Ultimate Algorithmic Trading System

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244GENETIC OPTIMIZATION, WALK FORWARDsame algorithm, except instead of adding the volatility level, it subtracts it. Here arethe choppy market long and short entries:Buy Next Barat Close + xPercent ∗ xAverage(Range, 20)stopSell Short Next Barat Close − xPercent ∗ xAverage(Range, 20)stopNow if the CMI indicates a trending market by exhibiting a higher value, thenthe system switches to the trend-following mechanism. Here, the system falls backon the old Donchian breakout algorithm. Longs are put on at the Highest High ofxTrendBars and shorts are put on at the Lowest Low of xTrendBars. The entry rulesfor trending markets are:BuyNextBarat Highest(High, xTrendBars)stopSell Short Next Barat Lowest(Low, xTrendBars)stopLooking at our entry rules, we can see there are three optimizable parameters:xLevel, xPercent, and xTrendBars. The last two parameters can be further optimizedfor long entries and short entries. If we allow these two parameters to be differentfor longs versus shorts, then instead of two parameters we have four. So all in all,there will be a total of five optimizable parameters:1. xLevel—Choppy Market value2. xPercentBuys—Percent of 20-day exponential moving average of Range3. xPercentSells—Percent of 20-day exponential moving average of Range4. xTrendBarsBuys—Number of Highest high/Lowest low lookback bars5. xTrendBarsSells—Number of Highest high/Lowest low lookback barsWe could simply pull out of thin air some values for these parameters or we coulduse TradeStation’s tools to help zero in on a robust solution. We now have a tradingsystem and a set of five optimizable parameters, so the next step is to come up witha range of values that we want to optimize the parameters across. These differentranges can also be called our search space. Here are the ranges and iterations foreach of the parameters for our system, TA1:1. xLevel { 25 to 75 by 5 increments } [11 iterations]2. xPercentBuys { 0.25 to 0.75 by 0.05 increments } [11 iterations]3. xPercentSells { 0.25 to 0.75 by 0.05 increments } [11 iterations]4. xTrendBarsBuy { 8to20by4} [4 iterations]5. xTrendBarsSell { 8to20by4} [4 iterations]If we do the math (11 * 11 * 11 * 4 * 4), we come up with a total of 21,296iterations. It will take this many iterations to search the entire space if we apply anexhaustive search algorithm. So the computer will need to run 21,296 tests on thewww.rasabourse.com
history of each market. This test was performed on 10 years of Euro currency dataand it took my ‘‘typical’’ computer 13 minutes. The search space could have beenexpanded but for brevity sake it was limited to 21,296. If just one increment wasadded to each of the parameters’ range, the total number of iterations would havenearly doubled. The results of the exhaustive search are shown in Table 8.1. (Note:Results are shown without a commission/slippage charge.)Now let’s do the same thing, but instead of using the Exhaustive method let’sgo with the genetic option. In less than one minute the results in Table 8.2 werecomputed.TABLE 8.1Results from the Exhaustive Search Algorithm’s Five ParametersxLevelxPercentBuyxPercentSellxTrendBarsBuyxTrendBarsSellTotalProfitMax Intra.Drawdown25 0.3 0.75 8 20 103615 −1523525 0.4 0.75 8 20 102435 −1608025 0.4 0.7 8 20 101005 −1576025 0.7 0.75 8 20 96050 −1780525 0.75 0.7 8 20 96680 −1834325 0.45 0.75 8 20 97435 −1725525 0.75 0.75 8 20 94020 −1848025 0.7 0.7 8 20 96015 −766825 0.5 0.75 8 20 93805 −1666025 0.4 0.65 8 20 98763 −15485TABLE 8.2xLevelxPercentBuyResults from the Genetic Search Algorithm’s Five ParametersxPercentSellxTrendBarsBuyxTrendBarsSellTotalProfitMax Intra.Drawdown25 0.3 0.75 8 20 103615 −1523525 0.4 0.75 8 20 102435 −1608025 0.4 0.7 8 20 101005 −1576025 0.7 0.75 8 20 96050 −1780525 0.45 0.75 8 20 97435 −1725525 0.5 0.75 8 20 93805 −1666025 0.55 0.75 8 20 91650 −1506325 0.45 0.7 8 20 95655 −1611025 0.35 0.75 8 16 91575 −1884325 0.6 0.75 8 20 86850 −15913245GENETIC 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
Page 207 and 208: >>> c = a + b>>> cAfter you hit ent
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Page 211 and 212: Shorting occurs when the low of the
Page 213 and 214: That was the long entry logic only
Page 215 and 216: FIGURE 6.4brackets.A Python run-tim
Page 217 and 218: prevAvg2 and today’s avg1 must be
Page 219 and 220: ■ 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)
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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 and 272: TABLE 8.6The Out-of-Sample (OOS) Re
Page 273 and 274: your algorithm are each written on
Page 275 and 276: That’s it! These seven lines of c
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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
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Page 305 and 306: APPENDIX AAmiBrokerHere is a brief
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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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