Problem: Our current method of selecting stock pairs for cointegration testing, using [current method], might miss out on valuable opportunities. We want to improve the selection process to identify more promising candidates.
Solution: I propose exploring alternative methods for stock pair selection:
Statistical Techniques: Correlation analysis, volatility ratios, or statistical arbitrage strategies.
Machine Learning: Training models to find hidden relationships between stocks.
Technical Analysis: Utilizing indicators like moving averages or RSI to identify cointegrated or tradeable pairs.
Alternatives: We haven't implemented any of these alternatives yet.
Additional Context: Enhancing the pair selection process could lead to better cointegration results and potentially more effective trading strategies.
@Akshat111111 I have raised an issue, can I start working on this?
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Problem: Our current method of selecting stock pairs for cointegration testing, using [current method], might miss out on valuable opportunities. We want to improve the selection process to identify more promising candidates.
Solution: I propose exploring alternative methods for stock pair selection:
Statistical Techniques: Correlation analysis, volatility ratios, or statistical arbitrage strategies. Machine Learning: Training models to find hidden relationships between stocks. Technical Analysis: Utilizing indicators like moving averages or RSI to identify cointegrated or tradeable pairs. Alternatives: We haven't implemented any of these alternatives yet.
Additional Context: Enhancing the pair selection process could lead to better cointegration results and potentially more effective trading strategies.
@Akshat111111 I have raised an issue, can I start working on this?