Ultimate Algorithmic Trading System

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Donald Lambert’s CCI—Commodity Channel IndexDonald Lambert developed the CCI in 1980, and it was featured in Commoditiesmagazine that same year. The Commodity Channel Index can be used in the samevein as RSI and stochastic: trend determination or warning of extreme conditions.Lambert originally developed CCI to identify cyclical turns in commodities, but theindicator can be successfully applied to indices, ETFs, stocks, and other securities. Ingeneral, CCI measures the current price level relative to an average price level overa given period of time. CCI is relatively high when prices are far above their average,and conversely, CCI is relatively low when prices are far below their average—henceits application as reversion to the mean tool.The main difference between CCI and other similar oscillators is that it is notnecessarily range bound. Most of the time, the CCI will oscillate between twoextremes, but as you will see in its calculations, there is not a fixed boundary. TheCCI introduces the concept of typical price and utilizes it in its calculations. Typicalprice is just the average of a bar’s high, low, and close: Typical price (TP) = (High +Low + Close)/3. This calculation looks at the three most important price points ona price bar and many believe it reveals a more realistic picture of the day’s action thanjust the close. The TP is also known as the daily pivot point. In the day, floor tradersused this easily calculable price to determine current support and resistance levels.48STOCHASTICS AND AVERAGES AND RSI! OH, MY!Calculation for a 20-day CCI:1. Going back in time, calculate the average of the past 20 days’ typical prices.2. Calculate the one standard deviation of the average found in step 1. This is wherea computer/calculator comes in handy.3. Divide the difference between today’s TP and the 20-day average of TP by theproduct of the standard deviation times 0.015.CCI = (Today’s TP − 20-day SMA of TP)∕(20-day standard deviation ∗ 0.015)Lambert set the constant at 0.015 in the denominator to ensure that approximately70 to 80 percent of CCI values would fall between −100 and +100. Since thenumerator can be positive or negative, so can the CCI. The standard deviationis inversely proportional to sample size; the larger the sample the smaller thedeviation. Since the standard deviation is in the denominator, a small lookbackperiod will cause a more volatile CCI. A shorter CCI (10 periods) will also produce asmaller percentage of values between +100 and −100. Conversely, a longer CCI (40periods) will have a higher percentage of values between +100 and −100.Since this oscillator usually falls between +100 and −100 and is momentumbased, most traders will use it as an overbought/oversold indicator. However, sincewww.rasabourse.com
it is boundless extended readings above +100 or below −100 can indicate verystrong trend moves.The weakness of the CCI is its variability due to the volatile nature of the underlyinginstrument that is being applied to. A volatile stock will cause the indicator to behaveerratically and jump well above/below the standard +100 and −100. Thesebenchmarks may have to be expanded based on the market being analyzed. Manytraders also apply a smoothing function to the CCI in an attempt to eliminateknee-jerk reactions.We will be discussing two trading algorithms based on the CCI (Box 2.5). Thefirst will utilize the CCI in the same manner as the RSI and stochastic. The secondalgorithm will use the CCI in a completely opposite manner—trend confirmation.Box 2.5 Donald Lambert’s CCI OB/OS System DescriptionIf 9-day average of the CCI 20-day crosses above −100, thenBuy MOCIf 9-day average CCI 20-day crosses below + 100, thenSell Short MOCIf position is longTake Profit: If close > entry price + 3 ATR Sell MOCProtective Stop: If close < entry price − 1 ATR Sell MOCIf position is shortTake Profit: If close < entry price − 3 ATR BuyToCover MOCProtective Stop: If close > entry price + 1 ATR, BuyToCover MOCNote that MOC indicates market on close.Donald Lambert’s CCI OB/OS System p–code‘‘Donald Lambert CCI OB/OSmyCCIVal = average(cci(cciLen),smooth);If myCCIVal crosses above -100 then buy this bar on closeIf myCCIVal crosses below 100 then sellShort this bar onclose49STOCHASTICS AND AVERAGES AND RSI! OH, MY!If marketPosition = 1 thenIf c > entryPrice + 3* avgTrueRange(10) then sell thisbar on closeif c < entryPrice - 1* avgTrueRange(10) then sell thisbar on closewww.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
Page 13 and 14: xivINTRODUCTION TO THE ULTIMATE ALG
Page 15 and 16: xviINTRODUCTION TO THE ULTIMATE ALG
Page 17 and 18: CHAPTER 1Introductionto TradingAlgo
Page 19 and 20: However, at this introductory stage
Page 21 and 22: completely above the tableau. If th
Page 23 and 24: Don’t get bogged down trying to u
Page 25 and 26: checking account was down the $10K,
Page 27 and 28: Short Liquidation Condition—What
Page 29 and 30: One BarStrengthTwo BarStrengthThree
Page 31 and 32: and inexperienced trading system pr
Page 33 and 34: Input = 6StartInput = 4Input <> 6In
Page 35 and 36: The diagram in Figure 1.4 looks cle
Page 37 and 38: observed. If the latter criterion i
Page 39 and 40: Start or NewBarIs C >AVG(C,200)YesN
Page 41 and 42: CHAPTER 2Stochastics andAverages an
Page 43 and 44: The basic theory behind the ADX is
Page 45 and 46: 4. Divide the 14-day smoothed plus
Page 47 and 48: if myADX < 35 thenif MarketPosition
Page 49 and 50: TABLE 2.1Performance of Directional
Page 51 and 52: FIGURE 2.4Source: TradeStationExamp
Page 53 and 54: Start orNew BarIs RSI < 30YesBuy Ma
Page 55 and 56: RSI DivergenceMarket Continuing to
Page 57 and 58: If marketPosition =-1 and close > e
Page 59 and 60: TABLE 2.3Performance of RSI Failure
Page 61 and 62: Start orNew BarIs Slow %K CrossingA
Page 63: TABLE 2.4Performance of Stochastic
Page 67 and 68: TABLE 2.5Performance of CCI algorit
Page 69 and 70: FIGURE 2.11Source: TradeStationThe
Page 71 and 72: We will discuss moving averages in
Page 73 and 74: MACDAvg = XAverage( MyMACD,9)MACDDi
Page 75 and 76: FIGURE 2.13Source: TradeStationThre
Page 77 and 78: LC0_E0B −25000 −41790 398 −62
Page 79 and 80: FIGURE 2.14Source: TradeStationA 60
Page 81 and 82: TABLE 2.8 Performance of Bollinger
Page 83 and 84: If filterTrade = false thenIf close
Page 85 and 86: Keltner ChannelChester Keltner brou
Page 87 and 88: FIGURE 2.15Source: TradeStationKelt
Page 89 and 90: 35000300002500020000150001000050000
Page 91 and 92: TABLE 2.12Best Parameter Sets of Ke
Page 93 and 94: CHAPTER 3Complete TradingAlgorithms
Page 95 and 96: Box 3.1 Turtle System #1Buy one tic
Page 97 and 98: TABLE 3.1Turtle System 1 without LT
Page 99 and 100: TABLE 3.3Turtle System 1 with LTLF
Page 101 and 102: TABLE 3.4Tandem Performance of Turt
Page 103 and 104: Pause the Battle—Let’s Talk Ris
Page 105 and 106: Box 3.3 Battle Royale—And the Com
Page 107 and 108: TABLE 3.5Single Moving Average (SMA
Page 109 and 110: TABLE 3.8Donchian Channel Breakout
Page 111 and 112: we have, because who has the time t
Page 113 and 114: %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
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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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