Recently had it done. Millennium Quant Intern OA is a very special experience. On the whole, this set of OA is not just algorithmic questions like the regular big companies, but it simulates real trading scenarios through the AmplifyME platform, combining programming and quantitative logic, which makes it feel more like a practical training program. I'd like to share some of the actual questions for students' reference and practice~
Testing process
Time of receipt: Not long after the pitch, I received an OA with an email stating that it had to be completed within 14 days, and once I clicked on it, it was a 24 hour time limit.
formalityThe overall style of the course is progressive, with a total of 4 main questions and a number of sub-tasks underneath each question. The first part is more basic, and the second part is gradually adding risk management, hedging and arbitrage, which is increasing in difficulty and complexity.
Platform environment: Use AmplifyME, which comes with AmplifyQuantTrading package, a lot of data and transaction objects are encapsulated. We just need to write the logic in the given functions/classes without having to build the framework from scratch ourselves.
Millennium Quant Intern OA Real Questions Review
Challenge 1: Price Making
In this challenge, your task is to build a simple, automated market maker. Using object-oriented programming, you will create a class that can provide a two-sided price quote (a bid and an offer) for any given asset. two-sided price quote (a bid and an offer) for any given asset. Your model will need to calculate quotes that are 1.5% away from the current market price.
What is Market Making?
When a large client (like a hedge fund) wants to trade, they ask a Market Maker for a price. The client tells you what they want to trade (e.g., a tech stock) and how many. The client tells you what they want to trade (e.g., a tech stock) and how many. They don't tell you if they want to buy or sell. Because of this, you must provide a price for both scenarios.
- Bid Price: The price the client can sell at.
- Offer Price: The price the client can buy at.
Calculation Rules
- Bid Price = Reference Price × (1 - 0.015)
- Offer Price = Reference Price × (1 + 0.015)
For Example
If a client requests a quote for an asset with a reference price of $120, your market maker would provide.
- Bid. $118.20
- Offer. $121.80
Challenge 2: Price Skewing & Risk Management
In this challenge, you'll build on the foundation of Challenge 1 by adding a new layer of real-world complexity. price skewingIn practice, a market maker doesn't always quote prices symmetrically around a reference price. Instead, they adjust their quotes to Instead, they adjust their quotes to manage the risk of their current inventory.
The Logic of Skewing
- If you are long an asset (own too much), you want to encourage clients to buy from you → shift prices down.
- If you are short an asset (owe it), you want to encourage clients to sell to you → shift prices up.
For Example
If the reference price for a stock is $120:
- When Long (you want to sell): your quote might be Bid $114 - Offer $120, shifted downward.
- When Short (you want to buy): your quote might be Bid $118 - Offer $124, shifted upward.
Challenge 3: Optimized ETF Hedging
Now you will extend the previous challenge to perform a more sophisticated, beta-adjusted hedge. Instead of a simple 1-for-1 hedge, you will use beta values to calculate the precise amount of ETF needed to offset your risk in each stock. Instead of a simple 1-for-1 hedge, you will use beta values to calculate the precise amount of ETF needed to offset your risk in each stock.
The Beta-Adjusted Hedge
Formula. Hedge Amount = Equity Risk Amount × Beta
Example
If you hold a $800,000 long position in a stock with a Beta of 0.72, your optimized hedge is to sell $576,000 of the ETF.
New Tools for Hedging
- ExchangeTrade object
- ticker: String (e.g., 'TECHETF')
- trade_volume: Integer
- ref_price: Float
- action: String ('Buy' or 'Sell')
- date: Integer
- Exchange.execute(trade) → submit hedge trade.
- Log ETF Position → keep track of hedge exposure.
Challenge 4: Arbitrage Trading
The final challenge introduces arbitrage trading: exploiting temporary price differences between an ETF and its underlying stocks.
Core Idea
Compare the traded price of the ETF (Real ETF) with a "fair value" calculated from its 5 component stocks (Synthetic ETF).
Trading Logic
- If Real ETFs > Synthetic ETFs → Sell ETFs, Buy components.
- If Real ETF < Synthetic ETF → Buy ETF, Sell components.
Tools: HedgeFund Object
.balance: account balance..current_positions: current holdings..commission_percentage: trading fee.hf.execute_order(ticker, volume, action, date): submit a trade order.
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