Ultimate Algorithmic Trading System

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for y in range(0,len(startTradeTuple)):print(startTradeTuple[y][0],’ ’,startTradeTuple[y][1],’ ’,startTradeTuple[y][2])if startTradeTuple[y][2] < minDD: minDD =startTradeTuple[y][2]if startTradeTuple[y][2] > maxDD: maxDD =startTradeTuple[y][2]numBins = 20binTuple = list()binMin = minDDbinMax = maxDDbinInc = (maxDD - minDD)/20.0binBot = binMinfor y in range(0,numBins):binTop = binBot + binIncbinTuple += ((y,binBot,binTop),)print(binTuple[y][1],’ ’,binTuple[y][2])binBot = binTop + ybins = list()bins[:] = []for x in range(0,numBins):bins.append(0)for x in range(0,len(startTradeTuple)):for y in range(0,numBins):tempDD = startTradeTuple[x][2]tempBot = binTuple[y][1]tempTop = binTuple[y][2]if (tempDD >= binTuple[y][1] and tempDD< binTuple[y][2]):# tempVal = bins(y) + 1# bins.insert(y,tempVal)bins[y] += 1freqSum = sum(bins)binProb = list()for y in range(0,numBins):if y == 0:binProb.append(bins[y]/freqSum)else:binProb.append(bins[y]/freqSum + binProb[y-1])for y in range(0,numBins):print("Probability of DD < %7d is %4.3f\n"% (binTuple[y][2], binProb[y]))321PYTHON SYSTEM BACKTESTERwww.rasabourse.com
Monte Carlo Analysis# Monte Carlo Analysis322PYTHON SYSTEM BACKTESTERmcTradeTuple = list()for x in range(0,5): # number of alternate historiesfor y in range(0,len(tradeTuple)):randomTradeNum = random.randint(0,len(tradeTuple)-1)mcTradeTuple += ((x,y,tradeTuple[randomTradeNum][1],tradeTuple[randomTradeNum][0]),)mcTradeResultsTuple = list()whichAlt = -1for x in range(0,len(mcTradeTuple)):if mcTradeTuple[x][1]==0:print(’New Alternate History Generated’)cumEquity = 0maxTradeDD = -99999999maxCumEquity = 0cumEquity += mcTradeTuple[x][2]print(’Randomized trade listing : ’,mcTradeTuple[x][3],’ ’,mcTradeTuple[x][2])maxCumEquity = max(maxCumEquity,cumEquity)maxTradeDD = max(maxTradeDD,maxCumEquity - cumEquity)if mcTradeTuple[x][1] == len(tradeTuple)-1 :mcTradeResultsTuple += ((cumEquity,maxTradeDD,cumEquity/len(tradeTuple)),)for x in range(0,len(mcTradeResultsTuple)):mcCumEquity = mcTradeResultsTuple[x][0]mcMaxDD = mcTradeResultsTuple[x][1]mcAvgTrd = mcTradeResultsTuple[x][2]print(’Alt history %5d Profit: %10d MaxDD:%10d Avg Trade %6d\n’ % (x,mcCumEquity,mcMaxDD,mcAvgTrd))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
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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
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
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: ■ Python-Specific Keywordsandasas
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 and 356: Mini Russell day trading system, na
Page 357 and 358: PPO. See Percentage price oscillato
Page 359 and 360: Time-based market order (MOC), exit
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Ultimate Algorithmic Trading System

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