Research Like a Quant

Anecdotes scale on social media. Edges scale on process. If you want time-based ideas to survive contact with live markets, you need a research loop.

You do not need a PhD. You need discipline: define, measure, forward-test, journal.

Step 1 Collect data. Step 2 Write a hypothesis before mining. Step 3 Backtest with clear rules. Step 4 Measure expectancy (not just win rate). Step 5 Forward test on unseen data or paper. Step 6 Journal live results and kill failures.

• Order matters — hypothesis before sightseeing
• Expectancy includes wins, losses, and costs
• Forward test is non-negotiable

Does BTC reverse more often after extreme positive funding for N intervals? Do Mondays show larger retracements after strong Sunday pumps? Do spot ETF inflow days correlate with same-day returns after costs? Does realized vol expand around major OPEX?

Each line must become a precise, testable statement with dates and definitions.

• Funding extremes → next-day return distribution
• Weekday effects → range or return stats by regime
• ETF flows → event study with lagged controls
• OPEX → vol and path metrics, not only direction

Win rate without payoff is a vanity metric. Expectancy ≈ (win% × avg win) − (loss% × avg loss) after fees and funding.

A calendar tendency with 55% wins and tiny winners vs fat losers is garbage. Run the math.

• Include fees and funding in the test
• Split bull/bear or high/low vol regimes
• Report sample size beside every %

Influencers sell certainty. Researchers sell distributions. When someone says “everyone knows OPEX dumps,” ask for their spreadsheet — or build yours.

TrendWave helps you track live P&L and funding so forward tests live in reality, not screenshots. The research still has to be honest.

• Demand mechanisms and numbers
• Replicate or discard
• Your journal is the forward-test log