Ultimate Algorithmic Trading System

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software-derived optimal set is selected (15 OOS% with 25 runs), then the followingparameters are suggested to be used for next 111 days.1. movAvgLen 1502. pivotHiLookBack 43. pivotHiStrength 14. pivotLowLookBack 55. pivotLowStrength 16. LprofitObjective 11507. LstopLoss 3508. SprofitObjective 9509. SstopLoss 50010. longExitDays 1311. shortExitDays 14Now the question that started this part of the chapter can be answered. TheWFO is an incredible tool that can help determine if a trading algorithm needs to beretrained periodically and also if the algorithm demonstrates sufficient robustness tomatch to some degree the results that were derived during its training.258■ Monte Carlo AnalysisGENETIC OPTIMIZATION, WALK FORWARDAs we have seen, walk-forward analysis can tell us much about an algorithm’srobustness. Another tool that can reveal algorithm robustness is Monte Carlosimulation. The key to this form of simulation can be found in random numbers.Once you develop what you consider a good trading algorithm and test it againsthistorical data, you then can really put it to the test by using random numbers andthe historical trade history.This trade history that is generated by testing your algorithm represents justone path your system traveled. What if you could create many paths by jumblingthe order of the trades that your algorithm generated. These different paths couldrepresent alternate universes. Let’s say that your algorithm got lucky and had abunch of winning trades in a row. This streak might be the portion of the equitycurve that pulled the system out of mediocre status. If these trades didn’t exist orhadn’t fallen in place like they did, then the equity curve might look quite a bitdifferent. A robust trading system should still produce robust performance metricseven when the trades are jumbled, some eliminated and some duplicated. If themajority of the alternate paths of an algorithm fail to produce good metrics, thenthe algorithm should be considered suspect.Creating alternate paths or parallel universes is a very simple process when youhave access to a random number generator and a computer. As you saw earlier inthis chapter an RNG is a very special tool. Imagine all of the trades generated bywww.rasabourse.com
your algorithm are each written on a separate piece of paper and all the pieces arethen placed in a bag. You create an alternative trade history by reaching into the bagand randomly selecting trades and writing them down in a trade log in sequentialmanner. You do this routine until you have recorded the same number of trades thatwere initially generated by the backtest. One very important thing to rememberis to always return the piece of paper to the bag after you record the trade. Thisprocess would be very time consuming and you might ask what’s the use. Don’tworry about the time involved because the computer can do it very quickly andrecreating hundreds of alternative paths is well worth the effort. Will it help createa better algorithm? Probably not but it will help you make a decision to allocatereal funds to an algorithm or not. Like I stated earlier, the robustness of a tradingalgorithm will reveal itself through Monte Carlo simulation.Implementation of Monte Carlo Simulation Using Python The concept of thisform of simulation is easy enough. And putting it into computer language is as easy.The hardest part is getting the initial trade list. Once you have that the rest falls intoplace relatively easily. Understanding how a computer implementation of a conceptworks usually helps the understanding of how the concept works in general and howit might be beneficial.Imagine all the trades generated by a backtest are stored in a list. The list issimply an ordered collection of the trade dates and trade profits. The list might looksomething like this:TradeList[0] = (20011015,−150)TradeList[1] = (20011105,+200)TradeList[2] = (20011202,−300)TradeList[3] = (20011222,+500)You could call this the source list. Now imagine you have another blank list that’scalled AlternateList1. The objective of the Monte Carlo simulation is to fill theAlternateList1 with trades from the source list. If there are only four elements inthe list, you would need an RNG to generate random numbers from 0 to 3. Let’spush the RNG button and start the process of filling up AlternateList1:The RNG generates the number 3AlternateList1[0] = TradeList[3]RNG generates the number 0AlternateList[1] = TradeList[0]RNG generates the number 1AlternateList[2] = TradeList[1]RNG generates the number 3AlternateList[3] = TradeList[3]259GENETIC OPTIMIZATION, WALK FORWARDwww.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
Page 221 and 222: 206AN INTRODUCTION TO EASYLANGUAGEt
Page 223 and 224: FIGURE 7.5The algorithm with a sing
Page 225 and 226: the Vars statement and initialize t
Page 227 and 228: TABLE 7.1EasyLanguage Keywords and
Page 229 and 230: 214AN INTRODUCTION TO EASYLANGUAGEF
Page 231 and 232: standard deviations. In our example
Page 233 and 234: 218AN INTRODUCTION TO EASYLANGUAGEt
Page 235 and 236: into a project makes it easier to a
Page 237 and 238: If marketPosition =-1 thenbeginend;
Page 239 and 240: or program any trading idea. Practi
Page 241 and 242: CHAPTER 8Genetic Optimization,Walk
Page 243 and 244: superfast processors, searching a l
Page 245 and 246: This equation involves four unknown
Page 247 and 248: For generation = 1 To 100 ' total g
Page 249 and 250: Converting Fitness in Terms of Slic
Page 251 and 252: 'the 6 darts landedRandomizeFor l =
Page 253 and 254: roulette(i) = Rnd(1#)If roulette(i)
Page 255 and 256: selection and reproduction may even
Page 257 and 258: This is just one possible solution.
Page 259 and 260: history of each market. This test w
Page 261 and 262: Crossover RateThe rate that determi
Page 263 and 264: TradeStation’s and AmiBroker’s
Page 265 and 266: TABLE 8.3In-Sample Testing TA2Le Se
Page 267 and 268: Here are the various calculations t
Page 269 and 270: 1. movAvgLen { 50 to 150 by 10 incr
Page 271: TABLE 8.6The Out-of-Sample (OOS) Re
Page 275 and 276: That’s it! These seven lines of c
Page 277 and 278: [Equity]350003000025000200001500010
Page 279 and 280: Calculating Start Trade Drawdown Wi
Page 281 and 282: A loop is used to flow through each
Page 283 and 284: The maximum drawdown of this algori
Page 285 and 286: ■ Fixed Fractional272PORTFOLIO AN
Page 287 and 288: FIGURE 9.2Add a strategy to your st
Page 289 and 290: 276FIGURE 9.6The UGTATS markets and
Page 291 and 292: 278FIGURE 9.9 How to adjust the set
Page 293 and 294: FIGURE 9.12 The Trade Analysis wind
Page 295 and 296: 282PORTFOLIO ANALYSISFIGURE 9.15thi
Page 297 and 298: FIGURE 9.18 Profit and drawdown are
Page 299 and 300: FIGURE 9.20 To set the portfolio st
Page 301 and 302: FIGURE 9.24Source: TradeStationThe
Page 303 and 304: 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
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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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