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MiyaLo's Projects

cta-strategy_fundamental-data-mining icon cta-strategy_fundamental-data-mining

We target commodities with robust liquidity, market engagement, and sound structure, possessing strong trading traits. Starting with fundamental data analysis, we adhere to subjective logic. Applying quantitative techniques, we build and compare multi-dimensional models to refine our market strategy and insights.

cta-strategy_grid-trading_commodity-arbitrage icon cta-strategy_grid-trading_commodity-arbitrage

The grid trading method fundamentally relies on regression. It typically confronts systemic risk with single assets, prompting a shift towards correlated asset portfolios to hedge systemic risk through pair trading.

cta-strategy_hedging-ratio icon cta-strategy_hedging-ratio

This study analyzes the hedging ratio using the CSI 300, CSI 500, and SSE 50 stock index futures as examples. It employs OLS, VAR, ECM, and GARCH models to calculate the hedging ratio, evaluates the net value curve and model effectiveness, and manages the risk exposure of long and short positions through risk management.

cta-strategy_machine-learning_commodity-fundamentals icon cta-strategy_machine-learning_commodity-fundamentals

As the excess returns of public factors gradually narrow, it is worthwhile to explore multi-faceted and multi-angle CTA factors. This strategy, with respect to the underlying fundamental logic as a premise, employs quantitative methods to establish a multi-dimensional model.

cta-strategy_price-volume-factors icon cta-strategy_price-volume-factors

This strategy targets China's futures market, analyzing sectors like metals, energy, agriculture, and precious metals. It uses four factors—momentum in time series, cross-sectional momentum, term structure, and volume of positions—to predict prices and trade for commodities commodities with good liquidity.

fof_asset-allocation_var icon fof_asset-allocation_var

This study applies VAR to economic fundamentals and financial environments, including overseas, valuation, market sentiment, inflation, and economic momentum data, to test its efficacy in predicting market style rotation based on index models.

fof_smartbeta_growth-value icon fof_smartbeta_growth-value

Value and growth styles are widely defined and attract significant market attention. This strategy primarily focuses on researching these two sectors. By employing market capitalization weighting and IC (Information Coefficient) weighting methods, we categorize funds into two styles and construct corresponding smart beta indices.

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