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Digital Currency

  “Digital Currency” Recently, Digital Currency is proceeding, but limited to wallets for purchase. To proceed more, digital currency must be applied to assets classes. Then, paper and coins currency can be invalid. If digital currency is applied to assets and used in smartphones, various types of Bonds and Securities are possible. Digital Currency is associated with Personal Information, and it causes fear and intrust to own government for some people. This problem can be solved by fear to lose money. If paper and coins are invalid, people must use digital currency. To proceed Digital Currency, deleting trust for Bitcoin is good way. Bitcoin is used for dark money, and not in control by trustable organizations. Digital Currency by government can compete Bitcoin if the government is trustable. In addition, Bitcoin has several deep security risk by hackers. Roll back is impossible when hackers steal Bitcoin.
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Secret Code(correlation 0.95189 between Nasdaq and Commodities)

  2024/4/8.  Secret Code These days, I wrote some Python codes about searching correlation between Nasdaq and Commodities. The latest score is 0.95189 now(2024/4/8). I don’t open this code because I will use it for my trading assistant program. I hope I can be rich with the program… (Additional things...) I changed my mind, but it is only small... This is the result picture between Predicted model vs Actual Nikkei 225 Close price. 2024/4/17.  Additional result I lost $200(¥30000)... My bad point is too much believing my thought...
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Cacao + Copper - suger = Nikkei225?

  (cacao_close ** 0.31) + (copper_close ** 0.46) - (suger_close ** 0.37) This shows highest correlation with Nikkei 225. The value is 0.631. I did great work! I uploaded whole code in below github. https://github.com/Kouhei-Takagi/PythonAlmostEveryday/tree/main/VSNikkei
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Nikkei index price analysis with Python

  2022/1/16.  Nikkei index price analysis with Python I wrote some Python codes for analysis about Nikkei index price. The time period is 2021/10 - 2021/12, and I uses Nikkei index price, Volume and Selling Volume as parameters. The codes are on GitHub. https://github.com/Kouhei-Takagi/NIKKEI-Price PPDAC is important for this task, PPDAC is consisted from Problem, Plan, Data, Analysis and Conclusion. {Ploblem} I want to know what happened about movement of Nikkei index price. {Plan} Can I know it from some parameters? {Data} I collected data with some Python codes. {Analysis} I visualized the data and thought about it. 2021/10 2021/11 2021/12 These figures show that (A) Volume and Selling Volume had strong relationships, and (B) perhaps Nikkei index price downed after 5 -10 day later when Selling Volume increased.   {Conclusion} Not secret. From thinking about above (A) and (B), Nikkei index price may be difficult to fall down or raise up. I thought
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Python coding

 I did Python coding and wrote one article on Qiita. https://qiita.com/K-TKG/items/cb198a0f63f98003d961 This is written in Japanese, so that perhaps you are not able to read it... Parts of my code are below... https://github.com/Kouhei-Takagi/NIKKEI-Price
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