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WiMi’s PBO Algorithm: Elevating Blockchain and Bitcoin Trading

WiMi Hologram Cloud Inc., a leading global Hologram Augmented Reality (“AR”) Technology provider, announced that the probability of backtest overfitting algorithm (PBO) was used to blockchain and bitcoin trading strategies. PBO is computed based on the combinatorial symmetric cross-validation framework (CSCV) and is designed to quantify the risk of overfitting in small sample backtests. With PBO, it is possible to assess the overfitting probability of a strategy in a single backtest, thus reducing the risk of a poorly performing live model. However, relying on backtesting results alone is not enough. In order to obtain models that perform better out-of-sample, WiMi has developed a dynamic optimization algorithm based on PBO, namely the “PBO-DOA algorithm”. This algorithm is important in quantitative trading because it can dynamically optimize the model’s parameters to help investors build optimal portfolios.

PBO-DOA could be applied in blockchain and bitcoin trading strategies. By optimizing the weight allocation of a portfolio, the algorithm is able to maximize returns and control risk. This provides an effective tool for investors to better manage their portfolios and thus realize better investment returns. Suppose there is a portfolio that contains multiple blockchain and bitcoin-related assets such as bitcoin, ethereum, litecoin, etc. At this point, using PBO-DOA it is possible to optimize the return of the portfolio by adjusting the weights of each asset:

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AIThority Predictions Series 2024 bannerFirst, WiMi collects historical market data for each asset, including key information such as price and trading volume. Subsequently, by employing PBO-DOA, the expected return and risk of each asset are precisely calculated. The core idea of the algorithm is to fully consider the correlation and covariance matrices between assets in order to develop a reasonable asset weight allocation. It aims to find an optimal portfolio that maximizes expected return and minimizes risk. Here is an example, there are three assets: bitcoin, ethereum, and Litecoin. Using PBO-DOA, the expected return and risk of each asset can be accurately calculated and the optimal asset weight allocation can be further found. Based on the calculation results of the algorithm, the optimal weight allocation scheme can be analyzed as 40% for Bitcoin, 30% for Ether, and 30% for Litecoin. Therefore, in order to realize the optimal portfolio, 40% of the funds should be invested in Bitcoin, 30% in Ether, and the remaining 30% in Litecoin.

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The PBO-DOA is based on in-depth analysis of large amounts of transaction data and model training, which can provide more accurate predictions and decision support. By studying historical Bitcoin price and transaction data, the algorithm is able to successfully identify market trends and cycles, thus helping traders develop more effective trading strategies. In addition, through in-depth mining and analysis of blockchain data, PBO-DOA reveals hidden trading laws and patterns.

Blockchain, as an open and transparent transaction record database, provides rich data resources, and it is based on these data that PBO-DOA uncovers traders’ behavioral patterns and potential market opportunities. By monitoring the data and analyzing it in real-time, the algorithm helps traders adjust their strategies in time to adapt to the dynamic changes in the market. Given the volatility of the Bitcoin market, real-time data monitoring and analysis is particularly important, and PBO-DOA’s powerful real-time data analytics help traders quickly capture market dynamics and make decisions accordingly.

WiMi’s PBO-DOA allows for more scientific optimization of blockchain and bitcoin trading strategies, thereby increasing portfolio returns and controlling risk. Algorithms in blockchain and bitcoin trading strategies are over-optimizing the weight allocation of portfolios, maximizing returns, and controlling risks. By considering the correlation and covariance matrices between assets, it helps investors find an optimal weight allocation scheme to construct an optimal portfolio.

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