[Coinvestor] Algo partner interview : Silentist(2)

$FCA : Portfolio strategies grounded in fundamental assessments of cryptocurrencies

▶︎ What strategy is Facto A?

• Facto A ($FCA) strategy utilizes on- and off-chain data for each project, managing portfolios through short- and long-term rebalancing driven by fundamental quantitative valuations. It applies institutional asset factor analysis methods to the crypto field. $FCA generates over 100 factors directly from data, refining them using reinforcement learning to create numerous synthetic factors. From these, it chooses the best, which are rigorously tested and refined quarterly. At the start of each month, chosen synthetic factors cast long/short votes on cryptocurrencies with over $1 billion market cap. A portfolio is constructed based on these votes, aiming for market-neutral balance. The process repeats each month, maintaining portfolio integrity and rebalancing.

▶︎ Which investors would you recommend the $FCA strategy to?

• The $FCA strategy is ideal for those experienced in institutional(traditional) assets. Unlike many algorithmic trading strategies relying on technical trading and market data, $FCA is entirely rooted in fundamentals. It deeply assesses factors like blockchain activity, revenue, and user engagement for each project. This strategy emphasizes purchasing fundamentally strong yet undervalued projects and selling those with strong market momentum. Consequently, $FCA aims to profit from the alignment of its fundamental analysis with market dynamics.

▶︎ Why do you use market-neutral risk factors, which are rarely used in crypto markets?

• Although market-neutral strategies have gained traction in institutional management, their implementation in the cryptocurrency for generating returns has been limited. However, with the emergence of factor analysis for crypto projects, the potential to establish long and short market-neutral portfolios based on these factors has expanded.

▶︎ What are the expected risks of the $FCA strategy?

• The $FCA strategy upholds a consistent 1:1 long/short ratio and focuses on stocks with a market capitalization of $1BM or more. This approach contributes to its relative stability even in times of market turbulence like bull runs and crashes. However, the primary anticipated risk is counterparty risk, arising from potential failures of the trading infrastructure like Binance, leading to the loss of assets. Furthermore, compared to institutional assets, the availability of vendors providing on- and off-chain data on crypto fundamentals is limited, prompting us to maintain a vigilant awareness of data accuracy.

▶︎ How do you manage the expected risks of the $FCA strategy?

• To mitigate counterparty risk, we need to diversify our trading infrastructure across multiple exchanges that provide crypto perpetual futures. As a precaution against data-related risks, we have devised a strategy to conduct routine data assessments and gradually expand our data sources to enhance cross-validation measures.

▶︎ Why did you decide to partner with Coinvestor, and what synergies do you expect from the collaboration?

• Coinvestor’s value proposition, which involves identifying promising crypto management teams and facilitating secure connections with new clients, aligns well with Silentist’s objectives. We are enthusiastic about this partnership and eagerly anticipate showcasing Coinvestor’s performance to broaden our potential client base.

▶︎ How does Silentist bridge the gap between backtesting and actual strategy execution?

• Factor-based rebalancing portfolios have low trade frequency and operate with longer time horizons. $FCA undergoes rebalancing approximately once a month. This infrequent trading approach reduces the occurrence of errors, such as slippage, during structural backtesting.
• The process of identifying the second significant factor begins with historical data fitting (in-sample test). However, only factors unused in deriving subsequent ones are used in actual operations (out-of-sample test). We validate these factors using future data performance (validation period), while testing conservatively with higher transaction costs. It’s like having an out-of-sample test twice.
• The process aims to avoid selecting over-optimized strategies in terms of $FCA factor or derivation time. By including only stocks with a market cap of $1BM or more, it mitigates issues like survivorship bias in backtesting.

▶︎ What is the recommended investment period for the Factor A strategy?

• We anticipate a rise in players adopting fundamental data-based investments, resulting in enhanced returns as these approaches gain traction. We suggest giving it around 6 months to a year for self-correction within WEB 3.0 and a more balanced investor landscape.

▶︎ Why do you pursue market-neutral returns solely based on fundamental quantitative data?

• A market-neutral portfolio mitigates the risk associated with directional bets, despite having fewer potential return sources. It permits selective application of directional bets during trading, provided stock selection relies on fundamental data with a dash of technical analysis. In the case of $FCA, we exclusively focus on return sources rooted in fundamental data. Given the complexity of multifaceted products, investors might find it challenging to comprehend their value. Therefore, we believe that a straightforwardly operated product is more appropriate.

▶︎ How often is the final portfolio of selected factors typically rebalanced?

• Factors are updated once every quarter, while stocks based on these factors are refreshed on the first day of each month.

▶︎ Is it worthwhile to generate accurate fundamental data for non-business tokens?

• Even the smallest projects possess financial metrics such as revenue and costs, and these metrics hold significance. Given that these fundamental metrics serve a foundational purpose, our team is of the opinion that every project qualifies for the integration of fundamental data.

▶︎ I was intrigued by your post regarding the creation of a crypto conspiracy paper with Facto and its free distribution. Could you please explain the criteria you employ to evaluate and score different tokens?

• There are five factors used for scoring:

Value Factor : Based on traditional indicators like PSR and PER, higher value indicates greater undervaluation, leading to a higher score.

Growth Factor : Assesses the absolute growth rate of crypto project drivers, including blockchain contributors and user counts.

Momentum Factor : Measures project market interest via metrics like market price and trading volume.

Quality Factor : Evaluates project efficiency in generating profit with less capital.

Native Factor : Focuses on crypto-specific indicators, like business and financial efficiency, including user-to-developer ratios.

▶︎ Do you have any content you’re preparing using Facto?

• We aspire to construct a ‘fundamental insight platform’ grounded in the content and insights generated via Facto, a quantitative valuation model built upon a framework for assessing crypto fundamentals. Initially, we developed a service enabling users to freely and interactively solicit fundamental evaluation outcomes or investment perspectives on projects by integrating Facto and ChatGPT.

Examples:

Explore the top 10 most captivating coins.

Receive recommendations tailored to your current investment criteria.

Engage in discussions about AI analysts’ fundamental assessments and investment recommendations for specific projects.

Compare objective fundamental metrics among distinct projects.

Obtain a SPOT portfolio score assessment for your present account.

Receive consultancy grounded in the outcomes of your current account’s portfolio assessment.

https://www.youtube.com/watch?v=DWvDJ8fk_VI (Example using the Hi Facto! Telegram bot)

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