Explore institutional-grade research bridging Canadian household finance (RRSP/FHSA tax arbitrage, semi-annual mortgage compounding, Big 6 & Desjardins MER drag, TSX ETFs) and quantitative portfolio econometrics (Deflated Sharpe Ratio, CPCV, Fractional Kelly, drawdown control).
Quantitative guide to Canadian registered account optimization: combined federal-provincial tax brackets, FHSA ($8,000/yr, $40,000 cap) vs. RRSP, the $60,000 HBP repayment drag, and the $100,000 tax-sheltered down payment strategy.
An institutional quantitative framework comparing homeownership and renting paired with TFSA/FHSA ETF investing across Canadian provinces: CMHC insurance, land transfer taxes, unrecoverable ownership drag, and OSFI B-20 renewal shocks.
Actuarial breakdown of Section 6 of the Canadian Interest Act, equivalent periodic rates, CMHC default insurance tiers, 30-year insured amortizations, and how accelerated bi-weekly payments cut 3.3 years off your mortgage.
An institutional quantitative breakdown of Management Expense Ratio (MER) compound drag in Canada: trailing commissions, CRM2 / Total Cost Reporting (TCR), SPIVA Canada data, and how switching from a 2.05% Series A bank fund to a 0.06% TSX ETF saves $195,000+.
A 2.05% MER on a retail Series A bank fund erases up to 37.4% of your terminal wealth over 25 years. Explore our Canadian Investment Funds Comparator across the Big Six banks to audit Series A vs. Series F and ETF fees in real time.
Since 1976, an inversion of the 10-year minus 2-year spread has preceded each of the six US recessions, 10 to 24 months ahead. Yet the record 2022-2024 inversion has not been followed by any recession so far. The official data, episode by episode.
0.25% annual fees versus 1.5%: over 30 years, the gap amounts to €57,100 on a capital of €247,454, calculated on a regular investment. What a robo-advisor actually does, with three real calculations on fees, Markowitz instability and rebalancing.
The 13F publishes positions that are at least 45 days old, up to 136 days before the next filing. And the 5-year annualized volatility of the S&P 500 (17%) and Bitcoin (51.6%) would place them in categories 4 and 6 respectively on the 7-tier PRIIPs risk scale. What regulatory reporting really reveals, and what it protects.
The 50%-margin close-out on a 30:1 EUR/USD CFD triggers at a 1.67% adverse move, which has happened on 1.2% of sessions since 1999. On a 5:1 equity CFD, the 10% threshold is crossed far more often. A real calculation of how often forced close-outs actually occur, instrument by instrument.
Applied to real bitcoin prices since 2017, the full Kelly criterion would have lost 85.8% in a single day and 99.3% at its worst point. Our calculations on nine years of data explain why, and show what holds up better.
A basic bitcoin trend strategy shows 211% a year in a naive backtest, and 41% once corrected. Using real data, we isolated each pitfall behind the gap: look-ahead, fees, funding, exchange choice and parameters.
Bitcoin has long been marketed as an asset uncorrelated with equity markets. Data since 2020 tells a more nuanced story, particularly during periods of stress. Here's what that concretely means for portfolio allocation.
A sector ETF promises higher upside, but at what risk cost? A comparison with the classic broad index ETF.
Between marketing promises and real-world use, where does AI in algorithmic trading actually stand today? A hype-free assessment of concrete contributions and structural limits.
The Sortino ratio fixes a major flaw in the Sharpe ratio by penalizing only downside volatility. Comparison, calculation, and a worked example.
Value at Risk is one of the most widely used risk metrics in finance. How to calculate it, what it actually tells you, and why it doesn't tell you everything.
Holding twenty positions isn't enough to diversify a portfolio if they all move together. Here's how to measure real correlation between your assets, and why it spikes exactly when you need diversification most.
A high Sharpe can hide a catastrophic drawdown or pure luck from multiple trials. Here are the blind spots of the most-used ratio in quant finance, and how to fill them.
Seven concrete, well-documented reasons backtested performance fails to replicate live, from survivorship bias to overfitting, with mitigations for each.
Why K-Fold cross-validation leaks future data in trading backtests, and how walk-forward analysis and CPCV provide a valid alternative for time series.
Understand how survivorship bias inflates equity backtest returns and how point-in-time data and delisting adjustments fix it.
Learn why zero-cost fill assumptions inflate backtests and how to model slippage, spread, and commissions realistically in Python.
A rigorous comparison of the Sharpe and Sortino ratios, with formulas, Python code, and a clear framework for choosing the right risk-adjusted metric.
Learn how Monte Carlo simulation in Python reveals the true drawdown risk of a trading strategy beyond a single historical backtest path.
The Kelly criterion formula explained: derivation, why full Kelly fails under real-world fat tails, and why fractional Kelly sizing is used instead.
Practical techniques to reduce overfitting in algorithmic trading: temporal splits, parameter reduction, regularization, and heatmap diagnostics.
Learn to calculate maximum drawdown and time underwater in Python with pandas, including a complete script, plots, and why duration matters as much as depth.
A technical comparison of VectorBT, Backtrader, Zipline-Reloaded and QuantConnect/LEAN for Python backtesting, covering architecture, speed and realism trade-offs.
The Omega ratio uses the entire return distribution instead of just mean and variance. Explanation, a worked example, and the limitations of this underused measure.
We tested the chartist rule "after 3 down days, a rebound is likely" on 55 years of Nasdaq data: the market rises less often, not more (49.4% vs 52.2%, p = 0.014). How confirmation bias turns a reversed signal into a conviction.
On €20,000 invested for 20 years at a 6% gross return, the PEA's after-tax value (18.6% social contributions in 2026) comes to €55,932, versus €50,282 for an accumulating brokerage-account ETF taxed at the 31.4% flat tax, and €49,066 for a distributing one. A calculated simulation using 2026 rates, to confirm with a professional.
Investing internationally mechanically exposes a portfolio to currency swings that can amplify or wipe out the underlying assets' performance. A close look at portfolio currency risk, with numeric examples.
A model that predicts the past perfectly and collapses in live conditions: overfitting is the number-one trap in machine learning applied to trading. Understanding why, and how to guard against it.
Fixed, fractional, volatility-based, or the 2% rule: a tour of position sizing methods beyond the Kelly criterion.
Why your allocation drifts over time, and how the 5/25 rule helps decide when to rebalance without overreacting.
Testing a strategy only on companies and funds still active today artificially inflates performance. Here's how to detect and correct survivorship bias.
Walk-forward analysis reproduces how a strategy would actually be re-optimized over time. Here's how it works and why it detects overfitting better than a simple in-sample/out-of-sample split.
A 25% maximum drawdown looks acceptable on paper. In practice, it's often exactly where traders give up. Here's why, and how to prepare for it.
The Calmar ratio compares annualized return to the worst observed drawdown. A blunt but telling metric, with its strengths and limitations.
Testing 1000 variants then keeping the best guarantees a nice backtest… and a live letdown. Here's how to spot data-snooping and protect yourself with the Deflated Sharpe Ratio.
The Kelly criterion is optimal in theory and ruinous in practice. Here's why, and how to fraction it to build position sizing that survives real-world uncertainty.
Check the items that apply to your strategy to calculate your live Tax Erosion Score and unlock the quantitative optimization protocol.
2 / 7 erosion factors selected
Select the potential friction points in your current setup:
Enter your email to immediately unlock all 7 quantitative remedies, Net Future Return (RNF) formulas, and download the complete guide.
100% Free · Zero spam · Unsubscribe in 1 click.