Investing tools, market essays, and developer-to-investor crossovers — tested or argued, never speculated.
83 articles
How to size a trade from risk instead of conviction: the fixed-fractional formula, fractional Kelly, R-multiples, and the drawdown math that decides whether a losing streak is survivable.
A measured look at why lump-sum investing usually beats dollar-cost averaging on expected return, when DCA still makes sense, and how to decide for your own cash.
The Sharpe ratio measures excess return per unit of volatility. Here is exactly what the number captures, the four assumptions that break it, and when to trust it.
A practical comparison of Tiingo and Polygon.io for solo quant builders in 2026 — pricing, rate limits, data coverage, and which one fits a weekend backtester.
A practical walkthrough of writing a Python rebalancing script: measuring allocation drift, generating a self-funding trade list, and using threshold bands to avoid over-trading.
Reliable backtests need reliable data, and pulling it live from an API on every run is slow, fragile, and costly. Here's how to build a local market-data pipeline that caches, respects rate limits, and handles gaps.
Market, limit, stop, and the time-in-force flags behind them decide whether your strategy fills where you expect. Here's what each order type actually does, and when the wrong one quietly costs you money.
Pairs trading bets that two related securities will revert to their usual relationship. Here's what cointegration actually means, why it's not the same as correlation, and how to think about building a pairs strategy.
PyPortfolioOpt makes Markowitz mean-variance optimization a few lines of Python. Here's what it does, why naive optimization produces fragile portfolios, and the techniques that make the output usable.
Standard k-fold cross-validation shuffles data and leaks the future into the past — fatal for trading models. Here's why time order matters, and how walk-forward and purged validation fix it.
Tax-loss harvesting sells losers to offset gains, and it's tempting to automate. Here's the mechanics, the wash-sale rule that trips up naive bots, and why the tax rules — not the code — are the hard part.
Paper trading is the step between a backtest and real money, and most people do it wrong. Here's what forward-testing actually proves, what it can't, and how to run it so the results mean something.
Risk parity allocates by risk contribution instead of dollars, so one volatile asset doesn't dominate your portfolio. Here's the intuition, a simple implementation path, and the caveats retail investors miss.
A return number means nothing without the risk behind it. Here's how the Sharpe, Sortino, and Calmar ratios each measure risk differently, and which one to trust when you're evaluating a strategy.
A strategy that's profitable on paper often dies the moment you add realistic trading costs. Here's what slippage, spread, and fees actually do to returns, and how to model them honestly.
Alpaca markets itself as an API-first brokerage. After building against it, here's where the developer experience shines, where paper trading hides real-world friction, and who it's actually for.
Interactive Brokers and Alpaca sit at opposite ends of the brokerage-API spectrum: depth and complexity versus simplicity and speed. Here's how to choose based on what you're actually building.
The Kelly criterion gives a mathematically optimal bet size, but applied naively to trading it will ruin you. Here's the intuition, the formula, and why almost everyone uses a fraction of it.
Before you pay for a fundamentals API, know that the SEC gives away structured financial-statement data through EDGAR. Here's what the API offers, its quirks, and how to actually use it.
Polygon and Alpha Vantage get all the attention, but Tiingo, EODHD, and Databento each fill a different gap for retail quants. Here's how they compare on coverage, cost, and what you actually get.
Four research-backed books on why markets misbehave and why investors do — bubbles, manias, cognitive bias, and fractal risk — chosen for readers who want history and psychology, not stock tips.
A research-based hardware guide for active traders and quant developers — color-accurate 4K monitors, a sturdy dual arm, a real ergonomic chair, and a stable standing desk that survive long market sessions.
A research-based reading list for new investors who think like engineers — index-fund logic, fee math, and behavior — with four books worth your first month of reading and why each earns its place.
A research-based personal finance reading list for engineers — high savings rates, low-cost index investing, automation, and FIRE math — with four books that respect a technical reader's time and skepticism.
A research-based reading path for engineers moving into quant and algorithmic trading — from building a first systematic strategy to market microstructure and modern ML methods, with four books worth owning.
A hands-on Python guide to CCXT — installing the library, generating testnet keys, fetching OHLCV and order books, coding an SMA crossover signal, placing orders, and surviving rate limits before you risk real money.
I rebuilt the same equity backtest on Financial Modeling Prep and Sharadar's SF1 to see which fundamental data source you can actually trust. The difference comes down to point-in-time data — and it decides whether your backtest is real or a fantasy.
A hands-on developer comparison of SnapTrade and Plaid Investments — trade execution vs read-only holdings, broker coverage, auth flows, data freshness, and pricing for fintech builders in 2026.
How I model and run a Treasury bill ladder for emergency funds and business cash — yield vs HYSA, TreasuryDirect vs brokerage, auto-roll, the state-tax-exempt angle, and a small Python model you can copy.
A hands-on guide to walk-forward optimization in Python — why a single train/test split lies to you, how rolling and anchored windows work, and the robustness metrics that catch overfit strategies before they lose money.
After building 8 side projects on both APIs, here's the real difference between Alpha Vantage's structured approach and Yahoo Finance's undocumented-but-free data pipeline.
A step-by-step guide from data to ranked results — the survivorship-bias trap, the look-ahead bug, transaction costs that destroy paper returns, and the smallest viable backtest harness.
A practical guide to constructing factor-based ETF portfolios — from correlation matrices to backtesting, plus the five mistakes that cost me 3.2% in annual returns before I caught them.
What sections of a 10-K actually contain signal for retail quant strategies, what's noise, and the 5-line Python harness that pulls the data points worth caring about. Written for engineers who'd rather grep than read.
Greenblatt's Magic Formula adapted to KOSPI/KOSDAQ — pulling ROIC and earnings yield from DART, ranking the universe, and the gotchas specific to Korean accounting that you don't hit running US screens.
Three years of using Polygon.io's REST and WebSocket APIs for everything from weekend projects to production dashboards — what the developer experience actually delivers.
Side-by-side tests of both APIs across the data-source workloads a retail quant actually runs — historical equity prices, fundamentals, intraday, real-time. Which one's worth paying for and which to use for free tier exploration.
Two years of building algorithmic trading strategies on QuantConnect's LEAN engine — what the cloud IDE gets right, where it breaks, and whether it's worth the $20/month.
Three backtest frameworks I've shipped real strategies in. The decision tree for picking depends on data needs, latency to first result, and whether you want to deploy live — not on framework features.
Eighteen months of using TIKR Terminal for fundamental stock research — how it stacks up against Koyfin, Bloomberg, and a spreadsheet, and where the data quality breaks.
A developer's hands-on review of Alpaca's trading API — paper trading setup, Python SDK, real-time websocket streams, order execution, and how it compares to IBKR for retail algo trading.
An engineer's guide to bond investing — Treasury bonds, TIPS, bond ETFs, yield curve mechanics, duration and convexity explained with code, and how to think about fixed income in a portfolio.
A developer's guide to implementing the Fama-French 3-factor and 5-factor models in Python — data sources, regression with statsmodels, alpha calculation, and what factor investing actually means for your portfolio.
A hands-on review of IBKR's trading API — from TWS setup to paper trading with ib_insync, market data streaming, order placement, and how it stacks up against other broker APIs for the developer-investor.
Build a Monte Carlo portfolio simulator from scratch in Python — model returns, volatility, and withdrawal rates to estimate retirement success probability with visualizations you can trust.
A developer's guide to building a personal dividend tracker — pulling data from free APIs, designing the data model, calculating yield on cost, and visualizing projected income without relying on a subscription service.
A developer's hands-on guide to the FRED API — from getting an API key to building a Python dashboard that pulls GDP, CPI, and unemployment data for investment research.
A developer's guide to options Greeks — delta as rate of change, gamma as acceleration, theta as exponential decay, and why these matter even if you never trade a single contract.
A developer's comparison of Wealthfront and Betterment in 2026: tax-loss harvesting mechanics, direct indexing, portfolio construction, fees, API access, and which robo-advisor makes sense for an engineer with RSUs and a high marginal tax rate.
A hands-on look at YCharts' fundamental data, stock screener, Excel integration, and charting capabilities — how it compares to Koyfin and Bloomberg for retail investors who think like engineers.
We tested Koinly, CoinTracker, and ZenLedger to find which crypto tax platform handles developer edge cases — DeFi, CSV imports from obscure exchanges, API sync failures, and programmatic reconciliation. Here is which earns its seat and which raises more questions.
We tested three financial data APIs — Alpha Vantage, Polygon.io, and Yahoo Finance — pulling 5 years of daily OHLCV data for SPY, AAPL, and BTC-USD. Here's how they compare on data quality, rate limits, and pricing for developers building investment tools.
We tested five portfolio rebalancing approaches — from Google Sheets scripts to platforms like Passiv and Portseido — to find what saves time versus adds complexity. A practical guide for maintaining target allocation without over-trading.
We implemented the same momentum rotation strategy in Backtrader, VectorBT, and Zipline-Reloaded and measured backtest execution time, code complexity, and accuracy against live trading results. Here is which framework actually fits your workflow — and which ones overpromise.
TradingView is the default charting tool for millions, but most users only scratch the surface. We tested Pine Script indicators, strategy tester, broker execution, and stock screener to find where it excels and where you need a different tool.
An honest assessment of Composer — the no-code strategy builder for ETFs and stocks, its backtesting engine, visual editor, and where the abstraction layer limits serious quant workflows.
A hands-on look at the IBKR Client Portal API and TWS API — authentication, paper trading, rate limits, and honest friction points that retail developers will encounter.
A realistic look at Koyfin's charting, fundamental data, and dashboard capabilities — what it replaces, what it does not, and how it fits into a retail investor's research stack.
A look at Portfolio Visualizer's Monte Carlo simulation, factor analysis, and asset correlation tools — what the free and paid tiers actually deliver for retail portfolio construction.
A developer's guide to Pine Script v5 — the language, built-in indicators, strategy backtesting, alerts, and where the platform's scripting model shows its limits.
Loss aversion, recency bias, overconfidence, and herding are predictable bugs in how people invest. Here is how each one works and how to build guardrails instead of relying on willpower.
A practical literacy guide to bonds, duration, and why fixed income plays a specific role in a portfolio — written for developers who already understand stock-index investing.
A developer-focused comparison of Alpaca, IBKR, and Tradier brokerage APIs — auth, paper trading, order types, rate limits, and honest caveats.
A pipeline-focused engineering guide to building a stock screener: universe selection, data sources, ranking metrics, storage, and the pitfalls that quietly break each stage.
Historical data favors deploying a lump sum immediately, but DCA reduces regret and worst-case outcomes. Here is how to think through the tradeoff rigorously.
Percentage losses and gains are not symmetric. Here is the arithmetic of drawdown recovery, why volatility drags down compound returns, and what it means for how you invest.
Expense ratios are deducted silently from your fund every day. A worked 30-year example shows how even a 1% annual fee erodes a meaningful fraction of your ending balance.
What investment factors are, the handful that have held up across decades of research, the two competing explanations for why they exist, and the caveats that rarely get stated.
How to model thousands of possible market paths instead of trusting one average return — a working Python approach to retirement projections and the assumptions that make or break it.
Evidence on portfolio rebalancing frequency: why it is primarily a risk-control tool, how drift works, and what the research actually says about costs and timing.
Skip the 200-page slog. Focus on Items 1, 1A, 7, and 8 — here is what each section tells you and how to read it efficiently.
A developer-investor's guide to P/E, EV/EBITDA, P/S, and P/B — what each multiple measures, where it breaks down, and why context is everything.
Why two portfolios with identical average returns can produce drastically different outcomes — and what to do about it near retirement.
A US-focused literacy guide to 401(k), IRA, Roth, and HSA accounts — the mechanics, the tradeoffs, and a contribution-priority framework for developers who invest.
How tax-loss harvesting works in a taxable brokerage account, why it defers rather than eliminates tax, and the wash-sale rule that quietly cancels the benefit.
Survivorship bias, look-ahead bias, point-in-time data, and the rest of the subtle ways your beautiful backtest is overstating returns. With concrete fixes for each.
Compound interest sounds like magic. The math is mundane and the most common rules of thumb are subtly wrong. Here's a calmer take on what compounding actually delivers.
The three labels overlap more than the marketing suggests. A developer-friendly breakdown of structure, tax treatment, and the cases where the difference actually changes your decision.
Polygon and Alpha Vantage are the two common entry points for developer-side stock data. A side-by-side on free tiers, coverage, and developer experience — without the trading-edge hype.
Most stock-data tutorials skip past the one free, public, no-rate-limit source for US company filings. Here's how to read fundamentals straight from SEC EDGAR, JSON-first.
Diversification reduces idiosyncratic risk, not market risk. When the distinction matters, what correlation looks like under stress, and why 60/40 isn't a free lunch.
Rules-based investing isn't AI, isn't HFT, and isn't a guarantee. It's a behavioral discipline: decide the action before the event. Here's what counts as a rule, what doesn't, and how it actually fails.
Tools, evidence, and disclosure are the same job whether the topic is a code editor or a brokerage account. Here's the editorial line for the new finance section.
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