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dsyoon
2023-08-27 21:05:59 +09:00
parent 0d69ead097
commit e7012c8fa3
6 changed files with 208 additions and 15 deletions
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30
analyzer/SPCorrelationAnalyzer.py
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@@ -1,4 +1,5 @@
import os
import json
import sqlite3
import pandas as pd
@@ -224,18 +225,25 @@ if __name__ == "__main__":
spCorrelationAnalyzer = SPCorrelationAnalyzer(stockFileName)
# {"stock_code": "122630", "stock_name": "KODEX 레버리지"}
# {"stock_code": "252670", "stock_name": "KODEX 200선물인버스2X"}
# {"stock_code": "^NDX", "stock_name": "NASDAQ 100"}
# {"stock_code": "TQQQ", "stock_name": "ProShares UltraPro QQQ"}
# {"stock_code": "SQQQ", "stock_name": "ProShares UltraPro Short QQQ"}
# {"stock_code": "SOXL", "stock_name": "Direxion Daily Semiconductor Bull 3X Shares"}
# {"stock_code": "SOXS", "stock_name": "Direxion Daily Semiconductor Bear -3X Shares"}
corr_scores = spCorrelationAnalyzer.analyze(master_code='SOXS')
corr_scores_list = sorted(corr_scores.items(), key=lambda item: item[1], reverse=True)
for item in corr_scores_list:
print("%s %4.3f" % (item[0], item[1]))
inputs = [
{"stock_code": "122630", "stock_name": "KODEX 레버리지", "corr": []},
{"stock_code": "252670", "stock_name": "KODEX 200선물인버스2X", "corr": []},
{"stock_code": "^NDX", "stock_name": "NASDAQ 100", "corr": []},
{"stock_code": "TQQQ", "stock_name": "ProShares UltraPro QQQ", "corr": []},
{"stock_code": "SQQQ", "stock_name": "ProShares UltraPro Short QQQ", "corr": []},
{"stock_code": "SOXL", "stock_name": "Direxion Daily Semiconductor Bull 3X Shares", "corr": []},
{"stock_code": "SOXS", "stock_name": "Direxion Daily Semiconductor Bear -3X Shares", "corr": []}
]
for input in inputs:
corr_scores = spCorrelationAnalyzer.analyze(master_code=input["stock_code"])
corr_scores_list = sorted(corr_scores.items(), key=lambda item: item[1], reverse=True)
for item in corr_scores_list:
input["corr"].append({item[0]: item[1]})
print("%s,%s,%4.3f" % (input["stock_code"], item[0], item[1]))
outFileName = os.path.join(PROJECT_HOME, 'analyzer/corr.json')
with open(outFileName, 'w', encoding='utf-8') as file:
json.dump(inputs, file, indent="\t", ensure_ascii=False)
print('done...')