Ultimate Algorithmic Trading System

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case(1):beginif h > state1Price then state1Price = h;if low < state1Price * .98 thenbeginstate = 2;print(date," State 2 :",low);value1 = text_new(date[1],time,l-10,"S2");text_setColor(value1,red);end;end;end;case(2):beginif isHiPivot and h[1] > state1Price thenbeginstate = 3;state3Price = h;print(date," State 3 :", state3Price);value1 = text_new(date[2],time,h+20,"S3");;text_setColor(value1,yellow);end;end;case(3):beginif h > state3Price then state3Price = h;if l < state3Price * .98 thenbeginbuy this bar on close;print(date," accept state : ",l);value1 = text_new(date[1],time,l-10,"S4");;text_setColor(value1,GREEN);state = 0;end;end;225AN INTRODUCTION TO EASYLANGUAGEIf barNumber - state1BarNumber > 30 then state = 0;Sell next bar at lowest(l,60) stop;www.rasabourse.com
CHAPTER 8Genetic Optimization,Walk Forward,and Monte CarloStart Trade Analysis227■ Utilizing TradeStation and AmiBrokerBoth TradeStation and AmiBroker software includes two very powerful tools that canbe utilized to help create robust trading algorithms. If you have used TradeStation orAmiBroker to develop trading algorithms but have not taken advantage of genetic orwalk-forward optimization, this chapter will hopefully provide enough informationto help enhance your research and unlock the potential of these powerful tools.These tools have been a part of both platforms for several years, but through myobservations of other users they have been either ignored or underutilized. Usersoften note these stumbling blocks:■■■They are hard to understand.They are hard to implement.They lead to over-curve-fitting.The objective of this chapter is to explain that a computer science degree isnot necessary to understand these tools; in some scenarios their use is absolutelyThe Ultimate Algorithmic Trading System Toolbox + Website: Using Today’s Technology to Help You Become a Better Trader. George Pruitt© 2016 by George Pruitt. Published by John Wiley & Sons, Inc.www.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
Page 189 and 190: inclusive, the number of iterations
Page 191 and 192: 176This indicator is programmed as
Page 193 and 194: if prices[curBar - offset] < prices
Page 195 and 196: All these files deal with opening a
Page 197 and 198: entryPrice,fileList,entryPrice,entr
Page 199 and 200: Python has a ton of reusable code a
Page 201 and 202: 186USING PYTHON TO BACKTEST YOUR AL
Page 203 and 204: portfolioClass. These classes are n
Page 205 and 206: percent wins, number of trades, and
Page 207 and 208: >>> c = a + b>>> cAfter you hit ent
Page 209 and 210: you don’t need to know exactly ho
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: or program any trading idea. Practi
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 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
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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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