| Overall Statistics |
|
Total Orders 14 Average Win 0.07% Average Loss -0.02% Compounding Annual Return 1.939% Drawdown 0.000% Expectancy 1.409 Start Equity 100000 End Equity 100190 Net Profit 0.190% Sharpe Ratio -12.65 Sortino Ratio 0 Probabilistic Sharpe Ratio 99.681% Loss Rate 50% Win Rate 50% Profit-Loss Ratio 3.82 Alpha -0.044 Beta 0.004 Annual Standard Deviation 0.003 Annual Variance 0 Information Ratio -5.945 Tracking Error 0.091 Treynor Ratio -9.688 Total Fees $0.00 Estimated Strategy Capacity $1000.00 Lowest Capacity Asset SPXW 32MQGIJEGAZXQ|SPX 31 Portfolio Turnover 0.01% |
#region imports
from AlgorithmImports import *
#endregion
# region imports
from datetime import *
TEN_DATES = \
{
'20220110': [('TTWO', -10.546780072904001, "TECH")],
'20220126': [('FFIV', -11.820566157185487, "TECH")],
'20220202': [('CHRW', -10.175002390743037, "TRANSPORTATION_LOGISTICS")],
'20220204': [('CLX',-12.598282327325514, "CONSUMER")],
'20220210': [('CDAY', -10.77027527465746, "TECH"), ('IPG', -16.176470588235297, "COMM"), ('CTL', -13.338533541341649, "COMM")],
'20220216': [('VIAC', -16.171158655181994, "COMM")],
'20220217': [('ALB', -11.930894308943095, "MATERIALS")],
'20220224': [('NTAP', -13.592579379236536, "TECH"), ('EPAM', -16.313989909858215, "TECH"), ('PCLN', -12.533251276403629, "CONSUMER")],
'20220321': [('NLSN', -16.407528641571194, "COMM")],
'20220419': [('XRAY', -13.731527093596057, "HEALTH")],
'20220422': [('HCA', -10.436460807600954, "HEALTH")],
'20220426': [('UHS', -12.460363217065446, "HEALTH")],
'20220427': [('FFIV', -11.469238306430823, "TECH")],
'20220428': [('CARR', -10.07230239128338, "MANUFACTURING")],
'20220429': [('AMZN', -10.129221661658491, "TECH"), ('VRSN', -11.241723443047682, "TECH")],
'20220503': [('EL', -12.661627594674435, "CONSUMER")],
'20220504': [('MTCH', -11.984307770184763, "TECH"), ('IDXX', -12.377282319478846, "HEALTH"), ('JCI', -12.695848812792768, "MANUFACTURING"), ('AKAM', -12.926186291739889, " TECHNOLOGY")],
'20220505': [('ETSY', -13.802250068599657, "RETAIL")],
'20220510': [('XRAY', -10.191082802547772, "HEALTH")],
'20220513': [('TWTR', -10.734087380793975, "TECH")],
'20220519': [('CSCO', -11.269644334160468, "TECH")],
'20220601': [('MHP', -10.435504177635337, "TRANSPORTATION"), ('DNB', -10.631030938090653, "TECH")],
'20220720': [('BHGE', -10.138248847926265, "INDUSTRIAL")],
'20220721': [('CCL R735QTJ8XC9X', -12.804328223624886, "TECH")],
'20220722': [('SIVB', -11.731664259348422, "FINANCE")],
'20220727': [('SHW', -11.636320736167367, "INDUSTRIAL")],
'20220728': [('SWK', -15.121328224776502, "CONSUMER")],
'20220729': [('DXCM', -10.024140705828255, "HEALTH"), ('INTC', -10.475950642155626, "TECH")],
'20220804': [('FTNT', -10.607506361323157, "TECH")],
'20220812': [('ILMN', -12.064720365810762, "HEALTH")],
'20220825': [('DLTR', -10.880829015544045, "RETAIL")],
'20220915': [('ADBE', -12.973729543496985, "TECH")],
'20220929': [('KMX', -14.019016697588112, "RETAIL")],
'20220930': [('NKE', -12.304626035875382, "CONSUMER")],
'20221019': [('GNRC', -17.517260051441728, "MANUFACTURING"), ('NTRS', -10.809268348362066, "FINANCE")],
'20221020': [('ALL', -13.29633740288568, "FINANCE")],
'20221021': [('RHI', -17.297094188376754, "COMMERCIAL"), ('SIVB', -15.691331085102156, "FINANCE"), ('HCA', -11.998466037102732, "HEALTH")],
'20221026': [('MAS', -11.884567645841956, "MANUFACTURING")],
'20221027': [('ALGN', -15.128135715574794, "HEALTH"), ('WST', -18.474842767295595, "HEALTH"), ('INVH', -10.305910305910306, "FINANCE")],
'20221028': [('AMZN', -11.679884643114628, "TECH"), ('EW', -16.083429895712626, "HEALTH")],
'20221101': [('XRAY', -10.512654120700846, "HEALTH")],
'20221103': [('FIS', -16.408707688435882, "FINANCE"), ('FTNT', -14.427954161187301, "TECH"), ('CTL', -15.744680851063823, "MINING"), ('CTSH', -13.757052771324258, "TECH")],
'20221108': [('TTWO', -15.22140221402214, "TECH")],
'20221109': [('NWS', -11.30334486735871, "CONSUMER"), ('NWSA', -11.936805149210068, "CONSUMER")],
'20221116': [('AAP', -14.809176905512677, "RETAIL"), ('CCL', -12.36559139784947, "CONSUMER"), ('DH', -16.675983899821112, "HEALTH")],
'20221206': [('NRG', -12.487757100881494, "ENERGY")],
'20221214': [('CHTR', -12.567484975043296, "CONSUMER")],
'20221222': [('KMX', -11.687436847423383, "RETAIL")],
'20230110': [('ILMN', -10.177994308041095, "HEALTH")],
'20230124': [('MA', -12.201178117653281, "FINANCE"), ('MCD', -12.206171785064422, "CONSUMER"), ('MET', -12.717498939029573, "FINANCE"), ('MOS', -16.670113753877963, "INDUSTRIAL"), ('MPC', -11.948297604035316, "ENERGY"), ('MS', -12.560485946669411, "FINANCE"), ('NKE', -12.417179826954564, "CONSUMER"), ('OKE', -11.917395669009034), ('PLD', -12.74688665027365, "FINANCE"), ('PPL', -15.109140518417465, "UTILITIES"), ('PRU', -12.411241124112406, "FINANCE"), ('PSX', -11.866467177856741, "ENERGY"), ('UPS', -12., "TRANSPORTATION"), ('VICI', -15.309155766944116, "FINANCE"), ('WFC', -15.352143968007118, "FINANCE")],
'20230126': [('SHW', -10.9636164960136, "INDUSTRIAL")],
'20230127': [('INTC', -10.103024260551678, "TECH")],
'20230201': [('WRK', -15.163098878695205, "INDUSTRIAL"), ('SYF', -10.15518649605227, "FINANCE")],
'20230213': [('FIS', -13.245823389021464, "COMMERCIAL")],
'20230222': [('CRL', -11.50656814449918, "COMMERCIAL"), ('KEYS', -12.213114754098358, "TECH")],
'20230228': [('UHS', -12.232582582103662, "HEALTH")],
'20230308': [('WTW', -14.718614718614715, "FINANCE")],
'20230310': [('SBNY', -17.62891141472014, "FINANCE")],
'20230313': [('TFC', -14.984552008238927, "FINANCE"), ('SCHW', -11.175468483816017, "FINANCE"), ('FITB', -12.347711557458014, "FINANCE"), ('HBAN', -10.845175766641733, "FINANCE")],
'20230317': [('FRC', -18.004085205719292, "FINANCE")],
'20230320': [('FRC', -19.81707317073171, "FINANCE")]
}
TWENTY_DATES = \
{
'20220121': [('NFLX', -21.300541072306935, "TECH")],
'20220127': [('TER', -22.91971821162027, "TECH")],
'20220202': [('PYPL', -20.60295790671217, "TECH")],
'20220203': [('FB', -24.148606811145513, "TECH")],
'20220420': [('NFLX', -29.720891540690168, "TECH")],
'20220428': [('ALGN', -21.987625891296506, "HEALTH")],
'20220506': [('UA', -21.15963855421686, "CONSUMER")],
'20220518': [('DH', -24.471299093655585, "HEALTH")],
'20220520': [('ROST', -24.649406688241633, "RETAIL")],
'20220803': [('MTCH', -21.705123191239725, "TECH")],
'20220916': [('FDX', -21.901693757016655, "TRANSPORTATION")],
'20221027': [('FB', -24.51086119242027, "TECH")],
'20221028': [('DVA', -20.206718346253226, "HEALTH")],
'20221101': [('CTLT', -23.53567625133121, "HEALTH")],
'20221103': [('LNC', -24.184261036468328, "FINANCE")],
'20230124': [('T', -20.377160817181768, "COMMUNICATIONS")],
'20230208': [('CTL', -20.24048096192385, "ENERGY")],
'20230309': [('SIVB', -34.096255087182165, "FINANCE")],
'20230310': [('FRC', -27.58620689655171, "FINANCE")],
'20230313': [('RF', -29.930966469428004, "FINANCE"), ('ZION', -35.41511771995044, "FINANCE"), ('FRC', -66.72782874617737, "FINANCE"), ('KEY', -21.455938697318004, "FINANCE"), ('CMA', -28.54956640027206, "FINANCE")],
'20230315': [('FRC', -25.58667676003029, "FINANCE")],
'20230316': [('FRC', -33.72913992297818, "FINANCE")],
'20230414': [('CTLT', -26.12541462644132, "HEALTH")]
}
POWELL = {
'20200211': '10:00',
'20200212': '10:00',
'20200326': '06:00',
'20200409': '09:00',
'20200513': '08:00',
'20200519': '09:00',
'20200521': '13:30',
'20200529': '10:00',
'20200616': '09:00',
'20200617': '11:00',
'20200619': '12:00',
'20200630': '11:30',
'20200827': '08:10',
'20200922': '09:30',
'20200923': '09:00',
'20200924': '09:00',
'20201006': '09:40',
'20201019': '07:00',
'20201112': '11:45',
'20201201': '10:00',
'20201202': '10:00',
'20210114': '12:30',
'20210210': '14:00',
'20210223': '10:00',
'20210224': '10:00',
'20210304': '12:05',
'20210322': '08:00',
'20210323': '11:00',
'20210324': '09:00',
'20210408': '11:00',
'20210414': '11:00',
'20210503': '13:20',
'20210604': '06:00',
'20210622': '13:00',
'20210714': '11:00',
'20210715': '08:30',
'20210817': '12:30',
'20210827': '09:00',
'20210924': '09:00',
'20210928': '09:00',
'20210929': '09:45',
'20211022': '10:00',
'20211108': '10:30',
'20211109': '09:00',
'20211129': '15:05',
'20211130': '10:00',
'20211201': '10:00',
'20220111': '10:00',
'20220302': '10:00',
'20220303': '10:00',
'20220321': '11:00',
'20220323': '07:00',
'20220421': '10:00',
'20220517': '13:00',
'20220524': '11:20',
'20220617': '07:45',
'20220622': '08:30',
'20220623': '09:00',
'20220629': '08:00',
'20220826': '09:00',
'20220908': '08:10',
'20220923': '13:00',
'20220927': '06:30',
'20220928': '09:15',
'20221130': '13:30',
'20230110': "PLACEHOLDER",
'20230207': "PLACEHOLDER",
'20230307': "PLACEHOLDER",
'20230308': "PLACEHOLDER",
'20230519': "PLACEHOLDER",
'20230621': "PLACEHOLDER",
'20230622': "PLACEHOLDER",
'20230628': "PLACEHOLDER",
'20230629': "PLACEHOLDER"}
POWELL_NEXT_DAY = {
'20200212': 'PLACEHOLDER',
'20200213': 'PLACEHOLDER',
'20200327': 'PLACEHOLDER',
'20200410': 'PLACEHOLDER',
'20200514': 'PLACEHOLDER',
'20200520': 'PLACEHOLDER',
'20200522': 'PLACEHOLDER',
'20200530': 'PLACEHOLDER',
'20200617': 'PLACEHOLDER',
'20200618': 'PLACEHOLDER',
'20200620': 'PLACEHOLDER',
'20200701': 'PLACEHOLDER',
'20200828': 'PLACEHOLDER',
'20200923': 'PLACEHOLDER',
'20200924': 'PLACEHOLDER',
'20200925': 'PLACEHOLDER',
'20201007': 'PLACEHOLDER',
'20201020': 'PLACEHOLDER',
'20201113': 'PLACEHOLDER',
'20201202': 'PLACEHOLDER',
'20201203': 'PLACEHOLDER',
'20210115': 'PLACEHOLDER',
'20210211': 'PLACEHOLDER',
'20210224': 'PLACEHOLDER',
'20210225': 'PLACEHOLDER',
'20210305': 'PLACEHOLDER',
'20210323': 'PLACEHOLDER',
'20210324': 'PLACEHOLDER',
'20210325': 'PLACEHOLDER',
'20210409': 'PLACEHOLDER',
'20210415': 'PLACEHOLDER',
'20210504': 'PLACEHOLDER',
'20210605': 'PLACEHOLDER',
'20210623': 'PLACEHOLDER',
'20210715': 'PLACEHOLDER',
'20210716': 'PLACEHOLDER',
'20210818': 'PLACEHOLDER',
'20210828': 'PLACEHOLDER',
'20210925': 'PLACEHOLDER',
'20210929': 'PLACEHOLDER',
'20210930': 'PLACEHOLDER',
'20211023': 'PLACEHOLDER',
'20211109': 'PLACEHOLDER',
'20211110': 'PLACEHOLDER',
'20211130': 'PLACEHOLDER',
'20211201': 'PLACEHOLDER',
'20211202': 'PLACEHOLDER',
'20220112': 'PLACEHOLDER',
'20220303': 'PLACEHOLDER',
'20220304': 'PLACEHOLDER',
'20220322': 'PLACEHOLDER',
'20220324': 'PLACEHOLDER',
'20220422': 'PLACEHOLDER',
'20220518': 'PLACEHOLDER',
'20220525': 'PLACEHOLDER',
'20220618': 'PLACEHOLDER',
'20220623': 'PLACEHOLDER',
'20220624': 'PLACEHOLDER',
'20220630': 'PLACEHOLDER',
'20220827': 'PLACEHOLDER',
'20220909': 'PLACEHOLDER',
'20220924': 'PLACEHOLDER',
'20220928': 'PLACEHOLDER',
'20220929': 'PLACEHOLDER',
'20221130': 'PLACEHOLDER',
'20230111': 'PLACEHOLDER',
'20230208': 'PLACEHOLDER',
'20230308': 'PLACEHOLDER',
'20230309': 'PLACEHOLDER',
'20230520': 'PLACEHOLDER',
'20230622': 'PLACEHOLDER',
'20230623': 'PLACEHOLDER',
'20230629': 'PLACEHOLDER',
'20230630': 'PLACEHOLDER',
'20230824': 'PLACEHOLDER',
'20230825': 'PLACEHOLDER'
}
FOMC_MEETINGS_FIRSTDAY = {
'20210126': 'PLACEHOLDER',
'20210316': 'PLACEHOLDER',
'20210427': 'PLACEHOLDER',
'20210616': 'PLACEHOLDER',
'20210727': 'PLACEHOLDER',
'20210921': 'PLACEHOLDER',
'20211102': 'PLACEHOLDER',
'20211214': 'PLACEHOLDER',
'20220125': 'PLACEHOLDER',
'20220315': 'PLACEHOLDER',
'20220503': 'PLACEHOLDER',
'20220614': 'PLACEHOLDER',
'20220726': 'PLACEHOLDER',
'20220920': 'PLACEHOLDER',
'20221101': 'PLACEHOLDER',
'20221213': 'PLACEHOLDER',
'20230131': 'PLACEHOLDER',
'20230321': 'PLACEHOLDER',
'20230402': 'PLACEHOLDER',
'20230613': 'PLACEHOLDER',
'20230725': 'PLACEHOLDER',
'20230919': 'PLACEHOLDER',
'20231031': 'PLACEHOLDER',
'20231212': 'PLACEHOLDER'
}
FARM_PAYROLL = { '20230106': 'PLACEHOLDER',
'20230203': 'PLACEHOLDER',
'20230310': 'PLACEHOLDER',
'20230407': 'PLACEHOLDER',
'20230505': 'PLACEHOLDER',
'20230602': 'PLACEHOLDER',
'20230707': 'PLACEHOLDER',
'20230804': 'PLACEHOLDER',
'20230901': 'PLACEHOLDER',
'20231006': 'PLACEHOLDER',}
FOMC_MEETINGS = {
'20210127': 'PLACEHOLDER',
'20210317': 'PLACEHOLDER',
'20210428': 'PLACEHOLDER',
'20210615': 'PLACEHOLDER',
'20210728': 'PLACEHOLDER',
'20210922': 'PLACEHOLDER',
'20211103': 'PLACEHOLDER',
'20211215': 'PLACEHOLDER',
'20220126': 'PLACEHOLDER',
'20220316': 'PLACEHOLDER',
'20220504': 'PLACEHOLDER',
'20220615': 'PLACEHOLDER',
'20220727': 'PLACEHOLDER',
'20220921': 'PLACEHOLDER',
'20221102': 'PLACEHOLDER',
'20221214': 'PLACEHOLDER',
'20230201': 'PLACEHOLDER',
'20230322': 'PLACEHOLDER',
'20230403': 'PLACEHOLDER',
'20230614': 'PLACEHOLDER',
'20230726': 'PLACEHOLDER',
'20230920': 'PLACEHOLDER',
'20231101': 'PLACEHOLDER',
'20231213': 'PLACEHOLDER'
}
FOMC_MINUTES = {
'20200103': 'PLACEHOLDER',
'20200219': 'PLACEHOLDER',
'20200408': 'PLACEHOLDER',
'20200520': 'PLACEHOLDER',
'20200610': 'PLACEHOLDER',
'20200701': 'PLACEHOLDER',
'20200819': 'PLACEHOLDER',
'20200916': 'PLACEHOLDER',
'20201007': 'PLACEHOLDER',
'20201125': 'PLACEHOLDER',
'20210106': 'PLACEHOLDER',
'20210217': 'PLACEHOLDER',
'20210407': 'PLACEHOLDER',
'20210519': 'PLACEHOLDER',
'20210707': 'PLACEHOLDER',
'20210818': 'PLACEHOLDER',
'20211013': 'PLACEHOLDER',
'20211124': 'PLACEHOLDER',
'20220105': 'PLACEHOLDER',
'20220216': 'PLACEHOLDER',
'20220406': 'PLACEHOLDER',
'20220525': 'PLACEHOLDER',
'20220706': 'PLACEHOLDER',
'20220817': 'PLACEHOLDER',
'20221012': 'PLACEHOLDER',
'20221123': 'PLACEHOLDER',
'20230104': 'PLACEHOLDER',
'20230222': 'PLACEHOLDER',
'20230412': 'PLACEHOLDER',
'20230524': 'PLACEHOLDER',
'20230705': 'PLACEHOLDER',
'20230816': 'PLACEHOLDER',
'20231011': 'PLACEHOLDER',
'20231122': 'PLACEHOLDER'
}
CPI = {
'20180323': 'PLACEHOLDER',
'20181107': 'PLACEHOLDER',
'20190823': 'PLACEHOLDER',
'20210113': 'PLACEHOLDER',
'20210210': 'PLACEHOLDER',
'20210310': 'PLACEHOLDER',
'20210413': 'PLACEHOLDER',
'20210512': 'PLACEHOLDER',
'20210610': 'PLACEHOLDER',
'20210713': 'PLACEHOLDER',
'20210811': 'PLACEHOLDER',
'20210914': 'PLACEHOLDER',
'20211013': 'PLACEHOLDER',
'20211110': 'PLACEHOLDER',
'20211210': 'PLACEHOLDER',
'20220112': 'PLACEHOLDER',
'20220210': 'PLACEHOLDER',
'20220310': 'PLACEHOLDER',
'20220412': 'PLACEHOLDER',
'20220511': 'PLACEHOLDER',
'20220610': 'PLACEHOLDER',
'20220713': 'PLACEHOLDER',
'20220810': 'PLACEHOLDER',
'20220913': 'PLACEHOLDER',
'20221013': 'PLACEHOLDER',
'20221110': 'PLACEHOLDER',
'20221213': 'PLACEHOLDER',
'20230112': 'PLACEHOLDER',
'20230214': 'PLACEHOLDER',
'20230314': 'PLACEHOLDER',
'20230331': 'PLACEHOLDER',
'20230412': 'PLACEHOLDER',
'20230510': 'PLACEHOLDER',
'20230613': 'PLACEHOLDER',
'20230712': 'PLACEHOLDER',
'20230810': 'PLACEHOLDER',
'20230829': 'PLACEHOLDER',
'20230913': 'PLACEHOLDER',
'20231012': 'PLACEHOLDER',
'20231114': 'PLACEHOLDER',
'20231212': 'PLACEHOLDER',
'20231213': "PLACEHOLDER",
'20231214': "PLACEHOLDER",
'20231215': "PLACEHOLDER",
'20241113': "PLACEHOLDER",
'20241101': "PLACEHOLDER",
'20241104': "PLACEHOLDER",
'20241105': "PLACEHOLDER",
'20241106': "PLACEHOLDER",
'20241107': "PLACEHOLDER",
'20241108': "PLACEHOLDER",
'20241211': "PLACEHOLDER",
'20241218': "PLACEHOLDER",
'20250129': "PLACEHOLDER",
}
CPI_NEXT_DAY = {
'20210114': 'PLACEHOLDER',
'20210211': 'PLACEHOLDER',
'20210311': 'PLACEHOLDER',
'20210414': 'PLACEHOLDER',
'20210513': 'PLACEHOLDER',
'20210611': 'PLACEHOLDER',
'20210714': 'PLACEHOLDER',
'20210812': 'PLACEHOLDER',
'20210915': 'PLACEHOLDER',
'20211014': 'PLACEHOLDER',
'20211111': 'PLACEHOLDER',
'20211211': 'PLACEHOLDER',
'20220113': 'PLACEHOLDER',
'20220211': 'PLACEHOLDER',
'20220311': 'PLACEHOLDER',
'20220413': 'PLACEHOLDER',
'20220512': 'PLACEHOLDER',
'20220611': 'PLACEHOLDER',
'20220714': 'PLACEHOLDER',
'20220811': 'PLACEHOLDER',
'20220914': 'PLACEHOLDER',
'20221014': 'PLACEHOLDER',
'20221111': 'PLACEHOLDER',
'20221214': 'PLACEHOLDER',
'20230113': 'PLACEHOLDER',
'20230215': 'PLACEHOLDER',
'20230315': 'PLACEHOLDER',
'20230413': 'PLACEHOLDER',
'20230511': 'PLACEHOLDER',
'20230614': 'PLACEHOLDER',
'20230713': 'PLACEHOLDER',
'20230811': 'PLACEHOLDER',
'20230914': 'PLACEHOLDER',
'20231013': 'PLACEHOLDER',
'20231115': 'PLACEHOLDER',
'20231213': 'PLACEHOLDER'
}
FED_INTEREST_RATE_DECISION = {
'20200129': 'PLACEHOLDER',
'20200303': 'PLACEHOLDER',
'20200315': 'PLACEHOLDER',
'20200419': 'PLACEHOLDER',
'20200610': 'PLACEHOLDER',
'20200729': 'PLACEHOLDER',
'20200916': 'PLACEHOLDER',
'20201105': 'PLACEHOLDER',
'20201216': 'PLACEHOLDER',
'20210127': 'PLACEHOLDER',
'20210317': 'PLACEHOLDER',
'20210428': 'PLACEHOLDER',
'20210616': 'PLACEHOLDER',
'20210728': 'PLACEHOLDER',
'20210922': 'PLACEHOLDER',
'20211103': 'PLACEHOLDER',
'20211215': 'PLACEHOLDER',
'20220126': 'PLACEHOLDER',
'20220316': 'PLACEHOLDER',
'20220504': 'PLACEHOLDER',
'20220615': 'PLACEHOLDER',
'20220727': 'PLACEHOLDER',
'20220921': 'PLACEHOLDER',
'20221102': 'PLACEHOLDER',
'20221214': 'PLACEHOLDER',
'20230201': 'PLACEHOLDER',
'20230322': 'PLACEHOLDER',
'20230503': 'PLACEHOLDER',
'20230614': 'PLACEHOLDER',
'20230726': 'PLACEHOLDER',
'20230920': 'PLACEHOLDER',
'20231101': 'PLACEHOLDER',
'20231213': 'PLACEHOLDER',
}
FED_INTEREST_RATE_DECISION_NEXTDAY = {
'20200130': 'PLACEHOLDER',
'20200304': 'PLACEHOLDER',
'20200316': 'PLACEHOLDER',
'20200420': 'PLACEHOLDER',
'20200611': 'PLACEHOLDER',
'20200730': 'PLACEHOLDER',
'20200917': 'PLACEHOLDER',
'20201106': 'PLACEHOLDER',
'20201217': 'PLACEHOLDER',
'20210128': 'PLACEHOLDER',
'20210318': 'PLACEHOLDER',
'20210429': 'PLACEHOLDER',
'20210617': 'PLACEHOLDER',
'20210729': 'PLACEHOLDER',
'20210923': 'PLACEHOLDER',
'20211104': 'PLACEHOLDER',
'20211216': 'PLACEHOLDER',
'20220127': 'PLACEHOLDER',
'20220317': 'PLACEHOLDER',
'20220505': 'PLACEHOLDER',
'20220616': 'PLACEHOLDER',
'20220728': 'PLACEHOLDER',
'20220922': 'PLACEHOLDER',
'20221103': 'PLACEHOLDER',
'20221215': 'PLACEHOLDER',
'20230202': 'PLACEHOLDER',
'20230323': 'PLACEHOLDER',
'20230504': 'PLACEHOLDER',
'20230615': 'PLACEHOLDER',
'20230727': 'PLACEHOLDER',
'20230921': 'PLACEHOLDER',
'20231102': 'PLACEHOLDER',
'20231214': 'PLACEHOLDER',
}
POWELL_NEXT_DAY_2023 = {
'20230111':"PLACEHOLDER",
'20230208':"PLACEHOLDER",
'20230308':"PLACEHOLDER",
'20230309':"PLACEHOLDER",
}
FOMC_MEETINGS_2023 = {
'20230131':"PLACEHOLDER",
'20230201':"PLACEHOLDER",
'20230321':"PLACEHOLDER",
'20230322':"PLACEHOLDER",
'20230402':"PLACEHOLDER",
'20230403':"PLACEHOLDER",
'20230613':"PLACEHOLDER",
'20230614':"PLACEHOLDER",
'20230725':"PLACEHOLDER",
'20230726':"PLACEHOLDER",
'20230919':"PLACEHOLDER",
'20230920':"PLACEHOLDER",
'20231031':"PLACEHOLDER",
'20231101':"PLACEHOLDER",
'20231212':"PLACEHOLDER",
'20231213':"PLACEHOLDER",
}
FOMC_MINUTES_2023 = {
'20230104':"PLACEHOLDER",
'20230222':"PLACEHOLDER",
'20230412':"PLACEHOLDER",
'20230524':"PLACEHOLDER",
'20230705':"PLACEHOLDER",
'20230816':"PLACEHOLDER",
'20231011':"PLACEHOLDER",
'20231122':"PLACEHOLDER",
}
CPI_2023 = {
'20230112':"PLACEHOLDER",
'20230214':"PLACEHOLDER",
'20230314':"PLACEHOLDER",
'20230412':"PLACEHOLDER",
'20230510':"PLACEHOLDER",
'20230613':"PLACEHOLDER",
'20230712':"PLACEHOLDER",
'20230810':"PLACEHOLDER",
'20230913':"PLACEHOLDER",
'20231012':"PLACEHOLDER",
'20231114':"PLACEHOLDER",
'20231212':"PLACEHOLDER",
'20231213':"PLACEHOLDER",
'20231214':"PLACEHOLDER",
}
CPI_NEXT_DAY_2023 = {
'20230113':"PLACEHOLDER",
'20230215':"PLACEHOLDER",
'20230315':"PLACEHOLDER",
'20230413':"PLACEHOLDER",
'20230511':"PLACEHOLDER",
'20230614':"PLACEHOLDER",
'20230713':"PLACEHOLDER",
'20230811':"PLACEHOLDER",
'20230914':"PLACEHOLDER",
'20231013':"PLACEHOLDER",
'20231115':"PLACEHOLDER",
'20231213':"PLACEHOLDER",
}
#region imports
from AlgorithmImports import *
#endregion
import calendar_info
import config
import pandas as pd
USE_FED_INTEREST_RATE_DECISION_NEXTDAY = True
class TenTwentyDates():
def __init__(self):
self.use_10 = config.TEN_RULE_ACTIVE
self.use_20 = config.TWENTY_RULE_ACTIVE
self.use_powell = config.POWELL_ACTIVE
self.use_powell_next_day = config.POWELL_NEXT_DAY_ACTIVE
self.use_fomc_meetings = config.FOMC_MEETINGS_ACTIVE
self.use_fomc_minutes = config.FOMC_MINUTES_ACTIVE
self.use_fed_interest_rate_decision = config.FED_INTEREST_RATE_DECISION_ACTIVE
self.use_fed_interest_rate_decision_nextday = config.FED_INTEREST_RATE_DECISION_NEXTDAY_ACTIVE
self.use_cpi = config.CPI_ACTIVE
self.use_cpi_next_day = config.CPI_NEXT_DAY_ACTIVE
self.use_farm_roll = config.FARM_PAYROLL_ACTIVE
# df = pd.read_csv('event_queries_4.csv') # Assuming it's a CSV file, adjust accordingly
# df['date'] = pd.to_datetime(df['date'])
# # Create a dictionary with date as key and time as value
# self.result_dict = dict(zip(df['date'].dt.strftime('%Y%m%d'), df['date'].dt.strftime('%H:%M')))
# Convert the DataFrame to a dictionary
def check_rules(self, date, time, date_tomorrow):
if self.use_powell:
if str(date) in calendar_info.POWELL.keys() or str(date_tomorrow) in calendar_info.POWELL.keys():
date_to_check = str(date) if str(date) in calendar_info.POWELL.keys() else str(date_tomorrow)
if calendar_info.POWELL[date_to_check] == 'PLACEHOLDER' or (date_to_check in calendar_info.POWELL.keys() and int(calendar_info.POWELL[date_to_check].replace(":", "")) < 1200 or int(time.replace(":", "")) >= int(calendar_info.POWELL[date_to_check].replace(":", ""))):
return True
if self.use_powell_next_day:
if str(date) in calendar_info.POWELL_NEXT_DAY.keys() or str(date_tomorrow) in calendar_info.POWELL_NEXT_DAY.keys():
return True
if self.use_fomc_meetings:
if str(date) in calendar_info.FOMC_MEETINGS.keys() or str(date_tomorrow) in calendar_info.FOMC_MEETINGS.keys():
return True
if self.use_farm_roll:
if str(date) in calendar_info.FARM_PAYROLL.keys() or str(date_tomorrow) in calendar_info.FARM_PAYROLL.keys():
return True
if self.use_fomc_minutes:
if str(date) in calendar_info.FOMC_MINUTES.keys() or str(date_tomorrow) in calendar_info.FOMC_MINUTES.keys():
return True
if self.use_fed_interest_rate_decision:
if str(date) in calendar_info.FED_INTEREST_RATE_DECISION.keys() or str(date_tomorrow) in calendar_info.FED_INTEREST_RATE_DECISION.keys():
return True
if self.use_fed_interest_rate_decision_nextday:
if str(date) in calendar_info.FED_INTEREST_RATE_DECISION_NEXTDAY.keys() or str(date_tomorrow) in calendar_info.FED_INTEREST_RATE_DECISION_NEXTDAY.keys():
return True
if self.use_cpi:
if str(date) in calendar_info.CPI.keys() or str(date_tomorrow) in calendar_info.CPI.keys():
return True
if self.use_cpi_next_day:
if str(date) in calendar_info.CPI_NEXT_DAY.keys() or str(date_tomorrow) in calendar_info.CPI_NEXT_DAY.keys():
return True
if self.use_10:
if str(date) in calendar_info.TEN_DATES or str(date_tomorrow) in calendar_info.TEN_DATES:
dates_to_check = [str(date), str(date_tomorrow)]
for date_check in dates_to_check:
for tuple in calendar_info.TEN_DATES.get(date_check, []):
if tuple[2] == "FINANCE" or tuple[2] == "RETAIL":
return True
if self.use_20:
if str(date) in calendar_info.TWENTY_DATES or str(date_tomorrow) in calendar_info.TWENTY_DATES:
dates_to_check = [str(date), str(date_tomorrow)]
for date_check in dates_to_check:
for tuple in calendar_info.TWENTY_DATES.get(date_check, []):
if tuple[2] == "FINANCE" or tuple[2] == "RETAIL":
return True
if self.use_20 and self.use_10:
if str(date) in calendar_info.TEN_DATES or str(date) in calendar_info.TWENTY_DATES or str(date_tomorrow) in calendar_info.TEN_DATES or str(date_tomorrow) in calendar_info.TWENTY_DATES:
dates_to_check = [str(date), str(date_tomorrow)]
for date_check in dates_to_check:
for tuple in calendar_info.TEN_DATES.get(date_check, []) + calendar_info.TWENTY_DATES.get(date_check, []):
if tuple[2] == "FINANCE" or tuple[2] == "RETAIL":
return True
return False
#region imports from AlgorithmImports import * from datetime import * ################################################################## IRON CONDOR SETTINGS RANGE ######################################################################## # Pick between "FIXED" or "AUTOMATIC" # Fixed will use the MINIMUM and MAXIMUM DISTANCE below # Automatic will use the AUTOMATIC RANGE PERCENTAGES below RANGE_MODE = "AUTOMATIC" TEST_MODE = True # Set this to False to disable the testing mode TEST_QUANTITY = 1 # The predefined test quantity # Control which range it is allowed to automatically select # Pick between 1, 1.5, 2, 2.5, 3 # If a number is included the set logic will be applied to automatically select that range if the conditions are met # Below are the conditions for each range: # 3% = VIX above 30 # 2.5% = VIX above 25 and ATR% above 2% # 2% = VIX below 25 or ATR% below 2% # 1.5% = VIX below 25 and ATR% below 2% # 1% = VIX below 20 and ATR% below 1.5% AUTOMATIC_RANGE_PERCENTAGES = [1, 1.5, 2, 2.5, 3] # Percentage added to the minimums in the list above to create the maximum for the ranges AUTOMATIC_RANGE_MAX_ADD = 0.3 # VIX Threshold for deciding if 3% range is needed VIX_THRESHOLD_1 = 30 # VIX Threshold for deciding if 2.5% or 2% range is needed VIX_THRESHOLD_2 = 25 # VIX Threshold for deciding if 1% or 1.5% range is needed VIX_THRESHOLD_3 = 20 # ATR Threshold for deciding if 1.5% or 2% or 2.5% range is needed ATR_THRESHOLD_1 = 0.02 # ATR Threshold for deciding if 1% or 1.5%range is needed ATR_THRESHOLD_2 = 0.02 POWELL_ACTIVE = False # True/False POWELL_NEXT_DAY_ACTIVE = False # True/False CPI_ACTIVE = True # True/False CPI_NEXT_DAY_ACTIVE = False # True/False FOMC_MEETINGS_ACTIVE = False # True/False FED_INTEREST_RATE_DECISION_ACTIVE = False # True/False FED_INTEREST_RATE_DECISION_NEXTDAY_ACTIVE = False # True/False FOMC_MINUTES_ACTIVE = False # True/False FOMC_MEETINGS_FIRSTDAY_ACTIVE = False # If a stock in major sectors of S&P 500 gapped down 10% then we dont enter that day TEN_RULE_ACTIVE = 0.0 # Calendar variables, need to remove these since we also use calendar api now USE_2023_CPI = 1.0 USE_2023_CPI_NEXT_DAY = 0.0 USE_2023_FOMC_MEETINGS = 0.0 USE_2023_FOMC_MINUTES = 0 USE_2023_POWELL = 0 USE_2023_POWELL_NEXT_DAY = 0 # If a stock in major sectors of S&P 500 gapped down 20% then we dont enter that day TWENTY_RULE_ACTIVE = 0 # Have to remove this again as this was added for farm 2023 before it came from the calendar api # True/False FARM_PAYROLL_ACTIVE = False # I have removed this since only scaling range for 1 event seems like overfitting # True/False FARM_PAYROLL_AS_SCALED_ACTIVE = False
# region imports
from AlgorithmImports import *
from setup_contracts import CreateCondors
from select_range import SelectRange
from check_calendar import TenTwentyDates
import numpy as np
import config
import pytz
from datetime import datetime, timedelta
# endregion
class PensiveSkyBlueZebra(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2024, 11, 1)
self.SetEndDate(2024, 12, 31)
self.SetCash(100000)
self.symbol = self.AddIndex("SPX", Resolution.Minute).Symbol
self.range_selector = SelectRange(self, self.symbol)
self.calendar_checker = TenTwentyDates()
self.option = self.AddIndexOption(self.symbol, "SPXW")
self.option_symbol = self.option.Symbol
self.option.SetFilter(self.UniverseFunc)
self.SetSecurityInitializer(self.CustomSecurityInitializer)
# Updated to use correct property for portfolio
self.Portfolio.SetPositions(SecurityPositionGroupModel.Null)
self.Portfolio.MarginCallModel = MarginCallModel.Null
self.open_price = None
self.trading_window = False
self.Schedule.On(self.DateRules.EveryDay(), self.TimeRules.BeforeMarketClose(self.symbol, 11), self.open_window)
self.Schedule.On(self.DateRules.EveryDay(), self.TimeRules.BeforeMarketClose(self.symbol, 0), self.get_close)
self.Schedule.On(self.DateRules.EveryDay(), self.TimeRules.AfterMarketOpen(self.symbol, 0), self.close_window)
self.Schedule.On(self.DateRules.EveryDay(), self.TimeRules.AfterMarketOpen(self.symbol, 1), self.get_range)
self.invested_today = False
self.puts_1dte = []
self.calls_1dte = []
self.condor_creator = CreateCondors(self)
self.slice = None
self.orders_to_send = []
self.current_symbols_list = []
self.prev_gap = 0
self.close = 1
self.intraday_movement = 0
self.daily_open = 0
self.test_mode = config.TEST_MODE
self.test_quantity = config.TEST_QUANTITY
def get_close(self):
# Reset daily_open to 0
# self.daily_open = 0
# Pull from history the most recent 9:31 AM price
history = self.History[TradeBar](self.symbol, timedelta(days=1), Resolution.Minute)
# Convert history to a list for easier manipulation
history_list = list(history)
# Ensure the returned history is not empty
if history_list:
# Determine if the bars start at 8 AM or 9 AM by checking the first bar
first_bar_time = history_list[0].Time
# self.Debug(f"First bar time: {first_bar_time}")
# Determine if the data starts at 8:30 AM (requiring a 1-hour shift)
needs_shift = first_bar_time.strftime("%H:%M") == "08:30"
# if needs_shift:
# self.Debug("Bars start at 8 AM, applying a 1-hour shift.")
# else:
# self.Debug("Bars start at 9 AM, no shift needed.")
# Iterate through the TradeBars to find the 9:31 AM NYSE time
for bar in history_list:
bar_time = bar.Time
if needs_shift:
# Apply the shift if bars start at 8 AM
bar_time += timedelta(hours=1)
# Debugging for raw and corrected bar times
# self.Debug(f"Raw bar time: {bar.Time}, Corrected bar time: {bar_time} {bar.Open}")
# Check if the corrected time matches 9:31 AM
if bar_time.strftime("%H:%M") == "09:30":
# Assign the most recent 9:31 AM price to daily_open
self.daily_open = bar.Open
# self.Debug(f"Daily Open: {self.daily_open} at {bar_time}")
# self.Debug("-------------------------------------------------------")
break
# Update close price and calculate intraday movement
self.close = self.Securities[self.symbol].Close
if self.daily_open != 0: # Ensure daily_open is not 0 to avoid division by zero
self.intraday_movement = abs((self.close - self.daily_open) / self.daily_open)
else:
self.intraday_movement = 0 # Fallback if daily_open could not be set
def CustomSecurityInitializer(self, security: Security) -> None:
# Disable trading fees
security.SetFeeModel(ConstantFeeModel(0))
if security.Type == SecurityType.IndexOption: # Option type
security.SetOptionAssignmentModel(NullOptionAssignmentModel())
# Next, overwrite the security buying power
security.SetBuyingPowerModel(SecurityMarginModel(10))
def UniverseFunc(self, universe):
price = self.Securities[self.symbol].Price
atm = self.Securities[self.symbol].Price * 0.06
return universe.IncludeWeeklys().Strikes(-atm, atm).Expiration(TimeSpan.FromDays(1), TimeSpan.FromDays(2))
def liq(self):
self.Liquidate()
def close_window(self):
self.trading_window = False
self.puts_1dte = []
self.calls_1dte = []
def get_range(self):
gap = abs((self.Securities[self.symbol].Open - self.close) / self.close)
self.Plot("Daily Gap", "Gap Percentage", gap * 100) # Plot as a percentage
self.daily_open = self.Securities[self.symbol].Open
# self.debug(f"Daily Open correct {self.daily_open} {self.time}")
if self.orders_to_send:
for order_info in self.orders_to_send:
current_premium = 0
# Compute current premium
for i, symbol in enumerate(order_info['symbols']):
if i % 2 == 0:
# Sell leg
premium = self.Securities[symbol].BidPrice
else:
# Buy leg
premium = -self.Securities[symbol].AskPrice
current_premium += premium
put_sell_premium = self.Securities[order_info['symbols'][0]].BidPrice
call_sell_premium = self.Securities[order_info['symbols'][2]].BidPrice
include_put_leg = put_sell_premium >= 0.1
include_call_leg = call_sell_premium >= 0.1
# Skip this order if neither leg meets the premium requirement
if not include_put_leg and not include_call_leg:
continue
# Build the legs list based on which sides are eligible
legs = []
if include_put_leg:
legs.append(order_info['legs'][0]) # Put Sell
legs.append(order_info['legs'][1]) # Put Buy
if include_call_leg:
legs.append(order_info['legs'][2]) # Call Sell
legs.append(order_info['legs'][3]) # Call Buy
# Calculate premium only if legs are eligible and proceed
if legs:
if (gap < 0.009 and self.prev_gap < 0.0125 and self.intraday_movement < 0.03) and current_premium > 0.2:
# Calculate the maximum number of contracts based on available cash
strike_difference = 25 * 100 # Strike difference multiplied by 100
max_contracts_based_on_cash = int(self.Portfolio.Cash / strike_difference)
# Ensure a minimum of 40 contracts
quantity = self.test_quantity if self.test_mode else max(1, int(max_contracts_based_on_cash * 0.85 * order_info['quantity_factor']))
self.ComboMarketOrder(legs, quantity, tag=f"range: {order_info['min_range']} gap: {gap} {self.prev_gap}, {order_info['tag_suffix']}")
else:
# Order conditions not met, discard
pass
# Clear orders to send
self.orders_to_send = []
self.prev_gap = gap
# Then reset ranges for next day
min_range, max_range = self.range_selector.set_range_today()
self.todays_min_range = min_range
self.todays_max_range = max_range
# Reset variables
self.orders_to_send = []
self.current_symbols_list = []
def open_window(self):
self.invested_today = False
if self.Time.weekday() != 4:
self.open_price = self.Securities[self.symbol].Open
no_entry_today = self.calendar_checker.check_rules((self.Time + timedelta(days=1)).strftime("%Y%m%d"), self.Time.strftime("%H%M"), (self.Time + timedelta(days=1)).strftime("%Y%m%d"))
if not no_entry_today and not (self.time.year == 2021 and self.time.month == 11 and not self.time.day == 24):
self.trading_window = True
self.puts_1dte, self.calls_1dte = self.condor_creator.populate_lists(self.symbol, None, self.open_price, None, None)
def OnData(self, data: Slice):
self.slice = data
if self.open_price is None:
return # Ensure we have an open price to work with
if not self.invested_today and self.trading_window and self.Time.day != 3 and not self.Portfolio.Invested and self.Time.hour <= 15:
self.SetUpIronCondor()
def SetUpIronCondor(self):
self.SetUpIronCondorForRange(self.todays_min_range, 0.75, '')
self.SetUpIronCondorForRange(self.todays_min_range + 0.005, 0.25, 'small')
def SetUpIronCondorForRange(self, min_range, quantity_factor, tag_suffix):
if not self.puts_1dte or not self.calls_1dte:
return
underlying_price = self.Securities[self.symbol].Close
sell_strike_put = round(underlying_price * (1 - min_range) / 5) * 5
sell_strike_call = round(underlying_price * (1 + min_range) / 5) * 5
buy_strike_put = sell_strike_put - 25
buy_strike_call = sell_strike_call + 25
put_1dte_sell = min(self.puts_1dte, key=lambda x: abs(x.StrikePrice - sell_strike_put))
call_1dte_sell = min(self.calls_1dte, key=lambda x: abs(x.StrikePrice - sell_strike_call))
put_1dte_buy = next((x for x in self.puts_1dte if x.StrikePrice == buy_strike_put), None)
call_1dte_buy = next((x for x in self.calls_1dte if x.StrikePrice == buy_strike_call), None)
if put_1dte_buy is None or call_1dte_buy is None:
return
if put_1dte_sell.Symbol == put_1dte_buy.Symbol or call_1dte_sell.Symbol == call_1dte_buy.Symbol:
return
current_put_1dte_sell_premium = self.Securities[put_1dte_sell.Symbol].AskPrice
current_put_1dte_buy_premium = -self.Securities[put_1dte_buy.Symbol].BidPrice
current_call_1dte_sell_premium = self.Securities[call_1dte_sell.Symbol].AskPrice
current_call_1dte_buy_premium = -self.Securities[call_1dte_buy.Symbol].BidPrice
if current_put_1dte_sell_premium == 0 or current_put_1dte_buy_premium == 0 or current_call_1dte_sell_premium == 0 or current_call_1dte_buy_premium == 0:
return
current_premium = current_put_1dte_sell_premium - current_put_1dte_buy_premium + current_call_1dte_sell_premium - current_call_1dte_buy_premium
if current_premium > 0.2:
if self.Time.hour != 1:
self.invested_today = True
self.trading_window = False
# Calculate the maximum number of contracts based on available cash
strike_difference = 25 * 100 # Strike difference multiplied by 100
max_contracts_based_on_cash = int(self.Portfolio.Cash / strike_difference)
# Ensure a minimum of 40 contracts
quantity = self.test_quantity if self.test_mode else max(1, int(max_contracts_based_on_cash * 0.85 * quantity_factor))
include_put_leg = put_sell_pcurrent_put_1dte_sell_premiumremium >= 0.1
include_call_leg = current_call_1dte_sell_premium >= 0.1
# Create order legs
# Build legs list based on which sides are eligible
legs = []
if include_put_leg:
legs.append(Leg.Create(put_1dte_sell.Symbol, -1)) # Put Sell
legs.append(Leg.Create(put_1dte_buy.Symbol, 1)) # Put Buy
if include_call_leg:
legs.append(Leg.Create(call_1dte_sell.Symbol, -1)) # Call Sell
legs.append(Leg.Create(call_1dte_buy.Symbol, 1)) # Call Buy
# Place combo market order
self.ComboMarketOrder(legs, quantity, tag=f"range: {min_range} {tag_suffix}")
else:
# For after-hours, store the order to send later
legs = []
legs.append(Leg.Create(put_1dte_sell.Symbol, -1))
legs.append(Leg.Create(put_1dte_buy.Symbol, 1))
legs.append(Leg.Create(call_1dte_sell.Symbol, -1))
legs.append(Leg.Create(call_1dte_buy.Symbol, 1))
self.orders_to_send.append({
'legs': legs,
'min_range': min_range,
'quantity_factor': quantity_factor,
'tag_suffix': tag_suffix,
'symbols': [put_1dte_sell.Symbol, put_1dte_buy.Symbol, call_1dte_sell.Symbol, call_1dte_buy.Symbol]
})
from AlgorithmImports import *
import config
class SelectRange():
def __init__(self, algorithm, symbol):
self.algorithm = algorithm
self.symbol = symbol
self.range_mode = config.RANGE_MODE
self.VIX = self.algorithm.AddIndex("VIX", Resolution.Minute).Symbol
self.range_max_today = None
self.range_min_today = None
self.automatic_range_percentages = config.AUTOMATIC_RANGE_PERCENTAGES
self.automatic_range_max_add = config.AUTOMATIC_RANGE_MAX_ADD/100
self.use_1 = False
self.use_1_5 = False
self.use_2 = False
self.use_2_5 = False
self.use_3 = False
self.algorithm.Schedule.On(self.algorithm.DateRules.EveryDay(), self.algorithm.TimeRules.AfterMarketOpen(self.symbol, 1), self.temporary_get_vix)
if self.range_mode == "FIXED":
self.range_max_today = config.MAXIMUM_DISTANCE/100
self.range_min_today = config.MINIMUM_DISTANCE/100
elif self.range_mode == "AUTOMATIC":
if 1 in self.automatic_range_percentages:
self.use_1 = True
if 1.5 in self.automatic_range_percentages:
self.use_1_5 = True
if 2 in self.automatic_range_percentages:
self.use_2 = True
if 2.5 in self.automatic_range_percentages:
self.use_2_5 = True
if 3 in self.automatic_range_percentages:
self.use_3 = True
else:
raise Exception(f"Selected RANGE MODE {self.range_mode} is incorrect, please check the input and either choose 'AUTOMATIC' or 'FIXED'")
def temporary_get_vix(self):
self.vix = self.algorithm.Securities[self.VIX].Open
def set_range_today(self):
if self.range_mode == "AUTOMATIC":
if self.vix >= 30:
self.range_max_today = 0.06 + self.automatic_range_max_add
self.range_min_today = 0.06
elif self.vix >= 20:
self.range_max_today = 0.04 + self.automatic_range_max_add
self.range_min_today = 0.04
else:
self.range_max_today = 0.02 + self.automatic_range_max_add
self.range_min_today = 0.02
elif self.range_mode == "FIXED":
pass
else:
raise Exception(f"Selected RANGE MODE {self.range_mode} is incorrect, please check the input and either choose 'AUTOMATIC' or 'FIXED'")
self.algorithm.Plot("Range Used", "Range", self.range_min_today)
return self.range_min_today , self.range_max_today
from AlgorithmImports import *
import numpy as np
import pandas as pd
from pandas import Timestamp
class CreateCondors():
def __init__(self, algorithm):
self.algo = algorithm
def populate_lists(self, symbol, symbolData, open_price, min_range, max_range):
if open_price is None:
open_price = self.algo.Securities[symbol].Open
put_3_expiry = self.generate_price_list(open_price, direction="PUT")
call_3_expiry = self.generate_price_list(open_price, direction="CALL")
time_1dte = self.algo.Time + timedelta(days=1)
# Ensure expiry is of the correct type
expiry = Timestamp(time_1dte.year, time_1dte.month, time_1dte.day)
chain = self.algo.option_chain(Symbol.create_canonical_option(underlying_symbol=symbol, target_option="SPXW", market=Market.USA, alias=None), flatten=True).data_frame
self.algo.debug(chain)
self.algo.debug(f"DataFrame columns: {list(chain.columns)}")
self.algo.debug(f"DataFrame index: {chain.index.to_list()}")
if chain is None or not isinstance(chain, pd.DataFrame):
self.algo.debug("The 'chain' variable is invalid or not a DataFrame.")
return None
else:
self.algo.debug(f"DataFrame head:\n{chain.head().to_string()}")
# Filter chain by expiry
if 'expiry' not in chain.columns:
return [], []
filtered_chain = chain[chain['expiry'] == expiry]
# Get available strikes
available_call_strikes = set(filtered_chain[filtered_chain.right == OptionRight.CALL]['strike'].unique())
available_put_strikes = set(filtered_chain[filtered_chain.right == OptionRight.PUT]['strike'].unique())
# Filter put and call expiry strikes
put_3_expiry = [strike for strike in put_3_expiry if strike in available_put_strikes]
call_3_expiry = [strike for strike in call_3_expiry if strike in available_call_strikes]
call_ivs = []
put_ivs = []
for original_call in call_3_expiry:
call_option_1 = Symbol.CreateOption(symbol, "SPXW", Market.USA,
OptionStyle.European, OptionRight.Call, original_call, time_1dte)
call_contract_1 = self.algo.AddIndexOptionContract(call_option_1, Resolution.Minute)
call_ivs.append(call_contract_1)
for original_put in put_3_expiry:
put_option_1 = Symbol.CreateOption(symbol, "SPXW", Market.USA,
OptionStyle.European, OptionRight.Put, original_put, time_1dte)
put_contract_1 = self.algo.AddIndexOptionContract(put_option_1, Resolution.Minute)
put_ivs.append(put_contract_1)
return put_ivs, call_ivs
def generate_price_list(self, open_price, increment=5, percentage_range=0.06, direction="CALL"):
variation = open_price * percentage_range
lower_bound = round((open_price - variation) / increment) * increment
upper_bound = round((open_price + variation) / increment) * increment
price_list = list(range(lower_bound, upper_bound + increment, increment))
open_price_rounded = round(open_price / increment) * increment
if open_price_rounded not in price_list:
price_list.insert(0, open_price_rounded)
return price_list