Why you should know this
Market making earns small spreads repeatedly, but those gains can be overwhelmed when inventory accumulates or informed traders trade against stale quotes.
Academy 12 is where ideas become operating rules. The aim is not to collect strategy names. It is to learn how to ask the same professional questions of every style: what is the decision rule, what market behavior is it trying to exploit, what assumptions support it, what costs sit between the signal and the result, and what evidence says the method no longer fits?
How the strategy actually makes a decision
A market maker posts both a bid and an ask, offering to buy below and sell above a reference price. The apparent business is simple: collect the spread. The real problem is managing inventory and information. If the market maker repeatedly buys while price is falling, inventory grows as the asset becomes less valuable. If better-informed traders hit stale quotes just before price moves, the spread received may be much smaller than the adverse price change.
The strategy therefore balances three competing objectives: quote closely enough to get trades, quote widely enough to cover risk and costs, and keep inventory from drifting into a large directional position.
What must be true before the strategy makes sense
A testable market-making model needs a reference price, quote width, order size, inventory limits, cancel/replace rules, latency assumptions, fee or rebate structure and a rule for suspending quotes during exceptional volatility. A backtest using mid-price bars alone is usually too optimistic because it does not model queue position or whether the maker would actually be filled.
The strategy also needs an inventory-skew rule. When inventory becomes long, quotes may need to encourage selling and discourage more buying. Without a rule, “neutral market making” can quietly become a leveraged directional bet.
Work through the decision, not just the definition
Assume a fictional mid-price of PHP 100.00 and quotes at PHP 99.80 bid / PHP 100.20 ask. A round trip appears to earn PHP 0.40 per unit. But if the maker buys at PHP 99.80 just before the true market reprices to PHP 98.80, the PHP 0.20 half-spread did not compensate for a PHP 1.00 adverse move.
Now imagine this happens repeatedly because the quoting system reacts slowly to news. The strategy may show many fills and positive quoted spreads while losing money after inventory mark-to-market. That is adverse selection in practical terms.
Where the strategy gives its edge back
A strategy can have a sensible market idea and still produce a poor result if its costs, timing or operating conditions are wrong. Before judging performance, separate market edge from execution drag. Fees, spread, slippage, missed signals, funding or borrowing costs, tax-record obligations and unavailable liquidity may matter differently for each style. The next lesson in this family turns those frictions into an explicit testing ledger rather than leaving them as footnotes.
Market making fails when quotes are stale, inventory limits are ignored, volatility expands faster than quote width, or fees and cancel costs erase the spread. It can also fail operationally through API outages, latency, incorrect market data or one-sided execution.
The most important stress test is not an average day. It is the moment when spreads widen, inventory is already imbalanced and the system’s ability to cancel or hedge is impaired.
Skill practice — build the rule before seeing the answer
Create a fictional quote simulator for twenty price updates. Record bid, ask, fills, inventory, mark-to-market P&L and spread P&L separately. Add one sudden 3% price jump and a temporary inability to cancel quotes. Then set a maximum inventory and a volatility threshold that forces quoting to pause. The lesson is complete when the reader can explain why gross spread capture is not the same as strategy profit.
Do not score the exercise only by whether the hypothetical trade made money. Score whether the rule was clear enough that another reader could make the same decision from the same information. A lucky outcome from an undefined process is not the skill Academy 12 is trying to build.
How this connects to market mastery
A strategy becomes useful only when it can be compared with alternatives, tested under different regimes and retired when its assumptions fail. That is the bridge from “I know what this strategy is called” to “I can decide whether this strategy belongs in this market and in my operating constraints.”
*Cryptocurrency and virtual asset transactions are highly volatile and irreversible, may result in significant losses, and do not guarantee returns; customers should trade only after understanding the risks involved.
