There is a lot to learn from this blog post, but I didn’t understand the sections on Market Taking vs Market Making and Market microstructure signals, so I asked AI to explain them:
Market Maker (做市商) = Restaurant Owner
- Role: Provide liquidity, waiting for others to trade
- How to make money: Bid‑ask spread
- Specific operation:
- Place both a buy order ($99) and a sell order ($101) simultaneously
- Wait for others to match
- Earn the $2 difference
- Risk assumed: Inventory holding risk (price could suddenly crash)
- Analogy: Like a restaurant owner who prepares ingredients and waits for customers
Market Taker (吃单者) = Customer
- Role: Actively consume liquidity, execute immediately
- How to make money: Predict imminent price movement and trade ahead
- Specific operation:
- See someone selling at $101
- Predict price will rise to $105
- Buy immediately at $101 (paying the spread)
- Wait for price to rise and then sell
- Risk assumed: Risk of wrong prediction
- Analogy: Like a hungry customer willing to pay the restaurant’s markup
Key Differences
| Dimension | Market Maker | Market Taker |
|---|---|---|
| Order Type | Passive orders (limit orders) | Active orders (market orders) |
| Speed Requirement | Relatively low | Must be fast (ahead of price movement) |
| Revenue Source | Earn spread | Earn price movement |
| Queue Position | Very important (front of the line gets filled first) | Not very important (since you are actively taking orders) |
The “queue position” you mentioned
For a Market Maker, queue position is extremely important:
- If your $99 buy order is in position 1, you get filled first when a large order arrives
- If it’s in position 1000, you won’t get filled
For a Market Taker, queue position is not very meaningful:
- You are already actively “taking” others’ orders
- No need to wait in line for others to fill your order
Simple mnemonic: Market Maker is the “stall holder”, Market Taker is the “buyer”. The stall position (queue) is important, while buying you just go straight to the stall.
Is that clear?
Market microstructure signals
What is this system doing?
Think of it as a super‑fast robot trader that watches the market and tries to predict where prices are going in the next few milliseconds, then buys or sells to make small profits.
The Three Main Parts
1. Input: Market Data (What’s happening right now?)
The system receives constant updates from the exchange about three things:
- Someone wants to buy/sell – A new order appears (e.g., “I’ll buy 10 shares at $98.75”)
- Someone cancels their order – An order disappears
- A trade happens – A buyer and seller actually exchange shares
These updates build an order book, which shows all pending buy and sell orders at different prices. Think of it like a two‑sided auction board.
2. The Strategy: Reading the “Signals”
The system uses patterns in the order book to guess where the price will go next. Here are the three signals explained:
Book Pressure – “Which side is heavier?”
- If there are more/bigger buy orders than sell orders, the price will probably go up
- It’s like a tug‑of‑war: the heavier side usually wins
- The calculation creates a weighted average that leans toward the stronger side
Trade Impulse – “Momentum matters”
- If you see a big sell trade, there’s often another sell trade coming immediately after
- Why? Multiple traders might react to the same news at almost the same time
- It’s like dominos falling – one trade often triggers similar trades
Related Trade – “What are similar stocks doing?”
- If a related stock (like Pepsi when you’re trading Coke) just moved, yours probably will too
- Uses correlation between securities
3. Output: Making a Trade
The system combines these signals into a “theoretical price” – what it thinks the true value is right now. If this price is different enough from the market price, it places an order to capture the difference.
The Example Walkthrough
The document shows a situation where:
- A big sell trade happens (9 shares)
- This creates trade impulse (momentum down)
- But it also removes buy orders, changing book pressure
- Combined, the theoretical price drops to $98.64
- But someone still wants to buy at $98.75
- So the system sells to them for an instant $0.11 profit per share!
Why This Works (or worked)
This strategy makes money by being faster and smarter than other traders at processing information that’s already public. However, as the document hints, this used to work better – nowadays everyone has similar systems, so the edges are much smaller and you need to be extremely fast and sophisticated to compete.