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Quantitative Research & AI Guides

Master Institutional Quantitative AI Trading

Explore in-depth educational guides written by quantitative researchers. Learn how L2 orderbook spoofing detection, quarter-Kelly position sizing with a hard 2% risk cap, multi-agent AI debate, and tokenomics risk models protect capital.

Featured Knowledge Guides (17)

Verified Quantitative Methodologies
Orderbook & Liquidity6 min read

What is L2 Order Book Spoofing & Fake Liquidity Walls in Crypto?

Learn how market manipulators use L2 orderbook spoofing and phantom buy/sell walls to trap retail traders, and how AI radars detect fake liquidity in real-time.

Key Takeaway

Spoofing is the illegal placement of large, non-bona-fide orders designed to create false market sentiment before being cancelled prior to execution.

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Orderbook & Liquidity5 min read

How Crypto Liquidation Heatmaps & Liquidity Squeeze Pools Work

Discover how market makers and high-frequency algorithms use liquidation heatmaps to locate over-leveraged long and short clusters before price cascades occur.

Key Takeaway

Liquidation heatmaps model where leverage traders' bankruptcy prices cluster based on open interest and leverage tiers (10x, 25x, 50x, 100x).

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Orderbook & Liquidity5 min read

What is Cumulative Volume Delta (CVD) in Crypto Trading?

Learn how Cumulative Volume Delta (CVD) measures aggressive market buy vs sell volume to spot hidden institutional absorption and trend exhaustion.

Key Takeaway

Volume Delta measures the net difference between market buy orders (aggressors hitting the ask) and market sell orders (aggressors hitting the bid).

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AI Committee8 min read

How 14-Agent AI Consensus Reconciles Conflicting Trading Signals

How multi-agent AI committees use structured debate rounds, recalibration and confidence weighting instead of one indicator.

Key Takeaway

Single-indicator technical strategies (like RSI or Moving Average crossovers) suffer from high false-positive rates in choppy markets.

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Risk Management5 min read

Fractional Kelly Position Sizing: The Math BlofinX Sizes Trades With

How the Kelly Criterion sets allocation per trade, why we stake a quarter of it behind a hard 2% risk cap, and when it refuses to size.

Key Takeaway

Over-leveraging and improper position sizing are the leading causes of account liquidation in crypto futures.

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AI Committee7 min read

Crypto Volatility Regimes: Identifying Compression vs Expansion Outbreaks

Understand how ATR, Bollinger Band Squeeze, and GARCH models classify market regimes to dynamically adjust profit targets and stop loss buffers.

Key Takeaway

Financial markets spend 70% of the time in low-volatility range-bound consolidation and only 30% in high-volatility directional expansion.

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Tokenomics6 min read

FDV vs Market Cap: How Supply Overhang Affects Crypto Valuation

Learn how Fully Diluted Valuation (FDV), circulating supply, cliff unlocks, and NVT ratios reveal hidden tokenomics risks in crypto assets.

Key Takeaway

Market Cap measures current circulating tokens multiplied by price, while FDV measures total future supply at current price.

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Risk Management7 min read

What a 35% Win Rate Actually Means (And Why Ours Is One)

Win rate alone says almost nothing about whether a strategy makes money. The arithmetic, worked through our own measured 35.29%.

Key Takeaway

Win rate is meaningless without the average size of a win and a loss beside it. A 35% strategy can outperform a 70% one.

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AI Committee8 min read

How to Read a Forecast Calibration Curve (Ours Says We Are Overconfident)

A calibration curve compares what a forecaster claimed against what happened. How to read one, using ours, which runs overconfident.

Key Takeaway

Calibration asks a different question from accuracy: when a model says 70%, does it happen about 70% of the time?

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AI Committee9 min read

How to Check Whether a Crypto Signal Service Is Telling the Truth

Six checks that separate a real published track record from a marketing number, with our own results against each one — including where we come off badly.

Key Takeaway

A track record with no sample size, no confidence interval and no losing trades is marketing, not evidence.

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Risk Management9 min read

Is Funding-Rate Carry Still Profitable? We Measured Six Years of It

The one strategy we have measured as profitable after costs, and it is barely paying in 2026. The full six-year record on BTC.

Key Takeaway

Across 7,000 eight-hour funding periods on BTC (April 2020 to August 2026), funding was positive 86.0% of the time. That is the structural reason carry works at all.

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AI Committee11 min read

How to Pre-Register a Backtest (And What Ours Found)

We wrote the pass mark down before running anything, spent the holdout once, and found no signal. The protocol and every number.

Key Takeaway

The bar was written down before any result existed: net return > 0, t >= 3.0 on validate, Bonferroni-corrected p, beats three baselines, same sign at both horizons, and t >= 2.0 on a holdout read exactly once.

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Tokenomics7 min read

The FDV You Are Quoted Uses Issued Supply, Not the Cap

Two different numbers are published as FDV, and the common one hides the dilution you are measuring. The arithmetic and a one-line test.

Key Takeaway

CoinGecko's `fully_diluted_valuation` is price x TOTAL (already-issued) supply, not price x MAX supply. Verified live: Bitcoin's FDV matches price x 20.07M issued, not price x the 21M cap.

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Risk Management9 min read

Your Reward-to-Risk Ratio Does Not Change Your Expectancy

Reward:risk cancels out of the expectancy equation. Tuning take-profit changes the shape of outcomes, not their worth. Two levers do.

Key Takeaway

Per-trade expectancy reduces to EV/risk = edge - cost/risk. Reward:risk cancels out of it entirely.

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AI Committee8 min read

Our RSI Was the Number 50, Every Bar, for Every Asset

An off-by-one seeded a recursive average from a NaN, so RSI was NaN all series, and fillna(50) turned it into a constant 50.0.

Key Takeaway

`close.diff()` leaves a NaN at index 0, so a 14-period rolling mean of the result is first valid at index 14 — not 13. Seeding the recurrence at 14 read index 13, which was NaN.

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AI Committee8 min read

We Shipped a Filter That Served the Losing Side for Weeks

A filter that separated winners from losers in-sample inverted once live, actively selecting the worse cohort rather than just failing.

Key Takeaway

On an in-sample time split, picks aligned with the futures long/short crowd won ~44% against ~28% for those against it. The split held across periods, which is normally the check that catches overfitting.

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Getting Started7 min read

How to Connect Your Binance API Keys to a Trading Terminal (Safely)

Creating Binance API keys for a third-party terminal: which permissions to grant, why withdrawals stay off, IP allow-listing, testnets.

Key Takeaway

Grant Enable Reading plus the one trading permission you need, and never Enable Withdrawals — a terminal that places orders has no use for a key that can move money out.

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Experience 14-Agent AI Consensus Live

Put quantitative principles into practice with real-time spoofing radar and 14-agent consensus signals.