Your go-to resource for answers to common inquiries about Fincanva and our services
Fincanva is a web-based Software as a Service (SaaS) for investing. It’s a risk-free sandbox where you can experiment, learn, and grow your investment knowledge. Whether you’re a seasoned investor or just starting, Fincanva provides the tools and knowledge you need to navigate the complexities of the financial markets and achieve your investment goals.
Fincanva is a comprehensive financial education platform designed for everyone, from seasoned investors and financial professionals to students and researchers.
A portfolio in Fincanva represents an overarching investment strategy designed to manage and achieve specific financial goals. It serves as a container for multiple investment models, each contributing a unique strategy within the portfolio. By combining different models, you can diversify your investments and manage risk more effectively. Think of a portfolio as a master plan that coordinates various models to create a balanced, goal-driven investment approach.
Setting up a portfolio in Fincanva involves tailoring your investment strategy through a series of important steps. First, you define the general settings, including the portfolio name, strategy description, and objectives. Next, you determine how to allocate capital across different models within the portfolio, ensuring each model fits into the broader financial goals. Following that, you add and configure one or more models, where each model represents a distinct investment approach, such as growth investing or momentum trading. Finally, you have the option to apply risk management settings, such as limiting exposure to certain asset types or setting constraints on overall portfolio risk. By incorporating multiple models, you can blend various strategies into a cohesive plan that balances performance and risk.
A model in Fincanva represents a specific investment strategy applied to a defined set of assets, such as stocks, ETPs, or crypto. Models are the building blocks of your portfolio, with each one designed to achieve a particular objective. You can think of models as individual investment recipes that can be combined within your portfolio to reach your overall financial goals. For example, one model may focus on value investing, targeting undervalued stocks, while another may apply momentum-based strategies by focusing on assets with strong upward price trends. Each model contributes its unique characteristics, making it possible to customize your portfolio’s strategy according to your needs.
Models play a critical role in shaping the structure and performance of a Fincanva portfolio. A portfolio can contain multiple models, each with its own strategy and goals. By combining these models, you create a diversified and risk-managed portfolio designed to achieve your overall financial objectives. For instance, a portfolio might include a model focusing on growth stocks and another that emphasizes defensive assets to provide stability during market downturns. This combination allows for greater flexibility and resilience, as different models can complement each other, adapting to varying market conditions. Ultimately, models enable you to structure a portfolio that balances both risk and reward, optimizing your chances for long-term success.
A Fincanva portfolio functions by coordinating a set of rules and settings that govern how the models it contains operate and interact. These rules include general settings, which define the portfolio’s objectives and strategy, as well as allocation settings, which specify how capital is distributed among different models. Additionally, risk management settings can be applied to control the portfolio’s overall exposure to risk by setting limits on factors such as maximum drawdown or volatility. Each model within the portfolio contributes its unique strategy, and the combined performance of these models determines the portfolio’s overall success. This structure allows for diversification and tailored investment strategies that adapt to your goals.
A Fincanva model works by applying a structured set of rules and settings to the assets it encompasses. These rules define the model’s general parameters, such as its objective and timeframe, along with asset selection criteria, which filter and screen assets based on user-defined factors. The model’s allocation settings determine how capital is distributed among the selected assets, using methods like equal weighting, risk scaling, or market cap weighting. Furthermore, model-specific risk management settings control exposure by setting constraints on allocation ranges or volatility limits. A portfolio can contain multiple models, each operating with its own strategy, enabling users to diversify and experiment with various investment approaches under a unified portfolio structure.
Risk management rules allow for dynamic adjustments in capital deployment and allocation based on market conditions, enabling flexibility in response to changing environments.
A portfolio is the actual investment strategy applied in real-time, guiding investment decisions. A simulation tests (or backtest) this strategy against historical data, allowing evaluation and refinement before implementation with real money. Simulations provide insights into portfolio performance under different market conditions, uncover hidden properties and relations, identify strengths and weaknesses, refine strategies, and build confidence in investment approaches.
Models offer several advantages: focused strategies, simulation and optimization, building blocks for portfolios, and granular control. They allow targeting specific market segments or asset classes with tailored approaches, testing strategies against historical data, creating a library of models to mix and match within portfolios, and fine-tuning investment decisions at the individual asset level.
Portfolios offer benefits like diversification between different models, strategic flexibility, efficiency, and performance tracking. They allow spreading investments across different models to reduce overall risk, tailoring the portfolio to match risk tolerance and financial objectives, managing multiple models within a unified structure, and easily monitoring and analyzing the performance of the investment strategy.
Simulations offer valuable insights such as evaluating portfolio performance, uncovering hidden properties and relations, identifying strengths and weaknesses, refining strategies, and building confidence. They provide empirical evidence and insights that help make informed investment decisions.
Yes. You can set country of residence, tax regime, short-term capital gain tax, long-term capital gain tax, and dividend tax.
Yes. In simulations we consider trading fees, slippage, borrowing rate, and short rate.
Traditional backtesting tools often suffer from issues like look-ahead bias, selection bias, survivorship bias, unrealistic assumptions, and low data quality. These flaws can lead to unrealistic results and an overly optimistic view of historical returns. Fincanva addresses these challenges effectively.
Fincanva’s simulation engine overcomes these challenges with a walk-forward approach, inclusion of delisted shares, use of screeners, bridging the gap between simulation and reality, incorporating real-world costs and frictions, and using high-quality data. This approach ensures accurate and trustworthy results, realistic simulations, and the ability to seamlessly implement investment ideas in a live trading environment.
Fincanva’s simulation engine offers benefits like eliminating look-ahead bias, countering survivorship bias, avoiding selection bias, bridging the gap between simulation and reality, incorporating real-world costs and frictions, and providing high-quality data. These features ensure realistic simulations and accurate, reliable results.
Fincanva prioritizes transparency, discourages harmful practices, avoids conflicts of interest, and focuses on long-term strategies over short-term gains. It also provides educational resources to empower investors to make informed and principled choices.
Fincanva screeners are filtering tools derived from a powerful portfolio simulation engine, designed for accuracy, versatility, and user empowerment.
Fincanva screeners are built on a simulation engine, free and powerful, unmatched in versatility, and offer real-world relevance due to meticulously curated and constantly updated data.
Yes, Fincanva screeners are completely free to use.
Fundamental indicators, technical indicators, and market sector indicators. Some filters are specific to certain asset types.
Yes, by clicking on the cog icon next to each filter, you can set parameters like reports ago, days, months ago, and many more.
While powerful and free, Fincanva screeners are designed to complement the core simulation engine. Their functionality may be subject to the requirements and limitations of the simulation environment.
It offers a complete view of all positions opened within the portfolio during the simulation period, detailing symbols, positions, trades, dividends, and splits.
It offers a complete view of all positions opened within the portfolio during the simulation period, detailing symbols, positions, trades, dividends, and splits.
It provides a detailed summary of the portfolio’s key performance indicators (KPIs), including performance metrics, risk metrics, and comparative ratios, allowing for assessment and comparison with a benchmark.
It offers a comprehensive summary of monthly and annual performance, along with drawdown for each period, and allows for comparison with a benchmark.
It chronicles the distribution of capital during a simulation within a portfolio model, showing capital evolution in terms of profit or allocation, percentage allocation evolution, and more.
It provides a comprehensive assessment of each model’s yearly performance, correlations, and placement in a performance-risk space, supporting informed investment decisions.
It examines how the start date of the portfolio impacts its performance over time, providing analysis for different time frames and displaying potential earnings and risks associated with different investment starting dates.
It allows monitoring of open positions at the end of the simulation, enabling users to conclude the simulation on the current date and view the positions that should presently be open in the real portfolio.
Fincanva sources and processes data from a wide variety of reliable providers to ensure extensive coverage and accuracy. We are continually committed to ensuring data quality and integrating new providers into our platform. As of today, we source data from Nasdaq Data Link, Sharadar, EODHD, Siblis Research, Quandl, the Federal Reserve, OECD, Professor Kenneth Fama-French’s Research Data, Macrotrends, and Coding Wand (Fincanva). We will continue to add new providers.
Fincanva prioritizes data quality and reliability through rigorous validation, careful provider selection, cleansing, and temporal accuracy measures. We meticulously select and preprocess historical data to eliminate biases and manage data revisions, ensuring the most up-to-date and relevant information is used in simulations, portfolios and models. We continuously add new data providers and plan to introduce new types of data.
Fincanva utilizes multiple types of data, inluding price data, fundamental data, and macroeconomic data and more.
Fincanva leverages data to provide powerful tools and insights through simulations, models, educational resources, and research reports, empowering users to make informed investment decisions.
Data is updated on a daily basis, sourced from both external suppliers and in-house analysts.
Yes, delays or inaccuracies may occur due to errors from third-party data providers. Users are advised to verify critical information independently
Fincanva is committed to providing users with the highest quality data and tools to support their investment journey, emphasizing informed and data-driven decisions as the key to achieving financial goals.
Currently, we offer various allocation strategies including Beta neutral, Equal weights, Fixed weights, Floating, Market cap weighted, Mimicking portfolio, Minimum correlation, several Modern Portfolio Theory (MPT), Ranking based allocation, Risk scaling, and Risk parity. We plan on expanding our offerings in the future.
‘Beta neutral’ investing aims to balance market risk by taking both long and short positions to create a portfolio with zero (or target) net beta. This minimizes market risk and suits market-neutral strategies, but frequent rebalancing can be costly.
‘Equal weights’ investing assigns equal weight to all assets in a portfolio or model, regardless of individual characteristics. It is simple to understand and promotes diversification, but ignores individual asset risk and return, and may not optimize the portfolio’s risk-return profile.
‘Fixed weights’ investing assigns pre-determined weights to assets, which remain constant over time. This offers control over portfolio composition and aligns with investor preferences, but does not adapt to market changes and necessitates periodic rebalancing.
‘Floating weights’ allocation allows asset weights to change with market fluctuations and reduces rebalancing frequency, minimizing transaction costs. It allows natural growth and market momentum to influence the portfolio. However, this approach can result in unintended asset concentration and deviation from original investment goals.
‘Market cap weighted’ investing assigns weights to assets based on their market capitalization. This approach aligns the portfolio with broader market trends, reflecting market consensus on asset values, and often requires less frequent rebalancing. However, it can lead to concentration in large-cap stocks, increasing risk, and may not suit smaller, high-growth potential assets.
‘Mimicking portfolio’ aims to replicate the performance of a chosen benchmark, such as an index. This passive investment strategy effectively tracks the benchmark’s performance and is useful for index replication. However, it may not capture all nuances of the benchmark and can be computationally intensive to construct.
‘Minimum correlation’ investing aims to reduce portfolio risk by combining assets with low or negative correlations, enhancing diversification benefits and portfolio stability. However, correlations can change over time, necessitating rebalancing, and this approach can be computationally intensive.
‘Modern portfolio theory (MPT)’ aims to optimize the balance between risk and return by constructing portfolios on the efficient frontier. It offers methods like Markowitz optimal portfolio, minimum variance, and maximum return, with constraints like positive weights, allocation range, and diversified constraint. MPT provides a systematic framework for diversification, but relies on historical data and can be computationally intensive.
‘Ranking based’ allocation ranks assets based on a chosen criterion (e.g., past performance) and assigns weights based on their percentile. This approach capitalizes on trends and is adaptable to various ranking methods, but rankings can change often, causing higher turnover and requiring reliable ranking criteria.
‘Risk scaling’ allocates capital inversely proportional to asset risk, aiming to balance risk contributions in the portfolio. It potentially lowers overall volatility and prioritizes stable assets, but may underweight high-growth assets and relies on accurate volatility estimation.
‘Risk parity’ allocates weights so that each asset contributes equally to the portfolio’s overall risk. This approach promotes portfolio stability and diversification, potentially leading to more consistent returns. However, it can involve high leverage for low-risk assets, increasing complexity and costs, and is computationally intensive.
Allocation methods are designed to align with the specific goals of either a model or a portfolio. We prioritize methods that make sense for each context. For example, allowing a portfolio to short a model isn’t practical. Similarly, concepts like beta neutral or mimicking are better suited for models while are not in line with portfolio management.
Fincanva offers five tiers: a free tier with limited access, and four paid tiers (Starter, Advanced, Premium, Ultimate), each offering varying levels of access to portfolio and simulation features.
Fincanva accepts various payment methods including Credit Cards, Debit Cards, Google Pay, Apple Pay, Link, PayPal and SEPA trasfers. International bank transfers and other payment methods are planned for the future.
Yes, all payments are processed through Stripe, a world leading company in online payment management.
Yes, you can cancel your subscription at any time. The service will continue until the end of your current billing period.
The services and digital content provided by Fincanva are non-refundable, as access to our products is immediate and tailored to your needs. However, we’re committed to providing you with the best possible experience!
Screening tools on Fincanva are completely free and do not require a trial or subscription. For advanced features like backtesting, advanced simulations, and reporting, we offer a 14-day free trial with no credit card required.
Customers will be moved to the free plan. This also applies to active subscriptions that are not renewed.
If you downgrade your subscription or it expires, any portfolios and simulations exceeding the limits of your new plan will be made inaccessible. Rest assured, no data will be lost or deleted. To regain full access to blocked or inaccessible data, you’ll need to upgrade to a suitable plan.
For a fiscal invoice, please contact us directly.
The public subscription prices listed on Fincanva are intended for private, personal use only. Businesses, professional investors, and institutions should contact us to discuss tailored pricing options that align with their specific needs and usage requirements.
Yes, educational and nonprofit pricing is available. Interested users should contact Fincanva for further details.
No, account sharing is strictly prohibited. Each account is intended for a single user.
Fincanva is designed for investing, not trading. Currently, it doesn’t allow direct broker integrations or order execution.
Not at the moment, but we plan to integrate with brokers in the future.
No, Fincanva does not sell or provide any signals. Your portfolio results are dependent on your strategies, settings, and personal interests.
Yes, Fincanva provides tutorials, examples, and explanations through its knowledge base and insight section. We also offer dedicated trining and courses. Please contact us if interested.
Yes, the insight section and other documentation on Fincanva cover these best practices.
The insight section includes educational content related to market updates and platform features.
You can contact Fincanva through the contact form in the footer.
Yes, Fincanva has a knowledge base with detailed explanations and tutorials.
You can currently submit feature requests through direct contact. A voting system for features is planned for future implementation.
A public roadmap is not yet available but will be published soon.
APIs and developer tools are not available at the moment. We plan to release them in the future for business and institutional users.
Data export and import options are available for certain types of data.
Fincanva is fully owned and developed by Coding Wand Srl, an Italian-based tech company focused on research and software development.
Yes, Fincanva adheres to GDPR standards regarding data protection and privacy.
No, Fincanva is not a registered advisor or broker. It is an educational platform designed for hypothetical analysis and does not provide personalized financial advice.
No, Fincanva does not guarantee any investment returns. All simulations and analyses are hypothetical, and users are advised to consult professional advisors.
Data accuracy is not guaranteed, and delays or discrepancies may occur due to third-party providers.
Users are solely responsible for their investment decisions. Fincanva’s outputs should be considered educational and not a substitute for professional advice.
Historical backtesting has limitations, including the fact that past performance is not indicative of future results. Hypothetical scenarios may not account for all real-world factors.
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