BEST MFForward performance tracking available. Use the Tracking tab to initialize positions, refresh NAVs, and track real outcomes with exit rules (TP/SL/max-hold).
System Analysis
DIAGNOSIS
How fund screening works:
Mutual funds are screened from Yahoo Finance using criteria including: trailing returns (1yr, 3yr, 5yr), expense ratio (MER), fund category, and Morningstar rating where available. Picks are generated based on trailing performance combined with risk metrics (standard deviation, Sharpe ratio approximation). Currently supports Canadian and US-listed mutual funds and ETFs.
Current status — critical gap:
• 45 picks have been generated but zero have forward-facing outcome tracking
• No entry prices recorded, no daily NAV refresh, no win/loss determination
• Without forward tracking, this system provides screening only — not performance data
• Backtests exist but are inherently biased (survivorship bias, look-ahead bias)
Platform comparison:
• This system: No forward tracking — cannot verify if picks outperform index funds
• Consolidated Stocks — full forward tracking (65 positions, daily price refresh)
• L vs O — real closed trades with verified P&L
• Building forward-facing tracking for mutual funds is a top-priority roadmap item
Near-term:
• Build forward-facing outcome tracker (same model as Consolidated Picks)
• Daily NAV refresh via GitHub Actions with entry price recording
• Win/loss tracking with configurable exit rules
Q2 2026:
• MER (Management Expense Ratio) impact analysis on returns
• Fund family comparison (Vanguard vs iShares vs Fidelity)
• Risk-adjusted metrics (Sharpe ratio, max drawdown per fund)
Long-term:
• Tax-efficient fund rotation strategies (RRSP vs TFSA optimization)
• Automated rebalancing recommendations
• Income vs growth fund comparison dashboard
This tool is for research and educational purposes only. Past performance does not guarantee future results. Mutual fund trades may have redemption fees and T+1 settlement. Consult a licensed financial adviser.
First-Time Setup
Initialize the database, import sample mutual fund picks, and fetch NAV data.
Run all 10 preset scenarios against the same fund picks and compare performance side by side.
Strategy Comparison (Ranked by Return)
#
Strategy
TR%
SL%
Hold
Trades
Win Rate
Return %
Final $
Max DD
Fee Drag
Sharpe
Profit Factor
Compare Algorithms
Test each algorithm independently under the same scenario rules.
Algorithm Performance (Ranked by Return)
#
Algorithm
Trades
Win Rate
Return %
Final $
Max DD
Fee Drag
Sharpe
Profit Factor
Avg Hold
Portfolio Templates
Pre-configured fund portfolio strategies. Click "Backtest" to run a simulation.
Name
Type
TR%
SL%
Hold
Capital
Pos Size
Action
Mutual Fund Picks
Symbol
Fund Name
Family
Category
Algorithm
Entry NAV
Pick Date
Score
Rating
Morningstar
Expense Ratio
Status
Saved Backtest Results
Run Name
Strategy
Algos
Trades
Win Rate
Return %
Final $
Max DD
Sharpe
Created
Forward Performance Tracking
Track real outcomes of mutual fund picks with daily NAV updates, exit rules (TP/SL/max-hold), and win/loss recording.
Open Positions
-
Closed
-
Win Rate
-
Avg Return
-
Avg Hold Days
-
Open Positions
Symbol
Fund Name
Algorithm
Pick Date
Entry NAV
Current NAV
Return %
Hold Days
Peak
Trough
Closed Positions
Symbol
Fund Name
Algorithm
Pick Date
Entry NAV
Exit NAV
Return %
Exit Reason
Hold Days
Exit Date
Algorithm Performance
Algorithm
Total Picks
Open
Wins
Losses
Avg Current Return
Honest System Assessment -- Mutual Funds Portfolio
This page provides complete transparency about our mutual fund analysis system's current state, known limitations, and planned improvements. We believe in honest disclosure -- this system has no forward-facing performance tracking whatsoever. All analysis is based on backtested scenarios using historical NAV data from Yahoo Finance.
Current Strengths
MULTIPLE ANALYSIS TOOLS
What-If Calculator -- Run custom scenarios with configurable target return, stop loss, and max hold days. 10 preset scenarios from Short Tactical (14d) to Buy & Hold 1-Year (252d). See equity curves, exit reason breakdowns, and per-algorithm performance.
STRATEGY COMPARISON
Side-by-Side Scenarios -- Compare all 10 preset strategies simultaneously with ranked performance tables. Includes win rate, return %, final value, max drawdown, fee drag, Sharpe ratio, and profit factor for each scenario.
ALGORITHM COMPARISON
Per-Algorithm Breakdown -- Test each algorithm independently under the same scenario rules. Identifies which algorithm works best for mutual funds under different market conditions and holding periods.
PORTFOLIO TEMPLATES
Pre-Configured Strategies -- Ready-made portfolio templates for different investor profiles (conservative, growth, income, aggressive rotation). Each template has preconfigured TP/SL/hold parameters and can be backtested with one click.
FUND PICKS DATABASE
45 Picks Across Multiple Algorithms -- Fund picks filterable by algorithm, with fund name, family, category, NAV price, score, rating, Morningstar stars, and expense ratio displayed for each pick.
USER-FRIENDLY INTERFACE
Multiple Analysis Angles -- Interactive charts via Chart.js, sortable tables, scenario presets with quick-apply, algorithm filtering, and saveable backtest results. Designed for both beginners and experienced fund investors.
ML System Status
Current State: ML Pipeline DEPLOYED (Feb 12, 2026)
• 45 picks with NAV history; optimizer uses backtest data
Status: ML deployed with walk-forward validation + expense ratio integration. Running weekly Sundays.
Known Flaws & Limitations
NO LIVE TRACKING
Zero Forward-Facing Data -- 45 picks generated, 0 tracked with real results. No entry prices recorded, no exit conditions monitored, no win/loss ratio. Every other asset class in our system (stocks, crypto, forex) has some form of live tracking except mutual funds.
NO EXPENSE RATIO OPTIMIZATION
Critical for Fund Selection -- Expense ratios are displayed but not factored into fund selection or ranking. Research shows each 1% in fees costs approximately 17% of returns over 20 years (Carhart 1997). This is the single highest-impact missing feature for mutual fund investors.
NO TAX-LOSS HARVESTING
Missing Tax Efficiency -- No recommendations for tax-loss harvesting, no RRSP vs TFSA optimization, no capital gains distribution tracking. Professional robo-advisors (Wealthfront, Betterment) make tax-loss harvesting a core feature.
NO REBALANCING ALERTS
No Drift Monitoring -- No automated rebalancing recommendations when portfolio allocation drifts from target. No alerts when a fund deviates from its stated strategy (style drift detection). Manual monitoring required.
NO MARKET REGIME CORRELATION
Context-Blind Analysis -- No correlation with broader market regime (bull/bear/sideways). Fund performance varies dramatically across market conditions but our analysis treats all periods equally.
SINGLE DATA SOURCE
Yahoo Finance Only -- All NAV data comes from Yahoo Finance. No Morningstar ratings integration, no Lipper data, no Bloomberg Terminal data. World-class fund analysis platforms use 5-10 data sources for comprehensive coverage.
NO STYLE DRIFT DETECTION
Manager Risk Ignored -- Mutual fund managers may change their investment strategy over time (style drift). A "large-cap value" fund may gradually shift to "large-cap growth." Without style drift detection, past performance may not reflect future approach.
NO MANAGER TENURE TRACKING
Key Predictor Missing -- Manager tenure is one of the strongest predictors of future fund performance consistency. When a star manager leaves, historical returns become less predictive. We do not track manager changes or tenure length.
NO FLOW DATA
Capital Flows Ignored -- Fund inflows and outflows affect returns. Large outflows force fund managers to sell holdings at unfavorable prices. Large inflows create cash drag. Professional fund analysis always includes flow data.
Improvement Roadmap
DONE: ANALYSIS TOOLS
Backtesting Infrastructure Complete -- What-If analysis, strategy comparison, algorithm comparison, portfolio templates, fund picks database, and backtest history are all operational. 10 preset scenarios, interactive charts, saveable results.
DONE: FUND PICKS
45 Picks Generated -- Multiple algorithms have generated fund picks with scores, ratings, and Morningstar star ratings. Yahoo Finance NAV data imported for backtesting against historical scenarios.
PHASE 1: EXPENSE OPTIMIZATION
Highest Impact Fix -- Factor expense ratios into fund selection and ranking. Build expense ratio vs returns scatter plot. Flag funds where fees erode returns below index fund alternatives. This single feature would provide more value than any other improvement.
PHASE 2: MORNINGSTAR DATA
Data Source Expansion -- Integrate Morningstar ratings and categories for richer fund analysis. Morningstar is the gold standard for mutual fund analysis ($200/yr subscription). Would add analyst ratings, category rankings, and style box classification.
PHASE 3: LIVE TRACKING
Forward-Facing Outcome Tracking -- Connect fund picks to actual NAV tracking with entry prices, exit conditions, and daily P&L recording. Same model as Consolidated Stock Picks. Daily refresh via GitHub Actions.
DONE: ML PIPELINE (Feb 12)
Walk-Forward ML Deployed -- mutualfund_ml_optimizer.py (1,438 lines) with walk-forward validation, Sharpe optimization, expense ratio drag, cross-algo correlation. Runs weekly via GitHub Actions. Also: concept drift detection, multi-timeframe regime, online incremental learning.
PHASE 5: TAX EFFICIENCY
Tax-Loss Harvesting & RRSP/TFSA Optimization -- Score funds by tax efficiency. Suggest tax-loss harvesting opportunities. Optimize fund placement across registered (RRSP, TFSA) and non-registered accounts based on distribution types.
PHASE 6: REBALANCING
Drift Monitoring & Alerts -- Automated detection of portfolio drift from target allocation. Style drift detection for individual funds. Rebalancing recommendations with tax-aware trade suggestions.
Is It Production Ready?
Short Answer: It is a research/analysis tool, not a trading system.
Here is where we actually stand:
The analysis tools work well for backtesting scenarios and comparing strategies
45 fund picks exist but none are tracked with real outcomes
✓ ML pipeline deployed (walk-forward grid search + drift detection)
No expense ratio optimization (the single most impactful feature for fund investors)
Data source: Yahoo Finance only -- no Morningstar, no Lipper, no Bloomberg
What milestones remain?
Expense ratio optimization → Factor fees into fund selection (highest impact)
Forward tracking built → Record entry/exit prices for every pick
20+ closed positions → ML grid search can activate
Morningstar integration → Gold standard fund data
Tax efficiency scoring → RRSP/TFSA optimization
Current Status: The backtesting and analysis tools on this page are fully functional for research purposes. Fund picks are generated by multiple algorithms with scores and ratings. However, there is zero forward-facing validation -- no picks have been tracked against real outcomes. Do NOT treat backtest results as predictive of future performance. Build the forward tracker before making allocation decisions.
Architecture
System components and data flow:
Data Source: Yahoo Finance
Daily NAV data for all tracked mutual funds. Single data source -- no Morningstar, no Lipper, no Bloomberg integration. NAV prices fetched via fetch_prices.php and stored in the database for backtesting.
API: mutual_fund_portfolio.php
PHP backend handles stats, fund data, and backtest operations. Actions: stats (portfolio overview), funds (fund list with NAV), backtests (saved scenario results). Located at findmutualfunds2/portfolio2/api/.
Backtest Engine: whatif.php
Runs What-If scenarios, strategy comparisons, and algorithm comparisons against historical NAV data. Configurable TP/SL/hold parameters. Supports saving results to database.
Fund Picks: data.php?type=picks
45 fund picks across multiple algorithms. Each pick includes ticker, fund name, family, category, NAV, score, rating, Morningstar stars, and expense ratio. Filterable by algorithm.
Portfolio Templates: data.php?type=portfolios
Pre-configured fund portfolio strategies with backtest capability. Different investor profiles (conservative, growth, income, aggressive). Each template has preset TP/SL/hold/capital/sizing parameters.
Frontend: This Portfolio Page
What-If analysis, strategy comparison, algorithm comparison, portfolio templates, fund picks, backtest history, system analysis, and research tabs. Interactive charts via Chart.js. Fully functional for backtesting and research.
Bottom Line
The mutual fund analysis tools are solid for backtesting and research -- multiple scenario presets, algorithm comparisons, portfolio templates, and saveable results. However, the system has zero forward-facing performance tracking. 45 picks exist but none have been tracked against real outcomes. The highest-impact next step is building expense ratio optimization, followed by a forward-facing outcome tracker modeled after the Consolidated Stock Picks system.
Research & Future Enhancements
This section documents our research into professional mutual fund analysis, the academic foundations behind fund selection, and the benchmarks we are targeting. Understanding how institutional fund analysis platforms operate is essential for building systems that can compete with Morningstar, Vanguard, and robo-advisors.
Core Methodology: Backtested Strategy Comparison
Our system evaluates mutual funds by backtesting different holding strategies against historical NAV data. The core approach:
Scenario-Based Backtesting
10 preset scenarios test different investment approaches: from Short Tactical (5% target, 14-day hold) to Buy & Hold 1-Year (25% target, 252-day hold). Each scenario applies configurable take-profit, stop-loss, and max hold rules to historical NAV data.
Multi-Algorithm Selection
Multiple algorithms generate fund picks using different selection criteria. Each algorithm can be tested independently or in combination. Algorithm comparison reveals which selection method works best for different market conditions and holding periods.
Key Mutual Fund Concepts
ACTIVE VS PASSIVE
The Central Debate in Fund Selection -- Fama & French (2010) demonstrated that most actively managed mutual funds underperform their benchmark indices after fees. Over 15-year periods, roughly 90% of active large-cap funds fail to beat the S&P 500. This makes expense ratios the single most predictive factor in fund selection.
EXPENSE RATIO IMPACT
The Silent Return Killer -- Carhart (1997) showed that each 1% in annual fees costs approximately 17% of total returns over a 20-year horizon due to compounding drag. A fund returning 8% gross with a 1.5% expense ratio delivers only 6.5% net -- and that 1.5% compounds against the investor every year.
FACTOR INVESTING
Beyond Market Cap Weighting -- Academic research identifies persistent return factors: momentum (recent winners keep winning), value (cheap stocks outperform), quality (profitable companies outperform), and low-volatility (boring stocks have higher risk-adjusted returns). Smart beta mutual funds target these factors systematically.
SHARPE RATIO
Risk-Adjusted Return Metric -- Measures excess return per unit of risk. A Sharpe ratio above 1.0 indicates good risk-adjusted performance. However, Sharpe ratio has limitations with non-normal return distributions (fat tails, skewness) common in mutual fund returns. Sortino ratio may be more appropriate for downside-focused investors.
SURVIVORSHIP BIAS
Hidden Data Problem -- Fund databases drop funds that close or merge, removing the worst performers from historical records. This inflates average reported returns by 1-2% annually. Any backtest using only currently-existing funds overstates expected performance. Our system is exposed to this bias.
MORNINGSTAR STARS
Backward-Looking Metric -- Kinnel (2010) showed that Morningstar's star ratings predict expense ratios more than future returns. 5-star funds do not consistently outperform 3-star funds. The stars reflect past risk-adjusted performance, not future potential. Expense ratio is a better predictor of future fund performance.
Market Inefficiencies in Mutual Funds
WINDOW DRESSING
Fund managers buy recent winners and sell losers before quarter-end reporting to make their portfolio look better. This creates predictable end-of-quarter trading patterns. Academic research documents this across thousands of funds.
TAX INEFFICIENCY
Many active mutual funds generate significant capital gains distributions that investors must pay taxes on, even if they did not sell their fund shares. Index funds and ETFs are typically more tax-efficient due to lower turnover. Tax drag can reduce after-tax returns by 1-2% annually.
CASH DRAG
Active funds typically hold 3-10% in cash for redemption buffers. In rising markets, this cash drag reduces returns vs fully-invested index funds. Some fund families use credit facilities to minimize cash drag, but most smaller funds absorb the cost.
CLOSET INDEXING
Some actively managed funds charge active management fees (1-2%) while closely tracking their benchmark index (R-squared > 0.95). Investors pay active fees for passive returns. "Active share" measures how different a fund is from its benchmark -- low active share indicates closet indexing.
Key Research Sources
Fama & French (2010)
"Luck versus Skill in the Cross-Section of Mutual Fund Returns" -- Demonstrated that the distribution of mutual fund returns matches what would be expected from chance alone. After fees, most active managers do not deliver alpha. The few that do are statistically indistinguishable from lucky coin flippers.
Carhart (1997)
"On Persistence in Mutual Fund Performance" -- Extended the Fama-French 3-factor model with a momentum factor. Showed that most apparent fund outperformance is explained by factor exposures and fees, not manager skill. Expense ratios are the strongest negative predictor of fund returns.
Kinnel (2010)
"How Expense Ratios and Star Ratings Predict Success" -- Morningstar's own director of research found that expense ratios are a better predictor of future fund performance than Morningstar's own star ratings. The cheapest quintile of funds outperformed the most expensive quintile in every category.
Cremers & Petajisto (2009)
"How Active Is Your Fund Manager?" -- Introduced the "active share" concept. Funds with high active share (truly different from benchmark) outperform after fees, while closet indexers (low active share) underperform. Active share is the best predictor of fund performance persistence.
How We Apply This Research
Our system incorporates academic findings in these ways:
Multiple Scenarios: 10 preset strategies test different holding periods and exit rules, matching academic findings on optimal rebalancing frequencies
Algorithm Comparison: Side-by-side testing reveals which fund selection approaches work for different market conditions
Disciplined Exits: TP/SL framework prevents the common mistake of holding underperforming funds indefinitely
Expense Ratio Display: Shown for each fund pick (but not yet factored into selection -- a known gap)
Morningstar Stars: Displayed as context but not the primary selection criterion (per Kinnel 2010 findings)
Not yet applied: Expense ratio optimization (Carhart 1997), active share filtering (Cremers & Petajisto), tax-loss harvesting, survivorship bias correction, factor exposure analysis, style drift detection, flow data tracking, rebalancing automation.
Future Enhancements & Research Priorities
Based on academic research and competitive analysis, these are the highest-impact improvements:
P1: Expense Ratio Optimization
Highest-impact feature. Factor expense ratios into fund selection and ranking. Build expense vs return scatter plots. Auto-flag funds where fees erode returns below comparable index fund alternatives. Carhart (1997) shows this is the strongest predictor of future fund returns.
P2: Morningstar Data Integration
Morningstar is the gold standard for fund analysis ($200/yr). Would add analyst ratings (gold/silver/bronze), category rankings, style box classification, and manager tenure data. Currently we rely on Yahoo Finance only.
P3: Live Tracking
Connect fund picks to actual NAV tracking for forward validation. Record entry prices at pick generation, monitor daily NAV changes, apply exit rules (TP/SL/max hold). Same model as Consolidated Stock Picks with daily GitHub Actions refresh.
P4: ML Pipeline
Learn which fund characteristics predict outperformance: expense ratio, AUM, manager tenure, category, factor exposures, active share, flow trends. Requires 20+ closed positions for grid search activation. Walk-forward validation before deployment.
P5: Tax Efficiency Scoring
Score funds by tax efficiency (turnover ratio, capital gains distribution history). Suggest tax-loss harvesting opportunities. Optimize fund placement: high-turnover funds in registered accounts (RRSP, TFSA), tax-efficient funds in taxable accounts.
P6: Rebalancing & Drift Monitoring
Automated detection of portfolio allocation drift from targets. Style drift detection for individual funds (when a "value" fund starts behaving like "growth"). Rebalancing alerts with tax-aware trade suggestions to minimize capital gains realization.
World-Class Benchmark Comparison
How top mutual fund analysis platforms compare, and where we need to be:
Metric
World-Class Target
Our Current
Gap
Data Sources
5-10 (Morningstar, Lipper, Bloomberg)
1 (Yahoo Finance)
Critical gap
Expense Optimization
Yes (core feature)
None (displayed only)
Highest priority
Tax Awareness
Full tax-lot tracking
None
Not implemented
ML Integration
Walk-forward validated
None (no data to train)
Need forward tracker first
Rebalancing
Automated with tax awareness
None
Not implemented
Live Tracking
Real-time NAV + P&L
None (backtest only)
Zero forward data
Forward Validation
Yes (with attribution)
0 tracked outcomes
45 picks, 0 tracked
Benchmark platforms: Vanguard ($7T AUM, lowest-cost index funds), Morningstar (gold standard analysis, $200/yr), Wealthfront (robo-advisor, tax-loss harvesting), Betterment (goal-based allocation), Fidelity (Zero-fee index funds, fund screener).
The Hard Truth
Our mutual fund system has solid backtesting and analysis tools but zero forward-facing performance data. The 45 fund picks are generated by algorithms but have never been tracked against real outcomes. Without live tracking, we cannot validate whether our fund selection actually outperforms simple index fund investing -- and academic research (Fama & French 2010) strongly suggests most active fund selection strategies do not. The single highest-impact improvement is adding expense ratio optimization, because research consistently shows that fees are the strongest predictor of future fund returns. Until we build forward tracking and expense optimization, this system is a research tool, not a fund selection platform that can compete with Morningstar or robo-advisors.