{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_myinvestment-ai","slug":"myinvestment-ai","name":"MyInvestment-AI","type":"product","url":"https://myinvestment-ai.com","page_url":"https://unfragile.ai/myinvestment-ai","categories":["data-analysis"],"tags":[],"pricing":{"model":"paid","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_myinvestment-ai__cap_0","uri":"capability://data.processing.analysis.risk.profile.based.portfolio.allocation","name":"risk-profile-based portfolio allocation","description":"Analyzes user-provided risk tolerance, investment timeline, and financial goals through a questionnaire interface to generate initial asset allocation recommendations. The system likely uses a decision tree or clustering algorithm to map user profiles to predefined allocation templates (e.g., aggressive/moderate/conservative), then personalizes weights across asset classes (stocks, bonds, alternatives) based on goal-specific parameters. This allocation serves as the foundation for all downstream recommendations.","intents":["I want to know what asset allocation matches my risk tolerance without hiring a financial advisor","I need a starting portfolio allocation that aligns with my 10-year retirement goal","I want to understand how my risk profile translates to a concrete portfolio mix"],"best_for":["Self-directed retail investors with $10K-$500K portfolios","First-time investors seeking structured guidance on asset allocation","Users migrating from static robo-advisors to adaptive AI-driven strategies"],"limitations":["Risk questionnaires are inherently subjective and may not capture true risk capacity vs. risk tolerance","No real-time behavioral adjustment during market stress — allocation remains static until user re-runs assessment","Limited to predefined allocation templates; no custom asset class support beyond major categories"],"requires":["User completion of risk assessment questionnaire (5-15 minutes)","Basic financial information: income, net worth, investment timeline","No API key or external service required for initial allocation"],"input_types":["structured questionnaire responses (risk tolerance, time horizon, goals)","numerical financial data (portfolio size, income, liabilities)"],"output_types":["asset allocation percentages (e.g., 60% stocks, 30% bonds, 10% alternatives)","allocation rationale document","recommended fund/ETF tickers for each allocation bucket"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_myinvestment-ai__cap_1","uri":"capability://data.processing.analysis.market.condition.responsive.recommendation.adjustment","name":"market-condition-responsive recommendation adjustment","description":"Continuously monitors market data (equity indices, volatility, interest rates, sector performance) and adjusts portfolio recommendations in real-time or near-real-time without requiring user action. The system likely ingests market feeds via APIs (Yahoo Finance, Bloomberg, or proprietary data), applies technical indicators and regime-detection algorithms (e.g., VIX thresholds, yield curve inversion detection) to identify market regime shifts, then triggers recommendation updates (e.g., 'reduce equity exposure during high volatility' or 'increase bond allocation when rates spike'). This creates a feedback loop where recommendations drift from the initial allocation based on market conditions.","intents":["I want my portfolio recommendations to automatically adjust when market conditions change, without me having to manually rebalance","I need alerts when my allocation drifts significantly from optimal due to market movements","I want to understand how current market conditions affect my recommended strategy"],"best_for":["Active investors who want algorithmic discipline without manual rebalancing","Users seeking protection during market downturns through dynamic de-risking","Portfolios with $50K+ where rebalancing costs are justified by improved returns"],"limitations":["Market regime detection algorithms can lag during rapid market transitions (flash crashes, gap events)","No guarantee that algorithmic adjustments outperform buy-and-hold; backtesting results not publicly disclosed","Adjustment frequency and magnitude are opaque — users cannot control how aggressively the system rebalances","Potential for 'whipsaw' behavior if market regime detection triggers false positives"],"requires":["Real-time or daily market data feed (equity indices, volatility indices, bond yields)","Computational infrastructure for continuous monitoring and recommendation generation","User account with active portfolio tracking enabled"],"input_types":["market data streams (OHLCV, volatility indices, yield curves)","user portfolio holdings and current allocation","historical market regimes and performance data"],"output_types":["updated allocation recommendations with confidence scores","rebalancing alerts with suggested trades","market regime classification (bullish/bearish/neutral with supporting metrics)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_myinvestment-ai__cap_10","uri":"capability://data.processing.analysis.scenario.analysis.and.stress.testing","name":"scenario analysis and stress testing","description":"Simulates portfolio performance under hypothetical market scenarios (recession, inflation spike, geopolitical crisis, interest rate shock) to evaluate strategy robustness. The system likely maintains a library of historical market scenarios or uses parameterized stress scenarios, then applies these to the recommended allocation to estimate potential losses and recovery times. This enables users to understand how their portfolio would perform in adverse conditions.","intents":["I want to know how my portfolio would perform in a recession or market crash","I want to understand the impact of inflation or interest rate changes on my allocation","I want to stress-test my strategy against historical crisis scenarios"],"best_for":["Risk-conscious investors wanting to understand worst-case scenarios","Users with low risk tolerance seeking reassurance about downside protection","Investors planning for specific economic scenarios (retirement, inflation, etc.)"],"limitations":["Scenario analysis is inherently speculative; actual market behavior may differ significantly from scenarios","Scenarios are based on historical patterns which may not repeat","No consensus on which scenarios are most relevant or likely","Stress test results do not account for behavioral responses (panic selling, forced liquidations)","Results are highly sensitive to scenario assumptions and parameter choices"],"requires":["Historical market data for scenario calibration","Scenario definitions (asset class returns, correlations, volatility)","Portfolio holdings and allocation"],"input_types":["portfolio allocation","scenario definitions (market returns, volatility, correlations)","historical scenario data"],"output_types":["portfolio returns under each scenario","maximum drawdown under each scenario","recovery time estimates","probability-weighted scenario analysis"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_myinvestment-ai__cap_11","uri":"capability://data.processing.analysis.dividend.and.income.optimization","name":"dividend and income optimization","description":"Analyzes portfolio holdings to identify dividend-paying securities and optimizes the portfolio for income generation based on user preferences. The system likely tracks dividend yields, payout ratios, and dividend growth rates, then recommends securities or allocations that maximize income while maintaining risk and diversification constraints. It may also provide tax-efficient income strategies (qualified vs. non-qualified dividends, dividend reinvestment decisions).","intents":["I want to optimize my portfolio for dividend income","I want to understand the income my portfolio generates and how it's taxed","I want to find high-yield securities that fit my risk profile"],"best_for":["Retirees or near-retirees seeking portfolio income","Income-focused investors with low risk tolerance","Users in high tax brackets seeking tax-efficient income strategies"],"limitations":["High-yield securities often have higher risk or lower quality; optimizing for yield can lead to concentration in risky assets","Dividend yields are backward-looking and may not be sustainable","Dividend cuts or suspensions can significantly impact income projections","Tax efficiency of dividends depends on user's tax situation which may not be fully known to the system","Income optimization may conflict with growth objectives"],"requires":["Dividend data for all portfolio holdings (yields, payout ratios, growth rates)","User's income needs and preferences","Tax situation (marginal tax rate, qualified dividend treatment)"],"input_types":["portfolio holdings","dividend data (yields, payout ratios, growth rates)","user income needs","tax situation"],"output_types":["portfolio dividend yield and income projections","dividend growth projections","tax-efficient income strategy recommendations","high-yield security recommendations"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_myinvestment-ai__cap_12","uri":"capability://data.processing.analysis.correlation.and.diversification.analysis","name":"correlation and diversification analysis","description":"Analyzes correlation between portfolio holdings and asset classes to identify diversification gaps and concentration risks. The system likely computes pairwise correlations between holdings, identifies clusters of highly-correlated assets, and recommends diversification improvements. It may also use principal component analysis or other dimensionality reduction techniques to identify the true number of independent risk factors in the portfolio.","intents":["I want to understand how diversified my portfolio really is","I want to identify holdings that are too correlated with each other","I want to find diversification improvements that reduce portfolio risk"],"best_for":["Investors seeking to optimize diversification","Users with concentrated portfolios wanting to reduce correlation risk","Sophisticated investors understanding correlation dynamics"],"limitations":["Correlations are time-varying and unstable during market stress (correlation breakdown risk)","Historical correlations may not predict future correlations","Diversification analysis assumes normal distributions; tail risk may not be captured","No guidance on optimal level of diversification or correlation targets"],"requires":["Historical price data for all holdings (typically 1-3 years)","Correlation computation methodology"],"input_types":["portfolio holdings with prices","historical price data","correlation window (1-year, 3-year, etc.)"],"output_types":["correlation matrix","correlation clusters and groups","diversification score","concentration risk metrics","diversification improvement recommendations"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_myinvestment-ai__cap_2","uri":"capability://data.processing.analysis.behavioral.pattern.driven.strategy.refinement","name":"behavioral-pattern-driven strategy refinement","description":"Tracks user investment behavior over time (trading frequency, hold periods, panic selling during downturns, concentration in certain sectors) and uses this behavioral data to refine future recommendations. The system likely maintains a user behavior profile that captures deviations from the recommended strategy, then applies reinforcement learning or Bayesian updating to adjust recommendations toward allocations the user is more likely to actually follow. For example, if a user consistently sells during market dips, the system might recommend a more conservative allocation that the user can psychologically tolerate.","intents":["I want recommendations that account for my actual investing behavior, not just my stated risk tolerance","I want the AI to learn from my past mistakes and suggest strategies I'm more likely to stick with","I want to understand how my behavior patterns affect my long-term returns"],"best_for":["Behavioral investors who struggle with emotional decision-making during volatility","Users with 1+ years of trading history on the platform","Portfolios where behavioral discipline is a key return driver"],"limitations":["Behavioral profiling requires significant historical data; new users receive generic recommendations until sufficient behavior is observed (typically 3-6 months)","Risk of reinforcing suboptimal behavior (e.g., if user consistently underperforms, system may recommend even more conservative allocation)","Privacy concerns: behavioral tracking requires detailed transaction-level data storage","No transparency into how behavioral signals are weighted vs. fundamental risk factors"],"requires":["Minimum 3-6 months of user trading history on platform","Transaction-level data access (buy/sell dates, amounts, prices)","Behavioral analytics infrastructure to compute deviation metrics"],"input_types":["historical transaction records (dates, amounts, asset classes)","user portfolio performance vs. benchmark","timing of trades relative to market events (volatility spikes, crashes)"],"output_types":["refined allocation recommendations adjusted for behavioral tolerance","behavioral profile summary (e.g., 'panic seller during >10% drawdowns')","personalized rebalancing frequency and trade size recommendations"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_myinvestment-ai__cap_3","uri":"capability://data.processing.analysis.goal.based.portfolio.decomposition.and.tracking","name":"goal-based portfolio decomposition and tracking","description":"Decomposes a user's overall portfolio into sub-portfolios, each aligned to a specific financial goal (retirement, home purchase, education funding) with its own time horizon and risk profile. The system likely uses a goal-based asset allocation framework where each goal receives a dedicated allocation strategy, then aggregates these into a unified portfolio recommendation. It continuously tracks progress toward each goal (comparing projected vs. actual returns) and alerts users when a goal is at risk of being underfunded, enabling proactive strategy adjustments.","intents":["I want to allocate my portfolio across multiple goals (retirement, home, kids' education) with different time horizons","I want to know if I'm on track to meet each of my financial goals","I want to understand how much risk I need to take to achieve each goal"],"best_for":["Users with multiple financial goals spanning different time horizons","Investors seeking goal-based accountability and progress tracking","Households with $100K+ portfolios where goal segmentation is meaningful"],"limitations":["Goal-based decomposition assumes goals are independent; no optimization across competing goals (e.g., if retirement is underfunded, system cannot automatically redirect education funding)","Projections are based on historical return assumptions which may not hold in future market regimes","No integration with external financial data (home prices, education costs, inflation forecasts) — projections are generic","Rebalancing across multiple goal-specific sub-portfolios may incur higher transaction costs"],"requires":["User definition of specific financial goals with target amounts and timelines","Current portfolio value and expected contribution amounts","Historical return data and volatility assumptions for asset classes"],"input_types":["goal specifications (name, target amount, target date, priority)","current portfolio holdings and values","expected future contributions"],"output_types":["per-goal allocation recommendations","projected probability of achieving each goal","consolidated portfolio allocation across all goals","goal-specific performance tracking dashboard"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_myinvestment-ai__cap_4","uri":"capability://data.processing.analysis.tax.loss.harvesting.opportunity.identification","name":"tax-loss-harvesting opportunity identification","description":"Analyzes user portfolio holdings against cost basis and current market prices to identify positions with unrealized losses that can be sold to offset capital gains or income. The system likely maintains a cost-basis database, monitors price movements, and applies tax-loss-harvesting rules (wash-sale rules, minimum holding periods) to generate actionable harvesting recommendations. It may also coordinate harvesting across multiple accounts (taxable, tax-deferred) to maximize tax efficiency while maintaining the user's target allocation.","intents":["I want to automatically identify tax-loss-harvesting opportunities in my portfolio","I want to offset capital gains with losses without disrupting my allocation","I want to understand how much tax I can save through strategic loss harvesting"],"best_for":["High-income investors in taxable accounts with $100K+ portfolios","Users with significant unrealized gains seeking tax-efficient strategies","Investors with multiple accounts (taxable, 401k, IRA) seeking coordinated tax planning"],"limitations":["Wash-sale rules complicate harvesting in volatile markets; system must track 30-day windows and prevent accidental violations","Tax-loss harvesting value depends on user's marginal tax rate and future income; system may not have visibility into user's full tax situation","Harvesting recommendations are generic and may not account for state taxes, AMT, or other tax complications","Requires accurate cost-basis data which may be incomplete for inherited securities or old positions"],"requires":["Complete cost-basis records for all holdings","Real-time price data for all positions","User's marginal tax rate (estimated or provided)","Ability to execute trades or generate trade recommendations"],"input_types":["portfolio holdings with cost basis and purchase dates","current market prices","user's marginal tax rate","tax-loss-harvesting rules and constraints"],"output_types":["list of harvestable positions with estimated tax savings","recommended replacement securities to maintain allocation","trade execution recommendations with wash-sale warnings"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_myinvestment-ai__cap_5","uri":"capability://data.processing.analysis.sector.and.factor.exposure.analysis","name":"sector-and-factor-exposure analysis","description":"Decomposes the user's portfolio into sector exposures (technology, healthcare, financials, etc.) and factor exposures (value, growth, momentum, quality) to provide transparency into what the portfolio is actually 'betting on'. The system likely uses factor models (Fama-French, Carhart, or proprietary) to decompose returns and exposures, then compares user's actual exposures against recommended exposures to identify unintended concentration or drift. This enables users to understand whether their portfolio is aligned with their stated investment thesis.","intents":["I want to understand what sectors and factors my portfolio is exposed to","I want to know if my portfolio has unintended concentration in certain sectors","I want to see how my exposures have drifted from my target allocation"],"best_for":["Sophisticated investors seeking factor-based portfolio analysis","Users building thematic or factor-tilted portfolios","Investors wanting to avoid unintended sector bets"],"limitations":["Factor decomposition is model-dependent; different factor models may produce different exposure estimates","Requires holdings-level data; cannot be computed from aggregate portfolio values alone","Factor exposures are backward-looking and may not predict future performance","No guidance on whether current exposures are optimal or should be adjusted"],"requires":["Detailed portfolio holdings with ticker symbols","Factor model data (factor loadings for each security)","Historical price and return data for factor analysis"],"input_types":["portfolio holdings with weights","factor model definitions (sector, factor loadings)","historical return data"],"output_types":["sector exposure breakdown (% in each sector)","factor exposure analysis (value, growth, momentum, quality, etc.)","exposure drift report vs. target allocation","concentration risk metrics"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_myinvestment-ai__cap_6","uri":"capability://data.processing.analysis.backtesting.and.historical.performance.simulation","name":"backtesting and historical performance simulation","description":"Simulates how the recommended allocation would have performed historically using past market data, enabling users to evaluate strategy robustness across different market regimes. The system likely uses a backtesting engine that applies the allocation strategy to historical price data, computes returns and drawdowns, and compares against benchmarks. It may also run Monte Carlo simulations to estimate future return distributions and drawdown probabilities under various market scenarios.","intents":["I want to see how my recommended allocation would have performed in the past","I want to understand the worst-case drawdown my portfolio might experience","I want to compare my strategy's historical performance against a benchmark"],"best_for":["Investors seeking evidence-based confidence in recommended strategies","Users evaluating different allocation strategies before committing capital","Risk-conscious investors wanting to understand historical drawdowns"],"limitations":["Past performance does not guarantee future results; backtests assume perfect execution and no slippage","Backtesting results are highly sensitive to start/end dates and market regime selection (look-ahead bias risk)","No transparency into backtesting methodology, data sources, or assumptions — results cannot be independently verified","Monte Carlo simulations are based on historical volatility and correlation assumptions which may not hold in future regimes","Backtests do not account for transaction costs, taxes, or rebalancing friction"],"requires":["Historical price data for all recommended asset classes (typically 10-20 years)","Backtesting engine with Monte Carlo simulation capability","Benchmark data for comparison"],"input_types":["allocation strategy (asset class weights)","historical price data (OHLCV)","rebalancing frequency and rules","transaction cost assumptions"],"output_types":["historical return and volatility metrics","maximum drawdown and recovery time","Sharpe ratio and other risk-adjusted return metrics","Monte Carlo simulation results (return distribution, drawdown probabilities)","comparison vs. benchmark performance"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_myinvestment-ai__cap_7","uri":"capability://automation.workflow.rebalancing.execution.and.trade.recommendation","name":"rebalancing execution and trade recommendation","description":"Generates specific trade recommendations to rebalance the portfolio back to target allocation when drift exceeds thresholds. The system likely monitors portfolio drift in real-time, calculates the minimum set of trades needed to restore target allocation, and accounts for transaction costs and tax implications when generating recommendations. It may also offer automated execution via connected brokers or manual trade instructions for users preferring to execute themselves.","intents":["I want specific trade recommendations to rebalance my portfolio back to target allocation","I want to rebalance efficiently while minimizing transaction costs and taxes","I want to automate rebalancing without manually calculating trades"],"best_for":["Busy investors wanting to automate rebalancing execution","Users with multiple accounts seeking coordinated rebalancing","Portfolios with $50K+ where rebalancing costs are material"],"limitations":["Trade recommendations assume immediate execution; market prices may move between recommendation and execution","No integration with all brokers — execution may require manual trade entry","Rebalancing frequency is opaque — users cannot control how often system recommends rebalancing","Tax-aware rebalancing requires accurate cost-basis data which may be incomplete","Automated execution requires API access to broker accounts, raising security and privacy concerns"],"requires":["Real-time portfolio holdings and current prices","Target allocation specification","Transaction cost data (commissions, spreads)","Tax-loss-harvesting rules and cost-basis data (for tax-aware rebalancing)","Broker API access (for automated execution) or manual trade entry capability"],"input_types":["current portfolio holdings and prices","target allocation","transaction cost assumptions","tax-loss-harvesting constraints"],"output_types":["specific trade recommendations (buy/sell quantities and securities)","estimated transaction costs and tax impact","projected portfolio allocation after trades","trade execution status (pending, executed, failed)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_myinvestment-ai__cap_8","uri":"capability://data.processing.analysis.performance.attribution.and.return.decomposition","name":"performance attribution and return decomposition","description":"Breaks down portfolio returns into components attributable to different sources: asset allocation decisions, security selection, market timing, and fees. The system likely uses return attribution models (Brinson-Fachler or similar) to quantify how much return came from being overweight/underweight different asset classes vs. picking better securities within each class. This enables users to understand whether outperformance (or underperformance) came from strategic allocation or tactical decisions.","intents":["I want to understand what drove my portfolio's returns","I want to know if my outperformance came from good allocation or good security selection","I want to see how much value the AI recommendations added vs. a simple benchmark"],"best_for":["Sophisticated investors seeking detailed performance analysis","Users evaluating the value-add of AI recommendations","Portfolio managers wanting to understand return drivers"],"limitations":["Attribution analysis is backward-looking and does not predict future performance","Results are sensitive to attribution methodology and benchmark selection","Requires detailed transaction history and timing data; cannot be computed from aggregate returns alone","No guidance on whether attribution results indicate skill or luck"],"requires":["Detailed transaction history with dates and prices","Benchmark allocation and returns","Historical portfolio weights and returns","Attribution model specification"],"input_types":["portfolio transactions (buys, sells, dividends)","portfolio weights over time","benchmark weights and returns","asset class returns"],"output_types":["allocation effect (return from over/underweighting asset classes)","selection effect (return from security selection within asset classes)","timing effect (return from tactical allocation changes)","fee impact on returns","total return decomposition"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_myinvestment-ai__cap_9","uri":"capability://data.processing.analysis.risk.metric.computation.and.monitoring","name":"risk metric computation and monitoring","description":"Continuously computes portfolio risk metrics (volatility, beta, Value-at-Risk, Conditional Value-at-Risk, Sharpe ratio, Sortino ratio) and monitors them against user-defined risk limits. The system likely uses rolling-window volatility calculations, correlation matrices, and historical/parametric VaR models to estimate downside risk. It alerts users when risk metrics exceed thresholds or when portfolio risk profile has drifted significantly from the target risk level.","intents":["I want to understand the risk characteristics of my portfolio","I want to be alerted if my portfolio's risk exceeds my tolerance","I want to compare my portfolio's risk metrics against benchmarks"],"best_for":["Risk-conscious investors wanting quantitative risk monitoring","Users with defined risk limits or risk budgets","Institutional investors requiring risk compliance reporting"],"limitations":["Risk metrics are backward-looking and based on historical data; future volatility may differ significantly","VaR models assume normal distributions which underestimate tail risk during market stress","Correlation estimates are unstable during market crises when correlations tend toward 1","Risk metrics do not account for liquidity risk or operational risk","No guidance on whether current risk level is appropriate for user's goals"],"requires":["Historical price data for all portfolio holdings (typically 1-3 years for volatility, 5-10 years for VaR)","Risk metric definitions and thresholds","Benchmark data for comparison"],"input_types":["portfolio holdings and weights","historical price data","risk metric definitions (volatility window, VaR confidence level, etc.)","risk limits and thresholds"],"output_types":["volatility and beta metrics","Value-at-Risk and Conditional Value-at-Risk estimates","Sharpe ratio and Sortino ratio","risk metric trends and alerts","comparison vs. benchmark risk metrics"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":43,"verified":false,"data_access_risk":"high","permissions":["User completion of risk assessment questionnaire (5-15 minutes)","Basic financial information: income, net worth, investment timeline","No API key or external service required for initial allocation","Real-time or daily market data feed (equity indices, volatility indices, bond yields)","Computational infrastructure for continuous monitoring and recommendation generation","User account with active portfolio tracking enabled","Historical market data for scenario calibration","Scenario definitions (asset class returns, correlations, volatility)","Portfolio holdings and allocation","Dividend data for all portfolio holdings (yields, payout ratios, growth rates)"],"failure_modes":["Risk questionnaires are inherently subjective and may not capture true risk capacity vs. risk tolerance","No real-time behavioral adjustment during market stress — allocation remains static until user re-runs assessment","Limited to predefined allocation templates; no custom asset class support beyond major categories","Market regime detection algorithms can lag during rapid market transitions (flash crashes, gap events)","No guarantee that algorithmic adjustments outperform buy-and-hold; backtesting results not publicly disclosed","Adjustment frequency and magnitude are opaque — users cannot control how aggressively the system rebalances","Potential for 'whipsaw' behavior if market regime detection triggers false positives","Scenario analysis is inherently speculative; actual market behavior may differ significantly from scenarios","Scenarios are based on historical patterns which may not repeat","No consensus on which scenarios are most relevant or likely","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.36666666666666664,"quality":0.78,"ecosystem":0.15000000000000002,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:31.858Z","last_scraped_at":"2026-04-05T13:23:42.551Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=myinvestment-ai","compare_url":"https://unfragile.ai/compare?artifact=myinvestment-ai"}},"signature":"G2wCxyBI63fLTq/LVTCR8N6nfSmQGOiWSThKT4YoYafq1T3qDgBMfiMOzMqDQ6hhetrPNrdlxp8ps39Z5gXtCQ==","signedAt":"2026-06-21T04:38:52.268Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/myinvestment-ai","artifact":"https://unfragile.ai/myinvestment-ai","verify":"https://unfragile.ai/api/v1/verify?slug=myinvestment-ai","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}