一名優秀的 Quant 都需要具備哪些職業素養和技能？

也可以說說你看到的一些優秀 Quant 都具有哪些職業素養和技能？

因為不同 Quant 的原因，技能可能要求不同，所以還望多說說素養：）

---

首先是對這個行業的興趣，很多人可能一開始覺得quant很酷炫就開始往這邊挪，但是對行業不了解，對需要克服的困難沒有清晰的認識，最終打了退堂鼓，所以一個持久的興趣很重要。

然後說一些會學到用到的東西：

1. 編程
2. 數學/統計
3. 金融

金融工程其實就是這三者的交叉學科。

編程是重中之重，沒有誰能不編程就說自己是quant的，只不過需要負責的編程任務不同罷了。對於quant來說，你可能需要學會很多種不同的語言，你需要會C++，並不是說你一定會用到它，而是你對C++的知識讓潛在僱主知道你能很快學會其他的語言。你需要會Matlab/R,這樣你可以做research, 跑simulation,然後制定策略等等，你還要回SQL,因為金融是一個高頻大數據的行業。其它根據不同崗位還會有不同需求。

到底是用數學還是統計，往往取決於你是個Q quant還是P quant，但是金融時間序列和布朗運動為主的difussion process是每個人都必須要會的，之後根據職業特點和個人興趣會有不同。Q quant會進一步學到各種change of measure, change of numéraire, jump/levy process等等。而P quant則會進一步學習各種數據挖掘演算法，濾波方法，貝葉斯統計等等。

最後是金融，這個每個人的看法不一樣，有的人說他完全不關心市場是什麼樣的，他只關心Data，我不太贊成這種看法。很多經典的模型被人詬病，批評別人的Model是很容易的，但是一個Model永遠只是現實的一種逼近，關鍵是這個模型能不能給你基本的直覺指引。很多人說CAPM沒用，但是CAPM起碼告訴了一個很簡單的道理，你不可能在降低風險的同時提高收益，起碼從期望上不能做到這個的，很多人做了半天model，最後把這個忽略了。你可能覺得APT很可笑，但是很多fund其實每天就干這個，找factor，然後hedge到各種risk。你可能覺得市場微觀結構沒意思，但是如果你要開發做市策略的時候這就是重中之重。你可能覺得utility function沒意思，但是他能幫助你理解人性。

關於金融，這取決於你怎麼看待quant這份工作，你覺得你是在做模型，只不過有人做流體你做金融，那你就會覺得金融理論很沒意思，但我會覺得金融學的知識是最重要的，也是最有意思的。

如果把市場比作一個水面，在正常的時候，有很多漣漪，你說你能發現其中的規律，然後你模擬了這個波動，你賺到了錢。但是如果你從來不去思考這個漣漪為什麼會出現，那你就不會知道有一個巨浪要來了。也許你10年都在賺錢，但是一天虧到底。

對風險的敬畏是每一個金融業者應有的素養，quant也不能忽略，甚至要多面臨兩層風險:模型風險和統計估算風險。

知道model什麼時候fail比知道model好在哪裡要重要的多。

At the end of the day, its the thing that you dont know you dont know that makes you lose all you pennies.

---

紮實的數學基礎。我面試的時候都會故意問一些問題讓臨時看wiki的貨暴露馬腳。

基礎的程序設計能力，根據工種細分不同對程序設計能力要求不同。高頻的要求高一些，但就算是衍生品礦工和中低頻礦工，寫個script的基礎的能力必須有。

耐心、耐心、耐心，細心，細心，細心。我以前是個馬大哈，幾年工作下來耐心大大的，不會放過任何小數點後8位以內的差距。開始磨死人，現在習慣了反而受不了別人馬大哈了。

好奇心和不氣餒的精神。好奇心幫你發現問題，不氣餒的精神讓你跟問題死嗑。最終哪怕只是在在一定程度上解決它，都會給你的公司帶來利潤。

交流能力和虛心精神。礦工是個需要緊密團隊合作的工作，一個人再聰明再努力也對付不了市場上一群人都在拚命的聰明人。

最後就是用於面對錯誤的精神。人人都會犯錯誤，如果不敢於承認、面對錯誤，對團隊、對公司都是很大的隱患。另外，礦工是個不進則退的行業，不敢於面對自己錯誤的也會限制自己的成長，這樣遲早會被淘汰。

--

安利一個我的live

理工生如何進投行交易部門

---

我心目中 Quant 所需要的素養也就是一名科學工作者所應該具備的素養。

第一是要有懷疑精神。別人給你講了一個思路，或是一項技術，也許那個人是你的老闆，或是你敬仰的業內大牛，那麼你就會信之不疑全盤接受嗎？你從事某項工作多年，比如說一段固定模式程序的寫法，很多年都那樣寫了，從來沒出過問題，那麼這種方法就一定是完全正確或是最優的嗎？你新加入了一家公司或是團隊，那裡的人看起來都很聰明且有高大上的學歷和工作背景，那麼他們的做事方法就是無可置疑，你只需照葫蘆畫瓢嗎？在我看來，一個優秀的 Quant，第一步要做的就是獨立運用自己所學對要面臨的任務和問題進行分析，不輕信其他人（包括自己的歷史經驗），對任何自己存疑的地方都積極調查，即便有時這會讓你看起來像個傻瓜。當然，做到這一點有一個前提，就是你要確保自己並不真的是一個傻瓜，對於給出的質疑要有充足的理由和確鑿的證據。

第二是要有紀律性。對於既定的紀律要堅決遵守，比如提交程序之前先跑測試，運行一個系統 之前先做例行檢查，寫程序的時候保證符合規範。即便有些時候一些紀律看起來很蠢，但如果你知道不遵守它們會導致什麼後果，那就絕對不要抱有僥倖心理，嚴格執行。

第三是能在抽象層面進行工作。這個抽象並非是指數學上那種程度抽象，而是說對一項技術或是產品，能夠深入理解其原理，進行有創造力的工作。 強調這一點，是因為我的確見過有些人雖然技術水平不錯，但是只能刻板的照做別人交待好的事情，一旦遇到一些別人沒有解釋過需要自己想辦法的問題，就會做的一塌糊塗。就其原因，就是因為對深入的概念和原理缺乏理解，只能浮於表象。

可以看出這三條沒有一條是講如何做交易的，甚至也不涉及具體的數學或是計算機技術。但是我相信能做到這三點的人，教他具體的業務或是技術細節絕非難事。而這三點其實恰恰是在任何科研工作中都需要的。雖然的確有些天才少年可以無師自通，但是對普通人來說，要磨練這些素養，讀一個理工科 PhD 經受正規的科研訓練是最有效的途徑，不論是數學物理還是計算機。我覺得這正是為什麼這一行如此偏愛 PhD 的原因。

---

數學得好，一般都對數理背景的很有好感。這裡我很感謝我本科的數學系

上面也說了，基本三方面混合的

數理邏輯+金融+編程

我個人方面編程能力不算強

金融的知識方面，其實學金融的時候就一個感覺，這不就是什麼和什麼加一加就完事了嘛

說到底好多東西，金融方面的，也是可以找到數學的邏輯在裡面的。

還有一點就是個人性格等方面的，抗壓能力與是否能夠靜的下來還是很重要的。

有些時候欲速不達，需要靜下心來，一點點一點點從頭開始做，每一步不能有問題，邏輯得反覆推演，要不容易出現沒有覆蓋的情況。

還有個人方面的感覺就是魄力吧，是否敢擔事的能力。

還有從失敗中吸取教訓的能力～

說這麼多也相當於是自我總結了，估計再過一段時間可能還會有更多的感受吧

---

其實每個quant都有自己的特點，這些特點決定了他們的不可或缺。比如說：

1. coding活好的，尤其是牽涉到系統架構這種事情，1個頂100個；

2. 統計和數學功底好的，這類人喜歡從數據中找盈利的策略，這也是老闆也最喜歡的員工，因為他們每個策略背後貌似都有數據支持，這類人是quant的主流；

3. 對市場有感覺的，看看K線或者基本面數據後策略就能信手拈來的。這類人無他，天生適合這一行，膽大心黑。懶一點的不想學數學和統計，勤奮的也照樣會學，不過通常前者居多，爆倉最多的也就這種人；

---

一是對各類事物尤其是數理方面有極強的好奇心和熱情。好奇心和熱情不光使得工作時的效率提高，還會自願增加處於工作和學習的狀態中的時間。

二是嚴謹和科學精神。嚴謹是各種事情不馬虎，不得過且過，專註細節，實事求是。科學精神是不會盲從（包括不盲從所謂的科學知識），具備懷疑精神，什麼事都會問為什麼，什麼事都不會十分篤定的精神。

這兩點其實都跟大五人格中的開放性(經驗開放性)有關，從前我們招聘的時候還會專門測這一項。

---

之前的大神回答都已經很好了。

加多一些，紮實的微觀市場知識。比如:

港交所分成的strict limit order和enhanced limit order的區別，會對市場造成什麼的影響，發去交易所以後會吃點多少active order對市場的order book有什麼影響，etc。

最基本的一些意義比如limit order provide liquidity 和market order consume liquidity。

市場的變化，東京交易所從一開始的五個tick level到現在的八個tick level對你的演算法會有如何衝擊？

延遲的敏感，一個best bid offer在你的系統收到有多大延遲，發出去一個order有多大延遲？如果做並發計算我可以做出什麼樣的事兒保證在用的是最fresh的data...?

---

聰明，細心，認真，低調，活兒好要求少。

---

數學、金融、計算機，進入這個行業，有以上其中一項技能即可，但要做的好，挺難的。多學多練，多向高手請教，雖然核心策略沒有人會告訴你。但一些方法論和注意事項還可以學習的。

優礦目前推出了兩千萬實盤FOF基金的投資經理徵選，入選後直接發實盤產品，還可以得到中信證券千里馬訓練營入場券，直接進入量化行業核心喲，報名地址：優礦

學習資料很多，樓主可參考。

新手入門

視頻學習帖：

優礦

新手入門手把手教學貼

量化分析師的Python日記【第1天：誰來給我講講Python？】

量化分析師的Python日記【第2天：再接著介紹一下Python唄】

量化分析師的Python日記【第3天：一大波金融Library來襲之numpy篇】

量化分析師的Python日記【第4天：一大波金融Library來襲之scipy篇】

量化分析師的Python日記【第5天：數據處理的瑞士軍刀pandas】

量化分析師的Python日記【第6天：數據處理的瑞士軍刀pandas】下篇

量化分析師的Python日記【第7天：QQuant 之初出江湖】

量化分析師的Python日記【第8天Q Quant兵器譜之函數插值】

量化分析師的Python日記【第9天Q Quant兵器譜之二叉樹】

量化分析師的Python日記【第10天 Q Quant兵器譜 -之偏微分方程1】

量化分析師的Python日記【第11天 Q Quant兵器譜之偏微分方程2】

量化分析師的Python日記【第12天：量化入門進階之葵花寶典：因子如何產生和回測】

量化分析師的Python日記【第13天 Q Quant兵器譜之偏微分方程3】

量化分析師的Python日記【第14天：如何在優礦上做Alpha對沖模型】

量化分析師的Python日記【第15天：如何在優礦上搞一個wealthfront出來】

乾貨進階

社區精華貼：優礦

社區分類貼：

首先是股票量化相關。

一 基本面分析

1.1 alpha多因子模型

• 《量化分析師的Python日記【第14天：如何在優礦上做Alpha對沖模型】》 優礦
• 《破解Alpha對沖策略——觀《量化分析師Python日記第14天》有感》 優礦
• 《熔斷不要怕，alpha model為你保駕護航！》 優礦
• 《尋找alpha之: alpha設計》 優礦

1.2 基本面因子選股

• Porfolio（現金比率+負債現金+現金保障倍數）+市盈率 優礦
• ROE選股指標 優礦
• 《成交量因子》 優礦
• 《ROICcashROIC》 優礦
• 《【國信金工】資產周轉率選股模型》
  優礦
• 《【基本面指標】Cash Cow》 優礦
• 《量化因子選股——凈利潤／營業總收入》 優礦
• 《營業收入增長率+市盈率》
  優礦

1.3 財報閱讀

• 《[米缸量化讀財報] 資產負債表-投資相關資產》

  優礦

1.4 股東分析

• 《技術分析入門 【2】 —— 大家搶籌碼（06年至12年版）》
  優礦
• 《技術分析入門 【2】 —— 大家搶籌碼（06年至12年版）— 更新版》
  優礦
• 《誰是中國A股最有錢的自然人》
  優礦

1.5 宏觀研究

• 《【乾貨包郵】手把手教你做宏觀擇時》
  優礦
• 《宏觀研究：從估值角度看當前市場》
  優礦
• 《追尋「國家隊」的足跡》
  優礦

二 套利

2.1 配對交易

• 《HS300ETF套利（上）》
  優礦
• 《【統計套利】配對交易》
  優礦
• 《相似公司股票搬磚》
  優礦
• 《Paired trading》
  優礦

2.2 期現套利

• 《通過股指期貨的期現差與ETF對沖套利》
  優礦

三 事件驅動

3.1 盈利預增

• 《盈利預增事件》
  優礦
• 《事件驅動策略示例——盈利預增》
  優礦

3.2 分析師推薦

• 《分析師的金手指？》
  優礦

3.3 牛熊轉換

• 《歷史總是相似 牛市還在延續》
  優礦
• 《歷史總是相似 牛市已經見頂？》
  優礦

3.4 熔斷機制

• 《股海拾貝之【熔斷錯殺股】》
  優礦

3.5 暴漲暴跌

• 《【實盤感悟】遇上暴跌我該怎麼做？》
  優礦

3.6 兼并重組、舉牌收購

• 《寶萬戰-大戲開幕》
  優礦

四 技術分析

4.1 布林帶

• 《布林帶交易策略》
  優礦
• 《布林帶回調系統-日內》
  優礦
• 《Conservative Bollinger Bands》
  優礦
• 《Even More Conservative Bollinger Bands》
  優礦
• 《Simple Bollinger Bands》
  優礦

4.2 均線系統

• 《技術分析入門 —— 雙均線策略》
  優礦
• 《5日線10日線交易策略》
  優礦
• 《用5日均線和10日均線進行判斷 --- 改進版》
  優礦
• 《macross》
  優礦

4.3 MACD

• 《Simple MACD》
  優礦
• 《MACD quantization trade》
  優礦
• 《MACD平滑異同移動平均線方法》
  優礦

4.4 阿隆指標

• 《技術指標阿隆（Aroon）全解析》
  優礦

4.5 CCI

• 《CCI 順勢指標探索》
  優礦

4.6 RSI

• 《重寫rsi》
  優礦
• 《RSI指標策略》
  優礦

4.7 DMI

• 《DMI指標體系的構建及簡單應用》
  優礦

4.8 EMV

• 《EMV技術指標的構建及應用》
  優礦

4.9 KDJ

• 《KDJ策略》
  優礦

4.10 CMO

• 《CMO策略模仿練習1》
  優礦
• 《CMO策略模仿練習2》
  優礦
• 《【技術指標】CMO》
  優礦

4.11 FPC

• 《FPC指標選股》
  優礦

4.12 Chaikin Volatility

• 《嘉慶離散指標測試》
  優礦
• 《嘉慶離散指標測試 （改進效率版）》
  優礦

4.13 委比

• 《實時計算委比》
  優礦

4.14 封單量

• 《按照封單跟流通股本比例排序，剔除6月上市新股，前50》
  優礦
• 《漲停股票封單統計》
  優礦
• 《實時計算漲停板股票的封單資金與總流通市值的比例》
  優礦

4.15 成交量

• 《決戰之地，IF1507！》
  優礦

4.16 K線分析

• 《尋找夜空中最亮的星》
  優礦

五 量化模型

5.1 動量模型

• 《Momentum策略》
  優礦
• 《【小散學量化】-2-動量模型的簡單實踐》
  優礦
• 《一個追漲的策略(修正版)》
  優礦
• 《動量策略（momentum driven）》
  優礦
• 《動量策略（momentum driven）——修正版》
  優礦
• 《最經典的Momentum和Contrarian在中國市場的測試》
  優礦
• 《最經典的Momentum和Contrarian在中國市場的測試-yanheven改進》
  優礦
• 《［策略］基於勝率的趨勢交易策略》
  優礦
• 《量化策略 Sharpe_Momentum (夏普率動量策略)》
  優礦
• 《策略探討（更新）：價量結合+動量反轉》
  優礦
• 《反向動量策略（reverse momentum driven）》
  優礦
• 《輕鬆跑贏大盤 - 主題Momentum策略》
  優礦
• 《Contrarian strategy》
  優礦

5.2 Joseph Piotroski 9 F-Score Value Investing Model

• 《基本面選股系統：Piotroski F-Score ranking system》
  優礦

5.3 SVR

• 《使用SVR預測股票開盤價 v1.0》
  優礦

5.4 決策樹、隨機樹

• 《決策樹模型（固定模型）》
  優礦
• 《基於Random Forest的決策策略》
  優礦

5.5 鐘擺理論

• 《鐘擺理論的簡單實現——完美躲過股災和精準抄底》
  優礦

5.6 海龜模型

• 《simple turtle》
  優礦
• 《俠之大者 一起賺錢》
  優礦

5.7 5217策略

• 《白龍馬的新手策略》
  優礦

5.8 SMIA

• 《基於歷史狀態空間相似性匹配的行業配置SMIA 模型—取交集》
  優礦

5.9 神經網路

• 《神經網路交易的訓練部分》
  優礦
• 《通過神經網路進行交易》
  優礦

5.10 PAMR

• 《PAMR： 基於均值反轉的投資組合選擇策略 - 修改版》
  優礦

5.11 Fisher Transform

• 《Using Fisher Transform Indicator》
  優礦

5.12 分型假說，Hurst指數

• 《分形市場假說，一個聽起來很美的假說》
  優礦

5.13 變點理論

• 《變點策略初步》
  優礦

5.14 Z-score Model

• 《Zscore Model Tutorial》
  優礦
• 《信用債風險模型初探之：Z-Score Model》
  優礦

5.15 機器學習

• 《Machine Learning 學習筆記（一） by OTreeWEN》
  優礦

5.16 DualTrust策略和布林強盜策略

• 《誰能夠幫忙實現DualTrust策略和布林強盜策略(BollingerBandit)？》
  優礦

5.17 卡爾曼濾波

• 《有沒有朋友懂如何用卡爾曼濾波進行金融數據分析的？》
  優礦

5.18 LPPL anti-bubble model

• 《今天大盤熔斷大跌，後市如何——based on LPPL anti-bubble model》
  優礦
• 《破解股市泡沫之謎——對數周期冪率（LPPL）模型》
  優礦

六 大數據模型

6.1 市場情緒分析

• 《通聯情緒指標策略》
  優礦
• 《互聯網+量化投資 大數據指數手把手》
  優礦

6.2 新聞熱點

• 《如何使用優礦之「新聞熱點」？》
  優礦
• 《技術分析【3】—— 眾星拱月，眾口鑠金?》
  優礦

七 排名選股系統

7.1 小市值投資法

• 《學習筆記：可模擬（小市值+便宜 的修改版）》
  優礦
• 《市值最小300指數》
  優礦
• 《市值最小300股票（篩選器版）》
  優礦
• 《流通市值最小股票（新篩選器版）》
  優礦
• 《持有市值最小的10隻股票》
  優礦
• 《10% smallest cap stock》
  優礦

7.2 羊駝策略

• 《羊駝策略》
  優礦
• 《羊駝反轉策略（修改版）》
  優礦
• 《羊駝反轉策略》
  優礦
• 《我的羊駝策略，選5隻股無腦輪替》
  優礦

7.3 低價策略

• 《專撿便宜貨(新版quartz)》
  優礦
• 《便宜就是 alpha》
  優礦

八 輪動模型

8.1 大小盤輪動

• 《新手上路 -- 二八ETF擇時輪動策略2.0》
  優礦

8.2 季節性策略

• 《Halloween Cycle》
  優礦
• 《Halloween cycle 2》
  優礦
• 《夏買電，東買煤？》
  優礦
• 《歷史的十一月板塊漲幅》
  優礦

8.3 行業輪動

• 《銀行股輪動》
  優礦
• 《申萬二級行業在最近1年、3個月、5個交易日的漲幅統計》
  優礦

8.4 主題輪動

• 《快速研究主題神器》
  優礦
• 《recommendation based on subject》
  優礦
• 《strategy7: recommendation based on theme》
  優礦
• 《板塊異動類》
  優礦
• 《風險因子（離散類）》
  優礦

8.5 龍頭輪動

• 《Competitive Securities》
  優礦
• 《Market Competitiveness》
  優礦
• 《主題龍頭類》
  優礦

九 組合投資

9.1 指數跟蹤

• 《【策略】指數跟蹤低成本建倉策略》
  優礦

9.2 GMVP

• 《Global Minimum Variance Portfolio (GMVP)》
  優礦

9.3 凸優化

• 《如何在Python中利用CVXOPT求解二次規劃問題》
  優礦

十 波動率

10.1 波動率選股

• 《風平浪靜 風起豬飛》
  優礦

10.2 波動率擇時

• 《基於VIX指數的擇時策略》
  優礦
• 《簡單低波動率指數》
  優礦

10.3 Arch/Garch模型

• 《如何使用優礦進行GARCH模型分析》
  優礦

十一 演算法交易

11.1 VWAP

• 《Value-Weighted Average Price (VWAP)》
  優礦

十二 中高頻交易

12.1 order book分析

• 《基於高頻limit order book數據的短程價格方向預測——via multi-class SVM》
  優礦

12.2 日內交易

• 《大盤日內走勢 (for擇時)》
  優礦

十三 Alternative Strategy

13.1 易經、傳統文化

• 《老黃曆診股》
  優礦

接著是基金、利率互換、固定收益類

一 分級基金

• 《「優礦」集思錄——分級基金專題》
  優礦
• 《基於期權定價的分級基金交易策略》
  優礦
• 《基於期權定價的興全合潤基金交易策略》
  優礦

二 基金分析

• 《Alpha基金「黑天鵝事件」 -- 思考以及原因》
  優礦

三 債券

• 《債券報價中的小陷阱》
  優礦

四 利率互換

• 《Swap Curve Construction》
  優礦
• 《中國Repo 7D互換的例子》
  優礦

然後是衍生品相關

一 期權數據

• 《如何獲取期權市場數據快照》
  優礦
• 《期權高頻數據準備》
  優礦

二 期權系列

• 《【50ETF期權】 1. 歷史成交持倉和PCR數據》
  優礦
• 《【50ETF期權】 2. 歷史波動率》
  優礦
• 《【50ETF期權】 3. 中國波指 iVIX》
  優礦
• 《【50ETF期權】 4. Greeks 和隱含波動率微笑》
  優礦
• 《【50ETF期權】 5. 日內即時監控 Greeks 和隱含波動率微笑》
  優礦

三 期權分析

• 《【50ETF期權】 期權擇時指數 1.0》
  優礦
• 《期權頭寸計算》
  優礦
• 《期權探秘1》
  優礦
• 《期權探秘2》
  優礦
• 《期權市場一周縱覽》
  優礦
• 《基於期權PCR指數的擇時策略》
  優礦
• 《期權每日成交額PC比例計算》
  優礦

四 期權日報

主要是@李自龍的期權擇時日報。

五 期貨分析

• 《Gifts from Santa Claus——股指期貨趨勢交易研究》
  優礦

現金獎勵活動：優礦

各種福利：優礦

---

必備：熱情，勤奮，小強精神。

加分：天賦

---

一個
優秀的 Quant 需要紮實的數學基礎和良好的編程能力，這裡的數學基礎主要思維和研究的能力，各種模型可在實踐中不斷學習，編程能力主要體現在把自己的想法或模型進行測試或實踐的能力。
金融是人在參與，
Quant需要面對的不只是數字和模型 ，只要是做投資的，我覺得都離不開對市場和參與者的了解，所以對財務知識、心理學以及行為學的研究也是很必要的。

---

在quantnet上看到的,希望對各位有幫助，侵刪。

How to Get a Quant Job, Advice from Wall Street Executives

Whether youre looking for your very first job, switching carers, or re-entering the job market after an extended absence, finding a job requires two main tasks: understanding yourself and understanding the job market. I received several emails asking me for advice regarding quant jobs and various companies that are hiring and what they are looking for specifically. I usually pass on the questions to someone I know and then reply back to them. Over the last month I have got an increased number of such emails. I decided to compile these questions and made some of my own and decided to ask some people who would be better suited to answer these questions.

Over the course of my blog I have had the pleasure to build some really good contacts. It has given me a chance to meet and talk to several Wall Street executives. I decided to send my questions to some of them to get answers to the most frequently asked questions. They are more than happy to provide guidance to members of http://quantnet.com in the condition that their names are not displayed due to their firms policy.

Can you please tell us a bit about your company and the department you work in?

• Managing Director 1 (MD 1): Market risk, major investment bank
• Managing Director 2 (MD 2): The company I work for is an International Bank involved Equities, Fixed income, FX and Commodities trading in addition to their IB activities. I work in the commodities trading division of the bank running a commodity index portfolio.
• Capital Management Firm Partner: I work in a capital management firm. The fund is a Hong Kong based corporate finance firm that provides wealth management and risk hedging advisor services. I worked in equity and derivatives trading desk before, and right now I am working in strategist department with some talents, give advice to other traders and analysts. Plan and make future trading ideas and decisions. I do most of the hiring for the equity division. We also have a relatively new and growing operation in North America with operations in Toronto, Chicago and San Francisco.
• CEO: My company creates quantitative investment strategies and sells them to high net worth, institutions, pension funds, endowments, etc.

What are the typical jobs that you interviewed candidates for over the course of your tenure as a hiring manager at your firm?

• MD 1: Desk risk management, model review, market risk methodology, market risk reporting, market risk quant analysis, head of model review, risk intern, risk analyst, administrative officer, treasury capital analyst, IT project manager
• MD 2: I generally interview candidates for various types of positions: Analyst, Trading or Quant positions. As an Analyst your job is to provide support on the desk, analysis of the markets, etc. Trading role means that you will, from the start, be involved on the trading side of the business and may be given your own book to manage risk and client flow. As a Quant you are expected to operate on a more analytical level and be able to understand the various models used for pricing the various products traded on the desk.
• Partner: Traders and analysts.
• CEO: I』ve interviewed potential researchers, programmers, strategists, and traders, over my career.

There are several MFE/MQF/MSCF/etc programs mushrooming all over the world. How do you distinguish the good from the bad and the ugly?

• MD 1: Anecdotal based upon the people I』ve seen. I had one person from a top-rated program turn out to be a real dud. That has biased me against that program. I』ve also had a great person from a poorly-ranked program who leads me to grant the benefit of the doubt. On the other had, I have found CMU and Wharton people to be consistently excellent.
• MD 2: I generally distinguish the good programs from the bad ones, when I find that the candidates coming out of the good programs have the right balance of practical and theoretical knowledge around quantitative finance. Candidates that have the analytical background and are able to quickly implement models and demonstrate their relevance to the business. I consider the 「bad programs」 the ones that just immerse candidates with enormous amount of theoretical information with very little hands-on or practical training.
• Partner: Depends on the skills of candidates, not depend on the programs.
• CEO: Honestly, I really have not focused on where they came from as long as it sounded nerdy and I』ve heard of it before.

Before, it was possible for MS, PhD in non-finance subjects like Statistics, Computer Science, etc to get quant positions. Is an MFE or an MFE type program a bar for those positions now? If there were two candidates, one MS in a non-finance subject and another with an MFE and all other credentials were at par, would the MFE have an obvious upper hand?

• MD 1: Not at all. MFE』s tend to be light in statistics, strong in programming.
• MD 2: I generally do not really have any sort of bias when it comes to considering a candidate with an analytical background, whether they have a non-finance degree or a pure MFE degree. What I generally look for is the ability to think 「outside the box」 or be able to withstand the stress of a trading environment. I also look for candidates that can potentially turn into good 「risk managers」 on the desk. I also look for a sense of passion to learn and to continue expanding their knowledge base. (Generally I find candidates with MBAs to be a little more obstinate in their adherence to looking at things from the Market Efficient Theory side).
• Partner: Actually, depends on the interview. I can not make a decision only depends on the degree but not the skill. We need some one really can do the job and get the profit. But if you compare MS or MFE, I may choose MFE, but if you choose MS from MIT or a MFE from a university and I never heard about it, I may consider about MS more than MFE. However, the final decision depends on interview results and skills, not programs.
• CEO: Doesn』t matter to me. I look for high GPAs for one. It says that the candidate took school seriously, then I look for something special about the resume, like first place in Math Olympics etc. social things like class President not as interesting to me.

What mathematics topics do you believe are essential to quantitative positions?

• MD 1: Calculus, linear algebra
• MD 2: A solid foundation in Stochastic Calculus is a must. Also , having backgrounds in areas like 「Information Theory」, 「Game Theory」, and pattern recognition or signal processing are huge pluses.
• Partner: For trading, the basic mathematics should be a given. Usually I prefer if they have a strong grasp on bond mathematics and basic financial mathematics too but most of it is just about understanding the markets.
• CEO: Minimum requirements, statistics, probability, econometrics, time-series, calculus, etc. Just the basic core stuff.

What technical skills do you believe are essential to quantitative positions?

• MD 1: SQL, inference, spreadsheet expertise, EDA, common sense
• MD 2: Knowing how to program a complex quantitative model efficiently and accurately. Sometimes I come across quants that are brilliant analytically but have very little programming skills, which means I need to hire an additional person to be the programmer. I highly recommend having several modern programming languages in your tool bag.
• Partner: C++ VBA, Modeling skills.
• CEO: Good programming skills in R, Matlab, C, or C++.

What do you believe are the top 5 credentials that you look for when interviewing a candidate for a quantitative position? Programming? Strong Mathematics? Good communication? Brand name University? Etc.

• MD 1: Communication is perhaps the most important. There are gazillion quants who can』t express themselves clearly. Having them on your staff is like having a 1 million horsepower engine that has no transmission to harness its power. Brand name school helps. It means there』s a higher probability that the person is smart. Not programming. If I want a programmer, I』ll hire one. We can train good people with all the programming skills they』ll need. For anything other than model review or model development, I don』t need a PhD in math.
• MD 2:
  1. Strong Communications Skills
  2. Strong Mathematics
  3. Strong programming skills
  4. Ability to think out-side the box type of mentality.
  5. Good interpersonal skills.Don』t really pay much attention to the brand name of the school they went to.
• Partner: Good Communication , passion, Knowledge and skills (not only in programming or mathematics, but also cover some other area), imagination ( think about use different method to find the solution) , and the most important thing is HE Really like math, programming and this job.
• CEO:
  1. High GPA, tells me they took their studies seriously,
  2. 「The Fit」, will the candidate fit in with the others in the group, or will he/she be too difficult to assimilate into corporate life,
  3. Past accomplishments and anything that took initiative,
  4. Charity work, or mentoring shows maturity and selflessness
  5. University is the last thing, although I notice that I』m kind of partial to places that I』ve been to. (but that shouldn』t really matter, as long as (1) is satisfied.)

What are the most common misconceptions of people seeking this line of work?

• MD 1: Soft skills are as important as hard skills. WE DONT CARE IF YOU HAVE A PRM/FRM/CFA THOSE REPRESENT MEANS AND NOT ENDS.People think it』s the fast track to big bucks. It』s not. It』s the fast track to mediocre bucks combined with high stress and long hours. Do this if you find it inherently interesting. Otherwise you』ll be a miserable, overworked, geek bouncing from firm to firm chasing the money.
• MD 2: That they will be moved into a trading role right off the bat. You have to earn this privilege over time.
• Partner: Hmmm there are lots of misconceptions and I am not really sure which one is most common.
• CEO: You just have to be smart. You also need some soft skills so people want to work with you. I think every hiring manager is looking for someone smarter than him/her and nicer than him/her. If you can show that you can do the most mundane tasks without complaining, and can master the most difficult tasks without getting a big head, then you will get many offers.

How important is having previous internship experience for an entry level job? How do students with no finance experience show they are worthy of the jobs too?

• MD 1: An internship helps, but isn』t crucial. Inexperienced people should do their homework. Don』t tell me you』re interested in fixed income analytics and equity research and foreign exchange modelling. Which one? Why? Prove to me you know something about the business.
• MD 2: Far more important for me is their technical and quantitative background, the business side of things can always be learned on the job. Much easier to teach a candidate about the business than to teach them about Stochastic Calculus.
• Partner: It is really important to have a internship experience for the future jobs. Students need to show their skills to connect math with real market, and actually sometimes head of quantitative department would like to hire someone without finance experience, but really like mathematics and find solutions of puzzle.
• CEO: To me, that』s not that important for junior positions, it just shows that you know how to behave in a corporate setting. If you have no experience, show me something you did. Show me a model you』ve built and what you know about back-testing, market microstructure, research design, about being creative. Show me one of your research working papers, when I read them I can tell what the candidate can and can not do. Resumes look very similar at the top-end of the spectrum.

Where do you think the largest job growth is within the quantitative finance industry ? Risk ? Structured ? Trading ? etc.

• MD 1: Risk for secular growth. Growth in trading and structuring tends to be cyclical. Marginal people often get hired here and are the first to be fired.
• MD 2: I think the largest job growth will be in Risk and in Trading. Given what』s happened in the world in the past few years, there will be more likely a movements towards better Risk management and Trading as opposed to the creation of more complex structured products.
• Partner: Risk Management.
• CEO: As regulation continues to gain steam, risk will always be a large employer of financial engineers. I personally like the trading part, creating new models and implementing them but I have found that this is not for everyone.

What is the best way for an entry level candidate to secure a job at the large investment banks? Through recruiters? Applying on the website? Campus recruitment? School Career Services?

• MD 1: Through referrals. Network.
• MD 2: The best way to secure an entry level type of position in the large investment banks is through a combination of the use of recruiters as well as campus recruitment events. Attending industry specific events are also an excellent way to meet industry experts that can provide guidance.
• Partner: Relationship and networking. Not only from campus recruitment, but also from some other places, like IAFE events, meetings, or even church. Candidate needs to show something to prove skills and know how to manage relationship. It is the best way to get the interview. Sometimes campus recruitment or school career services maybe a choice but you need to make yourself stand out the line.
• CEO: Network your alums, head-hunters can be a waste of time, but there are a few good ones. For entry-level jobs you can just Google quant-jobs and find a ton of listings, then just keep applying.

How important is networking for entry level candidates? What are some possible networking venues that you would suggest?

• MD 1: Networking is crucial. GARP, PRMIA, conferences, newsgroups, blogs. School, too. Talk to your professors.
• MD 2: Attending industry specific conferences I have always found to be excellent places to meet and network with people. These types of activities are normally used by industry participants as a means to recruit candidates. It also gives candidates a better understanding of the types of issues and problems that are being addressed in Risk Management and in Trading. From experience, I have always found these to be the best places to network.
• Partner: As I said above, it is really important not only for entry level candidate, but also for other managers, bankers, even traders. For the best venues, I am afraid I do not have any good suggestions, but if you can ask your professor to go out and have a drink, you may find the answer
• CEO: I think networking is important but probably for more senior level positions. Junior quants just entering the industry can find tons of open listing just using the internet.

Thank you for your time. I greatly appreciate it. Any last parting words that you would like to leave us with regarding securing a job at a company like yours?

• MD 1: If you put it on your resume, be ready to explain it. I』ve dinged many people for stating they knew how to do Monte Carlo simulation, but who couldn』t tell me what a random walk was (for time series MC) or what a Gaussian Copula was (for a VaR or credit risk MC).
• MD 2: Sometimes securing a job at a large IB may not be possible in a certain situation, but that does not mean that you many not take a job at a Technology company first and get an experience that help you later on to secure an even better job at an IB. It is always better to gain as much experience as possible in any type of job and work towards eventually getting the ideal job that you desire. The more experience you can get under your belt the better. From my own personal experience, before I landed the position I truly desired as a Trading Manager I went through various career changes along the way. These career changes have over the long run have helped me gain a better appreciation for managing people as well as managing risk on a trading desk. Spending time learning about the 『soft」 skills can be time well spent.
• Partner: Study, network, networking.
• CEO: Show me why you are better than everyone else that has a resume and test scores that looks just like yours. Show me one thing that makes you unique in all the world of quants. Good luck!

I hope this helped with any questions any readers had. Feel free to put questions below here and maybe I will do a Part 2 if there is enough demand. It will take a while as I do not want to pester all these busy people with questions.

#1Joy Pathak, 9/8/10

---

推薦閱讀：