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This interview features Andres Granger—an Asia-based fund manager and market-neutral spread trader—breaking down how he went from early false starts to running automated, execution-first strategies across futures, FX, and digital markets. Recorded while he’s living and trading from South Korea, Granger explains the path from “calling direction” to professional non-directional trading, why tape/DOM and Bookmap views matter more than pretty charts, and how disciplined review turned him from break-even to consistent.
In this piece, you’ll learn exactly how Andres Granger structures market-neutral spread trades (cash-and-carry, stat-arb style pairs), why execution and sizing beat prediction, and how automation (bots/auto-spreaders) scales a simple edge across accounts and SMAs. We’ll cover the journaling/review process that sharpened his decision-making, how he uses the order book and tape to anticipate prints, and when he layers a slow-timeframe trend-following sleeve on top of neutral strategies. Read this to copy his practical rules for consistency—tight execution, patient convergence, and relentless review—so you can trade more like a pro without trying to predict every tick.
Andres Granger Playbook & Strategy: How He Actually Trades
Core Philosophy: Predict Less, Capture Spreads
Andres Granger focuses on market-neutral trading—building positions that don’t require calling direction. The idea is simple: buy a little cheaper here, sell a little richer there, and let convergence do the heavy lifting.
- Trade when there’s a quantifiable spread you can define in advance (e.g., spot vs. futures basis in contango).
- Enter only when the live spread exceeds your “edge threshold” after fees, slippage, and funding/borrowing costs.
- Focus on correlation and cointegration over prediction; your thesis is convergence, not trend.
Core Setups: Cash-and-Carry, Exchange Spreads, and Stat-Arb Pairs
He leans on classic cash-and-carry and stat-arb style pairs: long the cheaper leg, short the richer one, then ride the basis toward parity. This is portable across asset classes and particularly effective in fragmented markets.
- Cash-and-carry: long spot, short futures when the term structure is rich enough to cover all costs with a safety margin.
- Cross-venue spread: buy on the bid at Exchange A, sell on the ask at Exchange B when the net price gap > total costs.
- Pairs/stat-arb: go long the laggard and short the leader only when correlation is stable and the z-score of the spread exceeds your trigger.
Execution Edge: DOM/Tape First, Pretty Charts Second
Execution is everything. Andres prioritizes DOM/tape (and heat-map style order flow) to get filled advantageously on both legs and improve average price.
- Work resting orders inside the spread; avoid crossing unless your edge decays fast.
- Leg management rule: if one leg fills and the other doesn’t within X seconds or Y ticks, scratch the filled leg.
- Record fills and queue position; aim to be maker > taker. Maker’s share target should be explicit in your plan.
Automation & Scaling: Bots That Trade Your Rules
Once rules are proven, Andres lets automation run the spreads 24/7 according to predefined parameters, then reviews fills and P&L. The bot enforces discipline and frees him to focus on research and risk.
- Encode entry/exit as hard conditions (spread > threshold; revert to mean by Δ). No discretionary overrides mid-trade.
- Separate signal from execution: one module decides “trade,” another optimizes order placement and queueing.
- Schedule human review windows (e.g., daily) to assess slippage, rejects, and parameter drift before re-deploying.
Risk Framework: Small Bites, Tight Leashes
Neutral doesn’t mean risk-free. Inventory risk, legging risk, borrow/funding changes, and exchange outages can all hurt you. Keep positions bite-sized and kill losers fast.
- Inventory cap: hard max exposure per leg and per spread; never add to rebalance beyond pre-set limits.
- Time-out stop: if convergence stalls beyond T minutes or expands by E ticks, flatten both legs.
- Venue risk: pre-define backup venues; if primary venue degrades (latency, rejects), route to secondary or stand down.
Sizing & Cost Control: Edge After Fees or No Trade
Andres emphasizes “execution-first” thinking—your realized edge is spread minus fees, borrow/funding, and slippage. If costs eat the edge, skip it.
- Pre-trade cost model must project net basis capture > 2× your average slippage + fees.
- Live slippage monitor: if rolling 20-trade slippage > budget, reduce size or disable that venue.
- Funding sensitivity: re-price thresholds when funding or borrow spikes; don’t rely on stale assumptions.
Playbook for Cash-and-Carry: Step-By-Step
This is the bread-and-butter neutral trade: long spot, short futures when futures trade rich. It’s simple to explain and rigorous to execute.
- Trigger: annualized basis above your hurdle (after all costs) with minimum lookback stability.
- Entry: place passive bids in spot and passive offers in futures; avoid market orders unless the basis is fleeting.
- Exit: unwind legs as basis compresses to your take-profit level or at expiry—whichever comes first.
Playbook for Exchange Spreads: Fragmentation = Opportunity
Fragmented order books create transient gaps. Andres exploits these by being first in the queue and disciplined on legging.
- Only trade when the net gap (A bid vs. B ask) exceeds fees + expected slippage + buffer.
- If only one leg fills, use a “leg stop timer” to scratch and re-quote; never chase the second leg across the spread.
- Update fair value every tick; cancel/replace aggressively when queue position deteriorates.
Pairs & Stat-Arb: Convergence With Guardrails
When two products are highly related, temporary divergence can be harvested. But the rules must protect you from regime change.
- Entry only when the z-score of the spread > entry Z and realized correlation above floor for the last N windows.
- Dynamic TP: scale out as z-score reverts; full exit near mean or on correlation breakdown.
- Hard fail-safes: if correlation < floor or volatility doubles, flatten and re-estimate parameters.
Review & Improvement: Film Study for Traders
Andres literally reviews recordings of his own trading to spot blind spots and sharpen execution. Treat it like an athlete watching game tape.
- Record DOM/heat-map + orders; tag trades by setup, venue, and outcome.
- Weekly audit: top 10 slippage offenders, top 10 missed fills—rewrite rules or venue lists accordingly.
- Convert lessons into parameter changes with version control; keep old configs to compare outcomes.
Operating Like a Pro: From Solo to Managing Capital
He highlights the difference between trading your own account and scaling with outside capital—discipline, process, and repeatability matter most.
- Document your strategy so another trader (or a bot) can run it unambiguously.
- Risk first: daily loss limits, venue risk rules, and emergency flatten procedures must be explicit.
- Performance is path-dependent—optimize stability over headline returns to be allocatable.
Context & Edge Durability: Why This Works
Fully automated, market-neutral strategies can be robust across regimes when grounded in microstructure and strict execution. That’s why Andres emphasizes automation and neutrality as core design choices.
- Build around structural edges (fragmentation, basis, liquidity rebates), not forecasts.
- Assume edges decay—monitor edge health with rolling hit-rate and net edge after costs; pause when metrics slip.
- Keep research pipelines alive: test new venues, products, and parameter sets continuously.
Stop Predicting Direction—Build Repeatable Edge With Market-Neutral Spreads
Andres Granger keeps it simple: stop guessing where the price goes and start harvesting the gap between two related prices. He treats the market like a machine—find a spread that routinely overpays, define costs, and let convergence do the lifting. Instead of calling tops and bottoms, he builds trades that make money if the relationship normalizes, not if a chart pattern “works.” That shift turns trading from fortune-telling into a process: identify, quantify, execute, repeat.
In practice, Andres Granger looks for situations where one instrument is slightly rich and another is slightly cheap, then structures long/short legs so direction barely matters. He times entries when the live spread clears his cost and slippage budget, then manages fills through queues and timeouts to avoid getting stuck on one leg. Risk is framed by thresholds and timers—if the spread doesn’t compress in a set window or blows out past a limit, he flattens fast. Over many iterations, the edge comes from consistency: same setup, same rules, same review—no crystal ball required.
Size Positions By Volatility And Real Edge After Costs
Andres Granger sizes positions to the market, not his mood. He ties exposure to realized volatility, so a choppy product gets a smaller size and a calmer one earns more weight. Before entering, he computes the net edge—spread minus fees, funding/borrow, and expected slippage—and refuses trades that don’t clear a preset hurdle. If the hurdle is met, size scales proportionally to edge and inversely to volatility, so the risk per trade stays consistent.
He also caps allocation with hard guardrails: maximum dollar exposure per leg, maximum leverage per venue, and a daily loss stop that shuts the strategy down. Andres Granger prefers fraction-of-Kelly style scaling but clips it aggressively to avoid ruin from variance and model error. He recalculates size when volatility regime shifts or execution quality degrades, shrinking immediately if slippage rises. The goal is simple: only bet big when the edge is real after costs—and even then, keep the leash tight.
Diversify By Underlying, Strategy, And Trade Duration To Smooth P&L
Andres Granger doesn’t let one idea or one market carry the month. He spreads risk across different underlyings (indexes, FX, commodities), mixes strategies (cash-and-carry, cross-venue spreads, pairs), and staggers holding periods from quick intraday clips to slow basis compressions. That blend keeps any single drawdown from snowballing and turns a lumpy equity curve into something steadier.
He treats correlation like a position size input, not a trivia stat. If two trades share the same basis risk or venue fragility, he sizes them as one and looks for a truly independent sleeve before scaling. Portfolios get regular “orthogonality checks,” using simple rolling correlations and outcome clustering to kill hidden overlap. Exits are staggered across time targets and profit bands so wins don’t all rely on the same tick. In short, Andres Granger builds a basket of uncorrelated small edges, then lets time diversification do as much work as market selection.
Write Hard Rules For Entries, Exits, And Defined Risk Limits
Andres Granger turns fuzzy ideas into yes/no rules before the session starts. Entry must only occur when the measured spread or setup exceeds a pre-set edge threshold after all costs; if it’s close, it’s a pass. Orders are placed in a specific sequence (e.g., rest both legs first, never cross unless decay is proven), and any partial fill has a timer—if the second leg isn’t filled within the window, flatten immediately. No “it looks good” exceptions; either the conditions are true or they aren’t.
Exits are equally explicit: take-profit at a defined convergence level or time-based expiry, whichever hits first, with a hard stop if the spread widens beyond a maximum adverse move. Every play carries a position cap per leg and a daily loss limit that shuts the book and ends discretion for the day. Venue risk is codified too—if rejects or latency exceed thresholds, routes switch or the strategy pauses. The result is simple: Andres Granger protects himself from his own optimism by letting the rules run the show.
Run The Mechanics, Automate Execution, Review Process Every Week
Andres Granger treats trading like running a factory line: the mechanics do the work, the human checks the gauges. He automates entries, exits, and risk actions so the same rules execute at 2:00 a.m. exactly as they do at 2:00 p.m. Every order, fill, cancel/replace, and reject is logged with timestamps and context so he can audit what the bot actually did—not what he hoped it did. Core KPIs are simple and unforgiving: hit rate, average net edge after costs, slippage per venue, queue position share, and time-to-fill. If edge decays or slippage drifts above budget, Andres Granger cuts size or disables the venue before damage compounds.
The weekly review is a ritual, not a suggestion. He replays DOM and heat-map moments around winners and losers, tags errors by root cause, and writes one small rule improvement per category. Parameters are versioned like code: the A/B tests are configured in parallel and only promote the winner to production. Post-mortems are short and blunt—what failed, what rule changes, what size adjustment—then he resets for the next cycle. The cycle never changes: automate the mechanics, measure the outcomes, tighten the rules, repeat.
In the end, Andres Granger’s edge comes from abandoning prediction and committing to process. He moved from trying to “call direction” to running market-neutral spread trades with tight execution rules, a shift he credits to learning how professionals actually operate and holding himself accountable for discipline lapses. That evolution—from break-even experimentation to rule-driven trading—anchors everything that follows: define the setup, price the costs, execute precisely, and let convergence—not forecasts—pay you.
The mechanics are deliberately simple but ruthlessly enforced. Core plays like cash-and-carry don’t care if price goes up or down; they monetize the basis as futures converge to spot at expiry, provided entries, exits, and risk limits are coded and followed. Execution is treated as a skill: DOM/tape awareness, queue positioning, and strict legging rules determine whether the theoretical spread survives fees and slippage in the real world.
Granger scales by sizing to volatility and real edge, not conviction, and by auditing his own behavior. He records, reviews, and iterates—spotting blind spots faster and converting lessons into rule updates—so the playbook stays durable across regimes. The result is a professional loop: small, repeatable edges; volatility-aware sizing; explicit risk limits; and relentless review. If you adopt only one takeaway, make it this: trade a rulebook you can execute perfectly at 2 a.m., not a hunch you can only believe at noon.
