Quant Fun
A strategy you can build. A market you understand.
Choose your market and your starting point. Build a real example, one decision at a time, then explore its recorded backtest.
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The next chapter starts here.
New features, platform introductions, and a closer look at what we’re building at Quant Fun.
Flip the Strategy and It Still Loses
Running a losing strategy backwards should print money, and almost never does. Why trading costs have no side, what the flip test really measures, and the checks that tell you whether your backtest is simulating a market or negating a number.
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500 Stocks Are Not 500 Experiments
Breadth across assets is the easiest way to overstate evidence. Why a market mode, sectors, and styles collapse a large universe into a handful of independent directions—and what that does to your t-statistic.
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Ten Years or Two Weeks? What a Backtest Length Really Measures
Calendar time is a poor measure of evidence. Effective sample size, overlapping signals, regime coverage, and the number of variants you tried decide whether a backtest tells you anything at all.
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Copy Trading on FOMO
What FOMO is, why manual copying is always late, how to size a copied trade so it means what the trader meant, and how to set up a first subscription.
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The Gap Between Their Fill and Yours
Four reasons copying a FOMO trader is harder than it looks, where a trader’s gain goes on its way to you, and the execution architecture it takes to close the gap honestly.
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Featured post ·
Simple Strategies That Made Money: Three Windows in Market History
Order-book imbalance, the 2020 perpetual basis, and the first months of crypto UP/DOWN markets: three eras when a strategy you could explain in a paragraph was enough, and what closed each window.
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State Machines: One Strategy That Can Change Its Mind
Memory for a trading strategy: up to ten complete lifecycles, and the rules that move each market between them.
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Queue Position: Why Being First Matters
Learn how queue position affects limit-order fills, adverse selection, and the decision to wait, reprice, or cancel—with lessons from market microstructure research.
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Iceberg Orders: The Liquidity You Cannot See
Understand hidden order size, native and synthetic icebergs, replenishment clues, and how to test execution decisions without mistaking suspicion for certainty.
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Funding Rates: Why Traders Pay the Other Side
Learn why perpetual futures need funding, how it resembles floating interest, and how hedged funding strategies earn—and lose—money after costs.
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A Dollar of Volume Is Not a Dollar of Risk
Compare Polymarket and Binance turnover through payoff, volatility, and time horizon. Learn where the embedded-leverage analogy helps and where it breaks.
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Perpetual Arbitrage: Why a Price Gap Is Not Locked-In Profit
Understand cross-exchange perpetual arbitrage through funding, basis risk, separate margin, contract differences, and a worked spread-profit example.
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Studio Strategies, Part 3: Build More Advanced Experiments
Add maker-order management, momentum confirmation, weather probabilities, and perpetual-market logic without turning the graph into a mystery.
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Studio Strategies, Part 2: Run a Backtest You Can Learn From
Replay the first strategy, understand fills and drawdown, diagnose empty results, and compare changes without fooling yourself.
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Studio Strategies, Part 1: Build Your First Strategy
Turn one clear idea into a working graph. Learn the main blocks, choose a market, and build a BTC prediction-market strategy step by step.
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Managed Accounts: One Strategy, One Pool, Your Share
Introducing a separate home for pooled yield, proprietary HFT and discretionary managed accounts that allocate to vaults on Quant Fun.
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Building an AI Trading Agent From Market Data to Orders
Preview an AI trading agent workflow in Quant Fun: connect market data, prompts, timed decisions, and order execution, with backtesting limits and AI costs.
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Random Walk Models: Pricing UP/DOWN Contracts
Explore a random walk model for trading UP/DOWN contracts: turn volatility, Gaussian price steps, and settlement TWAPs into probability estimates.
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Knight Capital: The Work Behind Reliable Trading
What Knight Capital’s trading failure teaches about algorithmic trading infrastructure, deployment checks, and the value of tested strategy components.
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Adverse Selection in Trading: Why Your Fills Go Bad
Learn how adverse selection in trading hurts market makers and takers, why fills can go against you, and what to test beyond fill rate.
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Dynamic Trading Universe: Choosing Which Markets to Trade
Automate trading universe selection in Quant Fun: filter assets by volume and market cap, use a custom list, and refresh your strategy’s markets on a schedule.
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Meet Quant Fun: Trading Infrastructure You Can Build On
Meet Quant Fun, a platform for building AI-assisted trading strategies with tested components. Connect market data, inspect logic, and refine execution.
Read postGet to know Quant Fun.
Explore the platform and learn how strategies come together.