最近剛做完 Millennium Quant Intern OA ，體驗感還是蠻特別的。 整體來說，這套 OA 不像常規大廠那樣就是單純的演算法題，而是通過 AmplifyME 平臺模擬了真實交易場景，把程式設計和量化邏輯結合起來，感覺更像是實訓專案。 接下來分享一下真題，供相關的同學參考練習~
測試流程
收到時間：投完沒多久就收到了 OA，郵件說明要在 14 天內完成，一旦點開就是 24 小時限時。
形式：總共 4 道大題，每題下面還有一些子任務，整體是循序漸進的風格。 前面比較基礎，後面逐漸加上 risk management、hedging 和 arbitrage，難度和複雜度都在遞增。
平台環境：使用 AmplifyME，自帶 AmplifyQuantTrading 包，很多數據和交易物件都封裝好了。 我們只需要在給定的函數/類里寫邏輯，不用自己從零搭架子。
Millennium Quant Intern OA 真題回顧
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 atwo-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) andhow 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 skewing. In practice, a market maker doesn’t always quote prices symmetrically around a reference price. Instead, they adjust their quotes tomanage 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 betavalues 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 ETF > Synthetic ETF → Sell ETF, 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.
Programhelp 專業助攻服務
我們團隊長期專注於 OA 代寫、筆試代寫、Hackerrank 包過 等服務，保證所有測試用例 100% 通過，如未通過不收費。 無論是 HackerRank、牛客網還是 Codesignal，我們都能通過遠端無痕操作，確保過程安全流暢。
除了筆試，我們還提供 面試輔助、VO 助攻，由北美資深 CS 專家人工即時給出思路和提示，遠勝 AI 自動化答題，説明你在關鍵問題上快速突破。
針對有需要的同學，我們也能提供 代面試服務：通過轉接攝像頭與變聲技術實現真實互動，團隊成員與你默契配合，確保過程自然無痕，説明你直達 Offer。
更重要的是，我們提供 全套護航服務 —— 從 OA 到面試，再到簽約談判，全程陪伴，直到你順利入職理想大廠。 採用 「預付少量定金 + 成功上岸再付尾款」 的模式，真正讓你無後顧之憂。
