CryptoracleExploration of CO Indicators Based on ESGPT and News-Driven Forecasting
This paper proposes an innovative method integrating Event Stream GPT (ESGPT) continuous event modeling with a News-driven forecasting framework (News-to-Forecast, N2F) to systematically explore the prediction of Crypto-Only (CO) indicators. By unifying event representation, multimodal fusion, and explainable prediction mechanisms, a cross-modal prediction framework is constructed.1. IntroductionCO indicators, as multi-dimensional measurement tools integrating price, volume, and commu...
CryptoracleCausal Path Modeling of Crypto Market Volatility
CryptoracleMomentum Rotation Strategy Based on Community Popularity and Price Momentum
Strategy 1: Investing in Top 3 Community Popularity Momentum 1. Cryptocurrency Clustering: Building Resonance SectorsA distance matrix (Euclidean distance) is constructed based on a Currency × Date mention volume matrix. The Ward hierarchical clustering method is then applied to group the currencies.Assets are divided into 10 categories to identify the "resonance sector" for each currency, supporting subsequent sector-level linkage identification and trading logic. CategoryCurrency ListCatego...
CryptoracleExploration of CO Indicators Based on ESGPT and News-Driven Forecasting
This paper proposes an innovative method integrating Event Stream GPT (ESGPT) continuous event modeling with a News-driven forecasting framework (News-to-Forecast, N2F) to systematically explore the prediction of Crypto-Only (CO) indicators. By unifying event representation, multimodal fusion, and explainable prediction mechanisms, a cross-modal prediction framework is constructed.1. IntroductionCO indicators, as multi-dimensional measurement tools integrating price, volume, and commu...
CryptoracleCausal Path Modeling of Crypto Market Volatility
CryptoracleMomentum Rotation Strategy Based on Community Popularity and Price Momentum
Strategy 1: Investing in Top 3 Community Popularity Momentum 1. Cryptocurrency Clustering: Building Resonance SectorsA distance matrix (Euclidean distance) is constructed based on a Currency × Date mention volume matrix. The Ward hierarchical clustering method is then applied to group the currencies.Assets are divided into 10 categories to identify the "resonance sector" for each currency, supporting subsequent sector-level linkage identification and trading logic. CategoryCurrency ListCatego...
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Institution Name:
Researcher / Contact Person:
Backtest Time Range: 2025-01-01 to 2025-08-27
Strategy Name: Multi-factor Strategy
Strategy Type: Multi-factor Time-series Strategy
Indicator Names Used: (Multiple selections allowed)
CO-A-01-01, CO-A-01-02, CO-A-01-03, CO-A-01-04, CO-A-01-05, CO-A-01-07, CO-A-01-08, CO-A-01-09
CO-A-02-01, CO-A-02-02, CO-A-02-03, CO-A-02-04, CO-A-02-05
Data Update Frequency: Every 15 minutes
Access Method: API
Metrics | Without Community Indicators | With Community Indicators | Improvement |
Annualized Return (%) | — | ||
Sharpe Ratio | — | ||
Maximum Drawdown (%) | — | ||
Profit/Loss Ratio | — | ||
Win Rate (%) | — | ||
Avg. Daily Trades | — | ||
Estimated Transaction Cost | — |
Changes in Strategy Performance: (e.g., increased sensitivity at key market nodes, avoidance of extreme drawdowns, etc.)
The indicators show weak effectiveness with no predictive effect in the short term; however, there is some predictive effect over longer time horizons.
Discovery of New Trading Assets: (e.g., assets not previously used in original business but added this time)
None
Indicator Contribution to Strategy Logic: (e.g., improved signal quality, noise filtering)
The indicators did not play a significant role in enhancing effectiveness.
User Experience: (e.g., ease of use, explanatory power, compatibility with internal models)
High ease of use, but effectiveness is relatively average.
Potential Improvement Suggestions: (e.g., suggestions for update frequency, desire for more community types, etc.)
Indicator frequency could be further reduced; noise filtering is still required during data integration.
Interested in continued use and discussing formal licensing
Interested in customized indicators / focusing on specific community types (please specify)
Requires a longer trial period / more historical data (please specify requirements)
Institution Name:
Researcher / Contact Person:
Backtest Time Range: 2025-01-01 to 2025-08-27
Strategy Name: Multi-factor Strategy
Strategy Type: Multi-factor Time-series Strategy
Indicator Names Used: (Multiple selections allowed)
CO-A-01-01, CO-A-01-02, CO-A-01-03, CO-A-01-04, CO-A-01-05, CO-A-01-07, CO-A-01-08, CO-A-01-09
CO-A-02-01, CO-A-02-02, CO-A-02-03, CO-A-02-04, CO-A-02-05
Data Update Frequency: Every 15 minutes
Access Method: API
Metrics | Without Community Indicators | With Community Indicators | Improvement |
Annualized Return (%) | — | ||
Sharpe Ratio | — | ||
Maximum Drawdown (%) | — | ||
Profit/Loss Ratio | — | ||
Win Rate (%) | — | ||
Avg. Daily Trades | — | ||
Estimated Transaction Cost | — |
Changes in Strategy Performance: (e.g., increased sensitivity at key market nodes, avoidance of extreme drawdowns, etc.)
The indicators show weak effectiveness with no predictive effect in the short term; however, there is some predictive effect over longer time horizons.
Discovery of New Trading Assets: (e.g., assets not previously used in original business but added this time)
None
Indicator Contribution to Strategy Logic: (e.g., improved signal quality, noise filtering)
The indicators did not play a significant role in enhancing effectiveness.
User Experience: (e.g., ease of use, explanatory power, compatibility with internal models)
High ease of use, but effectiveness is relatively average.
Potential Improvement Suggestions: (e.g., suggestions for update frequency, desire for more community types, etc.)
Indicator frequency could be further reduced; noise filtering is still required during data integration.
Interested in continued use and discussing formal licensing
Interested in customized indicators / focusing on specific community types (please specify)
Requires a longer trial period / more historical data (please specify requirements)
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