{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import yfinance as yf\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use(\"seaborn\")\n",
    "import seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# USARE QUESTO PER AVERE S&P500\n",
    "SeP500=[[\"Apple Inc.\",\"AAPL\",5.972092],[\"Microsoft Corporation\",\"MSFT\",5.195264],[\"Amazon.com Inc.\",\"AMZN\",2.379241],[\"Berkshire Hathaway Inc. Class B\",\"BRK-B\",1.744472],[\"Alphabet Inc. Class A\",\"GOOGL\",1.613283],[\"Alphabet Inc. Class C\",\"GOOG\",1.442776],[\"Johnson & Johnson\",\"JNJ\",1.410177],[\"UnitedHealth Group Incorporated\",\"UNH\",1.405054],[\"Exxon Mobil Corporation\",\"XOM\",1.37076],[\"JPMorgan Chase & Co.\",\"JPM\",1.236438],[\"NVIDIA Corporation\",\"NVDA\",1.195415],[\"Procter & Gamble Company\",\"PG\",1.105573],[\"Visa Inc. Class A\",\"V\",1.096749],[\"Home Depot Inc.\",\"HD\",0.997795],[\"Tesla Inc\",\"TSLA\",0.986464],[\"Mastercard Incorporated Class A\",\"MA\",0.966856],[\"Chevron Corporation\",\"CVX\",0.956271],[\"Meta Platforms Inc. Class A\",\"META\",0.89337],[\"AbbVie Inc.\",\"ABBV\",0.877282],[\"Merck & Co. Inc.\",\"MRK\",0.858758],[\"Eli Lilly and Company\",\"LLY\",0.846603],[\"Pfizer Inc.\",\"PFE\",0.833512],[\"PepsiCo Inc.\",\"PEP\",0.758131],[\"Coca-Cola Company\",\"KO\",0.747755],[\"Bank of America Corp\",\"BAC\",0.725827],[\"Broadcom Inc.\",\"AVGO\",0.716962],[\"Thermo Fisher Scientific Inc.\",\"TMO\",0.657217],[\"Costco Wholesale Corporation\",\"COST\",0.650236],[\"Walmart Inc.\",\"WMT\",0.627775],[\"Cisco Systems Inc.\",\"CSCO\",0.612083],[\"McDonald's Corporation\",\"MCD\",0.600491],[\"Abbott Laboratories\",\"ABT\",0.59982],[\"Verizon Communications Inc.\",\"VZ\",0.533153],[\"Walt Disney Company\",\"DIS\",0.530158],[\"Accenture Plc Class A\",\"ACN\",0.528608],[\"Danaher Corporation\",\"DHR\",0.512754],[\"NextEra Energy Inc.\",\"NEE\",0.512642],[\"Comcast Corporation Class A\",\"CMCSA\",0.497075],[\"Wells Fargo & Company\",\"WFC\",0.495657],[\"Texas Instruments Incorporated\",\"TXN\",0.493682],[\"Adobe Incorporated\",\"ADBE\",0.489401],[\"Linde plc\",\"LIN\",0.48837],[\"Philip Morris International Inc.\",\"PM\",0.486636],[\"NIKE Inc. Class B\",\"NKE\",0.482411],[\"Bristol-Myers Squibb Company\",\"BMY\",0.463078],[\"Salesforce Inc.\",\"CRM\",0.451387],[\"Raytheon Technologies Corporation\",\"RTX\",0.44971],[\"ConocoPhillips\",\"COP\",0.448749],[\"Amgen Inc.\",\"AMGN\",0.441853],[\"Honeywell International Inc.\",\"HON\",0.433987],[\"Netflix Inc.\",\"NFLX\",0.430386],[\"AT&T Inc.\",\"T\",0.416399],[\"Oracle Corporation\",\"ORCL\",0.407548],[\"United Parcel Service Inc. Class B\",\"UPS\",0.406095],[\"Union Pacific Corporation\",\"UNP\",0.39893],[\"Charles Schwab Corp\",\"SCHW\",0.398872],[\"International Business Machines Corporation\",\"IBM\",0.398262],[\"Caterpillar Inc.\",\"CAT\",0.393825],[\"QUALCOMM Incorporated\",\"QCOM\",0.393572],[\"Lowe's Companies Inc.\",\"LOW\",0.379872],[\"Intel Corporation\",\"INTC\",0.371181],[\"Starbucks Corporation\",\"SBUX\",0.368938],[\"CVS Health Corporation\",\"CVS\",0.368887],[\"Goldman Sachs Group Inc.\",\"GS\",0.368308],[\"Deere & Company\",\"DE\",0.360904],[\"Boeing Company\",\"BA\",0.358554],[\"S&P Global Inc.\",\"SPGI\",0.357988],[\"Morgan Stanley\",\"MS\",0.354531],[\"BlackRock Inc.\",\"BLK\",0.347126],[\"Elevance Health Inc.\",\"ELV\",0.345529],[\"Intuit Inc.\",\"INTU\",0.341941],[\"Advanced Micro Devices Inc.\",\"AMD\",0.332127],[\"Prologis Inc.\",\"PLD\",0.328743],[\"Lockheed Martin Corporation\",\"LMT\",0.328513],[\"Gilead Sciences Inc.\",\"GILD\",0.327529],[\"Medtronic Plc\",\"MDT\",0.316254],[\"American Tower Corporation\",\"AMT\",0.313324],[\"Automatic Data Processing Inc.\",\"ADP\",0.304246],[\"TJX Companies Inc\",\"TJX\",0.291838],[\"Intuitive Surgical Inc.\",\"ISRG\",0.289022],[\"Chubb Limited\",\"CB\",0.285456],[\"Cigna Corporation\",\"CI\",0.284308],[\"Citigroup Inc.\",\"C\",0.282546],[\"Mondelez International Inc. Class A\",\"MDLZ\",0.281817],[\"Applied Materials Inc.\",\"AMAT\",0.281125],[\"American Express Company\",\"AXP\",0.275858],[\"T-Mobile US Inc.\",\"TMUS\",0.273871],[\"PayPal Holdings Inc.\",\"PYPL\",0.269647],[\"Stryker Corporation\",\"SYK\",0.268699],[\"Analog Devices Inc.\",\"ADI\",0.26569],[\"Booking Holdings Inc.\",\"BKNG\",0.262864],[\"Marsh & McLennan Companies Inc.\",\"MMC\",0.259978],[\"Altria Group Inc\",\"MO\",0.253216],[\"Duke Energy Corporation\",\"DUK\",0.248565],[\"General Electric Company\",\"GE\",0.242898],[\"Schlumberger NV\",\"SLB\",0.242102],[\"Southern Company\",\"SO\",0.238857],[\"Progressive Corporation\",\"PGR\",0.237003],[\"ServiceNow Inc.\",\"NOW\",0.233859],[\"EOG Resources Inc.\",\"EOG\",0.23026],[\"Vertex Pharmaceuticals Incorporated\",\"VRTX\",0.223092],[\"Regeneron Pharmaceuticals Inc.\",\"REGN\",0.222935],[\"Target Corporation\",\"TGT\",0.221715],[\"Becton Dickinson and Company\",\"BDX\",0.220153],[\"Northrop Grumman Corp.\",\"NOC\",0.220079],[\"3M Company\",\"MMM\",0.215052],[\"Air Products and Chemicals Inc.\",\"APD\",0.212123],[\"Zoetis Inc. Class A\",\"ZTS\",0.210301],[\"CSX Corporation\",\"CSX\",0.207347],[\"PNC Financial Services Group Inc.\",\"PNC\",0.203314],[\"Colgate-Palmolive Company\",\"CL\",0.201143],[\"Boston Scientific Corporation\",\"BSX\",0.200375],[\"Fiserv Inc.\",\"FISV\",0.199639],[\"Eaton Corp. Plc\",\"ETN\",0.19594],[\"Illinois Tool Works Inc.\",\"ITW\",0.195523],[\"Aon Plc Class A\",\"AON\",0.194443],[\"CME Group Inc. Class A\",\"CME\",0.194055],[\"Equinix Inc.\",\"EQIX\",0.193415],[\"Crown Castle Inc.\",\"CCI\",0.193182],[\"U.S. Bancorp\",\"USB\",0.192707],[\"Lam Research Corporation\",\"LRCX\",0.18949],[\"Micron Technology Inc.\",\"MU\",0.18802],[\"Freeport-McMoRan Inc.\",\"FCX\",0.187285],[\"Humana Inc.\",\"HUM\",0.187147],[\"Moderna Inc.\",\"MRNA\",0.186492],[\"Truist Financial Corporation\",\"TFC\",0.186463],[\"Estee Lauder Companies Inc. Class A\",\"EL\",0.184866],[\"Waste Management Inc.\",\"WM\",0.182023],[\"Norfolk Southern Corporation\",\"NSC\",0.180603],[\"Intercontinental Exchange Inc.\",\"ICE\",0.179529],[\"KLA Corporation\",\"KLAC\",0.177062],[\"Emerson Electric Co.\",\"EMR\",0.176096],[\"Sherwin-Williams Company\",\"SHW\",0.170231],[\"General Dynamics Corporation\",\"GD\",0.1686],[\"Activision Blizzard Inc.\",\"ATVI\",0.168098],[\"Pioneer Natural Resources Company\",\"PXD\",0.167961],[\"McKesson Corporation\",\"MCK\",0.167932],[\"Dollar General Corporation\",\"DG\",0.167117],[\"Marathon Petroleum Corporation\",\"MPC\",0.166475],[\"HCA Healthcare Inc\",\"HCA\",0.164483],[\"O'Reilly Automotive Inc.\",\"ORLY\",0.159142],[\"Dominion Energy Inc\",\"D\",0.156789],[\"General Motors Company\",\"GM\",0.156594],\n",
    "        [\"American Electric Power Company Inc.\",\"AEP\",0.154449],[\"General Mills Inc.\",\"GIS\",0.15429],[\"Ford Motor Company\",\"F\",0.15325],[\"Valero Energy Corporation\",\"VLO\",0.152112],[\"Synopsys Inc.\",\"SNPS\",0.152103],[\"Sempra Energy\",\"SRE\",0.151562],[\"Phillips 66\",\"PSX\",0.149645],[\"MetLife Inc.\",\"MET\",0.146185],[\"American International Group Inc.\",\"AIG\",0.145617],[\"Edwards Lifesciences Corporation\",\"EW\",0.144928],[\"Amphenol Corporation Class A\",\"APH\",0.14448],[\"Roper Technologies Inc.\",\"ROP\",0.144299],[\"Archer-Daniels-Midland Company\",\"ADM\",0.143905],[\"Occidental Petroleum Corporation\",\"OXY\",0.142978],[\"AutoZone Inc.\",\"AZO\",0.142244],[\"Kimberly-Clark Corporation\",\"KMB\",0.141455],[\"Johnson Controls International plc\",\"JCI\",0.140653],[\"Moody's Corporation\",\"MCO\",0.140293],[\"FedEx Corporation\",\"FDX\",0.139394],[\"Cadence Design Systems Inc.\",\"CDNS\",0.13905],[\"Travelers Companies Inc.\",\"TRV\",0.137296],[\"Public Storage\",\"PSA\",0.135879],[\"Centene Corporation\",\"CNC\",0.135632],[\"Agilent Technologies Inc.\",\"A\",0.134541],[\"Corteva Inc\",\"CTVA\",0.134153],[\"Exelon Corporation\",\"EXC\",0.13388],[\"Motorola Solutions Inc.\",\"MSI\",0.132857],[\"DexCom Inc.\",\"DXCM\",0.131727],[\"NXP Semiconductors NV\",\"NXPI\",0.129153],[\"Autodesk Inc.\",\"ADSK\",0.127722],[\"Fidelity National Information Services Inc.\",\"FIS\",0.127459],[\"Ross Stores Inc.\",\"ROST\",0.126913],[\"Newmont Corporation\",\"NEM\",0.126425],[\"Trane Technologies plc\",\"TT\",0.126398],[\"Marriott International Inc. Class A\",\"MAR\",0.125934],[\"Aflac Incorporated\",\"AFL\",0.125663],[\"Devon Energy Corporation\",\"DVN\",0.124544],[\"Realty Income Corporation\",\"O\",0.124514],[\"Microchip Technology Incorporated\",\"MCHP\",0.124273],[\"Sysco Corporation\",\"SYY\",0.123393],[\"Arthur J. Gallagher & Co.\",\"AJG\",0.123355],[\"Chipotle Mexican Grill Inc.\",\"CMG\",0.122733],[\"Hess Corporation\",\"HES\",0.121821],[\"Biogen Inc.\",\"BIIB\",0.121754],[\"Williams Companies Inc.\",\"WMB\",0.121581],[\"Dow Inc.\",\"DOW\",0.121419],[\"Charter Communications Inc. Class A\",\"CHTR\",0.121023],[\"Parker-Hannifin Corporation\",\"PH\",0.121015],[\"Xcel Energy Inc.\",\"XEL\",0.120873],[\"Monster Beverage Corporation\",\"MNST\",0.118979],[\"IQVIA Holdings Inc\",\"IQV\",0.118883],[\"TE Connectivity Ltd.\",\"TEL\",0.118738],[\"MSCI Inc. Class A\",\"MSCI\",0.118715],[\"Simon Property Group Inc.\",\"SPG\",0.117793],[\"L3Harris Technologies Inc\",\"LHX\",0.117546],[\"Cintas Corporation\",\"CTAS\",0.116073],[\"Allstate Corporation\",\"ALL\",0.115739],[\"Paychex Inc.\",\"PAYX\",0.115561],[\"Capital One Financial Corp\",\"COF\",0.114384],[\"IDEXX Laboratories Inc.\",\"IDXX\",0.114092],[\"Kinder Morgan Inc Class P\",\"KMI\",0.11343],[\"Ecolab Inc.\",\"ECL\",0.113259],[\"Nucor Corporation\",\"NUE\",0.112968],[\"Yum! Brands Inc.\",\"YUM\",0.112868],[\"Halliburton Company\",\"HAL\",0.112683],[\"DuPont de Nemours Inc.\",\"DD\",0.11256],[\"Carrier Global Corp.\",\"CARR\",0.111964],[\"Prudential Financial Inc.\",\"PRU\",0.111664],[\"Bank of New York Mellon Corp\",\"BK\",0.108264],[\"Hilton Worldwide Holdings Inc\",\"HLT\",0.108158],[\"Constellation Brands Inc. Class A\",\"STZ\",0.107346],[\"PACCAR Inc\",\"PCAR\",0.106491],[\"Consolidated Edison Inc.\",\"ED\",0.106409],[\"Ameriprise Financial Inc.\",\"AMP\",0.106264],[\"Cummins Inc.\",\"CMI\",0.105227],[\"Otis Worldwide Corporation\",\"OTIS\",0.104566],[\"Kraft Heinz Company\",\"KHC\",0.102786],[\"TransDigm Group Incorporated\",\"TDG\",0.102601],[\"Hershey Company\",\"HSY\",0.101227],[\"Welltower Inc\",\"WELL\",0.10044],[\"Electronic Arts Inc.\",\"EA\",0.100289],[\"Mettler-Toledo International Inc.\",\"MTD\",0.100162],[\"Illumina Inc.\",\"ILMN\",0.100063],[\"AMETEK Inc.\",\"AME\",0.099793],[\"Fortinet Inc.\",\"FTNT\",0.098895],[\"Public Service Enterprise Group Inc\",\"PEG\",0.097223],[\"Keysight Technologies Inc\",\"KEYS\",0.097182],[\"Cognizant Technology Solutions Corporation Class A\",\"CTSH\",0.096263],[\"CoStar Group Inc.\",\"CSGP\",0.096193],[\"SBA Communications Corp. Class A\",\"SBAC\",0.096004],[\"Rockwell Automation Inc.\",\"ROK\",0.09463],[\"Baker Hughes Company Class A\",\"BKR\",0.094491],[\"Enphase Energy Inc.\",\"ENPH\",0.094397],[\"ResMed Inc.\",\"RMD\",0.094257],[\"VICI Properties Inc\",\"VICI\",0.093879],[\"Keurig Dr Pepper Inc.\",\"KDP\",0.093603],[\"WEC Energy Group Inc\",\"WEC\",0.093462],[\"Dollar Tree Inc.\",\"DLTR\",0.092927],[\"ONEOK Inc.\",\"OKE\",0.092837],[\"PPG Industries Inc.\",\"PPG\",0.092623],\n",
    "        [\"Kroger Co.\",\"KR\",0.092355],[\"State Street Corporation\",\"STT\",0.091104],[\"Eversource Energy\",\"ES\",0.090788],[\"Global Payments Inc.\",\"GPN\",0.090246],[\"D.R. Horton Inc.\",\"DHI\",0.089688],[\"Digital Realty Trust Inc.\",\"DLR\",0.08888],[\"American Water Works Company Inc.\",\"AWK\",0.088404],[\"Verisk Analytics Inc\",\"VRSK\",0.087383],[\"International Flavors & Fragrances Inc.\",\"IFF\",0.087129],[\"Discover Financial Services\",\"DFS\",0.086772],[\"Arista Networks Inc.\",\"ANET\",0.086568],[\"Old Dominion Freight Line Inc.\",\"ODFL\",0.085819],[\"Willis Towers Watson Public Limited Company\",\"WTW\",0.085698],[\"ON Semiconductor Corporation\",\"ON\",0.085633],[\"Constellation Energy Corporation\",\"CEG\",0.084289],[\"Fastenal Company\",\"FAST\",0.08349],[\"Copart Inc.\",\"CPRT\",0.082106],[\"Corning Inc\",\"GLW\",0.082084],[\"AmerisourceBergen Corporation\",\"ABC\",0.081986],[\"Aptiv PLC\",\"APTV\",0.081465],[\"Zimmer Biomet Holdings Inc.\",\"ZBH\",0.081167],[\"Albemarle Corporation\",\"ALB\",0.08052],[\"United Rentals Inc.\",\"URI\",0.080435],[\"CBRE Group Inc. Class A\",\"CBRE\",0.08027],[\"Republic Services Inc.\",\"RSG\",0.080228],[\"Walgreens Boots Alliance Inc.\",\"WBA\",0.080065],[\"M&T Bank Corporation\",\"MTB\",0.0798],[\"HP Inc.\",\"HPQ\",0.079725],[\"Gartner Inc.\",\"IT\",0.079622],[\"Edison International\",\"EIX\",0.079437],[\"T. Rowe Price Group\",\"TROW\",0.079182],[\"PG&E Corporation\",\"PCG\",0.07885],[\"Warner Bros. Discovery Inc. Series A\",\"WBD\",0.0779],[\"Ulta Beauty Inc.\",\"ULTA\",0.077217],[\"W.W. Grainger Inc.\",\"GWW\",0.077215],[\"Diamondback Energy Inc.\",\"FANG\",0.076901],[\"Equifax Inc.\",\"EFX\",0.076884],[\"Hartford Financial Services Group Inc.\",\"HIG\",0.075952],[\"CDW Corp.\",\"CDW\",0.075722],[\"Lennar Corporation Class A\",\"LEN\",0.07431],[\"Genuine Parts Company\",\"GPC\",0.07393],[\"Tractor Supply Company\",\"TSCO\",0.073814],[\"eBay Inc.\",\"EBAY\",0.073125],[\"Vulcan Materials Company\",\"VMC\",0.072605],[\"Fifth Third Bancorp\",\"FITB\",0.072077],[\"Fortive Corp.\",\"FTV\",0.071437],[\"Weyerhaeuser Company\",\"WY\",0.071345],[\"Delta Air Lines Inc.\",\"DAL\",0.071286],[\"Arch Capital Group Ltd.\",\"ACGL\",0.07113],[\"LyondellBasell Industries NV\",\"LYB\",0.071072],[\"Ameren Corporation\",\"AEE\",0.070927],[\"DTE Energy Company\",\"DTE\",0.070729],[\"FirstEnergy Corp.\",\"FE\",0.070382],[\"AvalonBay Communities Inc.\",\"AVB\",0.06975],[\"First Republic Bank\",\"FRC\",0.069522],[\"Baxter International Inc.\",\"BAX\",0.068614],[\"Ingersoll Rand Inc.\",\"IR\",0.068446],[\"PPL Corporation\",\"PPL\",0.067845],[\"Laboratory Corporation of America Holdings\",\"LH\",0.067336],[\"Entergy Corporation\",\"ETR\",0.067229],[\"Hewlett Packard Enterprise Co.\",\"HPE\",0.067117],[\"Alexandria Real Estate Equities Inc.\",\"ARE\",0.066364],[\"Raymond James Financial Inc.\",\"RJF\",0.065583],[\"ANSYS Inc.\",\"ANSS\",0.065464],[\"Martin Marietta Materials Inc.\",\"MLM\",0.065241],[\"McCormick & Company Incorporated\",\"MKC\",0.065064],[\"Nasdaq Inc.\",\"NDAQ\",0.064904],[\"Southwest Airlines Co.\",\"LUV\",0.064881],[\"GE Healthcare Technologies Inc.\",\"GEHC\",0.064733],[\"Regions Financial Corporation\",\"RF\",0.063589],[\"Huntington Bancshares Incorporated\",\"HBAN\",0.06324],[\"Cardinal Health Inc.\",\"CAH\",0.062395],[\"Citizens Financial Group Inc.\",\"CFG\",0.061825],[\"Principal Financial Group Inc.\",\"PFG\",0.061525],[\"Hologic Inc.\",\"HOLX\",0.06115],[\"Xylem Inc.\",\"XYL\",0.061036],[\"Church & Dwight Co. Inc.\",\"CHD\",0.060911],[\"Dover Corporation\",\"DOV\",0.060577],[\"Equity Residential\",\"EQR\",0.060478],[\"Coterra Energy Inc.\",\"CTRA\",0.060251],[\"Quanta Services Inc.\",\"PWR\",0.060078],[\"Extra Space Storage Inc.\",\"EXR\",0.059711],[\"Northern Trust Corporation\",\"NTRS\",0.059481],[\"VeriSign Inc.\",\"VRSN\",0.059426],[\"Conagra Brands Inc.\",\"CAG\",0.058812],[\"Tyson Foods Inc. Class A\",\"TSN\",0.058676],[\"Waters Corporation\",\"WAT\",0.058632],[\"CenterPoint Energy Inc.\",\"CNP\",0.058168],[\"STERIS Plc\",\"STE\",0.058101],[\"Ventas Inc.\",\"VTR\",0.057926],[\"Teledyne Technologies Incorporated\",\"TDY\",0.057838],[\"CMS Energy Corporation\",\"CMS\",0.057217],[\"EPAM Systems Inc.\",\"EPAM\",0.057009],[\"Westinghouse Air Brake Technologies Corporation\",\"WAB\",0.056127],[\"Amcor PLC\",\"AMCR\",0.05611],[\"Kellogg Company\",\"K\",0.056065],[\"Darden Restaurants Inc.\",\"DRI\",0.055523],[\"AES Corporation\",\"AES\",0.055017],[\"Expeditors International of Washington Inc.\",\"EXPD\",0.054957],[\"Quest Diagnostics Incorporated\",\"DGX\",0.054485],[\"Mid-America Apartment Communities Inc.\",\"MAA\",0.054357],[\"West Pharmaceutical Services Inc.\",\"WST\",0.053932],[\"IDEX Corporation\",\"IEX\",0.053837],[\"Omnicom Group Inc\",\"OMC\",0.053631],[\"Ball Corporation\",\"BALL\",0.053405],[\"Clorox Company\",\"CLX\",0.053085],[\"Las Vegas Sands Corp.\",\"LVS\",0.053033],[\"Cincinnati Financial Corporation\",\"CINF\",0.052906],[\"Invitation Homes Inc.\",\"INVH\",0.052831],[\"Molina Healthcare Inc.\",\"MOH\",0.052715],[\"Marathon Oil Corporation\",\"MRO\",0.052674],[\"J.M. Smucker Company\",\"SJM\",0.052602],[\"CF Industries Holdings Inc.\",\"CF\",0.052406],[\"Monolithic Power Systems Inc.\",\"MPWR\",0.052078],[\"Targa Resources Corp.\",\"TRGP\",0.051752],[\"Steel Dynamics Inc.\",\"STLD\",0.051639],[\"KeyCorp\",\"KEY\",0.051373],[\"Cooper Companies Inc.\",\"COO\",0.051345],[\"PerkinElmer Inc.\",\"PKI\",0.05131],[\"Align Technology Inc.\",\"ALGN\",0.050664],[\"Best Buy Co. Inc.\",\"BBY\",0.050637],[\"Take-Two Interactive Software Inc.\",\"TTWO\",0.050468],[\"Broadridge Financial Solutions Inc.\",\"BR\",0.050063],[\"Mosaic Company\",\"MOS\",0.049843],[\"FMC Corporation\",\"FMC\",0.049682],[\"Jacobs Solutions Inc.\",\"J\",0.04961],[\"First Solar Inc.\",\"FSLR\",0.048424],[\"Atmos Energy Corporation\",\"ATO\",0.048239],[\"SolarEdge Technologies Inc.\",\"SEDG\",0.04798],[\"Avery Dennison Corporation\",\"AVY\",0.047566],[\"Skyworks Solutions Inc.\",\"SWKS\",0.047287],[\"Etsy Inc.\",\"ETSY\",0.046548],[\"FactSet Research Systems Inc.\",\"FDS\",0.046409],[\"Garmin Ltd.\",\"GRMN\",0.046339],[\"Textron Inc.\",\"TXT\",0.045601],[\"W. R. Berkley Corporation\",\"WRB\",0.045534],[\"Howmet Aerospace Inc.\",\"HWM\",0.045264],[\"J.B. Hunt Transport Services Inc.\",\"JBHT\",0.045239],[\"NVR Inc.\",\"NVR\",0.044976],\n",
    "        [\"Incyte Corporation\",\"INCY\",0.04491],[\"Teradyne Inc.\",\"TER\",0.044697],[\"Synchrony Financial\",\"SYF\",0.044642],[\"Evergy Inc.\",\"EVRG\",0.044557],[\"Iron Mountain Inc.\",\"IRM\",0.044413],[\"LKQ Corporation\",\"LKQ\",0.044299],[\"SVB Financial Group\",\"SIVB\",0.044298],[\"Zebra Technologies Corporation Class A\",\"ZBRA\",0.04422],[\"Viatris Inc.\",\"VTRS\",0.04399],[\"FLEETCOR Technologies Inc.\",\"FLT\",0.043725],[\"Paycom Software Inc.\",\"PAYC\",0.043642],[\"United Airlines Holdings Inc.\",\"UAL\",0.043487],[\"Lamb Weston Holdings Inc.\",\"LW\",0.043345],[\"NetApp Inc.\",\"NTAP\",0.043318],[\"APA Corp.\",\"APA\",0.043298],[\"Healthpeak Properties Inc.\",\"PEAK\",0.042893],[\"Expedia Group Inc.\",\"EXPE\",0.042841],[\"Everest Re Group Ltd.\",\"RE\",0.042568],[\"Interpublic Group of Companies Inc.\",\"IPG\",0.042389],[\"Akamai Technologies Inc.\",\"AKAM\",0.04219],[\"Alliant Energy Corp\",\"LNT\",0.042032],[\"Essex Property Trust Inc.\",\"ESS\",0.041888],[\"Leidos Holdings Inc.\",\"LDOS\",0.041849],[\"Brown & Brown Inc.\",\"BRO\",0.041666],[\"International Paper Company\",\"IP\",0.040931],[\"Hormel Foods Corporation\",\"HRL\",0.040784],[\"Tyler Technologies Inc.\",\"TYL\",0.040759],[\"PTC Inc.\",\"PTC\",0.040425],[\"Jack Henry & Associates Inc.\",\"JKHY\",0.039837],[\"Trimble Inc.\",\"TRMB\",0.039799],[\"Nordson Corporation\",\"NDSN\",0.039797],[\"Bio-Techne Corporation\",\"TECH\",0.039564],[\"Kimco Realty Corporation\",\"KIM\",0.039545],[\"Cboe Global Markets Inc\",\"CBOE\",0.039469],[\"Snap-on Incorporated\",\"SNA\",0.039297],[\"EQT Corporation\",\"EQT\",0.038698],[\"Pool Corporation\",\"POOL\",0.038572],[\"Royal Caribbean Group\",\"RCL\",0.038376],[\"Packaging Corporation of America\",\"PKG\",0.038135],[\"Gen Digital Inc.\",\"GEN\",0.037863],[\"Match Group Inc.\",\"MTCH\",0.037692],[\"MGM Resorts International\",\"MGM\",0.03756],[\"Stanley Black & Decker Inc.\",\"SWK\",0.037092],[\"MarketAxess Holdings Inc.\",\"MKTX\",0.03671],[\"Camden Property Trust\",\"CPT\",0.036697],[\"Domino's Pizza Inc.\",\"DPZ\",0.036614],[\"Teleflex Incorporated\",\"TFX\",0.03642],[\"UDR Inc.\",\"UDR\",0.036124],[\"Brown-Forman Corporation Class B\",\"BF-B\",0.035797],[\"Western Digital Corporation\",\"WDC\",0.035754],[\"Celanese Corporation\",\"CE\",0.035601],[\"C.H. Robinson Worldwide Inc.\",\"CHRW\",0.035378],[\"Loews Corporation\",\"L\",0.03518],[\"Host Hotels & Resorts Inc.\",\"HST\",0.034877],[\"Masco Corporation\",\"MAS\",0.034733],[\"Charles River Laboratories International Inc.\",\"CRL\",0.034667],[\"Campbell Soup Company\",\"CPB\",0.034633],[\"NiSource Inc\",\"NI\",0.034486],[\"Henry Schein Inc.\",\"HSIC\",0.034064],[\"PulteGroup Inc.\",\"PHM\",0.034039],[\"Globe Life Inc.\",\"GL\",0.033289],[\"Eastman Chemical Company\",\"EMN\",0.033266],[\"CarMax Inc.\",\"KMX\",0.033087],[\"Seagate Technology Holdings PLC\",\"STX\",0.033013],[\"Tapestry Inc.\",\"TPR\",0.031763],[\"Live Nation Entertainment Inc.\",\"LYV\",0.03139],[\"Bath & Body Works Inc.\",\"BBWI\",0.031379],[\"Juniper Networks Inc.\",\"JNPR\",0.031345],[\"BorgWarner Inc.\",\"BWA\",0.030795],[\"Allegion Public Limited Company\",\"ALLE\",0.0305],[\"Universal Health Services Inc. Class B\",\"UHS\",0.030292],[\"Wynn Resorts Limited\",\"WYNN\",0.030269],[\"Paramount Global Class B\",\"PARA\",0.030186],[\"Qorvo Inc.\",\"QRVO\",0.030059],[\"Ceridian HCM Holding Inc.\",\"CDAY\",0.029434],[\"Fox Corporation Class A\",\"FOXA\",0.029317],[\"V.F. Corporation\",\"VFC\",0.029],[\"Regency Centers Corporation\",\"REG\",0.028952],[\"Molson Coors Beverage Company Class B\",\"TAP\",0.028838],[\"American Airlines Group Inc.\",\"AAL\",0.028624],[\"WestRock Company\",\"WRK\",0.028472],[\"Boston Properties Inc.\",\"BXP\",0.028276],[\"Caesars Entertainment Inc\",\"CZR\",0.028275],[\"Advance Auto Parts Inc.\",\"AAP\",0.028202],[\"Carnival Corporation\",\"CCL\",0.028164],[\"Bio-Rad Laboratories Inc. Class A\",\"BIO\",0.027751],[\"Huntington Ingalls Industries Inc.\",\"HII\",0.026944],[\"Comerica Incorporated\",\"CMA\",0.026891],[\"Invesco Ltd.\",\"IVZ\",0.026802],[\"Catalent Inc\",\"CTLT\",0.026233],[\"F5 Inc.\",\"FFIV\",0.026084],[\"Pinnacle West Capital Corporation\",\"PNW\",0.025971],[\"Rollins Inc.\",\"ROL\",0.025945],[\"Robert Half International Inc.\",\"RHI\",0.025398],[\"Whirlpool Corporation\",\"WHR\",0.025367],[\"Hasbro Inc.\",\"HAS\",0.024835],[\"A. O. Smith Corporation\",\"AOS\",0.024133],[\"Sealed Air Corporation\",\"SEE\",0.023985],[\"Pentair plc\",\"PNR\",0.023847],[\"Franklin Resources Inc.\",\"BEN\",0.02376],\n",
    "        [\"Zions Bancorporation N.A.\",\"ZION\",0.023517],[\"NRG Energy Inc.\",\"NRG\",0.023479],[\"Federal Realty Investment Trust\",\"FRT\",0.023081],[\"Organon & Co.\",\"OGN\",0.022834],[\"News Corporation Class A\",\"NWSA\",0.022747],[\"Signature Bank\",\"SBNY\",0.022084],[\"DENTSPLY SIRONA Inc.\",\"XRAY\",0.021835],[\"Assurant Inc.\",\"AIZ\",0.020374],[\"Generac Holdings Inc.\",\"GNRC\",0.020031],[\"DXC Technology Co.\",\"DXC\",0.019866],[\"Mohawk Industries Inc.\",\"MHK\",0.01762],[\"Norwegian Cruise Line Holdings Ltd.\",\"NCLH\",0.017473],[\"Alaska Air Group Inc.\",\"ALK\",0.017329],[\"Newell Brands Inc\",\"NWL\",0.01676],[\"Lumen Technologies Inc.\",\"LUMN\",0.015085],[\"Ralph Lauren Corporation Class A\",\"RL\",0.015011],[\"Lincoln National Corp\",\"LNC\",0.01452],[\"DaVita Inc.\",\"DVA\",0.013406],[\"Fox Corporation Class B\",\"FOX\",0.01319],[\"DISH Network Corporation Class A\",\"DISH\",0.011947],[\"News Corporation Class B\",\"NWS\",0.007208]]\n",
    "tickers=list(np.array(SeP500)[:,1])\n",
    "nomi=list(np.array(SeP500)[:,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  503 of 503 completed\n"
     ]
    }
   ],
   "source": [
    "dati=yf.download(tickers)[\"Adj Close\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# rimetto a posto le colonne che non ho capito perché me le scombina\n",
    "dati=dati.reindex(tickers, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 2500x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dati[\"AAPL\"].loc[\"2006\":].plot(figsize=(25,15))\n",
    "dati[\"AAPL\"].loc[\"2006\":].dropna().cummax().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 2500x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dati[\"JNJ\"].loc[\"2013\":].plot(figsize=(25,15))\n",
    "dati[\"JNJ\"].loc[\"2013\":].dropna().cummax().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "datimax=dati.cummax()\n",
    "drawdown=(dati-datimax)/datimax\n",
    "cummin=drawdown.cummin()\n",
    "logret=np.log(dati/dati.shift(1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Impedisco di investire nel primo anno"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "cummin2=cummin.copy()\n",
    "logret2=logret.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "times2=logret2.apply(pd.Series.first_valid_index)+ pd.offsets.DateOffset(years=1)\n",
    "for t in tickers:\n",
    "    logret2[t].loc[:times2[t]]=np.nan\n",
    "    cummin2[t].loc[:times2[t]]=np.nan "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "times=(logret2.index[-1]-times2)/np.timedelta64(1, 'Y')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Soglia 0.3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "soglia=0.3\n",
    "# strategia 1: buy and hold\n",
    "# strategia 2: compro quando scende sotto il drawdown e tengo fino alla fine\n",
    "# strategia 3: compro quando scende sotto il drawdown e tengo fino alla risalita"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AAPL     20.876250\n",
       "MSFT     22.824050\n",
       "AMZN     26.063171\n",
       "BRK-B    10.297233\n",
       "GOOGL    16.248101\n",
       "           ...    \n",
       "LNC       5.901550\n",
       "DVA       9.586076\n",
       "FOX      11.841338\n",
       "DISH      6.783176\n",
       "NWS       2.464485\n",
       "Length: 503, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "strategia1=100*(np.exp(logret2.sum()/times)-1)\n",
    "strategia1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AAPL     20.876250\n",
       "MSFT     21.302561\n",
       "AMZN     26.063171\n",
       "BRK-B     8.725525\n",
       "GOOGL    11.682944\n",
       "           ...    \n",
       "LNC       4.805727\n",
       "DVA      10.284578\n",
       "FOX      11.841338\n",
       "DISH      7.264221\n",
       "NWS       5.805999\n",
       "Length: 503, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "strategia2=100*(np.exp(((cummin2<-soglia)*logret2).sum()/times)-1)\n",
    "strategia2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>primo dato valido</th>\n",
       "      <th>prezzo1</th>\n",
       "      <th>data massimo</th>\n",
       "      <th>massimo</th>\n",
       "      <th>va sotto soglia</th>\n",
       "      <th>prezzo2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AAPL</th>\n",
       "      <td>1980-12-15</td>\n",
       "      <td>0.094519</td>\n",
       "      <td>1980-12-29</td>\n",
       "      <td>0.124869</td>\n",
       "      <td>1981-12-16</td>\n",
       "      <td>0.067638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSFT</th>\n",
       "      <td>1986-03-14</td>\n",
       "      <td>0.062823</td>\n",
       "      <td>1987-10-05</td>\n",
       "      <td>0.342277</td>\n",
       "      <td>1987-10-19</td>\n",
       "      <td>0.196051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMZN</th>\n",
       "      <td>1997-05-16</td>\n",
       "      <td>0.086458</td>\n",
       "      <td>1998-04-16</td>\n",
       "      <td>0.410417</td>\n",
       "      <td>1998-05-18</td>\n",
       "      <td>0.366927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BRK-B</th>\n",
       "      <td>1996-05-10</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>1998-06-19</td>\n",
       "      <td>54.099998</td>\n",
       "      <td>1999-09-28</td>\n",
       "      <td>37.400002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GOOGL</th>\n",
       "      <td>2004-08-20</td>\n",
       "      <td>2.710460</td>\n",
       "      <td>2007-11-06</td>\n",
       "      <td>18.563313</td>\n",
       "      <td>2008-02-01</td>\n",
       "      <td>12.910410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GOOG</th>\n",
       "      <td>2004-08-20</td>\n",
       "      <td>2.697639</td>\n",
       "      <td>2007-11-06</td>\n",
       "      <td>18.475498</td>\n",
       "      <td>2008-02-01</td>\n",
       "      <td>12.849338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JNJ</th>\n",
       "      <td>1962-01-03</td>\n",
       "      <td>0.065422</td>\n",
       "      <td>1962-02-07</td>\n",
       "      <td>0.074375</td>\n",
       "      <td>1963-01-04</td>\n",
       "      <td>0.053508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UNH</th>\n",
       "      <td>1984-10-18</td>\n",
       "      <td>0.121133</td>\n",
       "      <td>1985-07-22</td>\n",
       "      <td>0.306020</td>\n",
       "      <td>1985-10-21</td>\n",
       "      <td>0.258204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XOM</th>\n",
       "      <td>1962-01-03</td>\n",
       "      <td>0.099758</td>\n",
       "      <td>1964-11-20</td>\n",
       "      <td>0.203202</td>\n",
       "      <td>1966-08-29</td>\n",
       "      <td>0.141469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JPM</th>\n",
       "      <td>1980-03-18</td>\n",
       "      <td>1.122867</td>\n",
       "      <td>1981-06-16</td>\n",
       "      <td>2.016242</td>\n",
       "      <td>1982-07-07</td>\n",
       "      <td>1.364652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NVDA</th>\n",
       "      <td>1999-01-25</td>\n",
       "      <td>0.415857</td>\n",
       "      <td>1999-12-31</td>\n",
       "      <td>0.897438</td>\n",
       "      <td>2000-01-26</td>\n",
       "      <td>0.776743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PG</th>\n",
       "      <td>1962-01-03</td>\n",
       "      <td>0.262992</td>\n",
       "      <td>1962-01-03</td>\n",
       "      <td>0.262992</td>\n",
       "      <td>1963-01-04</td>\n",
       "      <td>0.217238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V</th>\n",
       "      <td>2008-03-20</td>\n",
       "      <td>14.495992</td>\n",
       "      <td>2008-05-07</td>\n",
       "      <td>19.936213</td>\n",
       "      <td>2009-03-23</td>\n",
       "      <td>12.578486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HD</th>\n",
       "      <td>1981-09-23</td>\n",
       "      <td>0.018070</td>\n",
       "      <td>1983-09-20</td>\n",
       "      <td>0.416502</td>\n",
       "      <td>1983-10-31</td>\n",
       "      <td>0.282627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TSLA</th>\n",
       "      <td>2010-06-30</td>\n",
       "      <td>1.588667</td>\n",
       "      <td>2010-11-24</td>\n",
       "      <td>2.364667</td>\n",
       "      <td>2011-07-01</td>\n",
       "      <td>1.934667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MA</th>\n",
       "      <td>2006-05-26</td>\n",
       "      <td>4.148601</td>\n",
       "      <td>2008-06-02</td>\n",
       "      <td>29.749420</td>\n",
       "      <td>2008-09-04</td>\n",
       "      <td>20.095049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CVX</th>\n",
       "      <td>1962-01-03</td>\n",
       "      <td>0.348873</td>\n",
       "      <td>1966-01-19</td>\n",
       "      <td>0.642091</td>\n",
       "      <td>1966-08-23</td>\n",
       "      <td>0.445683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>META</th>\n",
       "      <td>2012-05-21</td>\n",
       "      <td>34.029999</td>\n",
       "      <td>2012-05-21</td>\n",
       "      <td>34.029999</td>\n",
       "      <td>2013-05-22</td>\n",
       "      <td>25.160000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABBV</th>\n",
       "      <td>2013-01-03</td>\n",
       "      <td>22.915104</td>\n",
       "      <td>2015-07-28</td>\n",
       "      <td>51.486412</td>\n",
       "      <td>2015-10-22</td>\n",
       "      <td>35.212490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MRK</th>\n",
       "      <td>1962-01-03</td>\n",
       "      <td>0.067414</td>\n",
       "      <td>1962-03-23</td>\n",
       "      <td>0.074726</td>\n",
       "      <td>1963-01-04</td>\n",
       "      <td>0.064415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LLY</th>\n",
       "      <td>1972-06-02</td>\n",
       "      <td>0.924479</td>\n",
       "      <td>1973-07-25</td>\n",
       "      <td>1.305583</td>\n",
       "      <td>1974-02-11</td>\n",
       "      <td>0.908994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PFE</th>\n",
       "      <td>1972-06-02</td>\n",
       "      <td>0.172844</td>\n",
       "      <td>1973-07-25</td>\n",
       "      <td>0.225744</td>\n",
       "      <td>1974-04-25</td>\n",
       "      <td>0.154993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PEP</th>\n",
       "      <td>1972-06-02</td>\n",
       "      <td>0.416047</td>\n",
       "      <td>1972-12-07</td>\n",
       "      <td>0.437312</td>\n",
       "      <td>1974-02-05</td>\n",
       "      <td>0.304939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KO</th>\n",
       "      <td>1962-01-03</td>\n",
       "      <td>0.048557</td>\n",
       "      <td>1973-03-14</td>\n",
       "      <td>0.367059</td>\n",
       "      <td>1974-05-02</td>\n",
       "      <td>0.250945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BAC</th>\n",
       "      <td>1973-02-22</td>\n",
       "      <td>1.636574</td>\n",
       "      <td>1973-10-19</td>\n",
       "      <td>1.851479</td>\n",
       "      <td>1974-05-08</td>\n",
       "      <td>1.294933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AVGO</th>\n",
       "      <td>2009-08-07</td>\n",
       "      <td>12.105116</td>\n",
       "      <td>2011-07-06</td>\n",
       "      <td>29.020336</td>\n",
       "      <td>2011-08-08</td>\n",
       "      <td>20.190964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TMO</th>\n",
       "      <td>1980-03-18</td>\n",
       "      <td>1.655435</td>\n",
       "      <td>1980-09-17</td>\n",
       "      <td>2.798047</td>\n",
       "      <td>1981-07-08</td>\n",
       "      <td>1.916347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COST</th>\n",
       "      <td>1986-07-10</td>\n",
       "      <td>7.600136</td>\n",
       "      <td>1987-03-26</td>\n",
       "      <td>8.859586</td>\n",
       "      <td>1987-07-13</td>\n",
       "      <td>8.425297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WMT</th>\n",
       "      <td>1972-08-28</td>\n",
       "      <td>0.035747</td>\n",
       "      <td>1972-11-27</td>\n",
       "      <td>0.038058</td>\n",
       "      <td>1973-08-29</td>\n",
       "      <td>0.023378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CSCO</th>\n",
       "      <td>1990-02-20</td>\n",
       "      <td>0.056262</td>\n",
       "      <td>1991-02-13</td>\n",
       "      <td>0.147382</td>\n",
       "      <td>1991-02-21</td>\n",
       "      <td>0.139433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MCD</th>\n",
       "      <td>1966-07-06</td>\n",
       "      <td>0.127219</td>\n",
       "      <td>1967-06-21</td>\n",
       "      <td>0.214797</td>\n",
       "      <td>1967-07-07</td>\n",
       "      <td>0.208804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABT</th>\n",
       "      <td>1980-03-18</td>\n",
       "      <td>0.206454</td>\n",
       "      <td>1987-03-24</td>\n",
       "      <td>1.693849</td>\n",
       "      <td>1987-10-20</td>\n",
       "      <td>1.180671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VZ</th>\n",
       "      <td>1983-11-22</td>\n",
       "      <td>1.264168</td>\n",
       "      <td>1999-10-04</td>\n",
       "      <td>21.634064</td>\n",
       "      <td>2000-02-24</td>\n",
       "      <td>15.126930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DIS</th>\n",
       "      <td>1962-01-03</td>\n",
       "      <td>0.059182</td>\n",
       "      <td>1962-01-11</td>\n",
       "      <td>0.062710</td>\n",
       "      <td>1963-01-04</td>\n",
       "      <td>0.050705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACN</th>\n",
       "      <td>2001-07-20</td>\n",
       "      <td>10.939423</td>\n",
       "      <td>2002-03-11</td>\n",
       "      <td>21.791388</td>\n",
       "      <td>2002-07-22</td>\n",
       "      <td>10.822816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DHR</th>\n",
       "      <td>1979-01-02</td>\n",
       "      <td>0.078984</td>\n",
       "      <td>1979-08-28</td>\n",
       "      <td>0.133456</td>\n",
       "      <td>1980-01-03</td>\n",
       "      <td>0.095325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NEE</th>\n",
       "      <td>1973-02-22</td>\n",
       "      <td>0.389877</td>\n",
       "      <td>1973-03-13</td>\n",
       "      <td>0.423030</td>\n",
       "      <td>1974-02-25</td>\n",
       "      <td>0.262571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CMCSA</th>\n",
       "      <td>1980-03-18</td>\n",
       "      <td>0.068238</td>\n",
       "      <td>1981-05-21</td>\n",
       "      <td>0.187654</td>\n",
       "      <td>1981-09-25</td>\n",
       "      <td>0.131358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WFC</th>\n",
       "      <td>1972-06-02</td>\n",
       "      <td>0.148935</td>\n",
       "      <td>1973-10-30</td>\n",
       "      <td>0.245917</td>\n",
       "      <td>1974-05-10</td>\n",
       "      <td>0.170917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TXN</th>\n",
       "      <td>1972-06-02</td>\n",
       "      <td>0.770303</td>\n",
       "      <td>1973-10-12</td>\n",
       "      <td>1.286320</td>\n",
       "      <td>1973-12-13</td>\n",
       "      <td>0.887118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ADBE</th>\n",
       "      <td>1986-08-14</td>\n",
       "      <td>0.209060</td>\n",
       "      <td>1987-05-05</td>\n",
       "      <td>1.584453</td>\n",
       "      <td>1987-08-17</td>\n",
       "      <td>1.085644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LIN</th>\n",
       "      <td>1992-06-18</td>\n",
       "      <td>4.637535</td>\n",
       "      <td>1997-07-21</td>\n",
       "      <td>18.193213</td>\n",
       "      <td>1998-08-27</td>\n",
       "      <td>12.206602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PM</th>\n",
       "      <td>2008-03-18</td>\n",
       "      <td>24.800337</td>\n",
       "      <td>2008-08-12</td>\n",
       "      <td>28.226189</td>\n",
       "      <td>2009-03-19</td>\n",
       "      <td>19.205423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NKE</th>\n",
       "      <td>1980-12-03</td>\n",
       "      <td>0.127474</td>\n",
       "      <td>1982-12-08</td>\n",
       "      <td>0.313727</td>\n",
       "      <td>1983-01-11</td>\n",
       "      <td>0.219538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BMY</th>\n",
       "      <td>1972-06-02</td>\n",
       "      <td>0.332630</td>\n",
       "      <td>1972-08-10</td>\n",
       "      <td>0.381656</td>\n",
       "      <td>1973-12-11</td>\n",
       "      <td>0.261395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CRM</th>\n",
       "      <td>2004-06-24</td>\n",
       "      <td>4.190000</td>\n",
       "      <td>2004-11-02</td>\n",
       "      <td>5.490000</td>\n",
       "      <td>2005-06-27</td>\n",
       "      <td>5.060000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RTX</th>\n",
       "      <td>1962-04-03</td>\n",
       "      <td>0.094913</td>\n",
       "      <td>1966-04-20</td>\n",
       "      <td>0.341528</td>\n",
       "      <td>1966-10-06</td>\n",
       "      <td>0.223258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COP</th>\n",
       "      <td>1982-01-04</td>\n",
       "      <td>1.071323</td>\n",
       "      <td>1982-01-18</td>\n",
       "      <td>1.078038</td>\n",
       "      <td>1983-01-05</td>\n",
       "      <td>1.001867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMGN</th>\n",
       "      <td>1983-06-20</td>\n",
       "      <td>0.224463</td>\n",
       "      <td>1983-06-22</td>\n",
       "      <td>0.232204</td>\n",
       "      <td>1984-06-21</td>\n",
       "      <td>0.087077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HON</th>\n",
       "      <td>1962-01-03</td>\n",
       "      <td>1.116203</td>\n",
       "      <td>1962-01-08</td>\n",
       "      <td>1.126146</td>\n",
       "      <td>1963-01-04</td>\n",
       "      <td>0.907728</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      primo dato valido    prezzo1 data massimo    massimo va sotto soglia  \\\n",
       "AAPL         1980-12-15   0.094519   1980-12-29   0.124869      1981-12-16   \n",
       "MSFT         1986-03-14   0.062823   1987-10-05   0.342277      1987-10-19   \n",
       "AMZN         1997-05-16   0.086458   1998-04-16   0.410417      1998-05-18   \n",
       "BRK-B        1996-05-10  24.000000   1998-06-19  54.099998      1999-09-28   \n",
       "GOOGL        2004-08-20   2.710460   2007-11-06  18.563313      2008-02-01   \n",
       "GOOG         2004-08-20   2.697639   2007-11-06  18.475498      2008-02-01   \n",
       "JNJ          1962-01-03   0.065422   1962-02-07   0.074375      1963-01-04   \n",
       "UNH          1984-10-18   0.121133   1985-07-22   0.306020      1985-10-21   \n",
       "XOM          1962-01-03   0.099758   1964-11-20   0.203202      1966-08-29   \n",
       "JPM          1980-03-18   1.122867   1981-06-16   2.016242      1982-07-07   \n",
       "NVDA         1999-01-25   0.415857   1999-12-31   0.897438      2000-01-26   \n",
       "PG           1962-01-03   0.262992   1962-01-03   0.262992      1963-01-04   \n",
       "V            2008-03-20  14.495992   2008-05-07  19.936213      2009-03-23   \n",
       "HD           1981-09-23   0.018070   1983-09-20   0.416502      1983-10-31   \n",
       "TSLA         2010-06-30   1.588667   2010-11-24   2.364667      2011-07-01   \n",
       "MA           2006-05-26   4.148601   2008-06-02  29.749420      2008-09-04   \n",
       "CVX          1962-01-03   0.348873   1966-01-19   0.642091      1966-08-23   \n",
       "META         2012-05-21  34.029999   2012-05-21  34.029999      2013-05-22   \n",
       "ABBV         2013-01-03  22.915104   2015-07-28  51.486412      2015-10-22   \n",
       "MRK          1962-01-03   0.067414   1962-03-23   0.074726      1963-01-04   \n",
       "LLY          1972-06-02   0.924479   1973-07-25   1.305583      1974-02-11   \n",
       "PFE          1972-06-02   0.172844   1973-07-25   0.225744      1974-04-25   \n",
       "PEP          1972-06-02   0.416047   1972-12-07   0.437312      1974-02-05   \n",
       "KO           1962-01-03   0.048557   1973-03-14   0.367059      1974-05-02   \n",
       "BAC          1973-02-22   1.636574   1973-10-19   1.851479      1974-05-08   \n",
       "AVGO         2009-08-07  12.105116   2011-07-06  29.020336      2011-08-08   \n",
       "TMO          1980-03-18   1.655435   1980-09-17   2.798047      1981-07-08   \n",
       "COST         1986-07-10   7.600136   1987-03-26   8.859586      1987-07-13   \n",
       "WMT          1972-08-28   0.035747   1972-11-27   0.038058      1973-08-29   \n",
       "CSCO         1990-02-20   0.056262   1991-02-13   0.147382      1991-02-21   \n",
       "MCD          1966-07-06   0.127219   1967-06-21   0.214797      1967-07-07   \n",
       "ABT          1980-03-18   0.206454   1987-03-24   1.693849      1987-10-20   \n",
       "VZ           1983-11-22   1.264168   1999-10-04  21.634064      2000-02-24   \n",
       "DIS          1962-01-03   0.059182   1962-01-11   0.062710      1963-01-04   \n",
       "ACN          2001-07-20  10.939423   2002-03-11  21.791388      2002-07-22   \n",
       "DHR          1979-01-02   0.078984   1979-08-28   0.133456      1980-01-03   \n",
       "NEE          1973-02-22   0.389877   1973-03-13   0.423030      1974-02-25   \n",
       "CMCSA        1980-03-18   0.068238   1981-05-21   0.187654      1981-09-25   \n",
       "WFC          1972-06-02   0.148935   1973-10-30   0.245917      1974-05-10   \n",
       "TXN          1972-06-02   0.770303   1973-10-12   1.286320      1973-12-13   \n",
       "ADBE         1986-08-14   0.209060   1987-05-05   1.584453      1987-08-17   \n",
       "LIN          1992-06-18   4.637535   1997-07-21  18.193213      1998-08-27   \n",
       "PM           2008-03-18  24.800337   2008-08-12  28.226189      2009-03-19   \n",
       "NKE          1980-12-03   0.127474   1982-12-08   0.313727      1983-01-11   \n",
       "BMY          1972-06-02   0.332630   1972-08-10   0.381656      1973-12-11   \n",
       "CRM          2004-06-24   4.190000   2004-11-02   5.490000      2005-06-27   \n",
       "RTX          1962-04-03   0.094913   1966-04-20   0.341528      1966-10-06   \n",
       "COP          1982-01-04   1.071323   1982-01-18   1.078038      1983-01-05   \n",
       "AMGN         1983-06-20   0.224463   1983-06-22   0.232204      1984-06-21   \n",
       "HON          1962-01-03   1.116203   1962-01-08   1.126146      1963-01-04   \n",
       "\n",
       "         prezzo2  \n",
       "AAPL    0.067638  \n",
       "MSFT    0.196051  \n",
       "AMZN    0.366927  \n",
       "BRK-B  37.400002  \n",
       "GOOGL  12.910410  \n",
       "GOOG   12.849338  \n",
       "JNJ     0.053508  \n",
       "UNH     0.258204  \n",
       "XOM     0.141469  \n",
       "JPM     1.364652  \n",
       "NVDA    0.776743  \n",
       "PG      0.217238  \n",
       "V      12.578486  \n",
       "HD      0.282627  \n",
       "TSLA    1.934667  \n",
       "MA     20.095049  \n",
       "CVX     0.445683  \n",
       "META   25.160000  \n",
       "ABBV   35.212490  \n",
       "MRK     0.064415  \n",
       "LLY     0.908994  \n",
       "PFE     0.154993  \n",
       "PEP     0.304939  \n",
       "KO      0.250945  \n",
       "BAC     1.294933  \n",
       "AVGO   20.190964  \n",
       "TMO     1.916347  \n",
       "COST    8.425297  \n",
       "WMT     0.023378  \n",
       "CSCO    0.139433  \n",
       "MCD     0.208804  \n",
       "ABT     1.180671  \n",
       "VZ     15.126930  \n",
       "DIS     0.050705  \n",
       "ACN    10.822816  \n",
       "DHR     0.095325  \n",
       "NEE     0.262571  \n",
       "CMCSA   0.131358  \n",
       "WFC     0.170917  \n",
       "TXN     0.887118  \n",
       "ADBE    1.085644  \n",
       "LIN    12.206602  \n",
       "PM     19.205423  \n",
       "NKE     0.219538  \n",
       "BMY     0.261395  \n",
       "CRM     5.060000  \n",
       "RTX     0.223258  \n",
       "COP     1.001867  \n",
       "AMGN    0.087077  \n",
       "HON     0.907728  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t1=logret.apply(pd.Series.first_valid_index)\n",
    "t2=(cummin2<-soglia).eq(True).idxmax()\n",
    "d1=dati.iloc[0].copy()\n",
    "d2=dati.iloc[0].copy()\n",
    "mm=dati.iloc[0].copy()\n",
    "tm=t2.copy()\n",
    "for t in tickers:\n",
    "    d1[t]=dati[t].loc[t1[t]]\n",
    "    d2[t]=dati[t].loc[t2[t]]\n",
    "    temp=dati.index[dati.index.get_loc(t2[t])+1]\n",
    "    mm[t]=np.max(dati[t].loc[t1[t]:temp])\n",
    "    try:\n",
    "        tm[t]=dati[t].loc[t1[t]:temp].idxmax()\n",
    "    except:\n",
    "        tm[t]=np.nan\n",
    "pd.DataFrame({\"primo dato valido\":t1,\"prezzo1\":d1,\"data massimo\":tm,\"massimo\":mm,\"va sotto soglia\":t2,\"prezzo2\":d2})[:50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AAPL 0\n",
      "MSFT 1\n",
      "AMZN 2\n",
      "BRK-B 3\n",
      "GOOGL 4\n",
      "GOOG 5\n",
      "JNJ 6\n",
      "UNH 7\n",
      "XOM 8\n",
      "JPM 9\n",
      "NVDA 10\n",
      "PG 11\n",
      "V 12\n",
      "HD 13\n",
      "TSLA 14\n",
      "MA 15\n",
      "CVX 16\n",
      "META 17\n",
      "ABBV 18\n",
      "MRK 19\n",
      "LLY 20\n",
      "PFE 21\n",
      "PEP 22\n",
      "KO 23\n",
      "BAC 24\n",
      "AVGO 25\n",
      "TMO 26\n",
      "COST 27\n",
      "WMT 28\n",
      "CSCO 29\n",
      "MCD 30\n",
      "ABT 31\n",
      "VZ 32\n",
      "DIS 33\n",
      "ACN 34\n",
      "DHR 35\n",
      "NEE 36\n",
      "CMCSA 37\n",
      "WFC 38\n",
      "TXN 39\n",
      "ADBE 40\n",
      "LIN 41\n",
      "PM 42\n",
      "NKE 43\n",
      "BMY 44\n",
      "CRM 45\n",
      "RTX 46\n",
      "COP 47\n",
      "AMGN 48\n",
      "HON 49\n",
      "NFLX 50\n",
      "T 51\n",
      "ORCL 52\n",
      "UPS 53\n",
      "UNP 54\n",
      "SCHW 55\n",
      "IBM 56\n",
      "CAT 57\n",
      "QCOM 58\n",
      "LOW 59\n",
      "INTC 60\n",
      "SBUX 61\n",
      "CVS 62\n",
      "GS 63\n",
      "DE 64\n",
      "BA 65\n",
      "SPGI 66\n",
      "MS 67\n",
      "BLK 68\n",
      "ELV 69\n",
      "INTU 70\n",
      "AMD 71\n",
      "PLD 72\n",
      "LMT 73\n",
      "GILD 74\n",
      "MDT 75\n",
      "AMT 76\n",
      "ADP 77\n",
      "TJX 78\n",
      "ISRG 79\n",
      "CB 80\n",
      "CI 81\n",
      "C 82\n",
      "MDLZ 83\n",
      "AMAT 84\n",
      "AXP 85\n",
      "TMUS 86\n",
      "PYPL 87\n",
      "SYK 88\n",
      "ADI 89\n",
      "BKNG 90\n",
      "MMC 91\n",
      "MO 92\n",
      "DUK 93\n",
      "GE 94\n",
      "SLB 95\n",
      "SO 96\n",
      "PGR 97\n",
      "NOW 98\n",
      "EOG 99\n",
      "VRTX 100\n",
      "REGN 101\n",
      "TGT 102\n",
      "BDX 103\n",
      "NOC 104\n",
      "MMM 105\n",
      "APD 106\n",
      "ZTS 107\n",
      "CSX 108\n",
      "PNC 109\n",
      "CL 110\n",
      "BSX 111\n",
      "FISV 112\n",
      "ETN 113\n",
      "ITW 114\n",
      "AON 115\n",
      "CME 116\n",
      "EQIX 117\n",
      "CCI 118\n",
      "USB 119\n",
      "LRCX 120\n",
      "MU 121\n",
      "FCX 122\n",
      "HUM 123\n",
      "MRNA 124\n",
      "TFC 125\n",
      "EL 126\n",
      "WM 127\n",
      "NSC 128\n",
      "ICE 129\n",
      "KLAC 130\n",
      "EMR 131\n",
      "SHW 132\n",
      "GD 133\n",
      "ATVI 134\n",
      "PXD 135\n",
      "MCK 136\n",
      "DG 137\n",
      "MPC 138\n",
      "HCA 139\n",
      "ORLY 140\n",
      "D 141\n",
      "GM 142\n",
      "AEP 143\n",
      "GIS 144\n",
      "F 145\n",
      "VLO 146\n",
      "SNPS 147\n",
      "SRE 148\n",
      "PSX 149\n",
      "MET 150\n",
      "AIG 151\n",
      "EW 152\n",
      "APH 153\n",
      "ROP 154\n",
      "ADM 155\n",
      "OXY 156\n",
      "AZO 157\n",
      "KMB 158\n",
      "JCI 159\n",
      "MCO 160\n",
      "FDX 161\n",
      "CDNS 162\n",
      "TRV 163\n",
      "PSA 164\n",
      "CNC 165\n",
      "A 166\n",
      "CTVA 167\n",
      "EXC 168\n",
      "MSI 169\n",
      "DXCM 170\n",
      "NXPI 171\n",
      "ADSK 172\n",
      "FIS 173\n",
      "ROST 174\n",
      "NEM 175\n",
      "TT 176\n",
      "MAR 177\n",
      "AFL 178\n",
      "DVN 179\n",
      "O 180\n",
      "MCHP 181\n",
      "SYY 182\n",
      "AJG 183\n",
      "CMG 184\n",
      "HES 185\n",
      "BIIB 186\n",
      "WMB 187\n",
      "DOW 188\n",
      "CHTR 189\n",
      "PH 190\n",
      "XEL 191\n",
      "MNST 192\n",
      "IQV 193\n",
      "TEL 194\n",
      "MSCI 195\n",
      "SPG 196\n",
      "LHX 197\n",
      "CTAS 198\n",
      "ALL 199\n",
      "PAYX 200\n",
      "COF 201\n",
      "IDXX 202\n",
      "KMI 203\n",
      "ECL 204\n",
      "NUE 205\n",
      "YUM 206\n",
      "HAL 207\n",
      "DD 208\n",
      "CARR 209\n",
      "PRU 210\n",
      "BK 211\n",
      "HLT 212\n",
      "STZ 213\n",
      "PCAR 214\n",
      "ED 215\n",
      "AMP 216\n",
      "CMI 217\n",
      "OTIS 218\n",
      "KHC 219\n",
      "TDG 220\n",
      "HSY 221\n",
      "WELL 222\n",
      "EA 223\n",
      "MTD 224\n",
      "ILMN 225\n",
      "AME 226\n",
      "FTNT 227\n",
      "PEG 228\n",
      "KEYS 229\n",
      "CTSH 230\n",
      "CSGP 231\n",
      "SBAC 232\n",
      "ROK 233\n",
      "BKR 234\n",
      "ENPH 235\n",
      "RMD 236\n",
      "VICI 237\n",
      "KDP 238\n",
      "WEC 239\n",
      "DLTR 240\n",
      "OKE 241\n",
      "PPG 242\n",
      "KR 243\n",
      "STT 244\n",
      "ES 245\n",
      "GPN 246\n",
      "DHI 247\n",
      "DLR 248\n",
      "AWK 249\n",
      "VRSK 250\n",
      "IFF 251\n",
      "DFS 252\n",
      "ANET 253\n",
      "ODFL 254\n",
      "WTW 255\n",
      "ON 256\n",
      "CEG 257\n",
      "FAST 258\n",
      "CPRT 259\n",
      "GLW 260\n",
      "ABC 261\n",
      "APTV 262\n",
      "ZBH 263\n",
      "ALB 264\n",
      "URI 265\n",
      "CBRE 266\n",
      "RSG 267\n",
      "WBA 268\n",
      "MTB 269\n",
      "HPQ 270\n",
      "IT 271\n",
      "EIX 272\n",
      "TROW 273\n",
      "PCG 274\n",
      "WBD 275\n",
      "ULTA 276\n",
      "GWW 277\n",
      "FANG 278\n",
      "EFX 279\n",
      "HIG 280\n",
      "CDW 281\n",
      "LEN 282\n",
      "GPC 283\n",
      "TSCO 284\n",
      "EBAY 285\n",
      "VMC 286\n",
      "FITB 287\n",
      "FTV 288\n",
      "WY 289\n",
      "DAL 290\n",
      "ACGL 291\n",
      "LYB 292\n",
      "AEE 293\n",
      "DTE 294\n",
      "FE 295\n",
      "AVB 296\n",
      "FRC 297\n",
      "BAX 298\n",
      "IR 299\n",
      "PPL 300\n",
      "LH 301\n",
      "ETR 302\n",
      "HPE 303\n",
      "ARE 304\n",
      "RJF 305\n",
      "ANSS 306\n",
      "MLM 307\n",
      "MKC 308\n",
      "NDAQ 309\n",
      "LUV 310\n",
      "GEHC 311\n",
      "RF 312\n",
      "HBAN 313\n",
      "CAH 314\n",
      "CFG 315\n",
      "PFG 316\n",
      "HOLX 317\n",
      "XYL 318\n",
      "CHD 319\n",
      "DOV 320\n",
      "EQR 321\n",
      "CTRA 322\n",
      "PWR 323\n",
      "EXR 324\n",
      "NTRS 325\n",
      "VRSN 326\n",
      "CAG 327\n",
      "TSN 328\n",
      "WAT 329\n",
      "CNP 330\n",
      "STE 331\n",
      "VTR 332\n",
      "TDY 333\n",
      "CMS 334\n",
      "EPAM 335\n",
      "WAB 336\n",
      "AMCR 337\n",
      "K 338\n",
      "DRI 339\n",
      "AES 340\n",
      "EXPD 341\n",
      "DGX 342\n",
      "MAA 343\n",
      "WST 344\n",
      "IEX 345\n",
      "OMC 346\n",
      "BALL 347\n",
      "CLX 348\n",
      "LVS 349\n",
      "CINF 350\n",
      "INVH 351\n",
      "MOH 352\n",
      "MRO 353\n",
      "SJM 354\n",
      "CF 355\n",
      "MPWR 356\n",
      "TRGP 357\n",
      "STLD 358\n",
      "KEY 359\n",
      "COO 360\n",
      "PKI 361\n",
      "ALGN 362\n",
      "BBY 363\n",
      "TTWO 364\n",
      "BR 365\n",
      "MOS 366\n",
      "FMC 367\n",
      "J 368\n",
      "FSLR 369\n",
      "ATO 370\n",
      "SEDG 371\n",
      "AVY 372\n",
      "SWKS 373\n",
      "ETSY 374\n",
      "FDS 375\n",
      "GRMN 376\n",
      "TXT 377\n",
      "WRB 378\n",
      "HWM 379\n",
      "JBHT 380\n",
      "NVR 381\n",
      "INCY 382\n",
      "TER 383\n",
      "SYF 384\n",
      "EVRG 385\n",
      "IRM 386\n",
      "LKQ 387\n",
      "SIVB 388\n",
      "ZBRA 389\n",
      "VTRS 390\n",
      "FLT 391\n",
      "PAYC 392\n",
      "UAL 393\n",
      "LW 394\n",
      "NTAP 395\n",
      "APA 396\n",
      "PEAK 397\n",
      "EXPE 398\n",
      "RE 399\n",
      "IPG 400\n",
      "AKAM 401\n",
      "LNT 402\n",
      "ESS 403\n",
      "LDOS 404\n",
      "BRO 405\n",
      "IP 406\n",
      "HRL 407\n",
      "TYL 408\n",
      "PTC 409\n",
      "JKHY 410\n",
      "TRMB 411\n",
      "NDSN 412\n",
      "TECH 413\n",
      "KIM 414\n",
      "CBOE 415\n",
      "SNA 416\n",
      "EQT 417\n",
      "POOL 418\n",
      "RCL 419\n",
      "PKG 420\n",
      "GEN 421\n",
      "MTCH 422\n",
      "MGM 423\n",
      "SWK 424\n",
      "MKTX 425\n",
      "CPT 426\n",
      "DPZ 427\n",
      "TFX 428\n",
      "UDR 429\n",
      "BF-B 430\n",
      "WDC 431\n",
      "CE 432\n",
      "CHRW 433\n",
      "L 434\n",
      "HST 435\n",
      "MAS 436\n",
      "CRL 437\n",
      "CPB 438\n",
      "NI 439\n",
      "HSIC 440\n",
      "PHM 441\n",
      "GL 442\n",
      "EMN 443\n",
      "KMX 444\n",
      "STX 445\n",
      "TPR 446\n",
      "LYV 447\n",
      "BBWI 448\n",
      "JNPR 449\n",
      "BWA 450\n",
      "ALLE 451\n",
      "UHS 452\n",
      "WYNN 453\n",
      "PARA 454\n",
      "QRVO 455\n",
      "CDAY 456\n",
      "FOXA 457\n",
      "VFC 458\n",
      "REG 459\n",
      "TAP 460\n",
      "AAL 461\n",
      "WRK 462\n",
      "BXP 463\n",
      "CZR 464\n",
      "AAP 465\n",
      "CCL 466\n",
      "BIO 467\n",
      "HII 468\n",
      "CMA 469\n",
      "IVZ 470\n",
      "CTLT 471\n",
      "FFIV 472\n",
      "PNW 473\n",
      "ROL 474\n",
      "RHI 475\n",
      "WHR 476\n",
      "HAS 477\n",
      "AOS 478\n",
      "SEE 479\n",
      "PNR 480\n",
      "BEN 481\n",
      "ZION 482\n",
      "NRG 483\n",
      "FRT 484\n",
      "OGN 485\n",
      "NWSA 486\n",
      "SBNY 487\n",
      "XRAY 488\n",
      "AIZ 489\n",
      "GNRC 490\n",
      "DXC 491\n",
      "MHK 492\n",
      "NCLH 493\n",
      "ALK 494\n",
      "NWL 495\n",
      "LUMN 496\n",
      "RL 497\n",
      "LNC 498\n",
      "DVA 499\n",
      "FOX 500\n",
      "DISH 501\n",
      "NWS 502\n"
     ]
    }
   ],
   "source": [
    "sottoSoglia=(cummin2<-soglia)\n",
    "s3=(cummin2<-soglia) & (drawdown<0)\n",
    "j=0\n",
    "for t in tickers:\n",
    "    print(t,j)\n",
    "    j+=1\n",
    "    for i in dati.index:\n",
    "        if sottoSoglia[t].loc[i] and not (s3[t].loc[i]):\n",
    "            cummin2[t].loc[i:]=(drawdown[t].loc[i:]).cummin()\n",
    "            sottoSoglia[t]=(cummin2[t]<-soglia)\n",
    "            s3[t]=((cummin2[t]<-soglia) & (drawdown[t]<0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AAPL      9.505186\n",
       "MSFT      2.878837\n",
       "AMZN      5.304996\n",
       "BRK-B     2.938094\n",
       "GOOGL     2.823193\n",
       "           ...    \n",
       "LNC       2.183801\n",
       "DVA       6.606984\n",
       "FOX      22.403406\n",
       "DISH     -1.814198\n",
       "NWS       7.579232\n",
       "Length: 503, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "strategia3=100*(np.exp((s3*logret2).sum()/times)-1)\n",
    "strategia3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "buy&hold: 13.9 %  -  strategia 2: 12.8 %  -  strategia 3: 5.6 %\n"
     ]
    }
   ],
   "source": [
    "print(\"buy&hold:\",round(strategia1.mean(),1),\"%  -  strategia 2:\",round(strategia2.mean(),1),\"%  -  strategia 3:\",round(strategia3.mean(),1),\"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "buy&hold: 13.6 %  -  strategia 2: 12.4 %  -  strategia 3: 5.7 %\n"
     ]
    }
   ],
   "source": [
    "limite=50\n",
    "print(\"buy&hold:\",round(strategia1[limite:].mean(),1),\"%  -  strategia 2:\",round(strategia2[limite:].mean(),1),\"%  -  strategia 3:\",round(strategia3[limite:].mean(),1),\"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Frequenza')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2500x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xticks(range(-10,50,5))\n",
    "plt.xlabel(\"Rendimento annualizzato %\",fontdict = {'fontsize' : 24})\n",
    "strategia1.plot(kind=\"hist\",figsize=(25,15),range=[-10,50],bins=100,legend=True,label=\"buy&hold\",fontsize=20,color=\"green\")\n",
    "strategia2.plot(kind=\"hist\",figsize=(25,15),range=[-10,50],bins=100,legend=True,label=\"strategia 2\",fontsize=20,color=\"red\",alpha=0.8)\n",
    "strategia3.plot(kind=\"hist\",figsize=(25,15),range=[-10,50],bins=100,legend=True,label=\"strategia 3\",fontsize=20,color=\"blue\",alpha=0.6)\n",
    "plt.legend(fontsize=30)\n",
    "plt.ylabel(\"Frequenza\",fontdict = {'fontsize' : 24})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# soglia 0.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "cummin2=cummin.copy()\n",
    "logret2=logret.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "times2=logret2.apply(pd.Series.first_valid_index)+ pd.offsets.DateOffset(years=1)\n",
    "for t in tickers:\n",
    "    logret2[t].loc[:times2[t]]=np.nan\n",
    "    cummin2[t].loc[:times2[t]]=np.nan "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "times=(logret2.index[-1]-times2)/np.timedelta64(1, 'Y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "soglia=0.1\n",
    "# strategia 1: buy and hold\n",
    "# strategia 2: compro quando scende sotto il drawdown e tengo fino alla fine\n",
    "# strategia 3: compro quando scende sotto il drawdown e tengo fino alla risalita"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AAPL     20.876250\n",
       "MSFT     22.824050\n",
       "AMZN     26.063171\n",
       "BRK-B    10.297233\n",
       "GOOGL    16.248101\n",
       "           ...    \n",
       "LNC       5.901550\n",
       "DVA       9.586076\n",
       "FOX      11.841338\n",
       "DISH      6.783176\n",
       "NWS       2.464485\n",
       "Length: 503, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "strategia1=100*(np.exp(logret2.sum()/times)-1)\n",
    "strategia1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AAPL     20.876250\n",
       "MSFT     22.824050\n",
       "AMZN     26.063171\n",
       "BRK-B    10.297233\n",
       "GOOGL    16.248101\n",
       "           ...    \n",
       "LNC       5.901550\n",
       "DVA       9.586076\n",
       "FOX      11.841338\n",
       "DISH      6.783176\n",
       "NWS       2.464485\n",
       "Length: 503, dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "strategia2=100*(np.exp(((cummin2<-soglia)*logret2).sum()/times)-1)\n",
    "strategia2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>primo dato valido</th>\n",
       "      <th>prezzo1</th>\n",
       "      <th>data massimo</th>\n",
       "      <th>massimo</th>\n",
       "      <th>va sotto soglia</th>\n",
       "      <th>prezzo2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AAPL</th>\n",
       "      <td>1980-12-15</td>\n",
       "      <td>0.094519</td>\n",
       "      <td>1980-12-29</td>\n",
       "      <td>0.124869</td>\n",
       "      <td>1981-12-16</td>\n",
       "      <td>0.067638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSFT</th>\n",
       "      <td>1986-03-14</td>\n",
       "      <td>0.062823</td>\n",
       "      <td>1987-03-12</td>\n",
       "      <td>0.186032</td>\n",
       "      <td>1987-03-16</td>\n",
       "      <td>0.178179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMZN</th>\n",
       "      <td>1997-05-16</td>\n",
       "      <td>0.086458</td>\n",
       "      <td>1998-04-16</td>\n",
       "      <td>0.410417</td>\n",
       "      <td>1998-05-18</td>\n",
       "      <td>0.366927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BRK-B</th>\n",
       "      <td>1996-05-10</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>1997-05-13</td>\n",
       "      <td>26.620001</td>\n",
       "      <td>1997-05-12</td>\n",
       "      <td>26.440001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GOOGL</th>\n",
       "      <td>2004-08-20</td>\n",
       "      <td>2.710460</td>\n",
       "      <td>2005-07-21</td>\n",
       "      <td>7.856356</td>\n",
       "      <td>2005-08-22</td>\n",
       "      <td>6.857107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GOOG</th>\n",
       "      <td>2004-08-20</td>\n",
       "      <td>2.697639</td>\n",
       "      <td>2005-07-21</td>\n",
       "      <td>7.819192</td>\n",
       "      <td>2005-08-22</td>\n",
       "      <td>6.824669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JNJ</th>\n",
       "      <td>1962-01-03</td>\n",
       "      <td>0.065422</td>\n",
       "      <td>1962-02-07</td>\n",
       "      <td>0.074375</td>\n",
       "      <td>1963-01-04</td>\n",
       "      <td>0.053508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UNH</th>\n",
       "      <td>1984-10-18</td>\n",
       "      <td>0.121133</td>\n",
       "      <td>1985-07-22</td>\n",
       "      <td>0.306020</td>\n",
       "      <td>1985-10-21</td>\n",
       "      <td>0.258204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XOM</th>\n",
       "      <td>1962-01-03</td>\n",
       "      <td>0.099758</td>\n",
       "      <td>1962-12-26</td>\n",
       "      <td>0.121604</td>\n",
       "      <td>1963-01-04</td>\n",
       "      <td>0.121094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JPM</th>\n",
       "      <td>1980-03-18</td>\n",
       "      <td>1.122867</td>\n",
       "      <td>1981-03-19</td>\n",
       "      <td>1.766261</td>\n",
       "      <td>1981-03-19</td>\n",
       "      <td>1.766261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NVDA</th>\n",
       "      <td>1999-01-25</td>\n",
       "      <td>0.415857</td>\n",
       "      <td>1999-12-31</td>\n",
       "      <td>0.897438</td>\n",
       "      <td>2000-01-26</td>\n",
       "      <td>0.776743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PG</th>\n",
       "      <td>1962-01-03</td>\n",
       "      <td>0.262992</td>\n",
       "      <td>1962-01-03</td>\n",
       "      <td>0.262992</td>\n",
       "      <td>1963-01-04</td>\n",
       "      <td>0.217238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V</th>\n",
       "      <td>2008-03-20</td>\n",
       "      <td>14.495992</td>\n",
       "      <td>2008-05-07</td>\n",
       "      <td>19.936213</td>\n",
       "      <td>2009-03-23</td>\n",
       "      <td>12.578486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HD</th>\n",
       "      <td>1981-09-23</td>\n",
       "      <td>0.018070</td>\n",
       "      <td>1982-08-26</td>\n",
       "      <td>0.090490</td>\n",
       "      <td>1982-09-24</td>\n",
       "      <td>0.090490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TSLA</th>\n",
       "      <td>2010-06-30</td>\n",
       "      <td>1.588667</td>\n",
       "      <td>2010-11-24</td>\n",
       "      <td>2.364667</td>\n",
       "      <td>2011-07-01</td>\n",
       "      <td>1.934667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MA</th>\n",
       "      <td>2006-05-26</td>\n",
       "      <td>4.148601</td>\n",
       "      <td>2007-05-30</td>\n",
       "      <td>13.633222</td>\n",
       "      <td>2007-05-29</td>\n",
       "      <td>13.348755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CVX</th>\n",
       "      <td>1962-01-03</td>\n",
       "      <td>0.348873</td>\n",
       "      <td>1963-01-07</td>\n",
       "      <td>0.438568</td>\n",
       "      <td>1963-01-04</td>\n",
       "      <td>0.437709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>META</th>\n",
       "      <td>2012-05-21</td>\n",
       "      <td>34.029999</td>\n",
       "      <td>2012-05-21</td>\n",
       "      <td>34.029999</td>\n",
       "      <td>2013-05-22</td>\n",
       "      <td>25.160000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABBV</th>\n",
       "      <td>2013-01-03</td>\n",
       "      <td>22.915104</td>\n",
       "      <td>2013-12-18</td>\n",
       "      <td>37.165268</td>\n",
       "      <td>2014-01-06</td>\n",
       "      <td>34.476398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MRK</th>\n",
       "      <td>1962-01-03</td>\n",
       "      <td>0.067414</td>\n",
       "      <td>1962-03-23</td>\n",
       "      <td>0.074726</td>\n",
       "      <td>1963-01-04</td>\n",
       "      <td>0.064415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LLY</th>\n",
       "      <td>1972-06-02</td>\n",
       "      <td>0.924479</td>\n",
       "      <td>1973-03-15</td>\n",
       "      <td>1.279529</td>\n",
       "      <td>1973-06-04</td>\n",
       "      <td>1.191589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PFE</th>\n",
       "      <td>1972-06-02</td>\n",
       "      <td>0.172844</td>\n",
       "      <td>1972-08-11</td>\n",
       "      <td>0.194052</td>\n",
       "      <td>1973-06-04</td>\n",
       "      <td>0.174022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PEP</th>\n",
       "      <td>1972-06-02</td>\n",
       "      <td>0.416047</td>\n",
       "      <td>1972-12-07</td>\n",
       "      <td>0.437312</td>\n",
       "      <td>1973-06-04</td>\n",
       "      <td>0.394254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KO</th>\n",
       "      <td>1962-01-03</td>\n",
       "      <td>0.048557</td>\n",
       "      <td>1962-03-23</td>\n",
       "      <td>0.048997</td>\n",
       "      <td>1963-01-04</td>\n",
       "      <td>0.044255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BAC</th>\n",
       "      <td>1973-02-22</td>\n",
       "      <td>1.636574</td>\n",
       "      <td>1973-10-19</td>\n",
       "      <td>1.851479</td>\n",
       "      <td>1974-02-25</td>\n",
       "      <td>1.410650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AVGO</th>\n",
       "      <td>2009-08-07</td>\n",
       "      <td>12.105116</td>\n",
       "      <td>2010-06-17</td>\n",
       "      <td>17.203556</td>\n",
       "      <td>2010-08-09</td>\n",
       "      <td>16.872011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TMO</th>\n",
       "      <td>1980-03-18</td>\n",
       "      <td>1.655435</td>\n",
       "      <td>1980-09-17</td>\n",
       "      <td>2.798047</td>\n",
       "      <td>1981-03-19</td>\n",
       "      <td>2.456163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COST</th>\n",
       "      <td>1986-07-10</td>\n",
       "      <td>7.600136</td>\n",
       "      <td>1987-03-26</td>\n",
       "      <td>8.859586</td>\n",
       "      <td>1987-07-13</td>\n",
       "      <td>8.425297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WMT</th>\n",
       "      <td>1972-08-28</td>\n",
       "      <td>0.035747</td>\n",
       "      <td>1972-11-27</td>\n",
       "      <td>0.038058</td>\n",
       "      <td>1973-08-29</td>\n",
       "      <td>0.023378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CSCO</th>\n",
       "      <td>1990-02-20</td>\n",
       "      <td>0.056262</td>\n",
       "      <td>1991-02-13</td>\n",
       "      <td>0.147382</td>\n",
       "      <td>1991-02-21</td>\n",
       "      <td>0.139433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MCD</th>\n",
       "      <td>1966-07-06</td>\n",
       "      <td>0.127219</td>\n",
       "      <td>1967-06-21</td>\n",
       "      <td>0.214797</td>\n",
       "      <td>1967-07-07</td>\n",
       "      <td>0.208804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABT</th>\n",
       "      <td>1980-03-18</td>\n",
       "      <td>0.206454</td>\n",
       "      <td>1981-03-02</td>\n",
       "      <td>0.349108</td>\n",
       "      <td>1981-03-19</td>\n",
       "      <td>0.337638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VZ</th>\n",
       "      <td>1983-11-22</td>\n",
       "      <td>1.264168</td>\n",
       "      <td>1986-08-27</td>\n",
       "      <td>3.258860</td>\n",
       "      <td>1986-09-11</td>\n",
       "      <td>2.851505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DIS</th>\n",
       "      <td>1962-01-03</td>\n",
       "      <td>0.059182</td>\n",
       "      <td>1962-01-11</td>\n",
       "      <td>0.062710</td>\n",
       "      <td>1963-01-04</td>\n",
       "      <td>0.050705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACN</th>\n",
       "      <td>2001-07-20</td>\n",
       "      <td>10.939423</td>\n",
       "      <td>2002-03-11</td>\n",
       "      <td>21.791388</td>\n",
       "      <td>2002-07-22</td>\n",
       "      <td>10.822816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DHR</th>\n",
       "      <td>1979-01-02</td>\n",
       "      <td>0.078984</td>\n",
       "      <td>1979-08-28</td>\n",
       "      <td>0.133456</td>\n",
       "      <td>1980-01-03</td>\n",
       "      <td>0.095325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NEE</th>\n",
       "      <td>1973-02-22</td>\n",
       "      <td>0.389877</td>\n",
       "      <td>1973-03-13</td>\n",
       "      <td>0.423030</td>\n",
       "      <td>1974-02-25</td>\n",
       "      <td>0.262571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CMCSA</th>\n",
       "      <td>1980-03-18</td>\n",
       "      <td>0.068238</td>\n",
       "      <td>1980-09-23</td>\n",
       "      <td>0.147849</td>\n",
       "      <td>1981-03-19</td>\n",
       "      <td>0.142162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WFC</th>\n",
       "      <td>1972-06-02</td>\n",
       "      <td>0.148935</td>\n",
       "      <td>1973-01-16</td>\n",
       "      <td>0.229944</td>\n",
       "      <td>1973-06-04</td>\n",
       "      <td>0.200849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TXN</th>\n",
       "      <td>1972-06-02</td>\n",
       "      <td>0.770303</td>\n",
       "      <td>1973-05-24</td>\n",
       "      <td>0.933894</td>\n",
       "      <td>1973-06-04</td>\n",
       "      <td>0.857669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ADBE</th>\n",
       "      <td>1986-08-14</td>\n",
       "      <td>0.209060</td>\n",
       "      <td>1987-05-05</td>\n",
       "      <td>1.584453</td>\n",
       "      <td>1987-08-17</td>\n",
       "      <td>1.085644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LIN</th>\n",
       "      <td>1992-06-18</td>\n",
       "      <td>4.637535</td>\n",
       "      <td>1993-04-19</td>\n",
       "      <td>5.477890</td>\n",
       "      <td>1993-06-21</td>\n",
       "      <td>4.708661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PM</th>\n",
       "      <td>2008-03-18</td>\n",
       "      <td>24.800337</td>\n",
       "      <td>2008-08-12</td>\n",
       "      <td>28.226189</td>\n",
       "      <td>2009-03-19</td>\n",
       "      <td>19.205423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NKE</th>\n",
       "      <td>1980-12-03</td>\n",
       "      <td>0.127474</td>\n",
       "      <td>1981-12-01</td>\n",
       "      <td>0.161467</td>\n",
       "      <td>1981-12-04</td>\n",
       "      <td>0.158634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BMY</th>\n",
       "      <td>1972-06-02</td>\n",
       "      <td>0.332630</td>\n",
       "      <td>1972-08-10</td>\n",
       "      <td>0.381656</td>\n",
       "      <td>1973-06-04</td>\n",
       "      <td>0.316132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CRM</th>\n",
       "      <td>2004-06-24</td>\n",
       "      <td>4.190000</td>\n",
       "      <td>2004-11-02</td>\n",
       "      <td>5.490000</td>\n",
       "      <td>2005-06-27</td>\n",
       "      <td>5.060000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RTX</th>\n",
       "      <td>1962-04-03</td>\n",
       "      <td>0.094913</td>\n",
       "      <td>1962-12-05</td>\n",
       "      <td>0.112404</td>\n",
       "      <td>1963-04-04</td>\n",
       "      <td>0.102105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COP</th>\n",
       "      <td>1982-01-04</td>\n",
       "      <td>1.071323</td>\n",
       "      <td>1982-01-18</td>\n",
       "      <td>1.078038</td>\n",
       "      <td>1983-01-05</td>\n",
       "      <td>1.001867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMGN</th>\n",
       "      <td>1983-06-20</td>\n",
       "      <td>0.224463</td>\n",
       "      <td>1983-06-22</td>\n",
       "      <td>0.232204</td>\n",
       "      <td>1984-06-21</td>\n",
       "      <td>0.087077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HON</th>\n",
       "      <td>1962-01-03</td>\n",
       "      <td>1.116203</td>\n",
       "      <td>1962-01-08</td>\n",
       "      <td>1.126146</td>\n",
       "      <td>1963-01-04</td>\n",
       "      <td>0.907728</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      primo dato valido    prezzo1 data massimo    massimo va sotto soglia  \\\n",
       "AAPL         1980-12-15   0.094519   1980-12-29   0.124869      1981-12-16   \n",
       "MSFT         1986-03-14   0.062823   1987-03-12   0.186032      1987-03-16   \n",
       "AMZN         1997-05-16   0.086458   1998-04-16   0.410417      1998-05-18   \n",
       "BRK-B        1996-05-10  24.000000   1997-05-13  26.620001      1997-05-12   \n",
       "GOOGL        2004-08-20   2.710460   2005-07-21   7.856356      2005-08-22   \n",
       "GOOG         2004-08-20   2.697639   2005-07-21   7.819192      2005-08-22   \n",
       "JNJ          1962-01-03   0.065422   1962-02-07   0.074375      1963-01-04   \n",
       "UNH          1984-10-18   0.121133   1985-07-22   0.306020      1985-10-21   \n",
       "XOM          1962-01-03   0.099758   1962-12-26   0.121604      1963-01-04   \n",
       "JPM          1980-03-18   1.122867   1981-03-19   1.766261      1981-03-19   \n",
       "NVDA         1999-01-25   0.415857   1999-12-31   0.897438      2000-01-26   \n",
       "PG           1962-01-03   0.262992   1962-01-03   0.262992      1963-01-04   \n",
       "V            2008-03-20  14.495992   2008-05-07  19.936213      2009-03-23   \n",
       "HD           1981-09-23   0.018070   1982-08-26   0.090490      1982-09-24   \n",
       "TSLA         2010-06-30   1.588667   2010-11-24   2.364667      2011-07-01   \n",
       "MA           2006-05-26   4.148601   2007-05-30  13.633222      2007-05-29   \n",
       "CVX          1962-01-03   0.348873   1963-01-07   0.438568      1963-01-04   \n",
       "META         2012-05-21  34.029999   2012-05-21  34.029999      2013-05-22   \n",
       "ABBV         2013-01-03  22.915104   2013-12-18  37.165268      2014-01-06   \n",
       "MRK          1962-01-03   0.067414   1962-03-23   0.074726      1963-01-04   \n",
       "LLY          1972-06-02   0.924479   1973-03-15   1.279529      1973-06-04   \n",
       "PFE          1972-06-02   0.172844   1972-08-11   0.194052      1973-06-04   \n",
       "PEP          1972-06-02   0.416047   1972-12-07   0.437312      1973-06-04   \n",
       "KO           1962-01-03   0.048557   1962-03-23   0.048997      1963-01-04   \n",
       "BAC          1973-02-22   1.636574   1973-10-19   1.851479      1974-02-25   \n",
       "AVGO         2009-08-07  12.105116   2010-06-17  17.203556      2010-08-09   \n",
       "TMO          1980-03-18   1.655435   1980-09-17   2.798047      1981-03-19   \n",
       "COST         1986-07-10   7.600136   1987-03-26   8.859586      1987-07-13   \n",
       "WMT          1972-08-28   0.035747   1972-11-27   0.038058      1973-08-29   \n",
       "CSCO         1990-02-20   0.056262   1991-02-13   0.147382      1991-02-21   \n",
       "MCD          1966-07-06   0.127219   1967-06-21   0.214797      1967-07-07   \n",
       "ABT          1980-03-18   0.206454   1981-03-02   0.349108      1981-03-19   \n",
       "VZ           1983-11-22   1.264168   1986-08-27   3.258860      1986-09-11   \n",
       "DIS          1962-01-03   0.059182   1962-01-11   0.062710      1963-01-04   \n",
       "ACN          2001-07-20  10.939423   2002-03-11  21.791388      2002-07-22   \n",
       "DHR          1979-01-02   0.078984   1979-08-28   0.133456      1980-01-03   \n",
       "NEE          1973-02-22   0.389877   1973-03-13   0.423030      1974-02-25   \n",
       "CMCSA        1980-03-18   0.068238   1980-09-23   0.147849      1981-03-19   \n",
       "WFC          1972-06-02   0.148935   1973-01-16   0.229944      1973-06-04   \n",
       "TXN          1972-06-02   0.770303   1973-05-24   0.933894      1973-06-04   \n",
       "ADBE         1986-08-14   0.209060   1987-05-05   1.584453      1987-08-17   \n",
       "LIN          1992-06-18   4.637535   1993-04-19   5.477890      1993-06-21   \n",
       "PM           2008-03-18  24.800337   2008-08-12  28.226189      2009-03-19   \n",
       "NKE          1980-12-03   0.127474   1981-12-01   0.161467      1981-12-04   \n",
       "BMY          1972-06-02   0.332630   1972-08-10   0.381656      1973-06-04   \n",
       "CRM          2004-06-24   4.190000   2004-11-02   5.490000      2005-06-27   \n",
       "RTX          1962-04-03   0.094913   1962-12-05   0.112404      1963-04-04   \n",
       "COP          1982-01-04   1.071323   1982-01-18   1.078038      1983-01-05   \n",
       "AMGN         1983-06-20   0.224463   1983-06-22   0.232204      1984-06-21   \n",
       "HON          1962-01-03   1.116203   1962-01-08   1.126146      1963-01-04   \n",
       "\n",
       "         prezzo2  \n",
       "AAPL    0.067638  \n",
       "MSFT    0.178179  \n",
       "AMZN    0.366927  \n",
       "BRK-B  26.440001  \n",
       "GOOGL   6.857107  \n",
       "GOOG    6.824669  \n",
       "JNJ     0.053508  \n",
       "UNH     0.258204  \n",
       "XOM     0.121094  \n",
       "JPM     1.766261  \n",
       "NVDA    0.776743  \n",
       "PG      0.217238  \n",
       "V      12.578486  \n",
       "HD      0.090490  \n",
       "TSLA    1.934667  \n",
       "MA     13.348755  \n",
       "CVX     0.437709  \n",
       "META   25.160000  \n",
       "ABBV   34.476398  \n",
       "MRK     0.064415  \n",
       "LLY     1.191589  \n",
       "PFE     0.174022  \n",
       "PEP     0.394254  \n",
       "KO      0.044255  \n",
       "BAC     1.410650  \n",
       "AVGO   16.872011  \n",
       "TMO     2.456163  \n",
       "COST    8.425297  \n",
       "WMT     0.023378  \n",
       "CSCO    0.139433  \n",
       "MCD     0.208804  \n",
       "ABT     0.337638  \n",
       "VZ      2.851505  \n",
       "DIS     0.050705  \n",
       "ACN    10.822816  \n",
       "DHR     0.095325  \n",
       "NEE     0.262571  \n",
       "CMCSA   0.142162  \n",
       "WFC     0.200849  \n",
       "TXN     0.857669  \n",
       "ADBE    1.085644  \n",
       "LIN     4.708661  \n",
       "PM     19.205423  \n",
       "NKE     0.158634  \n",
       "BMY     0.316132  \n",
       "CRM     5.060000  \n",
       "RTX     0.102105  \n",
       "COP     1.001867  \n",
       "AMGN    0.087077  \n",
       "HON     0.907728  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t1=logret.apply(pd.Series.first_valid_index)\n",
    "t2=(cummin2<-soglia).eq(True).idxmax()\n",
    "d1=dati.iloc[0].copy()\n",
    "d2=dati.iloc[0].copy()\n",
    "mm=dati.iloc[0].copy()\n",
    "tm=t2.copy()\n",
    "for t in tickers:\n",
    "    d1[t]=dati[t].loc[t1[t]]\n",
    "    d2[t]=dati[t].loc[t2[t]]\n",
    "    temp=dati.index[dati.index.get_loc(t2[t])+1]\n",
    "    mm[t]=np.max(dati[t].loc[t1[t]:temp])\n",
    "    try:\n",
    "        tm[t]=dati[t].loc[t1[t]:temp].idxmax()\n",
    "    except:\n",
    "        tm[t]=np.nan\n",
    "pd.DataFrame({\"primo dato valido\":t1,\"prezzo1\":d1,\"data massimo\":tm,\"massimo\":mm,\"va sotto soglia\":t2,\"prezzo2\":d2})[:50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AAPL 0\n",
      "MSFT 1\n",
      "AMZN 2\n",
      "BRK-B 3\n",
      "GOOGL 4\n",
      "GOOG 5\n",
      "JNJ 6\n",
      "UNH 7\n",
      "XOM 8\n",
      "JPM 9\n",
      "NVDA 10\n",
      "PG 11\n",
      "V 12\n",
      "HD 13\n",
      "TSLA 14\n",
      "MA 15\n",
      "CVX 16\n",
      "META 17\n",
      "ABBV 18\n",
      "MRK 19\n",
      "LLY 20\n",
      "PFE 21\n",
      "PEP 22\n",
      "KO 23\n",
      "BAC 24\n",
      "AVGO 25\n",
      "TMO 26\n",
      "COST 27\n",
      "WMT 28\n",
      "CSCO 29\n",
      "MCD 30\n",
      "ABT 31\n",
      "VZ 32\n",
      "DIS 33\n",
      "ACN 34\n",
      "DHR 35\n",
      "NEE 36\n",
      "CMCSA 37\n",
      "WFC 38\n",
      "TXN 39\n",
      "ADBE 40\n",
      "LIN 41\n",
      "PM 42\n",
      "NKE 43\n",
      "BMY 44\n",
      "CRM 45\n",
      "RTX 46\n",
      "COP 47\n",
      "AMGN 48\n",
      "HON 49\n",
      "NFLX 50\n",
      "T 51\n",
      "ORCL 52\n",
      "UPS 53\n",
      "UNP 54\n",
      "SCHW 55\n",
      "IBM 56\n",
      "CAT 57\n",
      "QCOM 58\n",
      "LOW 59\n",
      "INTC 60\n",
      "SBUX 61\n",
      "CVS 62\n",
      "GS 63\n",
      "DE 64\n",
      "BA 65\n",
      "SPGI 66\n",
      "MS 67\n",
      "BLK 68\n",
      "ELV 69\n",
      "INTU 70\n",
      "AMD 71\n",
      "PLD 72\n",
      "LMT 73\n",
      "GILD 74\n",
      "MDT 75\n",
      "AMT 76\n",
      "ADP 77\n",
      "TJX 78\n",
      "ISRG 79\n",
      "CB 80\n",
      "CI 81\n",
      "C 82\n",
      "MDLZ 83\n",
      "AMAT 84\n",
      "AXP 85\n",
      "TMUS 86\n",
      "PYPL 87\n",
      "SYK 88\n",
      "ADI 89\n",
      "BKNG 90\n",
      "MMC 91\n",
      "MO 92\n",
      "DUK 93\n",
      "GE 94\n",
      "SLB 95\n",
      "SO 96\n",
      "PGR 97\n",
      "NOW 98\n",
      "EOG 99\n",
      "VRTX 100\n",
      "REGN 101\n",
      "TGT 102\n",
      "BDX 103\n",
      "NOC 104\n",
      "MMM 105\n",
      "APD 106\n",
      "ZTS 107\n",
      "CSX 108\n",
      "PNC 109\n",
      "CL 110\n",
      "BSX 111\n",
      "FISV 112\n",
      "ETN 113\n",
      "ITW 114\n",
      "AON 115\n",
      "CME 116\n",
      "EQIX 117\n",
      "CCI 118\n",
      "USB 119\n",
      "LRCX 120\n",
      "MU 121\n",
      "FCX 122\n",
      "HUM 123\n",
      "MRNA 124\n",
      "TFC 125\n",
      "EL 126\n",
      "WM 127\n",
      "NSC 128\n",
      "ICE 129\n",
      "KLAC 130\n",
      "EMR 131\n",
      "SHW 132\n",
      "GD 133\n",
      "ATVI 134\n",
      "PXD 135\n",
      "MCK 136\n",
      "DG 137\n",
      "MPC 138\n",
      "HCA 139\n",
      "ORLY 140\n",
      "D 141\n",
      "GM 142\n",
      "AEP 143\n",
      "GIS 144\n",
      "F 145\n",
      "VLO 146\n",
      "SNPS 147\n",
      "SRE 148\n",
      "PSX 149\n",
      "MET 150\n",
      "AIG 151\n",
      "EW 152\n",
      "APH 153\n",
      "ROP 154\n",
      "ADM 155\n",
      "OXY 156\n",
      "AZO 157\n",
      "KMB 158\n",
      "JCI 159\n",
      "MCO 160\n",
      "FDX 161\n",
      "CDNS 162\n",
      "TRV 163\n",
      "PSA 164\n",
      "CNC 165\n",
      "A 166\n",
      "CTVA 167\n",
      "EXC 168\n",
      "MSI 169\n",
      "DXCM 170\n",
      "NXPI 171\n",
      "ADSK 172\n",
      "FIS 173\n",
      "ROST 174\n",
      "NEM 175\n",
      "TT 176\n",
      "MAR 177\n",
      "AFL 178\n",
      "DVN 179\n",
      "O 180\n",
      "MCHP 181\n",
      "SYY 182\n",
      "AJG 183\n",
      "CMG 184\n",
      "HES 185\n",
      "BIIB 186\n",
      "WMB 187\n",
      "DOW 188\n",
      "CHTR 189\n",
      "PH 190\n",
      "XEL 191\n",
      "MNST 192\n",
      "IQV 193\n",
      "TEL 194\n",
      "MSCI 195\n",
      "SPG 196\n",
      "LHX 197\n",
      "CTAS 198\n",
      "ALL 199\n",
      "PAYX 200\n",
      "COF 201\n",
      "IDXX 202\n",
      "KMI 203\n",
      "ECL 204\n",
      "NUE 205\n",
      "YUM 206\n",
      "HAL 207\n",
      "DD 208\n",
      "CARR 209\n",
      "PRU 210\n",
      "BK 211\n",
      "HLT 212\n",
      "STZ 213\n",
      "PCAR 214\n",
      "ED 215\n",
      "AMP 216\n",
      "CMI 217\n",
      "OTIS 218\n",
      "KHC 219\n",
      "TDG 220\n",
      "HSY 221\n",
      "WELL 222\n",
      "EA 223\n",
      "MTD 224\n",
      "ILMN 225\n",
      "AME 226\n",
      "FTNT 227\n",
      "PEG 228\n",
      "KEYS 229\n",
      "CTSH 230\n",
      "CSGP 231\n",
      "SBAC 232\n",
      "ROK 233\n",
      "BKR 234\n",
      "ENPH 235\n",
      "RMD 236\n",
      "VICI 237\n",
      "KDP 238\n",
      "WEC 239\n",
      "DLTR 240\n",
      "OKE 241\n",
      "PPG 242\n",
      "KR 243\n",
      "STT 244\n",
      "ES 245\n",
      "GPN 246\n",
      "DHI 247\n",
      "DLR 248\n",
      "AWK 249\n",
      "VRSK 250\n",
      "IFF 251\n",
      "DFS 252\n",
      "ANET 253\n",
      "ODFL 254\n",
      "WTW 255\n",
      "ON 256\n",
      "CEG 257\n",
      "FAST 258\n",
      "CPRT 259\n",
      "GLW 260\n",
      "ABC 261\n",
      "APTV 262\n",
      "ZBH 263\n",
      "ALB 264\n",
      "URI 265\n",
      "CBRE 266\n",
      "RSG 267\n",
      "WBA 268\n",
      "MTB 269\n",
      "HPQ 270\n",
      "IT 271\n",
      "EIX 272\n",
      "TROW 273\n",
      "PCG 274\n",
      "WBD 275\n",
      "ULTA 276\n",
      "GWW 277\n",
      "FANG 278\n",
      "EFX 279\n",
      "HIG 280\n",
      "CDW 281\n",
      "LEN 282\n",
      "GPC 283\n",
      "TSCO 284\n",
      "EBAY 285\n",
      "VMC 286\n",
      "FITB 287\n",
      "FTV 288\n",
      "WY 289\n",
      "DAL 290\n",
      "ACGL 291\n",
      "LYB 292\n",
      "AEE 293\n",
      "DTE 294\n",
      "FE 295\n",
      "AVB 296\n",
      "FRC 297\n",
      "BAX 298\n",
      "IR 299\n",
      "PPL 300\n",
      "LH 301\n",
      "ETR 302\n",
      "HPE 303\n",
      "ARE 304\n",
      "RJF 305\n",
      "ANSS 306\n",
      "MLM 307\n",
      "MKC 308\n",
      "NDAQ 309\n",
      "LUV 310\n",
      "GEHC 311\n",
      "RF 312\n",
      "HBAN 313\n",
      "CAH 314\n",
      "CFG 315\n",
      "PFG 316\n",
      "HOLX 317\n",
      "XYL 318\n",
      "CHD 319\n",
      "DOV 320\n",
      "EQR 321\n",
      "CTRA 322\n",
      "PWR 323\n",
      "EXR 324\n",
      "NTRS 325\n",
      "VRSN 326\n",
      "CAG 327\n",
      "TSN 328\n",
      "WAT 329\n",
      "CNP 330\n",
      "STE 331\n",
      "VTR 332\n",
      "TDY 333\n",
      "CMS 334\n",
      "EPAM 335\n",
      "WAB 336\n",
      "AMCR 337\n",
      "K 338\n",
      "DRI 339\n",
      "AES 340\n",
      "EXPD 341\n",
      "DGX 342\n",
      "MAA 343\n",
      "WST 344\n",
      "IEX 345\n",
      "OMC 346\n",
      "BALL 347\n",
      "CLX 348\n",
      "LVS 349\n",
      "CINF 350\n",
      "INVH 351\n",
      "MOH 352\n",
      "MRO 353\n",
      "SJM 354\n",
      "CF 355\n",
      "MPWR 356\n",
      "TRGP 357\n",
      "STLD 358\n",
      "KEY 359\n",
      "COO 360\n",
      "PKI 361\n",
      "ALGN 362\n",
      "BBY 363\n",
      "TTWO 364\n",
      "BR 365\n",
      "MOS 366\n",
      "FMC 367\n",
      "J 368\n",
      "FSLR 369\n",
      "ATO 370\n",
      "SEDG 371\n",
      "AVY 372\n",
      "SWKS 373\n",
      "ETSY 374\n",
      "FDS 375\n",
      "GRMN 376\n",
      "TXT 377\n",
      "WRB 378\n",
      "HWM 379\n",
      "JBHT 380\n",
      "NVR 381\n",
      "INCY 382\n",
      "TER 383\n",
      "SYF 384\n",
      "EVRG 385\n",
      "IRM 386\n",
      "LKQ 387\n",
      "SIVB 388\n",
      "ZBRA 389\n",
      "VTRS 390\n",
      "FLT 391\n",
      "PAYC 392\n",
      "UAL 393\n",
      "LW 394\n",
      "NTAP 395\n",
      "APA 396\n",
      "PEAK 397\n",
      "EXPE 398\n",
      "RE 399\n",
      "IPG 400\n",
      "AKAM 401\n",
      "LNT 402\n",
      "ESS 403\n",
      "LDOS 404\n",
      "BRO 405\n",
      "IP 406\n",
      "HRL 407\n",
      "TYL 408\n",
      "PTC 409\n",
      "JKHY 410\n",
      "TRMB 411\n",
      "NDSN 412\n",
      "TECH 413\n",
      "KIM 414\n",
      "CBOE 415\n",
      "SNA 416\n",
      "EQT 417\n",
      "POOL 418\n",
      "RCL 419\n",
      "PKG 420\n",
      "GEN 421\n",
      "MTCH 422\n",
      "MGM 423\n",
      "SWK 424\n",
      "MKTX 425\n",
      "CPT 426\n",
      "DPZ 427\n",
      "TFX 428\n",
      "UDR 429\n",
      "BF-B 430\n",
      "WDC 431\n",
      "CE 432\n",
      "CHRW 433\n",
      "L 434\n",
      "HST 435\n",
      "MAS 436\n",
      "CRL 437\n",
      "CPB 438\n",
      "NI 439\n",
      "HSIC 440\n",
      "PHM 441\n",
      "GL 442\n",
      "EMN 443\n",
      "KMX 444\n",
      "STX 445\n",
      "TPR 446\n",
      "LYV 447\n",
      "BBWI 448\n",
      "JNPR 449\n",
      "BWA 450\n",
      "ALLE 451\n",
      "UHS 452\n",
      "WYNN 453\n",
      "PARA 454\n",
      "QRVO 455\n",
      "CDAY 456\n",
      "FOXA 457\n",
      "VFC 458\n",
      "REG 459\n",
      "TAP 460\n",
      "AAL 461\n",
      "WRK 462\n",
      "BXP 463\n",
      "CZR 464\n",
      "AAP 465\n",
      "CCL 466\n",
      "BIO 467\n",
      "HII 468\n",
      "CMA 469\n",
      "IVZ 470\n",
      "CTLT 471\n",
      "FFIV 472\n",
      "PNW 473\n",
      "ROL 474\n",
      "RHI 475\n",
      "WHR 476\n",
      "HAS 477\n",
      "AOS 478\n",
      "SEE 479\n",
      "PNR 480\n",
      "BEN 481\n",
      "ZION 482\n",
      "NRG 483\n",
      "FRT 484\n",
      "OGN 485\n",
      "NWSA 486\n",
      "SBNY 487\n",
      "XRAY 488\n",
      "AIZ 489\n",
      "GNRC 490\n",
      "DXC 491\n",
      "MHK 492\n",
      "NCLH 493\n",
      "ALK 494\n",
      "NWL 495\n",
      "LUMN 496\n",
      "RL 497\n",
      "LNC 498\n",
      "DVA 499\n",
      "FOX 500\n",
      "DISH 501\n",
      "NWS 502\n"
     ]
    }
   ],
   "source": [
    "sottoSoglia=(cummin2<-soglia)\n",
    "s3=(cummin2<-soglia) & (drawdown<0)\n",
    "j=0\n",
    "for t in tickers:\n",
    "    print(t,j)\n",
    "    j+=1\n",
    "    for i in dati.index:\n",
    "        if sottoSoglia[t].loc[i] and not (s3[t].loc[i]):\n",
    "            cummin2[t].loc[i:]=(drawdown[t].loc[i:]).cummin()\n",
    "            sottoSoglia[t]=(cummin2[t]<-soglia)\n",
    "            s3[t]=((cummin2[t]<-soglia) & (drawdown[t]<0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AAPL      6.766912\n",
       "MSFT      5.820563\n",
       "AMZN      1.738285\n",
       "BRK-B     2.685604\n",
       "GOOGL     4.160253\n",
       "           ...    \n",
       "LNC       1.700029\n",
       "DVA       3.156119\n",
       "FOX      12.233512\n",
       "DISH     -3.818712\n",
       "NWS      -1.116193\n",
       "Length: 503, dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "strategia3=100*(np.exp((s3*logret2).sum()/times)-1)\n",
    "strategia3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "buy&hold: 13.9 %  -  strategia 2: 13.9 %  -  strategia 3: 4.8 %\n"
     ]
    }
   ],
   "source": [
    "print(\"buy&hold:\",round(strategia1.mean(),1),\"%  -  strategia 2:\",round(strategia2.mean(),1),\"%  -  strategia 3:\",round(strategia3.mean(),1),\"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "buy&hold: 13.6 %  -  strategia 2: 13.6 %  -  strategia 3: 4.7 %\n"
     ]
    }
   ],
   "source": [
    "limite=50\n",
    "print(\"buy&hold:\",round(strategia1[limite:].mean(),1),\"%  -  strategia 2:\",round(strategia2[limite:].mean(),1),\"%  -  strategia 3:\",round(strategia3[limite:].mean(),1),\"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Frequenza')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 2500x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xticks(range(-10,50,5))\n",
    "plt.xlabel(\"Rendimento annualizzato %\",fontdict = {'fontsize' : 24})\n",
    "strategia1.plot(kind=\"hist\",figsize=(25,15),range=[-10,50],bins=100,legend=True,label=\"buy&hold\",fontsize=20,color=\"green\")\n",
    "strategia2.plot(kind=\"hist\",figsize=(25,15),range=[-10,50],bins=100,legend=True,label=\"strategia 2\",fontsize=20,color=\"red\",alpha=0.8)\n",
    "strategia3.plot(kind=\"hist\",figsize=(25,15),range=[-10,50],bins=100,legend=True,label=\"strategia 3\",fontsize=20,color=\"blue\",alpha=0.6)\n",
    "plt.legend(fontsize=30)\n",
    "plt.ylabel(\"Frequenza\",fontdict = {'fontsize' : 24})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# soglia 0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "cummin2=cummin.copy()\n",
    "logret2=logret.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "times2=logret2.apply(pd.Series.first_valid_index)+ pd.offsets.DateOffset(years=1)\n",
    "for t in tickers:\n",
    "    logret2[t].loc[:times2[t]]=np.nan\n",
    "    cummin2[t].loc[:times2[t]]=np.nan "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "times=(logret2.index[-1]-times2)/np.timedelta64(1, 'Y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "soglia=0.5\n",
    "# strategia 1: buy and hold\n",
    "# strategia 2: compro quando scende sotto il drawdown e tengo fino alla fine\n",
    "# strategia 3: compro quando scende sotto il drawdown e tengo fino alla risalita"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AAPL     20.876250\n",
       "MSFT     22.824050\n",
       "AMZN     26.063171\n",
       "BRK-B    10.297233\n",
       "GOOGL    16.248101\n",
       "           ...    \n",
       "LNC       5.901550\n",
       "DVA       9.586076\n",
       "FOX      11.841338\n",
       "DISH      6.783176\n",
       "NWS       2.464485\n",
       "Length: 503, dtype: float64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "strategia1=100*(np.exp(logret2.sum()/times)-1)\n",
    "strategia1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AAPL     20.876250\n",
       "MSFT     22.299687\n",
       "AMZN     17.061227\n",
       "BRK-B     7.292414\n",
       "GOOGL    14.389208\n",
       "           ...    \n",
       "LNC       4.805727\n",
       "DVA      10.038042\n",
       "FOX      17.547802\n",
       "DISH      8.354961\n",
       "NWS       9.801609\n",
       "Length: 503, dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "strategia2=100*(np.exp(((cummin2<-soglia)*logret2).sum()/times)-1)\n",
    "strategia2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>primo dato valido</th>\n",
       "      <th>prezzo1</th>\n",
       "      <th>data massimo</th>\n",
       "      <th>massimo</th>\n",
       "      <th>va sotto soglia</th>\n",
       "      <th>prezzo2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AAPL</th>\n",
       "      <td>1980-12-15</td>\n",
       "      <td>0.094519</td>\n",
       "      <td>1980-12-29</td>\n",
       "      <td>0.124869</td>\n",
       "      <td>1981-12-16</td>\n",
       "      <td>0.067638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSFT</th>\n",
       "      <td>1986-03-14</td>\n",
       "      <td>0.062823</td>\n",
       "      <td>1987-10-05</td>\n",
       "      <td>0.342277</td>\n",
       "      <td>1987-10-26</td>\n",
       "      <td>0.170055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMZN</th>\n",
       "      <td>1997-05-16</td>\n",
       "      <td>0.086458</td>\n",
       "      <td>1999-01-11</td>\n",
       "      <td>4.615625</td>\n",
       "      <td>1999-02-18</td>\n",
       "      <td>2.237500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BRK-B</th>\n",
       "      <td>1996-05-10</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>2007-12-10</td>\n",
       "      <td>99.699997</td>\n",
       "      <td>2009-02-20</td>\n",
       "      <td>47.740002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GOOGL</th>\n",
       "      <td>2004-08-20</td>\n",
       "      <td>2.710460</td>\n",
       "      <td>2007-11-06</td>\n",
       "      <td>18.563313</td>\n",
       "      <td>2008-10-07</td>\n",
       "      <td>8.658909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GOOG</th>\n",
       "      <td>2004-08-20</td>\n",
       "      <td>2.697639</td>\n",
       "      <td>2007-11-06</td>\n",
       "      <td>18.475498</td>\n",
       "      <td>2008-10-07</td>\n",
       "      <td>8.617948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JNJ</th>\n",
       "      <td>1962-01-03</td>\n",
       "      <td>0.065422</td>\n",
       "      <td>1972-11-01</td>\n",
       "      <td>0.899508</td>\n",
       "      <td>1977-04-25</td>\n",
       "      <td>0.443681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UNH</th>\n",
       "      <td>1984-10-18</td>\n",
       "      <td>0.121133</td>\n",
       "      <td>1986-04-08</td>\n",
       "      <td>0.404839</td>\n",
       "      <td>1986-10-08</td>\n",
       "      <td>0.200826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XOM</th>\n",
       "      <td>1962-01-03</td>\n",
       "      <td>0.099758</td>\n",
       "      <td>2014-06-23</td>\n",
       "      <td>70.642700</td>\n",
       "      <td>2020-03-12</td>\n",
       "      <td>31.404398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JPM</th>\n",
       "      <td>1980-03-18</td>\n",
       "      <td>1.122867</td>\n",
       "      <td>1986-03-27</td>\n",
       "      <td>4.759783</td>\n",
       "      <td>1987-10-20</td>\n",
       "      <td>2.375164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NVDA</th>\n",
       "      <td>1999-01-25</td>\n",
       "      <td>0.415857</td>\n",
       "      <td>2000-03-13</td>\n",
       "      <td>2.853636</td>\n",
       "      <td>2000-04-14</td>\n",
       "      <td>1.315384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PG</th>\n",
       "      <td>1962-01-03</td>\n",
       "      <td>0.262992</td>\n",
       "      <td>2000-01-11</td>\n",
       "      <td>31.823322</td>\n",
       "      <td>2000-03-08</td>\n",
       "      <td>15.718192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V</th>\n",
       "      <td>2008-03-20</td>\n",
       "      <td>14.495992</td>\n",
       "      <td>2008-05-07</td>\n",
       "      <td>19.936213</td>\n",
       "      <td>2009-03-23</td>\n",
       "      <td>12.578486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HD</th>\n",
       "      <td>1981-09-23</td>\n",
       "      <td>0.018070</td>\n",
       "      <td>1983-09-20</td>\n",
       "      <td>0.416502</td>\n",
       "      <td>1984-03-01</td>\n",
       "      <td>0.204946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TSLA</th>\n",
       "      <td>2010-06-30</td>\n",
       "      <td>1.588667</td>\n",
       "      <td>2017-09-18</td>\n",
       "      <td>25.666668</td>\n",
       "      <td>2019-05-24</td>\n",
       "      <td>12.708667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MA</th>\n",
       "      <td>2006-05-26</td>\n",
       "      <td>4.148601</td>\n",
       "      <td>2008-06-02</td>\n",
       "      <td>29.749420</td>\n",
       "      <td>2008-10-07</td>\n",
       "      <td>14.762232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CVX</th>\n",
       "      <td>1962-01-03</td>\n",
       "      <td>0.348873</td>\n",
       "      <td>1973-01-31</td>\n",
       "      <td>0.918014</td>\n",
       "      <td>1974-10-03</td>\n",
       "      <td>0.458657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>META</th>\n",
       "      <td>2012-05-21</td>\n",
       "      <td>34.029999</td>\n",
       "      <td>2012-05-21</td>\n",
       "      <td>34.029999</td>\n",
       "      <td>2013-05-22</td>\n",
       "      <td>25.160000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABBV</th>\n",
       "      <td>2013-01-03</td>\n",
       "      <td>22.915104</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1962-01-02</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MRK</th>\n",
       "      <td>1962-01-03</td>\n",
       "      <td>0.067414</td>\n",
       "      <td>2000-12-28</td>\n",
       "      <td>39.377083</td>\n",
       "      <td>2002-07-09</td>\n",
       "      <td>19.623020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LLY</th>\n",
       "      <td>1972-06-02</td>\n",
       "      <td>0.924479</td>\n",
       "      <td>1973-07-25</td>\n",
       "      <td>1.305583</td>\n",
       "      <td>1977-01-25</td>\n",
       "      <td>0.650307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PFE</th>\n",
       "      <td>1972-06-02</td>\n",
       "      <td>0.172844</td>\n",
       "      <td>1973-07-25</td>\n",
       "      <td>0.225744</td>\n",
       "      <td>1974-08-23</td>\n",
       "      <td>0.107551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PEP</th>\n",
       "      <td>1972-06-02</td>\n",
       "      <td>0.416047</td>\n",
       "      <td>1972-12-07</td>\n",
       "      <td>0.437312</td>\n",
       "      <td>1974-08-14</td>\n",
       "      <td>0.213882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KO</th>\n",
       "      <td>1962-01-03</td>\n",
       "      <td>0.048557</td>\n",
       "      <td>1973-03-14</td>\n",
       "      <td>0.367059</td>\n",
       "      <td>1974-08-22</td>\n",
       "      <td>0.183260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BAC</th>\n",
       "      <td>1973-02-22</td>\n",
       "      <td>1.636574</td>\n",
       "      <td>1973-10-19</td>\n",
       "      <td>1.851479</td>\n",
       "      <td>1974-06-27</td>\n",
       "      <td>0.903698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AVGO</th>\n",
       "      <td>2009-08-07</td>\n",
       "      <td>12.105116</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1962-01-02</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TMO</th>\n",
       "      <td>1980-03-18</td>\n",
       "      <td>1.655435</td>\n",
       "      <td>1980-09-17</td>\n",
       "      <td>2.798047</td>\n",
       "      <td>1982-02-09</td>\n",
       "      <td>1.259570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COST</th>\n",
       "      <td>1986-07-10</td>\n",
       "      <td>7.600136</td>\n",
       "      <td>1987-08-11</td>\n",
       "      <td>8.946448</td>\n",
       "      <td>1987-12-03</td>\n",
       "      <td>4.212648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WMT</th>\n",
       "      <td>1972-08-28</td>\n",
       "      <td>0.035747</td>\n",
       "      <td>1972-11-27</td>\n",
       "      <td>0.038058</td>\n",
       "      <td>1973-08-29</td>\n",
       "      <td>0.023378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CSCO</th>\n",
       "      <td>1990-02-20</td>\n",
       "      <td>0.056262</td>\n",
       "      <td>1994-03-09</td>\n",
       "      <td>1.580235</td>\n",
       "      <td>1994-07-14</td>\n",
       "      <td>0.772994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MCD</th>\n",
       "      <td>1966-07-06</td>\n",
       "      <td>0.127219</td>\n",
       "      <td>1968-05-07</td>\n",
       "      <td>0.429593</td>\n",
       "      <td>1968-05-21</td>\n",
       "      <td>0.189445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABT</th>\n",
       "      <td>1980-03-18</td>\n",
       "      <td>0.206454</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1962-01-02</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VZ</th>\n",
       "      <td>1983-11-22</td>\n",
       "      <td>1.264168</td>\n",
       "      <td>1999-10-04</td>\n",
       "      <td>21.634064</td>\n",
       "      <td>2002-07-22</td>\n",
       "      <td>9.758036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DIS</th>\n",
       "      <td>1962-01-03</td>\n",
       "      <td>0.059182</td>\n",
       "      <td>1973-01-02</td>\n",
       "      <td>1.668684</td>\n",
       "      <td>1973-11-09</td>\n",
       "      <td>0.820664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACN</th>\n",
       "      <td>2001-07-20</td>\n",
       "      <td>10.939423</td>\n",
       "      <td>2002-03-11</td>\n",
       "      <td>21.791388</td>\n",
       "      <td>2002-07-22</td>\n",
       "      <td>10.822816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DHR</th>\n",
       "      <td>1979-01-02</td>\n",
       "      <td>0.078984</td>\n",
       "      <td>1979-08-28</td>\n",
       "      <td>0.133456</td>\n",
       "      <td>1980-03-24</td>\n",
       "      <td>0.065366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NEE</th>\n",
       "      <td>1973-02-22</td>\n",
       "      <td>0.389877</td>\n",
       "      <td>1973-03-13</td>\n",
       "      <td>0.423030</td>\n",
       "      <td>1974-04-23</td>\n",
       "      <td>0.209526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CMCSA</th>\n",
       "      <td>1980-03-18</td>\n",
       "      <td>0.068238</td>\n",
       "      <td>1989-10-09</td>\n",
       "      <td>1.557343</td>\n",
       "      <td>1990-09-27</td>\n",
       "      <td>0.774257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WFC</th>\n",
       "      <td>1972-06-02</td>\n",
       "      <td>0.148935</td>\n",
       "      <td>1973-10-30</td>\n",
       "      <td>0.245917</td>\n",
       "      <td>1974-07-03</td>\n",
       "      <td>0.120893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TXN</th>\n",
       "      <td>1972-06-02</td>\n",
       "      <td>0.770303</td>\n",
       "      <td>1973-10-12</td>\n",
       "      <td>1.286320</td>\n",
       "      <td>1974-09-13</td>\n",
       "      <td>0.640568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ADBE</th>\n",
       "      <td>1986-08-14</td>\n",
       "      <td>0.209060</td>\n",
       "      <td>1987-05-05</td>\n",
       "      <td>1.584453</td>\n",
       "      <td>1987-10-20</td>\n",
       "      <td>0.777556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LIN</th>\n",
       "      <td>1992-06-18</td>\n",
       "      <td>4.637535</td>\n",
       "      <td>2008-06-23</td>\n",
       "      <td>73.864983</td>\n",
       "      <td>2008-11-20</td>\n",
       "      <td>35.635227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PM</th>\n",
       "      <td>2008-03-18</td>\n",
       "      <td>24.800337</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1962-01-02</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NKE</th>\n",
       "      <td>1980-12-03</td>\n",
       "      <td>0.127474</td>\n",
       "      <td>1982-12-08</td>\n",
       "      <td>0.313727</td>\n",
       "      <td>1984-02-01</td>\n",
       "      <td>0.155802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BMY</th>\n",
       "      <td>1972-06-02</td>\n",
       "      <td>0.332630</td>\n",
       "      <td>1972-08-10</td>\n",
       "      <td>0.381656</td>\n",
       "      <td>1974-09-30</td>\n",
       "      <td>0.182255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CRM</th>\n",
       "      <td>2004-06-24</td>\n",
       "      <td>4.190000</td>\n",
       "      <td>2008-06-23</td>\n",
       "      <td>18.607500</td>\n",
       "      <td>2008-10-07</td>\n",
       "      <td>8.985000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RTX</th>\n",
       "      <td>1962-04-03</td>\n",
       "      <td>0.094913</td>\n",
       "      <td>1967-06-27</td>\n",
       "      <td>0.396542</td>\n",
       "      <td>1969-07-23</td>\n",
       "      <td>0.197790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COP</th>\n",
       "      <td>1982-01-04</td>\n",
       "      <td>1.071323</td>\n",
       "      <td>2008-06-17</td>\n",
       "      <td>43.393795</td>\n",
       "      <td>2008-10-27</td>\n",
       "      <td>20.787249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMGN</th>\n",
       "      <td>1983-06-20</td>\n",
       "      <td>0.224463</td>\n",
       "      <td>1983-06-22</td>\n",
       "      <td>0.232204</td>\n",
       "      <td>1984-06-21</td>\n",
       "      <td>0.087077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HON</th>\n",
       "      <td>1962-01-03</td>\n",
       "      <td>1.116203</td>\n",
       "      <td>1965-03-02</td>\n",
       "      <td>1.312719</td>\n",
       "      <td>1970-04-16</td>\n",
       "      <td>0.641899</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      primo dato valido    prezzo1 data massimo    massimo va sotto soglia  \\\n",
       "AAPL         1980-12-15   0.094519   1980-12-29   0.124869      1981-12-16   \n",
       "MSFT         1986-03-14   0.062823   1987-10-05   0.342277      1987-10-26   \n",
       "AMZN         1997-05-16   0.086458   1999-01-11   4.615625      1999-02-18   \n",
       "BRK-B        1996-05-10  24.000000   2007-12-10  99.699997      2009-02-20   \n",
       "GOOGL        2004-08-20   2.710460   2007-11-06  18.563313      2008-10-07   \n",
       "GOOG         2004-08-20   2.697639   2007-11-06  18.475498      2008-10-07   \n",
       "JNJ          1962-01-03   0.065422   1972-11-01   0.899508      1977-04-25   \n",
       "UNH          1984-10-18   0.121133   1986-04-08   0.404839      1986-10-08   \n",
       "XOM          1962-01-03   0.099758   2014-06-23  70.642700      2020-03-12   \n",
       "JPM          1980-03-18   1.122867   1986-03-27   4.759783      1987-10-20   \n",
       "NVDA         1999-01-25   0.415857   2000-03-13   2.853636      2000-04-14   \n",
       "PG           1962-01-03   0.262992   2000-01-11  31.823322      2000-03-08   \n",
       "V            2008-03-20  14.495992   2008-05-07  19.936213      2009-03-23   \n",
       "HD           1981-09-23   0.018070   1983-09-20   0.416502      1984-03-01   \n",
       "TSLA         2010-06-30   1.588667   2017-09-18  25.666668      2019-05-24   \n",
       "MA           2006-05-26   4.148601   2008-06-02  29.749420      2008-10-07   \n",
       "CVX          1962-01-03   0.348873   1973-01-31   0.918014      1974-10-03   \n",
       "META         2012-05-21  34.029999   2012-05-21  34.029999      2013-05-22   \n",
       "ABBV         2013-01-03  22.915104          NaT        NaN      1962-01-02   \n",
       "MRK          1962-01-03   0.067414   2000-12-28  39.377083      2002-07-09   \n",
       "LLY          1972-06-02   0.924479   1973-07-25   1.305583      1977-01-25   \n",
       "PFE          1972-06-02   0.172844   1973-07-25   0.225744      1974-08-23   \n",
       "PEP          1972-06-02   0.416047   1972-12-07   0.437312      1974-08-14   \n",
       "KO           1962-01-03   0.048557   1973-03-14   0.367059      1974-08-22   \n",
       "BAC          1973-02-22   1.636574   1973-10-19   1.851479      1974-06-27   \n",
       "AVGO         2009-08-07  12.105116          NaT        NaN      1962-01-02   \n",
       "TMO          1980-03-18   1.655435   1980-09-17   2.798047      1982-02-09   \n",
       "COST         1986-07-10   7.600136   1987-08-11   8.946448      1987-12-03   \n",
       "WMT          1972-08-28   0.035747   1972-11-27   0.038058      1973-08-29   \n",
       "CSCO         1990-02-20   0.056262   1994-03-09   1.580235      1994-07-14   \n",
       "MCD          1966-07-06   0.127219   1968-05-07   0.429593      1968-05-21   \n",
       "ABT          1980-03-18   0.206454          NaT        NaN      1962-01-02   \n",
       "VZ           1983-11-22   1.264168   1999-10-04  21.634064      2002-07-22   \n",
       "DIS          1962-01-03   0.059182   1973-01-02   1.668684      1973-11-09   \n",
       "ACN          2001-07-20  10.939423   2002-03-11  21.791388      2002-07-22   \n",
       "DHR          1979-01-02   0.078984   1979-08-28   0.133456      1980-03-24   \n",
       "NEE          1973-02-22   0.389877   1973-03-13   0.423030      1974-04-23   \n",
       "CMCSA        1980-03-18   0.068238   1989-10-09   1.557343      1990-09-27   \n",
       "WFC          1972-06-02   0.148935   1973-10-30   0.245917      1974-07-03   \n",
       "TXN          1972-06-02   0.770303   1973-10-12   1.286320      1974-09-13   \n",
       "ADBE         1986-08-14   0.209060   1987-05-05   1.584453      1987-10-20   \n",
       "LIN          1992-06-18   4.637535   2008-06-23  73.864983      2008-11-20   \n",
       "PM           2008-03-18  24.800337          NaT        NaN      1962-01-02   \n",
       "NKE          1980-12-03   0.127474   1982-12-08   0.313727      1984-02-01   \n",
       "BMY          1972-06-02   0.332630   1972-08-10   0.381656      1974-09-30   \n",
       "CRM          2004-06-24   4.190000   2008-06-23  18.607500      2008-10-07   \n",
       "RTX          1962-04-03   0.094913   1967-06-27   0.396542      1969-07-23   \n",
       "COP          1982-01-04   1.071323   2008-06-17  43.393795      2008-10-27   \n",
       "AMGN         1983-06-20   0.224463   1983-06-22   0.232204      1984-06-21   \n",
       "HON          1962-01-03   1.116203   1965-03-02   1.312719      1970-04-16   \n",
       "\n",
       "         prezzo2  \n",
       "AAPL    0.067638  \n",
       "MSFT    0.170055  \n",
       "AMZN    2.237500  \n",
       "BRK-B  47.740002  \n",
       "GOOGL   8.658909  \n",
       "GOOG    8.617948  \n",
       "JNJ     0.443681  \n",
       "UNH     0.200826  \n",
       "XOM    31.404398  \n",
       "JPM     2.375164  \n",
       "NVDA    1.315384  \n",
       "PG     15.718192  \n",
       "V      12.578486  \n",
       "HD      0.204946  \n",
       "TSLA   12.708667  \n",
       "MA     14.762232  \n",
       "CVX     0.458657  \n",
       "META   25.160000  \n",
       "ABBV         NaN  \n",
       "MRK    19.623020  \n",
       "LLY     0.650307  \n",
       "PFE     0.107551  \n",
       "PEP     0.213882  \n",
       "KO      0.183260  \n",
       "BAC     0.903698  \n",
       "AVGO         NaN  \n",
       "TMO     1.259570  \n",
       "COST    4.212648  \n",
       "WMT     0.023378  \n",
       "CSCO    0.772994  \n",
       "MCD     0.189445  \n",
       "ABT          NaN  \n",
       "VZ      9.758036  \n",
       "DIS     0.820664  \n",
       "ACN    10.822816  \n",
       "DHR     0.065366  \n",
       "NEE     0.209526  \n",
       "CMCSA   0.774257  \n",
       "WFC     0.120893  \n",
       "TXN     0.640568  \n",
       "ADBE    0.777556  \n",
       "LIN    35.635227  \n",
       "PM           NaN  \n",
       "NKE     0.155802  \n",
       "BMY     0.182255  \n",
       "CRM     8.985000  \n",
       "RTX     0.197790  \n",
       "COP    20.787249  \n",
       "AMGN    0.087077  \n",
       "HON     0.641899  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t1=logret.apply(pd.Series.first_valid_index)\n",
    "t2=(cummin2<-soglia).eq(True).idxmax()\n",
    "d1=dati.iloc[0].copy()\n",
    "d2=dati.iloc[0].copy()\n",
    "mm=dati.iloc[0].copy()\n",
    "tm=t2.copy()\n",
    "for t in tickers:\n",
    "    d1[t]=dati[t].loc[t1[t]]\n",
    "    d2[t]=dati[t].loc[t2[t]]\n",
    "    temp=dati.index[dati.index.get_loc(t2[t])+1]\n",
    "    mm[t]=np.max(dati[t].loc[t1[t]:temp])\n",
    "    try:\n",
    "        tm[t]=dati[t].loc[t1[t]:temp].idxmax()\n",
    "    except:\n",
    "        tm[t]=np.nan\n",
    "pd.DataFrame({\"primo dato valido\":t1,\"prezzo1\":d1,\"data massimo\":tm,\"massimo\":mm,\"va sotto soglia\":t2,\"prezzo2\":d2})[:50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AAPL 0\n",
      "MSFT 1\n",
      "AMZN 2\n",
      "BRK-B 3\n",
      "GOOGL 4\n",
      "GOOG 5\n",
      "JNJ 6\n",
      "UNH 7\n",
      "XOM 8\n",
      "JPM 9\n",
      "NVDA 10\n",
      "PG 11\n",
      "V 12\n",
      "HD 13\n",
      "TSLA 14\n",
      "MA 15\n",
      "CVX 16\n",
      "META 17\n",
      "ABBV 18\n",
      "MRK 19\n",
      "LLY 20\n",
      "PFE 21\n",
      "PEP 22\n",
      "KO 23\n",
      "BAC 24\n",
      "AVGO 25\n",
      "TMO 26\n",
      "COST 27\n",
      "WMT 28\n",
      "CSCO 29\n",
      "MCD 30\n",
      "ABT 31\n",
      "VZ 32\n",
      "DIS 33\n",
      "ACN 34\n",
      "DHR 35\n",
      "NEE 36\n",
      "CMCSA 37\n",
      "WFC 38\n",
      "TXN 39\n",
      "ADBE 40\n",
      "LIN 41\n",
      "PM 42\n",
      "NKE 43\n",
      "BMY 44\n",
      "CRM 45\n",
      "RTX 46\n",
      "COP 47\n",
      "AMGN 48\n",
      "HON 49\n",
      "NFLX 50\n",
      "T 51\n",
      "ORCL 52\n",
      "UPS 53\n",
      "UNP 54\n",
      "SCHW 55\n",
      "IBM 56\n",
      "CAT 57\n",
      "QCOM 58\n",
      "LOW 59\n",
      "INTC 60\n",
      "SBUX 61\n",
      "CVS 62\n",
      "GS 63\n",
      "DE 64\n",
      "BA 65\n",
      "SPGI 66\n",
      "MS 67\n",
      "BLK 68\n",
      "ELV 69\n",
      "INTU 70\n",
      "AMD 71\n",
      "PLD 72\n",
      "LMT 73\n",
      "GILD 74\n",
      "MDT 75\n",
      "AMT 76\n",
      "ADP 77\n",
      "TJX 78\n",
      "ISRG 79\n",
      "CB 80\n",
      "CI 81\n",
      "C 82\n",
      "MDLZ 83\n",
      "AMAT 84\n",
      "AXP 85\n",
      "TMUS 86\n",
      "PYPL 87\n",
      "SYK 88\n",
      "ADI 89\n",
      "BKNG 90\n",
      "MMC 91\n",
      "MO 92\n",
      "DUK 93\n",
      "GE 94\n",
      "SLB 95\n",
      "SO 96\n",
      "PGR 97\n",
      "NOW 98\n",
      "EOG 99\n",
      "VRTX 100\n",
      "REGN 101\n",
      "TGT 102\n",
      "BDX 103\n",
      "NOC 104\n",
      "MMM 105\n",
      "APD 106\n",
      "ZTS 107\n",
      "CSX 108\n",
      "PNC 109\n",
      "CL 110\n",
      "BSX 111\n",
      "FISV 112\n",
      "ETN 113\n",
      "ITW 114\n",
      "AON 115\n",
      "CME 116\n",
      "EQIX 117\n",
      "CCI 118\n",
      "USB 119\n",
      "LRCX 120\n",
      "MU 121\n",
      "FCX 122\n",
      "HUM 123\n",
      "MRNA 124\n",
      "TFC 125\n",
      "EL 126\n",
      "WM 127\n",
      "NSC 128\n",
      "ICE 129\n",
      "KLAC 130\n",
      "EMR 131\n",
      "SHW 132\n",
      "GD 133\n",
      "ATVI 134\n",
      "PXD 135\n",
      "MCK 136\n",
      "DG 137\n",
      "MPC 138\n",
      "HCA 139\n",
      "ORLY 140\n",
      "D 141\n",
      "GM 142\n",
      "AEP 143\n",
      "GIS 144\n",
      "F 145\n",
      "VLO 146\n",
      "SNPS 147\n",
      "SRE 148\n",
      "PSX 149\n",
      "MET 150\n",
      "AIG 151\n",
      "EW 152\n",
      "APH 153\n",
      "ROP 154\n",
      "ADM 155\n",
      "OXY 156\n",
      "AZO 157\n",
      "KMB 158\n",
      "JCI 159\n",
      "MCO 160\n",
      "FDX 161\n",
      "CDNS 162\n",
      "TRV 163\n",
      "PSA 164\n",
      "CNC 165\n",
      "A 166\n",
      "CTVA 167\n",
      "EXC 168\n",
      "MSI 169\n",
      "DXCM 170\n",
      "NXPI 171\n",
      "ADSK 172\n",
      "FIS 173\n",
      "ROST 174\n",
      "NEM 175\n",
      "TT 176\n",
      "MAR 177\n",
      "AFL 178\n",
      "DVN 179\n",
      "O 180\n",
      "MCHP 181\n",
      "SYY 182\n",
      "AJG 183\n",
      "CMG 184\n",
      "HES 185\n",
      "BIIB 186\n",
      "WMB 187\n",
      "DOW 188\n",
      "CHTR 189\n",
      "PH 190\n",
      "XEL 191\n",
      "MNST 192\n",
      "IQV 193\n",
      "TEL 194\n",
      "MSCI 195\n",
      "SPG 196\n",
      "LHX 197\n",
      "CTAS 198\n",
      "ALL 199\n",
      "PAYX 200\n",
      "COF 201\n",
      "IDXX 202\n",
      "KMI 203\n",
      "ECL 204\n",
      "NUE 205\n",
      "YUM 206\n",
      "HAL 207\n",
      "DD 208\n",
      "CARR 209\n",
      "PRU 210\n",
      "BK 211\n",
      "HLT 212\n",
      "STZ 213\n",
      "PCAR 214\n",
      "ED 215\n",
      "AMP 216\n",
      "CMI 217\n",
      "OTIS 218\n",
      "KHC 219\n",
      "TDG 220\n",
      "HSY 221\n",
      "WELL 222\n",
      "EA 223\n",
      "MTD 224\n",
      "ILMN 225\n",
      "AME 226\n",
      "FTNT 227\n",
      "PEG 228\n",
      "KEYS 229\n",
      "CTSH 230\n",
      "CSGP 231\n",
      "SBAC 232\n",
      "ROK 233\n",
      "BKR 234\n",
      "ENPH 235\n",
      "RMD 236\n",
      "VICI 237\n",
      "KDP 238\n",
      "WEC 239\n",
      "DLTR 240\n",
      "OKE 241\n",
      "PPG 242\n",
      "KR 243\n",
      "STT 244\n",
      "ES 245\n",
      "GPN 246\n",
      "DHI 247\n",
      "DLR 248\n",
      "AWK 249\n",
      "VRSK 250\n",
      "IFF 251\n",
      "DFS 252\n",
      "ANET 253\n",
      "ODFL 254\n",
      "WTW 255\n",
      "ON 256\n",
      "CEG 257\n",
      "FAST 258\n",
      "CPRT 259\n",
      "GLW 260\n",
      "ABC 261\n",
      "APTV 262\n",
      "ZBH 263\n",
      "ALB 264\n",
      "URI 265\n",
      "CBRE 266\n",
      "RSG 267\n",
      "WBA 268\n",
      "MTB 269\n",
      "HPQ 270\n",
      "IT 271\n",
      "EIX 272\n",
      "TROW 273\n",
      "PCG 274\n",
      "WBD 275\n",
      "ULTA 276\n",
      "GWW 277\n",
      "FANG 278\n",
      "EFX 279\n",
      "HIG 280\n",
      "CDW 281\n",
      "LEN 282\n",
      "GPC 283\n",
      "TSCO 284\n",
      "EBAY 285\n",
      "VMC 286\n",
      "FITB 287\n",
      "FTV 288\n",
      "WY 289\n",
      "DAL 290\n",
      "ACGL 291\n",
      "LYB 292\n",
      "AEE 293\n",
      "DTE 294\n",
      "FE 295\n",
      "AVB 296\n",
      "FRC 297\n",
      "BAX 298\n",
      "IR 299\n",
      "PPL 300\n",
      "LH 301\n",
      "ETR 302\n",
      "HPE 303\n",
      "ARE 304\n",
      "RJF 305\n",
      "ANSS 306\n",
      "MLM 307\n",
      "MKC 308\n",
      "NDAQ 309\n",
      "LUV 310\n",
      "GEHC 311\n",
      "RF 312\n",
      "HBAN 313\n",
      "CAH 314\n",
      "CFG 315\n",
      "PFG 316\n",
      "HOLX 317\n",
      "XYL 318\n",
      "CHD 319\n",
      "DOV 320\n",
      "EQR 321\n",
      "CTRA 322\n",
      "PWR 323\n",
      "EXR 324\n",
      "NTRS 325\n",
      "VRSN 326\n",
      "CAG 327\n",
      "TSN 328\n",
      "WAT 329\n",
      "CNP 330\n",
      "STE 331\n",
      "VTR 332\n",
      "TDY 333\n",
      "CMS 334\n",
      "EPAM 335\n",
      "WAB 336\n",
      "AMCR 337\n",
      "K 338\n",
      "DRI 339\n",
      "AES 340\n",
      "EXPD 341\n",
      "DGX 342\n",
      "MAA 343\n",
      "WST 344\n",
      "IEX 345\n",
      "OMC 346\n",
      "BALL 347\n",
      "CLX 348\n",
      "LVS 349\n",
      "CINF 350\n",
      "INVH 351\n",
      "MOH 352\n",
      "MRO 353\n",
      "SJM 354\n",
      "CF 355\n",
      "MPWR 356\n",
      "TRGP 357\n",
      "STLD 358\n",
      "KEY 359\n",
      "COO 360\n",
      "PKI 361\n",
      "ALGN 362\n",
      "BBY 363\n",
      "TTWO 364\n",
      "BR 365\n",
      "MOS 366\n",
      "FMC 367\n",
      "J 368\n",
      "FSLR 369\n",
      "ATO 370\n",
      "SEDG 371\n",
      "AVY 372\n",
      "SWKS 373\n",
      "ETSY 374\n",
      "FDS 375\n",
      "GRMN 376\n",
      "TXT 377\n",
      "WRB 378\n",
      "HWM 379\n",
      "JBHT 380\n",
      "NVR 381\n",
      "INCY 382\n",
      "TER 383\n",
      "SYF 384\n",
      "EVRG 385\n",
      "IRM 386\n",
      "LKQ 387\n",
      "SIVB 388\n",
      "ZBRA 389\n",
      "VTRS 390\n",
      "FLT 391\n",
      "PAYC 392\n",
      "UAL 393\n",
      "LW 394\n",
      "NTAP 395\n",
      "APA 396\n",
      "PEAK 397\n",
      "EXPE 398\n",
      "RE 399\n",
      "IPG 400\n",
      "AKAM 401\n",
      "LNT 402\n",
      "ESS 403\n",
      "LDOS 404\n",
      "BRO 405\n",
      "IP 406\n",
      "HRL 407\n",
      "TYL 408\n",
      "PTC 409\n",
      "JKHY 410\n",
      "TRMB 411\n",
      "NDSN 412\n",
      "TECH 413\n",
      "KIM 414\n",
      "CBOE 415\n",
      "SNA 416\n",
      "EQT 417\n",
      "POOL 418\n",
      "RCL 419\n",
      "PKG 420\n",
      "GEN 421\n",
      "MTCH 422\n",
      "MGM 423\n",
      "SWK 424\n",
      "MKTX 425\n",
      "CPT 426\n",
      "DPZ 427\n",
      "TFX 428\n",
      "UDR 429\n",
      "BF-B 430\n",
      "WDC 431\n",
      "CE 432\n",
      "CHRW 433\n",
      "L 434\n",
      "HST 435\n",
      "MAS 436\n",
      "CRL 437\n",
      "CPB 438\n",
      "NI 439\n",
      "HSIC 440\n",
      "PHM 441\n",
      "GL 442\n",
      "EMN 443\n",
      "KMX 444\n",
      "STX 445\n",
      "TPR 446\n",
      "LYV 447\n",
      "BBWI 448\n",
      "JNPR 449\n",
      "BWA 450\n",
      "ALLE 451\n",
      "UHS 452\n",
      "WYNN 453\n",
      "PARA 454\n",
      "QRVO 455\n",
      "CDAY 456\n",
      "FOXA 457\n",
      "VFC 458\n",
      "REG 459\n",
      "TAP 460\n",
      "AAL 461\n",
      "WRK 462\n",
      "BXP 463\n",
      "CZR 464\n",
      "AAP 465\n",
      "CCL 466\n",
      "BIO 467\n",
      "HII 468\n",
      "CMA 469\n",
      "IVZ 470\n",
      "CTLT 471\n",
      "FFIV 472\n",
      "PNW 473\n",
      "ROL 474\n",
      "RHI 475\n",
      "WHR 476\n",
      "HAS 477\n",
      "AOS 478\n",
      "SEE 479\n",
      "PNR 480\n",
      "BEN 481\n",
      "ZION 482\n",
      "NRG 483\n",
      "FRT 484\n",
      "OGN 485\n",
      "NWSA 486\n",
      "SBNY 487\n",
      "XRAY 488\n",
      "AIZ 489\n",
      "GNRC 490\n",
      "DXC 491\n",
      "MHK 492\n",
      "NCLH 493\n",
      "ALK 494\n",
      "NWL 495\n",
      "LUMN 496\n",
      "RL 497\n",
      "LNC 498\n",
      "DVA 499\n",
      "FOX 500\n",
      "DISH 501\n",
      "NWS 502\n"
     ]
    }
   ],
   "source": [
    "sottoSoglia=(cummin2<-soglia)\n",
    "s3=(cummin2<-soglia) & (drawdown<0)\n",
    "j=0\n",
    "for t in tickers:\n",
    "    print(t,j)\n",
    "    j+=1\n",
    "    for i in dati.index:\n",
    "        if sottoSoglia[t].loc[i] and not (s3[t].loc[i]):\n",
    "            cummin2[t].loc[i:]=(drawdown[t].loc[i:]).cummin()\n",
    "            sottoSoglia[t]=(cummin2[t]<-soglia)\n",
    "            s3[t]=((cummin2[t]<-soglia) & (drawdown[t]<0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AAPL      8.672710\n",
       "MSFT      3.381718\n",
       "AMZN      8.228838\n",
       "BRK-B     2.668220\n",
       "GOOGL     3.974197\n",
       "           ...    \n",
       "LNC       4.634817\n",
       "DVA       2.919857\n",
       "FOX      22.107671\n",
       "DISH      0.727027\n",
       "NWS       7.723417\n",
       "Length: 503, dtype: float64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "strategia3=100*(np.exp((s3*logret2).sum()/times)-1)\n",
    "strategia3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "buy&hold: 13.9 %  -  strategia 2: 10.6 %  -  strategia 3: 4.7 %\n"
     ]
    }
   ],
   "source": [
    "print(\"buy&hold:\",round(strategia1.mean(),1),\"%  -  strategia 2:\",round(strategia2.mean(),1),\"%  -  strategia 3:\",round(strategia3.mean(),1),\"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "buy&hold: 13.6 %  -  strategia 2: 10.3 %  -  strategia 3: 4.8 %\n"
     ]
    }
   ],
   "source": [
    "limite=50\n",
    "print(\"buy&hold:\",round(strategia1[limite:].mean(),1),\"%  -  strategia 2:\",round(strategia2[limite:].mean(),1),\"%  -  strategia 3:\",round(strategia3[limite:].mean(),1),\"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Frequenza')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 2500x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xticks(range(-10,50,5))\n",
    "plt.xlabel(\"Rendimento annualizzato %\",fontdict = {'fontsize' : 24})\n",
    "strategia1.plot(kind=\"hist\",figsize=(25,15),range=[-10,50],bins=100,legend=True,label=\"buy&hold\",fontsize=20,color=\"green\")\n",
    "strategia2.plot(kind=\"hist\",figsize=(25,15),range=[-10,50],bins=100,legend=True,label=\"strategia 2\",fontsize=20,color=\"red\",alpha=0.8)\n",
    "strategia3.plot(kind=\"hist\",figsize=(25,15),range=[-10,50],bins=100,legend=True,label=\"strategia 3\",fontsize=20,color=\"blue\",alpha=0.6)\n",
    "plt.legend(fontsize=30)\n",
    "plt.ylabel(\"Frequenza\",fontdict = {'fontsize' : 24})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Crypto"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# USARE QUESTO PER AVERE S&P500\n",
    "crypto=[[\"Bitcoin\",\"BTC-USD\",1],[\"Ethereum\",\"ETH-USD\",1]]\n",
    "tickersc=list(np.array(crypto)[:,1])\n",
    "nomic=list(np.array(crypto)[:,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  2 of 2 completed\n"
     ]
    }
   ],
   "source": [
    "datic=yf.download(tickersc)[\"Adj Close\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# rimetto a posto le colonne che non ho capito perché me le scombina\n",
    "datic=datic.reindex(tickersc, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 2500x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "datic[\"BTC-USD\"].plot(figsize=(25,15))\n",
    "datic[\"BTC-USD\"].dropna().cummax().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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1dnY7A/e15tw4L4dZw0a1N4xqBw5BcA4AAAAAAMxFMRxvRHlU+6yPZnG9/6YvDdw3rVHtO+104L557zjPA/Hu10Ny/G5w7r0DHIDgHAAAAAAAmItRo9plnz033Xpn93be/p7W+alqnDeHJdUzMjCqfcjx5HenWRYXtnbj7f/mk/HhT94x7cMDlkRr3gcAAAAAAACspmJwXt6jPa1G9XHXajai3elMb1R7ReO8OefGeXkye51R7f/2P98eN33mr+KmiPjOp14z99Y8sPj8KwEAAAAAAMxFlhZ2nBvVXkseAGdT6pwv4o7zgcb5sOA8yUe1Z/HFO85279+u+J0AygTnAAAAAADAXPTtOB8Y1S45r9LMR7VP6fTsVjXO5z6qvfT1kHSrG5yn/RMLdnYHfyeAMsE5AAAAAAAwF2khHG8MjGqf9dEcD6398zS1Ue2VjfP5xknliyqGNs73DzPLsr7zo3EO1CE4BwAAAAAA5iLNek3ggca55LxSq9s4n9Ko9orG+byD83JOXmdUe/HsbO8IzoHxWvM+AAAAAAAAYDUVs99GaJzX0WrlO86no6pxPvdR7dH/+kNy80ga+aj2/rOz0xacA+NpnAMAAAAAAHNRbJyX91YLzqu1uvPIp/Pzqxrn+V71eTlQ49yodmBCgnMAAAAAAGAuiuPGG6XkPJtap/p4y8/LtEa17y7gqPZyUJ4MacA3C43ztHB+fuY3bo1drXNgDME5AAAAAAAwF51C47w5sON81kezmMoBeR5sr9So9oHGefXz8uPspINn5yOfvfOoDwtYMoJzAAAAAABgLtJRjXPJeUQMXkDwuEdetn//dM5P1aj2eTfOk1JyPnRUe6FxXj49F7faUzk2YHkIzgEAAAAAgLnIio3zRnlUOxH9Fxe84m98U6y19sLhaZ2fdqcqOJ9v47z01hg7qr1TGtUeEXH65NpUjg1YHoJzAAAAAABgLorh5vpas+8xhfM9xfOwsd6KiGTg/qNUNea8fFHDrJVff9io9kYhON/e6R85f4ngHBijNe8DAAAAAAAAVlNx3PiJVjMi2pWPrbLieUiiFxpP6/ykFcH5vBvnZcNGtRcb5zul5vxi/QbAItI4BwAAAAAA5qK/cd7f9ZOb7ymehiTJ/9+MG+dz3nFeVt55nsub6bvtdGBUe/lrgLLF+pcOAAAAAABYGWlhx/mJgVHtgs6IUuM8SbrN6WmdnarG+bCG97w0hsxqH3Z/RPXvBVAkOAcAAAAAAOYii/Ko9sJjcs6I6D8PSdItnE/twoKqxnk2tZj+YIbl+M1Rwbk3FDCG4BwAAAAAAJiLYph5Yr00qn3Bwtp5KQbkjSSJbud8hqPaF+FPsbHeu7BiWAN+dOP8yA8JWDKCcwAAAAAAYC6KofAJO84rpcMa51NKs6tGmi/C3+LURu/9MSw41zgHDkNwDgAAAAAAzEVf43xgVLugsyyJJLqF8yk3zr/x6tPd+x52+cZ0XmwCp04UL6yo/uUF58BhtMY/BQAAAAAA4Oj17TgfGNVORH/gmyS9tvW0cuC8cf7cb31ktJpJnLl0I85cemI6LzaBv/OC6+Idv/FHcfrkWmysV8dbo0a1Z1Uj6AEKBOcAAAAAAMBcFEPh4g7riMUYD74Isr5R7b1geBqj2rMs6/5Nms0kvuc7Hn3kr3FQT3n8lfGTr/prcebSE0MD8tGN82kdGbAsBOcAAAAAAMBcZFnavX2iVd5xLumM6D8P026cFy9kGBVCz8tjH3npyMdHBueSc2AMO84BAAAAAIC5yPoa5+XgfNZHs5iK56GRFHecH/0JKobLo8aeL6pRx2zHOTCO4BwAAAAAAJiLYpi53iqPahd0Rgyeh3xa+zTOTqcQnDeT4xecjx7V7v0EjCY4BwAAAAAA5qJ/NHh/ZCHm3FPecZ7EFEe1L3Pj3Kh2YAzBOQAAAAAAMBdZFPd394eeCsJ7io3zRtJrnE/jBPU1zo9hcF7Vkl9v7UVhcnNgHME5AAAAAAAwF1mWdm83kyRe9MxHFx6TdEZEpIXbSZJ0g/NpBMHHvXHebA7GXs3mfkNfcg6MITgHAAAAAADmIu1rUzfi77zgunjSo6+ICI3zXPECgr3QfHqB9nFvnFeF/Y39Kw3sOAfGEZwDAAAAAABzURzVnoee3YawoHNPacd5ng1P4/wc+8Z5VXDeEJwD9QjOAQAAAACAuSg3ziN6fWox5550oHG+Zxo5cCc75o3zih3n3cZ5OvAQQB/BOQAAAAAAMCfF4Hwv4EySvHE+lwNaOFlf47x3nqZxeo5947ypcQ4cnOAcAAAAAACYi6rGeUxxFPlx1L/jPJnq+enbcV7R3l50VS35/NcoXhQAUEVwDgAAAAAAzEWa9eZn503qbqNacB4R/Y3zRhQa+VN4rePeOK865u6odu8nYAzBOQAAAAAAMBfFKDMPPe0471c8D0mS9M7PtBvnjeMXIZVb8n/ruY/rvq8yO86BMY7fv3oAAAAAAMBSyCpGtdtx3q9/VHtv9Pg0rizoHPPGebPZH3u9+gefpnEO1CY4BwAAAAAA5iKLXg0430+d2HHep3gakiTpXlgwjZXdaV/j/PgF5+Wwv9lIuvcJzoFxBOcAAAAAAMBc5GFmlvWa5hrn/dJy47z71XRHtR/LxnnljvO9/02ncaUBsFQE5wAAAAAAwFx0W+VZL/Cc5g7v42hY43wapyc95sF5I6kIzjXOgZoE5wAAAAAAwFxkUdxxXhrVPo8DWkDlHee9+4/+tYqN89YxDM6rG+f7wXk68BBAH8E5AAAAAAAwF71QOOmGwka19yuehkaSdEePZ1O4tOC4N86bzeGNcxMMgHEE5wAAAAAAwFwUR7Un5ca5oDMiKs7DFC8sKDbOq9rbi64q7M/fV0a1A+MIzgEAAAAAgLlIK1rT3cb5rA9mQRXz3kaSlMa1H+1ZKobLx7FxXrnjfP+uYpseoIrgHAAAAAAAmIuqceOJoLNPecd5MRo+6jPUKSwCP47BeZX89/B2AsYRnAMAAAAAAPNRGNWeW2vuRRe7nbTqO1ZOMfBNkt5I+4g48uQ8Peaj2qvkLXQXYgDjCM4BAAAAAIC56I0G74W0J0+0IiJia7s9hyNaQOXGeSHPPuq93cUd51Vjz4+jXuNccA6MJjgHAAAAAADmompU+8Z6MyIiLm53Zn04C6nYu08i+hvnR/1aS9A4f9wjL42IiP/nS58SEYXGueAcGKM17wMAAAAAAABWUzc4L4xqP1VonGdZNtWg+Djo33Ge9O84Lzy22+7EbjuNUxtrB36tYnB+XHec/9Mbvz3ueWAzHnvNXoDe2K+QZka1A2NonAMAAAAAAHORVYxq39gPzrOI2N7VOs/6dpxH8VR1H+ukafxPv/yJ+B9+7ua49+zmgV+rswTB+Ym1Znzjw093L7joNc7neVTAcSA4BwAAAAAA5iJvnBcj2nzHeUTEpnHtfa3yRpL07R7PH/rq3Rfi7rObsdtO4zf/65cO/Fp54zxJlmfHeR6gp5JzYAzBOQAAAAAAMBfdULgwqv3kiWb39uZ2e9aHtHAGGufFx/YvPFhr9eKew1xs0Nl/seO637xKPqrdjnNgHME5AAAAAAAwF90d5wUn1wuN8x3BeX9wnvSF5/ljxfu2DnHO2p29H3hcx7RX6Y1qF5wDownOAQAAAACAuRi14zxC4zyif1T73orzwVHtxTHkWzuHaJx30oiIWGsuT3zUDc7TOR8IsPCW518+AAAAAADgWMkiTzN7YfCpQnC+Zcd5pCMa57Hf2O8UnrQ9YXDe7qSx2977O+zuB+etZQrO99vzmcY5MEZr/FMAAAAAAACOXlWUubFux3lRX+M86R/LnuflxTHkk4xqP3t+O37q3Z+ItVYj/sU/eHZ09ke1t5rLM6o9MaodqElwDgAAAAAAzEU3FM56Qe2JtV5wvr2rcV601zgfDLWLjfOtCc7Zv/392+PBi7sREfHndz4Y7aVsnO/9r1HtwDiCcwAAAAAAYC6y/c55MQou5sJKwv1N6UbSf67yx4o7znd26yfEn/uL+7u3/+CzX+sG5ksVnCdGtQP1CM4BAAAAAIC5yLrD2ntxcL7HO8uEnRGliwfKjfN8VHt6sPNU/Nkf/dxd3bZ/c4lGtec7zo1qB8ZZnkuGAAAAAACAY6UXjPcHtY3uXuoZH9ACGrXjPH+sc8ATtVMa656Pxl9bwsb5QS8uAFbH8vzLBwAAAAAAHDODO84jotuq1jjvb4U3SvvN84cO0qZO0yx22tVj3ZvLFJxrnAM1Lc+/fAAAAAAAwLGSVuw4j9jb5R0h7IwojrPfa5sXw/PsEKPat0tt86K1ZRrVnr+X6q9+B1aU4BwAAAAAAJiLYaPaE6Pau4rXDiSl83SYUe2jgnONc2AVLc+/fAAAAAAAwDEzZMf5fnphVHt/4Lu343ywDV5unNcJibd3hgfnrWUKzhPBOVDP8vzLBwAAAAAAHCvFMeRFebP6ICPIl01f4zzZ+79cOqRx3unUCM5HNM5byzSqveG9BNQjOAcAAAAAAOai1zcvj2rff1zW2de6T5KkLziPITvO64TEWyvSOG/uB+d1LiYAVtvy/MsHAAAAAAAcK1mW7t8qj2rf+3pYI32VFC8eaCT9FxnkZ2+gcV4jOF+Vxnl+EcBB9sADq0VwDgAAAAAAzEUejA82zvPx2jM/pIVTjHsHG+d7jx52x/mpE62+x5aqcb5/EUC7480EjLY8//IBAAAAAABLodEd1a4l3D+qvX/Hef5QJztc4/zUxhIH541e49z7CRhlef7lAwAAAAAAjo3f/dNPxwPNv9z/akjjXNDZN6o9iaR7biJ6bfSsPKq9Rru6uON8mYPz4tj5ZRzX/tE/+Xr829+/Pe5/cGvehwLH3vL8ywcAAAAAABwbv/3lf9+9naTNvsd6jfMZHtCCSsuN88JjeYO6HAiXR7dX2dlvnLeaSayv9Z//ZdpxnjfOI5YvON/e7cS7P/j5+N2PfyX+6b/6qEY9HJLgHAAAAAAAmLlkbad7+5IHv6X/MY3zrr7GeSk5zx8rB+Xl0e1V8lHtJ9aa0UyWJygv62ucL9me863tdvdigE6aLd2FATBrgnMAAAAAAGCmsiyLpLEXYu58+ZvjxM7VfY839oPcbLlyzoPZD8HzbLtqVHs5KK/TOG939p7TbDaiWWqY7y5RwNwsjJ1vL1mwvNPu/zvttpfn7wbzIDgHAAAAAABmqp22e1+kzb4wOCIi2Z/VnsZyBZ0HkWe9+cUEVaPaBxrntYLzvZC12Uj6xplHLFcA2984X673Uz5uP6dxDocjOAcAAAAAAGZqtxicZ40oTwrv7jgXBEYWwxvn+XUFB9lxnn9Pq5lEs9H/B2gvUXBe/N2WbVS7xjkcLcE5AAAAAAAwU33BeToYnPd2nM/woBZUbwr7YOP8/R/584g4WOM8f06zsdyj2ltLPKq9HJQv24UBMGuCcwAAAAAAYKZ2Ojvd21naGBih3W2cZ8sVdB5Efg4a3cZ577HP3HZvRAwG5bWC83xUe0XjfJmay8Xfrb1kwXJ5VPsyXfAA8yA4BwAAAAAAZmqns9v7ImvEnfdd7Hu81zgXnOenID8n5X3wEYON8zqj2tudvHGeDPzMZz35EQc51IVUbJwv3Y7z0gUO7SX7/WDWBOcAAAAAAMBM7XSKo9qbsV1qzjb2g1y5ea9xnlQ0ziP2Li44UOM83QtdW81GX7P/Wx57Jp5+7VWHOOLFUhxD306Xq5G90+7/3Cxbox5mTXAOAAAAAADMVLFxnqWN+N6/9ui+xxOj2rsGGucxOFa93Myv0zjv7Tjv/3nPecojK1vtx9VSN853y41zwTkcRmveBwAAAAAAAKyWYnD+vKc8Kn74b1zb93hvVPtMD2sh5aF4HmWXM+2d3c5AUN6p0azuFEa1F3P3Vmt5QvOI/gsDOksWLJd30beXaDc9zIPGOQAAAAAAMFO7hVHtj7zidHc0e66xn15onBcb5/t3lHLt7crgvMaO8+Ko9sL9rcZyRUd9jfMluxJjYFT7kv1+MGvL9a8fAAAAAACw8HbTXuO81RgcjtttnAsCu6H2sFHtO7vpQCBca1R7oXFerJwXg+ZlUGyct5d9VLvGORzKcv3rBwAAAAAALLziqPaq4DxvoCuc91r3ef5bHtV+0MZ5d8d5s9E/qr25XKPa+xvnyxUsa5zD0RKcAwAAAAAAM7Wb9ka1rzXWBh7PQ+JUcl4Y1b7fOK/Ycd7JDtA43w+Rm42kb1R7c9ka583lbZzvapzDkVquf/0AAAAAAICFt1tonK81h49ql5v3Gud5YD4wqr2dHmzH+X6I3GomfbvkNc6Pjz/76tm+r3c7y/X7wawJzgEAAAAAgJkq7jhvRnPgcY3znnGN8+2dzkBQXmtUe3fHeX9UZMf58fBX95yPr9x1vu++juAcDmW5/vUDAAAAAAAWXj6qPUsblaPB85BYcB6RRalxnpQb54M7zica1d5M+pr9xaB5GfQ3zpfn/XTHPecH7ttdogsDYB4E5wAAAAAAwEx1d5ynjWiUK9RhVHtRt3G+P6K9HIrv7KaHbJz3j2pftuC8f8f5cjeyl/33g2kTnAMAAAAAADN19/n79m6kzYHR4xG9Ue2Z5Lzbus/P0267Pxzd3u0MNPNv/dK9Y39u3jhvNRtR/O5yo/24ayRJ9+KMzhI1squmCgjO4XAE5wAAAAAAwMzc/dCD8fmH/jgiIrIsiUZFw7k7qn2JRmsfVLafheZ59k670/f4zu7gqPY/+fP74877Loz8ue202Djv3b9kuXlE9Frn+cUCy6B4EUBr//cTnMPhCM4BAAAAAICZ+YMv3tb7or1eOao9D9MVznuN80ZjL9Ipj2Hf3k0rm9S3f/XcyJ/bHdXeTLp71COWr3Ee0QuWl6lx3il8OE6sNSMior1Evx/Mg+AcAAAAAACYmXxXd0TEzl8+OapWaufZbXkE+SrKz0G+e/ybH3Mmzlx6ovd4mlXuND95ojX0Zz54YSfOb+5GRESr0YiXfecTIiJifa0RV12+cWTHviia+xcdtJeocV6cMrCeB+ft5fn9YB4E5wAAAAAAwOwk/SFvVcM5v09u3muY5xcYrLUa8dOvfnZcstHqPl41gnytNTwC+l9+67Pd281mEk/4hsvizT/6rHj7a54brebyRUfN7ijz5XlDFdvzvca54BwOY/jlRgAAAAAAAEesPyev3nGe35VJziPLg/PCeVpfa8apjVZc2GrHrV+6N+5+YHPg+0aFxF/62oPd23kb+5qHXXJUh7xw8nUA5V3wx1nvgoqke7HDMl0YAPOwfJcNAQAAAAAAC6sck1fuOM+DTsF5d5d1+TzlO8+rQvOI4e3j8sUIzapZ+Usm/x2XKTjPPxuNRtLd4b5Mo+hhHjTOAQAAAACAmUmKlb6s3EDff043OJ/NMS2yPOwtB9zj8u5hwfnF7Xbf13nousyW6UKMNMvi//jdL8RNt94ZEXvvi8YSXhgA86BxDgAAAAAAzMmYUe2CwG4YmpSD8zHJ+e6Q4PyBB7f7vm4u4U7zsvzcLcPb6YtfOdsNzSP2g3MXmsCRWP5/DQEAAAAAgIXRl/dm1aPaNc578nNQPk/Nqqp+Qbs9JDg/v115/zLL33PL0Mje3un0fd1oJC40gSMiOAcAAAAAAGYnKYZ7SeWo9v313ZGFIHDYqPZyAz0i4q9f/6ju7Xan+tydfag/ON8qjW5fRt0d50swqr2sb1T7Ev5+MEuCcwAAAAAAYGbKce/IxrkGbTcMLefk5SA9IuLSk2txYq0ZEcNHtW/t9jeWN0sN5mXUWKL303bp79dsJj4vcEQE5wAAAAAAwOyUkomq5nQedN5538X4o9vvncVRLaw8DG00+k9c1QUHzWYSrebe/cNGtZfv39pZ/sZ5skSN7HJw3kiKjfN5HBEsD8E5AAAAAAAwM31xb5YMNKkjom98+zt/87PTPqSF1kmrG+dV563ZSKLV2ot+2kMa5zul4Pz6664+/EEuuHLjfHu3Ew9e3JnnIR1Yecd5s5H0fr8luDAA5klwDgAAAAAAzE7SH+6NGtVORJaPai8l5eWvIyKajUasNfein2Gj2nfaveD1H738afHER19xREe6uJqFRna7k8b/9Msfjx//uZvj7rObcz6yyQ2Oam90L6Iwqh0OR3AOAAAAAADMTH8pNhk5qp1C47xOcN5MorUfnA8b1b67f/9Vl2/E06+96igPdWEVg+Uv3/VQ3HtuKzppFv/+pj+f74EdwKhR7ZnGORyK4BwAAAAAAJiZLNLiF2NHta+6fPx2s0Zw3mr0gvNhjfM8OF9rrU5E1N0BXmpkH8fR5gON8+Kodo1zOJTV+VcRAAAAAACYu3LjvKpdXhUKr6q0u+O8FJxXnLdmsxFrrb37253qEHUVg/OksAM8ieP93topN84b0Z3aIDeHw1mdfxUBAAAAAIC5K7d8q/aZa5z3pPvF8YFR7VXBeWP8qPad/fvXW80jPMrF1ttxnvW9t7Z2OvGRz34t7ju3Nacjm9z2bv/fNS1MbTiODXpYJK15HwAAAAAAALA6ssiKX1S2y+047+kMaZyXR7fn940d1b7fWF6lxnl3B3ipkv3ZL90Xn/3SffHIK0/FW1797Hkc2sS2d/ob51mWDR1FD0xmdf5VBAAAAAAA5q6/FZsM2XEuOM9l++erfIFBUhWcNxvdQLw9LDjvrN6o9vyig06aVYbLX7//4qwP6cDKO87TtPf7aZzD4azOv4oAAAAAAMDcZXVGtc/qYI6BbuN8YFT74HNbjSTWao9qX52IKOmOMu+dz+OqHJxnWdYLzqv/5EBNq/OvIgAAAAAAMHfF4DzLksqx7Mc72jxaeYu4HJRXjmpvJtFq5aPaq8/i7m7eOF/NHeedIU3842KgcZ5l0dhP+8oXpQCTEZwDAAAAAAAz1B/uNSqSio7qbFc6tHFeteO8Ea3m3v1GtfcUd5wf98b51na77+s0M6odjsrq/KsIAAAAAADMXTncqxrV3hnSll4mu+3O+CdFsXFeCs6rGueNJFrN0TvOd/Yby6s0qr2447x9zIPzi6XgPEuz7r77qv3tQH2r868iAAAAAAAwd33B+ZBR7ce9FTzOe/7TF+If/uxH4gtffqDv/u2dTnzsT78e585vd+/Lw9DyaPbK4LyZFNrH1a+92169xnmSFEe1H9/3VppmsbldMapd4xyOxOr8qwgAAAAAAMxdVmtU+3IHgDfd+rXYbafx9l/7TN/9/+b3vhi/+Dt/Gm/5N5/q3jfpqPbu3UNC1FUMzrs7ztPqNQBV++IX0eZOe+C+rBic23AAh7I6/yoCAAAAAABzl/UFukMa50PGjC+bLCLuf3Cr+/XNf/z1iIi452zvvvwagrqj2vN29bDy8c5+cL6+1jzoYR87+cUZWVa947yTZqX35WK6uDUYnKdZ7/fTOIfDEZwDAAAAAAAzU26cV+44L4Sb+c7uZVVunZd1JmmcN5Nu47wqRM2yrLv7fG3Jz2tRo8ao9uMw5aA6ODeqHY7K6vyrCAAAAAAAzF15x3mVYojZbB6PMdoHddcDmyMfz8/XQHBekfA0G70Gf1WG2i40+VsrNKo92T93nTSrHNUeEcdi9/nF7YpR7WnWfW9kxyD8h0W2Ov8qAgAAAAAAC2B8uFcc1d6saFavku6O89JpqBzV3uztOC83+yN6Y98jjs9e76PQ2wFePao9onr3+aK5uLU7cF+a9aY2yM3hcATnAAAAAADAzGicTyYPzpulinnVqPbWmB3naeG8rtJZzS8SyEaMam8fh8Z5xaj2LMu6F1WkknM4FME5AAAAAAAwOzWyvb7gfEWa0Xfed6Hy/mGN82HnZfSO897tqsb6smoUGtnDG+eLHzpXjWpPs96odjvO4XBa8z4AAAAAAABgdaSTjmpfgYD3U1+8J/7X9//xwP1p1hu4PrDjfMgI+1E7zovB6ipNwE/2a6Sjd5wv/qj2nfbeMZ5Ya8b2bici9i4G6F0YIDiHw9A4BwAAAAAAZiarMao9KYTEy9aMzirCzZ+vCM0j+kdvl89DUnFeTp9c6+04r2ycF4Pz5Tqvo/TtOB8ykv04NM7zcL94MUmWFhrni5/9w0ITnAMAAAAAADOT1Wic/72/eV339pnTJ6Z5ODNX9dsPuzigGHSXG+blJv5PvuqvxfpaM5IY3jjvG9W+QsF5ccd5e0hA3j4GjfM83G82k7jysr3PxStf9KTClIHFD/9hkRnVDgAAAAAAzEx/cF4d3l7zsEviWx57Jj7/5QeWbqZ4WhHcNpvJQOP5Y5/7etxxz/nu16NGtX/j1ZfEYx95aUTEyMb5qo5qL44yHzqq/Tg0zvN9940k/r//7TPj6/dfjCdcc1n83i13RET1ewuoT3AOAAAAAADMTN1W7KkTrfwbpng0s1f1+zcbjYjoD3R/8QN/2vd1uSFeDNL3vr//eVUZ6qo2zpPCKPPjPap97xhbjSQu2ViLb/qGyyOi9/tlsXdxwCr9beEoGdUOAAAAAADMTJ0d5xHRLaMvfpw5map8tjx2vUr5OcUv19d6cY8d54Pyc9VJs6EB+bBAfZHkbfnihRIR/RdBvO/3b4/f/fhXZnpcsCw0zgEAAAAAgJmps+M8ojfEffHjzMmUx2knsTeqfZykVIUsNs7XW8XgfPiO8+Jr18jql0YeNGfZiOB8yAj3RVLccV5UfC/8n5/YG9v+LY890x3fD9SjcQ4AAAAAAMxM7cnryXJWzsu/fxb1xqYPtIwLYelaq9m9PXLHeeF2skLJef6rpmkWnU51QN4+Do3z/WMcNX0gd/fZzVkcEiwVwTkAAAAAADAz/Y3z1Qlvc2lFoF1nano5HC2G7cVR7aN3nK9m47y4A3xYQH6sGucjRrX3nrv4vw8sGsE5AAAAAAAwO/vhbZZFvOkfPGvo03qj2he/CTyJquC8jsZAy7jYOK8a1V7ROE9Xdcd573fdHdI4P1Y7zkeMas+VVwIA4wnOAQAAAACAmUm7QXgSj7rqkqHPS5Z0yfkBc/OBVnFz7Kj20a9dZzz8siieq3a7F5z/nRdc2709bPf5MOc3d+M9/+kL8YnP33X4A6yp1zgfH5wfh9HzsGgE5wAAAAAAwAxlff9T89lLo6oJXCdML4elSSHhWa/bOM+KjfPxr7ksisHyTrsTERHf/Jgr4jlPeWT3/vaQJvowv/p7fxY33fq1+Fe//bmjOcgahu84H/xjbm23Z3JMsEwE5wAAAAAAwMzUHVW+rKPEqwLt3fb40DYpt4oLP6a447zbOK94reKXy3p+qxR/1fxcNxtJtAojzydtnH/2S/cdybFNots4b5Z2nFc0zi8KzmFignMAAAAAAGAO6gW3Bx1tvqiqLhzY3u2M/b5mKeguhu1rhSC1+KzyKxWD9IqsdWkVG9k7eXDebESz0TtvkwbnWeHsHnRv/aS6O84HGueDz93cHv+eAvoJzgEAAAAAgJlJs3ojsXtZ4HIl51X5bJ3GeblVvFP4nvW14o7zwpkrBbr9o9pXJzmv2nHebCTRLDbOJxzVXjy1nRntE59kVPvmjsY5TEpwDgAAAAAAzF42JrjNR44vV25eOap9nGsedioe+8hL++4rhu3FHefFTLX8UsWvq8Z7L6vi71oc1d4XqE8YfvcF5+lkoftBdUe1D+y7rwjOjWqHibXmfQAAAAAAAMDqqBsc51HgkuXmkY4YCf6MJ14dn/qze/rue8urnx0PP3NyoFW80+6N4m61ijvOi43z0mv3Nc4nOuxjrXjudju9Ue1Jsheed9Js8lHthXM56m96lLqj2ss7ziv+mFuCc5iYxjkAAAAAADAz+W7opOaO82UzKmM9sd4cuK/ZSCqD0UtPrnVvX3H6RPd20tc473+x4pT8qp+5rIoXExQb5xHRHde+O+mo9sLt9qyC82Gj2ivSvs0dO85hUhrnAAAAAADAzGS1O+T5rPapHcpcjGrcDwvOq3zn074hPn3bvXHJRiu+9fFXdu+v2zhfpeC8GCznTf3WfmC+sd6Knd2d2JpwJ3jx3M6ucV5/x/ms9q7DMhGcAwAAAAAAM9MNjsfsOE+6uflyBYCjQtaNtfrB+VqrET/+d54+cH8xQ03LjfMVHdXerNxxvpemnzzRigcv7MTm9mQN7eK5nFVInTfb64xqL//tgfGMagcAAAAAAGambp7XjQKXLP8b9ftXNc4bQ4LzYRojG+e928kKJedV5yQP00+d2Dvnm4fYCZ7vHp+2NO0fM5+reo8IzmFygnMAAAAAAGBmsshDxjHB7XJOah8ZaE7SOB+mb8d5DG+cT/hjj7Wk4pfNd5ufPLE3nPni1mTBefHv2FnAUe2zGh8Py0RwDgAAAAAAzMyqx3mjgvPqHeeTRTl1d5yvUuO8VRWcF0a1R0zeOC+e25kF5518VHv/75NUvEXk5jA5wTkAAAAAADAztXec71fOl23i9LRHtY/ecX7wn3uctVqDcVhvVPvBgvOiTieL85u78V//6K/i3PntA/+csa/TbZz3/z5V7fJ0RuPjYZkIzgEAAAAAgJkpjw8fakmXnI8aoX2idfhR7aN2nGd9jfOJfuyxttasCM7Lo9oPEZynWRa/8Dufi1/53S/G2371Mwf+OePku9TLDfqd9mBILjeHyQnOAQAAAACAmekF5+Ma5/nzl0s2onK+VtGMnjTg7h/V3v9axTB1lUa1V53Xo26cf+4v7o+IiK/ff/HAP6fO60QMjmrf3a0IzpdtVAPMQGveBwAAAAAAAFCWLFly/uCFnXjPf/rCyOdUtcsnDbiLzx7VOF+hSe3Rqmqcl3acb+10Ik2zA42w78yo3p2Pai8f4+Wn17u3Lzu1Fg9e3BWcwwFonAMAAAAAADOTh7fJmMb5svl3N30p/uj2e+OPbr936HMajST+0cufdqjXGdk47wvOV+f8VwbnpVHtERFbOwdrnXdGjN8/KlmWDd1x/vhrLouXfufj43u+4xvjeU+7Zu/5MzgmWDaCcwAAAAAAYIbqBnrJRM9eZBe2duOmW+8c+7xmsxHf8tgzh3qtYhk5TbNod3pt6GKOvuqj2vM94SdP9PbKb+10DvTzZxGcF1+jajLBS7/z8fF/+54ndkN1uTlMTnAOAAAAAADMTJpH4dmYHef7D4/aCX5c/Pz7/6TW85qNJFrNwwXaxUD8X/7aZ+If/9zNcd+5rYgoN84P9TLHStU5be630E+s9YLz7d0DBued/vfoNEa39wXnI94j+d81lZzDxATnAAAAAADA7NTM85Yp1/38lx+o9bxmIzn0CPXit997bivOb+7Gb/7XL0VEf3C+So3z6h3ne7//+lEE56WQ+qDN9ZGv0RndOM/l+887S3DBCcya4BwAAAAAAJiZrJucjwlu94PdVcr/Go3k0IF21ffv7AfCxXPZWKHKeVXQnN9XbJzv7B6sKV5umG9uH2xXet3XaDWGx3v572XHOUxOcA4AAAAAAMxMthRby6djVJO4rqofkTeus77G+aFf6tioupggH9W+vtaLyuo2zj9w81/0fV0ei761PYXGee1R7XuPpat0xQkcEcE5AAAAAAAwO/uBXjKmcZ4/ukrx31EE51Uhcb7ju1iMHnf+l11V43y75oj193+kPzjvpFnfhQibO0ffOC+22DfWW0Ofl//9p7BmHZae4BwAAAAAAJiZ2kF4Nzlfnei8OWIEd11VTfJmReP8CF7qWKvacb7TPviO82IAvzmFxvn5zd3u7UtPrQ19Xj6CX+McJrfi/ywCAAAAAACzVHfH+So0zssN86PYO17dON8Pzsc8b5Xk4877GucH3nHeH5xvTaFxfv5iLzg/fXJEcF74swrPYTKCcwAAAAAAYIYmDPOWOPtba/XHNEcyqr3ivlbeQi7syT6ClzrW8nZ/q5l0W/p1R7WXdTpp399y64A/Z5SHNmsG54U/bHn3OjCa4BwAAAAAAJiZrPaO8+VPdvMmeC5vQR9GVZG8VTGqfeUb5/sBc5Ik3bZ43VHt5VPXKQXU02h6X9gPzpuNJDbWm0Of1ygc3Nt/7TNx+1+dO/JjgWUlOAcAAAAAAGamGylmY4LbJH/+8rZmy43zxhGE2ZWj2lv53uujfa3jrHiRQh6cb+/WC84vPbXe93W52Z1NoemdN85Pn1obedFDsXF+21fPxVv+9aeO/FhgWQnOAQAAAACAmekG4fVy81jmNc1rpcZ56wga51WBeD6WPO1rnB/6pY61VqN37tfX9m7v7NTbcb7b7n9ee6BxfsiDK0nTLH7341+JiNFj2iNcEAGHITgHAAAAAABmaH9Ue83G+TJrlRvnR7HjvOJHlC9CSMKo9uI++W7jvOao9t3S8zqd/iD9qEe1/8lf3N+9fepEa+RzG0OSvz+6/d74jf9yW2xut4/y0GCpjP50AQAAAAAAHKFlbpBPqtw4n9ao9jzIzUeKr3poHlE9qn2nxqj2NMui3RncaV58Xx/1qPaz57e7t6/9xstHPrfqPZRlWbzzNz8bERHbu2n8yIuedKTHB8tC4xwAAAAAAJidJA8VR4e3yf7jyxy057vHI/Ya0HmgfempvXHcl59er/y+Uaoy8Tw4z8fkD2slr5Ji43w9b5zvjA/Oy2PaIyI6nayvZX7Uo9qLgf4PPf+bRj63ampB8Zj/4LN3Ht2BwZLROAcAAAAAAGYm2w8Yx3WeewHw8ibnxcZ5Mch9w498R/zhH98Zz3vqNRP/zKrGcbqfm3ZHtWucd/e+RxQa5xWheFllcJ6Wg/Ojfc/mx9VqNsaO86/6+xfHs7c79fa4wypyTREAAAAAADAH9cLb5Y3N94LQXDEQffgVJ+Nl3/WEuPqKkxP/zMrGeVoe1T7xjz32nv2UR/R9XTzf+a753Rqh8rDgvDiePT3iynneOF9vjY/1qi6KuGivOdQiOAcAAAAAAGYmi5qj2vcfXuZR7cWWeXNMk7iuUTvO87b/UexSP25e+b39e72zwhtrbX/feVUoXrbTHhznvtc47309rcb5+tr4WK/qfSQ4h3oE5wAAAAAAwMzkwfnqRbeDmkNGtR/GyMb5Co9qP3miFa/9wad2v873yEdErO03udsHHNWepmlfy/yoG+e7u/vBeas59rlV++s3t/qD805qXDtUseMcAAAAAACYoZqN8xWI1vsa582j6TrWa5wfyUsdO9/+xKvif/jhb4uHXbYRa4UQOh+Zf+BR7Z3yjvMjONiC7f2W+1qNxnnVNIFy4/z8xd24/PSJozk4WCKCcwAAAAAAYGZ6sfmY9LY7qn15Z7U3m71zcFTj06tCcY3zPUmSxFOf8LCB+/PGeZ1R7cXnNJIk0izbH9Xee58e9Xs2f806jfOkxqj2Or8nrCKj2gEAAAAAgNmpGSrm8d/yxuaz23H+V/deiK/c9dDKN86HmSQ4L+44P3liL8jupFkUp58f9aj2nd2911xvHaxx/ok/vavv6zrNelhFgnMAAAAAAGBmepFizfR2iZPzZmEhdbF9fhhVZfLbvnoufup/vyW+du+FvedIzvvko9rbE45q31jvBedZ36j2Iw7O88b5Wo0d5xV/2i985Wzf1xrnUE1wDgAAAAAAzFDNUHEFst2+Ue1TbJznPnPbvXuvtaKj2oc56Kj2jfW9jcidTtrXMk+POJeeqHFe430kOIdqgnMAAAAAAGBmsv3gfNyO8/zxJS6cRzM5+lHtdYIfuXm/tf3GeXlXeZXNwr7wSzZa3e8rftf0GueCc5gmwTkAAAAAADAz3YhxTL7XDXePOIRcJMXG+TR3nJdpnPdrFZrc7TGh8sWtveC82Uji5Im94Lw84v3Ig/P9xvlaq86o9hrBuR3nUElwDgAAAAAAzNy4xnlueWPz0o7zxtFENnUycbl5v7xxHjE+VL6wH5xfstGKZrN6xHuWHu27dneSxnmd4FzjHCoJzgEAAAAAgJnpDbWuGZwvaXJ+/XVX9bXMZ7HjfJLnrJK1QuN8XKh8cWs3IiJObax1/2bl75lW43y9TuPcqHY4sNa8DwAAAAAAAFglNXecL2m2+5THXxmvfNGT4mGXnYj/+NEvd+8/sh3nNX6MUe39io3zcaPai43zVh6cl0e1H3EuPdGO8xp/WsE5VBOcAwAAAAAAC2hJw90si4dfcTIi+sPyWe44l5v362ucjxnVPuvGeZZlsbO7H5wfVePcjnOoZFQ7AAAAAAAwM1lWs3Feev5xNer4+3ecH1VwPv45RzUWfllMMqq9b8f5/nlsl4Loo3zPtjtpN4g/sV4jOLfjHA5M4xwAAAAAAFg4y9KKHpWhNptz2nG+rG3+A2o1J2mc7wXnpzZake7/bdud/j9yeoTXemztdLq3N9aOasd5Z+xzYBVN3Dj/6Ec/Gq94xSvi27/92+N5z3tevOlNb4qtra2IiLj11lvjFa94RVx//fXxghe8IN73vvf1fe/73//+eOELXxhPf/rT44YbbojPfOYz3cc6nU687W1vi+c+97lx/fXXx2te85q4++67D/nrAQAAAAAAiySrueO8+/zjXTgfGNtd/Gpntxdgnj65diSvV6txLjfvU2ycj99x3hvV3kyGjGo/wuS8LzjXOIepmig4v//+++PHfuzH4u/9vb8Xn/zkJ+P9739/fOITn4hf/MVfjHPnzsWrX/3qeNnLXha33HJLvPnNb46f/umfjs9+9rMREfHxj3883vSmN8Vb3/rWuOWWW+IlL3lJvOY1r4nNzc2IiHjXu94VN998c/zWb/1WfOQjH4mNjY144xvfePS/MQAAAAAAMEd5qLga6W05RH3UVae7tx9x5lT39vf+tUcfyevVuSAhkZz3qbvjPE2zuLhdGNXerB7VfpQ7zrcLwXmtUe2lv+0TH33FwHN2O2ncdOvX4sOfvOPQxwfLZKLg/Morr4w//MM/jBtuuCGSJImzZ8/G9vZ2XHnllfGhD30orrjiirjxxhuj1WrFc57znHjxi18c733veyMi4n3ve1/8wA/8QDzjGc+ItbW1eNWrXhVnzpyJD37wg93Hf/RHfzSuueaaOH36dLzhDW+Im266Ke64w4cWAAAAAACWzdgd5/vN2WNeOB8IUV/2XY/v3v62a6+KH/4b18Z//4qnxaOuPl3+1gPROJ9c36j2EW3s85u73QkIl12y3t1x3ildHDG9xvn4Dczlv+23fdPDBp5z2x3n4j3/6Qvxqx++LT73l/cf+hhhWUw8qv306b1/uL/7u787XvziF8fVV18dN9xwQ9x2223xxCc+se+51157bXzhC1+IiIjbb7996OMPPfRQfP3rX+97/KqrrorLL788vvjFL078SwEAAAAAAIspH9U+rhjde/h4R+fF3Py//VvfEidP9MLPtVYjvu9Zj4mnfdNVR/Z6dUZ119mDvkr6GucjgvNzF3a6ty+7ZH3oPvGjXC+wtdvu3q7TOC9PE6g6lNv/6lz39pe//tCBjw2WzfhLU4b40Ic+FOfOnYvXve518Y/+0T+KRzziEXHy5Mm+52xsbMTFixcjIuLChQtDH79w4UJERJw6dWrg8fyxuprNia8FWDr5OXAuYHH4XMLi8bmExeNzCYvH5xIWj88lLCafzQkle1FeI5JotYafs8b+GOwsi5HPW3SN3V6Q2WqN/p2PQp0x4c3G9I9j3ib5XG4ULmYY9X7L95tHRDzsso2+wL0oi+zIzm8xyD99cm3sz11f6w/Xh4X7ubVWY+nfCyyOefz3cpLXOnBwvrGxERsbG/H6178+XvGKV8QrX/nKeOih/qtStra24pJLLomIiJMnT8bW1tbA42fOnOkG6vm+86rvr+uyy06Of9KKcC5g8fhcwuLxuYTF43MJi8fnEhaPzyUsJp/NevIR7Y1GI86cGZ4BnDy5vvf8JEY+b9E11ra7ty89vTH132W33Rn7nBMnWsf6nE6izudybaMXiJ+6ZH3ouWlHb6z5Yx51RfzpHWcrn9dsNo/s/DbXelHeIx9+aZw+tT7y+euF36WRRGxsrI18/unTJ1bmvcDiWNT/Xk4UnH/605+Of/bP/ln8zu/8Tqyv730wd3Z2Ym1tLa699tq4+eab+55/++23x3XXXRcREdddd13cdtttA48///nPj8svvzwe8YhH9I1zv+eee+Ls2bMD493HefDBzeh0ho/RWAXNZiMuu+ykcwELxOcSFo/PJSwen0tYPD6XsHh8LmEx+WxOJo29c5SlWTzwwPCps1ube2OxsyxGPm/RnTvfC843N3em/ru0a7wHx537ZTDJ53JzuzcO/fz57aHn5mt37RVIG0kS7Z3d2Cl8X9H2TvvIzu99D1zsHefF7djd3h3x7Ijtwk709bVmfNsTrux+fc3DTsWd913se/7O9tEdK4wzj/9e5q9Zx0TB+ZOe9KTY2tqKd7zjHfHjP/7jcc8998Tb3va2ePnLXx4vetGL4h3veEe85z3viRtvvDE+9alPxQc+8IH4+Z//+YiIePnLXx6vfe1r4/u///vjGc94Rrz3ve+N++67L174whdGRMQNN9wQ73rXu+KpT31qnDlzJt7ylrfEM5/5zHjMYx4z0S/f6aTRHrF/YpU4F7B4fC5h8fhcwuLxuYTF43MJi8fnEhaTz2ZN3Uniycjzle4/L8viWJ/X4qjtNM2m/ruk6fhR7Y1k9LlfJnU+l8XH2+3hzz/70N5FEJdeshZpJ4thQ9CP8t+Ci/vj4VvNRkSNz0Ka9h4/sd6MM6dPxL/4B8+KRiOJ3/jPtw0E50kc788Xx9Oi/vdyouD8kksuiV/+5V+Ot7zlLfG85z0vLr300njxi18cr33ta2N9fT3e/e53x5vf/OZ45zvfGVdeeWW88Y1vjGc/+9kREfGc5zwnfvInfzJ+6qd+Ku6666649tpr45d+6ZfiiiuuiIiI1772tdFut+PGG2+MCxcuxLOe9az42Z/92aP+fQEAAAAAgDnK8uQ8Gb17OX80i/FB8CIrBtmNMb/zUajzEs0xe69XTfHvMurddn4/xD59cm/8+bDzWOPahdq29hvkG+vNMc/ckxR+lytOn4iIiG+4am8U+5nLNgaeP4v3JBwXE+84v/baa+Pd73535WNPfepT49d//deHfu9LX/rSeOlLX1r52NraWrzuda+L173udZMeEgAAAAAAcMzUjuuOd24eaVYIzmcQWCc1glDBeb/iKcuy4W+4bD8Rb+5/w/DgvPpnnD2/HX92x9m4/rqrYq1VLwjfnjA4bzUb8fRrr4ov3nE2/sHffnLfY1ddPhicN5veC5CbODgHAAAAAAA4uL1QMRkTnS9LEbbYPp5VuzdJ9kbcDyMs7dcfnA9/XtodlrAfnDcblc/LhlTOf/bf3hpfuft8fO9fe3T83b95Xa1j29rdC85P1AzOIyL+ux96anTSbG+8e0FVcO6dAD3Vn2gAAAAAAIAFcMwL530N5lldDDAuoNc475fUHNWerw1o7KdrkzbOv3L3+YiI+NAtd9Q+tp08OF+rH5wnSTIQmkdEXHX5yYH7Okc5Vx6OOcE5AAAAAAAwM1m3cT46osjDzFEN4OOgb8f5jALrcQF9syEeKqo7qj3/W+bvzWF/zzQ9umPb2d37Yeutw//NHlbROE8F59DlX0YAAAAAAGB2knpBXb0O8OKbz6j2MY1zo9r7FNcGjLpQI3+scYAd58Na6OPstPca5+sTNM6HufTU2sAxH/S4YBkJzgEAAAAAgJkbF+7mjnuuV9x3PasJ6Ua1T6Z24zzLG+d7Xw9r7leF0ds7nb6vL261ax3bTvvoGueNJIkrTp/ou0/jHHoE5wAAAAAAwEx8/dwD0W7t7XneaJ4Y/eQlyXbTvh3ns/mlxo2EN6q9X9+O8xqN82RM47zqZ2xu9wfl9z24NfKY/uLOB+N9v3973P7VcxFxNI3ziIgzl5aCc7k5dLXmfQAAAAAAAMBq+A9/+tFIGnsN2hd+07NHPjdZkuS8GKLOasf5uEa5Ue2DkthbCjCqcZ4/lp/eYeexqsW9WWqclxvoRRe32vGmX/lk331HFZxfUQrOO4VjTbMsbrvjbDzq6tNx+uTakbweHCcuKQIAAAAAAGbiwu5mRERknWZ8x2Ovq/U9x31Ue7FxPqsd5+PyeaPaB+Ut8lFvtzwQH9c4rxrVvrXT3zhvd9Khr3PuwvbAfUcxqj0i4kxpVPsffPbO+OM/vy8iIj78ya/G2371M/FT//snjuS14LgRnAMAAAAAADPRyfbCwjpt8jxjzkZGmYuvb1T7jFKZ8aPaBedl3ffbqFHt+/+bn9+hwXlF43xru79h3k6HB+edzuD3r68dzZvnysv6g/Ov3nM+/ud/e2s88NB2/Pp/vi0iIu5/cDC4h1UgOAcAAAAAAGaik+6Hh9kEwe3xzs0jK+Sjs2qcjx/VLh4q6wXnw99wvcb53tfDdsVX/YzyjvN2RTjefawiVF9vHc2o9kddfUnl/fec3TySnw/HmX8ZAQAAAACAmUjzFDkbH08sSyd6HqPakzGvo3E+qM6o9t6O8/3G+bAd5xU/ZKu007zdnrBxfkSj2h/98Esr7187op8Px5lPAQAAAAAAMBNp5GFhjeC2RpB5HBTHds8oNx8b0AvOB+VnZGTjfP+h/Pw+4szJyr9p1aj2zfKO8xGj2qv2n6+vHU3j/PJL1ivvn9V7ExaZ4BwAAAAAAJiJvHGerFDjvBjEjts9fmQKL/Oy73x8POYRp/seFpwP6jbOR+04z/pHtZ/aWItvetTlA89LK35IuXFe1SrPtSuC96PacR4R8YPf9fjB1xxxPLAqBOcAAAAAAMBMdEe112qc926OagEvumIGOm6E+lEpvs5ll6zHs578iL7H7TgfVGvHealxHhFx7TdUBOcVwXf5vqpWea56VPvRNM4jIl78vMcPNM87peM5zp85OCj/MgIAAAAAADORZnut22TCeOI4R3j9O85n85rFl0mSiFaj/3xrnA/qBefDn5OWGucRexcmDD5v8HvLQfSohnc5xI442sZ5xOB+9nLLXW7OKhKcAwAAAAAAM9Ed1V6jcb4s0W6xaTyrUe3FYDdJkmiVQlLB+aD8PTkqL+6Nau+dv0tPrQ08r2pUe/m+kY3zqlHtR9g4j+hvzUcMhvVVxwDLrjXvAwAAAAAAAFZDGhPsOC8Ge1kc2yQ9m/Oo9kaSRJRGs5fbxtQb1Z4/VLwA4tJTg43zrCJ0Lv/YUcF51WPra0ccnJcunig34KvCf1h2GucAAAAAAMBMpPt93jqN86LsGA9rn8uo9qT/drlhXh7dTu9ig1FF63x6QP+o9pqN89IPrtpjnqsa437iqEe1DwTn/WF91Z52WHb+ZQQAAAAAAGYiy/JwbsId5/sZ3p/dcTbe8Rt/FF/48gNHe2BT1DeqfVaN88LtRpJEq9w4N6p9QO9PU6NxXhzVfrJix3lFmbycpe+OHNU++Njpk4MB/WGUG+flIN+odlaR4BwAAAAAAJiJfMd5o0Y8UZUxv/W9n47P/cX98S9/7TNHfWhTUwxI11qzimV6Jy9pxOCOc6PaB+SN81ETytNssHF+0B3nkzbOLznq4DzROIcywTkAAAAAADATWeTh3Pjgdlmi3e2dTvf2iSPeUz1MsUzcSJJoNsqNc/FQWX7KRu32zrrBee8ED9s9Xv455a9H7TivanuXpwYc1sCO8/IoecE5K8i/jAAAAAAAwEyk+8F5I6kRTxTCyVEt4EW3vbsXnCfJDBvnfTvOk4HGeTk0pfB2G/FeqxrVHhHx6hc/OZ75LQ+Plzzvcd37yo3y8nt4ZHA+4rGjUn4HlI8nO84fOjig1rwPAAAAAAAAWA154zypM6q99J3HVR6cn1hr9jWVp6n4Oo0komnH+ViTjGovn75nP+WR8eynPDL+4LN3du9rd9K+CyUGG+ejRrVPPzgvN8rtOAeNcwAAAAAAYEbyFmudHef93zeNo5mNfFT7rMa0R/RfdFDVOLfjfFB+rcGoUe15lpwMufCg1erdv9suN7j7n9tOJxvVftTKr2HHOQjOAQAAAACAGZmkcV5Mf49zhNdtnK/PMDhP+m+Xd5q37DgfkAwMLx+UpfmFH9XPXWv2/sbl4LwcRI9unM8gOC8F5eXgXOOcVeRfRgAAAAAAYCbyHed1RpbX7UTvttP43F/e3212L5q5NM77RrVXNM6Nah9Qr3Ge9T23bK3QOB+3M7zdHtU473/szKUnhj73oMrh/Cc+f3ff1xrnrCI7zgEAAAAAgBnZCwTrjGrvC9dHZHjv/b0vxk233hlPedyZ+PG/e/1hD/DIFXecz0p5VPvAjnOj2gfkb7dRawHyxxpDLjxYK5zngcZ5OTgfMaq9GGpf+6jL47/5vicNP6gDKofzX7//Yt/Xoy4ggGUlOAcAAAAAAGYi20/AG8mEO85HJOc33XpnRER87i8fOPiBTdH27l5AOctR7cXkvJFEtEpBb6tpIHFZ90KNEXnx+MZ5YVT7QOO8/7mdEePY8zHq1zzsVPyzVz5j+AEdwrhx8Ea1s4r8ywgAAAAAAMxEltRvnPd93zHO8LZ32hExv1HtSSMZaJif2tCrLMvPUDoiOc/HrTeGJOetwqj2sY3zzojG+X5oPc2R+uOCcaPaWUWCcwAAAAAAYCYmaZwvyzDxbuN8hsF5o3S73DA/uS44L6vXOC89t6RvVPuYxvmo4DxvnJdH7B+l8qj2wccF56wewTkAAAAAADAT2f6O86TOqPYlSc638h3nMx3VXmicJ8lAc3nYju5V1ttxPr5xPmxUe6vVe1+32+XgvNw4HzGqfT+0Lo/YP0qjRsVHjD4PsKwE5wAAAAAAwIxM0jjvhYbHOcPbyYPztdlFMsW4NUnsNK8jb5GPKlrn78Nho9r7GuflUe3pBKPa90Pt6TbO7TiHMv9SAgAAAAAAM5HvOG8mk7avj2+It72TB+ez3HHeu91oJNFqapiP0ztnw99refg9rHG+VmycjxnVPiqXzr93nn83O85ZRYJzAAAAAABgRvYb5zXiiWI4eZwjvJ32XnC+PtPgvDyqXRw0Tj7hYGTjPH//Dhmh3hrVOC8l5+mIHeN523uefzeNc1aRfykBAAAAAIDZ2G+c1xnVPk45iFxEWZZ1x27Pclx68aKDJLHTvI46O87zLDkZNqp9ksb58Nw8OhrnMBeCcwAAAAAAYDaS+k3avsZ5RYbX6Sx+sNcuHOPaDEPQ4isN28dNv+5pGtU43w+Th12H0Gwk3Z8ztnE+IqDv7jif4wUPx+HCFDhqgnMAAAAAAGAmuqOuazTOkxgdGh6HRmyxdTzbxnnv3AnO68nP2ai3Vf7YsHOaJEms7f+ddzvjRrUPf6HNnXZERGyst0Ye8zQZ1c4qEpwDAAAAAACzkTfOk8n2fVeNz+6MmnW9IIrhaas1w0imNKq96OnXXjW74zhGeoXz4YFx/j4cNqo9ojeuvdw4HxjVPqLRfXFrLzg/tTG94PyF3/HokY8fhwtT4KjN71IVAAAAAABgtezvOG/W2XFeHNUeg0Fj+xgEe+1CeLo2y8Z5DDbO//EPf1vc+qX74iXPe9zMjuM46YbhIxvno0e1R/QmCww0ztP6jfMLW7sREXHJFIPzH/ruJ8Tjr7k0PvH5u+OPbr934HGNc1aR4BwAAAAAAJi6dtrptp8bNRrn5WyyHDQejx3n82mcJxWN8299wsPiW5/wsJkdw3GTn6eqJniaZvHFrzzQbY3XaZy32/0/pzw1YVjjvN1JY2d3731zamOt1rEfxPpaM579lEfGV+46X/m4xjmrSHAOAAAAAABMXacQIjcbkzXOI6sIzitGtWdZNjLUnLXdQrg/28Z58YvFOR+LrFs4r8iL/8unvxq/+uHbBp5bpTuqfWDHef/zhm0auLA/pj1iuo3z3NqQCzpGjZKHZWXHOQAAAAAAMHW7nU73dp1R7cVx45//8gMDo6OrGrGLFvYVR7W3mrMLsPsuHliwc7Ko8nNWdbaKoXlERGPErPbuqPZ2p+/+cuN82Cj0i/tj2iOm2zjPDQ3ONc5ZQYJzAAAAAABg6vqC88b4Ue1Fv/A7nxsIxauCx0Ub374Io9rl5vXkp6wccFc+d0TlfGN97719sdAc/8Tn74rbvnqu73nDXufijBvn60Pel3acs4oE5wAAAAAAwNTtdnqBYK3GeSmbLAd5VSH5ooV9xeB8lqPaixbrjCyubhhe44SNGtX+sMs3IiLi3nNb3fv+1W9/buB5wxrdxVHtp+Y5qn3BPkswC4JzAAAAAABg6trpwRvnEVU7zrOB1u6ijWov7rluzTA4bxSS3ToNaiLy6et13kONEcn5VZefjIiIe89tRpZlQwPobMhrFUe1XzKTUe29z+I3P+aK7kqBjvcNK2j6l6oAAAAAAAAr76sP3Nu9ffnGJWOfXx6HXRWc12mhz1O73Tseo9qPhzrna1Tj/Or9xvnmdicubLXjwubu0OemaRaNZv8P29zpXWBy8sTkF5hMqtg4z7L9/e2d4YE/LDPBOQAAAAAAMHVfvPeO7u2nPPJxY59fziYHQvI0rbhvscK+eY1qbzQKjXPD2msZtbe8bGTj/IqT3dv3nN2M+x/cHvrcqmkAncJ7pjmD90xxx3kWEc39947gnFVkVDsAAAAAADB1X33ozr0b7bV49JmHTfz9A43zTjbQMO+kaSyS/lHt9YPZw/qh7/6maDUb8fArTsajH356Zq97nB3VqPYrTq93b5/f3I0LW8Mb51UXeuT3JWNe56j07TjPsu5rLtpFKDALGucAAAAAAMDU3bt9V8SJiBOdK6LRmLzXV9UuLwfli9aSbbfns+P86itOxs/8w+fFibVGNA9wrlfSfmB82FHtxZZ4p2LkebORdN/LVdd55M9vzuhCi7VS4zyfVlDnAgJYNoJzAAAAAABgqi7ubMXm2j2RRMQjTjyq1veUw8nq4PwYjWqf4Y7ziIjTJ9dm+nrHXf5+qzPavjgKv6xVeKzdGVwn0Go2opPu7TGvCqfbeXA+owse1lq9Pep9wfliDW+AmXCZEQAAAAAAMFU3f+lPI2nsJXHffs031/yu/nCy3C7vpGnFqPbFCs53C43z5oiwlflrTNQ4H/637GucV1zcURzZXzUhId9xPqv3S9+O8yzrBv/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      "text/plain": [
       "<Figure size 2500x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "datic[\"ETH-USD\"].plot(figsize=(25,15))\n",
    "datic[\"ETH-USD\"].dropna().cummax().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "datimaxc=datic.cummax()\n",
    "drawdownc=(datic-datimaxc)/datimaxc\n",
    "cumminc=drawdownc.cummin()\n",
    "logretc=np.log(datic/datic.shift(1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Impedisco di investire nel primo anno"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "cummin2c=cumminc.copy()\n",
    "logret2c=logretc.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "times2c=logret2c.apply(pd.Series.first_valid_index)+ pd.offsets.DateOffset(years=1)\n",
    "for t in tickersc:\n",
    "    logret2c[t].loc[:times2c[t]]=np.nan\n",
    "    cummin2c[t].loc[:times2c[t]]=np.nan "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "timesc=(logret2c.index[-1]-times2c)/np.timedelta64(1, 'Y')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Inizio analisi soglia 0.3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "soglia=0.3\n",
    "# strategia 1: buy and hold\n",
    "# strategia 2: compro quando scende sotto il drawdown e tengo fino alla fine\n",
    "# strategia 3: compro quando scende sotto il drawdown e tengo fino alla risalita"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BTC-USD    87.615168\n",
       "ETH-USD    62.779200\n",
       "dtype: float64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "strategia1=100*(np.exp(logret2c.sum()/timesc)-1)\n",
    "strategia1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BTC-USD    87.615168\n",
       "ETH-USD    62.779200\n",
       "dtype: float64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "strategia2=100*(np.exp(((cummin2c<-soglia)*logret2c).sum()/timesc)-1)\n",
    "strategia2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>primo dato valido</th>\n",
       "      <th>prezzo1</th>\n",
       "      <th>data massimo</th>\n",
       "      <th>massimo</th>\n",
       "      <th>va sotto soglia</th>\n",
       "      <th>prezzo2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BTC-USD</th>\n",
       "      <td>2014-09-18</td>\n",
       "      <td>424.440002</td>\n",
       "      <td>2014-09-23</td>\n",
       "      <td>435.790985</td>\n",
       "      <td>2015-09-19</td>\n",
       "      <td>231.492996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ETH-USD</th>\n",
       "      <td>2017-11-10</td>\n",
       "      <td>299.252991</td>\n",
       "      <td>2018-01-13</td>\n",
       "      <td>1396.420044</td>\n",
       "      <td>2018-11-11</td>\n",
       "      <td>211.339996</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        primo dato valido     prezzo1 data massimo      massimo  \\\n",
       "BTC-USD        2014-09-18  424.440002   2014-09-23   435.790985   \n",
       "ETH-USD        2017-11-10  299.252991   2018-01-13  1396.420044   \n",
       "\n",
       "        va sotto soglia     prezzo2  \n",
       "BTC-USD      2015-09-19  231.492996  \n",
       "ETH-USD      2018-11-11  211.339996  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t1=logretc.apply(pd.Series.first_valid_index)\n",
    "t2=(cummin2c<-soglia).eq(True).idxmax()\n",
    "d1=datic.iloc[0].copy()\n",
    "d2=datic.iloc[0].copy()\n",
    "mm=datic.iloc[0].copy()\n",
    "tm=t2.copy()\n",
    "for t in tickersc:\n",
    "    d1[t]=datic[t].loc[t1[t]]\n",
    "    d2[t]=datic[t].loc[t2[t]]\n",
    "    temp=datic.index[datic.index.get_loc(t2[t])+1]\n",
    "    mm[t]=np.max(datic[t].loc[t1[t]:temp])\n",
    "    try:\n",
    "        tm[t]=datic[t].loc[t1[t]:temp].idxmax()\n",
    "    except:\n",
    "        tm[t]=np.nan\n",
    "pd.DataFrame({\"primo dato valido\":t1,\"prezzo1\":d1,\"data massimo\":tm,\"massimo\":mm,\"va sotto soglia\":t2,\"prezzo2\":d2})[:50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BTC-USD 0\n",
      "ETH-USD 1\n"
     ]
    }
   ],
   "source": [
    "sottoSogliac=(cummin2c<-soglia)\n",
    "s3c=(cummin2c<-soglia) & (drawdownc<0)\n",
    "j=0\n",
    "for t in tickersc:\n",
    "    print(t,j)\n",
    "    j+=1\n",
    "    for i in datic.index:\n",
    "        if sottoSogliac[t].loc[i] and not (s3c[t].loc[i]):\n",
    "            cummin2c[t].loc[i:]=(drawdownc[t].loc[i:]).cummin()\n",
    "            sottoSogliac[t]=(cummin2c[t]<-soglia)\n",
    "            s3c[t]=((cummin2c[t]<-soglia) & (drawdownc[t]<0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BTC-USD    17.236391\n",
       "ETH-USD    36.416482\n",
       "dtype: float64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "strategia3=100*(np.exp((s3c*logret2c).sum()/timesc)-1)\n",
    "strategia3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Soglia 0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "cummin2c=cumminc.copy()\n",
    "logret2c=logretc.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "times2c=logret2c.apply(pd.Series.first_valid_index)+ pd.offsets.DateOffset(years=1)\n",
    "for t in tickersc:\n",
    "    logret2c[t].loc[:times2c[t]]=np.nan\n",
    "    cummin2c[t].loc[:times2c[t]]=np.nan "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "timesc=(logret2c.index[-1]-times2c)/np.timedelta64(1, 'Y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "soglia=0.5\n",
    "# strategia 1: buy and hold\n",
    "# strategia 2: compro quando scende sotto il drawdown e tengo fino alla fine\n",
    "# strategia 3: compro quando scende sotto il drawdown e tengo fino alla risalita"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BTC-USD    87.615168\n",
       "ETH-USD    62.779200\n",
       "dtype: float64"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "strategia1=100*(np.exp(logret2c.sum()/timesc)-1)\n",
    "strategia1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BTC-USD    87.615168\n",
       "ETH-USD    62.779200\n",
       "dtype: float64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "strategia2=100*(np.exp(((cummin2c<-soglia)*logret2c).sum()/timesc)-1)\n",
    "strategia2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>primo dato valido</th>\n",
       "      <th>prezzo1</th>\n",
       "      <th>data massimo</th>\n",
       "      <th>massimo</th>\n",
       "      <th>va sotto soglia</th>\n",
       "      <th>prezzo2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BTC-USD</th>\n",
       "      <td>2014-09-18</td>\n",
       "      <td>424.440002</td>\n",
       "      <td>2014-09-23</td>\n",
       "      <td>435.790985</td>\n",
       "      <td>2015-09-19</td>\n",
       "      <td>231.492996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ETH-USD</th>\n",
       "      <td>2017-11-10</td>\n",
       "      <td>299.252991</td>\n",
       "      <td>2018-01-13</td>\n",
       "      <td>1396.420044</td>\n",
       "      <td>2018-11-11</td>\n",
       "      <td>211.339996</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        primo dato valido     prezzo1 data massimo      massimo  \\\n",
       "BTC-USD        2014-09-18  424.440002   2014-09-23   435.790985   \n",
       "ETH-USD        2017-11-10  299.252991   2018-01-13  1396.420044   \n",
       "\n",
       "        va sotto soglia     prezzo2  \n",
       "BTC-USD      2015-09-19  231.492996  \n",
       "ETH-USD      2018-11-11  211.339996  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t1=logretc.apply(pd.Series.first_valid_index)\n",
    "t2=(cummin2c<-soglia).eq(True).idxmax()\n",
    "d1=datic.iloc[0].copy()\n",
    "d2=datic.iloc[0].copy()\n",
    "mm=datic.iloc[0].copy()\n",
    "tm=t2.copy()\n",
    "for t in tickersc:\n",
    "    d1[t]=datic[t].loc[t1[t]]\n",
    "    d2[t]=datic[t].loc[t2[t]]\n",
    "    temp=datic.index[datic.index.get_loc(t2[t])+1]\n",
    "    mm[t]=np.max(datic[t].loc[t1[t]:temp])\n",
    "    try:\n",
    "        tm[t]=datic[t].loc[t1[t]:temp].idxmax()\n",
    "    except:\n",
    "        tm[t]=np.nan\n",
    "pd.DataFrame({\"primo dato valido\":t1,\"prezzo1\":d1,\"data massimo\":tm,\"massimo\":mm,\"va sotto soglia\":t2,\"prezzo2\":d2})[:50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BTC-USD 0\n",
      "ETH-USD 1\n"
     ]
    }
   ],
   "source": [
    "sottoSogliac=(cummin2c<-soglia)\n",
    "s3c=(cummin2c<-soglia) & (drawdownc<0)\n",
    "j=0\n",
    "for t in tickersc:\n",
    "    print(t,j)\n",
    "    j+=1\n",
    "    for i in datic.index:\n",
    "        if sottoSogliac[t].loc[i] and not (s3c[t].loc[i]):\n",
    "            cummin2c[t].loc[i:]=(drawdownc[t].loc[i:]).cummin()\n",
    "            sottoSogliac[t]=(cummin2c[t]<-soglia)\n",
    "            s3c[t]=((cummin2c[t]<-soglia) & (drawdownc[t]<0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BTC-USD    21.662221\n",
       "ETH-USD    60.672968\n",
       "dtype: float64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "strategia3=100*(np.exp((s3c*logret2c).sum()/timesc)-1)\n",
    "strategia3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Soglia 0.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "cummin2c=cumminc.copy()\n",
    "logret2c=logretc.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "times2c=logret2c.apply(pd.Series.first_valid_index)+ pd.offsets.DateOffset(years=1)\n",
    "for t in tickersc:\n",
    "    logret2c[t].loc[:times2c[t]]=np.nan\n",
    "    cummin2c[t].loc[:times2c[t]]=np.nan "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "timesc=(logret2c.index[-1]-times2c)/np.timedelta64(1, 'Y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "soglia=0.1\n",
    "# strategia 1: buy and hold\n",
    "# strategia 2: compro quando scende sotto il drawdown e tengo fino alla fine\n",
    "# strategia 3: compro quando scende sotto il drawdown e tengo fino alla risalita"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BTC-USD    87.615168\n",
       "ETH-USD    62.779200\n",
       "dtype: float64"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "strategia1=100*(np.exp(logret2c.sum()/timesc)-1)\n",
    "strategia1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BTC-USD    87.615168\n",
       "ETH-USD    62.779200\n",
       "dtype: float64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "strategia2=100*(np.exp(((cummin2c<-soglia)*logret2c).sum()/timesc)-1)\n",
    "strategia2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>primo dato valido</th>\n",
       "      <th>prezzo1</th>\n",
       "      <th>data massimo</th>\n",
       "      <th>massimo</th>\n",
       "      <th>va sotto soglia</th>\n",
       "      <th>prezzo2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BTC-USD</th>\n",
       "      <td>2014-09-18</td>\n",
       "      <td>424.440002</td>\n",
       "      <td>2014-09-23</td>\n",
       "      <td>435.790985</td>\n",
       "      <td>2015-09-19</td>\n",
       "      <td>231.492996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ETH-USD</th>\n",
       "      <td>2017-11-10</td>\n",
       "      <td>299.252991</td>\n",
       "      <td>2018-01-13</td>\n",
       "      <td>1396.420044</td>\n",
       "      <td>2018-11-11</td>\n",
       "      <td>211.339996</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        primo dato valido     prezzo1 data massimo      massimo  \\\n",
       "BTC-USD        2014-09-18  424.440002   2014-09-23   435.790985   \n",
       "ETH-USD        2017-11-10  299.252991   2018-01-13  1396.420044   \n",
       "\n",
       "        va sotto soglia     prezzo2  \n",
       "BTC-USD      2015-09-19  231.492996  \n",
       "ETH-USD      2018-11-11  211.339996  "
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t1=logretc.apply(pd.Series.first_valid_index)\n",
    "t2=(cummin2c<-soglia).eq(True).idxmax()\n",
    "d1=datic.iloc[0].copy()\n",
    "d2=datic.iloc[0].copy()\n",
    "mm=datic.iloc[0].copy()\n",
    "tm=t2.copy()\n",
    "for t in tickersc:\n",
    "    d1[t]=datic[t].loc[t1[t]]\n",
    "    d2[t]=datic[t].loc[t2[t]]\n",
    "    temp=datic.index[datic.index.get_loc(t2[t])+1]\n",
    "    mm[t]=np.max(datic[t].loc[t1[t]:temp])\n",
    "    try:\n",
    "        tm[t]=datic[t].loc[t1[t]:temp].idxmax()\n",
    "    except:\n",
    "        tm[t]=np.nan\n",
    "pd.DataFrame({\"primo dato valido\":t1,\"prezzo1\":d1,\"data massimo\":tm,\"massimo\":mm,\"va sotto soglia\":t2,\"prezzo2\":d2})[:50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BTC-USD 0\n",
      "ETH-USD 1\n"
     ]
    }
   ],
   "source": [
    "sottoSogliac=(cummin2c<-soglia)\n",
    "s3c=(cummin2c<-soglia) & (drawdownc<0)\n",
    "j=0\n",
    "for t in tickersc:\n",
    "    print(t,j)\n",
    "    j+=1\n",
    "    for i in datic.index:\n",
    "        if sottoSogliac[t].loc[i] and not (s3c[t].loc[i]):\n",
    "            cummin2c[t].loc[i:]=(drawdownc[t].loc[i:]).cummin()\n",
    "            sottoSogliac[t]=(cummin2c[t]<-soglia)\n",
    "            s3c[t]=((cummin2c[t]<-soglia) & (drawdownc[t]<0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BTC-USD     2.176777\n",
       "ETH-USD    25.983291\n",
       "dtype: float64"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "strategia3=100*(np.exp((s3c*logret2c).sum()/timesc)-1)\n",
    "strategia3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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