Phillip K. Dick se preguntaba si soñaban los androides con ovejas eléctricas. Charles Sizemore nos alerta que los algos sueñan con Hollywood.
Posts Tagged ‘algorithmic trading
31
May
11
En defensa del value investing
…Ese es nombre de un articulo escrito por Donald MacKenzie. En el cual explora la transición hacia el trading electronico, algoritmico y de alta frecuencia. Explica muy detalladamente el Flash Crash. Y referencia varios algoritmos utilizados para hacer hacer plata (VWAP, spoofing). El unico pecado del texto: su longitud.
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The trigger was indeed an algorithm, but not one of the sophisticated ultra-fast high-frequency trading programs. It was a simple ‘volume participation’ algorithm, and while the official investigation does not name the firm that deployed it, market participants seem convinced that it was the Kansas City investment managers Waddell & Reed. The firm’s goal was to protect the value of a large position in the stock market against further declines, and it did this by programming the algorithm to sell 75,000 index future contracts. (These contracts track the S&P 500 stock-market index, and each contract was equivalent to shares worth a total of around $55,000. The seller of index futures makes money if the underlying index falls; the buyer gains if it rises.) The volume participation algorithm calculated the number of index futures contracts that had been traded over the previous minute, sold 9 per cent of that volume, and kept going until the full 75,000 had been sold. The total sell order, worth around $4.1 billion, was unusually large, though not unprecedented: the SEC/CFTC investigators found two efforts in the previous year to sell the same or larger quantities of futures in a single day. But the pace of the sales on 6 May was very fast.
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Por ultimo, en el escrito se habla de un paper de Hasbrouck y Saar, creo que es este.
En esta sexta entrega, Marco le explica a Gaston, lo que es Trading Electrónico, Trading Algorítmico y HFT. Para terminar, con su opinion sobre Latinoamerica y el Trading Electrónico.
Para un mayor disfrute de este video, le recomendamos que lo vea desde Vimeo directamente en Alta Definición.
10
Jan
11
Gráfico du Jour: Algo FX
(Fuente: BIS, via Econbrowser)
Nanex tiene breve e interesante estudio sobre un “fenomeno” que ellos llaman Quote Stuffing, pero que se da en secuencias poco convencionales (que no siguen una lógica económica).
As we continue to monitor the markets for evidence of Quote Stuffing and Strange Sequences (Crop Circles), we find that there are dozens if not hundreds of examples to choose from on any given day. As such, this page will be updated often with charts demonstrating this activity.
The common theme with the charts shown on this page is they are obviously all generated in code and are algorithmic. Some demonstrate bizarre price or size cycling, some demonstrate large burst of quotes in extremely short time frames and some will demonstrate both. In most cases these sequences are from a single exchange with no other exchange quoting in the same time frame.
07-12-10
NASDAQ “Ask Mountain”. Symbol IAU. Over 56,000 quotes in 10 seconds, all with same Ask Price and the Ask Size increasing or decreasing by 1 (to almost 40,000!).
18
Jul
10
7ma Reunion del QF Club
El pasado viernes se presentaron los siguientes trabajos en la 7ma Reunion del QF Club:
Matias Schapiro, “Software de backtesting y trading automático de estrategias de inversión algorítmicas – Arquitectura, Diseño y Ejemplos de uso”
Manuel Calderon, “Bonos Catastrofe, Analisis Financiero y Propuesta de Implementación en Argentina”
Llegue de casualidad, via el blog Zero Hedge, a un post –escrito por Paul Wilmott– sobre High Frequency Trading y su impacto en la liquidez y en la volatilidad de los mercados (Como diría un profesor de economía que tuve, “¿pero de que mercados me hablan?” cuando exigía claridad en los términos).
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I am concerned about High-frequency Trading (HFT) for two main reasons: Reduction of the relationship between value and price; Potential for positive feedback.
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But feedback can be positive or negative.
Negative feedback is when an up move in a stock leads to a sell signal, and thus a fall in the price, and a down leads to a buy, and thus a rise in the price. This dampens volatility.
Positive feedback is when an up begets a buy, which causes the stock to rise again, causing another buy, etc. etc. And when a fall begets a sell, causing another fall, and further selling, and…
So which is it? Does HFT result in a reduction of volatility via negative feedback or an increase via positive feedback? This is an easy one. If you are a hedge fund manager which of the following would you prefer? A or B?
A. Low volatility. Shares go up or go down fairly predictably. No skill is required to make money, even by the man on the street. Hedge funds can’t charge large fees.
B. High volatility. Very difficult markets, experts needed and can charge large fees. If a fund does well they make a killing because of the enormous profit they have made for their clients. But they are just as likely to lose all their clients’ money, in which case…nothing bad happens to the fund manager.
Yes, we are in that familiar territory of moral hazard. Of course the funds want to increase volatility and they have found themselves in exactly the place they want to be to make this happen.
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El Club de Finanzas Cuantitativas ha realizado una alianza educativa junto con Doctor Sapix, una empresa de soluciones para el mercado financiero, liderada por Matias Schapiro.
Doctor Sapix cuenta con simuladores de trading, que estarán disponibles en un futuro cercano para nuestros miembros. El acuerdo incluye, también, charlas relacionadas al trading automático y algorítmico.
Vox tiene un post sobre un tema donde la libre y la tortuga no condicen con su Fabula.
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According to the Securities and Exchange Commission, the number of active dark pools dealing in stocks on major US stock markets trebled to 29 in 2009 from about 10 in 2002. For April to June 2009, the total dark pools volume was about 7.2% of the total volumes of all US exchanges.
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Dark pools are a private or alternative trading system that allows participants to transact without displaying quotes publicly. Orders are anonymously matched and not reported to any entity, even the regulators (Younglai and Spicer 2009). Thus, the mainstream exchange-traded market does not have any clue about the volume of transactions happening in this parallel market or the prices at which they are being executed. Obviously, price discovery on the mainstream market, without dark pools information, becomes inefficient.
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But what is unacceptable is the practice of allowing a privileged minority to flash trade to track the reaction with high-speed processing capacity and the algorithm that can take advantage of the reaction to reap benefits – as this is akin to front-running. It is an example of high-frequency trading system with knowledge of asymmetric information that confuses common investors by simultaneously issuing and cancelling orders and entices them to shell out more for a particular security and, thus, squeezes out their profits.
It was the Chicago Board Options Exchange which pioneered flash orders early this decade to increase its execution speed (Patterson et al. 2009). Flash orders remained in the dark until a newspaper report in 2009 blew the whistle on how Goldman Sachs had made a killing through this route. According to Rosenblatt, flash trading accounted for about 2.4% of the total US stock volumes in June 2009.
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Por ultimo, una de las fuentes de este articulo es un trabajo de PWC, Dark Pools of liquidity
16
May
10
ETFs y HFT
Matt Hougan tiene un post donde agrupa distintas explicaciones del rol que tuvieron el ETFs en el crash de hace dos jueves atrás. Dejando en claro de que su protagonismo se debe a cuestiones de estrategias y no debido a fallas en los diversos productos.
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The most popular explanation I’ve heard is that ETFs are exposed to mistaken prices in underlying stocks. Computers are constantly monitoring the share price of ETFs and comparing those to the fair value of their underlying components. When prices get out of whack, computers will arbitrage the difference away, selling an ETF and buying its underlying securities, or vice versa.
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UPDATE:
Nota sobre el Crash del 6 de mayo
UPDATE -II:
Rajiv Sethi tiene una postura interesante sobre lo sucedido.
Quantitative Finance Club 