Ultimate Algorithmic Trading System

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346INDEXPPaintbar, 206Paradigms, example, 12–23ParametersBollinger performance, three-dimensionalcontour chart, 98fchange, 136fcomparison, 94tsets, 96t–97toptimization usage, 98trobust parameter sets, presence, 247fsetting, 126fstress, 248Partner matching probability, 238Pattern system, equity curve, 109fp-code, 36b, 46bPercentage price oscillator (PPO), 57Percent Risk, parameter (change), 281Performance metrics, 90–91example, 153fPeters, Tim, 170Pivot bar, strength (basis), 13Pivot high points, strength differences (examples),13fPivot point entry algorithm, example, 19bPivot-point long-entry algorithm, FSM modeling,18fPM. See Portfolio Maestropointvalue (AmiBroker keyword), 294Population, 231concepts, 230initial population setup, VBA Excel(usage), 232size, 246PortfolioAFL results, 128falgorithm, usage (decision), 101–108analysis, 271, 284–290backtesting, preparation, 279composition, 100–108correlation matrix, portion, 100fequity curve, stops (addition), 288fparameters, 104tprofit targets, examples, 286f, 287fsample, Turtle system 2 (example), 85tSettings, 285fsetup, PSB (usage), 182–193stop lossappearance, 287fsetting, 286fstop option, 287ftrades, stopsexclusion, 289tinclusion, 289tportfolioClass, 187–188, 313–315Portfolio Maestro (PM), 203, 271, 272–290backtest performance report, 279fequity curve chart, maximum drawdown(display), 280ffixed fractional money management overlay,281–284launch window, 273fManage Strategy Group button, indication, 278fMoney Management window, parameters(change), 283fMyFirstPortfolio, 277fMyFirstStrategyGroup, selection, 277fportfolioanalysis, 284–290backtesting, preparation, 279fequity curve, stops (addition), 288fprofit targets, examples, 286f, 287fstop loss, appearance, 287fstop option, 287fPortfolio Settings, 285fposition size, profit/drawdown (correlation),284fReturns and Equity tab, maximum drawdown(display), 282fstrategy group, 273–274list, 277fsettings, adjustment, 278fstrategy, addition, 274fStrategy Group settings button, 278fsymbol, addition, 275fTotal P/L option, 281fTrade Analysis window, trading statistics display,280fTurtle 55 strategy, appearance, 274fUGTATS list, selection, 275fUGTATS Market List, 274–281UGTATS markets, symbols, 276fPortfolio Profit Target, impact, 287Portfolio sample, Turtle system 1last trade was a loser filter (LTLF)absence, 81t, 82tinclusion, examples, 83t, 85tpositionscore (AmiBroker keyword), 294PositionSize, 123positionsize (AmiBroker keyword), 294Position size, profit/drawdown (correlation), 284fPosition sizing, 99–100futures mode (AmiBroker), 298–299usage, 328–332www.rasabourse.com
PPO. See Percentage price oscillatorPredictive indicator, 25Price bar interface, 118–120reserved words, 118Price-based indicators, 25, 58prices (sAverage function parameter), 172Probability of being hit by dart (calculation), VBAExcel (usage), 235Problem solving, 230–231Profit factor, performance metric, 91Profit/loss, performance metric, 91Profit objective, usage, 326–327Programming environment, 154–163Protective stop, usage, 326–327PSB. See Python System BacktesterPSBBollinger, opening, 170Pseudocode, 5–6algorithm pseudocde, 6conversion process, 12Pyramiding, usage, 80, 328–332Pythoncode, example, 6dynamic typing, 168–169installation, 169interpreted language, 168shell, presence, 168usage, 167, 259–261, 265–269reasons, 167–169user’s group, presence, 169Python Monte Carlo simulation, 261Python Programming (Zelle), 168Python System Backtester (PSB), 168, 309Bollinger Bands Function Module, 319classes, 310–315class rsiClass(object):, 317–318class stochClass(object):, 316–317configuration, 182–193data arrays, 309data import, 182–193equityClass, 310helper functions, 180–181Highest Function/Module, 318identifiers, 310IDLE, usage, 169–170import section, 179–193indicator classes/functions, 315–319indicator.py, 171–179installation, 169–170keywords, 310, 320–322lists, 309initiation, 181–182Lowest Function/Module, 319marketDataClass, 311Monte Carloanalysis, 322source code, 320–322portfolioClass, 313–315portfolio setup, 182–193rsiClass, 174–178sAverage, 172–174function, parameters, 172Simple Average Function/Module, 319Start Trade Drawdownsection, 320–321source code, 320–322structure, 171–193SystemTester.py, 178–179tradeInfoClass, 311–313usage, 193–202variables, initiation, 181–182QQuantitative Trading Systems (Bandy), 114RrampUp, 157Ramp-up data, insufficiency, 158fRandom number generator (RNG), 231, 239tool, 258–259usage, 260Rangefunction, 17410-day average, calculation, 111bReal Time data feeds, 114Relative strength (RS), 34–35Relative Strength Index (RSI), 5, 24, 32–42algorithm, performance, 38tdivergence, example, 39ffailure swing algorithm, performance, 43tfailure swing system description, 39b–41b14-day RSI, calculation, 32, 34pivots, value, 41profit objective, usage, 326protective stop, usage, 326systemdescription, 35b–36bp-code, 36btrading algorithm, flowchart (FC) diagram, 37fusage, example, 35fVBA function, 161removeDuplicates, 192Replacement function, VBA Excel (usage), 237–238Reproduction, 238–240concepts, 230347INDEXwww.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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count makes sense; trades are turne
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 and 354: ES. See Exhaustive searchESB. See E
Page 355: Mini Russell day trading system, na
Page 359 and 360: Time-based market order (MOC), exit
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