commit 777650df1286534be91b061d270776e64461e32e Author: SeanChenTaipei Date: Thu Mar 2 14:24:58 2023 +0800 init commit diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..d97f8e3 --- /dev/null +++ b/.gitignore @@ -0,0 +1,13 @@ +# Byte-compiled / optimized / DLL files +.ipynb_checkpoints/ +__pycache__/ +.DS_Store + +tpm/ + +# Distribution / packaging +main.old.py +postgres_runner.py +*.ipynb +tickers_sorted_tw.py +tickers_sorted.py \ No newline at end of file diff --git a/assets_tw.json b/assets_tw.json new file mode 100644 index 0000000..0ad7fa5 --- /dev/null +++ b/assets_tw.json @@ -0,0 +1 @@ +{"1101.TW": ["\u53f0\u6ce5", 39.150001525878906, "2000-01-05", "2023-02-24"], "1102.TW": ["\u4e9e\u6ce5", 44.75, "2000-01-05", "2023-02-24"], "1103.TW": ["\u5609\u6ce5", 20.399999618530273, "2000-01-05", "2023-02-24"], "1104.TW": ["\u74b0\u6ce5", 24.450000762939453, "2000-01-05", "2023-02-24"], "1108.TW": ["\u5e78\u798f", 14.149999618530273, "2000-01-05", "2023-02-24"], 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b/assets_us.json new file mode 100644 index 0000000..301a6f7 --- /dev/null +++ b/assets_us.json @@ -0,0 +1 @@ +{"A": ["Agilent Technologies, Inc.", 123.30000305175781, "2001-07-26", "2023-02-27"], "AAL": ["American Airlines Group Inc.", 1634.326416015625, "2017-11-10", "2023-02-27"], "AAP": ["Advance Auto Parts, Inc.", 5.460000038146973, "2006-09-07", "2023-02-27"], "AAPL": ["Apple Inc.", 60.650001525878906, "1973-01-03", "2023-02-27"], "ABBV": ["AbbVie Inc.", 183.1699981689453, "1980-03-18", "2023-02-27"], "ABC": ["AmerisourceBergen Corporation", 36.7599983215332, "1980-03-18", "2023-02-27"], "ABMD": ["Abiomed, Inc.", 212.1699981689453, "1980-01-03", "2023-02-27"], "ABT": ["Abbott Laboratories", 66.91999816894531, "1973-02-22", "2023-02-27"], "ACN": ["Accenture plc", 197.42999267578125, "2004-02-02", "2023-02-27"], "ADBE": ["Adobe Inc.", 225.30999755859375, "1982-06-03", "2023-02-27"], "ADI": ["Analog Devices, Inc.", 71.86000061035156, "1973-02-22", "2023-02-27"], "ADP": ["Automatic 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Robinson Worldwide, Inc.", 15.670000076293945, "2001-06-18", "2023-02-27"], "CHTR": ["Charter Communications, Inc.", 53.0099983215332, "1988-01-27", "2023-02-27"], "CI": ["Cigna Corporation", 126.25, "1980-03-18", "2023-02-27"], "CINF": ["Cincinnati Financial Corporation", 77.41000366210938, "1983-08-05", "2023-02-27"], "CL": ["Colgate-Palmolive Company", 71.63999938964844, "2017-04-10", "2023-02-27"], "CLX": ["The Clorox Company", 53.75, "1975-06-13", "2023-02-27"], "CMA": ["Comerica Incorporated", 33.02000045776367, "2003-12-03", "2023-02-27"], "CMCSA": ["Comcast Corporation", 17.469999313354492, "1995-08-28", "2023-02-27"], "CME": ["CME Group Inc.", 75.01000213623047, "1973-02-22", "2023-02-27"], "CMG": ["Chipotle Mexican Grill, Inc.", 50.84000015258789, "1973-05-04", "2023-02-27"], "CMI": ["Cummins Inc.", 230.6199951171875, "1962-01-03", "2023-02-27"], "CMS": ["CMS Energy Corporation", 162.22999572753906, "1973-02-22", "2023-02-27"], "CNC": ["Centene Corporation", 113.0, "2013-11-19", "2023-02-27"], "CNP": ["CenterPoint Energy, Inc.", 69.95999908447266, "1994-03-18", "2023-02-27"], "COF": ["Capital One Financial Corporation", 10.829999923706055, "1987-07-27", "2023-02-27"], "COG": ["Cabot Oil & Gas Corporation", 141.44000244140625, "1972-08-28", "2023-02-27"], "COP": ["ConocoPhillips", 106.72000122070312, "1973-05-04", "2023-02-27"], "COST": ["Costco Wholesale Corporation", 94.52999877929688, "1974-12-18", "2023-02-27"], "COTY": ["Coty Inc.", 41.75, "2014-09-25", "2023-02-27"], "CPB": ["Campbell Soup Company", 208.8800048828125, "2013-05-10", "2023-02-27"], "CPRI": ["Capri Holdings Limited", 39.65999984741211, "2015-07-07", "2023-02-27"], "CRM": ["salesforce.com, inc.", 483.32000732421875, "1984-10-18", "2023-02-27"], "CSCO": ["Cisco Systems, Inc.", 196.67999267578125, "1985-07-01", "2023-02-27"], "CSX": ["CSX Corporation", 100.44999694824219, "1962-01-03", "2023-02-27"], "CTAS": ["Cintas Corporation", 242.13999938964844, "1995-11-20", "2023-02-27"], "CTL": ["CenturyLink, Inc.", 845.9600219726562, "1993-04-26", "2023-02-27"], "CTSH": ["Cognizant Technology Solutions Corporation", 234.42999267578125, "1994-02-18", "2023-02-27"], "CTVA": ["Corteva, Inc.", 50.470001220703125, "1977-01-04", "2023-02-27"], "CTXS": ["Citrix Systems, Inc.", 61.52000045776367, "1980-01-03", "2023-02-27"], "CVS": ["CVS Health Corporation", 11.206999778747559, "2012-05-16", "2023-02-27"], "CVX": ["Chevron Corporation", 259.8999938964844, "1999-06-17", "2023-02-27"], "CXO": ["Concho Resources Inc.", 342.3699951171875, "2004-11-08", "2023-02-27"], "D": ["Dominion Energy, Inc.", 34.72999954223633, "1997-08-18", "2023-02-27"], "DAL": ["Delta Air Lines, Inc.", 323.0299987792969, "2002-05-24", "2023-02-27"], "DD": ["DuPont de Nemours, Inc.", 178.16000366210938, "2001-03-12", "2023-02-27"], "DFS": ["Discover Financial Services", 47.91999816894531, "1980-03-18", "2023-02-27"], "DG": ["Dollar General Corporation", 127.19999694824219, "2007-06-15", "2023-02-27"], "DGX": ["Quest Diagnostics Incorporated", 2572.25, "1991-04-03", "2023-02-27"], "DHI": ["D.R. Horton, Inc.", 102.91999816894531, "1992-04-02", "2023-02-27"], "DHR": ["Danaher Corporation", 234.4499969482422, "1983-06-20", "2023-02-27"], "DIS": ["The Walt Disney Company", 104.87999725341797, "2004-11-01", "2023-02-27"], "DISCA": ["Discovery, Inc.", 67.94000244140625, "1980-10-02", "2023-02-27"], "DISCK": ["Discovery, Inc.", 95.80999755859375, "1980-03-18", "2023-02-27"], "DISH": ["DISH Network Corporation", 175.91000366210938, "1972-06-02", "2023-02-27"], "DLR": ["Digital Realty Trust, Inc.", 506.510009765625, "1982-01-04", "2023-02-27"], "DLTR": ["Dollar Tree, Inc.", 17.360000610351562, "2011-02-14", "2023-02-27"], "DOV": ["Dover Corporation", 109.02999877929688, "1994-11-17", "2023-02-27"], "DRE": ["Duke Realty Corporation", 123.45999908447266, "2006-12-14", "2023-02-27"], "DTE": ["DTE Energy Company", 107.62999725341797, "1983-07-05", "2023-02-27"], "DUK": ["Duke Energy Corporation", 60.189998626708984, "1973-02-22", "2023-02-27"], "DVN": ["Devon Energy Corporation", 675.02001953125, "1973-02-22", "2023-02-27"], "DXC": ["DXC Technology Company", 354.80999755859375, "1994-11-11", "2023-02-27"], "EA": ["Electronic Arts Inc.", 166.80999755859375, "1973-02-22", "2023-02-27"], "ECL": ["Ecolab Inc.", 105.41999816894531, "2007-09-11", "2023-02-27"], "ED": ["Consolidated Edison, Inc.", 381.0199890136719, "1987-07-30", "2022-12-23"], "EFX": ["Equifax Inc.", 121.36000061035156, "1980-03-18", "2023-02-27"], "EIX": ["Edison International", 64.5050048828125, "1994-10-19", "2023-02-27"], "EL": ["The Estee Lauder Companies Inc.", 73.7699966430664, "2015-07-07", "2023-02-27"], "EMN": ["Eastman Chemical Company", 35.099998474121094, "1980-03-18", "2023-02-27"], "EMR": ["Emerson Electric Co.", 27.549999237060547, "1980-03-18", "2023-02-27"], "EOG": ["EOG Resources, Inc.", 83.83999633789062, "1962-01-03", "2023-02-27"], "EQIX": ["Equinix, Inc. (REIT)", 147.9199981689453, "1980-12-15", "2023-02-27"], "EQR": ["Equity Residential", 463.260009765625, "1997-12-19", "2023-02-27"], "ES": ["Eversource Energy", 111.31999969482422, "1984-09-10", "2023-02-27"], "ESS": ["Essex Property Trust, Inc.", 73.02999877929688, "1972-06-02", "2023-02-27"], "ETFC": ["E*TRADE Financial Corporation", 94.25, "1980-03-18", "2023-02-27"], "ETN": ["Eaton Corporation plc", 184.89999389648438, "1984-06-21", "2023-02-27"], "ETR": ["Entergy Corporation", 124.44000244140625, "1996-07-01", "2023-02-27"], "EVRG": ["Evergy, Inc.", 142.17999267578125, "1999-11-19", "2023-02-27"], "EW": ["Edwards Lifesciences Corporation", 129.25, "1998-07-02", "2023-02-27"], "EXC": ["Exelon Corporation", 221.94000244140625, "1980-03-18", "2023-02-27"], "EXPD": ["Expeditors International of Washington, Inc.", 239.97999572753906, "1962-01-03", "2023-02-27"], "EXPE": ["Expedia Group, Inc.", 75.1500015258789, "2007-04-11", "2023-02-27"], "EXR": ["Extra Space Storage Inc.", 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"2023-02-27"], "GILD": ["Gilead Sciences, Inc.", 39.22999954223633, "1979-05-16", "2023-02-27"], "GIS": ["General Mills, Inc.", 19.3700008392334, "1980-03-18", "2023-02-27"], "GL": ["Globe Life Inc.", 38.880001068115234, "1983-11-22", "2023-02-27"], "GLW": ["Corning Incorporated", 327.3900146484375, "1993-10-06", "2023-02-27"], "GM": ["General Motors Company", 174.3800048828125, "1972-06-02", "2023-02-27"], "GOOG": ["Alphabet Inc.", 90.8499984741211, "1962-01-03", "2023-02-27"], "GOOGL": ["Alphabet Inc.", 25.829999923706055, "1962-01-03", "2023-02-27"], "GPC": ["Genuine Parts Company", 164.27999877929688, "1980-03-18", "2023-02-27"], "GPN": ["Global Payments Inc.", 138.66000366210938, "1996-12-18", "2023-02-27"], "GPS": ["The Gap, Inc.", 114.75, "1989-10-05", "2023-02-27"], "GRMN": ["Garmin Ltd.", 138.8800048828125, "1980-03-18", "2023-02-27"], "GS": ["The Goldman Sachs Group, Inc.", 209.97999572753906, "2010-12-16", "2023-02-27"], "HAL": ["Halliburton Company", 341.9900207519531, "1991-10-25", "2023-02-27"], "HAS": ["Hasbro, Inc.", 22.139999389648438, "1996-10-30", "2023-02-27"], "HBAN": ["Huntington Bancshares Incorporated", 101.77999877929688, "2012-04-20", "2023-02-27"], "HBI": ["Hanesbrands Inc.", 2521.080078125, "1999-04-01", "2023-02-27"], "HCA": ["HCA Healthcare, Inc.", 71.83000183105469, "2005-12-22", "2023-02-27"], "HD": ["The Home Depot, Inc.", 43.060001373291016, "1980-03-18", "2023-02-27"], "HFC": ["HollyFrontier Corporation", 35.369998931884766, "2008-05-08", "2023-02-27"], "HIG": ["The Hartford Financial Services Group, Inc.", 239.22000122070312, "1980-03-18", "2023-02-27"], "HII": ["Huntington Ingalls Industries, Inc.", 421.6300048828125, "1972-06-02", "2023-02-27"], "HLT": ["Hilton Worldwide Holdings Inc.", 126.58000183105469, "2011-06-27", "2023-02-27"], "HOG": ["Harley-Davidson, Inc.", 73.30000305175781, "1999-11-01", "2023-02-27"], "HOLX": ["Hologic, Inc.", 11.210000038146973, "2013-06-14", "2023-02-27"], "HON": ["Honeywell International 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Flavors & Fragrances Inc.", 83.38999938964844, "1973-05-03", "2023-02-27"], "ILMN": ["Illumina, Inc.", 246.85000610351562, "1973-02-22", "2023-02-27"], "INCY": ["Incyte Corporation", 585.4500122070312, "2009-08-07", "2023-02-27"], "INFO": ["IHS Markit Ltd.", 182.22999572753906, "1999-11-11", "2023-02-27"], "INTC": ["Intel Corporation", 243.1999969482422, "1973-02-22", "2023-02-27"], "INTU": ["Intuit Inc.", 43.93000030517578, "2000-06-26", "2023-02-27"], "IP": ["International Paper Company", 428.989990234375, "1992-02-14", "2023-02-27"], "IPG": ["The Interpublic Group of Companies, Inc.", 73.05999755859375, "1973-02-22", "2023-02-27"], "IPGP": ["IPG Photonics Corporation", 35.6400032043457, "1980-03-18", "2023-02-27"], "IQV": ["IQVIA Holdings Inc.", 109.36000061035156, "1962-01-03", "2023-02-27"], "IR": ["Ingersoll Rand Inc.", 285.4200134277344, "1980-03-18", "2023-02-27"], "IRM": ["Iron Mountain Incorporated", 24.31999969482422, "1980-03-18", "2023-02-27"], "ISRG": ["Intuitive Surgical, Inc.", 173.24000549316406, "1994-03-14", "2023-02-27"], "IT": ["Gartner, Inc.", 204.24000549316406, "1980-03-18", "2023-02-27"], "ITW": ["Illinois Tool Works Inc.", 84.58999633789062, "1980-03-18", "2023-02-27"], "IVZ": ["Invesco Ltd.", 184.13999938964844, "2002-12-09", "2023-02-27"], "J": ["Jacobs Engineering Group Inc.", 5166.93017578125, "1985-07-23", "2023-02-27"], "JBHT": ["J.B. Hunt Transport Services, Inc.", 59.81999969482422, "1962-01-03", "2023-02-27"], "JCI": ["Johnson Controls International plc", 217.3800048828125, "2011-03-23", "2023-02-27"], "JKHY": ["Jack Henry & Associates, Inc.", 66.05999755859375, "1997-06-19", "2023-02-27"], "JNJ": ["Johnson & Johnson", 144.1300048828125, "2007-04-20", "2023-02-27"], "JNPR": ["Juniper Networks, Inc.", 110.58000183105469, "2005-04-15", "2023-02-27"], "JPM": ["JPMorgan Chase & Co.", 262.6199951171875, "1980-03-18", "2023-02-27"], "JWN": ["Nordstrom, Inc.", 523.1500244140625, "2007-10-26", "2023-02-27"], "K": ["Kellogg Company", 59.93000030517578, "1980-03-18", "2023-02-27"], "KEY": ["KeyCorp", 344.4800109863281, "1992-06-18", "2023-02-27"], "KEYS": ["Keysight Technologies, Inc.", 68.2300033569336, "1980-03-18", "2023-02-27"], "KHC": ["The Kraft Heinz Company", 139.77999877929688, "2008-04-24", "2023-02-27"], "KIM": ["Kimco Realty Corporation", 341.510009765625, "1973-02-22", "2023-02-27"], "KLAC": ["KLA 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"2023-02-27"], "LIN": ["Linde plc", 13.069999694824219, "2006-10-03", "2023-02-27"], "LKQ": ["LKQ Corporation", 188.3300018310547, "1992-06-02", "2023-02-27"], "LLY": ["Eli Lilly and Company", 232.8000030517578, "1973-03-14", "2023-02-27"], "LMT": ["Lockheed Martin Corporation", 20.6299991607666, "1991-11-25", "2023-02-27"], "LNC": ["Lincoln National Corporation", 693.2000122070312, "2000-08-14", "2023-02-27"], "LNT": ["Alliant Energy Corporation", 84.83999633789062, "1973-02-22", "2023-02-27"], "LOW": ["Lowe's Companies, Inc.", 16.329999923706055, "1962-01-03", "2023-02-27"], "LRCX": ["Lam Research Corporation", 77.83999633789062, "1995-12-18", "2023-02-27"], "LUV": ["Southwest Airlines Co.", 62.7400016784668, "1987-09-29", "2023-02-27"], "LVS": ["Las Vegas Sands Corp.", 36.34000015258789, "1991-12-18", "2023-02-27"], "LYB": ["LyondellBasell Industries N.V.", 4.460000038146973, "1994-09-14", "2023-02-27"], "LYV": ["Live Nation Entertainment, Inc.", 138.27000427246094, "2018-12-10", "2023-02-27"], "M": ["Macy's, Inc.", 20.010000228881836, "1980-03-18", "2023-02-27"], "MA": ["Mastercard Incorporated", 101.25, "2002-07-31", "2023-02-27"], "MAA": ["Mid-America Apartment Communities, Inc.", 40.779998779296875, "1972-06-02", "2023-02-27"], "MAR": ["Marriott International, Inc.", 91.48999786376953, "1992-06-08", "2023-02-27"], "MAS": ["Masco Corporation", 201.97000122070312, "1997-08-11", "2023-02-27"], "MCD": ["McDonald's Corporation", 65.63999938964844, "2001-06-14", "2023-02-27"], "MCHP": ["Microchip Technology Incorporated", 12.9399995803833, "2019-09-27", "2023-02-27"], "MDLZ": ["Mondelez International, Inc.", 66.62000274658203, "2016-07-06", "2023-02-27"], "MDT": ["Medtronic plc", 7.5, "1994-07-25", "2023-02-27"], "MET": ["MetLife, Inc.", 105.86000061035156, "1982-01-04", "2023-02-27"], "MGM": ["MGM Resorts International", 141.35000610351562, "1984-07-20", "2023-02-27"], "MHK": ["Mohawk Industries, Inc.", 145.08999633789062, "2013-12-13", "2023-02-27"], "MKTX": ["MarketAxess Holdings Inc.", 17.65999984741211, "2019-02-05", "2023-02-27"], "MLM": ["Martin Marietta Materials, Inc.", 49.709999084472656, "2014-10-01", "2023-02-27"], "MMC": ["Marsh & McLennan Companies, Inc.", 47.25, "1986-07-09", "2023-02-27"], "MMM": ["3M Company", 131.64999389648438, "1998-08-19", "2023-02-27"], "MNST": ["Monster Beverage Corporation", 129.22000122070312, "1980-03-18", "2023-02-27"], "MO": ["Altria Group, Inc.", 59.70000076293945, "1973-02-22", "2023-02-27"], "MOS": ["The Mosaic Company", 51.459999084472656, "1973-02-22", "2023-02-27"], "MPC": ["Marathon Petroleum Corporation", 49.98999786376953, "1993-08-16", "2023-02-27"], "MRK": ["Merck & Co., Inc.", 91.0199966430664, "1980-03-18", "2023-02-27"], "MRO": ["Marathon Oil Corporation", 20.40999984741211, "1992-02-06", "2023-02-27"], "MS": ["Morgan Stanley", 99.16000366210938, "1962-04-03", "2023-02-27"], "MSCI": ["MSCI Inc.", 53.70000076293945, "2013-11-08", "2022-10-27"], "MSFT": ["Microsoft Corporation", 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"2023-02-27"], "NVR": ["NVR, Inc.", 110.08000183105469, "1997-04-16", "2023-02-27"], "NWL": ["Newell Brands Inc.", 46.779998779296875, "1972-06-02", "2023-02-27"], "NWS": ["News Corporation", 240.64999389648438, "1990-03-30", "2023-02-27"], "NWSA": ["News Corporation", 82.62999725341797, "1985-04-19", "2023-02-27"], "O": ["Realty Income Corporation", 154.8800048828125, "1998-06-30", "2023-02-27"], "ODFL": ["Old Dominion Freight Line, Inc.", 235.00999450683594, "1999-01-25", "2023-02-27"], "OKE": ["ONEOK, Inc.", 73.27999877929688, "2007-04-11", "2023-02-27"], "OMC": ["Omnicom Group Inc.", 30.700000762939453, "1982-01-04", "2023-02-27"], "ORCL": ["Oracle Corporation", 37.02000045776367, "1972-06-02", "2023-02-27"], "ORLY": ["O'Reilly Automotive, Inc.", 140.5500030517578, "2007-03-23", "2023-02-27"], "OXY": ["Occidental Petroleum Corporation", 151.52999877929688, "1997-05-29", "2023-02-27"], "PAYC": ["Paycom Software, Inc.", 164.6199951171875, "2015-12-10", "2023-02-27"], "PAYX": ["Paychex, Inc.", 207.6300048828125, "2010-06-30", "2023-02-27"], "PBCT": ["People's United Financial, Inc.", 47.13999938964844, "1980-03-19", "2023-02-27"], "PCAR": ["PACCAR Inc", 83.9000015258789, "1998-01-05", "2023-02-27"], "PEAK": ["Healthpeak Properties, Inc.", 31.639999389648438, "2015-06-25", "2023-02-27"], "PEG": ["Public Service Enterprise Group Incorporated", 221.60000610351562, "1980-03-18", "2023-02-27"], "PEP": ["PepsiCo, Inc.", 83.87999725341797, "1980-03-18", "2023-02-27"], "PFE": ["Pfizer Inc.", 632.0800170898438, "1995-03-16", "2023-02-27"], "PFG": ["Principal Financial Group, Inc.", 43.40999984741211, "1962-01-03", "2023-02-27"], "PG": ["The Procter & Gamble Company", 106.83999633789062, "2005-07-22", "2023-02-27"], "PGR": ["The Progressive Corporation", 99.7699966430664, "1980-03-18", "2023-02-27"], "PH": ["Parker-Hannifin Corporation", 165.75, "1985-11-21", "2023-02-27"], "PHM": ["PulteGroup, Inc.", 14.420000076293945, "1980-03-18", "2023-02-27"], "PKG": ["Packaging Corporation of America", 77.08999633789062, "1973-02-22", "2023-02-27"], "PKI": ["PerkinElmer, Inc.", 379.0899963378906, "1980-10-09", "2023-02-27"], "PLD": ["Prologis, Inc.", 110.91999816894531, "1985-08-09", "2023-02-27"], "PM": ["Philip Morris International Inc.", 155.05999755859375, "1973-02-22", "2023-02-27"], "PNC": ["The PNC Financial Services Group, Inc.", 30.68000030517578, "1999-06-28", "2023-02-27"], "PNR": ["Pentair plc", 74.2699966430664, "1980-03-20", "2023-02-27"], "PPG": ["PPG Industries, Inc.", 22.1299991607666, "2020-04-02", "2023-02-27"], "PPL": ["PPL Corporation", 296.2300109863281, "1991-08-16", "2023-02-27"], "PRGO": ["Perrigo Company plc", 142.16000366210938, "1980-03-18", "2023-02-27"], "PRU": ["Prudential Financial, Inc.", 79.68000030517578, "1980-03-18", "2023-02-27"], "PSX": ["Phillips 66", 34.54999923706055, "1980-03-18", "2023-02-27"], "PVH": ["PVH Corp.", 34.209999084472656, "1973-02-22", "2023-02-27"], "PWR": ["Quanta Services, Inc.", 41.380001068115234, "1993-01-20", "2023-02-27"], "PXD": ["Pioneer Natural Resources Company", 29.93000030517578, "2014-11-03", "2023-02-27"], "PYPL": ["PayPal Holdings, Inc.", 46.81999969482422, "1992-05-20", "2023-02-27"], "QCOM": ["QUALCOMM Incorporated", 165.47000122070312, "2013-02-04", "2023-02-27"], "QRVO": ["Qorvo, Inc.", 363.3599853515625, "1992-02-27", "2023-02-27"], "RCL": ["Royal Caribbean Cruises Ltd.", 294.3900146484375, "1982-01-04", "2023-02-27"], "REG": ["Regency Centers Corporation", 365.489990234375, "2010-09-10", "2023-02-27"], "REGN": ["Regeneron Pharmaceuticals, Inc.", 289.7099914550781, "1994-11-01", "2023-02-27"], "RF": ["Regions Financial Corporation", 110.9000015258789, "1989-09-21", "2023-02-27"], "RHI": ["Robert Half International Inc.", 150.0, "1980-03-18", "2023-02-27"], "RJF": ["Raymond James Financial, Inc.", 77.33999633789062, "2000-03-28", "2023-02-27"], "RL": ["Ralph Lauren Corporation", 161.8800048828125, "2004-08-17", "2023-02-27"], "RMD": ["ResMed Inc.", 30.899999618530273, "1973-05-04", "2023-02-27"], "ROK": ["Rockwell Automation, Inc.", 310.0400085449219, "1995-11-20", "2023-02-27"], "ROL": ["Rollins, Inc.", 310.29998779296875, "2007-07-30", "2023-02-27"], "ROP": ["Roper Technologies, Inc.", 192.4499969482422, "1962-01-03", "2023-02-27"], "ROST": ["Ross Stores, Inc.", 155.07000732421875, "1980-03-18", "2023-02-27"], "RSG": ["Republic Services, Inc.", 75.69999694824219, "1973-05-09", "2023-02-27"], "RTX": ["Raytheon Technologies Corporation", 90.06999969482422, "1962-01-03", "2023-02-27"], "SBAC": ["SBA Communications Corporation (REIT)", 51.56999969482422, "1987-08-21", "2023-02-27"], "SBUX": ["Starbucks Corporation", 1494.75, "2006-01-27", "2023-02-27"], "SCHW": ["The Charles Schwab Corporation", 37.349998474121094, "1980-03-18", "2023-02-27"], "SEE": ["Sealed Air Corporation", 196.89999389648438, "2000-07-31", "2023-02-27"], "SHW": ["The Sherwin-Williams Company", 29.469999313354492, "1983-09-26", "2023-02-27"], "SIVB": ["SVB Financial Group", 85.83999633789062, "2004-09-30", "2023-02-27"], "SJM": ["The J. M. Smucker Company", 47.58000183105469, "1973-05-04", "2023-02-27"], "SLB": ["Schlumberger Limited", 95.61000061035156, "2002-07-31", "2023-02-27"], "SLG": ["SL Green Realty Corp.", 184.2899932861328, "1980-03-18", "2023-02-27"], "SNA": ["Snap-on Incorporated", 121.9000015258789, "2010-12-10", "2023-02-27"], "SNPS": ["Synopsys, Inc.", 53.70000076293945, "1980-03-18", "2023-02-27"], "SO": ["The Southern Company", 293.94000244140625, "1999-03-11", "2023-02-27"], "SPGI": ["S&P Global Inc.", 23.270000457763672, "1980-03-18", "2023-02-27"], "SRE": ["Sempra Energy", 44.7400016784668, "2014-05-23", "2023-02-27"], "STE": ["STERIS plc", 96.69999694824219, "1993-02-24", "2023-02-27"], "STT": ["State Street Corporation", 162.89999389648438, "1998-02-13", "2023-02-27"], "STX": ["Seagate Technology plc", 82.0999984741211, "1972-06-02", "2023-02-27"], "STZ": ["Constellation Brands, Inc.", 304.6600036621094, "1996-05-10", "2023-02-27"], "SWK": ["Stanley Black & Decker, Inc.", 101.51000213623047, "2005-11-17", "2023-02-27"], "SWKS": ["Skyworks Solutions, Inc.", 757.7899780273438, "1991-04-03", "2023-02-27"], "SYF": ["Synchrony Financial", 250.16000366210938, "1986-03-14", "2023-02-27"], "SYK": ["Stryker Corporation", 99.02999877929688, "2001-12-14", "2023-02-27"], "SYY": ["Sysco Corporation", 78.7699966430664, "1980-03-18", "2023-02-27"], "T": ["AT&T Inc.", 198.27999877929688, "1998-02-02", "2023-02-27"], "TAP": ["Molson Coors Beverage Company", 470.07000732421875, "1982-01-04", "2023-02-27"], "TDG": ["TransDigm Group Incorporated", 35.4900016784668, "2003-12-10", "2023-02-27"], "TEL": ["TE Connectivity Ltd.", 296.010009765625, "1981-09-23", "2023-02-27"], "TFC": ["Truist Financial Corporation", 151.07000732421875, "1988-06-23", "2023-02-27"], "TFX": ["Teleflex Incorporated", 27.489999771118164, "1973-02-22", "2023-02-27"], "TGT": ["Target Corporation", 173.3000030517578, "1972-06-02", "2023-02-27"], "TIF": ["Tiffany & Co.", 87.95999908447266, "1986-03-13", "2023-02-27"], "TJX": ["The TJX Companies, Inc.", 80.62000274658203, "1993-03-22", "2023-02-27"], "TMO": ["Thermo Fisher Scientific Inc.", 28.469999313354492, "1962-01-03", "2023-02-27"], "TMUS": ["T-Mobile US, Inc.", 57.900001525878906, "1984-06-04", "2023-02-27"], "TROW": ["T. Rowe Price Group, Inc.", 36.58000183105469, "1962-01-03", "2023-02-27"], "TRV": ["The Travelers Companies, Inc.", 111.44999694824219, "2007-06-15", "2023-02-27"], "TSCO": ["Tractor Supply Company", 65.23999786376953, "1980-03-18", "2023-02-27"], "TSLA": ["Tesla Inc.", 138.0, "2014-06-09", "2023-02-27"], "TSN": ["Tyson Foods, Inc.", 69.98999786376953, "1972-06-02", "2023-02-27"], "TT": ["Trane Technologies plc", 294.1199951171875, "2014-04-16", "2023-02-27"], "TTWO": ["Take-Two Interactive Software, Inc.", 169.61000061035156, "1998-03-24", "2023-02-27"], "TWTR": ["Twitter, Inc.", 359.94000244140625, "1994-02-18", "2023-02-27"], "TXN": ["Texas Instruments Incorporated", 59.7599983215332, "2018-04-30", "2023-02-27"], "TXT": ["Textron Inc.", 35.5, "2014-08-01", "2023-02-27"], "UA": ["Under Armour, Inc.", 181.1199951171875, "1973-02-22", "2023-02-27"], "UAA": ["Under Armour, Inc.", 85.19000244140625, "2004-06-14", "2023-02-27"], "UAL": ["United Airlines Holdings, Inc.", 35.38999938964844, "1980-03-18", "2023-02-27"], "UDR": ["UDR, Inc.", 35.43000030517578, "2019-03-13", "2023-02-27"], "UHS": ["Universal Health Services, Inc.", 68.12999725341797, "1973-05-04", "2023-02-27"], "ULTA": ["Ulta Beauty, Inc.", 1431.5899658203125, "1997-11-17", "2023-02-27"], "UNH": ["UnitedHealth Group Incorporated", 162.82000732421875, "1962-01-03", "2023-02-27"], "UNM": ["Unum Group", 332.29998779296875, "1983-01-24", "2023-02-27"], "UNP": ["Union Pacific Corporation", 216.10000610351562, "2009-11-16", "2023-02-27"], "UPS": ["United Parcel Service, Inc.", 37.06999969482422, "1973-02-22", "2023-02-27"], "URI": ["United Rentals, Inc.", 102.44000244140625, "1992-06-29", "2023-02-27"], "USB": ["U.S. Bancorp", 210.6999969482422, "1982-01-04", "2023-02-27"], "V": ["Visa Inc.", 63.06999969482422, "1993-11-01", "2023-02-27"], "VAR": ["Varian Medical Systems, Inc.", 8.539999961853027, "2016-03-24", "2023-02-27"], "VFC": ["V.F. 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"2013-06-20", "2023-02-27"], "2885.TW": ["\u5143\u5927\u91d1", 54.400001525878906, "1982-01-04", "2023-02-27"], "2886.TW": ["\u5146\u8c50\u91d1", 34.310001373291016, "1982-01-04", "2023-02-27"], "2887.TW": ["\u53f0\u65b0\u91d1", 236.88999938964844, "1973-02-22", "2023-02-27"], "2890.TW": ["\u6c38\u8c50\u91d1", 322.32000732421875, "1986-08-14", "2023-02-27"]} \ No newline at end of file diff --git a/main.py b/main.py new file mode 100644 index 0000000..13bd10a --- /dev/null +++ b/main.py @@ -0,0 +1,387 @@ +from flask import Flask, render_template, request, redirect, url_for, g, session, flash, jsonify +# from flask_login import LoginManager, UserMixin, login_user, logout_user, login_required, current_user +from markupsafe import escape + +from werkzeug.security import generate_password_hash, check_password_hash +from datetime import datetime, date, timedelta + + +import os +import json +import time +import random +import string +import numpy as np +import pandas as pd +import psycopg2 +import plotly +import plotly.express as px +from portfolio_builder import MVO +pd.options.plotting.backend = "plotly" + +# PARAMETERS +CONFIGS = { + "ENV": "development", + "DEBUG": True, + "SECRET_KEY": os.urandom(30), # Set the secret key for session authentication + "PERMANENT_SESSION_LIFETIME": timedelta(minutes=60) +} +SQL_CONFIG = { + 'database': "tpm", + 'user': "hsienchen", + 'host': "127.0.0.1", + 'port': "5432" +} + + +app = Flask(__name__) +app.config.from_mapping(CONFIGS) +# Login Manager, Flask_Login Stuff +# login_manager = LoginManager() +# login_manager.init_app(app) +# login_manager.login_view = 'login' +def login_required(): + if not 'username' in session: + return False + else: + return True +def get_stock(conn, stock_list, tw): + ## Query DB + if tw==1: + sql="SELECT ticker, date, price, return FROM stock_price_tw where ticker = ANY(%s);" + with conn: + with conn.cursor() as curs: + curs.execute(sql, (stock_list, )) + # print(curs.mogrify(sql, (stock_list,))) + data= curs.fetchall() + else: + sql1="SELECT ticker, date, price, return FROM stock_price where ticker = ANY(%s)" + sql2="SELECT ticker, date, price, return FROM stock_price_tw where ticker = ANY(%s) ;" + with conn: + with conn.cursor() as curs: + curs.execute(sql1, (stock_list,)) + data_us= curs.fetchall() + curs.execute(sql2, (stock_list,)) + data_tw= curs.fetchall() + data = data_us+data_tw + dfStock = pd.DataFrame(data, columns=['ticker', 'date', 'price', 'return']) + dfStock['date'] = pd.to_datetime(dfStock['date']) + dfStock = dfStock.drop_duplicates() + g = dfStock.groupby('ticker') + port = pd.concat([g.get_group(t).set_index('date')['price'] for t in stock_list], axis=1, join='inner') + port.columns=stock_list + return port + + +# Define the route for the index pages +@app.route('/') +def index(): + return render_template('base.html') + +# Login Page +@app.route('/login') +def login(): + # for key in session: + # print(key, session[key]) + # print(session.get('username'), session['username'], session.get('username') and session['username']) + return render_template('login.html') +@app.route('/login', methods=['POST']) +def login_post(): + # Get the username and password from the form + username = request.form.get('username') + password = request.form.get('password') + print(username, password) + + + ## Connect to the database + conn = psycopg2.connect(**SQL_CONFIG) + with conn: + with conn.cursor() as curs: + curs.execute("select * from users where username = %s;", (username, )) + data = curs.fetchone() + conn.close() + + # Authentication + if (data is None) or (username is None) or (password is None): + flash('使用者代號不對或密碼不對，請再試一次。', 'danger') + return render_template('login.html') + elif check_password_hash(data[2], password): + session['username'] = username + session['user_id'] = data[0] + session['privilege'] = data[-1] + session['update_freq'] = 100 + session['lastCreateTime'] = time.time() + session['tw'] = 1 + return redirect(url_for('index')) + else: + flash('使用者代號不對或密碼不對，請再試一次。', 'danger') + return render_template('login.html') + +# Registration Page +@app.route('/registration') +def registration(): + if login_required(): + return redirect(url_for('index')) + return render_template('registration.html') +@app.route('/registration', methods=['POST']) +def registration_post(): + # Get the username and password from the form + username = request.form.get('username') + password = request.form.get('password') + rep_password = request.form.get('rep-password') + # check password + if not password is None and password == rep_password: + print(username, password) + conn = psycopg2.connect(**SQL_CONFIG) + ## Connect to the database + with conn.cursor() as curs: + curs.execute("select * from users where username = %s;", (username, )) + data = curs.fetchone() + if data is None: + with conn: + with conn.cursor() as curs: + curs.execute("insert into users (username, password) values (%s, %s);", (username, generate_password_hash(password))) + # conn.commit() + else: + flash('使用者已存在。', 'warning') + return redirect(url_for('login')) + conn.close() + name = username.split('@')[0] + flash(f'註冊成功！ 歡迎您, {name}。', 'success') + return redirect(url_for('login')) + else: + flash('密碼不符合，請再次輸入。', 'warning') + return redirect(url_for('registration')) + +# Logout Page +@app.route('/logout', methods=['GET']) +def logout(): + if login_required(): + pass + else: + flash('請先登入。', 'warning') + return redirect(url_for('login')) + if 'username' in session: + # for key in list(session.keys()): + # print(key, session[key]) + # for key in list(session.keys()): + # session.pop(key) + session.clear() + return redirect(url_for('index')) + + + +@app.route('/strategy') +def strategy(): + if login_required(): + pass + else: + flash('使用投組功能請先登入。', 'warning') + return redirect(url_for('login')) + session['tw'] = 0 + # Load Assets + with open('assets_tw.json') as f: + data_tw = json.load(f) + with open('assets_us.json') as f: + data = json.load(f) + + print(request.args.get('data'), 666) + return render_template('strategy_tw.html', stockOpts={**data, **data_tw}) + + + + +@app.route('/strategy_tw') +def strategy_tw(): + if login_required(): + pass + else: + flash('使用投組功能請先登入。', 'warning') + return redirect(url_for('login')) + session['tw'] = 1 + # Load Assets + with open('assets_tw.json') as f: + data = json.load(f) + + print(request.args.get('data'), 666) + return render_template('strategy_tw.html', stockOpts=data) + + + + + +@app.route('/postStock', methods=['POST']) +def submit_stock_list(): + if login_required(): + pass + else: + print('NOT LOGIN!!') + return redirect(url_for('index')) + if not 'tw' in session: + return redirect(url_for('index')) + # Update Session + print("-"*10, "UPDATE ASSET", "-"*10) + if session['update_freq']==0: + print('update to frquent!') + return 'update to frquent!' + else: + session['update_freq']-=1 + flash('Looks like you have changed your name!', 'warning') + stock_list = request.form.get('stockList') # this is string + stock_list = json.loads(stock_list) # Load stock_list as list + session['currStockList'] = stock_list + + ## Query DB + conn = psycopg2.connect(**SQL_CONFIG) + port = get_stock(conn, stock_list, session['tw']) + conn.close() + + fig = port.plot(title = 'Assets in portfolio', + labels=dict(index="Date", value="Price", variable="Assets")) + fig['layout'] = dict( + autosize=True, + legend={'title': {'text': 'Assets'}, 'tracegroupgap': 0}, + title= {'text': 'Assets in portfolio'}, + xaxis= {'anchor': 'y', 'domain': [0.0, 1.0], 'title': {'text': 'Date'}}, + yaxis= {'anchor': 'x', 'domain': [0.0, 1.0], 'title': {'text': 'Price'}} + ) + fig.update_layout(legend=dict( + yanchor="top", + y=0.99, + xanchor="left", + x=0.01 + )) + + print(type(stock_list)) + + # 序列化 + graphJSON = json.dumps(fig, cls=plotly.utils.PlotlyJSONEncoder) + + + for key in request.form: + print(key, request.form[key]) + # Do something with the stock list heres + return graphJSON + # return jsonify({'message': 'Stock list received successfully, NOOOOO'}) + +@app.route('/postPort', methods=['POST']) +def buildPort(): + # Login Required + if login_required(): + pass + else: + print('NOT LOGIN!!') + return redirect(url_for('index')) + if not 'tw' in session: + return redirect(url_for('index')) + # Stop frequently building strategy + if time.time() - session['lastCreateTime'] < 20: + print("UNTIL: ", time.time()-session['lastCreateTime']) + return jsonify({'mes': '投資組合建立時間間隔(或與登入時間間隔)必須大於60秒！'}) + session['lastCreateTime'] = time.time() + + + print("-"*10) + for key in request.form: + print(key, request.form[key], type(request.form[key])) + + # Portfolio Info + name = request.form.get('name') + if name == '': + prefix=''.join(random.choices(string.ascii_uppercase + string.digits, k=6)) + name= prefix + f"-{round(time.time()%100, 2)}" + comp = request.form.get('comp') + ts = int(request.form.get('ts')) + ts = datetime.fromtimestamp(ts/1000) + role = request.form.get('role') + ratio = float(request.form.get('ratio')) + comment = request.form.get('comment') + stock_list = json.loads(request.form.get('stockList')) + + # Algorithm MVO + print("-"*10) + print("Enter Algorithms") + print("-"*10) + # time.sleep(20) + # Query DB + market_asset = '0050.TW' if session['tw']==1 else 'SPY' + conn = psycopg2.connect(**SQL_CONFIG) + if 'market_asset' in stock_list: + port = get_stock(conn, stock_list, session['tw']) + market = port[market_asset] + else: + port = get_stock(conn, stock_list+[market_asset], session['tw']) + market = port[market_asset] + port = port[stock_list] + + length, num = port.shape + tsize = int(length*ratio) + # time label + train_label = port.index[1:][:tsize] + test_label = port.index[1:][tsize:] + + # data + data_return = port.pct_change().dropna().to_numpy() + market_return = market.pct_change().dropna().to_numpy() + train = data_return[:tsize, :] + test = data_return[tsize:, :] + train_market = market_return[:tsize] + test_market = market_return[tsize:] + + # optimization + sol = MVO.opt(train, role=role) + train_info = MVO.portfolio_info(sol, train, train_market) + test_info = MVO.portfolio_info(sol, test, test_market) + # print(sol, train_info, test_info) + print("-"*10) + print(ts, name, session.get('username'), comp, + role, test_info['annual_ret'], test_info['vol'], test_info['mdd'], test_info['annual_sr'], + test_info['beta'], test_info['alpha'], test_info['var10'], test_info['R2'], True, comment, stock_list, list(sol), sep=", ") + print("-"*10) + data = (ts, name, session.get('username'), comp, + role, ratio, test_info['annual_ret'], test_info['vol'], test_info['mdd'], test_info['annual_sr'], + test_info['beta'], test_info['alpha'], test_info['var10'], test_info['R2'], True, comment, stock_list, list(sol)) + sql='insert into strategy \ + (date, name, username, competition, role, ratio, annual_ret, vol, mdd, annual_sr, beta, alpha, var10, R2, tw, comment, assets, assets_position)\ + values (%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s);' + with conn: + with conn.cursor() as curs: + curs.execute(sql, data) + conn.close() + print("\n------Write in Success--------\n") + return jsonify({'mes': '投資組合已完成建立，請至gooooooo查詢分析結果。'}) + +@app.route('/custom') +def custom(): + if login_required(): + pass + else: + flash('使用投組功能請先登入。', 'warning') + return redirect(url_for('login')) + return render_template('custom.html', message='No') +@app.route('/result') +def result(): + if login_required(): + pass + else: + flash('使用投組功能請先登入。', 'warning') + return redirect(url_for('login')) + return render_template('result.html') +@app.route('/result_tw') +def result_tw(): + if login_required(): + pass + else: + flash('使用投組功能請先登入。', 'warning') + return redirect(url_for('login')) + return render_template('result_tw.html') + +# handle login failed +@app.errorhandler(401) +def page_not_found(e): + return Response('

Failed

') + + + +if __name__ == "__main__": + app.run(host='0.0.0.0', port=8000) diff --git a/main_old.py b/main_old.py new file mode 100644 index 0000000..709c0da --- /dev/null +++ b/main_old.py @@ -0,0 +1,1347 @@ +# -*- coding: utf-8 -*- + +from flask import Flask, Blueprint, render_template, session, jsonify, request, redirect, url_for, flash, g, Markup, abort +# from flask_ipban import IpBan +from flask_login import login_required, current_user, login_user, logout_user +#from flask_environments import Environments +import sqlite3 +from werkzeug.security import generate_password_hash, check_password_hash +from datetime import datetime, timedelta +from dateutil.relativedelta import relativedelta +import os +import pytz +import re +import time +import json +import psycopg2 +import psycopg2.extras +from tickers_sorted import * +from tickers_sorted_tw import * +from black_list import black_list +from postgresql_config import * + +import numpy as np +import pandas as pd +import matplotlib.pyplot as plt + +import math +from cvxopt import matrix, solvers +from cvxopt.blas import dot +from cvxopt.solvers import qp + +import pickle + +UPLOAD_FOLDER = 'static/custom_data/' +ALLOWED_EXTENSIONS = {'csv'} +def allowed_file(filename): + return '.' in filename and \ + filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS + +main = Flask(__name__) + +# https://stackoverflow.com/questions/24222220/block-an-ip-address-from-accessing-my-flask-app-on-heroku +ip_ban_list = [] +@main.before_request +def block_method(): + ip = request.environ.get('REMOTE_ADDR') + if ip in ip_ban_list: + abort(403) + +main.config['SECRET_KEY'] = os.urandom(30) +main.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER + +from confidential_competitions import * +confidential_competitions_placeholder = ', '.join(['%s']*len(confidential_competitions_list)) + +# Source: https://uniwebsidad.com/libros/explore-flask/chapter-8/custom-filters +@main.template_filter('my_substitution') +def my_substitution(string): + return re.sub(r'@[a-zA-Z0-9_\-\.]+', r'', string) + +def ten_day_VaR(portfolio_value): + # 95% z value = 1.645 + z = 1.645 + return_value = portfolio_value.pct_change().shift(-1).dropna() + length = len(return_value) + daily_ret = return_value.mean() + daily_vol = return_value.std() + return -1*(daily_ret * 10 - daily_vol * (10**0.5) * z) + +# risk_free_rate = 0.00899 # U.S. 5 Year Treasury at 11:31 PM EDT, Jun 28, 2021 +risk_free_rate = 0 +def Linear_Reg(x,y): + ''' + input + x: market excess return, np.ndarray + y: portfolio excexx return, np.ndarray + + output + slope, intercept, R^2 + ''' + from sklearn import linear_model + import numpy + model = linear_model.LinearRegression(fit_intercept=True) + X = x[:,numpy.newaxis] + model.fit(X,y) + yhat = model.predict(X) + SSR = sum((y-yhat)**2) + SST = sum((y-numpy.mean(y))**2) + r_squared = 1 - (float(SSR))/SST + return model.coef_[0], model.intercept_, r_squared + # alpha = model.intercept_, beta = model.coef_[0] + +#env.filters['my_substitution'] = my_substitution + + + + +# def connect_db(): +# sql = sqlite3.connect('strategy.db', timeout=50) +# sql.row_factory = sqlite3.Row +# return sql +# +# def get_db(): +# if not hasattr(g, 'sqlite_db'): +# g.sqlite_db = connect_db() +# return g.sqlite_db +# +# @main.teardown_appcontext +# def close_db(error): +# if hasattr(g, 'sqlite_db'): +# g.sqlite_conn.close() + + +@main.route('/') +def index(): + + conn = psycopg2.connect(database=POSTGRESQL_DATABASE, user=POSTGRESQL_USER) + cur = conn.cursor(cursor_factory = psycopg2.extras.DictCursor) + + # Number of effective users + cur.execute('select count(b.a) as num_effective_users from (select min(strategy_id) as a from strategy group by author) as b') + num_effective_users = cur.fetchone()['num_effective_users'] + + # Number of effective strategies + cur.execute('select count(strategy_id) as num_effective_strategies from strategy where sharpe_ratio!=0') + num_effective_strategies = cur.fetchone()['num_effective_strategies'] + + cur.close() + conn.close() + + return render_template('index.html', num_effective_users=num_effective_users, num_effective_strategies=num_effective_strategies) + # return render_template('index_temp_0314.html') + + + + +@main.route('/create_strategy', methods=['GET', 'POST']) +def create_strategy(): + if not (session.get('USERNAME') and session['USERNAME']): + flash('使用此功能必須先登入。', 'danger') + return redirect('/login') + if session['USERNAME'] in black_list: + flash('我們已經暫停您建立策略的權利，有疑問請洽finteck@my.nthu.edu.tw', 'danger') + return redirect('/') + print('last_creation_time: ', session['last_creation_time']) + if time.time() - session['last_creation_time'] < 30: + flash('每兩次建立策略須間隔30秒', 'danger') + return redirect('/') + + if request.method == 'GET': + tw = request.values.get('tw') + tw_digit = 1 if tw=='true' else 0 if tw=='false' else None + + if request.method == 'POST': + strategy_name = request.form['strategy_name'] + create_date = datetime.strftime(datetime.now() + timedelta(hours=8), '%Y/%m/%d %H:%M:%S.%f') + session['last_creation_time'] = time.time() + + if strategy_name == '': + flash('請取一個名字', 'danger') + return render_template('create_strategy.html', asset_candidates=asset_candidates if tw=='false' else asset_candidates_tw if tw=='true' else None, tw=tw) + competition = request.form['competition'] + tw = request.form['tw'] + tw_digit = 1 if tw=='true' else 0 if tw=='false' else None + tickers = sorted(list(set(request.form.getlist('asset_ticker')))) + print('The list of assets: ', tickers) + + + # Turn off progress printing + solvers.options['show_progress'] = False + + start_dates = [ datetime(y, m, 1) for y in range(2015, datetime.now().year+1) + for m in [1, 4, 7, 10] + if datetime(y, m, 1)=start) & (all_data['Date']<=end) ] + data.set_index('Date', inplace=True) + data = data['Adj Close'] + return data + + + portfolio_value = pd.Series([100]) + optimal_weights = None + hist_return_series = pd.DataFrame(columns=['quarter', 'quarterly_returns']) + + index_returns_full = pd.Series() + portfolio_returns_full = pd.Series() + + for i in range(len(start_dates)-3): + + ### Take 6 months to backtest ### + + start = start_dates[i] + end = start_dates[i+2] + + data = pd.DataFrame({ ticker: stockpri(ticker, start, end) for ticker in tickers }) + data = data.dropna() + + returns = data.pct_change() + 1 + returns = returns.dropna() + log_returns = np.log(data.pct_change() + 1) + log_returns = log_returns.dropna() + + if log_returns.empty: + continue + + mu = np.exp(log_returns.mean()*252).values + # Markowitz frontier + profit = np.linspace(np.amin(mu), np.amax(mu), 100) + frontier = [] + w = [] + if len(tickers) >= 3: + for p in profit: + # Problem data. + n = len(tickers) + S = matrix(log_returns.cov().values*252) + pbar = matrix(0.0, (n,1)) + # Gx <= h + G = matrix(0.0, (2*n,n)) + G[::(2*n+1)] = 1.0 + G[n::(2*n+1)] = -1.0 + # h = matrix(1.0, (2*n,1)) + h = matrix(np.concatenate((0.5*np.ones((n,1)), -0.03*np.ones((n,1))), axis=0)) + A = matrix(np.concatenate((np.ones((1,n)), mu.reshape((1,n))), axis=0)) + b = matrix([1, p], (2, 1)) + + # Compute trade-off. + res = qp(S, -pbar, G, h, A, b) + + if res['status'] == 'optimal': + res_weight = res['x'] + s = math.sqrt(dot(res_weight, S*res_weight)) + frontier.append(np.array([p, s])) + w.append(res_weight) + elif len(tickers) == 2: + for p in profit: + S = log_returns.cov().values*252 + res_weight = [1 - (p-mu[0])/(mu[1]-mu[0]), (p-mu[0])/(mu[1]-mu[0])] + if (res_weight[0] < 0.03) or (res_weight[0] > 0.97): + continue + s = math.sqrt(np.matmul(res_weight, np.matmul(S, np.transpose(res_weight)))) + frontier.append(np.array([p, s])) + w.append(res_weight) + elif len(tickers) == 1: + res_weight = [1] + w.append(res_weight) + frontier.append(np.array([1, 1])) + + + frontier = np.array(frontier) + if frontier.shape == (0,): + continue + x = np.array(frontier[:, 0]) + y = np.array(frontier[:, 1]) + + frontier_sharpe_ratios = np.divide(x-1, y) + optimal_portfolio_index = np.argmax(frontier_sharpe_ratios) + optimal_weights = w[optimal_portfolio_index] + + + ### paper trade on the next three months ### + + start = start_dates[i+2] + end = start_dates[i+3] + + if tw=='true': + index_ticker = '0050.TW' + elif tw=='false': + index_ticker = 'SPY' + + data = pd.DataFrame({ ticker: stockpri(ticker, start, end) for ticker in tickers }) + data['market'] = stockpri(index_ticker, start, end) + data = data.dropna() + + returns = data.pct_change() + 1 + returns = returns.dropna() + log_returns = np.log(data.pct_change() + 1) + log_returns = log_returns.dropna() + + index_returns_3_months = returns['market'] + data = data.drop(columns=['market']) + returns = returns.drop(columns=['market']) + log_returns = log_returns.drop(columns=['market']) + portfolio_returns_3_months = pd.Series(np.dot(returns, optimal_weights).flatten()) + + index_returns_full = index_returns_full.append(index_returns_3_months, ignore_index=True) + portfolio_returns_full = portfolio_returns_full.append(portfolio_returns_3_months, ignore_index=True) + + portfolio_cum_returns = np.dot(returns, optimal_weights).cumprod() + portfolio_value_new_window = portfolio_value.iloc[-1].item() * pd.Series(portfolio_cum_returns) + portfolio_value_new_window.index = pd.to_datetime(returns.index, format='%Y-%m-%d') + portfolio_value = portfolio_value.append(portfolio_value_new_window) + + # produce quarterly return + hist_return_series.loc[len(hist_return_series)] = [str(start.year)+'Q'+str((start.month+2)//3), portfolio_cum_returns[-1]-1] + + if optimal_weights == None: + sharpe_ratio = avg_annual_return = annual_volatility = max_drawdown = alpha = beta = r_squared = ten_day_var = 0 + optimal_weights = [0, ]*len(tickers) + hist_returns = None + conn = psycopg2.connect(database=POSTGRESQL_DATABASE, user=POSTGRESQL_USER) + cur = conn.cursor(cursor_factory = psycopg2.extras.DictCursor) + cur.execute("""insert into strategy (strategy_name, + author, + create_date, + sharpe_ratio, + return, + volatility, + max_drawdown, + tw, + competition, + hist_returns, + alpha, + beta, + r_squared, + ten_day_var) + values (%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s) returning strategy_id;""", + (strategy_name, + session['USERNAME'], + create_date, + sharpe_ratio, + avg_annual_return, + annual_volatility, + max_drawdown, + tw_digit, + competition, + hist_returns, + alpha, + beta, + r_squared, + ten_day_var + ) ) + strategy_id = cur.fetchone()[0] + conn.commit() + + # record the list of tickers into database + # strategy_id = cur.execute('select * from strategy where create_date=?', [create_date]).fetchone()['strategy_id'] + for i in range(len(tickers)): + cur.execute('insert into assets_in_strategy (strategy_id, asset_ticker, weight) values (%s, %s, %s)', + (strategy_id, tickers[i], optimal_weights[i])) + conn.commit() + + cur.close() + conn.close() + + print('Strategy_id ' + str(strategy_id) + ' optimization fails.') + flash('無資料或無法畫出馬可維茲邊界，請換一個組合', 'danger') + return render_template('create_strategy.html', asset_candidates=asset_candidates if tw=='false' else asset_candidates_tw if tw=='true' else None, tw=tw) + + avg_annual_return = np.exp(np.log(portfolio_value.pct_change() + 1).mean() * 252) - 1 + annual_volatility = portfolio_value.pct_change().std() * math.sqrt(252) + sharpe_ratio = avg_annual_return/annual_volatility + max_drawdown = - np.amin(np.divide(portfolio_value, np.maximum.accumulate(portfolio_value)) - 1) + ten_day_var = ten_day_VaR(portfolio_value) + beta, alpha, r_squared = Linear_Reg(index_returns_full.to_numpy().flatten() - risk_free_rate, + portfolio_returns_full.to_numpy().flatten() - risk_free_rate) + + print('Sharpe ratio: ', sharpe_ratio, ', Return: ', avg_annual_return, ', Volatility: ', annual_volatility, ', Maximum Drawdown: ', max_drawdown) + + # hist_return_series.set_index('quarter', inplace=True) + hist_returns = pickle.dumps(hist_return_series) + + conn = psycopg2.connect(database=POSTGRESQL_DATABASE, user=POSTGRESQL_USER) + cur = conn.cursor(cursor_factory = psycopg2.extras.DictCursor) + cur.execute("""insert into strategy (strategy_name, + author, + create_date, + sharpe_ratio, + return, + volatility, + max_drawdown, + tw, + competition, + hist_returns, + alpha, + beta, + r_squared, + ten_day_var) + values (%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s) returning strategy_id;""", + (strategy_name, + session['USERNAME'], + create_date, + sharpe_ratio, + avg_annual_return, + annual_volatility, + max_drawdown, + tw_digit, + competition, + hist_returns, + alpha, + beta, + r_squared, + ten_day_var + ) ) + strategy_id = cur.fetchone()[0] + conn.commit() + + # record the list of tickers into database + # strategy_id = cur.execute('select * from strategy where create_date=?', [create_date]).fetchone()['strategy_id'] + for i in range(len(tickers)): + cur.execute('insert into assets_in_strategy (strategy_id, asset_ticker, weight) values (%s, %s, %s)', + (strategy_id, tickers[i], optimal_weights[i])) + conn.commit() + + cur.close() + conn.close() + + # fig, ax = plt.subplots() + # hist_return_series.hist(column='quarterly_returns', by='quarter', ax=ax) + hist_return_plot = hist_return_series.plot.bar(x='quarter', y='quarterly_returns').get_figure() + plt.tight_layout() + # plt.xticks(rotation=45) + hist_return_plot.savefig('static/img/quarterly_returns/'+str(strategy_id)+'.png') + plt.close() + + print(portfolio_value.head()) + print(portfolio_value.tail()) + plt.xticks(rotation=90) + # plt.tight_layout() + plt.plot(portfolio_value.iloc[1:]) + plt.savefig('static/img/portfolio_values/'+str(strategy_id)+'.png', bbox_inches='tight') + plt.close() + print('Strategy_id ' + str(strategy_id) + ' optimization succeeds.') + flash(Markup('回測已完成，詳情請點這裡查看。'), 'success') + return render_template('create_strategy.html', asset_candidates=asset_candidates if tw=='false' else asset_candidates_tw if tw=='true' else None, tw=tw) + + +@main.route('/create_strategy_upload', methods=['GET', 'POST']) +def create_strategy_upload(): + if not (session.get('USERNAME') and session['USERNAME']): + flash('使用此功能必須先登入。', 'danger') + return redirect('/login') + if session['USERNAME'] in black_list: + flash('我們已經暫停您建立策略的權利，有疑問請洽finteck@my.nthu.edu.tw', 'danger') + return redirect('/') + print('last_creation_time: ', session['last_creation_time']) + if time.time() - session['last_creation_time'] < 30: + flash('每兩次建立策略須間隔30秒', 'danger') + return redirect('/') + + if request.method == 'POST': + strategy_name = request.form['strategy_name'] + create_date = datetime.strftime(datetime.now() + timedelta(hours=8), '%Y/%m/%d %H:%M:%S.%f') + session['last_creation_time'] = time.time() + + if strategy_name == '': + flash('請取一個名字', 'danger') + return render_template('create_strategy_upload.html') + + f = request.files['fileToUpload'] + filename = create_date.replace('/', '').replace(' ', '').replace(':', '').replace('.', '') + '.csv' + f.save(os.path.join(main.config['UPLOAD_FOLDER'], filename)) + + all_data = pd.read_csv(main.config['UPLOAD_FOLDER'] + filename) + all_data['Date'] = pd.to_datetime(all_data['Date'], format='%Y-%m-%d') + + tickers = list(all_data.columns) + tickers.remove('Date') + print('The list of assets: ', tickers) + + competition = request.form['competition'] + + + # Turn off progress printing + solvers.options['show_progress'] = False + + + def stockpri(ticker, start, end): + data = all_data.loc[start:end, ['Date', ticker]] + data.set_index('Date', inplace=True) + data = data[ticker] + return data + + + portfolio_value = pd.Series([100]) + optimal_weights = None + hist_return_series = pd.DataFrame(columns=['quarter', 'quarterly_returns']) + + start_dates = list(np.arange(0, len(all_data), 63)) + [len(all_data)-1] + + + for i in range(len(start_dates)-3): + + ### Take 6 months to backtest ### + + start = start_dates[i] + end = start_dates[i+2]+1 + + data = pd.DataFrame({ ticker: stockpri(ticker, start, end) for ticker in tickers }) + data = data.dropna() + + returns = data.pct_change() + 1 + returns = returns.dropna() + log_returns = np.log(data.pct_change() + 1) + log_returns = log_returns.dropna() + + if log_returns.empty: + continue + + mu = np.exp(log_returns.mean()*252).values + # Markowitz frontier + profit = np.linspace(np.amin(mu), np.amax(mu), 100) + frontier = [] + w = [] + if len(tickers) >= 3: + for p in profit: + # Problem data. + n = len(tickers) + S = matrix(log_returns.cov().values*252) + pbar = matrix(0.0, (n,1)) + # Gx <= h + G = matrix(0.0, (2*n,n)) + G[::(2*n+1)] = 1.0 + G[n::(2*n+1)] = -1.0 + # h = matrix(1.0, (2*n,1)) + h = matrix(np.concatenate((0.5*np.ones((n,1)), -0.03*np.ones((n,1))), axis=0)) + A = matrix(np.concatenate((np.ones((1,n)), mu.reshape((1,n))), axis=0)) + b = matrix([1, p], (2, 1)) + + # Compute trade-off. + res = qp(S, -pbar, G, h, A, b) + + if res['status'] == 'optimal': + res_weight = res['x'] + s = math.sqrt(dot(res_weight, S*res_weight)) + frontier.append(np.array([p, s])) + w.append(res_weight) + elif len(tickers) == 2: + for p in profit: + S = log_returns.cov().values*252 + res_weight = [1 - (p-mu[0])/(mu[1]-mu[0]), (p-mu[0])/(mu[1]-mu[0])] + if (res_weight[0] < 0.03) or (res_weight[0] > 0.97): + continue + s = math.sqrt(np.matmul(res_weight, np.matmul(S, np.transpose(res_weight)))) + frontier.append(np.array([p, s])) + w.append(res_weight) + + + frontier = np.array(frontier) + if frontier.shape == (0,): + continue + x = np.array(frontier[:, 0]) + y = np.array(frontier[:, 1]) + + frontier_sharpe_ratios = np.divide(x-1, y) + optimal_portfolio_index = np.argmax(frontier_sharpe_ratios) + optimal_weights = w[optimal_portfolio_index] + + + ### paper trade on the next three months ### + + start = start_dates[i+2] + end = start_dates[i+3]+1 + + data = pd.DataFrame({ ticker: stockpri(ticker, start, end) for ticker in tickers }) + data = data.dropna() + + returns = data.pct_change() + 1 + returns = returns.dropna() + log_returns = np.log(data.pct_change() + 1) + log_returns = log_returns.dropna() + + + portfolio_cum_returns = np.dot(returns, optimal_weights).cumprod() + portfolio_value_new_window = portfolio_value.iloc[-1].item() * pd.Series(portfolio_cum_returns) + portfolio_value_new_window.index = pd.to_datetime(returns.index, format='%Y-%m-%d') + portfolio_value = portfolio_value.append(portfolio_value_new_window) + # portfolio_value.append(portfolio_value_new_window) + + # produce quarterly return + hist_return_series.loc[len(hist_return_series)] = [str(start), portfolio_cum_returns[-1]-1] + + + if len(tickers) == 1: + optimal_weights = [1] + portfolio_value = stockpri(tickers[0], 0, len(all_data)) + for i in range(len(start_dates)-1): + hist_return_series.loc[i] = [ str(start_dates[i]), portfolio_value[start_dates[i+1]] - portfolio_value[start_dates[i]] ] + + if optimal_weights == None: + sharpe_ratio = avg_annual_return = annual_volatility = max_drawdown = ten_day_var = 0 + optimal_weights = [0, ]*len(tickers) + hist_returns = None + conn = psycopg2.connect(database=POSTGRESQL_DATABASE, user=POSTGRESQL_USER) + cur = conn.cursor(cursor_factory = psycopg2.extras.DictCursor) + cur.execute("""insert into strategy (strategy_name, + author, + create_date, + sharpe_ratio, + return, + volatility, + max_drawdown, + tw, + competition, + hist_returns, + alpha, + beta, + r_squared, + ten_day_var) + values (%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s) returning strategy_id;""", + (strategy_name, + session['USERNAME'], + create_date, + sharpe_ratio, + avg_annual_return, + annual_volatility, + max_drawdown, + 0, # tw_digit, + competition, + hist_returns, + 0, # alpha, + 0, # beta, + 0, # r_squared, + ten_day_var + ) ) + strategy_id = cur.fetchone()[0] + conn.commit() + + # record the list of tickers into database + # strategy_id = cur.execute('select * from strategy where create_date=?', [create_date]).fetchone()['strategy_id'] + for i in range(len(tickers)): + cur.execute('insert into assets_in_strategy (strategy_id, asset_ticker, weight) values (%s, %s, %s)', + (strategy_id, tickers[i], optimal_weights[i])) + conn.commit() + + cur.close() + conn.close() + print('Strategy_id ' + str(strategy_id) + ' optimization fails.') + flash('無資料或無法畫出馬可維茲邊界，請換一個組合', 'danger') + return render_template('create_strategy_upload.html') + + avg_annual_return = np.exp(np.log(portfolio_value.pct_change() + 1).mean() * 252) - 1 + annual_volatility = portfolio_value.pct_change().std() * math.sqrt(252) + sharpe_ratio = avg_annual_return/annual_volatility + max_drawdown = - np.amin(np.divide(portfolio_value, np.maximum.accumulate(portfolio_value)) - 1) + ten_day_var = ten_day_VaR(portfolio_value) + + print('Sharpe ratio: ', sharpe_ratio, ', Return: ', avg_annual_return, ', Volatility: ', annual_volatility, ', Maximum Drawdown: ', max_drawdown) + + # hist_return_series.set_index('quarter', inplace=True) + hist_returns = pickle.dumps(hist_return_series) + + conn = psycopg2.connect(database=POSTGRESQL_DATABASE, user=POSTGRESQL_USER) + cur = conn.cursor(cursor_factory = psycopg2.extras.DictCursor) + cur.execute("""insert into strategy (strategy_name, + author, + create_date, + sharpe_ratio, + return, + volatility, + max_drawdown, + tw, + competition, + hist_returns, + alpha, + beta, + r_squared, + ten_day_var) values (%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s) returning strategy_id;""", + (strategy_name, + session['USERNAME'], + create_date, + sharpe_ratio, + avg_annual_return, + annual_volatility, + max_drawdown, + 0, # tw_digit, + competition, + hist_returns, + 0, # alpha, + 0, # beta, + 0, # r_squared, + ten_day_var + ) ) + strategy_id = cur.fetchone()[0] + conn.commit() + + # record the list of tickers into database + # strategy_id = cur.execute('select * from strategy where create_date=%s', (create_date,)).fetchone()['strategy_id'] + for i in range(len(tickers)): + cur.execute('insert into assets_in_strategy (strategy_id, asset_ticker, weight) values (%s, %s, %s)', + (strategy_id, tickers[i], optimal_weights[i])) + conn.commit() + + cur.close() + conn.close() + + # fig, ax = plt.subplots() + # hist_return_series.hist(column='quarterly_returns', by='quarter', ax=ax) + hist_return_plot = hist_return_series.plot.bar(x='quarter', y='quarterly_returns').get_figure() + plt.tight_layout() + hist_return_plot.savefig('static/img/quarterly_returns/'+str(strategy_id)+'.png') + plt.close() + + print(portfolio_value.head()) + print(portfolio_value.tail()) + plt.xticks(rotation=90) + # plt.tight_layout() + plt.plot(portfolio_value.iloc[1:]) + plt.savefig('static/img/portfolio_values/'+str(strategy_id)+'.png', bbox_inches='tight') + plt.close() + print('Strategy_id ' + str(strategy_id) + ' optimization succeeds.') + flash(Markup('回測已完成，詳情請點這裡查看。'), 'success') + return render_template('create_strategy_upload.html') + + + +@main.route('/create_strategy_topic', methods=['GET', 'POST']) +def create_strategy_topic(): + if not (session.get('USERNAME') and session['USERNAME']): + flash('使用此功能必須先登入。', 'danger') + return redirect('/login') + if session['USERNAME'] in black_list: + flash('我們已經暫停您建立策略的權利，有疑問請洽finteck@my.nthu.edu.tw', 'danger') + return redirect('/') + print('last_creation_time: ', session['last_creation_time']) + if time.time() - session['last_creation_time'] < 30: + flash('每兩次建立策略須間隔30秒', 'danger') + return redirect('/') + + if request.method == 'POST': + strategy_name = request.form['strategy_name'] + create_date = datetime.strftime(datetime.now() + timedelta(hours=8), '%Y/%m/%d %H:%M:%S.%f') + session['last_creation_time'] = time.time() + + if strategy_name == '': + flash('請取一個名字', 'danger') + conn = psycopg2.connect(database=POSTGRESQL_DATABASE, user=POSTGRESQL_USER) + cur = conn.cursor(cursor_factory = psycopg2.extras.DictCursor) + cur.execute("""select * from strategy where competition='topic' order by strategy_id desc""") + sql_results = cur.fetchall() + cur.close() + conn.close() + return render_template('create_strategy_topic.html', topic_results=sql_results) + + f = request.files['fileToUpload'] + filename = create_date.replace('/', '').replace(' ', '').replace(':', '').replace('.', '') + '.csv' + f.save(os.path.join(main.config['UPLOAD_FOLDER'], filename)) + + all_data = pd.read_csv(main.config['UPLOAD_FOLDER'] + filename) + all_data['Date'] = pd.to_datetime(all_data['Date'], format='%Y-%m-%d') + + tickers = list(all_data.columns) + tickers.remove('Date') + print('The list of assets: ', tickers) + + competition = request.form['competition'] + + + # Turn off progress printing + solvers.options['show_progress'] = False + + + def stockpri(ticker, start, end): + data = all_data.loc[start:end, ['Date', ticker]] + data.set_index('Date', inplace=True) + data = data[ticker] + return data + + + portfolio_value = pd.Series([100]) + optimal_weights = None + hist_return_series = pd.DataFrame(columns=['month', 'monthly_returns']) + + start_dates = list(np.arange(0, len(all_data), 21)) + [len(all_data)-1] + + + for i in range(len(start_dates)-3): + + ### Take 6 months to backtest ### + + start = start_dates[i] + end = start_dates[i+2]+1 + + data = pd.DataFrame({ ticker: stockpri(ticker, start, end) for ticker in tickers }) + data = data.dropna() + + returns = data.pct_change() + 1 + returns = returns.dropna() + log_returns = np.log(data.pct_change() + 1) + log_returns = log_returns.dropna() + + if log_returns.empty: + continue + + mu = np.exp(log_returns.mean()*252).values + # Markowitz frontier + profit = np.linspace(np.amin(mu), np.amax(mu), 100) + frontier = [] + w = [] + if len(tickers) >= 3: + for p in profit: + # Problem data. + n = len(tickers) + S = matrix(log_returns.cov().values*252) + pbar = matrix(0.0, (n,1)) + # Gx <= h + G = matrix(0.0, (2*n,n)) + G[::(2*n+1)] = 1.0 + G[n::(2*n+1)] = -1.0 + # h = matrix(1.0, (2*n,1)) + h = matrix(np.concatenate((0.5*np.ones((n,1)), -0.03*np.ones((n,1))), axis=0)) + A = matrix(np.concatenate((np.ones((1,n)), mu.reshape((1,n))), axis=0)) + b = matrix([1, p], (2, 1)) + + # Compute trade-off. + res = qp(S, -pbar, G, h, A, b) + + if res['status'] == 'optimal': + res_weight = res['x'] + s = math.sqrt(dot(res_weight, S*res_weight)) + frontier.append(np.array([p, s])) + w.append(res_weight) + elif len(tickers) == 2: + for p in profit: + S = log_returns.cov().values*252 + res_weight = [1 - (p-mu[0])/(mu[1]-mu[0]), (p-mu[0])/(mu[1]-mu[0])] + if (res_weight[0] < 0.03) or (res_weight[0] > 0.97): + continue + s = math.sqrt(np.matmul(res_weight, np.matmul(S, np.transpose(res_weight)))) + frontier.append(np.array([p, s])) + w.append(res_weight) + + + frontier = np.array(frontier) + if frontier.shape == (0,): + continue + x = np.array(frontier[:, 0]) + y = np.array(frontier[:, 1]) + + frontier_sharpe_ratios = np.divide(x-1, y) + optimal_portfolio_index = np.argmax(frontier_sharpe_ratios) + optimal_weights = w[optimal_portfolio_index] + + + ### paper trade on the next three months ### + + start = start_dates[i+2] + end = start_dates[i+3]+1 + + data = pd.DataFrame({ ticker: stockpri(ticker, start, end) for ticker in tickers }) + data = data.dropna() + + returns = data.pct_change() + 1 + returns = returns.dropna() + log_returns = np.log(data.pct_change() + 1) + log_returns = log_returns.dropna() + + + portfolio_cum_returns = np.dot(returns, optimal_weights).cumprod() + portfolio_value_new_window = portfolio_value.iloc[-1].item() * pd.Series(portfolio_cum_returns) + portfolio_value_new_window.index = pd.to_datetime(returns.index, format='%Y-%m-%d') + portfolio_value = portfolio_value.append(portfolio_value_new_window) + + # produce monthly return + hist_return_series.loc[len(hist_return_series)] = [str(start), portfolio_cum_returns[-1]-1] + + if optimal_weights == None: + sharpe_ratio = avg_annual_return = annual_volatility = max_drawdown = ten_day_var = 0 + optimal_weights = [0, ]*len(tickers) + hist_returns = None + conn = psycopg2.connect(database=POSTGRESQL_DATABASE, user=POSTGRESQL_USER) + cur = conn.cursor(cursor_factory = psycopg2.extras.DictCursor) + cur.execute("""insert into strategy (strategy_name, + author, + create_date, + explanation, + sharpe_ratio, + return, + volatility, + max_drawdown, + tw, + competition, + hist_returns, + alpha, + beta, + r_squared, + ten_day_var) + values (%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s) returning strategy_id;""", + (strategy_name, + session['USERNAME'], + create_date, + "", #explanation + sharpe_ratio, + avg_annual_return, + annual_volatility, + max_drawdown, + 0, # tw_digit, + competition, + hist_returns, + 0, # alpha, + 0, # beta, + 0, # r_squared, + ten_day_var + ) ) + strategy_id = cur.fetchone()[0] + conn.commit() + + # record the list of tickers into database + # strategy_id = cur.execute('select * from strategy where create_date=?', [create_date]).fetchone()['strategy_id'] + for i in range(len(tickers)): + cur.execute('insert into assets_in_strategy (strategy_id, asset_ticker, weight) values (%s, %s, %s)', + (strategy_id, tickers[i], optimal_weights[i])) + conn.commit() + + cur.close() + conn.close() + print('Strategy_id ' + str(strategy_id) + ' optimization fails.') + flash('無資料或無法畫出馬可維茲邊界，請換一個組合', 'danger') + conn = psycopg2.connect(database=POSTGRESQL_DATABASE, user=POSTGRESQL_USER) + cur = conn.cursor(cursor_factory = psycopg2.extras.DictCursor) + cur.execute("""select * from strategy where competition='topic' order by strategy_id desc""") + sql_results = cur.fetchall() + cur.close() + conn.close() + return render_template('create_strategy_topic.html', topic_results=sql_results) + + avg_annual_return = np.exp(np.log(portfolio_value.pct_change() + 1).mean() * 252) - 1 + annual_volatility = portfolio_value.pct_change().std() * math.sqrt(252) + sharpe_ratio = avg_annual_return/annual_volatility + max_drawdown = - np.amin(np.divide(portfolio_value, np.maximum.accumulate(portfolio_value)) - 1) + ten_day_var = ten_day_VaR(portfolio_value) + + print('Sharpe ratio: ', sharpe_ratio, ', Return: ', avg_annual_return, ', Volatility: ', annual_volatility, ', Maximum Drawdown: ', max_drawdown) + + # hist_return_series.set_index('month', inplace=True) + hist_returns = pickle.dumps(hist_return_series) + + conn = psycopg2.connect(database=POSTGRESQL_DATABASE, user=POSTGRESQL_USER) + cur = conn.cursor(cursor_factory = psycopg2.extras.DictCursor) + cur.execute("""insert into strategy (strategy_name, + author, + create_date, + explanation, + sharpe_ratio, + return, + volatility, + max_drawdown, + tw, + competition, + hist_returns, + alpha, + beta, + r_squared, + ten_day_var) values (%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s) returning strategy_id;""", + (strategy_name, + session['USERNAME'], + create_date, + "", #explanation + sharpe_ratio, + avg_annual_return, + annual_volatility, + max_drawdown, + 0, # tw_digit, + competition, + hist_returns, + 0, # alpha, + 0, # beta, + 0, # r_squared, + ten_day_var + ) ) + strategy_id = cur.fetchone()[0] + conn.commit() + + # record the list of tickers into database + # strategy_id = cur.execute('select * from strategy where create_date=%s', (create_date,)).fetchone()['strategy_id'] + for i in range(len(tickers)): + cur.execute('insert into assets_in_strategy (strategy_id, asset_ticker, weight) values (%s, %s, %s)', + (strategy_id, tickers[i], optimal_weights[i])) + conn.commit() + + cur.close() + conn.close() + + # fig, ax = plt.subplots() + # hist_return_series.hist(column='monthly_returns', by='month', ax=ax) + hist_return_plot = hist_return_series.plot.bar(x='month', y='monthly_returns').get_figure() + plt.tight_layout() + hist_return_plot.savefig('static/img/monthly_returns/'+str(strategy_id)+'.png') + plt.close() + + print(portfolio_value.head()) + print(portfolio_value.tail()) + plt.xticks(rotation=90) + # plt.tight_layout() + plt.plot(portfolio_value.iloc[1:]) + plt.savefig('static/img/portfolio_values/'+str(strategy_id)+'.png', bbox_inches='tight') + plt.close() + print('Strategy_id ' + str(strategy_id) + ' optimization succeeds.') + flash(Markup('回測已完成，詳情請點這裡查看。'), 'success') + conn = psycopg2.connect(database=POSTGRESQL_DATABASE, user=POSTGRESQL_USER) + cur = conn.cursor(cursor_factory = psycopg2.extras.DictCursor) + cur.execute("""select * from strategy where competition in ('topic_high_sharpe', 'topic_high_esg', 'topic_high_return', 'topic_high_volume') order by strategy_id desc""") + sql_results = cur.fetchall() + cur.close() + conn.close() + return render_template('create_strategy_topic.html', topic_results=sql_results) + + +@main.route('/login') +def login(): + return render_template('login.html') + + +@main.route('/login', methods=['POST']) +def login_post(): + password = request.form.get('password') + username = request.form.get('username') + + conn = psycopg2.connect(database=POSTGRESQL_DATABASE, user=POSTGRESQL_USER) + cur = conn.cursor(cursor_factory = psycopg2.extras.DictCursor) + cur.execute('select * from users where username=%s', (username,)) + sql_result = cur.fetchone() + cur.close() + conn.close() + + if (not sql_result) or (not check_password_hash(sql_result['password'], password)): + flash('使用者代號不對或密碼不對，請再試一次。', 'danger') + return redirect('/login') + + print(sql_result['username'], sql_result['user_id']) + session['login'] = True + session['user_id'] = sql_result['user_id'] + session['USERNAME'] = sql_result['username'] + session['last_creation_time'] = 0 + session['vip'] = sql_result['vip'] + return redirect('/') + + +@main.route('/logout') +#@login_required +def logout(): + #logout_user() + session['user_id'] = -1 + session['USERNAME'] = None + session['login'] = False + session['last_creation_time'] = None + session['vip'] = None + return redirect('/login') + + +@main.route('/signup') +def signup(): + return render_template('signup.html') + + +@main.route('/signup', methods=['POST']) +def signup_post(): + username = request.form.get('username') + password = request.form.get('password') + confirm_password = request.form.get('confirm_password') + + conn = psycopg2.connect(database=POSTGRESQL_DATABASE, user=POSTGRESQL_USER) + cur = conn.cursor(cursor_factory = psycopg2.extras.DictCursor) + cur.execute('select * from users where username=%s', (username,)) + sql_result = cur.fetchone() + if sql_result: # if a user is found, we want to redirect back to signup page so user can try again + cur.close() + conn.close() + flash('這個Email地址已經被使用', 'danger') + return redirect('/signup') + + if not (password == confirm_password): + cur.close() + conn.close() + flash('所輸入兩次密碼不同', 'danger') + return redirect('/signup') + + # create new user with the form data. Hash the password so plaintext version isn't saved. + cur.execute('insert into users (username, password) values (%s, %s)', (username, generate_password_hash(password))) + conn.commit() + cur.close() + conn.close() + + print('registered') + flash('已成功註冊', 'success') + + return redirect('/login') + + +@main.route('/forum') +def forum_index(): + if not (session.get('USERNAME') and session['USERNAME']): + flash('使用此功能必須先登入。', 'danger') + return redirect('/login') + conn = psycopg2.connect(database=POSTGRESQL_DATABASE, user=POSTGRESQL_USER) + cur = conn.cursor(cursor_factory = psycopg2.extras.DictCursor) + cur.execute("""select * from strategy + where competition not in (""" + confidential_competitions_placeholder + """) or author=%s + order by strategy_id desc;""", + confidential_competitions_list + (session['USERNAME'],)) + data = cur.fetchall() + cur.close() + conn.close() + + content_list = [] + for d in data: + content_list.append({ + "id": d['strategy_id'], + "time": d['create_date'], + "user_id": None, + "user_email": None, + "user_name": d['author'], + "comment": None, + "title": d['strategy_name'], + "video_id": None + }) + + return_data = { + "count": len(data), + "content": content_list + } + + return render_template('forum.html', forum_data=return_data) + + +@main.route('/post_page', methods=['GET']) +# @login_required +def post_page(): + if not (session.get('USERNAME') and session['USERNAME']): + flash('使用此功能必須先登入。', 'danger') + return redirect('/login') + post_id = int(request.values.get('post_id')) + + conn = psycopg2.connect(database=POSTGRESQL_DATABASE, user=POSTGRESQL_USER) + cur = conn.cursor(cursor_factory = psycopg2.extras.DictCursor) + + cur.execute('update strategy set click_count=click_count+1 where strategy_id=%s', (post_id,)) + conn.commit() + + cur.execute('select * from strategy where strategy_id=%s', (post_id,)) + strategy_content_list = cur.fetchone() + cur.execute('select * from assets_in_strategy where strategy_id=%s', (post_id,)) + asset_list = cur.fetchall() + cur.execute('select * from comment where strategy_id=%s', (post_id,)) + comment_list = cur.fetchall() + cur.close() + conn.close() + + print(asset_list) + + with open('latest_trading_data.txt') as all_data: + all_data_close = json.load(all_data) + + return_data = { + "strategy_content": strategy_content_list, + "asset_content": asset_list, + "comment_content": comment_list, + "comment_count": len(comment_list), + "asset_candidates": dict(asset_candidates + asset_candidates_tw), + "all_trading_data": all_data_close + } + + return render_template('post_page.html', data=return_data, strategy_id=str(post_id)) + +@main.route('/comment', methods=['POST']) +#@login_required +def post_comment_data(): + comment = request.form['comment'] + strategy_id = request.form['strategy_id'] + author = session['USERNAME'] + utc = datetime.utcnow() + utc = utc.replace(tzinfo=pytz.utc) + comment_date = datetime.strftime(utc.astimezone(pytz.timezone('Asia/Taipei')), '%Y/%m/%d %H:%M') + # comment_date = datetime.strftime(datetime.utcnow().astimezone(pytz.timezone('Asia/Taipei')), '%Y/%m/%d %H:%M') + # comment_date = datetime.strftime(datetime.utcnow().replace(tzinfo=pytz.timezone('Asia/Taipei')), '%Y/%m/%d %H:%M') + + conn = psycopg2.connect(database=POSTGRESQL_DATABASE, user=POSTGRESQL_USER) + cur = conn.cursor(cursor_factory = psycopg2.extras.DictCursor) + cur.execute('insert into comment (author, strategy_id, comment, date) values (%s, %s, %s, %s)', (author, strategy_id, comment, comment_date)) + conn.commit() + + cur.close() + conn.close() + return redirect('/post_page?post_id='+str(strategy_id)) + + +#@main.route('/profile') +#@login_required +#def profile(): +# return render_template('profile.html', name=current_user.name) + + +@main.route('/analysis_result') +#@login_required +def analysis_result(): + if not (session.get('USERNAME') and session['USERNAME']): + flash('使用此功能必須先登入。', 'danger') + return redirect('/login') + + sortby = request.values.get('sortby') + competition = request.values.get('competition') + tw = request.values.get('tw') + tw_digit = 1 if tw=='true' else 0 if tw=='false' else None + + conn = psycopg2.connect(database=POSTGRESQL_DATABASE, user=POSTGRESQL_USER) + cur = conn.cursor(cursor_factory = psycopg2.extras.DictCursor) + if sortby == 'competition': + if competition in confidential_competitions_list: + cur.close() + conn.close() + flash('本競賽暫不開放查詢', 'danger') + return redirect('analysis_result?sortby=default&tw=' + tw +'&competition=none') + cur.execute("""select b.strategy_id, + a.author, + b.create_date, + b.return, + a.sharpe_ratio, + b.max_drawdown, + b.strategy_name, + b.volatility + from (select author, max(sharpe_ratio) as sharpe_ratio from strategy group by author) as a + join strategy as b on a.author=b.author and a.sharpe_ratio=b.sharpe_ratio + where b.competition=%s + order by a.sharpe_ratio desc;""", (competition,)) + #cur.execute("""select strategy_id, + # author, + # create_date, + # return, + # max(sharpe_ratio) as sharpe_ratio, + # max_drawdown, + # strategy_name, + # volatility + # from strategy + # where competition=%s + # group by author + # order by sharpe_ratio desc;""", (competition,)) + sql_results = cur.fetchall() + num_records_hidden = min(5, len(sql_results)) + if session['vip']==False: + for i in range(num_records_hidden): + for key in sql_results[i].keys(): + if key not in ['return', 'sharpe_ratio', 'max_drawdown', 'volatility']: + sql_results[i][key] = '*****' + elif sortby == 'default': + cur.execute("""select * from strategy + where tw=%s and (competition not in (""" + confidential_competitions_placeholder + """) or author=%s) + order by strategy_id desc limit 200;""", + (tw_digit,) + confidential_competitions_list + (session['USERNAME'],)) + sql_results = cur.fetchall() + elif sortby == 'myself': + cur.execute("""select * from strategy + where tw=%s and author=%s + order by strategy_id desc;""", + (tw_digit, session['USERNAME'])) + sql_results = cur.fetchall() + elif sortby == 'return': + cur.execute("""select * from strategy + where tw=%s and (competition not in (""" + confidential_competitions_placeholder + """) or author=%s) + order by return desc limit 1000;""", + (tw_digit,) + confidential_competitions_list + (session['USERNAME'],)) + sql_results = cur.fetchall() + # num_records_hidden = min(5, len(sql_results)) + # if session['vip']==False: + # for i in range(num_records_hidden): + # for key in sql_results[i].keys(): + # if key not in ['return', 'sharpe_ratio', 'max_drawdown', 'volatility']: + # sql_results[i][key] = '*****' + if session['vip']==False: + return_threshold = 1 + for record in sql_results: + if record['return'] > return_threshold: + for key in record.keys(): + if key not in ['return', 'sharpe_ratio', 'max_drawdown', 'volatility']: + record[key] = '*****' + else: + break + elif sortby == 'sharpe': + cur.execute("""select * from strategy + where tw=%s and (competition not in (""" + confidential_competitions_placeholder + """) or author=%s) and sharpe_ratio!='NaN' + order by sharpe_ratio desc limit 1000;""", + (tw_digit,) + confidential_competitions_list + (session['USERNAME'],)) + sql_results = cur.fetchall() + if session['vip']==False: + sharpe_threshold = 5 + for record in sql_results: + if record['sharpe_ratio'] > sharpe_threshold: + for key in record.keys(): + if key not in ['return', 'sharpe_ratio', 'max_drawdown', 'volatility']: + record[key] = '*****' + else: + break + elif sortby == 'vol': + cur.execute("""select * from strategy + where tw=%s and (competition not in (""" + confidential_competitions_placeholder + """) or author=%s) and volatility!=0 + order by volatility asc limit 1000;""", + (tw_digit,) + confidential_competitions_list + (session['USERNAME'],)) + sql_results = cur.fetchall() + if session['vip']==False: + vol_threshold = 0.1 + for record in sql_results: + if record['volatility'] < vol_threshold: + for key in record.keys(): + if key not in ['return', 'sharpe_ratio', 'max_drawdown', 'volatility']: + record[key] = '*****' + else: + break + elif sortby == 'mdd': + cur.execute("""select * from strategy + where tw=%s and (competition not in (""" + confidential_competitions_placeholder + """) or author=%s) and max_drawdown!=0 + order by max_drawdown asc limit 1000;""", + (tw_digit,) + confidential_competitions_list + (session['USERNAME'],)) + sql_results = cur.fetchall() + if session['vip']==False: + mdd_threshold = 0.15 + for record in sql_results: + if record['max_drawdown'] < mdd_threshold: + for key in record.keys(): + if key not in ['return', 'sharpe_ratio', 'max_drawdown', 'volatility']: + record[key] = '*****' + else: + break + cur.close() + conn.close() + return render_template('result.html', results=sql_results, tw=tw) + + +if __name__ == "__main__": + main.run(host='0.0.0.0', port=80) diff --git a/non.html b/non.html new file mode 100644 index 0000000..cc12734 --- /dev/null +++ b/non.html @@ -0,0 +1,108 @@ + + + + + To-Do List + + + + +
+
+
+ + +
+ +
+ +
    +
    + + + + + + + + + + diff --git a/portfolio_builder.py b/portfolio_builder.py new file mode 100644 index 0000000..30b7ef2 --- /dev/null +++ b/portfolio_builder.py @@ -0,0 +1,126 @@ +import json +import time +import numpy as np +import pandas as pd + +from scipy.optimize import minimize + +class MVO(object): + def __init__(self, data, market, ratio, role='max-sharpe'): + length, self.num = data.shape + tsize = int(length*ratio) + self.data_return = data.pct_change().dropna().to_numpy() + self.market_return = market.pct_change().dropna().to_numpy() + self.train[:tsize, :] + self.test[tsize:, :] + self.train_market = self.market_return[:tsize] + self.test_market = self.market_return[tsize:] + @staticmethod + def portfolio_info(w, ret, market_ret, rf=0): + # return and drawdown + retPort = ret@w # T-dimensional array + cum_ret = (retPort+1).cumprod() + rolling_max=np.maximum.accumulate(cum_ret) + mdd = np.max((rolling_max - cum_ret)/rolling_max) + + ## Sharpe Ratio + stdPort = np.std(retPort) + vol = stdPort*15.87451 + annual_ret = np.mean(retPort) * 252 + annual_sr = (annual_ret-rf) / vol + + ## alpha, beta + cov = np.cov(retPort, market_ret) + beta = cov[0, 1] / cov[1, 1] + alpha = annual_ret - rf - beta*(np.mean(market_ret) * 252 - rf) + R2 = cov[0, 1]**2/(cov[0, 0] * cov[1, 1]) + + ## n-day 95% VaR + var10 = -annual_ret*(10/252) + 1.645*vol*(10/252)**(1/2) + d = dict(annual_ret = annual_ret, + vol=vol, + mdd=mdd, + annual_sr=annual_sr, + beta=beta, + alpha=alpha, + var10=var10, + R2=R2) + return {key: round(d[key], 2) for key in d} + @staticmethod + def sharpe_ratio(w, ret): + cov = np.cov(ret.T) + print(cov.shape, w.shape) + retPort = ret@w # T-dimensional array + stdPort = np.std(retPort) + return np.mean(retPort)/stdPort + @staticmethod + def sharpe_grad(w, ret, cov): + manual_ret = np.mean(ret, axis=0) + # print(cov.shape, w.shape) + retPort = ret@w # T-dimensional array + stdPort = np.std(retPort) + g1=manual_ret/stdPort + g2=np.mean(retPort)*(-0.5)*stdPort**(-3)*(2*cov@w) + return g1+g2 + @staticmethod + def sortino_ratio(w, ret): + retPort = ret@w # T-dimensional array + stdPort = np.std(np.maximum(-retPort, 0)) + return np.mean(retPort)/stdPort + @staticmethod + def sortino_grad(w, ret, cov_sor): + manual_ret = np.mean(ret, axis=0) + # print(cov.shape, w.shape) + retPort = ret@w # T-dimensional arrayss + stdPort = np.std(retPort) + g1=manual_ret/stdPort + g2=np.mean(retPort)*(-0.5)*stdPort**(-3)*(2*cov_sor@w) + return g1+g2 + @staticmethod + def volatility(w, ret): + retPort = ret@w # T-dimensional array + stdPort = np.std(retPort) + return stdPort + @staticmethod + def volatility_grad(w, ret, cov): + retPort = ret@w # T-dimensional array + stdPort = np.std(retPort) + return cov@w/stdPort**(0.5) + @classmethod + def opt(cls, ret, role="max_sharpe"): + n = ret.shape[1] + init=np.ones(n)/n + if role=="max_sharpe": + cov=np.cov(ret.T) + loss = lambda w: -cls.sharpe_ratio(w, ret) + grad = lambda w: -cls.sharpe_grad(w, ret, cov) + elif role=="max_sortino": + cov = np.cov(np.maximum(ret, 0).T) + loss = lambda w: -cls.sortino_ratio(w, ret) + grad = lambda w: -cls.sortino_grad(w, ret, cov) + elif role=="min_volatility": + cov=np.cov(ret.T) + loss = lambda w: cls.volatility(w, ret) + grad = lambda w: cls.volatility_grad(w, ret, cov) + else: + return init + bnds = [[0, 0.6] for i in range(n)] + opts = {'maxiter': 10000, 'disp': False} + cons = ({'type': 'eq', 'fun': lambda w: np.sum(w) - 1}) + result = minimize(loss, init, method="SLSQP",\ + options=opts, bounds=bnds, tol = None, jac = grad, constraints=cons) + sol = result['x'] + return np.round(sol, 2) + + + + + + + + + + + + + \ No newline at end of file diff --git a/sql_command.py b/sql_command.py new file mode 100644 index 0000000..e7dfed6 --- /dev/null +++ b/sql_command.py @@ -0,0 +1,5 @@ +insert_strategy = \ +""" +insert into strategy (date, name, username, competition, role, ratio, annual_ret, vol, mdd, annual_sr, beta, alpha, var10, R2, tw, comment, assets, assets_position) values (%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s); +""" +# (date, name, username, competition, role, ratio, annual_ret, vol, mdd, annual_sr, beta, alpha, var10, R2, tw, comment, assets, assets_position) diff --git a/sql_script/create_stockDB.sql b/sql_script/create_stockDB.sql new file mode 100644 index 0000000..10c0601 --- /dev/null +++ b/sql_script/create_stockDB.sql @@ -0,0 +1,44 @@ +DROP TABLE IF EXISTS stock_price; +-- DROP TABLE IF EXISTS stock_price_tw; +-- DROP TABLE IF EXISTS stock_info; +-- DROP TABLE IF EXISTS stock_info_tw; + +CREATE TABLE stock_price ( + id SERIAL PRIMARY KEY, + ticker VARCHAR(64) NOT NULL, + date DATE NOT NULL, + price REAL NOT NULL, + return REAL NOT NULL +); +-- you need to add () +CREATE INDEX idx_ticker ON stock_price (ticker); + +-- CREATE TABLE stock_price_tw ( +-- id SERIAL PRIMARY KEY, +-- ticker VARCHAR(64) NOT NULL, +-- date DATE NOT NULL, +-- price REAL NOT NULL, +-- return REAL NOT NULL +-- ); +-- -- you need to add () +-- CREATE INDEX idx_ticker_tw ON stock_price_tw (ticker); + +-- CREATE TABLE stock_info_tw ( +-- id SERIAL PRIMARY KEY, +-- ticker VARCHAR(64) NOT NULL, +-- last_price REAL NOT NULL, +-- start DATE NOT NULL, +-- end DATE NOT NULL +-- ); +-- you need to add () +-- CREATE INDEX idx_info_tw ON stock_info_tw (ticker); + +-- CREATE TABLE stock_info ( +-- id SERIAL PRIMARY KEY, +-- ticker VARCHAR(64) NOT NULL, +-- last_price REAL NOT NULL, +-- start DATE NOT NULL, +-- end DATE NOT NULL +-- ); +-- you need to add () +-- CREATE INDEX idx_info ON stock_info (ticker); \ No newline at end of file diff --git a/sql_script/create_strategy.sql b/sql_script/create_strategy.sql new file mode 100644 index 0000000..ea77353 --- /dev/null +++ b/sql_script/create_strategy.sql @@ -0,0 +1,24 @@ +DROP TABLE IF EXISTS strategy; +CREATE TABLE strategy ( + id SERIAL PRIMARY KEY, + date DATE NOT NULL, + name VARCHAR(64) NOT NULL, + username VARCHAR(64) NOT NULL, + competition VARCHAR(64) NOT NULL, + role VARCHAR(20) NOT NULL, + ratio REAL NOT NULL, + annual_ret REAL NOT NULL, + vol REAL NOT NULL, + mdd REAL NOT NULL, + annual_sr REAL NOT NULL, + beta REAL NOT NULL, + alpha REAL NOT NULL, + var10 REAL NOT NULL, + R2 REAL NOT NULL, + tw BOOLEAN DEFAULT TRUE, + comment VARCHAR(255), + assets TEXT[] NOT NULL, + assets_position REAL[] NOT NULL +); +CREATE INDEX idx_user ON strategy (username); + diff --git a/sql_script/create_users.sql b/sql_script/create_users.sql new file mode 100644 index 0000000..c8b90a5 --- /dev/null +++ b/sql_script/create_users.sql @@ -0,0 +1,10 @@ +DROP TABLE IF EXISTS users; +CREATE TABLE users ( + user_id SERIAL PRIMARY KEY, + username VARCHAR(64) UNIQUE NOT NULL, + password VARCHAR(128) NOT NULL, + vip BOOLEAN DEFAULT FALSE +); +INSERT INTO users (username, password) +VALUES ('admin', 'pbkdf2:sha256:260000$Z5bK5pp0D8HEDAps$abb43b1b1c543ff334de8fb3aeba9c0460c37de5b5363e5210e68b00739d5e2c'), +('R10246002@ntu.edu.tw', 'pbkdf2:sha256:260000$Z5bK5pp0D8HEDAps$abb43b1b1c543ff334de8fb3aeba9c0460c37de5b5363e5210e68b00739d5e2c'); \ No newline at end of file diff --git a/static/img/cat.png b/static/img/cat.png new file mode 100644 index 0000000..2e96417 Binary files /dev/null and b/static/img/cat.png differ diff --git a/static/img/frontier.jpg b/static/img/frontier.jpg new file mode 100644 index 0000000..ccc41f0 Binary files /dev/null and b/static/img/frontier.jpg differ diff --git a/static/img/money.jpeg b/static/img/money.jpeg new file mode 100644 index 0000000..7bb16d3 Binary files /dev/null and b/static/img/money.jpeg differ diff --git a/static/img/stock.jpeg b/static/img/stock.jpeg new file mode 100644 index 0000000..8c9e451 Binary files /dev/null and b/static/img/stock.jpeg differ diff --git a/static/js/addStock.js b/static/js/addStock.js new file mode 100644 index 0000000..449c996 --- /dev/null +++ b/static/js/addStock.js @@ -0,0 +1,156 @@ +// Initialize empty stock list +let stockList = ['2330.TW']; +// Cache frequently-used DOM elements +const $stockForm = $('#stock-form'); +const $stockSelect = $('#stock-select'); +const $compSelect = $('#competition'); +const $stockList = $('#stock-list'); +const $submitBtn = $('#submit-btn'); +const $addStockBtn = $('#addStockBtn'); +const $submitPort = $('#submit-port'); +const $sendPort = $('#sendPort'); +const $commentPort = $('#commentPort'); + +// Function to add a new stock item to the list +function addStockItem(stock, text) { + // Add item to array + stockList.push(stock); + // Update HTML list + const $newItem = $(`
  • + ${text} + + + +
    • `); + $stockList.append($newItem); +} + +// Function to delete a stock item from the list +function deleteStockItem(itemIndex) { + // Remove item from array + stockList.splice(itemIndex, 1); + + // Update HTML list + $stockList.children().eq(itemIndex).remove(); +} + +// Event listener for delete button clicks +$stockList.on('click', '.delete-btn', function(){ + var itemIndex = $(this).closest('li').index() + deleteStockItem(itemIndex); + // console.log(stockList); + }); +// $stockList.on('click', '.delete-btn', function(event) { +// // Get index of item to delete +// const $deleteBtn = $(event.target); +// const itemIndex = $deleteBtn.parent().index(); + +// // Delete item from list +// deleteStockItem(itemIndex); +// }); + + +// Event listener for submit button click +$addStockBtn.click(function(event) { + event.preventDefault(); + $('#search').val(''); + // Get selected stock from form + const selectedStock = $stockSelect.val(); + var text = $('#stock-select option:selected').text(); + if (selectedStock != null && stockList.indexOf(selectedStock)===-1) { + // Add new item to list + addStockItem(selectedStock, text); + + // Clear input field + $stockSelect.val(''); + } + console.log(stockList); +}); + +// Event listener for submit button click +$submitPort.click(function(event) { + event.preventDefault(); + if (stockList.length > 1){ + $('#portModal').modal('show'); + console.log('asset confirm'); + // $(this).prop('disabled', true); + } +}); +// Event listener for submit button click +$sendPort.click(function(event) { + if (stockList.length > 1){ + $('#confirmMes').text("投資組合已開始建立，請等待完成訊息，或１分鐘後至分析結果區查看！") + $('#confirmModal').modal('show'); + + + $submitPort.prop('disabled', true); + $.ajax({ + url: '/postPort', //todo create_strategy + method: 'POST', + data: { + name: $('input[name=portName]').val(), + ts: Date.now(), + comp: $('#competition').val(), + ratio: $('#ratio-select').val(), + role: $('#role-select').val(), + comment: $commentPort.val(), + stockList: JSON.stringify(stockList) + }, + success: function(response) { + console.log(response); + // var res = JSON.parse(response); + event.preventDefault(); + $('#confirmMes').text(response.mes) + if (stockList.length > 0){ + $('#confirmModal').modal('show'); + } + $submitPort.prop('disabled', false); + }, + error: function(xhr) { + console.log('Error submitting stock list: ' + xhr.responseText); + } + }); + $commentPort.val(''); +} + // Get selected stock from form +}); + +// Event listener for submit button click +$submitBtn.click(function(event) { + // Send stock list to server + // console.log(event.target) + // console.log(stockList) + // console.log(cacheList.value, stockList); + if (stockList.length > 0) { + // cacheList = stockList; + $.ajax({ + url: '/postStock', //todo create_strategy + method: 'POST', + data: { stockList: JSON.stringify(stockList) }, + success: function(response) { + var graphs = JSON.parse(response); + Plotly.newPlot("graph", + graphs.data, graphs.layout, {responsive: true}); + console.log(response.layout); + }, + error: function(xhr) { + console.log('Error submitting stock list: ' + xhr.responseText); + } + }); + } +}); +$(document).ready(function(){ + $("#search").on("keyup", function() { + var value = $(this).val().toLowerCase(); + $("#stock-select option").filter(function() { + $(this).toggle($(this).text().toLowerCase().indexOf(value) > -1) + }); + }); +}); + +// window.onresize = function() { +// Plotly.relayout('graph', { +// 'xaxis.autorange': true, +// 'yaxis.autorange': true +// }); +// }; diff --git a/templates/base.html b/templates/base.html new file mode 100644 index 0000000..decf0a7 --- /dev/null +++ b/templates/base.html @@ -0,0 +1,284 @@ + + + + + + + {% block title %}NTHU Trading Platform{% endblock title%} + + + + {% block link %} + + + {% endblock %} + + + +
    + + {% set navigation_bar = [ + ('/', 'index', '首頁', 'bi bi-house-fill'), + ('/strategy', 'strategy', '建立策略', 'bi bi-piggy-bank-fill'), + ('/strategy_tw', 'strategy_tw', '台股建立策略', 'bi bi-piggy-bank-fill'), + ('/custom', 'custom', '自訂數據建立策略', 'bi bi-database-fill-add'), + ('/result', 'result', '分析結果排行', 'bi bi-graph-up-arrow'), + ('/result_tw', 'result_tw', '分析結果排行（台股）', 'bi bi-graph-up-arrow'), + ('mailto:r10246002@ntu.edu.tw', 'error', '錯誤回報', 'bi bi-bug-fill') + ] -%} + {% set active_page = active_page|default('index') -%} + +
    + + {% block content %} +
    + + +
    +

    平台簡介

    +

    + 我們常聽到的「鷄蛋不要放在同個籃子裏」， + 爲的就是要分散股市不確定性所帶來的風險。 + 一般來説，在風險分散的同時，收益也會跟著降低。 + 於是問題就變成：我們如何在風險與報酬率中取捨。 + 被稱爲資産配置之父的哈利·馬可維兹（Harry Max Markowitz）就提供了以下的想法。 +

    +

    + 在給定各個標的（underlying）的權重後， + 對該投資組合（portfolio）做均值–變異數分析（mean-variance analysis）， + 其中平均數爲平均報酬率，變異數爲波動率（意即「風險」）。 + 在這樣定義不同投資組合的報酬與風險的情况下， + 我們可以發現：在不同的預期報酬下， + 都可以找到一個投資組合（或權重）使波動率達到最小值。 + 將這些報酬與對應到的最小風險記錄下來後所形成的開口向右的二維拋物線圖形（如下圖）， + 就是所謂的「效率前緣（efficient frontier）」。 +

    +
    + frontier-example +
    +
    + + +

    + 本網站使用S&P 500、元大臺灣50以及Nasdaq 100指數的成份股的歷史日資料。 + 每次計算標的權重，都是取六個月的資料，依照馬可維兹的理論， + 畫出效率前緣，然後取夏普值最大的權重，再用接下來三個月的資料， + 觀察投資組合的價值如何變化。我們每三個月更新一次權重， + 再把許多三個月區間內投資組合的價值變動拼接起來， + 得到2015年中到2020年中投資組合的價值變動， + 再藉此求出平均年報酬、年波動率，再把報酬除以波動率得到整個投資期間的夏普值。 + 另外我們還算出最大跌幅（maximum drawdown），就是投資過程中， + 會從到目前為止的最高點，最多下跌多少幅度。 +

    +
    +
    +
    +
    + 固定預期報酬 $p$，令投資組合權重為 $w$, 則將波動率最小化的數學問題為: +
    +
    +

    + $$\begin{equation} + \begin{aligned} + \min_{w} \quad &\frac{1}{2}w^{T}\Sigma w\quad\\ + \textrm{s.t.} \quad &\sum_{i=1}^{n}w_i = 1\\ + &\sum_{i=1}^{n}w_i R_i \geq p\\ + &0\leq w_i \leq 1 \quad , 1 \leq i \leq n + \end{aligned} + \end{equation}$$ +

    +
    +
    +
    +
    +
    +
    + 令投資組合權重為 $w$, 則將夏普率最大化的數學問題為: +
    +
    +

    + $$\begin{equation} + \begin{aligned} + \min_{w} \quad &\frac{w^T R}{\sqrt{w^{T}\Sigma w}}\quad\\ + \textrm{s.t.} \quad &\sum_{i=1}^{n}w_i = 1\\ + &0\leq w_i \leq 1 \quad , 1 \leq i \leq n + + \end{aligned} + \end{equation}$$ +

    +
    +
    +
    +
    +
    +

    投資組合的$\alpha$、$\beta$值簡介

    +

    + 在評估投資組合的表現時，經常使用的是絕對性的指標， + 例如報酬率、波動率、夏普指數，MDD等， + 不過也有相對性的指標alpha、beta值。 + 比較投組與另一個標的（例如大盤指數， + 我們可以透過回歸式，估計出作為超額報酬的alpha值， + 以及作為相關性的beta值。Alpha值表示投組的獲利能力， + 越高越好。Beta值代表了投組對系統性風險的曝險程度，越低越好。 +

    +

    + 根據CAPM模型，給定投組報酬 $r_{p}$, 大盤指數 $r_m$ 以及無風險利率 $r_f$ , + $\beta$ 值可以藉由以下公式得出： + $$ \mathbb{E}[r_p] - r_f = \beta (\mathbb{E}[r_m] - r_f)$$ + $\alpha$ 值則是藉由計算實際的投組報酬與由CAPM得到的理論報酬的差得到，公式為: + $$\alpha = r_p - (r_f + \beta (r_m - r_f))$$ +

    +

    + 當給定一組時間序列時，alpha、beta值可以利用線性回歸的方式得出。 + 我們可以將上述的式子改寫成以下的形式： + $$Y = \alpha + \beta X + \epsilon,$$ 而 $$Y = r_p-r_f , X = r_m-r_f$$ + 透過此回歸式，我們除了可以得到alpha、beta值外，也可以計算出值，用以衡量此模型的解釋力。 + 在本擂台上，不限台股的投組使用的大盤指數 ($r_m$) 是SPY， + 台股的投組使用的大盤指數是0050.TW，無風險利率暫定為0。 +

    +
    +

    VaR 簡介

    +
    +
    + {% endblock content %} + + + + + + + {% block script %} + + + + + {% endblock %} + + diff --git a/templates/competitions.html b/templates/competitions.html new file mode 100644 index 0000000..94c6f4f --- /dev/null +++ b/templates/competitions.html @@ -0,0 +1,53 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/templates/custom.html b/templates/custom.html new file mode 100644 index 0000000..f13d16e --- /dev/null +++ b/templates/custom.html @@ -0,0 +1,6 @@ +{% extends 'base.html' %} +{% set active_page = 'custom' %} + + +{% block title %}Strategy Page{% endblock%} +{% block content %}Hello world - {{ active_page|e }} {% endblock %} diff --git a/templates/login.html b/templates/login.html new file mode 100644 index 0000000..3ebd9a7 --- /dev/null +++ b/templates/login.html @@ -0,0 +1,85 @@ +{% extends 'base.html' %} +{% set active_page = none %} + + +{% block title %}Login Page{% endblock %} +{% block style %} +.card-body { + background-image: linear-gradient(rgba(255,255,255,0.5), rgba(255,255,255,0.5)), url({{ url_for('static', filename='img/cat.png') }}); + background-attachment: fixed-bottom; + background-position: right bottom -10px; + background-repeat: no-repeat; + +} +{% endblock %} + +{% block content %} +
    +
    +
    +
    +
    +
    +
    +
    +

    登入

    +
    + {% with messages = get_flashed_messages(with_categories=true) %} + {% if messages %} + {% for category, message in messages %} + + {% endfor %} + {% endif %} + {% endwith %} +
    + + +
    We'll never share your email with anyone else.
    +
    +
    + + +
    +
    + + +
    +
    +
    + 沒有帳號？ +
    + +
    +
    +
    + +
    +
    +
    +
    +
    +
    +
    + + + +{% if error %} + +{% endif %} +{% endblock %} \ No newline at end of file diff --git a/templates/registration.html b/templates/registration.html new file mode 100644 index 0000000..242d39c --- /dev/null +++ b/templates/registration.html @@ -0,0 +1,73 @@ +{% extends 'base.html' %} +{% set active_page = none %} + + +{% block title %}Registration Page{% endblock %} +{% block content %} +
    +
    +
    +
    +
    +
    +
    +
    + +

    註冊

    +
    + {% with messages = get_flashed_messages(with_categories=true) %} + {% if messages %} + {% for category, message in messages %} + + {% endfor %} + {% endif %} + {% endwith %} +
    + + +
    We'll never share your email with anyone else.
    +
    +
    + + +
    +
    + + +
    +
    +
    + 已有帳號？ +
    + + +
    +
    + + +
    +
    + + SINGUP IMAGE + +
    +
    +
    +
    +
    +
    +
    +
    + + + + +{% endblock %} \ No newline at end of file diff --git a/templates/result.html b/templates/result.html new file mode 100644 index 0000000..d14c449 --- /dev/null +++ b/templates/result.html @@ -0,0 +1,6 @@ +{% extends 'base.html' %} +{% set active_page = 'result' %} + + +{% block title %}Strategy Page{% endblock%} +{% block content %}Hello world - {{ active_page|e }} {% endblock %} diff --git a/templates/result_tw.html b/templates/result_tw.html new file mode 100644 index 0000000..4aa5b2a --- /dev/null +++ b/templates/result_tw.html @@ -0,0 +1,8 @@ +{% extends 'base.html' %} +{% set active_page = 'result_tw' %} + + +{% block title %}Strategy Page{% endblock%} +{% block content %}Hello world - {{ active_page|e }} + +{% endblock %} diff --git a/templates/strategy_tw.html b/templates/strategy_tw.html new file mode 100644 index 0000000..8b3b1fa --- /dev/null +++ b/templates/strategy_tw.html @@ -0,0 +1,175 @@ +{% extends 'base.html' %} +{% if session.tw == 1 %} + {% set active_page = 'strategy_tw' %} +{% else %} + {% set active_page = 'strategy' %} +{% endif %} + + + +{% block title %}Strategy Page{% endblock%} +{% block style %} +body { + background-image: url({{ url_for('static', filename='img/money.jpeg') }}); + background-repeat: no-repeat; /* Do not repeat the image */ + background-size: cover; +} +.input-group{ + border: 1px solid #8E8E8E; + border-radius: 7px; +} +.input-group > span{ + bg-dark; + font-bold; +} +{% endblock style %} + +{% block content %} +
    +
    +
    +
    + ... +
    +
    投資組合建立步驟
    +
      +
    1. 輸入投資組合名稱
      2. +
    2. 選擇所參加的課程或競賽
      3. +
    3. 選擇資產 (可使用搜索功能篩選)
      4. +
    4. 確認資產後按下 "確認資產", 查看資產價格動態圖表
      5. +
    5. 選擇訓練 / 回測數據比例與最佳化目標函數
      6. +
    6. 確認後按下 "建立投資組合", 查看回傳訊息
      7. +
    +
    +
    +
    + 投資組合名稱 + +
    +
    + 請選擇所參加的課程/競賽 + +
    +
    + 搜尋 + + 下方加入資產 +
    +
    + 請選擇資產 + + + +
    +
    +
    +
    + 已選擇的資產 +
    +
    + + +
    +
    +
    +
    + 請選擇訓練/回測數據比例 + + +
    +
      +
    1. + 2330.TW | 台積電 | 2000-01-05 ~ + + + +
      2. +
    +
    +
    +
    +
    + + +
    +
    +
    + +{% endblock %} + + + + +{% block script %} + + +{% endblock script %} +