1. Succumbing to Python in the Financial Markets David Cerezo Sánchez http://cerezo.name

2. Python Advantages & Drawbacks Interactive, expressiveness: very quick prototyping Reduced development cycle: C++/Python=10:1 Time distribution in algorithmic trading (25% devising new strategies; 25% coding; 50% model fine-tuning and code maintenance): Python improvements impact 75% of development Free, nonproprietary (vs. Matlab, TradeStation,…) Multi-threading from Python 3.2! SEC mandating cashflow disclosure of ABS securities in Python Dynamic, not strongly typed (Java): errors at runtime!

3. Must-Have Python Financial Packages IbPy: Interactive Brokers Python API ultra-finance, MarWiz, pyfinancial, profitpy, QSToolKit: algorithmic trading libraries Quantlib-python: quantitative finance library NumPy, SciPy, PyIMSL: computational, scientific, numerical libraries xlrd: extract data from .xls/.xlsx files RPy2: wrapper to R, allows R function execution within Python

4. Code Samples

5. Combo Orders with IbPy # define the contract for each leg shortContract= makeOptContract(‘MSFT', '', 26, '') longContract= makeOptContract(‘AAPL', '', 350, '') # instantiate each leg shortLeg= makeComboLeg(getConId(1,shortContract), 'SELL', 1) longLeg= makeComboLeg(getConId(2,longContract), 'BUY', 1) # build a bag with these legs calendarBagContract= makeBagContract(‘MSFT', [shortLeg, longLeg]) # build order to buy 1 spread at $0.5 buyOrder= makeOrder(‘BUY', 26, 0.5) # buy! buy! buy! con.placeOrder(nextOrderId, calendarBagContract, buyOrder) # watch the messages for a bit sleep(100)

6. Basket Options with Quantlib_python # Dates, risk-free rate & option parameters todaysDate = Date(8,May,2011); Settings.instance().evaluationDate = todaysDate settlementDate = Date(12,May,2011); riskFreeRate = FlatForward(settlementDate, 0.06, Actual365Fixed()) exercise = EuropeanExercise(Date(12,May,2011)); payoff = PlainVanillaPayoff(Option.Call, 10.0) # Market data underlying1 = SimpleQuote(8.0); volatility1 = BlackConstantVol(todaysDate, TARGET(), 0.12, Actual365Fixed()) dividendYield1 = FlatForward(settlementDate, 0.06, Actual365Fixed()) underlying2 = SimpleQuote(8.0); volatility2 = BlackConstantVol(todaysDate, TARGET(), 0.12, Actual365Fixed()) dividendYield2 = FlatForward(settlementDate, 0.06, Actual365Fixed()) process1 = BlackScholesMertonProcess(QuoteHandle(underlying1), YieldTermStructureHandle(dividendYield1), YieldTermStructureHandle(riskFreeRate), BlackVolTermStructureHandle(volatility1)) process2 = BlackScholesMertonProcess(QuoteHandle(underlying2), YieldTermStructureHandle(dividendYield2), YieldTermStructureHandle(riskFreeRate), BlackVolTermStructureHandle(volatility2)) procs = StochasticProcessVector(); procs.push_back(process1); procs.push_back(process2) matrix = Matrix(2,2); matrix[0][0] = 1.0; matrix[1][1] = 1.0; matrix[0][1] = 0.5; matrix[1][0] = 0.5 process = StochasticProcessArray(procs, matrix) basketoption = BasketOption(AverageBasketPayoff(payoff, 2), exercise) basketoption.setPricingEngine(MCEuropeanBasketEngine(process,'lowdiscrepancy ',timeSteps= 1,requiredSamples =65536)) print basketoption.NPV()

7. Bermuda Swaption with Quantlib_python swaptionVols = [ (Period(1, Years), Period(5, Years), 0.12), (Period(2, Years), Period(4, Years), 0.11), (Period(3, Years), Period(3, Years), 0.10), (Period(4, Years), Period(2, Years), 0.09), (Period(5, Years), Period(1, Years), 0.08) ] todaysDate = Date(8,May,2011); Settings.instance().evaluationDate = todaysDate; calendar = TARGET(); settlementDate = Date(12,May,2011); rate = QuoteHandle(SimpleQuote(0.05)); termStructure = YieldTermStructureHandle(FlatForward(settlementDate,rate,Actual365Fixed())) fixedLegFrequency = Annual; fixedLegTenor = Period(1,Years); fixedLegConvention = Unadjusted; floatingLegConvention = ModifiedFollowing; fixedLegDayCounter = Thirty360(Thirty360.European); floatingLegFrequency = Semiannual; floatingLegTenor = Period(6,Months) payFixed = VanillaSwap.Payer; fixingDays = 2; index = Euribor6M(termStructure); floatingLegDayCounter = index.dayCounter() swapStart = calendar.advance(settlementDate,1,Years,floatingLegConvention); swapEnd = calendar.advance(swapStart,5,Years,floatingLegConvention) fixedSchedule = Schedule(swapStart, swapEnd, fixedLegTenor, calendar, fixedLegConvention, fixedLegConvention, DateGeneration.Forward, False) floatingSchedule = Schedule(swapStart, swapEnd, floatingLegTenor, calendar, floatingLegConvention, floatingLegConvention, DateGeneration.Forward, False) dummy = VanillaSwap(payFixed, 100.0,fixedSchedule, 0.0, fixedLegDayCounter,floatingSchedule, index, 0.0, floatingLegDayCounter) swapEngine = DiscountingSwapEngine(termStructure); dummy.setPricingEngine(swapEngine);atmRate = dummy.fairRate() atmSwap = VanillaSwap(payFixed, 1000.0,fixedSchedule, atmRate, fixedLegDayCounter,floatingSchedule, index, 0.0,floatingLegDayCounter) otmSwap = VanillaSwap(payFixed, 1000.0,fixedSchedule, atmRate*1.4, fixedLegDayCounter,floatingSchedule, index, 0.0,floatingLegDayCounter) itmSwap = VanillaSwap(payFixed, 1000.0,fixedSchedule, atmRate*0.6, fixedLegDayCounter,floatingSchedule, index, 0.0,floatingLegDayCounter) atmSwap.setPricingEngine(swapEngine);otmSwap.setPricingEngine(swapEngine);itmSwap.setPricingEngine(swapEngine) helpers = [ SwaptionHelper(maturity, length,QuoteHandle(SimpleQuote(vol)),index, index.tenor(), index.dayCounter(),index.dayCounter(), termStructure) for maturity, length, vol in swaptionVols ] times = dict([(t,1) for t in h.times() for h in helpers]) times = times.keys(); times.sort(); grid = TimeGrid(times, 30); BKmodel = BlackKarasinski(termStructure) for h in helpers: h.setPricingEngine(TreeSwaptionEngine(BKmodel,grid)) calibrate(BKmodel, helpers, 0.05);bermudanDates = [ d for d in fixedSchedule ][:-1]; exercise = BermudanExercise(bermudanDates) atmSwaption = Swaption(atmSwap, exercise);otmSwaption = Swaption(otmSwap, exercise);itmSwaption = Swaption(itmSwap, exercise) tse=TreeSwaptionEngine(BKmodel, 50); atmSwaption.setPricingEngine(tse);otmSwaption.setPricingEngine(tse);itmSwaption.setPricingEngine(tse) print ('Black-Karasinski numerical', itmSwaption.NPV(), atmSwaption.NPV(), otmSwaption.NPV())

8. Fast implementation Investment Strategies “Portable Alphas from Pension Mispricing”, Journal of Portfolio Management, Summer 2006, 44-53 Pure alpha strategy 1.51% (monthly), S=0.26 Just 200 lines of Python: Heavy use of map, reduce, filter, lambda SciPy: OLS scikits.timeseries Easier to implement using RPy2 (R wrapper)

9. What lies ahead…

10. Substitutes vs Complements Paradox Quant/algo trading focused at human trader substitution, but… Moravec’s Paradox: Computer’s excel where humans are weak, and vice versa Vg. Advanced Chess (Computer-Augmented Chess Playing): computer chess programs allowed at human competitions Computers better at brute-force position evaluation, opening and endgame databases, transposition and refutation tables… Respect human common sense and judgment Promoted by top players: Kasparov, Anand, Topalov, … Computer-assisted Playchess.com Freestyle Chess 2005 Tournament: Amateurs+computers+better process >> specialized chess supercomputers >> grandmasters+computer+inferior process

11. Backtesting vs. Forward Testing Why do people love backtesting so much? overfitted model calibrations will always prove their strategies to have very high alpha&Sharpe ratio With hindsight, everyone’s a winner In HFT/algo/quant trading, forward testing should be the golden standard: Extremely fast changing market conditions Reverse-engineered strategies that stop working

12. Market Microstructure Don’t forget about optimal execution sizes! Or expected trading costs given trading volume and volatility!