Quantlytica Index
Currently Quantlytica Index is only on testnet
Introduction
Through our adopted risk control strategies, our goal is to construct an index that embodies both robustness and accuracy, effectively reflecting the current state of the cryptocurrency market. In selecting components for the index, we utilized quantitative factors to pick the top cryptocurrencies featured in factors style from a whitelist, and we combined their price movements using an equal-weighting approach to ensure that each component contributes evenly to the index.
These risk control strategies not only help protect investors from unnecessary risks but also provide them with a reliable and valuable market indicator. We believe that through this approach, we can offer investors stable and trustworthy investment options in the dynamic and ever-changing cryptocurrency market. The uniqueness of this index lies in its integration of fundamental and quantitative factors, aiming to provide investors with a more comprehensive, dynamic, and competitive investment tool.
Index construction risk management
Preliminary Screening Criteria
We will perform a preliminary screening of all cryptocurrencies listed on the Binance exchange.
To reduce the high risk associated with newly listed cryptocurrencies, our strategy involves excluding all cryptocurrencies that have been listed for less than 90 days.
This measure is based on an in-depth analysis of market data, aimed at reducing the excessive volatility brought about by the uncertainty of new cryptocurrencies and market speculation. Through this method, we can better ensure the stability of the index and provide a more mature and tested market reflection.
Whitelist Mechanism
We will establish a whitelist based on the trading pair data from most popular Cex and Dex.
Cryptocurrencies on the whitelist must have a stable transaction history on these major exchanges.
The purpose of this is to ensure that the selected cryptocurrencies not only perform well on one exchange but are also recognized for their market acceptance and liquidity across a broader spectrum.
We focus on those cryptocurrencies that have a high market value and are widely accepted within the crypto community. Through this approach, we can ensure that the index components accurately reflect the main and influential cryptocurrencies in the market.
Balancing Reliability and Limitations
In the process of establishing the whitelist, we recognize that this might lead to the exclusion of some cryptocurrencies with higher volatility but significant market capitalization (which may not be listed on Coinbase).
This selection helps us reduce the downside risk of the index, though we are aware that it could also limit the index’s upside potential.
We will balance these risks and potential rewards through continuous market monitoring and analysis. It is necessary to regularly review and update the whitelist to ensure the index continuously reflects the market conditions, including manual additions to the whitelist (e.g., BNB).
Index construction mechanism
Dataset Selection and Preprocessing
When selecting the dataset for index modeling, we specifically used 1-hour perpetual contract data. The primary motivation for this decision is to align the model more closely with actual market fluctuations through finer time granularity, specifically hourly price data. This high-resolution data allows us to capture subtle price movement features more accurately, thereby increasing the model’s sensitivity to market behaviors. With this data selection, we aim to achieve more precise and reliable fitting results, leading to more meaningful analysis and conclusions in subsequent backtesting reports.
Index Construction Factors
In index modeling, we employed a unique and effective method by combining fundamental and quantitative factors to select index components. Specifically, the model prioritizes the top 10 cryptocurrencies by in-house factors style from a whitelist over a certain past period. This strategy's design aims to combine the robustness of fundamental aspects with the precision of quantitative factors to build a more stable and reliable index.
Choosing cryptocurrencies with the highest trading volumes as index components helps ensure the index’s representativeness and liquidity. Prioritizing trading volume allows the index to more accurately reflect actual capital flows and investment trends in the market. Through this joint selection based on quantitative factors and trading volume, the index components are likely to exhibit relative stability in volatile markets, enhancing the overall credibility and applicability of the index. This strategy of integrating fundamental and quantitative approaches provides a more comprehensive and dynamic perspective for index construction.
Index Fitting Process
During the index fitting phase, a selection is first made through quantitative factors to filter multiple cryptocurrencies. The purpose of this step is to pick assets that show relatively robust market performance, in order to build a more robust index. By analyzing quantitative factors, we are able to exclude cryptocurrencies that are highly volatile or exhibit unstable performance, thus enhancing the overall reliability of the index.
Subsequently, in the process of building the index, we adopted an equal weighting distribution strategy. The rise and fall of each cryptocurrency are included in the index calculation with equal weight, ensuring an equal contribution from each component. This weighting distribution not only simplifies the model but also ensures that the overall index better reflects the composite market movements. This method is advantageous as it effectively captures the overall market performance of multiple cryptocurrencies, providing a more comprehensive and balanced foundation for further analysis.
Finally, the components of the index are rebalanced and adjusted every 30 days. Since the index components we designed are relatively stable, significant adjustments are unlikely.
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