EUA planeiam novas restrições sobre tecnologia de chips na China

[LoneWolf]

Olá a todos,

Obrigado pela partilha da noticia. Nao sou trader, apenas aspirante, mas ando há já muitos anos a jogar com "fake money" com resultados decentes. Naturalmente este artigo deu-me "pica"!

Obrigado tb pela partilha dos links sobre quant trading. Este tópico interessa-me imenso, mas estou como o JohnyRobaz, ainda tenho imenso para aprender.
Deixo mais alguns que coleccionei nos últimos tempos. Espero q nao sejam repetidos.

https://www.quantconnect.com/blog/

https://iknowfirst.com/2016/11

Cumps,
LoneWolf

[JohnyRobaz]

Amigo Jonhny

Para esta afirmação tenho uma resposta humilde...
Eu sei bem que os meus conhecimentos do mercado são limitados. E os conhecimentos matemáticos são de um simples mortal.
A minha ferramenta são um simples computador, uma tabela com cerca de 20 anos de histórico do SPX, com H,L,C.

Umas quantas variáveis temporais, e voilá...saem 2 indicadores fresquinhos para a mesa 4.

Uma coisa aprendi ao longo dos tempos...Eu sei qual é a minha posição no mercado. Eu não sou ninguém, a não ser um microcagésimo de investidor a que ninguém ligará com certeza. Mas é nas coisas simples do preço (H,L,C), que está a chave do meu destino no mercado.
Muitas vezes vejo gente a preocupar-se demais com o sistema, quando não tem a capacidade técnica para tal.

Nós deve-mo-nos conhecer primeiro. Até onde podemos ir. Não estou a dizer que não podemos evoluir, claro que podemos. Mas muitas vezes perde-se demasiado tempo a encontrar o rumo, e nesse caminho já se perdeu metade de uma vida e o mais natural é ir cometer erros aleatórios.

MHO.

Amigo bogos,

Sim, eu tenho noção disso tudo. No entanto, desde pequeno que me ensinaram que o saber não ocupa espaço e a minha curiosidade é grande. Não tenciono discutir aqui este tipo de abordagens ao trading que pouco conheço para usar neste sistema que estou a desenvolver, esse já está orientado. É sim para, caso aprenda o suficiente, usar na criação de outros sistemas no futuro. Podemos sempre evoluir, não devemos simplesmente ficar satisfeitos com o que já conseguimos. Pelo menos eu penso assim.

Abraço!

[bogos]

O caso do fundo do Simons é sem duvida muito particular,

Estive por curiosidade a verificar que pessoas estao a contratar e a maioria são ou fisicos ou engenheiros de rede de telecomunicações (servidores) especialistas em routers / switchs da cisco (muito rapidos)
Outra particularidade interessante é o seu DD, eles indicam que neste tempo tiveram um DD de 0.5% o que concluo que eles trabalham com latencia ou seja eles são na pratica uma empresa de HFT, ou seja detectam a anomalia ou arbitragem e o objectivo deles é chegar em primeiro lugar (só assim consigo perceber um DD de 0.5%)

Ha muitos blogues e sites que pensam que eles são um esquema em ponzi, mas se fosse assim estavam sempre a aceitar dinheiro novo (o que não está a acontecer)

A grande desvantagem ou o seu pé de aquiles talvez seja futura regulação, se nos USA começarem a taxar por operação (TOBIN) então perderão a sua mina de ouro.

Quanto a nós simples mortais penso que nao vale muito apena ir atraz dos segredos do Simons pois se ele necessita de rapidez de execução, e está quase 20 anos a nossa frente em questões de investigação de anomalias ou ineficiências de mercado

Como o VirtualGod disse e mito bem, acho que é mais interessante para nós conhecer a vida do Simons que tentar descrutrinar as suas tacticas.

PS: adoro o canal Numberphile sem duvida muito educativo e explicam a matematica de uma forma que até uma criança de 4 anos é capaz de perceber (bem...estou a exagerar um bocado )

Sem dúvida, também me parece que o que eles conseguem é aproveitar ineficiências do mercado com a sua muito maior velocidade. Devem ter também as melhores máquinas para o fazer, juntamente com essas optimizações feitas pelos melhores cérebros da área. Algo que está fora do alcance de individuais, pelo menos como eu. No entanto, a forma de abordar o trading usando a matemática e a lógica é algo que me fascina, e que tento perseguir. Acho que a intuição é muito bonita mas não me parece que seja para mim, um novato e pequeníssimo investidor.

No entanto, gostava de saber mais sobre o quantitative trading, vocês conhecem algum "essential" a nível de literatura? Ou aquilo é basicamente o que o Cem e o bogos e mesmo eu tentamos fazer, que é olhar para os dados do preço e volume e tentar criar interpretações que nos permitam prever a sua direcção futura com certo grau probabilístico através do uso da matemática e lógica para estabelecer ordens? Apenas pergunto isto porque parece-me que o quantitative trading envolve matemática mais complexa que aquela que tenho usado, e que me parece que o Cem e o bogos usam, pelo que fiquei curioso que matemática seria essa.

Cumpts.

Amigo Jonhny

Para esta afirmação tenho uma resposta humilde...
Eu sei bem que os meus conhecimentos do mercado são limitados. E os conhecimentos matemáticos são de um simples mortal.
A minha ferramenta são um simples computador, uma tabela com cerca de 20 anos de histórico do SPX, com H,L,C.

Umas quantas variáveis temporais, e voilá...saem 2 indicadores fresquinhos para a mesa 4.

Uma coisa aprendi ao longo dos tempos...Eu sei qual é a minha posição no mercado. Eu não sou ninguém, a não ser um microcagésimo de investidor a que ninguém ligará com certeza. Mas é nas coisas simples do preço (H,L,C), que está a chave do meu destino no mercado.
Muitas vezes vejo gente a preocupar-se demais com o sistema, quando não tem a capacidade técnica para tal.

Nós deve-mo-nos conhecer primeiro. Até onde podemos ir. Não estou a dizer que não podemos evoluir, claro que podemos. Mas muitas vezes perde-se demasiado tempo a encontrar o rumo, e nesse caminho já se perdeu metade de uma vida e o mais natural é ir cometer erros aleatórios.

MHO.

[danieljpires]

JonyRobaz

Conheço perfeitamente esse canal ate ha um podcast que da para ouvir no telemovel quando estamos a conduzir , mas esta muito virado para a analise tecnica e quando falam de estratégias de "algoritmos / quantitativos" e de modelização / analise de estatísticas parece que ainda estão no inicio do estagio de aprendizagem (pela forma como abordam a analise das estatisticas operacionais).

Deixo aqui um canal que ate tem uns videos interessantes

https://www.youtube.com/channel/UCOjD18 ... g4IozkF_7w

[Scientista]

Amigo Ténis:

Nesta altura creio que não, parece que o Fundo está reservado apenas para os cerca de 270 / 300 trabalhadores / sócios "sortudos", ou sábios, que ficaram obviamente milionários.

Este Fundo está num grupo de 3 fundos geridos pelo grupo Renaissance Technologies e o Medallion está nesta altura fechado a terceiros, osoutros estão disponíveis mas os resultados não chegam lá perto.

De todas as formas o Medallion destaca-se claramente dos outros devido à rentabilidade histórica estratosférica que ronda nesta altura os 35% anuais em 28 anos de existência (1 dólar investido nessa altura valeria hoje mais de 13.000 dólares).

Por curiosidade a última vez que o Medallion esteve aberto ao público em geral exigia uma quantidade mínima de investimento acima de 1 milhão de USD e cobrava duas taxas anuais: uma de 5% do total investido para management fee e outra de 44% sobre os retornos positivos!!!

Abraço.

Amigo Cem, mesmo com essas taxas altissimas, e tendo uma rendibilidade anual de 35%, o cliente consegue uma rendibilidade liquida (sem contar impostos) de 14,6%. è um valor bastante razoável...

[JohnyRobaz]

Sim, a parte da gestão de risco é outro mundo, e também já me dediquei a ela. Neste momento interessava-me mais pela parte do edge sobre o mercado, e discussão de ideias sobre isso. Percebo que quem encontra um edge, não o queira partilhar, mas as ferramentas ou as bases que se tem que adquirir para perseguir ou procurar um edge é bom partilhar e discutir. Por exemplo, sistemas que necessitem de grande velocidade de processamento e HFT a mim não me interessa tanto, por exemplo.

Logo à noite vou tentar começar a explorar este canal do youtube:

https://www.youtube.com/channel/UCdnzT5 ... TOiDsPhqcg

Abrç

[danieljpires]

Este tema é super extenso com muitos grupos e sub-grupos

Arbitragens de latencia intermercados mesmo activo ou sinteticos- o mais rapido apanha tudo (no video LS parte 4 é um classico deste sistema)
Arbitragem intermercados volume ordens- exemplo:uma grande ordem de call ATM entra num activo, o pessoal ve essa ordem e entra nas acções (Exemplo: passou na semana passada no GFI - gold field) como passou uma ordem call OTM na Vale ontem
Correlações - parte 3 do video LS da para ter uma ideia
Correlações curtas e longas entre activos
Tempo de correlações - exemplo: um activo sobe muito por algum motivo estrutural (exemplo: caida do dollar) outros irão seguir o caminho
Fractais - a probabilidade de um activo subir ou descer muito ou pouco relativo ao esforço de movimento (ex: um titulo pouco liquido com pouco capital é possivel move-lo muito com o menor esforço possivel (pouco dinheiro)

Depois temos os algoritmos detectores de padrões: detectam padrões passados (esta não consigo engolir) e quando se repete entram no activo

Algoritmos classicos: tendenciais , onde atravez de varios parametros de preço indica compra e venda (o classico dos classicos cruzamentos de medias - tambem nao engulo este )
Algoritmos rompimento (quando rompe uma zona de pivots/clusters (resistencia) compra ou se for um algoritmo contrarian vende esse rompimento

Mas nenhuma destas tecnicas é robusta o suficiente para ser rentavel a longo prazo sem um sistema de gestao de risco automatizavel e que previna o risco de falencia

Ou seja, o meu conselho é primeiro aprenderes tudo sobre gestao de risco e sistemas que diminuam a probabilidade de falencia e depois experimentar esses sistemas em conjunto (assim terias uma curva de DD ao estilo de Parrondo)

[JohnyRobaz]

Ha vários sites quantitativos que podem ser de ajuda, mas se estas a começar o youtuve é a melhor ferramenta antes diziamos se tens duvidas vai ao google sabe tudo, agora dizemos vai ao youtube ensina tudo

Aqui alguns sites que são técnicos mas dao grande ajuda

http://www.nuclearphynance.com/ - um dos melhores foruns quantitativos que conheço (modelização)

https://www.quantopian.com/ - aqui encontras modelos de toda a gente (e até codigos abertos para experimentares)

https://www.quantstart.com/ - o nome diz tudo

O mais importante para mim é saber aquilo que nao funciona, porque não funciona e quais as desvantagens dos varios sistemas que existem que "por enquanto" funcionam

O primeiro e o terceiro já conhecia, vou explorar o segundo também!
Vídeos no youtube, vou explorar também! Tens toda a razão. A questão é que a informação disponível neste momento é tanta que é preciso saber filtrá-la porque o dia só tem 24h....

De qualquer forma, mesmo nós poderíamos começar um tópico para abordar mais este assunto e discutir ideias. Até pode ser neste. Se tiveres já ideia dessas coisas que achas que já não funcionam ou reflectem um edge pequeno, partilha aí! Eu assim que arranjar tempo para me dedicar ao assunto também irei partilhar as minhas questões e opiniões aqui.

Abraço!

[danieljpires]

Ha vários sites quantitativos que podem ser de ajuda, mas se estas a começar o youtuve é a melhor ferramenta antes diziamos se tens duvidas vai ao google sabe tudo, agora dizemos vai ao youtube ensina tudo

Aqui alguns sites que são técnicos mas dao grande ajuda

http://www.nuclearphynance.com/ - um dos melhores foruns quantitativos que conheço (modelização)

https://www.quantopian.com/ - aqui encontras modelos de toda a gente (e até codigos abertos para experimentares)

https://www.quantstart.com/ - o nome diz tudo

O mais importante para mim é saber aquilo que nao funciona, porque não funciona e quais as desvantagens dos varios sistemas que existem que "por enquanto" funcionam

[JohnyRobaz]

O caso do fundo do Simons é sem duvida muito particular,

Estive por curiosidade a verificar que pessoas estao a contratar e a maioria são ou fisicos ou engenheiros de rede de telecomunicações (servidores) especialistas em routers / switchs da cisco (muito rapidos)
Outra particularidade interessante é o seu DD, eles indicam que neste tempo tiveram um DD de 0.5% o que concluo que eles trabalham com latencia ou seja eles são na pratica uma empresa de HFT, ou seja detectam a anomalia ou arbitragem e o objectivo deles é chegar em primeiro lugar (só assim consigo perceber um DD de 0.5%)

Ha muitos blogues e sites que pensam que eles são um esquema em ponzi, mas se fosse assim estavam sempre a aceitar dinheiro novo (o que não está a acontecer)

A grande desvantagem ou o seu pé de aquiles talvez seja futura regulação, se nos USA começarem a taxar por operação (TOBIN) então perderão a sua mina de ouro.

Quanto a nós simples mortais penso que nao vale muito apena ir atraz dos segredos do Simons pois se ele necessita de rapidez de execução, e está quase 20 anos a nossa frente em questões de investigação de anomalias ou ineficiências de mercado

Como o VirtualGod disse e mito bem, acho que é mais interessante para nós conhecer a vida do Simons que tentar descrutrinar as suas tacticas.

PS: adoro o canal Numberphile sem duvida muito educativo e explicam a matematica de uma forma que até uma criança de 4 anos é capaz de perceber (bem...estou a exagerar um bocado )

Sem dúvida, também me parece que o que eles conseguem é aproveitar ineficiências do mercado com a sua muito maior velocidade. Devem ter também as melhores máquinas para o fazer, juntamente com essas optimizações feitas pelos melhores cérebros da área. Algo que está fora do alcance de individuais, pelo menos como eu. No entanto, a forma de abordar o trading usando a matemática e a lógica é algo que me fascina, e que tento perseguir. Acho que a intuição é muito bonita mas não me parece que seja para mim, um novato e pequeníssimo investidor.

No entanto, gostava de saber mais sobre o quantitative trading, vocês conhecem algum "essential" a nível de literatura? Ou aquilo é basicamente o que o Cem e o bogos e mesmo eu tentamos fazer, que é olhar para os dados do preço e volume e tentar criar interpretações que nos permitam prever a sua direcção futura com certo grau probabilístico através do uso da matemática e lógica para estabelecer ordens? Apenas pergunto isto porque parece-me que o quantitative trading envolve matemática mais complexa que aquela que tenho usado, e que me parece que o Cem e o bogos usam, pelo que fiquei curioso que matemática seria essa.

Cumpts.

[danieljpires]

O caso do fundo do Simons é sem duvida muito particular,

Estive por curiosidade a verificar que pessoas estao a contratar e a maioria são ou fisicos ou engenheiros de rede de telecomunicações (servidores) especialistas em routers / switchs da cisco (muito rapidos)
Outra particularidade interessante é o seu DD, eles indicam que neste tempo tiveram um DD de 0.5% o que concluo que eles trabalham com latencia ou seja eles são na pratica uma empresa de HFT, ou seja detectam a anomalia ou arbitragem e o objectivo deles é chegar em primeiro lugar (só assim consigo perceber um DD de 0.5%)

Ha muitos blogues e sites que pensam que eles são um esquema em ponzi, mas se fosse assim estavam sempre a aceitar dinheiro novo (o que não está a acontecer)

A grande desvantagem ou o seu pé de aquiles talvez seja futura regulação, se nos USA começarem a taxar por operação (TOBIN) então perderão a sua mina de ouro.

Quanto a nós simples mortais penso que nao vale muito apena ir atraz dos segredos do Simons pois se ele necessita de rapidez de execução, e está quase 20 anos a nossa frente em questões de investigação de anomalias ou ineficiências de mercado

Como o VirtualGod disse e mito bem, acho que é mais interessante para nós conhecer a vida do Simons que tentar descrutrinar as suas tacticas.

PS: adoro o canal Numberphile sem duvida muito educativo e explicam a matematica de uma forma que até uma criança de 4 anos é capaz de perceber (bem...estou a exagerar um bocado )

[BearManBull]

Acho a história de vida do Simmons 1000 vezes mais interessante que este artigo (que tb já tinha partilhado no meu tópico).

A Billionaire Mathematician’s Life of Ferocious Curiosity

Nada disso não mete armas, nem petróleo, nem escândalos sexuais, claramente não é americano.

Em entrevista num dos meus canais de eleição no youtube.

[Cem pt]

Amigo Ténis:

Nesta altura creio que não, parece que o Fundo está reservado apenas para os cerca de 270 / 300 trabalhadores / sócios "sortudos", ou sábios, que ficaram obviamente milionários.

Este Fundo está num grupo de 3 fundos geridos pelo grupo Renaissance Technologies e o Medallion está nesta altura fechado a terceiros, osoutros estão disponíveis mas os resultados não chegam lá perto.

De todas as formas o Medallion destaca-se claramente dos outros devido à rentabilidade histórica estratosférica que ronda nesta altura os 35% anuais em 28 anos de existência (1 dólar investido nessa altura valeria hoje mais de 13.000 dólares).

Por curiosidade a última vez que o Medallion esteve aberto ao público em geral exigia uma quantidade mínima de investimento acima de 1 milhão de USD e cobrava duas taxas anuais: uma de 5% do total investido para management fee e outra de 44% sobre os retornos positivos!!!

Abraço.

[Tennis]

Alguém sabe se alguma corretora ou banco nacional negoceia o fundo medallion?

Obrigado,

Tennis

[JohnyRobaz]

Acho a história de vida do Simmons 1000 vezes mais interessante que este artigo (que tb já tinha partilhado no meu tópico).

A Billionaire Mathematician’s Life of Ferocious Curiosity

Vou ler, thanks!

[VirtuaGod]

Acho a história de vida do Simmons 1000 vezes mais interessante que este artigo (que tb já tinha partilhado no meu tópico).

A Billionaire Mathematician’s Life of Ferocious Curiosity

[JohnyRobaz]

Amigo Bogos:

Ao menos o artigo deixa no ar a ilusao que é possível ficar milionário seguindo algoritmos computarizados, portanto podemos sonhar, embora o busílis esteja em possuir um programa que resulte! Eles ali sao mais que as maes cheios de canudos universitários, assim também nao vale, mas enfim, com umas ideias meio malucas, traquinetas e camionetas pode ser que cheguemos a bom porto, sabe-se lá...







Abraço.

É a minha esperança neste mundo do trading. Qualquer um pode tentar construir a sua maquineta, como tu e o bogos exemplificam. A jogar com fundamentais e inside information, o micro investidor está sempre em desvantagem, a meu ver. Quando joga com o "data", com o preço, joga quase de igual para igual (os dados que eles arranjam podem ser de melhor qualidade, algo que eu acho estranho mas que acredito, mas a informação que esses dados transmitem não podem diferir muito).
É também a única forma de, a meu ver, deitar o psicológico fora e seguir disciplinadamente um plano previamente definido e testado.

Esse Fundo é a prova de que é assim que se tem maior probabilidade de ganhar dinheiro neste "jogo".

Entretanto encontrei este artigo, interessante para novatos como eu, acaba por confirmar a ideia que já apanhei aqui no caldeirão com vocês.

https://www.quantstart.com/articles/Beg ... ve-Trading

Eu espero no próximo ano iniciar-me nessa onda dos sistemas, vamos ver como corre.

Abraço aos dois.

[bogos]

Amigo Bogos:

Ao menos o artigo deixa no ar a ilusao que é possível ficar milionário seguindo algoritmos computarizados, portanto podemos sonhar, embora o busílis esteja em possuir um programa que resulte! Eles ali sao mais que as maes cheios de canudos universitários, assim também nao vale, mas enfim, com umas ideias meio malucas, traquinetas e camionetas pode ser que cheguemos a bom porto, sabe-se lá...







Abraço.

Não vale a pena pensar naquilo que eles desenvolveram. Esse fundo é imparável. Não está sequer ao alcance de seres mortais como eu.

Eu sempre soube qual era o meu lugar no mercado. Sou um mero curioso, paciente e hoje disciplinado.

A minha traquineta este ano está...fraca, fraquinha.

O sinal B que decidi meter para harmonizar com o Sinal A tem-se mostrado um regressor...

Por curiosidade peguei no sinal A e calculei o seu resultado.....

com o mesmo capital inicial de $22.500 e cada posição assumida em CFD's de 15, acumulando em lotes de 15, o resultado final iria em $9.950, ou seja 44% de lucro.

Mas o que me adianta isso neste momento.....zero.

Mas deixa-me confiante para 2017, pois irei rever a questão do sinal B.

Mas Cem, concerteza que não almejas os 40% anuais......e o importante é ter resultados acima de zero. Obvio que quanto maior, melhor, mas nem sempre é possivel.

Cumprimentos e abraço.

[Cem pt]

Amigo Bogos:

Ao menos o artigo deixa no ar a ilusao que é possível ficar milionário seguindo algoritmos computarizados, portanto podemos sonhar, embora o busílis esteja em possuir um programa que resulte! Eles ali sao mais que as maes cheios de canudos universitários, assim também nao vale, mas enfim, com umas ideias meio malucas, traquinetas e camionetas pode ser que cheguemos a bom porto, sabe-se lá...







Abraço.

[bogos]

Aqui está o artigo integral do fundo mais misterioso do mundo baseado em trend-following de uma carteira baseada em trading de futuros sobre commodities, será que mais algum maluco terá tido uma ideia semelhante? Se calhar sim, só que os resultados estao certamente muito longe, longíssimo deste Medallion...

Há só um detalhe no artigo que nao corresponde à realidade, o que aliás pode ser comprovado no gràfico das rentabilidades anualizadas do Fundo, que por acso consegui colocar, que mostra claramente retornos anualizados médios na zona dos 30% aos 40%, mas isso nao retira o mérito do Jim Simons e deste grupo de cientistas milionários!






Sixty miles east of Wall Street, a spit of land shaped like a whale’s tail separates Long Island Sound and Conscience Bay. The mansions here, with their long, gated driveways and million-dollar views, are part of a hamlet called Old Field. Locals have another name for these moneyed lanes: the Renaissance Riviera.

That’s because the area’s wealthiest residents, scientists all, work for the quantitative hedge fund Renaissance Technologies, based in nearby East Setauket. They are the creators and overseers of the Medallion Fund—perhaps the world’s greatest moneymaking machine. Medallion is open only to Renaissance’s roughly 300 employees, about 90 of whom are Ph.D.s, as well as a select few individuals with deep-rooted connections to the firm.

The fabled fund, known for its intense secrecy, has produced about $55 billion in profit over the last 28 years, according to data compiled by Bloomberg, making it about $10 billion more profitable than funds run by billionaires Ray Dalio and George Soros. What’s more, it did so in a shorter time and with fewer assets under management. The fund almost never loses money. Its biggest drawdown in one five-year period was half a percent.

Black_Box-CMYK-V2
Illustrator: Martin Ansin
“Renaissance is the commercial version of the Manhattan Project,” says Andrew Lo, a finance professor at MIT’s Sloan School of Management and chairman of AlphaSimplex, a quant research firm. Lo credits Jim Simons, the 78-year-old mathematician who founded Renaissance in 1982, for bringing so many scientists together. “They are the pinnacle of quant investing. No one else is even close.”

Few firms are the subject of so much fascination, rumor, or speculation. Everyone has heard of Renaissance; almost no one knows what goes on inside. (The company also operates three hedge funds, open to outside investors, that together oversee about $26 billion, although their performance is less spectacular than Medallion’s.) Apart from Simons, who retired in 2009 to focus on philanthropic causes, relatively little has been known about this small group of scientists—whose vast wealth is greater than the gross domestic product of many countries and increasingly influences U.S. politics 1 —until now. Renaissance’s owners and executives declined to comment for this story through the company’s spokesman, Jonathan Gasthalter. What follows is the product of extensive research and more than two dozen interviews with people who know them, have worked with them, or have competed against them.

This story appears in the December 2016 / January 2017 issue of Bloomberg Markets.
This story appears in the December 2016 / January 2017 issue of Bloomberg Markets.
Renaissance is unique, even among hedge funds, for the genius—and eccentricities—of its people. Peter Brown, who co-heads the firm, usually sleeps on a Murphy bed in his office. His counterpart, Robert Mercer, rarely speaks; you’re more likely to catch him whistling Yankee Doodle Dandy in meetings than to hear his voice. 2 Screaming battles seem to help a pair of identical twins, both of them Ph.D. string theorists, produce some of their best work. Employees aren’t above turf wars, either: A power grab may have once lifted a Russian scientist into a larger role within the highly profitable equity business in a new guard vs. old guard struggle.

For outsiders, the mystery of mysteries is how Medallion has managed to pump out annualized returns of almost 80 percent a year, before fees. “Even after all these years they’ve managed to fend off copycats,” says Philippe Bonnefoy, a former Medallion investor who later co-founded Eleuthera Capital, a Switzerland-based quantitative macro firm. Competitors have identified some likely reasons for the fund’s success, though. Renaissance’s computers are some of the world’s most powerful, for one. Its employees have more—and better—data. They’ve found more signals on which to base their predictions and have better models for allocating capital. They also pay close attention to the cost of trades and to how their own trading moves the markets.

But as computing power becomes ever cheaper and competitors sharpen their skills, will Medallion continue to mint money?

Quants seem like saviors to investors disappointed with how mere mortals have managed their money of late. In 2016 clients plugged $21 billion into quant hedge funds, while pulling $60 billion from those that do everything else. One noteworthy quant shop, Two Sigma, managed just $5 billion during the financial crisis and has seen assets jump to $37 billion. Even old-fashioned traders such as Paul Tudor Jones and Steve Cohen are adding to their computer scientist ranks in hopes of boosting returns.

Renaissance’s success, of course, ultimately lies with the people who built, improved upon, and maintain Medallion’s models, many of whom met at IBM in the 1980s, where they used statistical analysis to tackle daunting linguistic challenges. This is their story.

rentec-main-WEB
Simons is already well-known: math genius, professor at MIT and Harvard, recipient of the Oswald Veblen Prize in Geometry, and co-creator of the Chern-Simons theory. He was also a code breaker for the Institute for Defense Analyses, where he worked finding messages amid the noise.

The goal of quant trading is similar: to build models that find signals hidden in the noise of the markets. Often they’re just whispers, yet they’ll help predict how the price of a stock or a bond or a barrel of oil might move. The problem is complex. Price movements depend on fundamentals and flows and the sometimes irrational behavior of people who are doing the buying and selling.

Although Simons lost the IDA job after denouncing the Vietnam War in a letter to the New York Times, the connections he made through his work in cryptography helped create Renaissance and, a few years later, Medallion. Over the next decade, while chairing the math department at Stony Brook University, Simons dabbled in trading commodity futures. In 1977 he left academia for good to try his hand at managing money. 3

Initially he bought and sold commodities, making his bets based on fundamentals such as supply and demand. He found the experience gut wrenching, so he turned to his network of cryptographers and mathematicians for help looking at patterns: Elwyn Berlekamp and Leonard Baum, former colleagues from IDA, and Stony Brook professors Henry Laufer and James Ax. “Maybe there were some ways to predict prices statistically,” Simons said in a 2015 interview with Numberphile. “Gradually we built models.”

rentec-web-1-SUB
At their core, such models usually fall into one of two camps, trend-following or mean-reversion. Renaissance’s system had a foot in both. Its results were mixed at first: up 8.8 percent in 1988, its first year, and down 4.1 percent in 1989. But in 1990, after focusing exclusively on shorter-term trading, Medallion chalked up a 56 percent return, net of fees. “I was confident that the models would work better,” says Berlekamp, who returned to academia in 1991 and is now a professor emeritus at the University of California at Berkeley. “I didn’t think they would be as good as they were.”

Eventually the scientists went so far as to develop an in-house programming language for their models. Today, Medallion uses dozens of “strategies” that run together as one system. The code powering the fund includes several million lines, according to people familiar with the company. Various teams are responsible for specific areas of research, but in practice everybody can work on everything. There’s a meeting every Tuesday to hash out ideas.

In the early 1990s, big annual returns became the norm at Renaissance: 39.4 percent, 34 percent, 39.1 percent. Prospective investors clamored to get into Medallion, but the company didn’t pay them much heed—or coddle clients for that matter. Bonnefoy recalls dialing a Manhattan phone number to hear a recording of the monthly returns; Renaissance’s legal department doubled as unhelpful customer service representatives. (To this day the company’s website, rentec.com, looks like it dates from the Netscape era.) In 1993, Renaissance stopped accepting new money from outsiders. Fees were also ratcheted up—from 5 percent of assets and 20 percent of profits, to 5 percent and 44 percent. “They raised their fees to exorbitant levels and were still head and shoulders above everyone else,” says Bonnefoy, who, along with every other outsider, was finally booted from Medallion in 2005.

Encouraged by Medallion’s success, Simons by the mid-’90s was looking for more researchers. A résumé with Wall Street experience or even a finance background was a firm pass. “We hire people who have done good science,” Simons once said. The next surge of talent—much of which remains the core of the company today—came from a team of mathematicians at the IBM Thomas J. Watson Research Center in Yorktown Heights, N.Y., who were wrestling with speech recognition and machine translation.

In the early days of tackling these problems, computer scientists teamed with linguists and tried to code grammar. At IBM, a group including Mercer and Brown reasoned that the problems would be better solved using statistics and probabilities. (Their boss, Frederick Jelinek, liked to say, “Whenever I fire a linguist, the system gets better.”) According to scientists who worked at the research center then, the team fed reams of data into its computers. Documents from the Canadian Parliament, for instance, were available in both English and French, which none of the scientists spoke. (Mercer once disappeared for several months to type French verb conjugations into a computer, according to a source.) The data allowed them to write an algorithm that found the most likely match for the phrase Le chien est battu par Jean was “John does beat the dog.” A similar approach applied to speech recognition: Given auditory signal x, the speaker probably said the word y.

“Speech recognition and translation are the intersection of math and computer science,” says Ernie Chan, who worked at the research center in the mid-1990s and now runs quant firm QTS Capital Management. The scientists weren’t just working on academic problems; they were also developing theories and writing software to implement the solutions, he says. The group’s work eventually paved the way for Google Translate and Apple’s Siri.

Mercer and Brown went to IBM’s management in 1993 with a bold proposition, says a person who knows the two: Let them build models to manage a portion of the colossal company’s then-$28 billion pension fund. IBM balked, questioning what computational linguists would know about overseeing investments. But the duo’s fascination with financial markets was just beginning.

rentec-web-2
That same year, Nick Patterson, a former code breaker for British and U.S. intelligence agencies, joined Renaissance and approached acquaintances Brown and Mercer. “IBM was in serious trouble, and morale was poor, so it was something of a recruiting opportunity,” says Patterson, who worked at Renaissance until 2001 and is now a senior computational biologist researching genetics at the Broad Institute of MIT and Harvard. The two decided to join, drawn by the 50 percent pay raise. They roomed in an attic apartment in Setauket and often dined together. When the bill came, they would pull out a special calculator that could generate random numbers. Whoever produced the higher number picked up the tab.

“Renaissance was started by a couple of mathematicians,” Brown said in a 2013 conference for computational linguists. “They had no idea how to program. They’re people who learned how to program by reading computer manuals, and that’s not a particularly good way of learning.” He and Mercer had learned how to build large systems—with many people working on them simultaneously—which was a skill set they used to Renaissance’s advantage. Not that their new field was without challenges. “It’s all noise in finance,” he said.

More IBM veterans joined them on Long Island, including Stephen and Vincent Della Pietra, the string-theorist twins; Lalit Bahl, who had created algorithms to recognize human speech; Mukund Padmanabhan, whose specialty was digital-signal processing; David Magerman, a programmer; and Glen Whitney, who wrote software as a summer intern. “The takeaway from IBM was that the whole is greater than the sum of its parts,” says Chan. “They all worked together.”

The atmosphere at Renaissance was different than what they’d left behind. “We quickly learned that the financial world is different from IBM,” Brown said at the conference. “It’s ruthless. Either your models work better than the other guy’s, and you make money, or they don’t, and you go broke. That kind of pressure really focuses one’s attention.”

Renaissance also spent heavily collecting, sorting, and cleaning data, as well as making it accessible to its researchers. “If you have an idea, you want to test it quickly. And if you have to get the data in shape, it slows down the process tremendously,” says Patterson.

Cerebral challenges weren’t the only incentive for Renaissance’s data-hungry scientists. They also enjoyed something more intangible: a sense of family.

James Simons, Renaissance Technologies Corp, chairman and president
James Simons, Renaissance Technologies Corp, chairman and presidentPhotographer: Jin Lee/Bloomberg
Simons was the benevolent father figure. No other Renaissance senior executive has possessed his people skills, those who know him and the company say, and he inspired the supernerds to stick together. “It’s an open atmosphere,” Simons said in a speech at MIT in 2010. “We make sure everyone knows what everyone else is doing, the sooner the better. That’s what stimulates people.”

When the IBM crew arrived at Renaissance, Medallion was already producing annual returns, after fees, of at least 30 percent almost exclusively from futures trading. In the early days, anomalies were easy to spot and exploit. A Renaissance scientist noted that Standard & Poor’s options and futures closing times were 15 minutes apart, a detail he turned into a profit engine for a time, one former investor says. The system was full of such aberrations, he says, and the scientists researched each of them to death. Adding them all up produced serious money—millions at first, and before long, billions.

But as financial sophistication grew and more quants plied their craft at decoding markets, the inefficiencies began disappearing. When Mercer and Brown joined they were assigned to different research areas, but it soon became apparent they were better together than apart. They fed off each other: Brown was the optimist, and Mercer the skeptic. “Peter is very creative with a lot of ideas, and Bob says, ‘I think we need to think hard about that,’ ” says Patterson. They took charge of the equities group, which people say was losing money. “It took them four years to get the system working,” says Patterson. “Jim was very patient.” The investment paid off. Today the equities group accounts for the majority of Medallion’s profits, primarily using derivatives and leverage of four to five times its capital, according to documents filed with the U.S. Department of Labor. 4

rentec-web-3
“You need to build a system that is layered and layered,” Simons said in a 2000 interview with Institutional Investor, explaining some of the philosophy behind the firm and the Medallion model. “And with each new idea, you have to determine: Is this really new, or is this somehow embedded in what we’ve done already?” Once that’s determined, the team would figure out how much weighting to give it. Signals may eventually go cold over time but will usually be kept around because they can sometimes reemerge—or have unintended consequences if removed. A source says positions are held anywhere from seconds to seasons.

At the 2013 conference, Brown referenced an example they once shared with outside Medallion investors: By studying cloud cover data, they found a correlation between sunny days and rising markets from New York to Tokyo. “It turns out that when it’s cloudy in Paris, the French market is less likely to go up than when it’s sunny in Paris,” he said. It wasn’t a big moneymaker, though, because it was true only slightly more than 50 percent of the time. Brown continued: “The point is that, if there were signals that made a lot of sense that were very strong, they would have long ago been traded out. ... What we do is look for lots and lots, and we have, I don’t know, like 90 Ph.D.s in math and physics, who just sit there looking for these signals all day long. We have 10,000 processors in there that are constantly grinding away looking for signals.”

In addition to language specialists, astrophysicists have historically had an outsize impact on the system’s success, according to people familiar with the firm. These scientists excel at screening “noisy” data. String theorists have also had a major role, and the Della Pietra brothers—who reunited with their former IBM bosses to work on equities—were the first of many with that background. The identical twins, now 56, have never strayed far from each other: They took an honors science program at Columbia University as high school students; attended Princeton as undergraduates, studying physics; and received doctorates from Harvard in 1986.

“They always sat next to each other,” says Steven Strogatz, a math professor at Cornell University who remembers them as Princeton freshmen in a junior-year-level abstract algebra class. “Their talking involved a lot of arguing. It was passionate mathematical discussion, and they were always correcting the teacher or explaining something to each other.” Chan, who worked with them at IBM, remembers them screaming at each other—but never at anyone else, to whom they were kind and humble. Their twinship added another dimension, too. “They are almost telepathic,” he says.

At Renaissance, the Della Pietras have shared adjacent offices separated by an internal window to facilitate discussion. “They are creative people and very competitive with each other,” says Patterson, to whom they reported for a time.

The IBM crew focused on improving the system’s performance and efficiency. Since Renaissance’s models were short-term oriented, they spent time looking at execution costs and researching how their trades moved the markets—a particularly difficult problem to crack, according to other quants. They also ensured that the trades and profits matched what the system had intended, since a bad price or other glitch could throw off the whole operation.

rentec-web-4
Return sources: Labor Department, More Money Than God by Sebastian Mallaby, Investing in the Modern Age by Rachel and William Ziemba, Internal company documents, Bloomberg reporting
How much money an employee has in Medallion depends on his overall contribution to the firm—and collaboration is key to getting a bigger piece of the pie. Employees are awarded an allocation of shares they can buy. In addition, a quarter of one’s pay is deferred and invested in Medallion, where it stays for four years. Employees must also pay fees of as much as “5 and 44.”

Simons determined, almost from the beginning, that the fund’s overall size can affect performance: Too much money destroys returns. Renaissance currently caps Medallion’s assets between $9 billion and $10 billion, about twice what it was a decade ago. Profits get distributed every six months.

Thanks to Medallion, Simons—who still owns as much as 50 percent of the firm—has a net worth of $15.5 billion, according to estimates by the Bloomberg Billionaires Index. Laufer, who owns the next-largest stake (possibly as much as 25 percent), Brown, and Mercer are among other employees worth hundreds of millions of dollars.

In some ways, money, not unlike the company’s familial feel, even binds the place together. With the exception of the scientists who depart for academia or to pursue philanthropy, folks don’t leave Renaissance. Why would they? The problems are complex, the colleagues first-rate, and the paychecks huge.

As everyone became rich off Medallion, lifestyles changed. Trains to Manhattan gave way to helicopter commutes. Scientists swapped Hondas for Porsches. Fancy hobbies became normal. Simons’s cousin, Robert Lourie, who heads futures research, built an equestrian arena for his daughter, with arches so large that a bridge into New York City had to be shut down at night to facilitate their journey. Yachts also became a thing. Mercer has commissioned a succession of them, each called Sea Owl. For his part, Simons’s 222-foot Archimedes has a wood-burning fireplace. Both vessels have a propulsion system so novel that they don’t require an anchor. Always the merry ringleader, Simons planned company trips—to Bermuda, the Dominican Republic, Florida, Vermont—and encouraged employees to bring their families. Company lore is that on one of the firm’s ski trips, Simons, a longtime smoker, bought an insurance policy for a restaurant so he wouldn’t have to forgo his beloved Merits.

Money has also threatened to destroy the family atmosphere. In 2001, Renaissance hired a Russian scientist who, like many of his peers, came west after the collapse of the Soviet Union: Alexander Belopolsky. Patterson was against bringing him aboard, he says, because he had recently worked on Wall Street, where he had job-hopped. His fears proved prescient. In 2003 he and another Russian, Pavel Volfbeyn, announced they were leaving for hedge fund Millennium Partners, where they’d negotiated healthy bonuses and the right to keep a large part of their own profits. Renaissance sued them and Millennium, worried the researchers would take the firm’s secrets with them. All parties later settled out of court.

Around that time another of Renaissance’s Russian-born researchers, Alexey Kononenko, who received his Ph.D. from Penn State in 1997 and had also done a brief stint on Wall Street, was promoted within the equities group. Senior staffers ended up discussing Kononenko’s advancement during one of their regular dinners at Simons’s house. One person familiar with the situation says the scientists were just questioning why he had moved ahead of colleagues who had been there much longer, much the way an academic might complain about a younger colleague getting tenure. Other people with knowledge of the firm say Kononenko’s promotion was a significant event in Renaissance’s history and that the Russian had actually executed a power play.

Whatever the reasons for Kononenko’s advancement, the outcome has safeguarded the well from which Renaissance’s wealth flows: Medallion has averaged more than a 40 percent return, after fees, since the dinner.

When rivals and former investors are asked how Renaissance can continue to make such mind-blowing returns, the response is unanimous: They run faster than anyone else. Yet all that running hasn’t always kept them on their feet when everyone else stumbled.

In August 2007, rising mortgage defaults sent several of the largest quant hedge funds, including a $30 billion giant run by Goldman Sachs, into a tailspin. Managers at these firms were forced to cut positions, worsening the carnage. Insiders say the rout cost Medallion almost $1 billion—around one-fifth of the fund—in a matter of days. Renaissance executives, wary that continued chaos would wipe out their own fund, braced to turn down their own risk dial and begin selling positions. They were on the verge of capitulating when the market rebounded; over the remainder of the year, Medallion made up the losses and more, ending 2007 with an 85.9 percent gain. The Renaissance executives had learned an important lesson: Don’t mess with the models.

Another lesson may one day prove even more important: Beware of the damage others can cause. In a letter that same month to investors in his public institutional equities fund, Simons wrote: “While we believe we have an excellent set of predictive signals, some of these are undoubtedly shared by a number of long/short hedge funds.”

No system lasts forever, say quants. They ask how long Medallion’s magic can continue. But seven years after Simons’s retirement, the fund’s money-printing ways persist. Even in the first half of 2016, while many hedge funds struggled, it made more than 20 percent. Wealth and influence at Renaissance have grown apace.

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Yet as successful as Renaissance has been under Brown and Mercer—who are 61 and 70, respectively—industry insiders wonder how the firm will handle its next succession. They also reserve their reverence. Take, for instance, the anecdote from an invite-only conference earlier this year. An audience member asked a panel of quant managers, “Who would be your dream hire?” After a bit of nervous laughter, one of them gave his honest answer: Jim Simons.


Katherine Burton covers hedge funds for Bloomberg in New York. With Pamela Roux and Zachary R. Mider

Medallion Fund Returns.png

Caro amigo Cem...

30% a 40% ?
Temos hipóteses...

E até te digo mais.... para dificultar a coisa, até vou lá de modo contrarien....

De facto essa malta cheira e domina a matemática e estatística, conjugada com volatilidade e sentimento...
Isso é muita fruta para a minha camioneta. Mas deixa-me ao menos, na minha simplicidade de ver o trading, algo muito tranquilo.

Estou convencido, que sem tanta panóplia de factores, é possível fazer ali uns 10% ao ano.
Mas tenho que demonstrar, o resto é conversa.

Cumprimentos

[Cem pt]

Aqui está o artigo integral do fundo mais misterioso do mundo baseado em trend-following de uma carteira baseada em trading de futuros sobre commodities, será que mais algum maluco terá tido uma ideia semelhante? Se calhar sim, só que os resultados estao certamente muito longe, longíssimo deste Medallion...

Há só um detalhe no artigo que nao corresponde à realidade, o que aliás pode ser comprovado no gràfico das rentabilidades anualizadas do Fundo, que por acso consegui colocar, que mostra claramente retornos anualizados médios na zona dos 30% aos 40%, mas isso nao retira o mérito do Jim Simons e deste grupo de cientistas milionários!






Sixty miles east of Wall Street, a spit of land shaped like a whale’s tail separates Long Island Sound and Conscience Bay. The mansions here, with their long, gated driveways and million-dollar views, are part of a hamlet called Old Field. Locals have another name for these moneyed lanes: the Renaissance Riviera.

That’s because the area’s wealthiest residents, scientists all, work for the quantitative hedge fund Renaissance Technologies, based in nearby East Setauket. They are the creators and overseers of the Medallion Fund—perhaps the world’s greatest moneymaking machine. Medallion is open only to Renaissance’s roughly 300 employees, about 90 of whom are Ph.D.s, as well as a select few individuals with deep-rooted connections to the firm.

The fabled fund, known for its intense secrecy, has produced about $55 billion in profit over the last 28 years, according to data compiled by Bloomberg, making it about $10 billion more profitable than funds run by billionaires Ray Dalio and George Soros. What’s more, it did so in a shorter time and with fewer assets under management. The fund almost never loses money. Its biggest drawdown in one five-year period was half a percent.

Black_Box-CMYK-V2
Illustrator: Martin Ansin
“Renaissance is the commercial version of the Manhattan Project,” says Andrew Lo, a finance professor at MIT’s Sloan School of Management and chairman of AlphaSimplex, a quant research firm. Lo credits Jim Simons, the 78-year-old mathematician who founded Renaissance in 1982, for bringing so many scientists together. “They are the pinnacle of quant investing. No one else is even close.”

Few firms are the subject of so much fascination, rumor, or speculation. Everyone has heard of Renaissance; almost no one knows what goes on inside. (The company also operates three hedge funds, open to outside investors, that together oversee about $26 billion, although their performance is less spectacular than Medallion’s.) Apart from Simons, who retired in 2009 to focus on philanthropic causes, relatively little has been known about this small group of scientists—whose vast wealth is greater than the gross domestic product of many countries and increasingly influences U.S. politics 1 —until now. Renaissance’s owners and executives declined to comment for this story through the company’s spokesman, Jonathan Gasthalter. What follows is the product of extensive research and more than two dozen interviews with people who know them, have worked with them, or have competed against them.

This story appears in the December 2016 / January 2017 issue of Bloomberg Markets.
This story appears in the December 2016 / January 2017 issue of Bloomberg Markets.
Renaissance is unique, even among hedge funds, for the genius—and eccentricities—of its people. Peter Brown, who co-heads the firm, usually sleeps on a Murphy bed in his office. His counterpart, Robert Mercer, rarely speaks; you’re more likely to catch him whistling Yankee Doodle Dandy in meetings than to hear his voice. 2 Screaming battles seem to help a pair of identical twins, both of them Ph.D. string theorists, produce some of their best work. Employees aren’t above turf wars, either: A power grab may have once lifted a Russian scientist into a larger role within the highly profitable equity business in a new guard vs. old guard struggle.

For outsiders, the mystery of mysteries is how Medallion has managed to pump out annualized returns of almost 80 percent a year, before fees. “Even after all these years they’ve managed to fend off copycats,” says Philippe Bonnefoy, a former Medallion investor who later co-founded Eleuthera Capital, a Switzerland-based quantitative macro firm. Competitors have identified some likely reasons for the fund’s success, though. Renaissance’s computers are some of the world’s most powerful, for one. Its employees have more—and better—data. They’ve found more signals on which to base their predictions and have better models for allocating capital. They also pay close attention to the cost of trades and to how their own trading moves the markets.

But as computing power becomes ever cheaper and competitors sharpen their skills, will Medallion continue to mint money?

Quants seem like saviors to investors disappointed with how mere mortals have managed their money of late. In 2016 clients plugged $21 billion into quant hedge funds, while pulling $60 billion from those that do everything else. One noteworthy quant shop, Two Sigma, managed just $5 billion during the financial crisis and has seen assets jump to $37 billion. Even old-fashioned traders such as Paul Tudor Jones and Steve Cohen are adding to their computer scientist ranks in hopes of boosting returns.

Renaissance’s success, of course, ultimately lies with the people who built, improved upon, and maintain Medallion’s models, many of whom met at IBM in the 1980s, where they used statistical analysis to tackle daunting linguistic challenges. This is their story.

rentec-main-WEB
Simons is already well-known: math genius, professor at MIT and Harvard, recipient of the Oswald Veblen Prize in Geometry, and co-creator of the Chern-Simons theory. He was also a code breaker for the Institute for Defense Analyses, where he worked finding messages amid the noise.

The goal of quant trading is similar: to build models that find signals hidden in the noise of the markets. Often they’re just whispers, yet they’ll help predict how the price of a stock or a bond or a barrel of oil might move. The problem is complex. Price movements depend on fundamentals and flows and the sometimes irrational behavior of people who are doing the buying and selling.

Although Simons lost the IDA job after denouncing the Vietnam War in a letter to the New York Times, the connections he made through his work in cryptography helped create Renaissance and, a few years later, Medallion. Over the next decade, while chairing the math department at Stony Brook University, Simons dabbled in trading commodity futures. In 1977 he left academia for good to try his hand at managing money. 3

Initially he bought and sold commodities, making his bets based on fundamentals such as supply and demand. He found the experience gut wrenching, so he turned to his network of cryptographers and mathematicians for help looking at patterns: Elwyn Berlekamp and Leonard Baum, former colleagues from IDA, and Stony Brook professors Henry Laufer and James Ax. “Maybe there were some ways to predict prices statistically,” Simons said in a 2015 interview with Numberphile. “Gradually we built models.”

rentec-web-1-SUB
At their core, such models usually fall into one of two camps, trend-following or mean-reversion. Renaissance’s system had a foot in both. Its results were mixed at first: up 8.8 percent in 1988, its first year, and down 4.1 percent in 1989. But in 1990, after focusing exclusively on shorter-term trading, Medallion chalked up a 56 percent return, net of fees. “I was confident that the models would work better,” says Berlekamp, who returned to academia in 1991 and is now a professor emeritus at the University of California at Berkeley. “I didn’t think they would be as good as they were.”

Eventually the scientists went so far as to develop an in-house programming language for their models. Today, Medallion uses dozens of “strategies” that run together as one system. The code powering the fund includes several million lines, according to people familiar with the company. Various teams are responsible for specific areas of research, but in practice everybody can work on everything. There’s a meeting every Tuesday to hash out ideas.

In the early 1990s, big annual returns became the norm at Renaissance: 39.4 percent, 34 percent, 39.1 percent. Prospective investors clamored to get into Medallion, but the company didn’t pay them much heed—or coddle clients for that matter. Bonnefoy recalls dialing a Manhattan phone number to hear a recording of the monthly returns; Renaissance’s legal department doubled as unhelpful customer service representatives. (To this day the company’s website, rentec.com, looks like it dates from the Netscape era.) In 1993, Renaissance stopped accepting new money from outsiders. Fees were also ratcheted up—from 5 percent of assets and 20 percent of profits, to 5 percent and 44 percent. “They raised their fees to exorbitant levels and were still head and shoulders above everyone else,” says Bonnefoy, who, along with every other outsider, was finally booted from Medallion in 2005.

Encouraged by Medallion’s success, Simons by the mid-’90s was looking for more researchers. A résumé with Wall Street experience or even a finance background was a firm pass. “We hire people who have done good science,” Simons once said. The next surge of talent—much of which remains the core of the company today—came from a team of mathematicians at the IBM Thomas J. Watson Research Center in Yorktown Heights, N.Y., who were wrestling with speech recognition and machine translation.

In the early days of tackling these problems, computer scientists teamed with linguists and tried to code grammar. At IBM, a group including Mercer and Brown reasoned that the problems would be better solved using statistics and probabilities. (Their boss, Frederick Jelinek, liked to say, “Whenever I fire a linguist, the system gets better.”) According to scientists who worked at the research center then, the team fed reams of data into its computers. Documents from the Canadian Parliament, for instance, were available in both English and French, which none of the scientists spoke. (Mercer once disappeared for several months to type French verb conjugations into a computer, according to a source.) The data allowed them to write an algorithm that found the most likely match for the phrase Le chien est battu par Jean was “John does beat the dog.” A similar approach applied to speech recognition: Given auditory signal x, the speaker probably said the word y.

“Speech recognition and translation are the intersection of math and computer science,” says Ernie Chan, who worked at the research center in the mid-1990s and now runs quant firm QTS Capital Management. The scientists weren’t just working on academic problems; they were also developing theories and writing software to implement the solutions, he says. The group’s work eventually paved the way for Google Translate and Apple’s Siri.

Mercer and Brown went to IBM’s management in 1993 with a bold proposition, says a person who knows the two: Let them build models to manage a portion of the colossal company’s then-$28 billion pension fund. IBM balked, questioning what computational linguists would know about overseeing investments. But the duo’s fascination with financial markets was just beginning.

rentec-web-2
That same year, Nick Patterson, a former code breaker for British and U.S. intelligence agencies, joined Renaissance and approached acquaintances Brown and Mercer. “IBM was in serious trouble, and morale was poor, so it was something of a recruiting opportunity,” says Patterson, who worked at Renaissance until 2001 and is now a senior computational biologist researching genetics at the Broad Institute of MIT and Harvard. The two decided to join, drawn by the 50 percent pay raise. They roomed in an attic apartment in Setauket and often dined together. When the bill came, they would pull out a special calculator that could generate random numbers. Whoever produced the higher number picked up the tab.

“Renaissance was started by a couple of mathematicians,” Brown said in a 2013 conference for computational linguists. “They had no idea how to program. They’re people who learned how to program by reading computer manuals, and that’s not a particularly good way of learning.” He and Mercer had learned how to build large systems—with many people working on them simultaneously—which was a skill set they used to Renaissance’s advantage. Not that their new field was without challenges. “It’s all noise in finance,” he said.

More IBM veterans joined them on Long Island, including Stephen and Vincent Della Pietra, the string-theorist twins; Lalit Bahl, who had created algorithms to recognize human speech; Mukund Padmanabhan, whose specialty was digital-signal processing; David Magerman, a programmer; and Glen Whitney, who wrote software as a summer intern. “The takeaway from IBM was that the whole is greater than the sum of its parts,” says Chan. “They all worked together.”

The atmosphere at Renaissance was different than what they’d left behind. “We quickly learned that the financial world is different from IBM,” Brown said at the conference. “It’s ruthless. Either your models work better than the other guy’s, and you make money, or they don’t, and you go broke. That kind of pressure really focuses one’s attention.”

Renaissance also spent heavily collecting, sorting, and cleaning data, as well as making it accessible to its researchers. “If you have an idea, you want to test it quickly. And if you have to get the data in shape, it slows down the process tremendously,” says Patterson.

Cerebral challenges weren’t the only incentive for Renaissance’s data-hungry scientists. They also enjoyed something more intangible: a sense of family.

James Simons, Renaissance Technologies Corp, chairman and president
James Simons, Renaissance Technologies Corp, chairman and presidentPhotographer: Jin Lee/Bloomberg
Simons was the benevolent father figure. No other Renaissance senior executive has possessed his people skills, those who know him and the company say, and he inspired the supernerds to stick together. “It’s an open atmosphere,” Simons said in a speech at MIT in 2010. “We make sure everyone knows what everyone else is doing, the sooner the better. That’s what stimulates people.”

When the IBM crew arrived at Renaissance, Medallion was already producing annual returns, after fees, of at least 30 percent almost exclusively from futures trading. In the early days, anomalies were easy to spot and exploit. A Renaissance scientist noted that Standard & Poor’s options and futures closing times were 15 minutes apart, a detail he turned into a profit engine for a time, one former investor says. The system was full of such aberrations, he says, and the scientists researched each of them to death. Adding them all up produced serious money—millions at first, and before long, billions.

But as financial sophistication grew and more quants plied their craft at decoding markets, the inefficiencies began disappearing. When Mercer and Brown joined they were assigned to different research areas, but it soon became apparent they were better together than apart. They fed off each other: Brown was the optimist, and Mercer the skeptic. “Peter is very creative with a lot of ideas, and Bob says, ‘I think we need to think hard about that,’ ” says Patterson. They took charge of the equities group, which people say was losing money. “It took them four years to get the system working,” says Patterson. “Jim was very patient.” The investment paid off. Today the equities group accounts for the majority of Medallion’s profits, primarily using derivatives and leverage of four to five times its capital, according to documents filed with the U.S. Department of Labor. 4

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“You need to build a system that is layered and layered,” Simons said in a 2000 interview with Institutional Investor, explaining some of the philosophy behind the firm and the Medallion model. “And with each new idea, you have to determine: Is this really new, or is this somehow embedded in what we’ve done already?” Once that’s determined, the team would figure out how much weighting to give it. Signals may eventually go cold over time but will usually be kept around because they can sometimes reemerge—or have unintended consequences if removed. A source says positions are held anywhere from seconds to seasons.

At the 2013 conference, Brown referenced an example they once shared with outside Medallion investors: By studying cloud cover data, they found a correlation between sunny days and rising markets from New York to Tokyo. “It turns out that when it’s cloudy in Paris, the French market is less likely to go up than when it’s sunny in Paris,” he said. It wasn’t a big moneymaker, though, because it was true only slightly more than 50 percent of the time. Brown continued: “The point is that, if there were signals that made a lot of sense that were very strong, they would have long ago been traded out. ... What we do is look for lots and lots, and we have, I don’t know, like 90 Ph.D.s in math and physics, who just sit there looking for these signals all day long. We have 10,000 processors in there that are constantly grinding away looking for signals.”

In addition to language specialists, astrophysicists have historically had an outsize impact on the system’s success, according to people familiar with the firm. These scientists excel at screening “noisy” data. String theorists have also had a major role, and the Della Pietra brothers—who reunited with their former IBM bosses to work on equities—were the first of many with that background. The identical twins, now 56, have never strayed far from each other: They took an honors science program at Columbia University as high school students; attended Princeton as undergraduates, studying physics; and received doctorates from Harvard in 1986.

“They always sat next to each other,” says Steven Strogatz, a math professor at Cornell University who remembers them as Princeton freshmen in a junior-year-level abstract algebra class. “Their talking involved a lot of arguing. It was passionate mathematical discussion, and they were always correcting the teacher or explaining something to each other.” Chan, who worked with them at IBM, remembers them screaming at each other—but never at anyone else, to whom they were kind and humble. Their twinship added another dimension, too. “They are almost telepathic,” he says.

At Renaissance, the Della Pietras have shared adjacent offices separated by an internal window to facilitate discussion. “They are creative people and very competitive with each other,” says Patterson, to whom they reported for a time.

The IBM crew focused on improving the system’s performance and efficiency. Since Renaissance’s models were short-term oriented, they spent time looking at execution costs and researching how their trades moved the markets—a particularly difficult problem to crack, according to other quants. They also ensured that the trades and profits matched what the system had intended, since a bad price or other glitch could throw off the whole operation.

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Return sources: Labor Department, More Money Than God by Sebastian Mallaby, Investing in the Modern Age by Rachel and William Ziemba, Internal company documents, Bloomberg reporting
How much money an employee has in Medallion depends on his overall contribution to the firm—and collaboration is key to getting a bigger piece of the pie. Employees are awarded an allocation of shares they can buy. In addition, a quarter of one’s pay is deferred and invested in Medallion, where it stays for four years. Employees must also pay fees of as much as “5 and 44.”

Simons determined, almost from the beginning, that the fund’s overall size can affect performance: Too much money destroys returns. Renaissance currently caps Medallion’s assets between $9 billion and $10 billion, about twice what it was a decade ago. Profits get distributed every six months.

Thanks to Medallion, Simons—who still owns as much as 50 percent of the firm—has a net worth of $15.5 billion, according to estimates by the Bloomberg Billionaires Index. Laufer, who owns the next-largest stake (possibly as much as 25 percent), Brown, and Mercer are among other employees worth hundreds of millions of dollars.

In some ways, money, not unlike the company’s familial feel, even binds the place together. With the exception of the scientists who depart for academia or to pursue philanthropy, folks don’t leave Renaissance. Why would they? The problems are complex, the colleagues first-rate, and the paychecks huge.

As everyone became rich off Medallion, lifestyles changed. Trains to Manhattan gave way to helicopter commutes. Scientists swapped Hondas for Porsches. Fancy hobbies became normal. Simons’s cousin, Robert Lourie, who heads futures research, built an equestrian arena for his daughter, with arches so large that a bridge into New York City had to be shut down at night to facilitate their journey. Yachts also became a thing. Mercer has commissioned a succession of them, each called Sea Owl. For his part, Simons’s 222-foot Archimedes has a wood-burning fireplace. Both vessels have a propulsion system so novel that they don’t require an anchor. Always the merry ringleader, Simons planned company trips—to Bermuda, the Dominican Republic, Florida, Vermont—and encouraged employees to bring their families. Company lore is that on one of the firm’s ski trips, Simons, a longtime smoker, bought an insurance policy for a restaurant so he wouldn’t have to forgo his beloved Merits.

Money has also threatened to destroy the family atmosphere. In 2001, Renaissance hired a Russian scientist who, like many of his peers, came west after the collapse of the Soviet Union: Alexander Belopolsky. Patterson was against bringing him aboard, he says, because he had recently worked on Wall Street, where he had job-hopped. His fears proved prescient. In 2003 he and another Russian, Pavel Volfbeyn, announced they were leaving for hedge fund Millennium Partners, where they’d negotiated healthy bonuses and the right to keep a large part of their own profits. Renaissance sued them and Millennium, worried the researchers would take the firm’s secrets with them. All parties later settled out of court.

Around that time another of Renaissance’s Russian-born researchers, Alexey Kononenko, who received his Ph.D. from Penn State in 1997 and had also done a brief stint on Wall Street, was promoted within the equities group. Senior staffers ended up discussing Kononenko’s advancement during one of their regular dinners at Simons’s house. One person familiar with the situation says the scientists were just questioning why he had moved ahead of colleagues who had been there much longer, much the way an academic might complain about a younger colleague getting tenure. Other people with knowledge of the firm say Kononenko’s promotion was a significant event in Renaissance’s history and that the Russian had actually executed a power play.

Whatever the reasons for Kononenko’s advancement, the outcome has safeguarded the well from which Renaissance’s wealth flows: Medallion has averaged more than a 40 percent return, after fees, since the dinner.

When rivals and former investors are asked how Renaissance can continue to make such mind-blowing returns, the response is unanimous: They run faster than anyone else. Yet all that running hasn’t always kept them on their feet when everyone else stumbled.

In August 2007, rising mortgage defaults sent several of the largest quant hedge funds, including a $30 billion giant run by Goldman Sachs, into a tailspin. Managers at these firms were forced to cut positions, worsening the carnage. Insiders say the rout cost Medallion almost $1 billion—around one-fifth of the fund—in a matter of days. Renaissance executives, wary that continued chaos would wipe out their own fund, braced to turn down their own risk dial and begin selling positions. They were on the verge of capitulating when the market rebounded; over the remainder of the year, Medallion made up the losses and more, ending 2007 with an 85.9 percent gain. The Renaissance executives had learned an important lesson: Don’t mess with the models.

Another lesson may one day prove even more important: Beware of the damage others can cause. In a letter that same month to investors in his public institutional equities fund, Simons wrote: “While we believe we have an excellent set of predictive signals, some of these are undoubtedly shared by a number of long/short hedge funds.”

No system lasts forever, say quants. They ask how long Medallion’s magic can continue. But seven years after Simons’s retirement, the fund’s money-printing ways persist. Even in the first half of 2016, while many hedge funds struggled, it made more than 20 percent. Wealth and influence at Renaissance have grown apace.

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Yet as successful as Renaissance has been under Brown and Mercer—who are 61 and 70, respectively—industry insiders wonder how the firm will handle its next succession. They also reserve their reverence. Take, for instance, the anecdote from an invite-only conference earlier this year. An audience member asked a panel of quant managers, “Who would be your dream hire?” After a bit of nervous laughter, one of them gave his honest answer: Jim Simons.


Katherine Burton covers hedge funds for Bloomberg in New York. With Pamela Roux and Zachary R. Mider

Rentabilidades anualizadas do Fundo Medallion