331 and 332: ■ Python-Specific Keywordsandasas
Page 333 and 334: Monte Carlo Analysis# Monte Carlo A
Page 335 and 336: FIGURE D.1324TRADESTATION AND EASYL
Page 337 and 338: Simple RSI with Profit Objective an
Page 339 and 340: If condition1 and condition2 then b
Page 341 and 342: 330TRADESTATION AND EASYLANGUAGEIf(
Page 343 and 344: if lastTradeLoser thenbeginif curre
Page 345 and 346: Value1 = swingHighBar(1,h,pivotHiSt
Page 347 and 348: ■■■■■■■Statistically
Page 349 and 350: INDEXNote: Page references followed
Page 351 and 352: calculation2 standard deviations, u
Page 353: ES. See Exhaustive searchESB. See E
Page 357 and 358: PPO. See Percentage price oscillato
Page 359 and 360: Time-based market order (MOC), exit
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